DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 03/17/2026.
Claims 1, 9, and 16 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant’s arguments, see page 1 of Remarks, filed 03/17/2026, with respect to the drawing and specification objections have been fully considered and are persuasive. The amendments to the specification have obviated both the drawing and specification objections. The drawing and specification objections have been withdrawn.
Applicant’s arguments, see pages 1-3 of Remarks, filed 03/17/2026, with respect to the 35 U.S.C. 112(f) interpretation of “a computing system” have been fully considered and are persuasive. Examiner agrees with Applicant that “a computing system” would be recognized by one of ordinary skill in the art as providing sufficient structure for performing the functions of the claims. The 35 U.S.C. 112(f) interpretation of “a computing system” has been withdrawn as indicated below.
Applicant’s arguments, see pages 3-10 of Remarks, filed 03/17/2026, with respect to the 35 U.S.C. 101 rejections of claims 1-20 have been fully considered but are not persuasive. The 35 U.S.C. 101 rejections have been maintained.
After summarizing various 35 U.S.C. 101 guidance across pages 3-5 and summarizing features of the claims across pages 5-6, Applicant argues in Section II. that the claims do not recite a mental process at Step 2A Prong One.
First, Examiner notes that Applicant’s arguments regarding eligibility incorporate limitations that reside in dependent claims. Examiner notes that there are no method nor memory device claims that recite all the features argued, and that none of the independent claims recite all of the argued features either. For example, Applicant argues the process includes “Clustering geographic sectors "based on the edge weights" to produce price-linked neighborhoods; and generating a heatmap whose color mapping is derived from the computed weights from a target sector to others. - Programmatic generation and ranking of a comp list based on the computed edge weights, and rendering results via a user interface”. However, Examiner notes that the sorting/ranking argued by Applicant appears in method claim 7 while the heatmap is found in method claims 5-6. There is no dependency between claims 5-6 and 7. A similar situation occurs with the mirrored memory device claims, and none of independent claims 1, 9, and 16 recite a heatmap, clustering, and sorting. Therefore, even assuming arguendo, that Applicant’s arguments for the combination of features were to be persuasive, not all of the claims require all of the argued features.
Regarding the mental process arguments specifically, Applicant argues that the construction of the edge-weight table, clustering, the UI/heatmap, and comp/selection sorting are not mental processes and are “inherently machine implemented” at the scale of thousands of sectors and property pairs. Examiner notes, however, that the broadest reasonable interpretation of the claimed invention does not require “thousands of map sectors” or multiyear sales history, as there are no size nor time requirements recited in the claims. Furthermore, MPEP 2106.04(a)(2) III.C. recites “examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process” and MPEP 2106.04(a)(2) III.B. recites “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea”. Under the broadest reasonable interpretation of the current claims, the generation of edge weights covers “a simple addition of a fixed value for each identified sale pair (e.g., operating as a counter with ‘1’ added for each identified sale pair)” as stated by Applicant in [0026] of the specification as filed. A human could keep a tally (either mentally or using a pen and paper) of sale pairs between sectors and arrive at edge weights for the sectors. A human could then write out a table of the tallied edge weights (i.e. writing out the table from Fig. 2 of the specification as filed). Regarding the clustering, clustering sectors covers “an algorithm that looks for groups of geographic sectors having shared edge strengths over a selected threshold value” (specification [0036]). A human, having tallied edge weights by counting sale pairs, could then judge which of the edges have a number greater than a threshold number to perform the clustering. Regarding the UI/heatmap, a human could draw gridlines on a map and mark the sectors using different colors/gradation for sectors according to the tallied edge weights. For the UI, Examiner points to MPEP 2106.04(a)(2) III.C. above, with the use of a UI to generate such a heatmap instead of human drawing is performing the mental process in a computer environment and thus does not preclude the claim from reciting a mental process. Regarding the comp selection/sorting, the generation of the set of recent comps based on the edge weights from claim 1 covers determining comparable sales based on a comparison of land size, square footage, or other attributes in [0042] of the specification and choosing comps from sectors that made it into the price-linked neighborhood by having an edge weight high enough to clear the clustering threshold. This comparison of property attributes and edge weights could be done by a human with pen and paper. Finally, regarding sorting, a human could list comparable properties in decreasing order of sector edge weight as covered in specification [0059]. Accordingly, the broadest reasonable interpretation of the claimed features as argued, and the various subsets of the features actually recited in the claims, falls into the Mental Process category.
Applicant’s arguments regarding McRo and Enfish are also not persuasive. Applicant argues that McRo “held that automation by specific rules is not an abstract mental process, , and supports that rule-constrained automation of similarity mapping (e.g., edge weights from pairwise sales signals and sector graph clustering) is patent-eligible when recited with specific steps/constraints”. Applicant argues that Enfish supports that the weighted sector-adjacency table is a “computer-centric data structure” improving the how comparable properties are computed and retrieved. Examiner respectfully disagrees. First, MPEP 2106.05(a) recites “ For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea”. In contrast, the claimed invention is taking a process that can be performed according to rules outside of a computer environment (i.e. counting pairs of similar properties between sectors, checking whether the number of similar pairs passes a threshold to cluster the sectors, presenting a list of comparable properties in decreasing order of the previously counted number of similar pairs between the sector of the comp and a target property sector) and using a computer as a tool to perform the steps. Regarding Enfish, the self-referential database of Enfish provided an improvement to the functioning of a computer (see MPEP 2106.05(a)). In contrast, the present claims cover the creation of table based on the counting of similar pairs of properties sales across sectors. While this table and corresponding methodology might be an improvement to the determination of comp properties, the performance of the computer system itself is not improved. Instead, the abstract idea of finding comp properties may be improved. MPEP 2106.05(a) II. states “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.” Accordingly, the instant invention is not analogous to McRo and Enfish, and is not patent eligible for the reasons found in those cases. The claimed invention recites a Mental Process.
Additionally, Applicant’s arguments that the claims do not recite a Certain Method of Organizing Human Activity for the same reasoning they allegedly do not recite a Mental Process are also not persuasive for similar reasoning to that discussed above. Furthermore, the claimed process of performing a property valuation falls into commercial interactions of Certain Methods of Organizing Human Activity because the process at least recites “business relations” as discussed in MPEP 2106.04(a)(2) II.B. The claims also recite a Certain Method of Organizing Human Activity.
Next, Applicant argues in Section III. that the claims are allegedly integrated into a practical application at Step 2A Prong Two and should be found eligible. Specifically, Applicant argues that the claimed invention improves computer functionality and the technical field of “computer-implemented valuation analytics”. Applicant argues that the processes argued above at Prong One are “specific computer operations and data flows” and that the claimed invention overcomes the “traditional limitations of geographic polygons or radius-based approaches to identifying comp properties and is “computer-centric” solution. Applicant argues that Examiner’s analysis of the claims did not evaluate whether the claims as a whole provide an improvement. Examiner respectfully disagrees. First, as discussed above regarding Prong One, the argued limitations recite a Mental Process and Certain Method of Organizing Human Activity. For the reasoning discussed above, the claimed processes are not “computer-centric” and in the broadest reasonable interpretation are not grounded in computer technology. The “computing system” and the UI of the dependent claims have been indicated as additional elements of the invention at Prong Two, but they amount to no more than mere instructions to apply the exception using generic computing components. Turning to the “apply it” analysis of MPEP2106.05(f), the MPEP states “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 (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application.” In claim 1, the claim recites an abstract process for the reasons discussed above and states that the operation is executed “via a computing system”. Instead of an improvement to the functioning of the computing system, the claim is merely applying the abstract idea “via a computing system” by using a generic “computing system” in its ordinary capacity to perform the method. Regarding the “specificity” of the steps, MPEP 2106.04 I. states “The Court has held that a claim may not preempt abstract ideas, laws of nature, or natural phenomena, even if the judicial exception is narrow (e.g., a particular mathematical formula such as the Arrhenius equation).” Accordingly, the abstract idea steps allegedly being “specific” or narrow does not indicate an improvement to computer technology. Additionally, MPEP 2106.05(a) I. states “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:… ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)”. In the instant case, the acceleration of abstract processes, or the handling of large data sets as Applicant argues regarding Prong One, brough about by the capabilities of a generic computer does not amount to an improvement to computer function. The claims make no indication that “a computing system” goes beyond a generic computing system, and using a generic computing system as a tool to perform an abstract idea quickly does not improve the functioning of a computer.
Regarding the improvement over the geographic polygons and radius-based approaches, Examiner notes that this improvement is an improvement to the abstract idea itself, not to the functioning of computer or a technical field. Specifically, the traditional limitations were with how a map of properties was divided leading to suboptimal comp identification. The claimed invention may improve upon the traditional method of identifying comps, but that improvement is achieved by improving the abstract process. In particular, the replacement of a radius or polygon with comp properties from sectors clustered based on a count of similar property sale pairs between sectors. The steps of this alleged improvement are part of the abstract idea as discussed above regarding Prong One. As cited above, MPEP 2106.05(a) II. states “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.” In other words, instead of a traditional method being performed “via a computing system”, the claimed invention performs the improved abstract idea “via a computing system”. The computing system itself, or another technology, is not improved.
For the above reasoning, Examiner did evaluate the claims as a whole. Regarding claim 1 as argued by Applicant, the entirety of the claim outside of “via a computing system” does fall into the abstract idea as discussed above. When viewing the additionally element of “via a computing system” in the claim as a whole, the computing system follows the MPEP 2106.05(f) analysis of using a generic computing system as a tool to perform the abstract idea for the reasoning above. While the claimed invention may provide improvements to the abstract idea of the claim that provide advantages, MPEP 2106.05(a) states that an improved idea does not mean an improvement to technology. Therefore, viewed as a whole, the claims do not integrate their judicial exceptions into a practical application. Applicant’s arguments at Step 2A Prong Two are not persuasive.
Next, Applicant argues in Section IV. that the claims recite an inventive concept at Step 2B. Applicant argues that the Office Action provided no Berkheimer evidence and therefore a finding of ineligibility at Step 2B cannot hold. Applicant also makes the argument regarding an alleged improvement to the functioning of a computer or other technology in this section again. Examiner respectfully disagrees. MPEP 2106.05 II. recites “in Step 2B, examiners should: • Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two; • Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h): • Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and • Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d)” (emphasis added). In the present case, the features argued by Applicant are not additional elements that are carried over into Step 2B analysis but are part of the abstract idea. Accordingly, Berkheimer evidence for these parts of the abstract idea is not required. Indeed, MPEP 2106.04 I. recites “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions.” Accordingly, Applicant’s argument that the parts of the claim reciting the abstract idea need to be shown to be “WURC” are not persuasive.
Regarding the additional elements that are present in the claimed invention, Examiner notes that the additional elements (a computer system, training an automated valuation model using machine learning, a user interface, a tool via the user interface, etc.) have been classified as mere instructions to apply an exception, individually and as a combination, according to MPEP 2106.05(f). Per the MPEP above, conclusions from section 2106.05(f) are not re-evaluated to determine whether they are WURC at Step 2B and instead are carried over from Step 2A Prong Two. Furthermore, MPEP 2016.05(f) states “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 (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more” (emphasis added). Therefore, as none of the additional elements of the claimed invention have been classified as extra-solution activity and instead have been classified as mere instructions to apply the exception, Berkheimer WURC evidence was not required in the previous Office Action and is not required below.
Regarding the argument that the claimed invention amount to an improvement to technology are not persuasive for the reasons discussed above. Applicant’s arguments at Step 2B are not persuasive.
Applicant finally argues in section V. that Examiner is oversimplifying the claims out from their “concrete computer-specific implementations”. Examiner respectfully disagrees. As shown above, the claims recite an abstract idea that is merely being executed “via a computing system” in claim 1. Therefore, the steps argued by Applicant are not rooted in technology like the case law Applicant cites to. Furthermore, the improvements argued by Applicant, i.e. the improvement over polygon/radius-based comps, are improvements to the abstract idea and not to technology for the reasoning discussed above. Applicant’s arguments regarding eligibility are therefore unpersuasive. The 35 U.S.C. 101 rejections of claims 1-20 have been maintained.
Applicant’s arguments, see pages 10-15 of Remarks, filed 03/17/2026, with respect to the 35 U.S.C. 103 rejections of claims 1-20 have been fully considered but are either not persuasive or moot. Claims 1-20 still stand rejected under 35 U.S.C. 103.
After summarizing the rejections and discussing the burdens of 35 U.S.C. 103 rejections across pages 10-12, Applicant argues on pages 12-15 that the combination of Spieckerman and Geng fails to render obvious all of the elements of Applicant’s independent claims. Applicant argues that the previous Office Action relies “on broad functional paraphrasing” and omits “critical detail” from the claims. Specifically, Applicant argues that Spieckerman allegedly does not teach identifying pairs of similar sales in historical property sale records, generating a table of edges between the geographic sectors, creating edges between sectors, and use “long-term” historical sale pairs to generate a pricing similarity topology.
Regarding the identification of pairs of similar sales in historical property sale records and the use of “long-term” historical sale pairs, Examiner notes that Applicant appears to be trying to argue that the “recent” sales of Spieckerman [0056] and [0065] do not recite “historical property sale records” as recited in the claims. Examiner notes that “historical” is not recited in the claims as to preclude “recent” sales as Applicant argues, and that “historical” is not defined in the specification as specifically covering only “long-term” records. Indeed, paragraph [0051] of Applicant’s specification as filed explicitly states “At 512, the method may include determining a list of recently-sold comparable properties…” (emphasis added). Therefore, “recent” sales records fall under “historical sales records” in the context of the claimed invention. Accordingly, the broadest reasonable interpretation of “historical property sale records” covers “recent” property sales because these “recent” property sales still happened in the past. Accordingly, the recent property sales of Spieckerman [0056] and [0065] recite “historical property sale records” in the context of the claimed invention.
Regarding the pairing of historical property sales, Examiner noted in the previous Office Action that Spieckerman did not teach this feature and that Geng was being used to teach the pairing of similar property records. Arguments regarding Geng will be addressed below.
Regarding the creation of creating edges between sectors, the broadest reasonable interpretation of an “edge” in the context of the claimed invention is a connection between two regions. Spieckerman’s inter-region variance of [0058] is an edge between two regions of properties based on recent sales within the regions because the inter-region variance describes a strength of connection between the regions in terms of how similar the recently sold properties are. Again regarding the edge weight being based on pairs of sales, Geng is used to teach the pairs of property records being used to generate the edge weight as discussed below.
Regarding the creation of table of the edges, Applicant’s arguments are moot because the Liu et al. (U.S. Pre-Grant Publication No. 2022/0101474, hereafter known as Liu) is used to teach the generation of a table of edges between nodes and Spieckerman and Geng are not relied upon to teach the generation of a table.
Next, Applicant argues that Examiner misunderstands the function of Geng and that Geng allegedly does not teach cure the deficiencies of Spieckerman. Specifically, Applicant argues that Geng uses subject-comp pairs that appear in appraisal reports, creates “links” between block only because of these similar appraisals, and allegedly teaches away from using historical sales records by using recent sales instead.
Regarding the use of professional appraisal reports, Examiner is citing Geng to tech the pairwise linkage between properties. As discussed above, Spieckerman teaches determination of similarities between properties, just not pairs of properties. Geng is used to teach that properties can be paired together in their similarity instead of evaluated as a group. In other words, in combination Spieckerman and Geng are teaching that the property similarity determinations of Spieckerman can be carried out pairing individual properties together.
Furthermore, the fact that Geng teaches the human generated appraisal reports are used to identify comps does not prevent Geng from remedying the deficiency of selecting pairs of properties with recent sales of Spieckerman. First, the present claims recite “identifying pairs of similar property sales in historical property sale records”, but do not restrict or preclude human-indicated similarity. Second, Geng explicitly teaches that, contrary to Applicant’s implication in the Remarks, Geng uses features from the property to determine similarity, with indications of similarity in an appraisal as one of several weighted features (see Geng Col. 22-39 “the property clustering component may include a feature extraction component 24 that determines features or characteristics of properties by analyzing the property-level data… The clustering component 22 preferably uses these extracted features, among others (such as property locations, sale prices, zip codes, etc.), to group together similar properties” with appraisal reports in Col. 59-64 “the clustering component gives significant weight to “comparable properties” data obtained from recent appraisal reports (e.g., appraisal reports issued in the last 3 years), such that properties identified as comparable by an appraiser will have a strong tendency to be grouped into a common neighborhood”).
Regarding the links between neighbor blocks, Geng explicitly teaches in Col. 5 lines 42-47 "(1) For each comparable property pair in each appraisal report, connect (form a link between) the two neighbor blocks in which the two properties reside. This may be repeated for each of multiple types of neighbor blocks. FIGS. 4 and 5 show examples of links formed between neighbor blocks based on appraisal reports" and Col. 5 lines 51-58 "(2) Aggregate/combine the links between each pair of connected blocks, and generate a normalized score representing a degree of connectivity or similarity between the two blocks. This results in a connectivity matrix. Normalization removes the effects of block size (number of properties) may be performed by, for example, dividing the number of linkages over the total number of properties in the blocks". The broadest reasonable interpretation of “edge”, as discussed above, in the context of the claimed invention is a connection between two regions of properties. Geng’s counting of linkages of similar properties between neighbor blocks to determine a degree of connectivity between the blocks and normalizing the degree of connectivity based on the numbers of properties in the blocks is assigning a weight (strength) of connection between the neighbor block regions based on a count of pairs of similar properties (in combination with Spieckerman, pairs of recent property sales). Accordingly, Applicant’s argument that the combination of Spieckerman and Geng does not teach the assigning of a weight based on pairs of similar property sales is not persuasive.
Regarding Geng allegedly “teaching away” by using recent sales, Examiner finds Applicant’s argument unpersuasive for similar reasoning discussed above about the “recent vs. historical” distinction that Applicant appears to be making. Applicant’s claims do not specify a time frame for the “historical” records that precludes “recent” records, and Applicant’s specification does not provide a definition for “historical” that excludes “recent” records. Because the recent sales still occurred prior to the time of the execution of the method of Spieckerman and Geng, the “recent” sales fall under “historical” sales. Therefore, instead of “teaching away” as Applicant argues, Geng teaches historical transactions under the broadest reasonable interpretation of the claimed invention.
Regarding Applicant’s arguments against the combining of Spieckerman and Geng, the argument that Geng teaches away from historical property sale records is unpersuasive for the reasoning discussed above. Regarding Applicant’s arguments against alleged hindsight reasoning in the combination of Spieckerman and Geng, Examiner respectfully disagrees. First Examiner notes that both Spieckerman and Geng both use property data to determine adjacent physical regions of properties that are similar enough to each other to group or cluster together. Both Spieckerman and Geng teach using property data from recent sales to determine individual properties that are similar to each other. While the methods used to link regions together in Spieckerman and Geng differ, one of ordinary skill in the art would have had motivation to incorporate the pairwise linkage method of Geng into the system of Spieckerman at least because of the reason cited in the previous Office Action, namely that the technique of Geng optimizes the size of the created neighborhoods of merged neighbor blocks by checking internal cohesion vs. external links (see Col. 6 lines 16-23). One of ordinary skill in the art would have recognized the need for a stopping criterion like that taught in Geng because at some point all of the similar properties have been considered and further additions to neighborhood would become counterproductive as less and less similar properties/regions are added. Therefore, the cited motivation to combine is not a broad assertion of “predictable results” as Applicant appears to argue but instead is a recognition explicitly stated in the prior art that the Geng methodology results in optimally sized neighborhoods for determining similar properties. As this motivation is explicitly provided in the cited prior art of record, the combination does not rely unduly on Applicant’s invention. Accordingly, Applicant’s arguments that the combination uses impermissible hindsight are not persuasive.
Accordingly, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of the amended independent claims as discussed in more detail below. Claims 1, 9, and 16 still stand rejected under 35 U.S.C. 103.
Regarding Applicant’s arguments regarding Den Herder and Shahbazi allegedly not curing the alleged deficiencies of Spieckerman and Geng regarding the independent claims, Applicant’s arguments are moot. As discussed above, Liu cures the deficiency of Spieckerman and Geng caused by limitations newly amended into the independent claims, so Den Herder and Shahbazi are not required to teach the limitations at issue.
Applicant’s arguments that dependent claims 2-8, 10-15, and 17-20 are distinguished over the art of record by virtue of their dependence on their respective independent claims are accordingly unpersuasive. Claims 1-20 still stand rejected under 35 U.S.C. 103.
Claim Objections
Claim 16 is objected to because of the following informalities:
Claim 16 recites “generatea table of edges between the geographic sectors” when it appears it should recite “generate a table of edges between the geographic sectors” to correct an apparent typographical error
Appropriate correction is required.
Claim Interpretation
As discussed in the Response to Arguments above, “a computing system” is no longer be interpreted as invoking 35 U.S.C. 112(f). “A computing system” would be recognized as providing sufficient structure to performed the claimed functions. Accordingly, “a computing system” would not satisfy the three prong test to invoke 35 U.S.C. 112(f).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite performing a property valuation.
As an initial matter, claims 1-8 fall into at least the method category of statutory subject matter. Claims 9-15 fall into at least the manufacture category of statutory subject matter. Finally, claims 16-20 fall into at least the machine category of statutory subject matter. Therefore, all claims fall into at least one of the statutory categories. Eligibility analysis proceeds to Step 2A.
In claim 1, the limitation of “A method comprising: executing a property valuation operation via a computing system, including: identifying pairs of similar property sales in historical property sale records”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “via a computing system,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “determining geographic sectors corresponding to locations of properties involved in the similar property sales; generating a table of edges between the geographic sectors; assigning edge weights between the geographic sectors in the table based on a number of pairs of similar property sales involving the geographic sectors in the historical property sale records; and generating a set of recent sales of properties comparable to a target property based on the edge weights”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 1 recites the concept of performing a property valuation which is a certain method of organizing human activity including commercial interactions. A method comprising: executing a property valuation operation, including: identifying pairs of similar property sales in historical property sale records; determining geographic sectors corresponding to locations of properties involved in the similar property sales; generating a table of edges between the geographic sectors; assigning edge weights between the geographic sectors in the table based on a number of pairs of similar property sales involving the geographic sectors in the historical property sale records; and generating a set of recent sales of properties comparable to a target property based on the edge weights all, as a whole, fall under the category of commercial interactions. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a computer system. The recited additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing system amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-3 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 4 further limits the abstract idea of claim 2 while introducing the additional element of training an automated valuation model using machine learning. The claim does not integrate the abstract idea into a practical application because the element of training an automated valuation model using machine learning is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 2 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 5 further limits the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above, this claim does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 6 further limits the abstract idea of claim 5 while introducing the additional elements of a user interface and a tool via the user interface. The claim does not integrate the abstract idea into a practical application because the elements of a user interface and a tool via the user interface are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 5 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 7 further limits the abstract idea of claim 1 while introducing the additional element of a user interface. The claim does not integrate the abstract idea into a practical application because the element of a user interface is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 1 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 8 further limits the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above, this claim does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
In claim 9, the limitation of “execute a property valuation operation via a computing system, including: identify pairs of similar property sales in historical property sale records”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “A memory device storing instructions that, when executed, cause a processor to” and “via a computing system,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “determine geographic sectors corresponding to locations of properties involved in the similar property sales; generate a table of edges between the geographic sectors; assign edge weights between the geographic sectors in the table based on a number of pairs of similar property sales involving the geographic sectors in the historical property sale records; perform clustering of the geographic sectors based on the edge weights; and define a price-linked neighborhood of geographic sectors based on the clustering”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 9 recites the concept of performing a property valuation which is a certain method of organizing human activity including commercial interactions. Execute a property valuation operation, including: identify pairs of similar property sales in historical property sale records; determine geographic sectors corresponding to locations of properties involved in the similar property sales; generate a table of edges between the geographic sectors; assign edge weights between the geographic sectors in the table based on a number of pairs of similar property sales involving the geographic sectors in the historical property sale records; perform clustering of the geographic sectors based on the edge weights; and define a price-linked neighborhood of geographic sectors based on the clustering all, as a whole, fall under the category of commercial interactions. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a memory device storing instructions, a processor, and a computing system. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a memory device storing instructions, a processor, and a computing system amounts to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 10 further limits the abstract idea of claim 9 without adding any new additional elements. Therefore, by the analysis of claim 9 above, this claim does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 11 further limits the abstract idea of claim 10 while introducing the additional element of a user interface. The claim does not integrate the abstract idea into a practical application because the element of a user interface is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 10 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 12 further limits the abstract idea of claim 9 without adding any new additional elements. Therefore, by the analysis of claim 9 above, this claim does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 13 further limits the abstract idea of claim 9 while introducing the additional element of training an automated valuation model using machine learning. The claim does not integrate the abstract idea into a practical application because the element of training an automated valuation model using machine learning is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 9 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 14 further limits the abstract idea of claim 9 without adding any new additional elements. Therefore, by the analysis of claim 9 above, this claim does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 15 further limits the abstract idea of claim 14 while introducing the additional elements of a user interface and a tool via the user interface. The claim does not integrate the abstract idea into a practical application because the elements of a user interface and a tool via the user interface are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 14 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
In claim 16, the limitation of “instructions that cause the processor to: identify pairs of similar property sales in historical property sale records”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “An apparatus comprising: a processor; and a memory device storing instructions that cause the processor to,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “determine geographic sectors corresponding to locations of properties involved in the similar property sales; generatea table of edges between the geographic sectors; assign edge weights between the geographic sectors in the table based on a number of pairs of similar property sales involving the geographic sectors in the historical property sale records; receive a selection identifying a target property; identify a target geographic sector including the target property; generate a heatmap of geographic sectors based on the edge weights between the target geographic sector and other geographic sectors, including: depict geographic sectors having higher edge weights as hotter; and depict geographic sectors having lower edge weights as colder”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 16 recites the concept of performing a property valuation which is a certain method of organizing human activity including commercial interactions. Identify pairs of similar property sales in historical property sale records; determine geographic sectors corresponding to locations of properties involved in the similar property sales; generatea table of edges between the geographic sectors; assign edge weights between the geographic sectors in the table based on a number of pairs of similar property sales involving the geographic sectors in the historical property sale records; receive a selection identifying a target property; identify a target geographic sector including the target property; generate a heatmap of geographic sectors based on the edge weights between the target geographic sector and other geographic sectors, including: depict geographic sectors having higher edge weights as hotter; and depict geographic sectors having lower edge weights as colder all, as a whole, fall under the category of commercial interactions. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of an apparatus, a processor, and a memory device storing instructions. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an apparatus, a processor, and a memory device storing instructions amounts to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 17-18 further limit the abstract idea of claim 16 without adding any new additional elements. Therefore, by the analysis of claim 16 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 19 further limits the abstract idea of claim 18 while introducing the additional element of a user interface. The claim does not integrate the abstract idea into a practical application because the element of a user interface is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 18 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 20 further limits the abstract idea of claim 16 while introducing the additional elements of a user interface and a tool via the user interface. The claim does not integrate the abstract idea into a practical application because the elements of a user interface and a tool via the user interface are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 16 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-5, 9-10, 12-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Spieckerman (U.S. Pre-Grant Publication No. 2014/0164260, hereafter known as Spieckerman) in view of Geng et al. (U.S. Patent No. 11,501,100; hereafter known as Geng) and Liu et al. (U.S. Pre-Grant Publication No. 2022/0101474, hereafter known as Liu).
Regarding claim 1, Spieckerman teaches:
A method comprising (see Fig. 2 and [0049]-[0071] for overall method)
executing a property valuation operation via a computing system, including: identifying (see Fig. 1 and [0020] for the property valuation system being performed via a computing system. See [0028] for retrieving sales transaction history and prices from a multiple listing service. See [0056]-[0057] for the generation of scores based on comparing properties, particularly "Regions in which the intra-region variance is low represent areas in which the properties have generally similar characteristics" for identifying similar property sales in historical property sale records)
determining geographic sectors corresponding to locations of properties involved in the similar property sales (see [0051]-[0054] for determining a geographic area and dividing the area into multiple regions based on the number of properties with recent sales. See [0055] "the number of regions in the geographic area surrounding the subject property is selected dynamically based at least partly on the number of recent sales in the geographic area. Such implementations advantageously may improve the statistical quality of the analysis by providing a statistically sufficient number of properties in each region. In some cases, one or more sales thresholds can be established, and the number of regions can be based on which sales threshold is passed")
generating (see [0058] "the method 200 analyzes the statistical characteristics of different regions to identify patterns in the statistical characteristics among the regions. In some implementations, the pattern analyzer 128 performs block 212. These statistical characteristics can be referred to as inter-region statistical characteristics, because they reflect the difference between statistical characteristics of different regions…two (or more) regions can be compared by calculating the statistical variance (or numerical difference) of a region score (or other statistical value) of a first region as compared to a corresponding region score (or other statistical value) of a second region (that is different from the first region). Groups of regions in which the inter-region variance is low are likely to be similar in characteristics, whereas regions in which the inter-region variance is high are likely to be dissimilar" for generating edges between region by generating inter-region statistical characteristics)
assigning edge weights between the geographic sectors (see [0057] for region scores including a weighted average of stats of properties of the region including sales prices. See [0058] " two (or more) regions can be compared by calculating the statistical variance (or numerical difference) of a region score (or other statistical value) of a first region as compared to a corresponding region score (or other statistical value) of a second region (that is different from the first region). Groups of regions in which the inter-region variance is low are likely to be similar in characteristics, whereas regions in which the inter-region variance is high are likely to be dissimilar" for determining edge weights between regions based on the number of similar property sales between regions)
and generating a set of recent sales of properties comparable to a target property based on the edge weights (see [0044]-[0045] for generating a list of comparable properties, [0065] for properties having recent sales within the identified contiguous region being comps, and [0070] for providing list of comparable properties having recent sales to property valuers to determine the valuation of a target property)
While Spieckerman teaches the identification of similar property sales in historical records and assigning edge weights between sectors as discussed above, Spieckerman does not explicitly teach identifying pairs of similar sales, generating a table of edges between the geographic sectors, and assigning edge weights to the edges in the table based on the number of pairs of similar sales involving the sectors. However, Geng teaches:
identifying pairs of similar property sales in historical property sale records (see Col. 4 lines 48-64 "Subject-comp pairs in appraisal reports can be treated as explicit expert endorsements of two properties' similarity. Given this, we can use these appraisals as input to data-based algorithms that seek to model and group properties based off their similarity. In one embodiment, the clustering component uses subject-comp occurrences as simple counts connecting our units of aggregation. Alternatively, more complex representations of the connections can be used using graph Spectral Clustering, which is a known connectivity-based clustering method. Regardless of the particular clustering algorithm used, the algorithm preferably treats the count of how many times two properties have been designated as comparable in appraisal reports as a measure of the similarity or relationship between these properties. The clustering algorithm may optionally give more weight to more recent appraisal reports, such as by applying an age-based decay factor during the counting process". In combination with the sales history of Spieckerman, the property-comp pairs are based on sales records of Spieckerman)
assigning edge weights between the geographic sectors (see Col. 5 lines 20-22 "the concept of using appraisal reports to measure the similarity between properties is extended to “neighbor blocks.”" as well as Col. 5 lines 42-47 "(1) For each comparable property pair in each appraisal report, connect (form a link between) the two neighbor blocks in which the two properties reside. This may be repeated for each of multiple types of neighbor blocks. FIGS. 4 and 5 show examples of links formed between neighbor blocks based on appraisal reports" and Col. 5 lines 51-58 "(2) Aggregate/combine the links between each pair of connected blocks, and generate a normalized score representing a degree of connectivity or similarity between the two blocks. This results in a connectivity matrix. Normalization removes the effects of block size (number of properties) may be performed by, for example, dividing the number of linkages over the total number of properties in the blocks" for assigning edge weights based on a number of pairs of similar sales involving the sectors. In combination with Spieckerman, this degree of connectivity between regions can further serve to identify sectors/blocks that can be aggregated/combined into a region)
One of ordinary skill in the art would have recognized that applying the known technique of identifying pairs of similar property sales and assigning edge weights between sectors based on a number of similar pairs involving the sectors of Geng to the system of Spieckerman would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Geng to the teaching of Spieckerman would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such identifying pairs of similar property sales and assigning edge weights between sectors based on a number of similar pairs involving the sectors. Further, applying identifying pairs of similar property sales and assigning edge weights between sectors based on a number of similar pairs involving the sectors to Spieckerman would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more optimal sizing of neighborhoods. Geng Col. 6 lines 16-23 states “a stopping criterion based on information entropy may compare the strength of the linkages within a block (which may have been formed from a block merge) to the strengths of the linkages between that block and other blocks. If the internal linkage strength is higher, the neighbor block may be considered optimal in size, and may be treated/defined as a neighborhood”. One of ordinary skill in the art would have recognized that comparing a neighborhood’s internal and external linkages to determine whether the neighborhood is the optimal size would further refine Spieckerman’s identification of contiguous neighborhoods in [0063]-[0064] to ensure that the neighborhood boundary is not including properties that are too dissimilar to each other.
The combination of Spieckerman and Geng still does not explicitly teach generating a table of the edges between regions and assigning edge weights in the table. However, Liu teaches:
generating a table of edges between the geographic sectors; assigning edge weights between the geographic sectors in the table (see Fig.8 and [0113] “The adjacency matrix A illustrated in FIG. 8 may represent a graph G including N vertices where N is an integer above 2. The rows and columns of the adjacency matrix A may represent the N vertices. A row and a column having the same index number may represent a same vertex. The adjacency matrix A may store weights of the edges of the corresponding graph. As illustrated in FIG. 8, a weight w.sub.i,j may correspond to an edge (i,j) starting from the ith vertex V.sub.j, to the jth vertex V.sub.j, where i and j are integers between 1 and N” for generating a table of edges and assigning weights to the edges in the table. In combination with Spieckerman and Geng, the edges and corresponding edge weights are assigned to the table with the geographic regions as the “N vertices” of the table)
It would have been obvious to one of ordinary skill in the art at the time of the invention to include generating a table of edges and assigned edge weights as taught by Liu in the combination of Spieckerman and Geng, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Specifically, as Spieckerman teaches that edges and edge weights are generated between geographic regions when searching for comparable properties, one of ordinary skill in the art would have recognized that the edges and edge weights of Spieckerman and Geng would have been able to be presented/stored in a generated table like that taught by Liu.
Regarding claim 2, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 1. Spieckerman further teaches:
performing clustering of the geographic sectors based on the edge weights (see Fig. 3 and [0063]-[0065], particularly [0063] "the method 200 can analyze the patterns among the regions and the subject property to identify contiguous regions having similar characteristics and that also intersect the location of the subject property...The group of regions having a particular closeness in statistical characteristics to each other (e.g., regions with sufficiently low inter-region variance) can be analyzed to determine whether the regions are spatially adjacent to each other. Additionally, the contiguous regions can then be analyzed to determine if the contiguous region includes or intersects the subject property. For example, the contiguous regions enclosed by the dashed line 312 in FIG. 3 include or intersect with the subject property at the center 312 of the graphic 300" for adjacent regions with sufficiently low variance (i.e. highest degree of similarity being defined as the neighborhood))
and defining a price-linked neighborhood of geographic sectors based on the clustering (see [0065] "Properties located in the contiguous region (e.g., within the neighborhood) are likely to be the properties that a buyer would look to as substitutes for the subject real estate property. Also, if the buyer is interested in the subject property, the buyer also is more likely to be interested in other properties in the same neighborhood. Therefore, properties having recent sales that are located in the contiguous region, e.g., within the neighborhood, are likely to be the best matches as "comps" to the subject property" for the continuous cluster of regions being defined as a neighborhood. See [0057] for the region scores used as edge weights including sales prices for the neighborhoods being price linked)
Regarding claim 3, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 2. Spieckerman further teaches:
receiving a selection identifying the target property (see [0021] "A user of the system 100 can use one of the computing devices 112 to request or access various information from the system 100 including information related to a valuation of a particular property (e.g., lists of comparable properties, property valuations, information from the data stores 108a, 108b, and so forth)" for a user requesting comparable properties for a target property)
identifying a target geographic sector including the target property (see [0051] "FIG. 3 is an example of a graphic 300 showing regions 304 (squares in this example) mapped in the geographic area 308 near the subject real estate property (at the center 302 of the graphic 300)" and [0053] " the central region 304a surrounding the subject property is subdivided into smaller regions 304b (e.g., quadrants in this example) leading to a total of 28 regions within the geographic area 308")
defining the price-linked neighborhood based on identifying the geographic sectors sharing highest edge weights with the target geographic sector (see Fig. 3 and [0063]-[0065], particularly [0063] "the method 200 can analyze the patterns among the regions and the subject property to identify contiguous regions having similar characteristics and that also intersect the location of the subject property...The group of regions having a particular closeness in statistical characteristics to each other (e.g., regions with sufficiently low inter-region variance) can be analyzed to determine whether the regions are spatially adjacent to each other. Additionally, the contiguous regions can then be analyzed to determine if the contiguous region includes or intersects the subject property. For example, the contiguous regions enclosed by the dashed line 312 in FIG. 3 include or intersect with the subject property at the center 312 of the graphic 300" for adjacent regions with sufficiently low variance (i.e. highest degree of similarity being defined as the neighborhood)
and generating the set of recent sales based on properties comparable to the target property located within the price-linked neighborhood (see [0065] "Properties located in the contiguous region (e.g., within the neighborhood) are likely to be the properties that a buyer would look to as substitutes for the subject real estate property…Therefore, properties having recent sales that are located in the contiguous region, e.g., within the neighborhood, are likely to be the best matches as "comps" to the subject property" and [0066] "the method 200 can select properties within the contiguous region(s) as the comparables" and [0070] "at blocks 220, 226, 230, or prior to ending, can provide the selected properties to an entity. For example, the method 200 can communicate the selected properties to... lender, real estate entity, loan provider, etc.")
Regarding claim 4, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 2. Spieckerman further teaches providing the determined comparable properties to an Automated Valuation Model to determine the value of a subject property (see at least [0039], [0042]-[0043]). However, Spieckerman does not explicitly teach training an AVM using machine learning based on the price-linked neighborhoods. Geng further teaches:
training an automated valuation model (AVM) using machine learning based on the price-linked neighborhood (see Col. 3 lines 19-39 "the system 20 also preferably includes a model generator 30 that generates neighborhood-specific AVMs for some or all of the defined neighborhoods. As is known in the art, the model generator 30 may use a machine learning process...Each neighborhood-specific AVM may be based primarily or exclusively on the property data of the properties within the respective neighborhood. As a result, the neighborhood-specific AVMs tend to be more accurate at estimating property values...the model generator updates the neighborhood-specific AVMs substantially in real time (e.g., on an hourly or daily basis) as new data becomes available on properties within the respective neighborhoods" for updating machine learning AVMs based on the neighborhood and Col. 6 line 52 thru Col. 7 line 12 for initial training)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the training of an AVM using machine based on the price-linked neighborhood of Geng into the combination of Spieckerman and Geng. As Geng states in Col. 3 lines 19-39 above, “Each neighborhood-specific AVM may be based primarily or exclusively on the property data of the properties within the respective neighborhood. As a result, the neighborhood-specific AVMs tend to be more accurate at estimating property values”. Therefore, one of ordinary skill in the art would have recognized that incorporating the training of an AVM based on the price-linked neighborhood would result in a more accurate valuation model than the AVMs in Spieckerman alone.
Regarding claim 5, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 1. Spieckerman further teaches:
receiving a selection identifying the target property (see [0021] "A user of the system 100 can use one of the computing devices 112 to request or access various information from the system 100 including information related to a valuation of a particular property (e.g., lists of comparable properties, property valuations, information from the data stores 108a, 108b, and so forth)" for a user requesting comparable properties for a target property)
identifying a target geographic sector including the target property (see [0051] "FIG. 3 is an example of a graphic 300 showing regions 304 (squares in this example) mapped in the geographic area 308 near the subject real estate property (at the center 302 of the graphic 300)" and [0053] " the central region 304a surrounding the subject property is subdivided into smaller regions 304b (e.g., quadrants in this example) leading to a total of 28 regions within the geographic area 308")
generating a heatmap of geographic sectors based on the edge weights between the target geographic sector and other geographic sectors, including: depicting geographic sectors having higher edge weights as hotter; and depicting geographic sectors having lower edge weights as colder (see Fig. 3 and [0039] "the polygons can be colored or shaded to represent, for example, closeness of match of the properties in the polygon to the subject real estate property" and [0062] "a plurality of thresholds can be established to reflect the degree to which a region matches the subject property. For example, if the variance between a region and the subject property is below a first (low) threshold, there is a close match; if the variance is above the first threshold and below a second (higher) threshold, there is a less close match, and so forth. In some cases, different colors, shades, or hues (or other graphical pattern) can be set to correspond to different closeness thresholds, e.g., green represents a close match to the subject property, yellow represents an intermediate match, and red represents a remote or distant match" for regions that are most similar to the target region shown as green and dissimilar regions red. Examiner is interpreting "hotter" and "colder" to cover indications of high/low similarity to the target region)
Regarding claim 9, Spieckerman teaches:
A memory device storing instructions that, when executed, cause a processor to (see [0090] "Code modules or any type of data may be stored on any type of non-transitory computer-readable medium, such as physical computer storage including hard drives, solid state memory, random access memory (RAM), read only memory (ROM), optical disc, volatile or non-volatile storage, combinations of the same and/or the like")
Regarding the limitations of “execute a property valuation operation via a computing system, including: identify pairs of similar property sales in historical property sale records; determine geographic sectors corresponding to locations of properties involved in the similar property sales; generate a table of edges between the geographic sectors; assign edge weights between the geographic sectors in the table based on a number of pairs of similar property sales involving the geographic sectors in the historical property sale records”, see the rejection of claim 1 above.
Regarding the limitations of “perform clustering of the geographic sectors based on the edge weights; and define a price-linked neighborhood of geographic sectors based on the clustering”, see the rejection of claim 2 above.
Regarding claim 10, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 9. Spieckerman further teaches:
receive a selection identifying a target property; identify a target geographic sector including the target property (see [0021] "A user of the system 100 can use one of the computing devices 112 to request or access various information from the system 100 including information related to a valuation of a particular property (e.g., lists of comparable properties, property valuations, information from the data stores 108a, 108b, and so forth)" for a user requesting comparable properties for a target property. See [0051] "FIG. 3 is an example of a graphic 300 showing regions 304 (squares in this example) mapped in the geographic area 308 near the subject real estate property (at the center 302 of the graphic 300)" and [0053] " the central region 304a surrounding the subject property is subdivided into smaller regions 304b (e.g., quadrants in this example) leading to a total of 28 regions within the geographic area 308")
and generate a set of recent sales of properties comparable to a target property based on edge weight values between the target geographic sector and other geographic sectors (see Fig. 3 and [0063]-[0065], particularly [0063] "the method 200 can analyze the patterns among the regions and the subject property to identify contiguous regions having similar characteristics and that also intersect the location of the subject property...The group of regions having a particular closeness in statistical characteristics to each other (e.g., regions with sufficiently low inter-region variance) can be analyzed to determine whether the regions are spatially adjacent to each other. Additionally, the contiguous regions can then be analyzed to determine if the contiguous region includes or intersects the subject property. For example, the contiguous regions enclosed by the dashed line 312 in FIG. 3 include or intersect with the subject property at the center 312 of the graphic 300" for adjacent regions with sufficiently low variance (i.e. highest degree of similarity being defined as the neighborhood. See [0065] "Properties located in the contiguous region (e.g., within the neighborhood) are likely to be the properties that a buyer would look to as substitutes for the subject real estate property…Therefore, properties having recent sales that are located in the contiguous region, e.g., within the neighborhood, are likely to be the best matches as "comps" to the subject property" and [0066] "the method 200 can select properties within the contiguous region(s) as the comparables" and [0070] "at blocks 220, 226, 230, or prior to ending, can provide the selected properties to an entity. For example, the method 200 can communicate the selected properties to... lender, real estate entity, loan provider, etc." for generating the comparable properties based on the highest edge weight geographic sectors)
Regarding claim 12, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 9. Regarding the limitations introduced in claim 12, see the rejection of claim 3 above.
Regarding claim 13, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 9. Regarding the limitations introduced in claim 13, see the rejection of claim 4 above.
Regarding claim 14, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 9. Regarding the limitations introduced in claim 14, see the rejection of claim 5 above.
Regarding claim 16, Spieckerman teaches:
An apparatus comprising: a processor; and a memory device storing instructions that cause the processor to (see [0088] "Each of the processes, methods, and algorithms described herein and/or depicted in the attached figures may be embodied in, and fully or partially automated by, code modules executed by one or more physical computing systems, hardware computer processors, application-specific circuitry, and/or electronic hardware configured to execute computer instructions" and [0090] "Code modules or any type of data may be stored on any type of non-transitory computer-readable medium, such as physical computer storage including hard drives, solid state memory, random access memory (RAM), read only memory (ROM), optical disc, volatile or non-volatile storage, combinations of the same and/or the like")
Regarding the limitations of “identify pairs of similar property sales in historical property sale records; determine geographic sectors corresponding to locations of properties involved in the similar property sales; generatea table of edges between the geographic sectors; assign edge weights between the geographic sectors in the table based on a number of pairs of similar property sales involving the geographic sectors in the historical property sale records”, see the rejection of claim 1.
Regarding the limitations of “receive a selection identifying a target property; identify a target geographic sector including the target property; generate a heatmap of geographic sectors based on the edge weights between the target geographic sector and other geographic sectors, including: depict geographic sectors having higher edge weights as hotter; and depict geographic sectors having lower edge weights as colder”, see the rejection of claim 5.
Regarding claim 17, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 16. Regarding the limitations introduced in claim 17, see the rejection of claim 2 above.
Regarding claim 18, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 17. Regarding the limitations introduced in claim 18, see the rejection of claim 3 above.
Claims 6-7, 11, 15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Spieckerman in view of Geng, Liu, and Den Herder et al. (U.S. Pre-Grant Publication No. 2013/0101234, hereafter known as Den Herder).
Regarding claim 6, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 5 above. Spieckerman further teaches:
providing a user interface to a user (see [0021] "The computing devices 112 can include general purpose computers, data input devices (e.g., terminals or displays), web or application interfaces, portable or mobile computers, laptops or tablets, smart phones, etc" and [0039] "the reporting module 136 can output a graphic, such as the example shown in FIG. 3, showing the polygons mapped by the region mapper 124. In some such cases, the polygons can be colored or shaded to represent, for example, closeness of match of the properties in the polygon to the subject real estate property")
However, while Spieckerman teaches outputting a user interface showing the heatmap of geographic sectors and the neighborhood boundary as discussed above and regarding claim 5, the combination of Spieckerman, Geng, and Liu does not explicitly teach providing a tool in the user interface to allow a user to define the price-linked neighborhood based on the heatmap, defining the price-linked neighborhood based on the input, and generating the set of recent sales based on comparable properties within the price-linked neighborhood defined by the user input. Den Herder teaches:
providing a tool, via the user interface, to enable the user to define a price-linked neighborhood based on the heatmap (see [0051] "A map image is displayed 404 and necessary input is obtained to define the geographic area" and [0052] "The Carve In and Carve Out modes entail interfacing with the user to receive indications to define the shape that in turn defines the geographic area. This may be a manual stringing of segments to define a shape such as a polygon that forms a perimeter of the defined geographic area. Alternatively, a shape tool allows the user to overlay and then resize and manipulate the shape to configure it as desired, so as to match it to whatever the user deems to be the appropriate neighborhood. Automated assistance may also be provided, wherein the application identifies and then suggests a possible boundary of the shape". Also see [0040] and module 205)
defining a price-linked neighborhood based on user input via the tool (see [0053] "Once the defined geographic area is established, the automated valuation model is applied 406 to corresponding property data for properties designated by the defined geographic area" and [0030] for the boundaries defining the neighborhood of interest)
and generating the set of recent sales based on properties comparable to the target property located within the price-linked neighborhood (see [0054] "Application of the model identifies a set of model-chosen comparable properties. The rendering 408 of the map image is then updated to include the subject property and the comparable properties so as to illustrate their relative locations. The boundaries of the defined geographic area may be retained in the map image rendering for appreciation that the comparables are within the desired neighborhood. Additionally, grid data concerning comparable property details may be concurrently displayed alongside the map image")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the user interface tools to allow a user to define a price-linked neighborhood boundary, defining the price-linked neighborhood based on input via the tool, and generating a set of comparables within the defined price-linked neighborhood boundary of Den Herder into the combination of Spieckerman, Geng, and Liu. As Den Herder states in [0006] “Traditional AVM models have implemented fixed geographical standards to define the area subject to automated valuation. AVM systems that accommodate a more tailored approach to property value estimation are needed”. By incorporating the customizability of neighborhood boundary determination into the combination of Spieckerman, Geng, and Liu, property valuation can be more tailored to user needs/preferences to satisfy the need identified by Den Herder. Accordingly, the combined system can be more responsive to user needs instead of simply applying the same neighborhood boundary determining criteria to all users.
Regarding claim 7, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 1 above. Spieckerman further teaches:
sorting the set of recent sales of properties comparable to the target property based on the edge weights (see [0068] "the regions in the geographic area 308 can be weighted or ranked based at least in part on each region's respective variance with respect to the subject property and/or each region's intra-region variance. For example, regions that are close matches to the subject property, and which have low intra-region variance, can be weighted more highly than regions that are less close matches to the subject property and/or have higher intra-region variance. Therefore, comparable properties can be selected preferentially from the more highly weighted or ranked regions" for selecting properties as comps preferentially from higher ranked regions)
and presenting a list of the sorted set of recent sales at a user interface (see [0070] "the method 200 can communicate the selected properties to an AVM for valuation of the subject property, communicate the selected properties to a lender, real estate entity, loan provider, etc.")
While Spieckerman teaches preferentially selecting comparable properties from highly weighted or ranked regions compared to properties from lower-ranked regions above, Spieckerman, Geng, and Liu do not explicitly teach that the selected comparable properties are particularly sorted such that properties from higher edge weight sectors are listed above properties from lower edge weight sectors. Specifically, while Spieckerman teaches sorting to only including properties from higher edge weight sectors in the list shown to the user, Spieckerman, Geng, and Liu does not further teach that the list of properties is further presented in a “highest-to-lowest” order as claimed. Den Herder teaches:
wherein recent sales from geographic sectors sharing higher edge weights with a target geographic sector containing the target property are listed above recent sales from geographic sectors sharing lower edge weights with the target geographic sector (see [0124] "The comparable properties may then be listed according to the weighting, or a ranking from the highest weighted comparable property to the lowest. This listing may be variously limited to accommodate listing them within a display area. For example, a default setting might be 20 comparable properties. The overall list of comparable properties includes, of course, the model-chosen comparable properties". In combination with Spieckerman, the comparable properties preferentially selected are then sorted in ranked order and displayed to the user)
One of ordinary skill in the art would have recognized that applying the known technique of listing higher ranked/weighted property sales above lower ranked/weighted property sales of Den Herder to the combination of Spieckerman, Geng, and Liu would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Den Herder to the teaching of the combination of Spieckerman, Geng, and Liu would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such listing higher ranked/weighted property sales above lower ranked/weighted property sales. Further, applying listing higher ranked/weighted property sales above lower ranked/weighted property sales to the combination of Spieckerman, Geng, and Liu would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient presentation of comparable properties. Specifically, one of ordinary skill in the art would have recognized that organizing the list of comparable properties in order of rank would make it easier for the user to find the best comparables to the target property out of the list of comparables.
Regarding claim 11, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 10. Spieckerman further teaches:
sort the set of recent sales of properties comparable to the target property based on the edge weights (see [0068] "the regions in the geographic area 308 can be weighted or ranked based at least in part on each region's respective variance with respect to the subject property and/or each region's intra-region variance. For example, regions that are close matches to the subject property, and which have low intra-region variance, can be weighted more highly than regions that are less close matches to the subject property and/or have higher intra-region variance. Therefore, comparable properties can be selected preferentially from the more highly weighted or ranked regions" for selecting properties as comps preferentially from higher ranked regions)
and present a list of the sorted set of recent sales at a user interface (see [0070] "the method 200 can communicate the selected properties to an AVM for valuation of the subject property, communicate the selected properties to a lender, real estate entity, loan provider, etc.")
While Spieckerman teaches preferentially selecting comparable properties from highly weighted or ranked regions compared to properties from lower-ranked regions above, the combination of Spieckerman, Geng, and Liu do not explicitly teach that the selected comparable properties are particularly sorted such that properties from higher edge weight sectors are listed above properties from lower edge weight sectors. Specifically, while Spieckerman teaches sorting to only including properties from higher edge weight sectors in the list shown to the user, Spieckerman, Geng, and Liu does not further teach that the list of properties is further presented in a “highest-to-lowest” order as claimed. Den Herder teaches:
wherein recent sales from geographic sectors sharing higher edge weights with the target geographic sector are listed above recent sales from geographic sectors sharing lower edge weights with the target geographic sector (see [0124] "The comparable properties may then be listed according to the weighting, or a ranking from the highest weighted comparable property to the lowest. This listing may be variously limited to accommodate listing them within a display area. For example, a default setting might be 20 comparable properties. The overall list of comparable properties includes, of course, the model-chosen comparable properties". In combination with Spieckerman, the comparable properties preferentially selected are then sorted in ranked order and displayed to the user)
One of ordinary skill in the art would have recognized that applying the known technique of listing higher ranked/weighted property sales above lower ranked/weighted property sales of Den Herder to the combination of Spieckerman, Geng, and Liu would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Den Herder to the teaching of the combination of Spieckerman, Geng, and Liu would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such listing higher ranked/weighted property sales above lower ranked/weighted property sales. Further, applying listing higher ranked/weighted property sales above lower ranked/weighted property sales to the combination of Spieckerman, Geng, and Liu would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient presentation of comparable properties. Specifically, one of ordinary skill in the art would have recognized that organizing the list of comparable properties in order of rank would make it easier for the user to find the best comparables to the target property out of the list of comparables.
Regarding claim 15, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 14. Regarding the limitations introduced in claim 15, see the rejection of claim 6 above.
Regarding claim 19, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 18. Spieckerman further teaches:
sort the set of recent sales of properties comparable to the target property based on the edge weights (see [0068] "the regions in the geographic area 308 can be weighted or ranked based at least in part on each region's respective variance with respect to the subject property and/or each region's intra-region variance. For example, regions that are close matches to the subject property, and which have low intra-region variance, can be weighted more highly than regions that are less close matches to the subject property and/or have higher intra-region variance. Therefore, comparable properties can be selected preferentially from the more highly weighted or ranked regions" for selecting properties as comps preferentially from higher ranked regions)
and present a list of the sorted set of recent sales at a user interface (see [0070] "the method 200 can communicate the selected properties to an AVM for valuation of the subject property, communicate the selected properties to a lender, real estate entity, loan provider, etc.")
While Spieckerman teaches preferentially selecting comparable properties from highly weighted or ranked regions compared to properties from lower-ranked regions above, the combination of Spieckerman, Geng, and Liu do not explicitly teach that the selected comparable properties are particularly sorted such that properties from higher edge weight sectors are listed above properties from lower edge weight sectors. Specifically, while Spieckerman teaches sorting to only including properties from higher edge weight sectors in the list shown to the user, Spieckerman, Geng, and Liu does not further teach that the list of properties is further presented in a “highest-to-lowest” order as claimed. Den Herder teaches:
wherein recent sales from geographic sectors sharing higher edge weights with the target geographic sector are listed above recent sales from geographic sectors sharing lower edge weights with the target geographic sector (see [0124] "The comparable properties may then be listed according to the weighting, or a ranking from the highest weighted comparable property to the lowest. This listing may be variously limited to accommodate listing them within a display area. For example, a default setting might be 20 comparable properties. The overall list of comparable properties includes, of course, the model-chosen comparable properties". In combination with Spieckerman, the comparable properties preferentially selected are then sorted in ranked order and displayed to the user)
One of ordinary skill in the art would have recognized that applying the known technique of listing higher ranked/weighted property sales above lower ranked/weighted property sales of Den Herder to the combination of Spieckerman, Geng, and Liu would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Den Herder to the teaching of the combination of Spieckerman, Geng, and Liu would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such listing higher ranked/weighted property sales above lower ranked/weighted property sales. Further, applying listing higher ranked/weighted property sales above lower ranked/weighted property sales to the combination of Spieckerman, Geng, and Liu would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient presentation of comparable properties. Specifically, one of ordinary skill in the art would have recognized that organizing the list of comparable properties in order of rank would make it easier for the user to find the best comparables to the target property out of the list of comparables.
Regarding claim 20, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 16. Spieckerman further teaches:
provide a user interface to a user (see [0021] "The computing devices 112 can include general purpose computers, data input devices (e.g., terminals or displays), web or application interfaces, portable or mobile computers, laptops or tablets, smart phones, etc" and [0039] "the reporting module 136 can output a graphic, such as the example shown in FIG. 3, showing the polygons mapped by the region mapper 124. In some such cases, the polygons can be colored or shaded to represent, for example, closeness of match of the properties in the polygon to the subject real estate property")
However, while Spieckerman teaches outputting a user interface showing the heatmap of geographic sectors and the neighborhood boundary as discussed above and regarding claim 5, the combination of Spieckerman, Geng, and Liu does not explicitly teach providing a tool in the user interface to allow a user to define the price-linked neighborhood based on the heatmap, defining the price-linked neighborhood based on the input, and generating the set of recent sales based on comparable properties within the price-linked neighborhood defined by the user input. Den Herder teaches:
provide a tool, via the user interface, to enable the user to define a price-linked neighborhood based on the heatmap (see [0051] "A map image is displayed 404 and necessary input is obtained to define the geographic area" and [0052] "The Carve In and Carve Out modes entail interfacing with the user to receive indications to define the shape that in turn defines the geographic area. This may be a manual stringing of segments to define a shape such as a polygon that forms a perimeter of the defined geographic area. Alternatively, a shape tool allows the user to overlay and then resize and manipulate the shape to configure it as desired, so as to match it to whatever the user deems to be the appropriate neighborhood. Automated assistance may also be provided, wherein the application identifies and then suggests a possible boundary of the shape". Also see [0040] and module 205)
define a user-selected price-linked neighborhood based on user input via the tool (see [0053] "Once the defined geographic area is established, the automated valuation model is applied 406 to corresponding property data for properties designated by the defined geographic area" and [0030] for the boundaries defining the neighborhood of interest)
and generate a set of recent sales based on properties comparable to the target property located within the user-selected price-linked neighborhood (see [0054] "Application of the model identifies a set of model-chosen comparable properties. The rendering 408 of the map image is then updated to include the subject property and the comparable properties so as to illustrate their relative locations. The boundaries of the defined geographic area may be retained in the map image rendering for appreciation that the comparables are within the desired neighborhood. Additionally, grid data concerning comparable property details may be concurrently displayed alongside the map image")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the user interface tools to allow a user to define a price-linked neighborhood boundary, defining the price-linked neighborhood based on input via the tool, and generating a set of comparables within the defined price-linked neighborhood boundary of Den Herder into the combination of Spieckerman, Geng, and Liu. As Den Herder states in [0006] “Traditional AVM models have implemented fixed geographical standards to define the area subject to automated valuation. AVM systems that accommodate a more tailored approach to property value estimation are needed”. By incorporating the customizability of neighborhood boundary determination into the combination of Spieckerman, Geng, and Liu, property valuation can be more tailored to user needs/preferences to satisfy the need identified by Den Herder. Accordingly, the combined system can be more responsive to user needs instead of simply applying the same neighborhood boundary determining criteria to all users.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Spieckerman in view of Geng, Liu, and Shahbazi et al. (U.S. Patent No. 11,861,748; hereafter known as Shahbazi).
Regarding claim 8, the combination of Spieckerman, Geng, and Liu teaches all of the limitations of claim 1. Spieckerman teaches dividing the area into geographic regions, adjusted so that there are a threshold number of sales in each region in paragraphs [0052]-[0053]. However, the combination of Spieckerman, Geng, and Liu does not explicitly teach the size of the sector based upon a density of properties on the map. Shahbazi teaches:
dividing a map into the geographic sectors, wherein a size of the geographic sectors is set based on a density of properties on the map (see Col. 4 lines 4-6 "In some embodiments regions of a certain size contain the same or similar number of homes, and may have areas and/or dimensions that vary significantly" for dividing the regions such that they have a similar number of homes. More property dense areas would accordingly have smaller size regions than less property dense areas in order for the regions to have a similar number of properties within their boundaries)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of dividing an area into sectors based on having a similar number of properties in each region of Shahbazi for the dividing of areas based on a threshold number of sales of the combination of Spieckerman, Geng, and Liu.
Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Aron et al. (U.S. Patent No. 11,455,089) teaches providing a user interface comprising a heatmap
Flint et al. (U.S. Patent No. 10,380,653) teaches receiving information regarding associated properties and obtaining an estimated valuation of a target property
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/M.C.M./Examiner, Art Unit 3628
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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