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 1-4, 6-16 and 18-20 are all the claims pending in the application.
Claims 5 and 17 are cancelled.
Claims 1-4, 6, 9, 12, 14-16, 18 and 20 are amended.
Claims 1-4, 6-16 and 18-20 are rejected.
The following is a Final Office Action in response to amendments and remarks filed July 8, 2026.
Response to Arguments
Regarding the 101 rejections, the rejections are maintained for the following reasons. First, under Step 2A Pong 1, Applicant asserts the claims do not recite an abstract idea because the claims are directed to establishing real-rime communication and using a combination of machine learning models. Examiner respectfully does not find this assertion persuasive at least because the claims still recite generating lease parameters for properties which is a part of forming contracts. Thus, the claims recite an abstract idea under Step 2A Prong 1.
Second, under Stepn 2A Prong 2, Applicant asserts the claims reflect an improvement in calculating risk probabilities by using multiple machine learning models. Examiner respectfully does not find this assertion persuasive because an improvement in the abstract idea itself is not an improvement in technology, see MPEP 2106.05(a) (discussing Trading Technologies Int’l v. IBG). That is, calculating risk probabilities is an abstract idea and using machine learning to improve the calculation does not reflect an improvement in a technology.
Third, Applicant asserts the claims affect a transformation by transforming raw data into weighted scores. Examiner respectfully does not find this assertion persuasive because manipulating data as claimed does not reflect a practical application, see MPEP 2106.05(c) (discussing CyberSource v. Retail Decisions).
Fourth, under Step 2B, Applicant asserts the rejections should be withdrawn because the Office Action did not provide evidence of the conventionality of the additional elements. Examiner respectfully does not find this assertion persuasive because the Office Action did not reject the additional elements as well-understood, routine or conventional.
Accordingly, the 101 rejections are maintained. Please see below for the complete analysis of the claims as amended.
Claim Objections
Claim 1 is objected to because of the following informalities: claim 1 appears to contain a type and should be amended to read (emphasized): “…sent over a communication network at a net lease management server configured to communicate with the at [[the]] least one third-party application…” Appropriate correction is required.
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 1-4, 6-16 and 18-20 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 1-4 and 6-11 are directed to a process; and claims 12-16 and 18-20 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:
… continuously receiving, over an expense network, real-time market data associated with a specific region from at least one third-party application sent over a communication network at a net lease management server configured to communicate with at the least one third-party application;
generating net lease parameters for the specific region based on a calculated profitability evaluation based on the real-time 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;
identifying properties that fall within the generated net lease parameters;
determining fixed costs and variable costs based on the real-time market data associated with at least one of the identified properties and extracted data points from stored invoice data;
generating a set of net lease terms associated with the at least one of the identified properties, based on first inputs including the determined fixed costs and variable costs, wherein first weights are assigned to each first input;
receiving an approval from a property owner of the generated set of net lease terms;
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;
updating the generated net lease terms with changes based on the identified one or more combinations of net lease terms;
generating a dynamic predictions of risk probabilities by a second machine-learning model based on the plurality of risk probabilities predicted by the first machine-learning model;
generating pattern market data of real estate properties by a third machine-learning model based on the plurality of risk probabilities predicted by the first machine-learning model;
predicting estimates of a residential net lease for the real estate properties by a fourth machine-learning model based on the dynamic predictions of risk probabilities from the second machine-learning model and the pattern market data from the third machine- learning model;
generating weighted scores for one or more rules associated with approving the set of net lease terms by a fifth machine-learning model based on the predicted estimates of a residential net lease from the fourth machine-learning model; and
providing the set of net lease terms to a user device based on the weighted scores exceeding a predetermined threshold.
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 based on the predictions and a weighted scoring metric (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):
…continuously receiving, over an expense network, real-time market data associated with a specific region from at least one third-party application sent over a communication network at a net lease management server configured to communicate with at the least one third-party application;
generating net lease parameters for the specific region based on a calculated profitability evaluation based on the real-time 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;
identifying properties that fall within the generated net lease parameters;
determining fixed costs and variable costs based on the real-time market data associated with at least one of the identified properties and extracted data points from stored invoice data;
generating a set of net lease terms associated with the at least one of the identified properties, based on first inputs including the determined fixed costs and variable costs, wherein first weights are assigned to each first input;
receiving an approval from a property owner of the generated set of net lease terms;
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;
updating the generated net lease terms with changes based on the identified one or more combinations of net lease terms;
generating a dynamic predictions of risk probabilities by a second machine-learning model based on the plurality of risk probabilities predicted by the first machine-learning model;
generating pattern market data of real estate properties by a third machine-learning model based on the plurality of risk probabilities predicted by the first machine-learning model;
predicting estimates of a residential net lease for the real estate properties by a fourth machine-learning model based on the dynamic predictions of risk probabilities from the second machine-learning model and the pattern market data from the third machine- learning model;
generating weighted scores for one or more rules associated with approving the set of net lease terms by a fifth machine-learning model based on the predicted estimates of a residential net lease from the fourth machine-learning model; and
providing the set of net lease terms to a user device based on the weighted scores exceeding a predetermined threshold.
These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements continuously receiving, over an expense network, real-time market data, with a server, and providing the terms to a user device, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass generic computer functions of receiving and sending data (e.g. receiving data from the internet and displaying data), 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).
Second, 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.
Third, the additional elements of using the second, third, fourth and fifth machine-learning models to make the various calculations based on the claimed inputs, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited sufficiently broadly and generally such that it amounts to no more than mere instructions to apply the exception.
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 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 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 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.
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 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).
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 BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 11-7.
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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.
