DETAILED ACTION
This communication is a Non-Final Office Action on the merits in response to communications received on 11/20/2025. Claims 42-46, 49-52, and 56-59 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/20/2025 has been entered.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 42-61 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
4. 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.
5. Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
6. 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 recites 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.
7. 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.
8. 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.
9. 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
Applicant's arguments filed 20 November 2025 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 solution for training valuation models. Specifically, the independent claims provide a technical solution for training valuation models when insufficient training data exists. 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].”
“In the present response, the claims reflect these technical improvements discussed in the specification by reciting details of how a listing price adjustment model including a plurality of submodels 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. Independent claims 42, 49, and 56 therefore recite improvements in training valuation models. Further, as recited in the claims, the first valuation model is included in a plurality of valuation models used by a metamodel to determine the overall valuation for a home. As recited in the claims, the metamodel may use input weighting based on the accuracy of the valuation models to determine the overall valuation. Accordingly, the generation of the synthetic sale data affects not only the first valuation model but also affects the valuation process downstream by affecting the configuration of the metamodel; because the synthetic sale data allows for the first valuation model to be trained and determine more accurate valuations, the weights determined by the metamodel are affected.
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. 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. The Applicant argues that “the technical solution for training valuation models when insufficient training data exists” and provides passages, [i.e., ¶ 0023, 0038] for consideration, however, there are no technical improvements are recited. Merely relying upon the type of data or information being trained or used by the models to determine the valuation for the distinguished home does not alter the analysis or solve any technical problem. As an ordered combination, the limitations that describe how the models may be trained or updated recite result-oriented functions rather than technical improvements to computers or technology. See Ericsson Inc. v. TCL Communication Tech. Holdings Ltd., 955 F.3d 1317, 1328 (Fed. Cir. 2020) Also, the courts have previously held "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)
The Examiner asserts the scope of the claimed invention, as discussed in the Specification, is not directed towards a technical solution to a technical problem, but directed to applying or training machine learning models while performing a valuation for a distinguished home which amounts to generic data processing. The Specification’s disclosure on training and implementing the machine learning models is/are a high-level general explanation of generic machine learning technology and applying it to the abstract idea. For example, Applicant’s claimed invention is not improving upon or resolving a problem that arose in machine learning, machine learning training, or meta-models and is merely relying upon or utilizing a meta-model and/or submodels for the known advantages they provide, e.g., increased accuracy, error reduction, efficiency, etc. At best, the claimed invention is directed towards generic techniques of how machine learning models, submodels, and a meta-model function or are trained and applying it to the abstract idea. 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.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. 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. 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, the claimed invention remains ineligible under Step 2B. It is important for applicant to note that considerations under Step 2A - Prong Two overlap with consideration under Step 2B, thus may of the considerations need not to be reevaluated in Step 2B because the result will be the same as is the case here.
Applicant relies upon features for improving the technical manner in which a valuation model is trained in a conclusory manner. At best, the features may add some specificity to the claim but the claimed invention focuses on an improvement to the abstract idea of generating a valuation for a distinguished home, not improvements to the functioning of a computer itself or to machine learning technology. The ordered combination of additional elements are recited at a high-level of generality in the claim and merely behave in their ordinary or normal capacity to aid in performing the abstract idea, which does not integrate the judicial exception into a practical application or provide an inventive concept. For these reasons, the rejections under 101 are being maintained.
Conclusion
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