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 .
Priority
This application claims priority to Provisional Application # 63/11,1052 and therefore has an priority date corresponding to the filing date of 08 November 2020.
Response to Amendment and Claim Status
In the Amendment received on 29 July 2025 claims 1,19,21,22 were amended and Claims 24 was newly added. Claims 1-8,10-22,24 are rejected herein.
Distinguishing Subject Matter
No combination of prior art appeared to address the totality of the invention as described in the independent claim amendment. For example, the prior art of note failed to teach or describe the section beginning at (g) retrieving… and ending in the 5,000 coaching profiles:
(G) retrieving, in response to receiving the request, a plurality of coaching profiles, wherein each coaching profile in the plurality of coaching profiles: (i) is associated with a corresponding coach in a plurality of coaches, (ii) comprises corresponding one or more wellness programs, each wellness program in the corresponding one or more wellness programs, each wellness programs in the corresponding one or more wellness programs comprising a corresponding structured series of tasks administrated to a respective subject, at least in part, by the corresponding coach that physically engages the respective client with the corresponding coach, and (iii) a first corresponding multidimensional data set associated with a corresponding first historical performance of the corresponding coach during a respective wellness program in the corresponding one or more wellness programs, the first corresponding multidimensional data set comprising a first value data subset associated with an interactive activity of the respective wellness program and a first text data subset associated with a second corpus of communications of a record of one or more communication channels associated with the respective wellness program, and wherein the plurality of coaching profiles comprises at least 5,000 coaching profiles;….
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 1-8,10-22,24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Statutory Categories
Claims 1, 19 and 20 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method of matching a first client and a coach, a computer system comprising one or more processors and a memory storing at least one program for execution, and a non-transitory computer readable storage medium stored on a computer system. All are in a statutory class for subject matter eligibility purposes.
Step 2A Prong One: The Abstract Idea
The limitations of (Claim 1 being representative) …for each client in a plurality of clients comprises at least 5,000 clients (A) obtaining […] a plurality of text-based data sets, wherein each text-based data set int eh plurality of text-based data sets is associated with at least one respective attributed in a plurality of attributes comprising at least 500 attributes; (B) assigning at least a portion of each respective text-based data set in the plurality of text-based data sets to a corresponding corpus of communications in a plurality of corpus of communications, wherein each respective corpus of communications in the plurality of corpus of communications is associated with a unique attribute in the plurality of attributes and wherein the plurality of attributes is based at least in part on an aggregation of each unique attribute associated with plurality of corpus of communications; (c) maintaining the plurality of corpus of communications by transforming the portion of each respective text-based data set in the plurality of text-based data sets from a native data structure form into a predetermined data structure form and updating the plurality of corpus of communications using the predetermined data structure form of the portion of each respective text-based data set, wherein an operation to perform the transforming includes one or more of inputting the portion of each respective text-based data set to a term frequency-inverse document frequency computational model, in a plurality of computational models, in order to receive as output from the term frequency inverse document frequency computational model, in a plurality of computational models, in order to receive as output from the term frequency inverse document frequency computational a model a corresponding first plurality of fixed-length vectors associated with the corresponding corpus of communications int eh plurality of corpus of communications; (d) training, responsive to maintaining (C), a first neural network computational model in the plurality of computational models, wherein the first neural network computational model is trained using the plurality of corpus of communications, and wherein an operation to perform the training comprises one or more of inputting a first set of corpus of communications associated with a corresponding first set of attributes in the plurality of attributes to the firs neural network computational model by applying a first plurality of parameters comprising one or more weighting factors, one or more bias values, and one or more threshold values using a gradient descent algorithm or a backward propagation algorithm in order to receive as output from the first neural network computational model a first set of one or more correlated similarities between the first set of corpus of communications and a second corpus of communications in the plurality of corpus of communications, adjusting one or more weighting factors, one or more bias values, or one or more threshold values of the first plurality of parameters in or der to form a second plurality of parameters, the second plurality of parameters in order to form a second plurality of parameters, the second plurality of parameters associated with a second set of one or more correlated similarities between the first set of corpus of communications and the second corpus of communications have a best fit exceed that of the first plurality of parameters, thereby forming a trained first neural network computational model; (E) receiving […] a request for a wellness program, the request comprising a plurality of responses elicited from the first client responsive to a survey […]; (F) inputting the plurality of responses to an ensemble computational model comprising the trained first neural network computational model in order to receive as output from the ensemble computational model a set of attributes assigned to the first client from the plurality of attributes wherein the set of attributes comprises at least 20 attributes; (G) retrieving, in response to receiving the request, a plurality of coaching profiles, wherein each coaching profile in the plurality of coaching profiles: (i) is associated with a corresponding coach in a plurality of coaches, (ii) comprises corresponding one or more wellness programs, each wellness program in the corresponding one or more wellness programs, each wellness programs in the corresponding one or more wellness programs comprising a corresponding structured series of tasks administrated to a respective subject, at least in part, by the corresponding coach that physically engages the respective client with the corresponding coach, and (iii) a first corresponding multidimensional data set associated with a corresponding first historical performance of the corresponding coach during a respective wellness program in the corresponding one or more wellness programs, the first corresponding multidimensional data set comprising a first value data subset associated with an interactive activity of the respective wellness program and a first text data subset associated with a second corpus of communications of a record of one or more communication channels associated with the respective wellness program, and wherein the plurality of coaching profiles comprises at least 5,000 coaching profiles; further retrieving, in response to receiving the request, a plurality of wellness programs comprising at least 10,000 wellness programs, wherein each respective wellness program in the plurality of wellness programs: (i) is associated with the one or more corresponding coaches in the plurality of coaches, (ii) comprises two or more attributes, in the plurality of attributes, improved for a subject by the respective wellness program, and (iii)a second corresponding multidimensional data set associated with a second historical performance of a respective client during the respective wellness program in the plurality of wellness programs, the second corresponding multidimensional data set comprising a second value data subset associated with the one or more physiological measurements captured during the interactive activity of the respective wellness program and the second text data subset associated with a subjective measuring during the interactive activity of the respective wellness program; (I) inputting the first corresponding multidimensional data set associated with each respective coaching profile in plurality of coaching profiles, the second corresponding multidimensional data set associated with each respective wellness program in the plurality of wellness programs, and the set of attributes assigned to the first client to the ensemble computational model in the plurality of computational models, thereby producing a respective result in a plurality of results from each computational model into first ensemble computational model, wherein each respective result in the plurality of results is a data set associated with a wellness program in the plurality of wellness programs or a coach in the plurality of coaches and comprises a corresponding semantic graph defining a proximity and a relationship between at least two attributes in the plurality of attributes, a first computational model in the ensemble computational model is trained on a first training corpus of labeled communication data associated with the plurality of coaching profiles and is further configured to produce a first data set associated with each respective coach in the plurality of coaches using at least the plurality of coaching profiles and the set of attributes, and a second computational in the ensemble computational model is trained on a second training corpus of labeled performance data associated with a plurality of client profiles and is further configured to produce a second data set associated with teach respective wellness program in the plurality of wellness programs using at least the plurality of wellness programs and the set of attributes; (J) collectively inputting the plurality of results to a second set of computational models, in the plurality of computational models, comprising a third computational model that uses a comparison or amalgamation algorithm, thereby producing a set of at least one coaching profile and at least one wellness program; and (K) communicating […] the set of the at least one coaching profile and the at least one wellness program thereby matching the first client and the coach; (L) receiving […] a corresponding performance data set associated with a performance of a first wellness program in the at least one wellness program, selected by the first client, with the coach, wherein the corresponding performance data set comprises (i) a subjective data set associated with a self-evaluation of the first wellness program by the first client and (ii) an objective data set associated with one or more value measurements captured during performance of the first wellness program by the first client; (M) inputting the corresponding performance data set to the term frequency-inverse document frequency computational model in order to receive as output from the term frequency inverse document frequency computational model a corresponding second plurality of fixed-length vectors associated with a corresponding corpus of communications of the first wellness program; and (N) communicating […] one or more recommendations for modifying the first wellness program based on the corresponding second plurality of fixed-length vectors, thereby providing feedback responsive to matching the first client as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components or mathematical concepts (the mathematical modelling component clearly falls under this category).
That is other than reciting remote devices, a computer system comprising one or more processors and a memory storing at least one program for execution by at least one processors, and a non-transitory computer readable storage medium stored on a computer system the claimed invention amounts to managing personal behavior or interaction between people (i.e., a person following a series of rules or steps), which falls under certain methods of organizing human activity or mathematical concepts. For example, but for the various general-purpose computer elements, the claims encompass a person receiving data, obtaining coaching profiles, obtaining wellness programs, processing coaching profiles, the wellness program and the set attributes to produce a respective result for each computational model and collectively considering each result and communicating to a client coaching profiles and wellness programs in the manner identified in the abstract idea, supra. The Examiner notes that a “certain method of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)) and that mathematical concepts may be described generally (i.e. a computational model) that If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within “certain methods of organizing human activity” or into the mathematical grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Note, the broadest reasonable interpretation of the term wellness program was used and the information concerning the GUI administration of the wellness program that is contained in the Specification was not imported into the analysis of the claims. As such, the term “wellness program” is intentionally included in the abstract idea and was not analyzed as an additional element.
Step 2A Prong Two: Practical Application
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of remote devices, a computer system comprising one or more processors and a memory storing at least one program for execution by at least one processors, and a non-transitory computer readable storage medium stored on a computer system that implements the identified abstract idea. These additional elements are not exclusively described by the applicant and are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using a generic computer component. Accordingly, 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. The claims are directed to an abstract idea.
Step 2B: Significantly More
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a general-purpose computer (or components thereof) to perform the noted steps amounts to no more than mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, the additional element of “communicating, in electronic format, to a second remote device…” was considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field [US 2019/0108704 A1 (hereafter Witowski) at the Abstract; US 20030179112 A1 (hereafter Parry) at the Abstract; US 6980137 B2 (hereafter Parry) at the Abstract]. As such the claim is not patent eligible.
Claims 2-8,10-18,21-22,24 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 2 merely describes a set of attributes. Claim 3 merely describes one or more accounting attributes. Claim 4 merely describes the corresponding first historical data set for a corresponding coach. Claim 5 merely describes the corresponding first historical data set comprises the communications corpus associated with the corresponding coach. Claim 6 merely describes each communication channel in the plurality of communication channels. Claim 7 merely describes a computational model for each sentiment in a plurality of sentiments. Claim 8 merely describes wherein the first client is associated with a specific enterprise. Claim 10 merely describes a weighted average of a subset of attributes. Claim 11 merely describes each respective attribute in the set of attributes comprises an independent weight. Claim 12 merely describes the data set associated with the one or more wellness program comprises a first return of investment of the first client and/or a second return on investment of a respective coach associated with a coaching profile. Claim 13 merely describes generating for display a listing of the set of at least one coaching profile and at least one wellness program. Claim 14 merely describes wherein the at least one coaching profile and the at least one wellness program have a one-to-one relationship. Claim 15 merely describes wherein the remote device associated with a subject other than the first client. Claim 16 merely describes a number of results based on attributes. Claim 17 merely describes a data set quality of a coach a quality of a respective wellness program, popularity of the respective coach, a popularity of the respective wellness program. Claim 18 merely describes a first subset of attributes assigned to the first client by the plurality of computational models and a second subset of attributes assigned by a human subject. Claim 21 merely describes generating a first communication channel and storing a record of corresponding plurality of messages. Claim 22 merely describes inputting a record corresponding to a plurality of messages to determine subject matter associated with the record and closing in accordance with a determination the subject matter associated with the record satisfies a threshold characteristic, the first communication channel. Claim 24 merely describes the first computational model is a support vector machine computational model trained to parse a respective data set with a hyperplane that is maximally distance from one or more labelled attributes associated with the first training corpus of labeled communication data.
Response to Arguments
Applicant argues that the claims are not directed to an abstract idea. Applicant argues that a method for providing feedback responsive to matching the presentive client using the ensemble computational model having the trained neural network computational model, as reflected by independent claim 1, does not fall into any of the “enumerated sub-groupings.” In particular, techniques for providing feedback responsible to matching the presentive client using the ensemble computational model having the trained neural network computational model is not a method of organizing human activity. For instance, the method is not managing personal behavior or personal interactions between people. Moreover, the method does not manage social activities, teaching, or following rules or instructions.
The Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to match a client and a coach. Furthermore, the Examiner submits that healthcare itself is inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to match a client and a coach, the claimed invention is directed to an abstract idea.
Moreover, the Examiner respectfully disagrees. Multiple CAFC decisions that the Office has characterized as Certain Method of Organizing Human Activity did not actively recite a person or persons performing the steps of the claims (see, e.g., EPG, TLI communications, Ultramercial). Because whether a human is required to perform the step of the claim is not a requirement for claims to encompass certain method of organizing human activity, this argument is not persuasive.
Applicant argues that the claims are directed to the technical operation of the plurality of computational models that uses at least three differently trained computational models to process data to detect correlation between available coaches, wellness programs offered by the available coaches, and generate outputs in the form of varying data structures such as fixed length vectors or semantic graphs using multidimensional data sets. These processes are inherently computational and algorithmic, focusing on the training and application of the computational models in order to provide actional feedback to coaches for modifying wellness programs based not only on prior performance but also communications between the coach and respective clients during prior wellness programs, as recited by the claims. This training and application of the computational models is not a mere general or minor step, but explicitly integrated throughout the claims as an essential process. The training involves multiple steps using multiple computational models, such as the term frequency-inverse document frequency computational model to transform text data into fixed-length vectors that applying training data to output predicted wellness programs, coach pairings for the respective client that is then utilized in real-time with subject provided responses, and further utilized to provide feedback to the coach through wellness program modification recommendations. These steps reflect a clear technical application of machine learning that is far more than a mere abstract idea or general concept. For instance, the additional technical steps are tied to each of the recited computational training models and/or their respective outputs, such that the elements work together to achieve the claimed result, further demonstrating that the claim does not merely recite an abstract idea or a method of organizing human activity. Accordingly, independent claim 1 is patent eligible under Prong One of Step 2A because the claim does not recite a judicial exception.
See response above. This argument is redundant.
Applicant argues that the claims are patent eligible under the second prong of step 2A because any alleged abstract idea is integrated into a practical application. Applicant argues that the claims focus on a technological improvement by specifying a particular way of running a computer-implemented process through defined training data acquisition steps, training steps, and client request data collection and evaluation using multi-dimensional data sets. At least the claimed limitations pertain to technologically complex systems and methods using machine learning processes with at least the term frequency-inverse document frequency computational model and the trained neural network computational model, which allows the system to generate feedback for coaches after matching the coach with the respective client and evaluating text and value data associated with performance of the wellness program. Moreover, the claims enhance computer functionality be enabling the creation of a sophisticated machine learning model and generating outputs predictive of results from wellness programs completed by pairing of user and coaches, such that the overall performance and capabilities of the trained machine learning models are advanced.
MPEP 2106.04(d) Integration of a Judicial Exception Into a Practical Application indicates that relevant considerations for evaluating whether additional elements integrate a judicial exception into a practical application includes:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
This same section of the MPEP indicates that the courts have also identified limitations that did not integrate a judicial exception into a practical application:
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of user, as discussed in MPEP 2016.05(h).
The Examiner maintains that as amended claim are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).
The Applicant asserts that the claims are patent eligible under step 2B because they amount to significantly more than the abstract idea. The claim recite a technological solution to a technological problem. Applicant argues that the non-conventional and specific arrangement of steps provides a technical improvement in the field, aligning with the principles set forth in Berkheim.
Also, the additional element of “communicating, in electronic format, to a second remote device…” was considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field [US 2019/0108704 A1 (hereafter Witowski) at the Abstract; US 20030179112 A1 (hereafter Parry) at the Abstract; US 6980137 B2 (hereafter Parry) at the Abstract]. As such the claim is not patent eligible.
Applicant argues that the training process involves applying particular data sets to particular algorithms, resulting in outputs that are actionable and directly tied to real-world applications in client coach matching and wellness program analysis. This involved solving specific technical problems, just as the ANN of Example 47 addressed real-time network security. These steps are not routine and conventional but are specifically designed to improve prediction accuracy and practical usability in complex datasets.
The judicial exception was not integrated into a practical application. See MPEP 2106.04(d) Integration of a Judicial Exception Into a Practical Application under section I. Relevant Considerations For Evaluating Whether Additional Elements Integrate a Judicial Exception Into a Practical Application. One of the limitations that courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: an improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP 2106.04(d)(1) and 2106.05(a). Here, there is no improvement in the functioning of the computer, or an improvement to other technology or technical field. Applicant argues that the inventive concept is found in the use of a DNN to separate speech signal through clustering embedding vectors and synthesizing distinct waveforms. This process allows the DNN to solve a technical problem of distinguishing speech sources in a mixed audio signal, achieving results superior to prior systems that required prior knowledge of speaker identities. The application of binary masks and clustering, combined with downstream processes for generating a clear audio signal, reflected an inventive solution to a longstanding challenge in speech separation. The present claims mirror these inventive features by integrating the plurality of computational models, including the different trained computational models and application processes that solves technical problems in providing actional feedback to coaches back on client coach matches. Specifically, the claims recite training techniques that enable the neural network model to reliability and accuracy predict client coach matches and provide feedback to coaches through recommended modifications to wellness programs. Additionally, the claims include the integration of unique data elements, such as first and second multi-dimensional data sets, which are used to generate outputs by computational models. This novel combination of features reflects a technical solution to the limitations of traditional models, providing significantly more than a mere abstract idea or routine use of machine learning.
However, the additional element of “communicating, in electronic format, to a second remote device…” was considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field [US 2019/0108704 A1 (hereafter Witowski) at the Abstract; US 20030179112 A1 (hereafter Parry) at the Abstract; US 6980137 B2 (hereafter Parry) at the Abstract.
Moreover, respectfully, the cited subject matter does not teach nor suggest a technical improvement to any technology nor an improvement to the computer, the technological environment to which the claims are confined. Respectfully, neither does the claimed subject matter claim an improvement to any other technology via reciting a technical solution to a technical problem in the claims.
Additionally, Examiner’s consider 2106.05(a) Improvements to the Functioning of a Computer or to Any other Technology or Technical Field when addressing Step 2A Prong Two of the Subject Matter Eligibility Analysis and when re-evaluating these consideration in Step 2B. This same section indicates that “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” This same section indicates that: … if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art… Examiner asserts there is no improvement to the computer, certainly that is claimed, but also there is no improvement outlined in the Specification. The Examiner reviewed the Specification and found no clear improvements to any technologies or to the computer.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11250942 B1 (hereafter Ahmad) teaches a health monitoring and coaching system that reads on the instant claims.
US 11056223 B1 (hereafter Ahmad 2) also teaches a health monitoring and coaching system that reads on the instant claims.
US 20210205660 A1 (hereafter Shavit) teaches an outdoors training systems and methods for designing, monitoring and providing feedback of training.
US 11610675 B1 (hereafter Stoertz) teaches dynamic and target allocation of resources in a coaching service environment that clearly reads on the instant claims.
Perlman, Lawrence. A Multidimensional Wellness Group Therapy Program for Veterans with Comorbid Psychiatric and Medical Conditions. Professional Psychology: Research and Practice 2010, Vol. 41, No. 2, 120-127. (Year: 2010)
Carol Ann Maher et al. A Physical Activity and Diet Program Delivered by Artificial Intelligent Virtual Health Coach: Proof-of-Concept Study. JMIR Mhealth Uhealth 2020;8(7):e17558) doi: 10.2196/17558. (Year: 2020)
Pratiwi. Towards Personalization of Physical Activity E-Coach Using Stage-Matched Behavior Change and Motivational Interviewing Strategies. 2017 IEEE Life Sciences Conference (LSC). Digital Object Identifier: 10.1109/LSC.2017.8268130. 2017 IEEE Life Sciences Conference (LSC). Pratiwi teaches a mechanism of physical activity e-coaching is relevant to the subject matter herein.
While not used in this office action, the prior art here is at least tangentially related to the claimed subject matter and may or may not be used in the future.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRISTAN ISAAC EVANS whose telephone number is (571)270-5972. The examiner can normally be reached Mon-Thurs 8:00am-12:00pm & 1:00pm-7:00pm, off Fridays.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683