Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are pending. Claims 1, 15 and 20 are amended.
Response to Arguments
Applicant’s arguments, filed 02/28/2026, with respect to the 101 arguments have been considered but are not persuasive.
Applicant’s arguments, on pages 11-14, that the claims are not directed to an abstract idea. Applicant argues that the claims require the steps of training a model and therefore cannot fall into the category of abstract idea.
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 and training a machine learning model) 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.
Applicant argues, on page 14-15, that the claims integrate the judicial exception into a practical application and provide an improvement.
Examiner respectfully disagrees. 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 15-16, 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.
Applicant’s arguments, filed 02/28/2026, with respect to the 103 arguments have been considered and are persuasive. Examiner has included the closest prior art of record below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claim 1-20 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);
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;
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);
upon receiving the STD from the orchestrator, 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;
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:
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 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.
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)
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
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, 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;
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 2-14 and 15-19 further narrow the same abstract ideas recited in Claims 1 and 15, respectively. Therefore, claims 2-14 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
Claim 19
Mapping server
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.
Novelty/Non-Obviousness
The closest prior art of record is:
Lyons (US2013/0041841A1)
Faucher (US20220129988A1)
Gitt (US20160321587A1)
Weber (US20140343970A1)
Lyons teaches ISP 104 offers investor 102 a set of tools to perform investment due diligence on properties 106-110. ISP 104 gives investor 102 the ability to search multiple investment properties 106-110 by property attributes or one or more key investment indicators, such as capitalization rate, debt service ratio, net operating income, cash flow, cash-on-cash return, or return on investment, and provide reliable and comprehensive investment analysis and risk assessment of each investment property. ISP 104 rank orders each investment property 106-110 based upon a selected sort field. ISP 104 saves investor 102 considerable time and money by providing access to a comprehensive, reliable, and objective investment model or comparative investment service. The key investment indicators and rankings of properties 106-110 can be stored and utilized by investor 102 to identify one or more properties that represent a sound investment based on their particular investment strategy.
Lyons teaches FIG. 3 illustrates a commerce system 112 including real property 114 presently held by owner 116. Owner 116 can be a person, group of persons, or business entity. Property 114 has value and revenue generating potential. (i.e. inferring future value based on data)
Lyons in Fig 12a-12b show further detail of exit strategy block 258 for property 246 with an investment analysis generated by ISP 104. The exit strategies are based on rehab and sell (R&R) or buy and hold (B&H). In FIG. 12a, the investment analysis for the rehab and resell exit strategy proposes an after repair value of $325,000.00, closing cost of $26,000.00, rehab and improvements of $32,500.00, holding costs of $4959.36, acquisition cost of $245,500.00, net profit before taxes and inflation of $16,040.64, and return on investment of 18.55%. In FIG. 12b, the investment analysis for the buy and hold exit strategy proposes a years to hold period of 5 years, future value of $271,031.83, closing costs at sale of $21,684.15, cash flow over 5 years of $20,812.80, acquisition cost of $245,000.00, net profit before taxes and inflation of $24,680.48, and return on investment of 50.37% for the years to hold period, as described in more detail below.
However, Lyons does not teach the steps of the model training.
Faucher teaches the degree of commitment associated with the financial goals is performed using a trained machine learning model. The degree of commitment corresponds to a predicted probability outputted by the trained machine learning model that a specific financial goal will be achieved. Historical income data and historical expense data is inputted to the trained machine learning model, and the prediction or importance to assign to a goal is determined based in the historical data. Preferably, trained machine learning models can be used to predict the probability that the client (or related entities) will achieve the goals set. Two clients with identical financial wealth data, monthly income and spending and socio-economic data may have very different propensities to achieve particular life goals. For one, the goals may be a vague wish, or the client may have little discipline to save money to achieve the goal. For the other client, the goal may be a first priority and he will adjust his spending to achieve the goal. Financial data relating to spending habits can be used to predict the likelihood of achieving specific life goals. For each life goal, the “degree of commitment to goal determination module” 146 can collect or access existing client financial data, personal information data, socio-economic data and behavioural data and whether the client achieved or did not achieve the goal. The collected data can be labelled accordingly, and an AI model can be trained with this training data to predict the likelihood that a client will achieve the same goal. According to a possible implementation, different machine learning models can be trained for different life goals. In the example of FIG. 2, three trained AI-model (18, 18′, 18″) are shown, each having been trained and being able to predict the likelihood that a given client will be able to take a sabbatical year, will be able to retire early, or will be able to buy a house, but of course, there can be as many model as possible life goals that can be created in the system 10. However, it does not teach the remaining limitations as claimed.
Gitt teaches the Project Asset Value is a monetary present value of a project when executed at a given time (past, present or future) in a home property influencing its current valuation, and represents an added value to a property's overall value. NPV( . . . )=Net present value considering a rate index for currency calculations for given cash flow, and has units of monetary currency. OC.sub.current(t.sub.i)=Operation costs for current solution for period t.sub.i, and has units of monetary currency. OC.sub.new(t.sub.i)=Operation costs for current solution for period t.sub.i, and has units of monetary currency. [0117] Acc.sub.i=Scenario index for period t.sub.i, and has dimensionless units. M(t,s,P,RSP)=NPV((M.sub.current(t.sub.i)M.sub.new(t.sub.i)*Bcc.sub.i) [0119] NPV( . . . )=Net present value considering a rate index for currency calculations for given cash flow, and has units of monetary currency. [0120] M.sub.current(t.sub.i)=Maintenance costs for current solution for period t.sub.i, and has units of monetary currency. [0121] M.sub.new(t.sub.i)=Maintenance costs for current solution for period t.sub.i, and has units of monetary currency. [0122] Bcc.sub.i=Scenario index for period t.sub.i, and has dimensionless units. However, it does not teach the remaining limitations as claimed.
Weber teaches FIG. 5 shows the initial, annual, and final cash payments to be made to the bank by the developer for the property referenced in Tables 1-5, the total annual cash flow and total cumulative cash flow to the bank based on these payments, the net present value to the bank of these cash payments and flows. and the cash flows of the developer with respect to the property during the real estate transaction process. The term "Other YO" means income and expenses after the property is acquired but prior to its lease to a third-party (typically, 3 months but this time period can vary). The last two rows show the total cash flow for the developer for the property and the net present value to the developer of that cash flow. "net cash flow"--the percentage increase in the selling bank's net present value as compared to book value of the nonperforming mortgage at signing of the agreement. However, it does not teach the remaining limitations as claimed.
In conclusion, it would not have been obvious to one of ordinary skill in the art before the effective filing date to combine the above references to teach all the limitations of the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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