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 .
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.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims 1-11 are directed to a process; and claims 12-20 are directed to a machine.
Independent Claims
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
The independent claims recite an abstract idea. Specifically, independent claim 1 recites an abstract idea in the limitations (emphasized)1:
…receiving, over an expense network, market data associated with a specific region sent over a communication network at a net lease management server configured to communicate with at least one third-party application;
initiating, by a net lease module, a reserve module;
generating, by the reserve module, net lease parameters for the specific region based on a calculated profitability evaluation based on the market data received via the expense network, wherein the calculated profitability evaluation determines a threshold margin based on a percentage of an average rental rate and average fixed costs in the specific region;
initiating, by the net lease module, an owner module;
identifying, by the owner module, properties that fall within the net lease parameters generated by the reserve module;
initiating, by the net lease module, a manage module;
determining, by the manage module, fixed costs and variable costs based on data associated with at least one of the identified properties and extracted data points from stored invoice data;
generating, by the reserve module, a set of net lease terms associated with the at least one of the properties identified by the net lease module, based on first inputs including the fixed costs and variable costs determined the manage module, wherein first weights are assigned to each first input;
receiving an approval from a property owner of the generated set of net lease terms;
initiating, by the net lease module, a risk module;
inputting, in a first machine-learning model of the risk module, due diligence data of the property owner and the identified properties and the generated set of net lease terms;
predicting, by the first machine-learning model, a plurality of risk probabilities associated with respective risk factors;
running a gradient-based optimization process of the first machine-learning model to identify one or more combinations of the net lease terms that minimize an overall risk level based on the predicted risk probabilities for the respective risk factors; and
updating the generated net lease terms with changes based on the identified one or more combinations of net lease terms.
These limitations recite an abstract idea because these encompass commercial or legal interactions (i.e., agreements in the form of contracts, or marketing or sales activities or behaviors). These limitations recite commercial or legal interactions (i.e., agreements in the form of contracts, or marketing or sales activities or behaviors) because these limitations essentially encompass analyzing properties to identify potentially profitable properties and contract terms, and drafting the contracts to minimize risk (i.e., performing market research and drafting contracts based on the research). Claims that encompass commercial or legal interactions fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Claims 1, 12, and 20 recite an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 1 recites the additional elements (emphasized):
…receiving, over an expense network, market data associated with a specific region sent over a communication network at a net lease management server configured to communicate with at least one third-party application;
initiating, by a net lease module, a reserve module;
generating, by the reserve module, net lease parameters for the specific region based on a calculated profitability evaluation based on the market data received via the expense network, wherein the calculated profitability evaluation determines a threshold margin based on a percentage of an average rental rate and average fixed costs in the specific region;
initiating, by the net lease module, an owner module;
identifying, by the owner module, properties that fall within the net lease parameters generated by the reserve module;
initiating, by the net lease module, a manage module;
determining, by the manage module, fixed costs and variable costs based on data associated with at least one of the identified properties and extracted data points from stored invoice data;
generating, by the reserve module, a set of net lease terms associated with the at least one of the properties identified by the net lease module, based on first inputs including the fixed costs and variable costs determined the manage module, wherein first weights are assigned to each first input;
receiving an approval from a property owner of the generated set of net lease terms;
initiating, by the net lease module, a risk module;
inputting, in a first machine-learning model of the risk module, due diligence data of the property owner and the identified properties and the generated set of net lease terms;
predicting, by the first machine-learning model, a plurality of risk probabilities associated with respective risk factors;
running a gradient-based optimization process of the first machine-learning model to identify one or more combinations of the net lease terms that minimize an overall risk level based on the predicted risk probabilities for the respective risk factors; and
updating the generated net lease terms with changes based on the identified one or more combinations of net lease terms.
These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the various modules and initiating the various modules, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recite at a high-level of generality (i.e., as generic software) such that it amounts to no more than mere intrusions to apply the exception.
Second, the additional elements receiving, over an expense network, market data, with a server, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving data (e.g. receiving user input over a network), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Third, the additional elements of inputting data into the first machine learning model, and running a gradient-based optimization, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (i.e., using machine learning techniques) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims.
Claims 12 and 20 recite similar additional elements as claim 1 and further recite “a storage configured to store instructions” and “one or more processors”; and a “non-transitory computer readable medium comprising instructions”, respectively. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Claims 1, 12 and 20 are directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent claims do not include additional elements that 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 amount to no more than mere instructions to apply the exception and a general link to a field of use. Mere instructions to apply an exception using a generic computer component and a general link to a field of use cannot provide an inventive concept. Claims 1, 12 and 20 are not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Regarding claims 2 and 14, claims 2 and 14 recite the same abstract idea as the independent claims because outputting dynamic predictions of risk of mitigations based on due diligence data is a part of analyzing properties to identify potentially profitable properties and contract terms (i.e., performing market research and drafting contracts based on the research).
Claims 2 and 14 further recite the additional elements of using the underwrite module and second machine learning model. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception using generic computer components.
Regarding claims 3 and 15, claims 3 and 15 recite the same abstract idea as the independent claims because outputting pattern market data based on location, market rate metrics and due diligence data is a part of analyzing properties to identify potentially profitable properties and contract terms (i.e., performing market research and drafting contracts based on the research).
Claims 3 and 15 further recite the additional elements of using the market module and third machine learning model. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception using generic computer components.
Regarding claims 4 and 16, claims 4 and 16 recite the same abstract idea as the independent claims because outputting estimates of financial projections based on due diligence, predictions of risks and pattern market data is a part of analyzing properties to identify potentially profitable properties and contract terms (i.e., performing market research and drafting contracts based on the research).
Claims 4 and 16 further recite the additional elements of using the financial module and fourth machine learning model. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception using generic computer components.
Regarding claims 5 and 17, claims 5 and 17 recite the same abstract idea as the independent claims because outputting weighted scores for rules based on estimates of financial projections is a part of analyzing properties to identify potentially profitable properties and contract terms (i.e., performing market research and drafting contracts based on the research).
Claims 5 and 17 further recite the additional elements of using the checklist module and fifth machine learning model. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception using generic computer components.
Claims 6 and 18 recite the additional elements of the machine learning models being part of a neural network and retraining the network based on the claimed data. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception using generic computer components.
Claims 7 and 19 recite the additional elements of using a machine learning model to determine weights. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are claimed too broadly and generally to be more than mere instructions to apply the exception using generic computer components.
Claim 8 recites the same abstract idea as the independent claims because performing simulations using comparable data and presenting options for changes in contracts is a part of analyzing properties to identify potentially profitable properties and contract terms (i.e., performing market research and drafting contracts based on the research).
Claim 9 recites the additional elements of recording the claimed data by the accounting module and sending an instruction. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of storing and sending data (e.g. storing user input and sending information over a network), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claims 10, 11, and 13 recite the same abstract idea as the independent claims because the various claimed data is still a part of analyzing properties to identify potentially profitable properties and contract terms (i.e., performing market research and drafting contracts based on the research).
Examiner’s Note – Claim Interpretation
Examiner notes the claims in the present application recite various modules but Examiner finds these modules do not invoke 112(f) for the following reasons. First, Examiner notes the claims recite various generic placeholders (e.g., the module in the reserve module), modified by functional language (e.g., generates a plurality of net lease parameters). However Examiner finds the various modules do not invoke 112(f) because the various modules are all modified by sufficient structure (i.e., the various hardware components recited in the independent claims) such that the limitations do not invoke 112(f) and because the Specification describes the net lease system that contains the various modules as software, ¶[0026] of the Specification as filed.
Examiner’s Note – No Prior Art Rejections
Regarding claims 1, 12, and 20, based on prior art search results, the prior art neither anticipates nor renders obvious the claims subject matter of the instant application as a whole either taken alone or in combination. In particular the prior art does not teach (emphasized):
…generating, by the reserve module, a set of net lease terms associated with the at least one of the properties identified by the net lease module, based on first inputs including the fixed costs and variable costs determined the manage module, wherein first weights are assigned to each first input…
…running a gradient-based optimization process of the first machine-learning model to identify one or more combinations of the net lease terms that minimize an overall risk level based on the predicted risk probabilities for the respective risk factors….
The closest prior art of record includes: Ogundunmade, Tayo P., Muyiwa Abidoye, and Oladapo M. Olunfunbi. "Modelling Residential Housing Rent Price Using Machine Learning Models." Mod Econ Manag 2 (2023) teaches using gradient based optimization, e.g., pg. 4 § 2.3.2, to determine weightings of real estate features in housing rental prices, pgs. 6-7 and Tbl. 5, but does not teach assigning weights to fixed and variable costs or minizine risk based on terms lease terms.
Fakieh et al., US Pub. No. 2025/0078051 teaches generating smart contracts using historical real estate transactions, e.g., ¶¶[0045], [0230], including gradient boosting machine learning techniques, ¶[0284], to suggest terms of the agreement, ¶¶[0232], [0239], for minimizing risks associated with the property, ¶[0083], but does not teach assigning weights to fixed and variable costs.
Garcia et al., US Pub. No. 2024/0346612 teaches using gradient boosting, ¶¶[0068], [0076], to determine outcomes of financial outcomes of real estate including rental, e.g., ¶¶[0083]-[0084]; see also ¶¶[0080], [0090] discussing optimization real estate portfolios but does not teach assigning weights to fixed and variable costs or minizine risk based on terms lease terms.
Gupta et al., US Pub. No. 2020/0334744 teaches using a gradient boosting model, ¶[0056], to minimize risk in contract terms, ¶[0080] but does not teach assigning weights to fixed and variable costs or minizine risk based on terms lease terms.
Accordingly, claims 1-20 are not rejected under 35 USC 102 or 103.
Conclusion
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
1 Examiner notes the exact language of claims 1, 12, and 20 varies slightly but these differences do not significantly alter the eligibility analysis and so the claims are analyzed concurrently here for the sake of brevity.
Prosecution Insights