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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/08/2026 has been entered.
Detailed Action
This Non-Final Office Action is in response to Applicant’s Request for Reconsideration filed 01/08/2026.
The effective filing date of the present application is 04/20/2022.
Response to Amendment
Applicant's reply and remarks of 01/08/2026 have been entered.
Claims 1 – 2, 5 – 13, and 15 – 20, are pending, claims 3 – 4 being presently cancelled.
The examiner will address applicant's remarks at the end of this office action.
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 – 2, 5 – 13, and 15 – 20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At Step 1 of analysis, the instant claims are directed towards a method (process) and a system (apparatus). Thus, all claims recite one of the statutory categories of invention.
At Step 2A, Prong One, of analysis, the Examiner maintains that the claims, as a whole, set forth a process for receiving parcel data associated with a property of interest, determining certain attributes (evaluating the data), and then predicting and adjusting a property value (making a judgment or opinion as to the value). This can be described as a mental process, and thus, recites an abstract idea.
Claim 1, which is illustrative of claims 11, contains the elements that define this abstract idea (and are highlighted below):
A method, comprising: receiving a top-down image of a property of interest;
extracting a set of measurements depicting the property of interest from the top-down image by image segmentation;
retrieving auxiliary property information indicative of property value;
determining parcel data associated with the property of interest from the auxiliary property information;
determining a set of property condition attributes using a set of attribute models based on the set of measurements and the parcel data;
determining a set of uncertainty parameters associated with each property condition attribute of the set of property condition attributes;
training a third-party automated valuation model based on a set of auxiliary properties, wherein each auxiliary property of the set of auxiliary properties is associated with a set of auxiliary property attribute values and an actual auxiliary property value,
wherein the third-party automated valuation model is trained by:
for each auxiliary property of the set of auxiliary properties:
generating a predicted auxiliary property value using the third-party automated valuation model, given the respective set of auxiliary property attribute values of each auxiliary property; and
training the third-party automated valuation model based on a comparison between the actual auxiliary property value of the auxiliary property and the respective predicted auxiliary property value to account for nonstructural property attributes:
receiving a predicted property value from the trained third-party automated
valuation model, wherein the trained third-party automated valuation model is configured to receive the set of property condition attributes and the set of uncertainty parameters as inputs; and
adjusting the predicted property value for the property of interest, given the set of property condition attributes and the set of uncertainty parameters.
Claim 11, adds elements that further define this abstract idea (and are highlighted below):
determine an adjustment factor based on the set of property condition attributes by: determining a discount curve associated with a property condition attribute of the set of property condition attributes; and
determining a property discount value using the discount curve based on a property condition attribute value for the property condition attribute, wherein the adjustment factor comprises the property discount value.
At Step 2A, Prong Two, of analysis, the Examiner has determined that the identified abstract idea (judicial exception) is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f). Further, in MPEP 2106.05(f) it is noted that "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology.
Claims 1 and 11 recite the following additional elements:
image segmentation;
automated valuation model;
a system, comprising: a processing system.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has described the system generically in their disclosure, at Specification [0029], and Figure 13, as filed. The automated valuation model is also broadly defined, namely; “…method can include and/or be used with: one or more property attribute models, one or more automated valuation models, one or more adjustment models, and/or any other suitable models.” Specification [0060 and 0062]. Further, image segmentation requires inputting an image data into a property attribute model; thus, utilizing a general-purpose computer to gather and analyze readily obtainable imagery. See Specification [0034].
Newly added elements of training a model based on a set of auxiliary attribute values covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. Specifically, this step describes that the “… models can be trained (e.g. pre-trained) using: self-supervised learning, semi-supervised learning, supervised learning, unsupervised learning, reinforcement learning, transfer learning, Bayesian optimization, positive-unlabeled learning, using backpropagation methods (e.g., by propagating a loss calculated based on a comparison between the predicted and actual training target back to the model; by updating the architecture and/or weights of the model based on the loss; etc.), and/or otherwise learned.” Specification [0061]. See also an abstract concept defined by: “…property attribute model can be a neural network (NN) that extracts the property attribute value from property measurements. The NN can be trained using training property measurements (and/or features extracted therefrom) associated with ground-truth attribute values (e.g., determined by a user, calculated from a ranking, etc.). Specification [0066]. (Emphasis added). Accordingly, alone and in combination, these additional elements are directed to the abstract idea and do not integrate the abstract idea into a practical application.
At Step 2B of eligibility analysis, the Examiner has determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not amount to more than simply instructing one to practice the abstract idea within a computer environment to perform the steps that define the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of: (image segmentation; automated valuation model; a processing system), amounts to no more than mere instructions to implement an abstract idea on a computer and 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).
Newly added elements describing training a model; however, it does not provide any details about how the trained model operates, other than receive property information and provide an output (an adjusted value). Therefore, this is also a recitation of a computer to perform the training and amount, to mere instructions to apply the judicial exception using a generic computer component, per MPEP 2106.05(f). The claims are not patent eligible.
Dependent claims 2, 9, and 12 contain further embellishments to the same abstract idea found in claims 1 and 11. Recitations to certain condition attributes of the property as well as 3D measurement and aerial imagery of the property are descriptive of the information about a property and are the crux of the abstract idea – information that is to be evaluated and judged. Further, claim 11 relies on the instructions to implement the abstract idea on a computer, using the generically described devices noted above. This does not render the claims as being patent eligible. See MPEP 2106.04(d).
Dependent claims 5 – 6, 10, and 16 – 18, contain further embellishments to the same abstract idea found in claims 1 and 11. Recitations to ways to train the model, determining trends, a discount curve, and predicted property values, are descriptive of the evaluation method employed. The use of a machine-learning model is broadly defined and amounts to instructing one to perform the abstract idea on a computer. See MPEP 2106.04(d). Accordingly, the claims are directed to the abstract idea.
Dependent claims 7, 8, 13, 15, 19, and 20, contain further embellishments to the same abstract idea found in claims 1 and 11. Recitations to ingest data, and extract data, describe the information about a property and are the crux of the abstract idea – information that is to be evaluated and judged. Further, these claims rely on the on instructions to implement the abstract idea on a computer, using the generically described devices noted earlier. This does not render the claims as being patent eligible. See MPEP 2106.04(d).
Therefore, for the reasons cited above, claims 1 – 2, 5 – 13, and 15 – 20, are directed to an abstract idea without integration into a practical application and without reciting significantly more.
Response to Arguments
Applicant's arguments filed 01/08/2026 have been fully considered but they are not persuasive. Applicant first discusses rejection of all prior claims under 35 U.S.C. § 101, and traverses this rejection. The Applicant disagrees with the Office conclusion that the claims do not include limitations that are significantly more than an abstract idea. See page 7. Applicant discusses the Office procedures regarding such rejections, on pages 7 – 9, and concludes that the amended claims are similar to Desjardins; in that they are directed to a method of training a machine learning that provides an improvement to the machine learning technology itself. See page 10. Based on the reasoning that follows, the Examiner respectfully disagrees.
First, as to Desjardins, that memo did not intend to announce any new USPTO practice or procedure and was meant to be consistent with existing USPTO guidance. As such, the Examiner maintains a conclusion that the amended claims are directed to an abstract idea without significantly more. The Ex Parte Desjardins decision analyzed eligibility in terms of whether the claims were directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field under longstanding Federal Circuit precedent. In Desjardins, the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. (Emphasis added).
The Examiner concludes there are no such steps recited within the claims or described within the disclosure that an improvement in the functioning of a computer, or an improvement to another technology or technical field as Applicant argues. Desjardins delineated the improvements to the technology; no such improvement is within the instant application. The amended claims merely recite receiving data (image and attribute parameters), making determinations, providing (input data) and receiving an adjusted value (data output). The Examiner maintains that these recitations describe a process for receiving parcel data associated with a property of interest, determining certain attributes (evaluating the data), and then predicting and adjusting a property value (making a judgment or opinion as to the value). They do not recite any improvement. Applicant’s reliance on Desjardins is not persuasive.
Further regarding Applicant’s argument that the claims recite an improvement to AVMs; this argument is not persuasive. Valuation of property is not a technical field. An improvement made to how a property is valued, may be an improvement to a method a human may employ to value a property, but an improvement to an abstract idea (a mental process) is not an improvement to technology. Making a better evaluation or judgment, or finalizing a better opinion, may improve an abstract process performed in the mind, but it does not improve component performance, storage operations, or preservation of important data as Desjardins exemplified as improvements. Applicant’s improvements include “more accurate predictions” and “account for non-structural attributes.” Thus, Applicant is pointing to more robust data and improved analysis and evaluation steps. These are not technology-centric and do not reflect improvements to technology. Applicant’s arguments are not persuasive.
Conclusion
Upon updated search, the Examiner maintains a conclusion that the instant claims are distinguished over prior art. This conclusion is detailed within the Office Action filed 10/08/2025.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DON EDMONDS whose telephone number is (571) 272-6171. The examiner can normally be reached M-F 8am-4pm EST.
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DONALD J. EDMONDS
Examiner
Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629