DETAILED ACTION
This action is made FINAL in response to the amendments filed on 5/22/2026.
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 - 3, 5 - 10, 12 - 17, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 8, and 15,
Step 2A, Prong One
The claim recites in part:
creating an initial expert layer of an expert hierarchy with a plurality of initial experts trained for prediction;
generating, by each of the initial and augmented experts in the expert hierarchy, a respective expert prediction based on the input.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example: (1) A human can create an initial expert hierarchy by mentally organizing knowledge, ranking areas of expertise, and deciding which “experts” (skills, perspectives, or strategies) should take priority in different situations. (2) A human expert can generate prediction based on their expertise.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving an input provided to the expert hierarchy for a prediction;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained via machine learning for the prediction, based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s)of the expert hierarchy so that an expert of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy
wherein the step of deriving comprises:
generating an augmented expert at a first augmented expert layer based on first training data and a plurality of predictions generated by the plurality of initial experts based on the first training data, and
generating an augmented expert at an augmented expert layer above the first augmented expert layer based on second training data, a plurality of predictions generated by the plurality of initial experts based on the second training data, and one or more predictions generated by respective one or more augmented experts at any lower augmented expert layer based on the second training data;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a processor, a memory, communication platform, and machine readable and non-transitory medium which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of user segment, initial expert layer, expert hierarchy, augmented expert layer, machine learning, input, expert hierarchy, and expert prediction amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receiving an input provided to the expert hierarchy for a prediction;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained via machine learning for the prediction, based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s)of the expert hierarchy so that an expert of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy
wherein the step of deriving comprises:
generating an augmented expert at a first augmented expert layer based on first training data and a plurality of predictions generated by the plurality of initial experts based on the first training data, and
generating an augmented expert at an augmented expert layer above the first augmented expert layer based on second training data, a plurality of predictions generated by the plurality of initial experts based on the second training data, and one or more predictions generated by respective one or more augmented experts at any lower augmented expert layer based on the second training data;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The processor, a memory, communication platform, and machine readable and non-transitory medium are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of user segment, initial expert layer, expert hierarchy, augmented expert layer, machine learning, input, expert hierarchy, and expert prediction amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2, 9, and 16, the recitation of “wherein the plurality of initial experts are heterogeneous experts” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 3, 10, and 17, the recitation of “wherein when the expert hierarch has multiple augmented expert layers, each augmented expert at an augmented expert layer higher than a first augmented expert layer additionally augments any augmented expert at a lower augmented expert layer” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 5, 12, and 19, the limitations “wherein the step of generating an augmented expert at a first augmented expert layer comprises: accessing the first training data having input features and ground truth labels; sending the input features to the plurality of initial experts; receiving expert predictions from the respective plurality of initial experts; and iteratively learning the augmented expert at the first augmented expert layer based on the input features, the expert predictions from the respective plurality of initial experts, and the ground truth labels” which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As to claims 6 and 13, the limitations “wherein the step of generating an augmented expert at an augmented expert layer above the first augmented expert layer comprises: accessing the second training data having input features and ground truth labels; sending the input features to the plurality of initial experts and one or more augmented experts at each lower augmented expert layer; receiving both initial expert predictions from the respective plurality of initial experts and augmented expert predictions from respective previously trained augmented experts at each lower augmented expert layer; and iteratively learning the augmented expert based on the input features, the initial expert predictions, the augmented expert predictions, and the ground truth labels” which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As to claims 7, 14, and 20, the limitations “accessing a nonlinear integration model provided for integrating different expert predictions; combining, in accordance with the nonlinear integration model, expert predictions from the initial and augmented experts in the expert hierarchy generated based on the input; and generating an integrated expert prediction based on a result of the combining” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Response to Arguments
Applicant's arguments filed 12/16/205 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 102 & 103
The amended claims overcome all art rejections. Both the 102 and 103 Rejections have been withdrawn.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
The Office Action alleges that claim 1 falls under the "Mental Processes." Office Action, pages 3-4. Applicant respectfully disagrees. Initially, claim 1 as a whole is related to "machine learning" (see the preamble), which cannot be performed by the human mind.
Particularly, claim 1 recites "deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained, via machine learning for the prediction, based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s) of the expert hierarchy so that an expert at any of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy, wherein the step of deriving comprises: generating an augmented expert at a first augmented expert layer based on first training data and a plurality of predictions generated by the plurality of initial experts based on the first training data, and generating an augmented expert at an augmented expert layer above the first augmented expert layer based on second training data, a plurality of predictions generated by the plurality of initial experts based on the second training data, and one or more predictions generated by respective one or more augmented experts at any lower augmented expert layer based on the second training data." These claim features are related to machine learning via an expert hierarchy, wherein experts at different layers are generated based on different data, and an expert at any augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy. The above-quoted claim features cannot be performed by a human mind or a human with pen/paper at least because a human mind cannot perform machine learning via an expert hierarchy, wherein an expert at any augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy. Thus, claim 1 does not fall within the "mental processes" grouping of abstract ideas.
The Office Action on page 17 alleges that "[a]s claimed the 'machine learning' is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words 'apply it' (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea." Applicant respectfully disagrees at least because "machine learning" is not recited in a level of generality that encompasses abstract manipulation of data but is recited in details - "wherein each augmented expert at any of the at least one augmented expert layer is trained, via machine learning for the prediction, based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s) of the expert hierarchy so that an expert at any of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy, wherein the step of deriving comprises: generating an augmented expert at a first augmented expert layer based on first training data and a plurality of predictions generated by the plurality of initial experts based on the first training data, and generating an augmented expert at an augmented expert layer above the first augmented expert layer based on second training data, a plurality of predictions generated by the plurality of initial experts based on the second training data, and one or more predictions generated by respective one or more augmented experts at any lower augmented expert layer based on the second training data." Human minds cannot machine generate/train an augmented expert in an expert hierarchy based on training data and expert outputs from all of experts from all lower expert layer(s) of the expert hierarchy, as recited.
Also, these claim features are not directed to certain methods of organizing human activity or mathematical concepts.
Accordingly, claim 1 does not fall into any of the abstract ideas exceptions provided by the MPEP, and thus claim 1 is patent eligible under Prong One of the Step 2A Analysis.
The examiner disagrees. Applicant’s arguments are not persuasive because they just focus on an overly narrow interpretation of the claims Under the broadest reasonable interpretation, the claims remain directed to the abstract idea as identified in the Office Action. Merely reciting machine learning, expert layers, experts, training data, and predication outputs does not remove the claims from the mental processes or integrate the exception into a practical application.
The “expert hierarchy, expert, and machine learning model” are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The applicant argues:
Moreover, claim 1 is patent eligible because the claimed concepts are integrated into a practical application. MPEP 2106.04(d) states: "after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two." MPEP 2106.04(d) also states "Limitations the 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)."
Initially, as mentioned previously, the Office Action has improperl analyzed claim 1 when determining whether claim 1 recites a judicial exception because claim 1 does not fall into any of the abstract idea exceptions - mathematical concepts, certain methods of organizing human activity, or mental processes.
Even assuming, for the sake of argument, that claim 1 does recite an abstract idea (whichthe Applicant disagrees), Applicant respectfully submits that claim 1 is patent eligible under Prong Two of the Step 2A Analysis.
The claims provide an improvement in technical fields of ensemble learning. As a known problem of the traditional ensemble learning, "due to the complexity of inter-relationships among data and different data sources, it is not possible to capture such inter-relationships via linear models" (para. [0004]), and "linearly combining their outputs using a linear combination cannot capture the actuality of the world" (para. [0005]).
"The present teaching discloses solutions that address challenges in the art. To resolve the issues associated with task heterogeneity, data long-tailness, and data availability in predicting based on online data, the present teaching presents a scheme of augmenting experts at one or more levels to not only leverage the learned expertise from original experts but also expand the expertise in terms of aspects of knowledge not yet learned by the existing experts including inter-relationships among existing experts that the traditional systems completely ignore. To achieve that, in deriving a new augmented expert, in addition to training data, the outputs from previously trained experts (including original and previously augmented experts) are also used to train the new augmented expert, where the outputs from the previously trained experts are generated by these experts based on the same training data. The disclosed expert augmentation scheme yields heterogeneous experts which form an expert hierarchy. This expert hierarchy provides an expanded range of knowledge learned by different experts SO that their respective expertise on the same task may be integrated to enhance the quality of the prediction as compared with the traditional systems." (Para. [0031]) "The present teaching also discloses a nonlinear framework for integrating outputs from different experts to overcome the deficiencies of the traditional approaches that use linear weighted sum in integrating different experts. The present teaching presents a scheme of combining multiple experts in a nonlinear manner via learning. The multiple experts being combined using the scheme as disclosed herein may include homogeneous and/or heterogeneous experts. In some embodiments, the experts being combined may include conventional experts and/or augmented experts created based on some given existing experts using the augmentation scheme as disclosed herein. In some embodiments, an artificial neural network (ANN) is employed for integration so that embeddings of the ANN may be learned to capture the nonlinear complex relationships and serve as a nonlinear integration function for combining multiple expert outputs. Such a trained ANN with learned embeddings, when receiving outputs from multiple experts as input, yields an integrated expert via complex non-linear function learned and implicitly specified via the parameterized ANN." (Para. [0032]). "As discussed herein, in some embodiments, experts in the hierarchy are trained one layer at a time. That is, the initial experts may be trained first. When the initial experts are trained, they are used in training augmented experts at the next layer. For example, in Fig. 3C, when training augmented expert 21 320-1, the trained initial expert 11 310-1 takes the same training data used for training the augmented expert 21 320-1 as input and produces its expert prediction which is provided to the augmented expert 21 320-1 as input to facilitate the learning. Once the augmented expert 21 is trained, both the initial expert 11 310-1 and augmented expert 21 320-1 are used in training augmented expert 31 340-1 by providing expert outputs thereto based on the training data used to train augmented expert 31 340-1, etc. So, the training of augmented expert N1 350-1 use training data as well as outputs from all experts, whether initial or augmented, from lower layers. In this manner, an augmented expert created at a certain layer not only learns from the training data used but also leverages the learned expertise from all lower-level experts." (Para. [0048]). Also, Applicant respectfully submits that the recited "deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained, via machine learning for the prediction, based on training data directed thereto and an expert output from an expert at any lower layer of the expert hierarchy so that an expert at any of the at least one augmented expert augments the expertise of experts at any lower layer of the expert hierarchy" is not recited at a high level of generality at least because the claim recites details about how the step of deriving is performed, e.g., training augmented experts via machine learning based on training data directed thereto and expert outputs from all experts at all lower expert layer(s) of the expert hierarchy.
The recited claim features are clearly tied to a practical application i.e., machine learning.
The examiner disagrees with the applicant’s position, as the arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the applicant assert, describe, or even suggest the limitations of “specific neural networks, nonlinear implementations, and/or specific training procedures.” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language. Without clear support in the claim language, the examiner cannot give weight to arguments premised on these alleged limitations.
Further, the applicant’s arguments rely on alleged technical improvements described in the Specification rather than the claim language. The claims do not recite any additional elements that integrate the judicial exception into a practical application. Instead, the claims merely recite the abstract idea performed using generic computer components. The so-called improvements to ensemble learning, neural networks, and expert augmentation are not recited in the claims and therefore the claims are not eligible,
The applicant argues:
Also, the Office Action on page 7 alleges that "the limitations 'generating an augmented expert at a first augmented expert layer based on first training data and a plurality of predictions generated by the plurality of initial experts based on the first training data; and generating an augmented expert at an augmented expert layer above the first augmented expert layer based on second training data, a plurality of predictions generated by the plurality of initial experts based on the second training data, and one or more predictions generated by respective one or more augmented experts at any lower augmented expert layer based on the second training data' are recited at a high-level of generality and amounts to no more than adding the words 'apply it' to the judicial exception." Applicant respectfully disagrees at least because these limitations are recited in great details - how augmented experts in different layers are generated/trained based on different data "so that an expert at any of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy," as recited.
Thus, Applicant respectfully submits that, under the Prong Two of the Step 2A Analysis from the Guidance, the claimed concept is integrated into a practical application and therefore is not directed to a judicial exception. Therefore, Applicant respectfully submits that the claims are directed to patent eligible subject matter.
Further, claim 1 amounts to significantly more than the judicial exception. The Berkheimer V. HP Inc, No. 2017-1437 (Fed. Cir. Feb. 8, 2018)¹ ("Berkheimer") decision re-emphasized that, "[a]t step two, we consider the elements of each claim both individually and 'as an ordered combination' to determine whether the additional elements 'transform the nature of the claim' into a patent eligible application." Berkheimer, pages 11 and 12. Berkheimer resolved that the "inventive concept" is not restricted to only the additional elements, but may include one or more allegedly abstract elements that, in combination with the additional elements, form the claim's inventive concept. See Id., page 12 (stating, without reference to an "additional" element, that "[t]he question of whether a claim element or combination of elements is well-understood routine and conventional to a skilled artisan in the relevant field is a question of fact"). For example, while the Berkheimer Court held independent claim 1 to be directed to "the abstract idea of parsing and comparing data with conventional computer components, the Berkheimer Court nevertheless concluded that dependent claim 4 (which depended on claim 1) was potentially patent-eligible. In concluding that claim 4 could be patent-eligible, the Berkheimer Court did not narrow the inventive concept to merely the additional limitation of "storing a reconciled object structure in the archive without substantial redundancy." Indeed, the general operation of storing data/object structures in some archive without substantial redundancy by itself would clearly have been found to be well- understood, routine, and conventional. Despite this, however, the Berkheimer Court concluded that the claimed invention of claim 4 could be patent-eligible.
In the instant application, the Office Action appears to allege that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Applicant respectfully disagrees with the contentions, and further submits that claim 1 is patent eligible under Step 2B Analysis from the Guidance.
Berkheimer showed that it is not merely the additional elements that are to be viewed for eligibility, but the claimed concept described by the additional elements in conjunction with the non-additional elements. Furthermore, even assuming arguendo that each of the claim limitations individually is abstract, or is performed by or is a generic computer, so too are the BASCOM claim limitations (e.g., BASCOM Global Internet V. AT&T Mobility LLC, No. 2015-1763 (Fed. Cir. Jun. 27, 2016)² ("BASCOM")).
In addition to failing to consider Applicant's claims as an ordered combination and as a whole, the Office Action has improperly analyzed the claims without considering the "additional element(s)" in combination with the non-additional elements. As a result, the Office Action has also incorrectly and improperly identified that the alleged "additional elements" do not amount to significantly more than the alleged judicial exception.
Accordingly, Applicant respectfully submits that claim 1 is patent eligible under the Step 2B Analysis of the Guidance Applicant respectfully submits that claim 1 is directed to patent eligible subject matter.
Accordingly, no further analysis is necessary to find claim 1 patent eligible under 35 U.S.C. § 101.
The examiner disagrees. Applicant has not provided sufficient evidence or persuasive
reasoning demonstrating that the additional elements, whether considered individually
or as an ordered combination, amount to significantly more than the abstract idea.
While, Berkheimer recognizes that whether certain elements are well- understood, routine, and conventional may present a factual issue, the present record
supports the determination that the additional elements, whether considered individually
or as an ordered combination, amount to significantly more than abstract. The present set of claims are generic computer components performing routine and conventional functions. The claim does not recite any specific technologically improvement or unconventional arrangement that transforms the nature of the claim into patent-eligible subject matter.
Applicant's reliance on BASCOM is also un persuasive, Unlike in BASCOM,
where the claims recited a specific, non-conventional arrangement of known
components, the instant claims merely implement the abstract idea using generic
computer elements without any meaningful limitation.
Furthermore, the Examiner has considered the claims both individually and as an
ordered combination. The combination of elements does not add any inventive concept,
as the elements operate in their expected and conventional manner. Claim 1 does not
included additional elements sufficient to amount to significantly more than the judicial
exception.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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