DETAILED ACTION
This communication is a Final Office Action on the merits in response to communications received on 05/06/2026. Claims 49 and 56 have been amended. Therefore, claims 42-61 are pending and have been addressed below. The present application is being examined under the pre-AIA first to invent provisions.
Claim Rejections - 35 USC § 101
1. 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.
2. Claims 42-61 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
3. Under Step 1 of the two-part analysis from Alice Corp: claim 42 recites a process (a series of acts or steps), claim 49 recites a manufacture (an article that is given a new form, quality, property, or combination through man-made or artificial means), claim 56 recites a machine (consisting of parts, or of certain devices and combination of devices). Thus, each of these claims falls within one of the four statutory categories.
4. Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
5. Claim 42 which is representative of claims 49 and 56 recites:
“using synthetic sale data”, “receiving a first set of training items, the first set of training items comprising a set of homes that have been sold, each training item of the first set of training items including a sale price of a first home, a listing price of the first home, and a value for a plurality of attributes associated with the first home;”, “using the first set of training items”, “generate a synthetic sale price for a home based on a listing price of the home”, “selecting, from the first set of training items, a first subset of training items and a subset of attributes from the plurality of attributes;”, “using the subset of training items”, “generate the synthetic sale price based on the subset of attributes;”, “determining an error value associated, wherein determining the error value associated includes: selecting, from the first set of training items, a second subset of training items;”, “for at least one training item of the second subset of training items”, “a value for at least one attribute of the subset of attributes to generate an estimated sale price;”, “determining a difference between the estimated sale price and the corresponding sale price;”, “determining an item error value based on the difference;” and “determining the error value based on the item error values;”, “assigning a weight…based on the error value;”, “generating a second set of training items, the second set of training items comprising a set of homes listed for sale prior to being sold, each training item of the second set of training items including a synthetic sale price for a second home, the synthetic sale price being determined…based on a listing price of the second home and a value for a plurality of attributes associated with the second home;”, “filtering the first set of training items and the second set of training items to remove outlier homes, wherein the outlier homes include distressed homes;”, “using the first set of training items and the second set of training items, based on (i) determining an error value associated…during a first training routine using the first set of training items and the second set of training items, and (ii) assigning a weight…associated with the determined error value”, “accept valuations determined”, “determining a weight for …based on an accuracy;”, “generating an estimated value of a distinguished home…to a set of values of home attributes of the distinguished home”, “generates the estimated value using the assigned weights;”, generating…an overall valuation based on the estimated values generated and a corresponding weight…to produce…the estimated value.
The limitations as drafted under their broadest reasonable interpretation limitations recite the abstract idea of performing a “home valuation” which encompasses fundamental economic principles or practices (i.e., hedging, mitigating transaction risk), mental processes (i.e., observations, evaluations, judgments, and opinions), mathematical concepts (i.e., mathematical relationships, mathematical calculations) and covers subject matter that falls within the certain methods of organizing human activity, mental processes and mathematical concepts groupings of abstract ideas. See MPEP 2106.04(a)(2)
The Applicant’s Specification in ¶ [0002-0004] emphasizes in many roles, it can be useful to be able to accurately determine the value of residential real estate properties ("homes"). As examples, by using accurate values for homes: taxing bodies can equitably set property tax levels; sellers and their agents can optimally set listing prices; buyers and their agents can determine appropriate offer amounts; insurance firms can properly value their insured assets; and mortgage companies can properly determine the value of the assets securing their loans. A variety of conventional approaches exist for valuing homes. One example is, for a home that was very recently sold, attributing its selling price as its value. Another widely-used conventional approach to valuing homes is appraisal, where a professional appraiser determines a value for a home by comparing some of its attributes (more precisely, the values of its attributes) to the attributes of similar nearby homes that have recently sold ("comps"). The appraiser arrives at an appraised value by subjectively adjusting the sale prices of the comps to reflect differences between the attributes of the comps and the attributes of the home being appraised.
For example, the claim recite a series of steps for “generating an estimated value of a distinguished home” and “generating an overall valuation” which covers concepts related to commerce and the economy. The recited limitations cover tasks for performing a home valuation which may be reasonably characterized as a fundamental economic practice that has been widely used in the real estate industry. The claimed steps recite mental processes for comparing and evaluating determined valuation(s) for error and accuracy which are acts that can be performed in the human mind and/or with the aid of pen and paper. The claim also recites mathematical operations or an act of calculating using mathematical methods to generate a valuation for the distinguished home. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, the step of "generating" a variable or value using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. Therefore, the claim as a whole recites an abstract idea.
6. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this
judicial exception is not integrated into a practical application because the additional elements of: “training a first valuation model”, “wherein training the first valuation model includes”, “training…, a listing price adjustment model comprising a plurality of submodels”, “wherein the listing price adjustment model is trained to”, “wherein training the listing price adjustment model includes:”, “for at least one submodel in the plurality of submodels”, “training… the submodel to”, “with the submodel”, “applying…to the submodel”, “to the submodel”, “by the trained listing price adjustment model”, “periodically training the first valuation model comprising a plurality of data models”, “with each of the plurality of data models”, “to each data model of the plurality of data models”, “wherein the plurality of data models includes a configurable number of data models;”, “configuring a metamodel to”, “by a plurality of valuation models, the plurality of valuation models including the first valuation model, wherein to configure the metamodel to”, “for at least one valuation model of the plurality of valuation models:”, “the valuation model”, “for at least one valuation model of the plurality of valuation models”, “by applying the valuation model”, “wherein the first valuation model”, “using the metamodel”, “by at least one valuation model of the plurality of valuation models”, “for the at least one valuation model of the plurality of valuation models”, “a graphical display of”, “within a user interface of a computing system” - see claim 42, “a computing system”, “one or more processors”, “one or more memories”, “a non-transitory computer-readable storage medium”, – see claims 49, 56 are recited at a high level of generality in light of the specification. The Specification [¶ 0025, 0039, 0074] provides a general explanation of the additional elements devoid any technical implementation details, such that the additional elements may be construed as generic computer components and machine learning technology being used to aid in performing the abstract idea. Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, computer, machine learning, and display technology for implementing the abstract idea. Thus, the additional elements merely add the words “apply it” with the judicial exception or mere instructions to implement the abstract idea on a computer, as discussed in MPEP 2106.05 (f)
The other additional element of: “a method for reducing model error associated with providing a valuation of a home, the method comprising:” in the preamble is merely indicating a field of use or technological environment in which to apply the judicial exception, as discussed in MPEP 2106.05 (h)
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
7. The claims do not include additional elements that, when analyzed individually and in combination, 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: “training a first valuation model”, “wherein training the first valuation model includes”, “training…, a listing price adjustment model comprising a plurality of submodels”, “wherein the listing price adjustment model is trained to”, “wherein training the listing price adjustment model includes:”, “for at least one submodel in the plurality of submodels”, “training… the submodel to”, “with the submodel”, “applying…to the submodel”, “to the submodel”, “by the trained listing price adjustment model”, “periodically training the first valuation model comprising a plurality of data models”, “with each of the plurality of data models”, “to each data model of the plurality of data models”, “wherein the plurality of data models includes a configurable number of data models;”, “configuring a metamodel to”, “by a plurality of valuation models, the plurality of valuation models including the first valuation model, wherein to configure the metamodel to”, “for at least one valuation model of the plurality of valuation models:”, “the valuation model”, “for at least one valuation model of the plurality of valuation models”, “by applying the valuation model”, “wherein the first valuation model”, “using the metamodel”, “by at least one valuation model of the plurality of valuation models”, “for the at least one valuation model of the plurality of valuation models”, “a graphical display of”, “within a user interface of a computing system” - see claim 42, “a computing system”, “one or more processors”, “one or more memories”, “a non-transitory computer-readable storage medium”, – see claims 49, 56 at best amounts to mere instructions to implement an abstract idea on a computer which does not provide an inventive concept. Thus, the claims are ineligible at Step 2B.
8. Claims 43-48, 50-55, and 57-61 are dependent of claims 42, 49, and 56.
Claims 43, 50, and 57 recite “wherein the plurality of submodels includes a plurality of tree data models, and wherein the synthetic sale price for the second home is determined by: initializing a data structure for collecting synthetic sale price estimations from each of the plurality of tree data models; for each tree of the plurality of tree data models, traversing edges of the tree to reach a leaf node whose range of encompassed attribute values or listing prices corresponds to an attribute value or listing price of the second home; and adding a sale price associated with the leaf node to the data structure; and selecting a statistical element in the data structure, such that an identified median element in the data structure is the synthetic sale price for the second home” which are acts for collecting and organizing information. The recited additional elements of “the plurality of submodels includes a plurality of tree data models” and “the data structure” are recited at a high-level of generality and behave in their ordinary or normal capacity for processing, retrieving, and storing data., claims 44, 51, and 58 recite “wherein the plurality of tree data models is a random forest of decision trees, and wherein training the listing price adjustment model includes: training each tree data model of the plurality of tree data models such that each leaf node of the tree data model represents a distinct combination of ranges of values of one or more attributes associated with the first home, each first home of each training item of the first second set of training items being represented by exactly one leaf node;” which further describes the types of models that may be used, however, this does not change the analysis. The recited additional element of “training the listing price adjustment model” at best amounts to generic data processing and is nothing more than mere instructions to implement the abstract idea on a computer – See MPEP 2106.05(f), claims 45, 48, 52, 55, and 59 recites “wherein the plurality of data models includes a plurality of tree data models, wherein a number of tree models included in the plurality of tree data models is configurable, and wherein training the first valuation model includes determining a relative weight for each tree data model of the plurality of tree data models based on one or more sets of test data items, and wherein the one or more sets of test data items are sampled subsets from a list of recent listing transactions occurring within a defined geographic area” which further describes the types of models that may be used, however, this does not change the analysis. The recited additional element of “training the first valuation model” at best amounts to generic data processing and is nothing more than mere instructions to implement the abstract idea on a computer – See MPEP 2106.05(f), claims 46, 53, and 60 recite “wherein each test data item of the test data items includes one or more home attributes, a listing price, and a sale price of a test data home, and wherein determining a relative weight for each tree data model of the plurality of tree data models includes: applying each test data item to at least one tree data model; determining a valuation for the test data home associated with the test data item based on the one or more home attributes or the listing price; determining an error measure based on the valuation for the test data home and sales price of the test data home; and recording the error measure for the test data home of each test data item” which further describes the data or information recited with the abstract idea and further narrows how the abstract idea may be performed., claims 47, 54, and 61 recite “obtaining an overall error measure for the at least one tree data model based on the recorded error measure of each test data item; and assigning the weight to the at least one tree data model inversely related to the at least one tree data model's overall error measure.” which further describes the data or information recited with the abstract idea and further narrows how the abstract idea may be performed. None of these features change the analysis. Accordingly, when viewed individually and as an ordered combination with the abstract idea the dependent claims do not integrate the abstract idea into a practical application or provide an inventive concept.
Response to Arguments
9. Applicant's arguments filed 05/06/2026 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “Claims 42-61 were rejected under 35 U.S.C. § 101 as being an abstract idea without significantly more. Applicant respectfully traverses the 35 U.S.C. § 101 rejection. Reconsideration and withdrawal of the rejection of claims 42-61 are respectfully requested for at least the following reasons.
Under Step 2A Prong Two, Examiners are to evaluate if the claims recite additional elements that integrate a judicial exception into a practical application. MPEP 2106.111. In examples, an additional element (or combination of elements) may integrate an exception into a practical application by improving the functioning of a computer or through an improvement to other technology or technical fields. MPEP 2106.04(d)(1).
The independent claims provide a technical improvement to machine learning
technology. Specifically, the independent claims provide a technical solution for training valuation models when insufficient training data exists. The claims recite details of how a listing price adjustment model including a plurality of sub-models is trained to generate synthetic sale prices, and generating a training set including these synthetic sale prices to train a first valuation model to generate an estimated value for a home. The claimed solution thereby allows for accurate and quick valuations of homes by the valuation model even when data on actual home sale transactions (e.g., within a given time period and region) is insufficient to train the first valuation model using traditional methods. The Examiner respectfully disagrees.
Contrary to the remarks, the claimed invention remains ineligible under Step 2A-Prong Two of the Alice two-part analysis. The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification [i.e., ¶ 0002-0004, 0025], is not directed towards the improvement of machine learning, but directed towards computer-implemented methods for estimating a value for a home. Here, the claim limitations for training valuation models when insufficient training data exists are not improving upon or resolving a problem that arose in machine learning or machine learning training.
The Applicant’s remarks rely upon utilizing machine learning technology for its known advantages, e.g., increased accuracy, error reduction, efficiency, etc. Thus, the remarks directed towards improving the accuracy of the home valuations is an alleged improvement to the abstract idea itself, not a technological improvement. See BSG Tech LLC V. Buyseasons, Inc., 899 F.3d 1281, 1287-88 (Fed. Cir. 2018). Also, relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible. See Alice, 134 S. Ct. at 2359 ("use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept). At best, the remarks are discussing the type of information that can be inputted into valuation models to output a corresponding result. The Examiner maintains merely relying upon the type of data or information being trained or used by the valuation models to determine the valuation for the distinguished home does not alter the analysis or solve any technical problem. As an ordered combination, the claim limitation describe how the models may be trained or updated and recite result-oriented functions at a high-level of generality rather than technical improvements to computers or machine learning technology. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “These improvements are similar to the improvements in the claims at issue in Ex Parte Desjardins, which were found to be eligible by reciting an improvement in machine learning technology by overcoming the problem of "catastrophic forgetting" i.e., both the claims of the present application and the claims at issue in Ex Parte Desjardins provide improvements in the training of machine learning models, thus providing an improvement in machine learning technology. Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025) (Appeals Review Panel Decision).” The Examiner respectfully disagrees.
The Applicant’s remarks are not persuasive. In the Ex Parte Desjardins court decision, the court cited to the Specification and limitations reflected in the claim. The court held that the claimed invention improves the operation of a machine learning system, such as by enhancing its training efficiency or preserving prior learning, it is not “directed to” an abstract idea under Alice Step 1. The Applicant’s presently recited claim limitations do not recite a comparable technological solution. The Applicant’s Specification [¶ 0023, 0038] does not support a finding that the claims are directed to a technological improvement in machine learning functionality. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The specification supports these technical improvements. As stated in the specification, in traditional methods that perform valuations based on comps, if the set of homes includes a few recent sales, it may not be possible to train a valuation model or otherwise support accurate home valuation estimates. See Specification at [0023]. By supplementing actual home sale transaction data with synthetic sale transaction data, the size of the training and testing data sets can be increased. See Specification at [0038]. Accordingly, with the larger training and testing sets including the synthetic sale data, the valuation model can be properly trained for sets of homes for which valuation models would be unable to be trained using traditional methods. See Specification at [0023], [0038].”
“Based at least on the above cited sections of the specification, Applicant respectfully asserts that the specification provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in machine learning technology. MPEP 2106.04(d)(1). Even if it is alleged that the specification does not explicitly set forth the improvements, which Applicant does not concede, it is enough that the improvement would be apparent to one of ordinary skill in the art. MPEP 2106.04(d)(1).” The Examiner respectfully disagrees.
Contrary to the remarks, the claimed invention remains ineligible under Step 2A-Prong Two of the Alice two-part analysis. The cited passages [¶ 0023,0038] from the Applicant’s Specification discuss benefits of using machine learning for performing accurate home valuations rather than technical improvements to machine learning technology. As for supplementing actual home sale transaction data with synthetic sale transaction data, the type of data being trained or applied by the models to determine the valuation for the distinguished home does not solve any technical problem. The Specification’s disclosure on training and applying the machine learning models is a high-level general explanation of generic machine learning technology and applying it to the abstract idea. See MPEP 2106.05(f) For these reasons, the rejections under 101 are being maintained.
Applicant further argues “For at least these reasons, the claimed solution provides a technical solution for training valuation models when insufficient training data exists. By incorporating synthetic training data generated, at least in part, by a trained listing price adjustment model, the training data can be expanded to levels needed to train the valuation model. Independent claims 42, 49, and 56 therefore recite improvements in machine learning technology, including improvements in the training of valuation models. As such, Applicant respectfully submits that these claims, and claims depending therefrom, are directed to eligible subject matter, and requests reconsideration and withdrawal of the rejection on this basis.” The Examiner respectfully disagrees.
Contrary to the remarks, the claimed invention remains ineligible under Step 2A-Prong Two of the Alice two-part analysis. As previously explained, the Specification [i.e., ¶ 0002-0004, 0023, 0025, 0038] frames the inventor’s problem in terms of how to improve upon existing home valuation methods. The remarks confirm that the problem facing the inventor was how to perform the abstract idea of estimating a valuation for a home when insufficient data exists, not to any technical improvements related to machine learning technology. The cited passages from the Specification and remarks make clear the claimed invention is not directed to any specific technological solutions. See MPEP 2106.05(a) For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Further, under Step 2B, Examiners are to evaluate if additional elements of the claim provide an inventive concept (or "significantly more") than the recited judicial exception. MPEP 2106.111. Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. MPEP 2106.05.1. Under Step 2B, adding a specific limitation other than what is well-understood, routine, conventional activity in the field or adding unconventional steps that confine the claim to a particular useful application qualifies as significantly more than an abstract idea. MPEP 2106.05(d). Additionally, limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include improvements to a technology or technical field. Id. For at least the same or similar reasons as described above, the claims recite elements improving the technical manner in which a valuation model is trained, specifically in cases in which the training data is insufficient to train the valuation model using traditional techniques.” The Examiner respectfully disagrees.
Contrary to the remarks, the claimed invention remains ineligible under Step 2B of the Alice two-part analysis. Here, the Applicant’s remarks cite to different sections of the MPEP, however, merely restating sections from the MPEP in this case does not provide sufficient support as to how the claimed invention provides an inventive concept. The remarks also do not persuasively identify any specific claim limitations of the claimed invention that, considered individually or in combination, amount to significantly more than the abstract ideas.
The Applicant’s remarks discuss the claim limitations improve the technical manner in which a valuation model is trained, specifically in cases in which the training data is insufficient to train the valuation model using traditional techniques. It is important for Applicant to note, the courts have previously held ("A claim for a new abstract idea is still an abstract idea.") – See Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016). Thus, the remarks include conclusory statements from the Applicant and are devoid of any technological implementation details for improving the manner in which a machine learning model functions or operates. The recited training steps being performed in the claim are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f). For these reasons, the rejections under 101 are being maintained.
Applicant further argues “In view of the above-provided reasons, Applicant respectfully submits that independent claims 42, 49, and 56 qualify as eligible subject matter. The other claims each ultimately depend from one of the independent claims and therefore recite patent eligible subject matter for at least the same or similar reasons. Applicant respectfully requests reconsideration and withdrawal of the rejection under 35 U.S.C. § 101.” The Examiner respectfully disagrees.
Contrary to the remarks, independent claims 49 and 56 recite subject matter substantially similar to claim 42, and are therefore being held rejected under the same grounds. The Examiner presented findings for each of these claims and explained why the additional limitations recited by these claims do not impart subject matter eligibility. For these reasons, the rejections under 101 are being maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EHRIN PRATT whose telephone number is (571)270-3184. The examiner can normally be reached 8-5 EST Monday-Friday.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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