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 .
DETAILED ACTION
1. The following is a NON-FINAL Office Action in response to the communicationreceived on 5/14/26. Claims 1-20 are now pending in this application.
2. A request for continued examination (RCE) under 37 CFR 1.114, including thefee 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the FINALITY of the previous Office Action has been WITHDRAWN pursuant to 37 CFR 1.114.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 5/14/26 has been entered.
Response to Amendments
3. Applicants Amendment has been acknowledged in that: Claims 1-5, 8-13, and 16-20 have been amended; hence such, Claims 1-20 are now pending in this application.
RESPONSE TO ARGUMENTS
Applicant argues#1
Double Patenting Rejection
Claims 1-20 are rejected on the ground of obviousness type double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 11,948,214 and nonstatutory double patenting as being unpatentable over claims 1-20 of copending application, U.S. Patent Application No. 18/607,385. Office Action, 18. The Applicant respectfully notes that the scope of the present claims have been amended and the double patenting rejections may no longer apply. The Examiner is requested to hold the double-patenting rejection in abeyance in the final scope of any otherwise allowable claim is determined.
Examiner Response
Examiner respectfully disagrees.
The limitations from (claims 1-20) from the instant application are still rejected under the doctrine of obviousness type patenting over claims 1-13 of US Patent 11,948,214.
Claim 2 & Claim 6 of the dependent claim recite the same limitations that are present in the amendments to independent claims 1,10, 18 of the instant application.
The limitations from (claims 1-20) from the instant application are still rejected under the doctrine of obviousness type patenting over claims 1, 3-20 of copending application 18/607,385, herein the ‘385 app.
The amendments to claims 1, 9, 17 of the *385 app are present in the amendments to independent claims 1,10, 18 of the instant application.
The limitations from (claims 1-20) from the instant application are still rejected under the doctrine of obviousness type patenting over claims 1-20 of copending application 18/393,298 herein the ‘298 app.
The amendments to claims 1, 9, 17 of the *385 app are present in the amendments to independent claims 1,10, 18 of the instant application.
The rejection is maintained.
Applicant argues#2
Claim Rejections under 35 U.S.C. §101
The Office Action rejects claims 1-20 under 35 U.S.C. $ 101 because the claimed invention is purportedly directed to abstract ideas without significantly more. Id. at 19. The Applicant respectfully disagrees.
Regarding Step 1 of the Alice analysis, the present claims concern at least a process and/or a machine and thus recite a patent-eligible subject matter under 35 U.S.C. $ 101.
Regarding Step 2A Prong 1 of the Alice analysis, the present claims are not directed to an abstract idea.
The Office Action argues that the claims The Office Action argues that the claim limitations "cover performance of the limitation as certain methods of organizing human activity" and relate to "commercial interaction." Id. at 21 et seq. Claim 1 has been currently amended to recite, in-part, as follows:
establishing a connection to at least one third-party application over a communication network at a net lease management server configured to communicate with at the least one third-party application, wherein real- time data associated with a specific region sent from the at least one third- party application is continuously received by the net lease management server, the real-time data including fixed factors, variable factors, and net lease terms;
using the real-time data to continuously train one or more weights in a machine-learning model, wherein the machine-learning model identifies one or more patterns based on historical net lease terms;
(emphasis added). Independent claims 10 and 18 have been similarly amended to incorporate the same language as the amended claim 1.
The recited limitations find support in the Specification as filed, which describes that the expenses network "connects to a plurality of third-party networks for the market data" and "continuously update the market data." Specification, [0056], [0065]. A machine-learning model that "trains and retrains on historical and real-time data" is used to "output a set of neat lease terms" and calculate the multipliers for stress scenarios. Id. at [0026], [0030].
Rather that relating to a commercial interaction, the claims are instead directed to establishing communication with various third-party networks to continuously receive
real-time data, using the real-time data to train and generate a machine-learning model to determine the patterns in historical data and weights for generated terms.
Thus, the claims are not directed to organizing human activity or abstract ideas and the claims qualify as patent-eligible under Step 2A, Prong One.
Examiner Response
Examiner respectfully disagrees.
The limitations (wherein real- time data associated with a specific region is continuously received, the real-time data including fixed factors, variable factors, and net lease terms; identifies one or more patterns based on historical net lease terms) is part of the identified abstract idea (a commercial interaction, steps for potential tenants and landlords communicating for the management of a leased property), which squarely fall in to the abstract category of Certain Methods of Organizing Human Activity (A commercial interaction), which the MPEP defines a commercial interaction:
MPEP 2106.04(a)(2) Abstract Idea Groupings [R-07.2022]:
II. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY
The phrase "methods of organizing human activity" is used to describe concepts relating to:
• fundamental economic principles or practices (including hedging, insurance, mitigating risk);
• commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
• managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
Second, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in MPEP § 2106.04(a)(3). Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping
Another example of subject matter where the commercial or legal interaction is business relations includes:
i. processing information through a clearing-house, where the business relation is the relationship between a party submitted a credit application (e.g., a car dealer) and funding sources (e.g., banks) when processing credit applications, Dealertrack v. Huber, 674 F.3d 1315, 1331, 101 USPQ2d 1325, 1339 (Fed. Cir. 2012).
The spec paras that application refers to are reproduced below:
[0026] The present disclosure relates to a system and method for providing a residential net lease network with a credit enhancement module, with a focus on providing residential net leases as a part of the exchange. The credit enhancement module may serve to monitor a backstop database that retains an accounting based on a determined multiplier of predicted future expenses calculated based on historical trend data that impact the fixed costs and variable costs associated with rental properties. In some cases, the multiplier and the predicted future expense are calculated based on one or more stress scenarios that select the predicted amount between an upper bound and a lower bound and a multiplier between a multiplier upper bound and a multiplier lower bound based on the extracted historical data. In some cases, the multiplier and the predicted future expenses may be calculated by a machine-learning model that trains and retrains on historical and real-time data such that the multiplier and the predicted future expenses are more accurately attuned to a changing economy.
[0030] In some cases, a machine-learning model is used to output the set of net lease terms. The machine-learning model may determine the weights based on training data including past net lease terms associated with the one or more regions.
[0056] Further, embodiments may include an expenses network 148, which includes a plurality of market data for the properties engaged or about to be engaged in a long-term net lease with the net lease network 102. The expenses network 148 may contain data for specific locations, cities, regions, or states to allow the most up-to-date market data for the net lease network 102 to use to create the net lease terms. The expenses network 148 may contain, for each specific location, the starting market rent, the market growth rate, the inflation rate, the vacancy rate, the rent collectability rate, the home price appreciation, and the operating expenses, which are stored as a data file and may contain the local taxes, the insurance rates, the management amounts, the maintenance budget, the homeowner’s association amounts, the cost of utilities, and the asset management amounts. In some embodiments, the expenses network 148 may be connected to a plurality of third-party networks to compile the market data. In some embodiments, the expenses network 148 may continuously update the market data or may collect the specific market data based on a request from the net lease network 102. In some embodiments, the expenses network 148 may store the market data in a plurality of databases to extract and send the data as it is requested from the net lease network 102.
[0065] The method begins with the net lease module 104 connecting to the expenses network 148 at step 200. For example, the net lease module 104 connects to the expenses network 148 through the cloud 150. In some embodiments, the connection may include a request from the net lease module 104 to receive the market data stored in the expenses network 148. In some embodiments, if the expenses network 148 connects to a plurality of third-party networks for the market data, the net lease module 104 may connect to each of the third-party networks to request the market data individually. In some embodiments, the request from the net lease module 104 may include a specific location, city, region, state, etc., for the desired market data.
The additional elements, outside of the abstract idea (a third-party application, communication network, net lease management server and training of the machine learning model using weights) are recited at a high level of generality, operating in their ordinary capacity, is being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
The rejection
Applicant argues#3
Regarding Step 2A Prong 2 of the Alice analysis, the present claims implement a practical application. With respect to the practical application inquiry, the Office Action argues that the additional elements are "recited at a high-level of generality" and "do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea." Office Action at 8. The Applicant respectfully disagrees.
Rather than merely using a computer as a tool to perform an abstract idea, the claims recite technical improvement to stress scenario generation by utilizing machine-learning models to process specific real-time data received from third party networks, identify patterns in the historical data, and determine weights used in the stress scenarios, integrating various networks and databases into a practical application.
Further, the claims affect a transformation or reduction by transforming raw real-time data received from different third party networks into multipliers for one or more stress scenarios that predict future expenses and determine sufficiency of reserve funds to account for various scenarios. The claims are therefore patent-eligible under Step 2A, Prong 2.
Examiner Response
Examiner respectfully disagrees.
Applicant argued the claims present a technical improvement. Examiner does not find this argument persuasive. Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is addressing a business problem (steps for potential tenants and landlords communicating for the management of a leased property) with a business solution. Applicant is merely using existing technology (for its intended purpose) to implement the business solution. Any improvements lie in the abstract idea itself, not in underlying technology
Also see the response to Applicant argues#2 above.
The rejection is maintained.
Applicant argues#4
Regarding Step 2B of the Alice analysis, Applicant's claims include an inventive concept. The Office Action argues that "the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception" and "there are no additional elements recited in the claim beyond the judicial exception." Id. at 25. These arguments in the Office Action are stated as conclusions, with no evidence provided. Without highly specific programming (e.g., such as processors that train the machine-learning algorithm), a general-purpose computer cannot train a machine-learning model, generate net lease parameters, net lease terms, and generate stress scenarios based on the trained machine-learning model. MPEP $ 2106.07(a)(III) requires that in step 2B:
Examiner should not assert that an additional element (or combination of elements) is well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
(A) A citation to an express statement in the Specification or to a statement made by an applicant during prosecution that demonstrates the well- understood, routine, conventional nature of the additional element(s). A finding that an element is well-understood, routine, or conventional cannot be based only on the fact that the Specification is silent with respect to describing such element.
The Office Action fails to "expressly support" its "rejection in writing" with any of the types of evidence listed in MPEP 2106.07(a)(III). Thus, the Office Action fails to show conventionality of any of the elements in the Applicant's claims.
At least 'establishing a connection to at least one third-party application' to continuously receive a specific type of real-time data associated with a specific region that includes 'fixed factors and variable factors, and net lease terms' and 'using the real-time data to continuously train one or more weights in a machine-learning model' to be used as weights to determine a set of net lease terms and multipliers for 'one or more stress scenarios' are not routine or conventional.
The Office Action fails to further consider whether each element outside of the purported abstract idea -individually or in every ordered combination-adds significantly more so as to qualify as an inventive concept under the second part of the Alice analysis, thereby failing to establish the lack thereof with clear and convincing evidence as required by Berkheimer. Berkheimer V. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018). In particular, the Office Action fails to consider the additional elements in combination in a way that deviates from what is routine or conventional.
Based on the foregoing, the Office Action fails to establish lack of patent-eligible subject matter under Section 101. Accordingly, Applicant respectfully requests reconsideration and withdrawal of the 35 U.S.C. $ 101 rejection.
Examiner Response
Examiner respectfully disagrees.
Applicant misapprehends when a Berkheimer analysis is required under current examination policy. Simply put, Examiner is not required under current Examination policy to evaluate under Step 2B, whether additional elements constitute “well-understood, routine, and conventional activities,” [“WURC activities”] unless an additional element(s) were found to be insignificant extra-solution activity in Step 2A, Prong 2. MPEP § 2106.05(d)(I). Here, the condition precedent was not met and the Non-Final Office Action determined the additional elements were no more than mere instructions to apply the abstract idea exception using a computer. MPEP § 2106.05(f). Thus, Examiner was not required to determine a Berkheimer analysis. MPEP § 2106.05(d)(I). (See Section 101 rejection below).
The rejection is maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness- type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).A timely filed terminal disclaimer in compliance with 37 CFR 1.321 (c) or 1.321 (d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
3. Claims 1-20 are rejected under the judicially created doctrine of obviousness type double patenting as being unpatentable over claims 1-13 of US Patent 11,948,214, herein the *214 patent. 4. Although the conflicting claims are not identical, they are not patentably distinct from each other because both the scope and function of the instant invention and the *214 patent are the same and the claimed limitations are almost identical.
5. Claims 1-20 are provisionally rejected on the grounds of non-statutory double patenting as being unpatentable over claims 1, 3-20 of application no. 18/607,385 (the reference application), herein the *385 application and claims 1-20 of application no. 18/393,298 (the reference application), herein the *298 application. Although the claims at issue are not identical, they are not patentable distinct from each other because both the claims of the instant invention and the claims of both the *298 application and the *385 are the same except for minor changes to the claim language. However, the scope of the claims and function of the claimed invention are identical to both the *298 and *385 applications.
Claim Rejections- 35 U.S.C § 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.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10,18 are directed to a method, system and computer readable medium which are statutory categories of invention. (Step 1: YES).
Representative Claim 1 recites the limitations of:
A computer-implemented method of machine learning automation for residential net lease management comprising:
establishing a connection to at least one third-party application over a communication network at a net lease management server configured to communicate with the at least one third-party application, wherein real-time associated data associated with a specific region sent from the at least one third-party application is continuously received by the net lease management server, the real-time data including fixed factors, variable factors, and net lease terms;
using the real-time data to continuously train one or more weights in a machine-learning model, wherein the machine-learning model identifies one or more patterns based on historical net lease terms;
generating, net lease parameters for the specific region based on a calculated profitability evaluation, wherein the calculated profitability evaluation is based on the real-time data and 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 factors and variable factors costs based on 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 for a residential net lease tenant, wherein the set of net lease terms is associated with the at least one of the identified properties based on inputs including the fixed factors and variable factors, wherein weights are assigned to each input;
generating, one or more stress scenarios based on the real-time data, wherein the one or more stress scenarios selects a multiplier based on the weights from the machine learning model and predicts an amount of future expenses based on the identified patterns in expenses from the machine-learning model in varied scenarios, wherein the machine-learning model is further updated by the updating market data to initialize a new stress scenario;
determining, that a backstop database or a single reserve database does not have a sufficient backstop amount to cover the multiplier of the predicted amount based on results of the stress scenario; and
generating, based upon the determination and over the communication network, an instruction to trigger a transfer of a difference between the sufficient backstop amount and an accounting at the backstop database to the backstop database.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity.
The claim recites elements that are in bold above, which covers performance of the limitation as a commercial interaction, steps for conducting a commercial interaction between potential tenants and landlords for the management of a leased property, (e.g., wherein real-time associated data associated with a specific region is continuously received, the real-time data including fixed factors, variable factors, and net lease terms; identifies one or more patterns based on historical net lease terms; generating, net lease parameters for the specific region based on a calculated profitability evaluation, wherein the calculated profitability evaluation is based on the real-time data and 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 factors and variable factors costs based on 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 for a residential net lease tenant, wherein the set of net lease terms is associated with the at least one of the identified properties based on inputs including the fixed factors and variable factors, wherein weights are assigned to each input; generating, one or more stress scenarios based on the real-time data, wherein the one or more stress scenarios selects a multiplier based on the weights and predicts an amount of future expenses based on the identified patterns in expenses in varied scenarios, to initialize a new stress scenario; determining, that a backstop or a single reserve does not have a sufficient backstop amount to cover the multiplier of the predicted amount based on results of the stress scenario; and generating, based upon the determination an instruction to trigger a transfer of a difference between the sufficient backstop amount and an accounting at the backstop to the backstop)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a Commercial Interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Claims 10, 18 are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 1, 10,18 includes the following additional elements:
-A communication network
-A net lease management server
-A third party application
-A storage configured to store instructions
-One or more processors configured to execute the instructions
-A single reserve database
-A backstop database
- A non-transitory computer readable medium
-Establishing a connection to a third party application
-Continuously training and updating a machine learning model based on updated data
The communication network, net lease management server, third party application, storage configured to store instructions, one or more processors configured to execute the instructions, a single reserve database, a backstop database, ,non-transitory computer readable medium, establishing a connection to a third party application, continuously training and updating a machine learning model based on updated data are recited at a high level of generality and are being used in their ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Therefore, the claim as a whole, looking at the additional elements individually and in combination, are no more than mere instructions to apply the exception using generic computing components and is not a practical application. MPEP 2106.05(f).
The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore claims 1, 10,18 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Representative claim 1 fails STEP 2B because the claims as a whole, looking at the additional elements individually and in combination, are not sufficient to amount to significantly more than the identified abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited in the claim beyond the judicial exception.
Mere instructions to implement an abstract idea, on or with the use of generic computer components, or even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 1,10, 18 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-9, 11-17, 19-20 further define the abstract idea that is present in their respective independent claims 1,10, 18 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above.
Claims 4,13 further define the abstract idea recited in their respective independent claims 1&10. The additional element of the “machine learning model being further trained” is recited a high level of generality, operating in their ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims (2-9, 11-17, 19-20) are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 7/23/2026
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