Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 22, 2026 has been entered.
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: Claims 1-7 recite a method (process) system, Claims 8-16 recite a system (machine), and Claims 17-20 recite one or more non-transitory computer readable medium (manufacture) and therefore fall into a statutory category.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
Referring to claims 1-20, the claims are directed to a manner of organizing the processing of 1031 exchanges of real properties, which under its broadest reasonable interpretation covers concepts covered under the Certain Methods of Organizing Human Activity grouping of abstract ideas.
The abstract idea portion of the claims is as follows:
Claim 1
A [computer-implemented] method [of automating a residential net lease management tool with a 1031 exchange module], comprising: receiving, [over an expense network], an initial exchange amount [over a communication network at a net lease management server configured to communicate with at least one third-party application], wherein the initial exchange amount is associated with a property that is sold or on sale;
continuously receiving in real-time, [over the expense network connected to a plurality of third party-networks], market data associated with one or more regions sent [over the communication network at the net lease management server], wherein the market data includes historical net lease terms;
storing market data received [from the expense network] in the one or more regions, wherein the market data is used as training data for a [machine-learning] algorithm;
generating a [machine-learning] model based on the market data received [from the plurality of third-party networks], wherein the [machine-learning] model identifies one or more patterns in the market data and one or more weights based on historical net lease terms using the [machine-learning] algorithm;
continuously updating the [machine-learning] model based on continuously updating market data;
generating net lease parameters for the one or more regions 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 one or more regions;
identifying replacement properties that fall within the net lease parameters;
determining, based on the initial exchange amount, one or more bundles of the replacement properties of the identified replacement properties that have a total exchange amount that is considered to have a value equal to or greater than the initial exchange amount;
generating a set of net lease terms associated with the at least one of the identified replacement properties, based on inputs including fixed costs and variable costs wherein the set of net lease terms are further based on the identified patterns from the [machine-learning] model, wherein the weights identified by the [machine-learning] model are assigned to each input;
storing [in a lease database], the net lease terms, wherein the net lease terms are used to continually update the [machine-learning] model;
recording, [in a reserve database associated with a single reserve fund], a first accounting for a first amount funded by one or more investors that are not respective owners of the respective properties;
recording, [in the reserve database associated with the single reserve fund], a second accounting for a second amount remunerated to the investors based on determined profit margins over term of lease and the net lease terms stored [at the lease database];
generating [a custom interface that includes] tools for adjusting the initial exchange amount and the first amount, and a visualization of predicted exchange amount based on the identified patterns from the [machine-learning] model based on the continuously updating market data;
customizing the [machine-learning] model based on input received from the tools, wherein the customized [machine-learning] model identifies a different bundle of replacement properties based on difference between the total exchange amount and the adjusted initial exchange amount;
and sending, based upon the accountings of the reserve database [over the communication network], an instruction to trigger a transfer to the single reserve fund.
Claim 9
[A system for automating a residential net lease management tool with an exchange module, comprising: a storage configured to store instructions;]
[and one or more processors configured to execute the instructions and cause the one or more processors to]:
receive, [over an expense network], an initial exchange amount [over a communication network at a net lease management server configured to communicate with at least one third-party application], wherein the initial exchange amount is associated with a property that is sold or on sale;
continuously receive in real-time, [over the expense network connected a plurality of third-party networks], market data associated with one or more regions sent [over the communication network at the net lease management server], wherein the market data includes historical net lease terms;
store market data received [from the expense network in the one or more regions], wherein the market data is used as training data for a [machine-learning] algorithm;
generate a [machine-learning] model based on the market data received [from the plurality of third-party network], wherein the [machine-learning] model identifies one or more patterns in the market data and one or more weights based on historical net lease terms using the [machine-learning] algorithm;
continuously update the [machine-learning] model based on continuously updating market data;
generate, [by the reserve module], net lease parameters for the one or more regions 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 one or more regions;
identify replacement properties that fall within the generated net lease parameters;
determine, based on the initial exchange amount, one or more bundles of the replacement properties of the identified replacement properties that have a total exchange amount that is considered to have a value equal to or greater than the initial exchange amount;
generate a set of net lease terms associated with the at least one of the replacement properties identified [by the net lease module], based on inputs including fixed costs and variable costs determined by [the manage module] wherein the set of net lease terms are further based on the identified patterns from the [machine learning] model, wherein the weights identified by the [machine-learning] model are assigned to each input;
store [in a lease database], the net lease terms, wherein the net lease terms are used to continually update the [machine-learning] model; record, [by the accounting module in a reserve database associated with a single reserve fund], a first accounting for a first amount funded by one or more investors that are not respective owners of the respective properties;
record [in the reserve database associated with the single reserve fund], a second accounting for a second amount remunerated to the investors based on determined profit margins over term of lease and the net lease terms stored [at the lease database];
generate [a custom interface that includes] tools for adjusting the initial exchange amount and the first amount, and a visualization of predicted exchange amount based on the identified patterns from the [machine-learning] model based on the continuously updating market data;
customize the [machine-learning] model based on input received from the tools, wherein the customized [machine-learning] model identifies a different bundle of replacement properties based on difference between the total exchange amount and the adjusted initial exchange amount;
and send, [based upon the accountings of the reserve database over the communication network], an instruction to trigger a transfer to the single reserve fund.
Claim 17
[A non-transitory computer readable medium comprising instructions, the instructions, when executed by a computing system, cause the computing system to]:
receiving, [over an expense network], an initial exchange amount [over a communication network at a net lease management server configured to communicate with at least one third-party application], wherein the initial exchange amount is associated with a property that is sold or on sale;
continuously receive in real-time, [over the expense network connected to a plurality of third-party networks], market data associated with one or more regions sent [over the communication network at the net lease management server], wherein the market data includes historical net lease terms;
storing market data received [from the expense network] in the one or more regions, wherein the market data is used as training data for a [machine-learning] algorithm;
generating a [machine-learning] model based on the market data received [from the plurality of third-party networks], wherein the [machine-learning] model identifies one or more patterns in the market data and one or more weights based on historical net lease terms using the [machine-learning] algorithm;
continuously updating the [machine-learning] model based on continuously updating market data;
generating net lease parameters for the one or more regions 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 one or more regions;
identifying replacement properties that fall within the generated net lease parameters;
determining, based on the initial exchange amount, one or more bundles of the replacement properties of the identified replacement properties that have a total exchange amount that is considered to have a value equal to or greater than the initial exchange amount;
generating a set of net lease terms associated with the at least one of the identified replacement properties, based on inputs including fixed costs and variable costs, wherein the set of net lease terms are further based on the identified patterns from the [machine-learning] model, wherein the weights identified by the [machine-learning] model are assigned to each input;
storing [in a lease database], the net lease terms, wherein the net lease terms are used to continually update the [machine-learning] model;
recording [in a reserve database associated with a single reserve fund], a first accounting for a first amount funded by one or more investors that are not respective owners of the respective properties;
recording [in the reserve database associated with the single reserve fund], a second accounting for a second amount remunerated to the investors based on determined profit margins over term of lease and the net lease terms stored at the lease database;
generating [a custom interface that includes] tools for adjusting the initial exchange amount and the first amount, and a visualization of predicted exchange amount based on the identified patterns from the [machine-learning] model based on the continuously updating market data;
customizing the [machine-learning] model based on input received from the tools, wherein the customized [machine-learning] model identifies a different bundle of replacement properties based on difference between the total exchange amount and the adjusted initial exchange amount;
and sending, based upon the accountings of the reserve database [over the communication network], an instruction to trigger a transfer to the single reserve fund.
Where the portions not bracketed recite the abstract idea.
Here the claims recite concepts capable performed in Certain Methods of Organizing Human Activity in particular managing personal interactions between people (including teaching and following rules), but for the recitation of generic computer components. In the present application concepts reciting a manner of organizing the processing of 1031 exchanges of real properties (See paragraphs 3 and 25).
If a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in managing personal interactions between people (including teaching and following rules), it falls under the Certain Methods of Organizing Human Activity grouping of abstract ideas. See MPEP 2106.04.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?):
The Examiner views the following as the additional elements:
A computer. (See paragraphs 71 and 185-186)
A residential net lease management tool. (See paragraph 4)
1031 exchange module. (See paragraph 7)
Communication network. (See paragraph 7)
A net lease management server. (See paragraph 40 and 186)
At least one third-party application. (See paragraph 74)
Expense network. (See paragraphs 26, 28 70, 74-75)
A reserve module. (See paragraph 30, 99-101)
An owner module. (See paragraph 78, 107-112))
An exchange module. (See paragraphs 52 and 148)
A net lease module. (See paragraph 28)
A manage module. (See paragraphs 34 and 121)
Lease database. (See paragraphs 6 and 8-9)
An accounting module. (See paragraph 35)
A reserve database associated with a single reserve fund. (See paragraphs 6 and 9)
A system. (See paragraphs 7 and 25)
A storage. (See paragraph 7)
Instructions. (See paragraph 7)
One or more processors. (See paragraph 7)
A non-transitory computer readable medium. (See paragraph 40)
Computing system. (See paragraphs 180-181, 183-184, and 186)
A plurality of third-party networks. (See paragraphs 70 and 74)
Machine learning. (See paragraphs 42, and 84-86)
Custom interface. (See paragraph 50)
These additional elements are recited at a high-level of generality such that they act to merely “apply” the abstract idea using generic computing components and do not integrate the abstract idea into a practical application. (See MPEP 2106.05 (f))
The combination of these additional elements and/or results oriented steps are no more than mere instructions to apply the exception using generic computing components. (See MPEP 2106.05 (f)) Accordingly, even in combination these 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, the claim is directed to an abstract idea.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?):
As noted above, the claims as a whole merely describes a method that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
Dependent claims 2-3 further define the abstract idea as identified. Additionally, the claim recites the generic machine-learning algorithm (See paragraphs 42 and 84) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 2-3 are considered to be patent ineligible.
Dependent claim 4 further defines the abstract idea as identified. Additionally, the claim recites the generic manage module (See paragraph 34 and 121), accounting module in the reserve database associated with the single reserve fund (See paragraphs 6 and 9), the lease database (See paragraphs 6 and 8-9) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 4 is considered to be patent ineligible.
Dependent claims 5-7 and 13-14 further define the abstract idea as identified. Therefore claims 5-7 and 13-14 are considered to be patent ineligible.
Dependent claim 8 further defines the abstract idea as identified. Additionally, the claim recites the generic communication network (See paragraph 7), net lease management server (See paragraphs 40 and 186), machine-learning algorithm (See paragraphs 42 and 84) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 8 is considered to be patent ineligible.
Dependent claims 10-11 further defines the abstract idea as identified. Additionally, the claim recites the generic machine learning algorithm (See paragraphs 42 and 84), one or more processors (See paragraph 7), and instructions (See paragraph 7) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 10-11 are considered to be patent ineligible.
Dependent claim 12 further defines the abstract idea as identified. Additionally, the claim recites the generic manage module (See paragraph 34 and 121), accounting module in the reserve database associated with the single reserve fund (See paragraphs 6, 9, and 35), the lease database (See paragraphs 6 and 8-9), one or more processors (See paragraph 7), and instructions
(See paragraph 7) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 12 is considered to be patent ineligible.
Dependent claim 15 further defines the abstract idea as identified. Additionally, the claim recites the generic one or more processors (See paragraph 7), and instructions (See paragraph 7) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 15 is considered to be patent ineligible.
Dependent claim 16 further defines the abstract idea as identified. Additionally, the claim recites the generic communication network, (See paragraph 7), the net lease management server (See paragraphs 40 and 186), machine-learning algorithm (See paragraphs 42 and 84), one or more processors (See paragraph 7), and instructions (See paragraph 7) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 16 is considered to be patent ineligible.
Dependent claims 18-19 further define the abstract idea as identified. Additionally, the claim recites the generic a machine-learning algorithm (See paragraphs 42 and 84), computer readable medium (See paragraph 40), instructions (See paragraph 7), and computing system (See paragraphs 180-181, 183-184, and 186) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 18-19 are considered to be patent ineligible.
Dependent claim 20 further defines the abstract idea as identified. Additionally, the claim recites the generic manage module (See paragraphs 34 and 121), accounting module in the reserve database associated with the single reserve fund (See paragraphs 6, 9, and 35), the lease database (See paragraphs 6 and 8-9), computer readable medium (See paragraph 40), instructions (See paragraph 7), and computing system (See paragraphs 180-181, 183-184, and 186) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 20 is considered to be patent ineligible.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed April 22, 2026 have been fully considered.
Applicant’s amendments and arguments, on page 16 of the Remarks, regarding the 112 (b) rejection the Examiner finds Applicant’s amendments persuasive. Therefore, the Examiner has withdrawn the 112(b) rejection.
Applicant’s amendments and arguments, on pages 16-21 of the Remarks, regarding the 101 rejection the Examiner finds unpersuasive.
Applicant argues under Step 2A Prong 1 that the amend claims recite
generating a custom interface that includes tools for adjusting the initial exchange amount and the first amount, and a visualization of predicted exchange amount based on the identified patterns from the [machine-learning] model based on the continuously updating market data;
customizing the machine-learning model based on input received from the tools, wherein the customized machine-learning model identifies a different bundle of replacement properties based on difference between the total exchange amount and the adjusted initial exchange amount[.]
According to Applicant these limitations provide for custom interfaces for investors to review and modify exchange terms before finalizing the transaction which offers tools for investors to customize their machine learning models based on their specific strategies and preferences, where the model is used to optimize property selection such that the model outputs replacement property that minimize the difference between total exchange amount and the initial exchange response. (See paragraphs 50, 84-85, and 152). Applicant contends that the claims are directed to establishing communications with various third-party networks to continuously receive data in real-time, using the data to train and generate a machine-learning model, updating the machine-learning model with continuously updating data, and generating a custom interface, that includes tools and visualization based on the machine-learning model to help investors understand market trends and adjust their investment strategies accordingly. (See paragraphs 85-86).
The Examiner respectfully disagrees views the claims outline concepts reciting a manner of organizing the processing of 1031 exchanges of real estate properties. The limitations cited by Applicant relate to displaying information (tools for adjusting the initial exchange amount and the first amount and the visualization that is based on the analysis of the collected information), and customizing the model based on information received (the input from the tools) where the model identifies a different bundle of properties based on the analysis of information (difference between the total exchange amount and the adjusted initial exchange amount) as part of facilitating the organizing the processing of 1031 exchanges of real estate properties. The Examiner views the use of the machine learning algorithm/model is mere instructions to apply the abstract idea using a generic computing component in the form of a specific type of model/algorithm applied being utilized. Further the Examiner views the custom interface is mere instructions to apply the abstract idea using a generic computing component for facilitating steps of the abstract idea including collecting and displaying data. The Examiner views that a professional that has acquired experience/training would be capable of making an assessment to determine a new bundle of property or graphic to create to share a customer based on the information received (including updated information) from a customer. Further, the Examiner views that a professional as part of their service gathers knowledge over time through training and experience to provide recommendations on the terms based on identified patterns, customizing inputs, and the weight inputs should be given. Therefore, the Examiner maintains the claims recite an abstract idea
Applicant argues that under Step 2A Prong 2 the claims recite technical improvements to processing of data received from third party networks utilizing machine-learning models to optimize exchange process with accurate and up-to-date forecasts, integrating various networks, modules, and databases into a practical application. Further Applicant contends that the claims affect a transformation or reduction by transforming raw market data received from different third party networks into a visual representation on a custom interface and customized machine learning model.
The Examiner respectfully disagrees viewing the additional elements alone and in combination of machine learning model, custom interface, third party networks, integrating various networks, the different modules, and databases as recited are mere instructions to apply the abstract idea using generic computing components. MPEP 2106.05 (f) The Examiner views that the improvement to up-to-date processes and collecting information is an improvement to the abstract idea and is not an improvement as enumerated under MPEP 2106.04(d). Therefore, the Examiner maintains the claims are not integrated into a practical application.
Applicant argues under Step 2B without highly specific programming, a general-purpose computer cannot generate a machine-learning model based on real-time data received from a plurality of third-party networks, let alone generate a custom interface and a custom machine-learning model recited herein. According to Applicant the Examiner failed to illustrate that the additional elements are conventional under MPEP 2106.07 (a)(III). Applicant contends “generating a custom interface that includes tools for adjusting the initial exchange amount and the first amount, and a visualization of predicted exchange amount based on the identified patterns from the machine-learning model based on the continuously updating market data;” and “customizing the machine-learning model based on input received from the tools, wherein the customized machine-learning model identifies a different bundle of replacement properties based on difference between the total exchange amount and the adjusted initial exchange amount” are not routine or conventional. Applicant contends the Examiner fails to consider individually or in combination whether the additional elements add significantly more.
The Examiner respectfully disagrees maintaining the steps proffered by Applicant are steps of the recited abstract idea under Step 2A Prong 1 and are not considered to be additional elements. Further the additional elements identified by the Examiner are mere instructions to apply the abstract idea using generic computing components as identified in the Step 2A Prong 2 Analysis and do not integrate the abstract idea into a practical application alone or in combination. MPEP 2106.05 (f). The Examiner further cited the associated paragraph numbers to illustrate the computing components are generic.
The Examiner cites to respectively MPEP 2106.05 (d) and MPEP 2106.05 (II) that states
In this respect, the well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a particular element or combination of elements is well-understood, routine, conventional activity.
Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should: Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two; Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h): Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d).
Here the Examiner carried over their identification that the additional elements alone or in combination are mere instructions to apply the abstract idea using generic computing components. MPEP 2106.05 (f). Therefore, the Examiner maintains the additional elements do not add significantly more alone or in combination to the abstract idea.
Therefore, for the foregoing reasons the Examiner has maintained the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang et al. (US 20220129932) – directed to machine learning pricing adjustment in a property system.
Pekelis et al. (US 20200074480) -directed to a machine learning baseline optimization system.
Kabello et al. (US 20160314522) – directed to a lease purchase system and method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J MONAGHAN whose telephone number is (571)270-5523. The examiner can normally be reached on Monday- Friday 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached on (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J. Monaghan/Examiner, Art Unit 3629
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