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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/19/2026 has been entered.
Claim Objections
Claim 19 is objected to because of the following informalities:
Claim 19 is objected to for reciting ‘a mapping server’. Claim 15, upon which claim 19 is dependent upon, has been amended to recite ‘a mapping server. Claim 19 should instead recite ‘the mapping server’. This appears to be a typographical error, and for the purposes of compact prosecution will be interpreted as such.
Appropriate correction is required.
Status of the Claims
Claims 1-6 and 8-20 are pending. Claims 1, 15 and 20 are amended.
Response to Arguments
Applicant’s arguments, filed 08/19/2026, with respect to the 101 arguments have been considered but are not persuasive.
Applicant’s arguments, on pages 15-19, that the claims are not directed to an abstract idea. Applicant argues that the claims are similar to example 39 and that the claims provide a specific method of data manipulation and use of models to obtain a specific type of prediction data.
Examiner respectfully disagrees. The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as valuation of assets based on their type. Additionally, the claim limitations are analogous to Mathematical Concepts (mathematical formulas/calculations) such as the valuation of the property based on some variables. The generic computer implementations (see below) do not change the character of the limitations. Accordingly, the claims recite an abstract idea. These additional elements (computer elements, models, and knowledge graph) are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Therefore, the claims recite an abstract idea. With respect to example 39, the prediction data is not similar to facial detection. The claims in example 39 are describing the steps of training a neural network for facial detection and do not recite a judicial exception.
Applicant argues, on pages 19-27, that the claims integrate all concepts therein into the practical application. Applicant argues that the claims recite an adaptive blocking model with lowers the computational cost of constructing a data structure. Applicants argue Desjardin and McRo, stating the claims similarly provide an improvement to a technology or technical field.
Examiner respectfully disagrees. With respect to Desjardin, those claims were considered eligible because they provided an improvement to model training. Furthermore, the decision in McRo provided an improvement to the technological process of animation software. As mentioned above, the additional elements do not integrate the judicial exception into a practical application. it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Here, the alleged improvement to a method for generating an asset valuation using a trained model is an improvement to business process of valuation and not to a technology or technical field.
Applicant argues, on pages 27-28, that the claims provide significantly more.
Examiner respectfully disagrees. As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the claims are ineligible.
Novelty/Non-Obviousness
The closest prior art of record is included in the previous office action mailed on 05/21/2026. The claims would be considered allowable if re-written or amended to overcome the rejections in 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 1-6 and 8-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-6 and 8-20 are directed to a series of steps, and therefore is a process.
Independent Claims
Step 2A Prong One
The limitation of Claim 1 recites:
A method for managing valuations of real property assets, the method comprising:
obtaining, …, an asset dataset (AD);
analyzing, …, the AD that is received from the orchestrator to generate a weighted average lease expiry (WALE) of a known lease and to generate a net present value (NPV) of a known cash flow;
obtaining, …, a sale transactions dataset (STD);
obtaining, …, a fingerprint, wherein obtaining the fingerprint comprises sending a location address associated with the …, wherein … queries a knowledge graph (KG) using the location address to return the fingerprint as a unique parcel identifier associated with the location address, wherein the KG is generated by …, wherein … groups records from one or more record sources and for each entity of a plurality of entities reduces the grouped records by consolidating records that are determined to refer to a same entity of the plurality of entities, wherein … is iterated until a number of the grouped records is reduced below a predetermined value to lower a computational cost of generating the KG, and wherein … aggregates the consolidated records onto entities of the plurality of entities, attributes the entities to the KG, and generates a confidence score indicating whether entities were attributed to the KG in a correct structure;
upon receiving the STD …, combining, …, the NPV of the known cash flow, the WALE of the known lease, and the STD using a fingerprint to generate an augmented dataset, wherein the combining comprises identifying, based on the fingerprint, transaction records in the STD corresponding to the fingerprint, wherein the fingerprint corresponds to an entity of the plurality of entities, and associating the transaction records with the entity based on a historical state of the KG corresponding to respective transaction dates;
obtaining, …, a future rent and asset sale price (FRASP) value for a transaction based on the augmented dataset, wherein the FRASP value for the transaction is used to generate a FRASP dataset;
obtaining, …, an economic and demographic dataset (EDD), a market dataset (MD), an asset characteristics dataset (ACD), and a location dataset (LD);
combining, …, the EDD, the MD, the ACD, and the LD that are received from … with the FRASP dataset …;
…
inferring, …, a FRASP value of a real property asset based on an inferencing dataset received …, wherein the real property asset is one selected from a group consisting of a commercial asset and a non-commercial asset;
upon receiving the FRASP value of the real property asset, appending, …, the FRASP value of the real property asset to the inferencing dataset to generate an inferred FRASP value output;
generating, …, an asset valuation value for the real property asset based on the FRASP value of the real property asset and the NPV of the known cash flow; and
initiating, …, a display of the asset valuation value for the real property asset ….
The limitation of Claim 15 recites:
A method for managing valuation of an asset, the method comprising:
obtaining, …, an asset dataset (AD);
analyzing, …, the AD that is received from … to generate a weighted average lease expiry (WALE) of a known lease and to generate a net present value (NPV) of a known cash flow;
obtaining, …, a sale transactions dataset (STD);
obtaining, …, a fingerprint, wherein obtaining the fingerprint comprises sending a location address associated with the AD to …, wherein … queries a knowledge graph (KG) using the location address to return the fingerprint as a unique parcel identifier associated with the location address, wherein the KG is generated …, wherein … groups records from one or more record sources and for each entity of a plurality of entities reduces the grouped records by consolidating records that are determined to refer to a same entity of the plurality of entities, and wherein … aggregates the consolidated records onto entities of the plurality of entities, attributes the entities to the KG, and generates a confidence score indicating whether entities were attributed to the KG in a correct structure;
upon receiving the STD from …, combining, …, the NPV of the known cash flow, the WALE of the known lease, and the STD using a fingerprint to generate an augmented dataset, wherein the combining comprises identifying, based on the fingerprint, transaction records in the STD corresponding to the fingerprint, wherein the fingerprint corresponds to an entity of the plurality of entities, and associating the transaction records with the entity based on a historical state of the KG corresponding to respective transaction dates;
obtaining, …, a future rent and asset sale price (FRASP) value for a transaction based on the augmented dataset, wherein the FRASP value for the transaction is used to generate a FRASP dataset;
obtaining, …, an economic and demographic dataset (EDD), a market dataset (MD), an asset characteristics dataset (ACD), and a location dataset (LD);
combining, …, the EDD, the MD, the ACD, and the LD that are received from …with the FRASP dataset to generate a training dataset (TD), wherein an engine is instructed by the analyzer to generate a model that predicts FRASP values for transactions and wherein the TD is sent to the engine;
…
and initiating, …, notification of an administrator about the trained model …..
The limitation of Claim 20 recites:
A method for managing valuation of an asset, the method comprising:
… generated based on a knowledge graph (KG), wherein the KG is generated …, wherein … records from one or more record sources and for each entity of a plurality of entities reduces the grouped records by consolidating records that are determined to refer to a same entity of the plurality of entities, wherein … aggregates the consolidated records onto entities of the plurality of entities, attributes the entities to the KG, and generates a confidence score indicating whether entities were attributed to the KG in a correct structure,
…
inferring, …, a future rent and asset sale price (FRASP) value of an asset based on an inferencing dataset received from an analyzer;
upon receiving the FRASP value, appending, …, the FRASP value to the inferencing dataset to generate an inferred FRASP value output;
generating, …, an asset valuation value for the asset based on the FRASP value and a net present value (NPV) of a known cash flow; and
initiating, …, notification of an administrator about the asset valuation value for the asset using a graphical user interface (GUI).
The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as valuation of assets based on their type. Additionally, the claim limitations are analogous to Mathematical Concepts (mathematical formulas/calculations) such as the valuations based on some variables and the knowledge graph. The generic computer implementations (see below) do not change the character of the limitations. Accordingly, the claims recite an abstract idea.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
Claim 1:
Orchestrator
Analyzer
Engine
generate a training dataset (TD), wherein an engine is instructed by the analyzer to generate a model that predicts FRASP values for transactions and wherein the TD is sent to the engine;
training, after receiving the TD, by the engine, and based on the TD, the model to obtain a trained model, wherein training the model comprises:
selecting a model type of a plurality of model types based on an absolute percentage error score of the model type generated using the TD, and based on a root mean squared score of the model type generated using the TD;
selecting one or more features of the TD to train the model using a recursive feature elimination model; and
training the model, using a set of linear and non-linear machine-learning models, based on the selected model type, and based on the one or more features of the TD, to obtain the trained model;
Graphical user interface (GUI)
Mapping server
a set of linear and non-linear machine-learning models
an adaptive blocking model of the set of linear and non-linear machine-learning models
a second model of the set of linear and non- linear machine-learning models
Claim 15:
Orchestrator
Analyzer
Engine
generate a training dataset (TD), wherein an engine is instructed by the analyzer to generate a model that predicts FRASP values for transactions and wherein the TD is sent to the engine;
training, by the engine and based on the TD, the model to obtain a trained model, wherein training the model comprises:
selecting a model type of a plurality of model types based on an absolute percentage error score of the model type generated using the TD, and based on a root mean squared score of the model type generated using the TD;
selecting one or more features of the TD to train the model using a recursive feature elimination model; and
training the model, using a set of linear and non-linear machine-learning models, based on the selected model type, and based on the one or more features of the TD, to obtain the trained model;
Graphical user interface (GUI)
Claim 20:
an engine
a set of linear and non-linear machine-learning models
an adaptive blocking model of the set of linear and non-linear machine-learning models
a second model of the set of linear and non- linear machine-learning models
trained model
training, by an engine and based on a training dataset (TD), a model to obtain a trained model, wherein training the model comprises:
selecting a model type of a plurality of model types based on an absolute percentage error score of the model type generated using the TD, and based on a root mean squared score of the model type generated using the TD;
selecting one or more features of the TD to train the model using a recursive feature elimination model; and
training the model, based on the selected model type, and based on the one or more features of the TD, to obtain the trained model;
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h))
Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
Dependent Claims
Dependent claims 2-6, 8-14 and 15-19 further narrow the same abstract ideas recited in Claims 1 and 15, respectively. Therefore, claims 2-6, 8-14 and 15-19 are directed to an abstract idea for the reasons given above.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the dependent claims recite the following additional elements:
Claim 5:
Recursive feature elimination model
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISMAIL A MANEJWALA whose telephone number is (571)272-8904. The examiner can normally be reached M-F 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISMAIL A MANEJWALA/Primary Examiner, Art Unit 3626
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