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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Referring to claims 1, 9 and 17,
The claims recite “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, 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 a machine-learning algorithm It is unclear to the Examiner whether the two instances of a machine learning algorithm are the same. The Examiner is interpreting the machine learning algorithm is the same.
Referring to claim 9,
The claim recites “A system for automating a residential net lease management tool with an exchange module, comprising: a storage configured to store instructions; a net lease module that controls a reserve module, an owner module, a manage module, and an accounting module… an exchange module that identifies” It is unclear to the Examiner whether the two instances of an exchange module are the same. The Examiner is interpreting the exchange modules are the same module.
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, 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 a [machine-learning] algorithm; continuously updating the [machine-learning] model based on continuously updating market data;
[initiating, by a net lease module, a reserve module];
generating, [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;
[initiating, by the net lease module, an owner module];
identifying, [by the owner module], replacement properties that fall within the net lease parameters generated [by the reserve module];
[initiating, by the net lease module, the 1031 exchange module that initiates an exchange module];
determining, [by the exchange module and] 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;
[initiating, by the net lease module, a manage module];
generating, [by the reserve module], 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;
storing, [by the net lease module in a lease database], the net lease terms, wherein the net lease terms are used to continually update the [machine-learning] model;
recording, [by an 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;
recording, [by the accounting module 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 visualization of predicted exchange amount based on the identified patterns from the [machine-learning] model based on the continuously updating market data;
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; a net lease module that controls a reserve module, an owner module, a manage module, and an accounting module];
[the reserve module that] generates a plurality of net lease parameters for different regions;
[the owner module that] identifies replacement properties that fall within a particular net lease parameter;
[an exchange module that] identifies like-properties to the identified replacement properties for 1031 exchanges;
[the manage module that] determines fixed costs and variable costs; [the accounting module that] records accountings;
[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];
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, 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 a [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, [by the owner module], replacement properties that fall within the net lease parameters generated [by the reserve module];
determine, [by the exchange module] and 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, [by the reserve module], 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, [by the net lease module 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, [by the accounting module 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 visualization of predicted exchange amount based on the identified patterns from the [machine-learning] model based on the continuously updating market data;
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]:
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];
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, 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 a [machine-learning] algorithm;
continuously updating the [machine-learning] model based on continuously updating market data;
[initiating, by a net lease module, a reserve module];
generating, [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;
[initiating, by the net lease module, an owner module];
identifying, [by the owner module], replacement properties that fall within the net lease parameters generated [by the reserve module];
[initiating, by the net lease module, the 1031 exchange module that initiates an exchange module];
determining, [by the exchange module and] 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;
[initiating, by the net lease module, a manage module];
generating, [by the reserve module], 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;
storing, [by the net lease module in a lease database], the net lease terms, wherein the net lease terms are used to continually update the [machine-learning] model;
recording, [by an 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;
recording, [by the accounting module 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 visualization of predicted exchange amount based on the identified patterns from the [machine-learning] model based on the continuously updating market data;
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)
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))
Referring to (Claim 1) “initiating, by a net lease module, a reserve module, … initiating, by the net lease module, an owner module, … initiating, by the net lease module, the 1031 exchange module that initiates an exchange module, … initiating, by the net lease module, a manage module”, (Claim 9) “a net lease module that controls a reserve module, an owner module, an exchange module, a manage module, and an accounting module”, and (Claim 17) “initiate, by a net lease module, a reserve module, … initiate, by the net lease module, an owner module, … initiate, by the net lease module, an exchange module” the Examiner views as results-oriented solution steps lacking details and therefore equivalent to merely applying the abstract idea using generic computing components. (See MPEP 2106.05(f) and Specification paragraphs 29, 35, 52, 77-78, 92, 107, 121)
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 November 26, 2025 have been fully considered.
Applicant’s amendments and arguments, on page 15 of the Remarks, regarding the claim objections the Examiner finds Applicant’s amendments persuasive. Therefore, the Examiner has withdrawn the claim objections.
Applicant’s amendments and arguments, on page 15 of the Remarks, regarding the 112 (a) rejection the Examiner finds Applicant’s amendments persuasive. Therefore, the Examiner has withdrawn the 112(a) rejection.
Applicant’s amendments and arguments, on pages 15-20 of the Remarks, regarding the 112 (a) rejection the Examiner finds unpersuasive. Therefore, the Examiner has withdrawn the 112(a) rejection.
Applicant argues under Step 2A Prong 1 that the claims recite concepts for 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 visualization based on the machine-learning model to help investors understand market trends and adjust their investment strategies accordingly. (See paragraphs 37, 70, and 84-86). According to Applicant this is reflected in the limitations of:
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, 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 a machine-learning algorithm; continuously updating the machine-learning model based on continuously updating market data;
generating, [by the reserve module], 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;
storing, [by the net lease module in a lease database], the net lease terms, wherein the net lease terms are used to continually update the [machine-learning] model;
generating a visualization of predicted exchange amount based on the identified patterns from the machine-learning model based on the continuously updating market data;
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 emphasized by Applicant related to organizing information (receiving and storing market data), analyzing the stored information (generating a model based on the stored information for identifying patterns and weights based on historical net lease terms using an algorithm), updating the model based on receiving updated information, analyzing information (generating a set of net lease terms based on inputs where the net lease terms are based on the identified patterns and weights for the model assigned to each input), storing information (the generated net lease terms for updating the model) and analyzed information for display (generating a visualized of the predicted amount based on the identified patterns from the model based on the continuously updating market data) 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 specific type of model/algorithm applied utilized. However 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 and the weight inputs should be given.
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. According to Applicant the net lease network utilizes the data received from the expense network connected to a plurality of third-party networks, to generate a machine-learning model to generate a set of net lease terms by the reserve model which is further used to update the machine-learning model, which are working in conjunction to perform the functions of the claims and thus are integral to the claims.
The Examiner respectfully disagrees viewing the additional elements alone and in combination of machine learning model, 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).
Applicant argues under Step 2B the steps of 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, 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 a machine-learning algorithm, continuously updating the machine-learning model based on continuously updating market data, generating, by the reserve module, 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, storing, by the net lease module in a lease database, the net lease terms, wherein the net lease terms are used to continually update the machine-learning model, and generating a visualization of predicted exchange amount based on the identified patterns from the machine-learning model based on the continuously updating market data are not routine or conventional.
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 and do not integrate the abstract idea into a practical application or adds significantly more. MPEP 2106.05 (f).
Therefore 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.
Chang et al. (US 20230027774) -directed to real estate evaluation.
Adhav et al. (US Patent No. 11,798,068) -directed to a lease management system.
THIS ACTION IS MADE FINAL. 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.
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/M.J.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629