DETAILED ACTION
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 . Claims 1-5, 7-10, 13-20, 22-25, and 28-30 are pending.
Claim Objections
Claims 7-10, 13-15, 22-25, and 28-29 are objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim in the alternative only, and/or cannot depend from any other multiple dependent claim. See MPEP § 608.01(n). Accordingly, the claims have not been further treated on the merits.
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 1-5, 16-20, and 30 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a system (i.e., a machine), claim 16 is directed to a method (i.e., a process), and claim 30 is directed to a non-transitory computer-readable medium (i.e., machine or manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 USC § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea from claims 1, 16, and 30 (claim 1 being representative) is:
retrieve user data relating to one or more types of lien cases;
process the user data for identifying and prioritizing the one or more types of lien cases, based at least on one or more case factors, wherein the one or more case factors comprise, a ratio of amount pending to amount received, a percentage of completion of job, a number of jobs previously done with same employer, years of association with the same employer, a project type, a property size, a property owner, a financial entity, end-user client, availability of documentation, and a real estate value of a property location;
process the identified one or more types of lien cases, the training data comprising one or more inputs and one or more predictive outputs derived from the machine learning model's processing of the one or more inputs, wherein the one or more predictive outputs comprise calculation of total contract, progress payment received, lien percentage of total contract, percentage of project completed, type of construction lien or claim, contract start date, length3 (completion date), early termination date, current filed lien status, field lien date, discharge bond filed date, lien law demand filed date, demand letter lien removal date, and lien foreclosure action filed date;
determine each lien cases metric of resolution, collectability, and resolution time parameters;
create a case study, a knowledge base summary, predictable sequential steps, an artificial intelligence summary, a machine learning efficiencies summary, and a combinational optimization sequential step summary, by utilizing the one or more predictive outputs.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, organizing cases, i.e., observing, evaluating, and judging cases information to determine cases metrics. This is further supported by paragraphs 0003 - 0006 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally and alternatively, the claim is directed to mediating lien disputes in a contractual context, which constitutes a process that, under its BRI, covers commercial activity. This is further supported by paragraphs 0003 - 0006 of applicant’s specification as filed. If a claim limitation, under its BRI, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: User device; server; cloud; processor; memory; for training machine learning model (112), wherein the machine learning model (112) is trained based at least on training data; using the machine learning model (112); by the machine learning model (112).
Claim 16 recites the following additional elements: for training machine learning model (112), wherein the machine learning model (112) is trained based at least on training data; using the machine learning model (112); by the machine learning model (112).
Claim 30 recites the following additional elements: Non-transitory computer-readable medium; processor; for training machine learning model (112), wherein the machine learning model (112) is trained based at least on training data; using the machine learning model (112); by the machine learning model (112).
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraph 0092 and 0253 of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Regarding claims 2-5 and 17-20, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Accordingly, claims 1-5, 16-20, and 30 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 16-20, and 30 are rejected under 35 U.S.C. § 103 as being unpatentable over Nelson (US 20070055637) in view of Cella (US 20200294138).
Claims 1, 16, and 30
Regarding claims 1, 16, and 30, Nelson discloses:
(claim 1) A system (100) for mediating lien disputes, the system (100) comprising {“A system and method for investigating and mediating construction claims is presented.” (paragraph 0013)}
(claim 16) A method (200) for mediating lien disputes, the method (200) comprising: {“A system and method for investigating and mediating construction claims” (paragraph 0013)}
retrieving user data relating to one or more types of lien cases; {The system stores case data entered by the user, which can be used to generate reports (i.e., retrieves and processes this data). A person can maintain databases, and cross-reference and research claims, including its specific aspects (e.g., claim type). (paragraph 0054)}
processing the user data for identifying and prioritizing the one or more types of lien cases, based at least on one or more case factors {Claim data enters the system and is processed and reduced it a list of numerical parameters such as vectors. “Claims can be ranked by probability of profitability (e.g., a case factor) in a priority queue.” (paragraph 0057)}
wherein the one or more case factors comprise, a ratio of amount pending to amount received, {Factors include “amount of the claim”, “time […] outstanding”, reduced to numerical parameters for decisions (paragraph 0044)} a percentage of completion of job, {End user refuses payment due to incomplete work; mediator contacts parties, arranges steps tied to completion; punch list affects payment (paragraphs 0038-0039)} a number of jobs previously done with same employer, {“completed many constructions projects” for the same client (paragraph 0173)} years of association with the same employer, {“10-year relationship” (paragraph 0173)} a project type, {construction claims include lien and non-lien; applies across tiers and claim types (paragraph 0021)} a property size, {mediator “conceptualize[s] the scope of the project […] estimated total contract […] in relation to the size of the overall construction project” (paragraph 0164)} a property owner, {A mediator can contact the owner of the property (i.e., is listed as a basic lien component) (paragraph 0033)} a financial entity, {strategic outreach to financing entity; lien documentation sent; facilitates meeting/resolution (paragraph 0035)} end-user client, {“end user” is referenced as the GC’s client; demand for payment made to end user) (paragraph 0038)} availability of documentation, {bring/send “supporting documentation […] lien and a demand letter” (paragraph 0033)} and a real estate value of a property location; {location indicates “affluent area […] prestigious building […] moderate to expensive real estate”, etc.; used to evaluate viability/collectability (paragraph 0170).}
wherein the one or more predictive outputs comprise {case data is reduced to numerical parameters (vectors), processed by metrics and algorithms to guide decisions (paragraphs 0044-0052); metrics are calculated and reported at each stage of a case (paragraph 0064)} calculation of total contract, {“ The mediator's review of the lien case file indicated that the client's total contract, including change orders, was $2.5 million dollars” (i.e., supports calculation and use of total contract amount) (paragraph 0173)} progress payment received, {“The claimee and the fashion chain's Director of Construction signed a two-way agreement and agreed upon a weekly progress payment of $24,000 per week” (paragraph 0179)} lien percentage of total contract, {Lien amounts relative to recovery percentages are discussed (paragraph 0168)} percentage of project completed, {Payment withheld due to incomplete work; punch list to completion (paragraphs 0038-0039)} type of construction lien or claim, {“Construction claim” is defined to include mechanic’s, materialman’s public project liens, bonded claims (paragraph 0021)} contract start date, length (completion date), {“time it has been outstanding” is a core factor (i.e., start/end date) (paragraph 0044)} early termination date, {Decision paths include abandonment, litigation, or continued mediation (0063)} current filed lien status, {It’s identified whether a mechanic’s lien has been filed and its attributes (paragraph 0154)} field lien date, {“the mechanics lien […] was eight months old” (paragraph 0191)} discharge bond filed date, {Its discussed payment and performance bond contexts (paragraph 0036)} lien law demand filed date, {“demand letter directed to the owner’s representative or attorney” (paragraph 0033)} demand letter lien removal date, {Demand letters and status updates leading to lien resolution is discussed (paragraph 0195)} and lien foreclosure action filed date; {Threat of foreclosure is used as leverage (paragraph 0038).}
create a case study, {The system creates and stores structured case studies derived from claim data and outcomes (paragraph 0153)} a knowledge base summary, {The system aggregates resolved cases, industry data, and practices into a centralized knowledge base (paragraph 0054, 0236-0237)} predictable sequential steps, {Predefined, repeatable, sequential steps are generated and followed by the system (paragraphs 0056-0064, 0125)} an artificial intelligence summary, {The expert system encapsulates encoded intelligence and produces analytical outputs summarizing AI-driven reasoning (paragraphs 0042-0043, 0048)} a machine learning efficiencies summary,{The system improve performance over time via stored outcomes and parameter adjustment, supporting efficiency summaries derived from learning behavior (paragraph 0049, 0051, 0119)} and a combinational optimization sequential step summary, {The system selects sequences combinations of actions based on comparative metrics and vector optimization (paragraphs 0022, 0044-0045, 0050)} by utilizing the one or more predictive outputs by the machine learning model (112). {The system’s calculated outputs directly feed the generation of summaries, case studies, and optimized sequential actions (paragraphs 0044, 0057-0064).}
Nelson does not explicitly disclose:
(Claim 1) A user device (102) communicatively coupled to a server (104) over a cloud (106), wherein the server (104) comprises;
(Claim 1) At least one processor (108);
(Claim 1) a memory (110) for storing a set of instructions configuring the at least one processor (108) to (claim 1);
(Claim 30) a non-transitory computer-readable medium including instructions for causing a processor (108) to perform functions including (claim 30);
(Claims 1, 16, and 30) processing the identified one or more types of lien cases for training machine learning model (112), wherein the machine learning model (112) is trained based at least on training data, the training data comprising one or more inputs and one or more predictive outputs derived from the machine learning model's processing of the one or more inputs; and
(Claims 1, 16, and 30) determining each lien cases metric of resolution, collectability, and resolution time parameters, using the machine learning model (112).
However, Cella, in a similar field of endeavor directed to adaptive intelligent systems used to enable lending transactions, teaches:
(Claim 1) a user device (102) communicatively coupled to a server (104) over a cloud (106), wherein the server (104) comprises {“In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.” (paragraph 2018); “In embodiments, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform.” (paragraph 2014)}
(Claim 1) at least one processor (108) {“The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor.” (paragraph 2014)}
(Claim 1) a memory (110) for storing a set of instructions configuring the at least one processor (108) to {“The processor, or any machine utilizing one, may include non-transitory memory that stores methods, codes, instructions and programs as described herein and elsewhere.” (paragraph 2014)}
(Claim 30) A non-transitory computer-readable medium including instructions for causing a processor (108) to perform functions including {“The processor may access a non-transitory storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere.” (paragraph 2014)}
(Claims 1, 16, and 30) processing the identified one or more types of lien cases for training machine learning model (112), wherein the machine learning model (112) is trained based at least on training data, the training data comprising one or more inputs and one or more predictive outputs derived from the machine learning model's processing of the one or more inputs {The platform processes data to train machine learning models (including neural nets), based on input parameters and generating outputs for pattern classification (i.e., predictive modeling). (paragraph 0055) The data may be a lien. (paragraphs 0140 and 0142)}
(Claims 1, 16, and 30) determining each lien cases metric of resolution, collectability, and resolution time parameters, using the machine learning model (112) {Figs. 65 and 67 show valuation circuits using machine learning (e.g., valuation model improvement circuit 6342, 6530, 6740). The automated agent circuit may be structured to set or modify terms and conditions 6514 for the loan such as a duration (i.e., resolution time), foreclose condition (i.e., collectability), default condition or foreclosing on a property (i.e., resolution) (paragraphs 0918, 0920, and 0922)}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the mediation and collection of liens features of Nelson, to include the loan information automation and machine learning features of Cella to improve information transparency and simplify application and negotiation processes in determining the reliability or financial health of entities. (see paragraph 0008 of Cella).
Claims 2 and 17
Regarding claims 2 and 17 Nelson discloses:
determining a nature of lien dispute, wherein the nature of lien dispute includes disputes related to, but not limited to, delay in payments, change orders, job not completed, job un-satisfaction, and project timeline extension; {“[F]ully investigating the details and context of the construction claim; determining the actual causes for the lack of payment. (i.e., delay in payment) (paragraph 0020); “recreate a time line and quantify specifically as to the dates and individuals that were present when they were instructed to execute the change order work.” (i.e., dispute related to change orders) (paragraph 0032); As a result of a dispute of final payment because of unfinished items in a job, “a review of open punch list items with the Subcontractors can be undertaken.” (paragraph 0039); A mediator can conceptualize how far from the scope of the project is the work done versus the underlying claim, including “whether it is ongoing or near completion.” Examiner notes that “near completion” may mean the project is in the punch list stage. (i.e., job un-satisfaction or project timeline extension) (paragraph 0164)}
calculating percentage completion of job changes orders, based at least on the determination of the nature of lien dispute; and {The calculation or quantification of the executed change order work to put a number or value on each change order. This correlates the nature of the dispute and the extent of completed work. (paragraph 0032)}
identifying completed jobs based at least on the calculated percentage. {The system can “[d]etermine the amount of the undisputed portion of the lien or construction claim, i.e., the percentage of value of completed work in relation to the total contract and change order balance.” (paragraph 0081)}
Claims 3 and 18
Regarding claims 3 and 18 Nelson discloses:
identifying incomplete punch list items for proposing timeline for completion of all items, based at least on the calculated percentage. {Reviewing open punch list items to identify work that remain incomplete can be done. (paragraph 0039) Further, it could be obtained timelines for completion of that work from subcontractors. (paragraph 0037) The system can “[d]etermine the amount of the undisputed portion of the lien or construction claim, i.e., the percentage of value of completed work in relation to the total contract and change order balance.” (paragraph 0081)}
Claims 4 and 19
Regarding claims 4 and 19 Nelson discloses:
wherein the one or more types of lien cases are identified and prioritized by performing a lien collectability assessment using a lien mediation data and a lien collectability data. {The system can use case-specific data to analyze lien claims based on factors such as claim amount, aging, and industry sector. These are treated as “numerical parameters” to evaluate collectability. Further, it prioritizes which claims to pursue based on expected profitability or resolution likelihood, effectively performing a collectability assessment. (paragraph 0044)}
Claims 5 and 20
Regarding claims 5 and 20 Nelson discloses:
wherein the lien collectability assessment categories each lien case's collectability into a high category, a medium category, and a low category based on the one or more case factors. {The system evaluates lien cases using quantified case factors, ranking those cases by likelihood of profitability, and distinguishing cases that are viable from those unlikely to be collected (i.e., categorizes lien collectability according to tiers) (paragraphs 0044, 0057-0058, 0062).}
Response to Arguments
Applicant’s arguments filed on 10/06/2025 have been carefully considered. The headings and page numbers below correspond to those used by applicant.
Claim objections
Claim objections regarding claims 1, 5, 16, 20, and 30 are withdrawn. However, claim objections regarding claims 7-10, 13-15, 22-25, and 28-29 are maintained. Examiner directs Applicant’s attention to the analysis above for a detailed explanation.
Claim Rejections - 35 USC § 112(b)
Claim rejections under § 112(b) are withdrawn in view of Applicant’s amendments.
Claim Rejections - 35 USC § 101
On pages 12-21, applicant offer remarks regarding the rejections under 35 U.S.C. §101. While well taken, they are not persuasive.
The amended claims remain directed to an abstract idea. Reciting lien specific data fields, predictive metrics, and general summaries does not change the focus of the claims from information analysis and reporting within a legal and/or commercial process.
The recitation of “training a machine learning model” does not render the claims patent-eligible. The claims do not improve machine learning technology itself or computer functionality. Rather, machine learning is used as a generic analytical tool to evaluate lien data and generate predictions and summaries, i.e., an abstract idea application.
The claimed outputs are informational results of data processing and do not reflect a technical improvement to computer operation.
Accordingly, rejections under 35 U.S.C. § 101 are maintained.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to patentability under 35 U.S.C. § 103 have been considered but are not persuasive. Regarding the arguments of the amended portions of the independent claim 1, Examiner directs Applicant’s attention to the analysis above.
Applicant offers in page 27:
Therefore, Examiner relies on Cella that discloses predictive analytics in a generic dispute management context, but does not disclose or suggest generating the particular lien-related predictive outputs required by amended claim 1. Cella is directed to predictive modeling in the loan underwriting and lending context. Its focus is on outputs such as borrower creditworthiness, repayment likelihood, or loan performance metrics. Cella does not disclose lien disputes, lien foreclosure timelines, discharge bond filings, or other lien-resolution specific outputs. Importantly, Cella's predictive modeling is tied to borrower behavior and loan portfolios, not to lien enforcement processes.
The specific predictive outputs recited in amended claim 1 are not trivial extensions of generic ML modeling. They represent a domain-specific application of machine learning uniquely tailored to lien dispute resolution in the construction industry. Identifying, extracting, and predicting such outputs is not suggested in Cella and would not have been obvious to a POSITA absent impermissible hindsight.
Since the cited references fail to teach or suggest the recited the one or more predictive outputs, they also fail to teach the step of "processing the identified one or more types of lien cases for training a machine learning model". In other words, the novelty resides not only in the predictive outputs themselves but also in the training and processing operation that explicitly relies on those outputs. Absent these outputs, the prior art cannot perform the processing step as claimed.
Accordingly, the entire limitation of claim 1 directed to processing lien cases for training a machine learning model to generate the recited predictive outputs is novel and patentably distinct over Cella.
Accordingly, Nelson and Cella, alone or in combination, fail to teach or suggest the predictive outputs expressly required by amended claim 1. Claim 1 is therefore patentably distinct, and the rejection under 35 U.S.C. § 103 should be withdrawn.
Cella teaches training predictive models on dispute and collection case data to generate outcomes for managing and resolving claims, without being limited to a specific industry or out put labels. Applying those same techniques to construction lien cases by using lien related dates, amounts, and status data is straightforward adaptation to an analogous domain. Merely reciting lien-specific outputs reflects a change in data selection, not a new nonobvious training or processing technique.
Applicant further offers in pages 31-32:
Argument 4: Nelson and Cella Cannot Be Combined
The Examiner relies on Nelson in view of Cella to reject the pending claims. Applicant respectfully submits that the proposed combination is improper and fails to render the claims obvious.
Nelson discloses general systems for dispute mediation, and while lien disputes are mentioned, Nelson does not teach or suggest the specific application of machine learning models trained with defined predictive outputs to generate lien case resolution metrics such as collectability, resolution probability, and timelines.
Cella, on the other hand, teaches machine learning in the context of lending and financial services, but does not address lien disputes or the unique factors that drive lien collectability or case resolution strategies.
The Examiner's rejection rests on combining Nelson's general case management disclosures with Cella's predictive modeling. However, such a combination is only apparent with hindsight reconstruction guided by Applicant's own claim language. There is no articulated reason why a person of ordinary skill in the art would have been motivated to modify Nelson's system with Cella's loan-based predictive analytics to arrive at the presently claimed invention.
The Federal Circuit and Supreme Court have cautioned against such hindsight-driven combinations. In KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 421 (2007), the Court noted that "a patent composed of several elements is not proved obvious merely by demonstrating that each element was, independently, known in the prior art." Similarly, in In re Rouffet, 149 F.3d 1350, 1357 (Fed. Cir. 1998), the court emphasized that the PTO must identify a teaching, suggestion, or motivation to combine references, and cannot rely on impermissible hindsight reconstruction.
Applicant's invention provides a non-obvious, domain-specific solution tailored to lien disputes, addressing issues such as lien collectability, resolution metrics, foreclosure timelines, and lien documentation problems not recognized, taught, or suggested in Nelson or Cella. The specific integration of machine learning with lien-specific predictive outputs is therefore not rendered obvious by the cited combination.
Therefore, the claimed invention as a whole remains non-obvious under § 103, and withdrawal of the rejection is respectfully requested.
Further, Applicant respectfully submits that independent claims 16 and 30 have been amended to recite subject matter analogous to amended independent claim 1, which is shown to be allowable as above.
Further, applicant respectfully submits that dependent claims 2-4 and 17-19 are also allowable under 35 USC§ 103 for their dependency on the amended independent claims 1 and 16 respectively.
Therefore, Applicant requests the Examiner to allow claims 1-4, 16-19, and 30 over 35 USC § 103, as a matter of law.
Nelson already teaches a computerized lien dispute mediation system that processes lien case data using metrics, decision vectors, and learned weighting factors to assess collectability, prioritize cases, and guide resolution steps and timelines. Cella teaches predictive modeling using machine learning to forecast outcomes and support dispute and collection decisions. A POSITA would have been motivated to apply Cella’s predictive analytics to Nelson’s lien-specific framework to improve case evaluation and resolution efficiency. The combination reflects a sensible integration of known techniques (which Nelson lays the groundwork), not hindsight.
In summary, examiner has responded to all arguments and found them unpersuasive. Accordingly, the rejections under 35 U.S.C. §103 are maintained.
Conclusion
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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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629