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 .
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim Rejections - 35 USC § 101
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Each of the independent claims recites steps that result in generating a synthetic question and synthetic answer pairs from source text. This appears to be a general purpose computer, for example a processor, processing units, a system, with no significantly more specialized elements. All of the recited steps are processes that, under its broadest reasonable interpretation, covers the limitations under the organized human activity. That is, other than reciting “processor comprising one or more processing units”, “A system” nothing in the claim element precludes the steps away from organizing human activity. The claims recite language models and machine learning models but lack of detail in the claims as to the form of the machine learning model (e.g., layers, nodes, etc. and what they do) and how the language models applied in generating question and answer pairs.
The claim features in italics above as drafted, under its broadest reasonable interpretation, are certain methods of organizing human activity performed by generic computer components. That is, other than reciting "processor comprising one or more processing units " and “A system”, nothing in the claim element precludes the step from practically being a method of organized human activity. For example, but for the “generating” [human behavior: to cause or create] and “updating” [human behavior: revising or giving latest information] in the context of this claim encompasses methods of organized human activity. If the claim limitations, under its broadest reasonable interpretation, covers fundamental economic practice, commercial or legal interaction or managing personal behavior or relationships or interactions between people but for the recitation of generic computer components, then it falls within the "process of organized human activity" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element: "a processor" and “A system”. The processor comprising one or more processing units is recited at a high-level of generality such that it amounts to no more than mere instructions in memory and executed by a processor to apply the exception using a generic computer component. 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. The claim is directed to an abstract idea.
"[A]fter determining that a claim is directed to a judicial exception, 'we then ask, [w]hat else is there in the claims before us?"' MPEP 2106.05 (emphasis in MPEP) citing Mayo, 566 U.S. at 78. "What is needed is an inventive concept in the non-abstract application realm." SAP Inc. v. lnvestPic, LLV, Appeal No. 2017-2081 (Fed. Cir. 2018). For step two, the examiner must "determine whether the claims do significantly more than simply describe [the] abstract method" and thus transform the abstract idea into patent-eligible subject matter. Ultramercial, Inc. v. Hutu, LLC, 772 F.3d 709 (Fed. Cir. 2014).
A primary consideration when determining whether a claim recites "significantly more" than abstract idea is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. See MPEP 2106.0S{d). "If the additional element (or combination of elements) is a specific limitation other than what is well- understood, routine and conventional in the field, for instance because it is an unconventional step that confines the claim to a particular useful application of the judicial exception, then this consideration favors eligibility. If, however, the additional element {or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility." Id.
The Federal Circuit has held that "[w]hether something is well-understood, routine, and conventional to a skilled artisan at the time of the patent is a factual determination." Bahr, Robert (April 19, 2018). Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.) citing Berkheimer at 1369. "As set forth in MPEP 2106.05(d)(I), an examiner should conclude that an element (or combination of elements) represents well-understood, routine, conventional activity only when the examiner can readily conclude that the element(s) is widely prevalent or in common use in the relevant industry. This memo [] clarifies that such a conclusion must be based upon a factual determination that is supported as discussed in section III [of the memo]." Berkheimer Memo at 3 (emphasis in memo).
Generally, "[i]f a patent uses generic computer components to implement an invention, it fails to recite an inventive concept under Alice step two." West View Research v. Audi, CAFC Appeal Nos. 2016-1947-51 (Fed. Cir. 04/19/2017) citing Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324-25 (Fed. Cir. 2016) (explaining that "generic computer components such as an 'interface,' 'network,' and 'database' ... do not satisfy the inventive concept requirement"; but see Bascom (finding that an inventive concept may be found in the non-conventional and non-generic arrangement of the generic computer components, i.e., the installation of a filtering tool at a specific location, remote from the end- users, with customizable filtering features specific to each end user).
In accordance with the above guidance, the examiner has searched the claim(s) to determine whether there are any "additional elements" in the claims that constitute "inventive concept," thereby rendering the claims eligible for patenting even if they are directed to an abstract idea. Alice, 134 S. Ct. 2347 (2014). Those "additional features" must be more than "well understood, routine, conventional activity." See Alice. To note, "under the Mayo/Alice framework, a claim directed to a newly discovered ... abstract idea [] cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility." Genetic Techs. Ltd v. Merial LLC, 818 F.3d 1369, 1376 (Fed. Cir. 2016); Diamond v. Diehr, 450 U.S. 175, 188-89 (1981).
As an example, the Federal Circuit has indicated that "inventive concept" can be found where the claims indicate the technological steps that are undertaken to overcome the stated problem(s) identified in Applicant's originally-filed Specification. See Trading Techs. Inc. v. CQG, Inc., No. 2016-1616 (Fed. Cir. 2017); but see IV v. Erie Indemnity, No. 2016-1128 (Fed. Cir. March 7, 2017) ("The claims are not focused on how usage of the XML tags alters the database in a way that leads to an improvement in technology of computer databases, as in Enfish.") (emphasis in original) and IV. v. Capital One, Nos. 2016-1077 (Fed. Cir. March 7, 2017) ("Indeed, the claim language here provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it. Our law demands more. See Elec. Power Grp., 830 F.3d 1356 (Fed. Cir. 2016) (cautioning against claims 'so result focused, so functional, as to effectively cover any solution to an identified problem.')"). Furthermore, "[a]bstraction is avoided or overcome when a proposed new application or computer-implemented function is not simply the generalized use of a computer as a tool to conduct a known or obvious process, but instead is an improvement to the capability of the system as a whole." Trading Techs. Int'l, Inc. v. CQG, Inc., No. 2016-1616 (Fed. Cir. 2017) (emphasis added).
In the search for inventive concept, the Berkheimer Memo describes "an additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).
A citation to one or more of the court decisions discussed in the MPEP as noting the well-understood, routine, conventional nature of the additional element(s).
A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s).
A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s).
See Berkheimer Memo at 3-4.
Accordingly, the examiner refers to the following generically-recited computer elements with their associated functions (and associated factual finding(s)), which are considered, individually and in combination, to be routine, conventional, and well-understood:
“a processor comprising: one or more processing units”,
“a system comprising one or more processing units”
“a method”
As set forth in MPEP § 2106.0S(d)(I), an examiner should conclude that an element (or combination of elements) represents well-understood, routine, conventional activity only when the examiner can readily conclude that the element(s) is widely prevalent or in common use in the relevant industry. The Berkhiemer memo clarifies that such a conclusion must be based upon a factual determination that is supported as discussed in section III the memo. As seen in paragraphs ([28, 99, 104, 111]) of the instant Specification and Symantec.. 838 F.3d at 1.321, 110 USPQ2d at. 1362, the elements are viewed to be well-understood, routine and conventional.
In sum, the Examiner finds that the claims "are directed to the use of conventional or generic technology in a nascent but well-known environment, without any claim that the invention reflects an inventive solution to any problem presented by combining the two." In re TLI Communications LLC, No. 2015-1372 (May 17, 2016). Similar to the claims in SAP v. lnvestPic, "[t]he claims here are ineligible because their innovation is an innovation in ineligible subject matter." Appeal No. 2017-2081 (Fed. Cir. 2018). In other words, "the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm." Id. Accordingly, when considered individually and in ordered combination, the examiner finds the claims to be directed to in-eligible subject matter.
Next, it is determined whether the claim integrates the judicial expectation into a practical application by identifying whether “any additional elements recited in the claim beyond the judicial exception(s)” and evaluate those elements to determine whether the integrate the judicial exception into a recognized practical application.
In this case, the additional elements do not integrate the judicial application into a practical application. The claim does not recite (i) an improvement to the functionality of a computer or other technology or technical field ; (ii) a "particular machine" to apply or use the judicial exception; (iii) a particular transformation of an article to a different thing or state; or (iv) any other meaningful limitation.
The additional elements beyond the judicial exception are (i) by a computer comprising a processor, memory storage and instruction program. Using a computing device to identify and determine a value and disposition of an object is merely applying the judicial exception using a generic computing component. Additionally, the claim identifies and determines a value and disposition of an object - the claim does not improve the functioning of the computing device, or other technology or field.
The claims do not recite specific limitations (alone or when considered as an ordered combination) that were not well understood, routine, and conventional. As set forth in the Specification, the disclosed subject matter can be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter.
Dependent claims 2-9, 11-18, and 20 include further recited limitations, do not integrate the abstract idea into a practical application, and the additional elements taken individually and in combination, do not contribute to an inventive concept, In other words, the dependent claims are directed to an abstract idea without significantly more.
Claim Objections
2. Claims 19-20 are objected to because of the following informalities: Claims 19-20 are not falling within one of the four statutory categories of invention. Supreme Court precedent and recent Federal Circuit decisions indicate that a statutory "process" under 35 U.S.C. 101 must (1) be tied to another statutory category (such as a particular apparatus), or (2) transform underlying subject matter (such as an article or material) to a different state or thing. While the instant claim(s) recite a series of steps or actsto be performed, the claim(s) neither transform underlying subject matter norpositively tie to another statutory category that accomplishes the claimed methodsteps, and therefore do not qualify as a statutory process. Claim 9 defines a "method" and it appears that applicant is defining a series of steps of “generate” and “update” that not tied to any apparatus. Appropriate correction is required. Failure to make appropriate correction(s) would lead to 35 U.S.C. 101 rejection(s).
Claim Rejections - 35 USC § 103
3. Claims 1-2, 9-11, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhat et al. (2025/0005276) in view of Khosla et al. (2025/0005058).
As to claim 1, Bhat teaches a processor comprising one or more processing units (abstract) to: wherein at least one synthetic question of the synthetic question and synthetic answer pairs is generated based at least one applying a representation of a unit of source text from a repository of textual data to one or more first language models (abstract, [0017] - LLMs have been employed in the form of a chatbot application in different industries, such as Information Technology (IT) trouble shoot, customer service, online learning, and/or the like. For example, long-form question-answering is a type of NLP tasks that generate an explanatory answer to a question; [0018] - provide a LLM recommendation mechanism for building a customized generative AI stack Specifically, given a target NLP task such as a chatbot application implementing long-form question-answering, a source document for evaluation in the relevant domain of the target chatbot application may be selected. A language model may then generate a summary of the source document, based on which a number of questions may be generated based on the summary and a number of corresponding answers distilled from the summary. The generated questions may then be fed to different external LLMs to generate answers; [0029] - one or more “complex” questions may be selected from the generated questions 112 (e.g., questions 112b generated based on source document 104) for question-answering; [0025, 0079, 0085] – a language model employed to generate a summary of source document of source document to generate question(s)); and at least one synthetic answer of the one or more the synthetic question and synthetic answer pairs is generated by applying a representation of the at least one synthetic question to one or more second language models (abstract; [0030] - prompt 102 to guide the LLM to generate a long-form answer to the input question. LLMs 105a-n may then respectively generate a plurality of sets of answers 116a, 116b, . . . , 116n based on an input of a combination of questions 112. For example, each set of answers 116a may include one or more answers to questions 112 answered by the corresponding LLM 105a; [0031-0035] – each LLM 105a-n may be prompt to generate an answer to the generated question using a prompt 102 similar to the following: Given the context, answer the question below: Context: {context} Question: {question} Answer: {Answer}). Bhat does not explicitly discuss execute one or more iterations of training one or more machine learning models based at least on training data that includes one or more synthetic question and synthetic answer pairs.
Khosla teaches the LLM may be trained at least on QA pairs from search systems associated with network-based service providers (e.g., regarding network-based storage service, network-based analytics, etc.) such that the LLM may generate answers that are specific to those network-based service providers ([0013]); the natural language question answering service 102 may comprise a trained LLM that is trained at least on the QA pairs from the search systems 124 in order to provide answer to questions and prompts ([0019]); and ([0022, 0024, 0028, 0062]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Khosla into the teachings of Bhat for the purpose of training the LLM on QA pairs so that the LLM generating answers that are specific to the network based service providers.
As to claims 2 and 11, Bhat teaches the processor of claim 1 and the system of claim 10, wherein the one or more processing units are further to generate the at least one the synthetic answer based at least on applying the representation of the at least one synthetic question and the unit of source text from the repository to the one or more second language model (abstract; [0030-0031] – generating answer(s) by one or more neural network based NLP models or LLMs based on input of a combination of questions; [0046] – answer submodule receives a user defined prompt and the questions from the second language model; answer submodule generates a prompt to one or more NLP models such that the NLP models generate a plurality of answers based on an input of a user defined prompt combined with the questions generated).
As to claims 9, 18, and 20, Bhat teaches the processor of claim 1, the system of claim 10, and the method of claim 19, wherein the processor, system and method is comprised in at least one of a system for performing conversational AI operations; a system implementing one or more language models; a system implementing one or more large language models (abstract; [0025-0028, 0079-0081, 0084-0086]).
Claim 10 is rejected for the same reasons discussed above with respect to claim 1. Bhat does not explicitly discuss one or more sequences of source text from a repository of the one or more first language models. However, Bhat teaches the summary is sent to the same or a different external LLM than model 106, together with a user prompt to generate a plurality of questions based on the summary ([0025]); and memory 220 incudes instructions for LLM selection module that used to implement and emulate the systems and models and implement any of the methods described further herein (Fig. 2A and related texts). It would have been obvious to have one or more sequences of source text thus improves neural network technology in language model selection.
Claim 19 is rejected for the same reasons discussed above with respect to claim 1.
4. Claims 3-4, 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Bhat et al. (2025/0005276) and Khosla et al. (2025/0005058) in view of Nishida et al. (2021/0125516).
As to claims 3 and 12, Bhat and Khosla do not explicitly discuss the processor of claim 1 and the system of claim 10, wherein the one or more processing units are further to determine to exclude one or more synthetic questions generated using the one or more first language models from the training data based at least on using one or more third language models tuned for question entailment to predict that the one or more synthetic questions cannot be answered from the unit of source text.
Nishida teaches predict accuracy was evaluated for an answer type T, an answer A, and a basis sentence S ([0211]); a question that cannot be properly answered with Yes or No can be excluded from learning data ([0238]).
It would have been obvious before the effective date of the claimed invention to incorporate the teachings of Nishida into the teachings of Bhat and Khosla for the purpose of achieving more appropriate learning.
As to claims 4 and 13, Nishida teaches a technique for an extractive task cannot output an answer in a format unwritten in a text and in order to output an answer in a format unwritten in a text it is necessary for a machine to determine an answer to a question from a part related to the question as well as focus on the related part in the text ([0006]) and predict accuracy was evaluated for an answer type T, an answer A, and a basis sentence S ([0211]); a question that cannot be properly answered with Yes or No can be excluded from learning data ([0238]). It would have been obvious to incorporate the teachings of excluding the one or more synthetic answers to predict that the synthetic answer was not extracted from the source text so that the machine to determine an answer to a question from a part related to the question as well as focus on the related part in the text.
5. Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bhat et al. (2025/0005276) and Khosla et al. (2025/0005058) in view of White, Jr. et al. (2024/0386253)
As to claims 5 and 14, Bhat and Khosla do not explicitly discuss the processor of claim 1 and the system of claim 10, wherein the one or more processing units are further to determine, using one or more third language models tuned for at least one of question entailment or answer entailment, that at least one of the one or more synthetic questions generated using the one or more first language models or one or more synthetic answers generated using the one or more second language models represents a hallucination.
White teaches a pre-trained large language model used as zero-shot classifier to determine whether the answer provide by the generative model is a hallucination or not; providing a classification output with a probability of its prediction ([0038, 0040-0042]).
It would have been obvious before the effective date of the claimed invention to incorporate the teachings of White into the teachings of Bhat and Khosla for the purpose of performing by providing the zero-shot classifier with the consolidated ground truth context, the generated text output by the generative model and a system prompt requesting the zero-shot classifier to provide a classification output with a probability of its prediction.
6. Claims 6-8 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bhat et al. (2025/0005276) and Khosla et al. (2025/0005058) in view of Kumbi et al. (2025/0103822).
As to claims 6 and 15, Bhat teaches generated summary passed to a language model to generate a plurality of questions based on summary; the language model may be the same or a different language model implemented at server 110; the language model may be one of the LLMs and the summary sent to the same or different external LLM than model 106 and together with a user prompt to generate a plurality of questions based on the summary ([0025]); generating by the second language model a plurality of initial questions based on at least one of the summary of the source document, prompting the second language model with a complexity evaluation question for evaluating a complexity of each of the initial questions, determining a percentage of the plurality of initial questions that passes the complexity evaluation question and selecting the percentage of the plurality of initial questions to be the one or more questions ([0085]). Bhat and Khosla do not explicitly discuss the processor of claim 1, the system of claim 10, wherein the one or more processing units are further to generate the one or more first language models based at least on tuning a base language model to tailor the base language model to a question generation task.
Kumbi teaches the online interaction system fine-tunes a pre-trained LLM using a set of digital content from the entity. The fine-tuned LLM can be prompted to generate a set of questions and answers based on the set of digital content from the entity ([0016]), and The questions-answer generation module 104 can implement a pre-trained LLM to generate the set of question-answer pairs. The pre-trained LLM can be fine-tuned from the online platform 120 before generating the question-answer pairs so that the questions and answers generated by the LLM are aligned with the style of the digital content 126. The operator associated with the online platform 120 can also provide a prompt to the fine-tuned pre-trained LLM for generating question-answer pairs ([0026]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Kumbi into the teachings of Bhat and Khosla for the purpose of validating the question using a textual entailment model to ensure that the answers corresponding to the generated questions are accurately derived.
As to claims 7 and 16, Bhat teaches generating question fed to different external LLMs to generate answer which are evaluated based on one or more metrics, e.g., factual consistency, accuracy, etc. to determine the LLM with the highest overall score ([0018]); an AI gateway that sends a task request to one or more selected LLMs and the task request comprises a user prompt to guide the selected LLM to perform the task, e.g., “answer the input question based on the input document with reasoning and explanatory details” and a corresponding LLM APIs receive the task request and translate the task request into a specific input format for the vendor-specific LLM to generate an answer [0022]; generated question fed to the pool of LLMs through LLM APIs, together with a prompt to guide the LLM to generate a long form answer to the input question; LLMs generate a plurality of sets of answers based on an input of a combination of questions and prompt) [0030]. Bhat and Khosla do not explicitly discuss the processor of claim 1, the system of claim 10, wherein the one or more processing units are further to generate the one or more second language models based at least on tuning a base language model to tailor the base language model to an answer generation task.
Kumbi teaches the online interaction system fine-tunes a pre-trained LLM using a set of digital content from the entity. The fine-tuned LLM can be prompted to generate a set of questions and answers based on the set of digital content from the entity ([0016]), and The questions-answer generation module 104 can implement a pre-trained LLM to generate the set of question-answer pairs. The pre-trained LLM can be fine-tuned from the online platform 120 before generating the question-answer pairs so that the questions and answers generated by the LLM are aligned with the style of the digital content 126. The operator associated with the online platform 120 can also provide a prompt to the fine-tuned pre-trained LLM for generating question-answer pairs ([0026]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Kumbi into the teachings of Bhat and Khosla for the purpose of validating the question using a textual entailment model to ensure that the answers corresponding to the questions are accurately derived.
As to claims 8 and 17, Bhat teaches long form question answering, a source document for evaluation in the relevant domain of the target chatbot application selected and a customer AI stack built by choosing one or more LLMs most suitable to a particular domain and design of the AI application ([0017-0021]); the server 110 may build and host a custom AI stack, such as AI applications implementing a specific natural language processing (NLP) task in a specific domain, and the like. For example, the domains include physics, entertainment, history, computer science, social sciences, society, economics, medicine, and sports. One or more LLMs from LLMs 105a-n selected to build the AI application [0092]. Bhat and Khosla do not explicitly the processor of claim 1, the system of claim 10, wherein the one or more processing units are further to generate at least one of the one or more first language models or the one or more second language models based at least on tuning one or more question-answer pairs in a common domain as the unit of source text for the synthetic question.
Kumbi teaches a question-answer generation module 104, answer augmentation module 106, a question matching module 108, and a data store 110. The question-answer generation module 104 is configured to generate a set of question-answer pairs using a set of digital content 126 from the online platform 120. The questions-answer generation module 104 can implement a pre-trained LLM to generate the set of question-answer pairs. The pre-trained LLM can be fine-tuned from the online platform 120 before generating the question-answer pairs so that the questions and answers generated by the LLM are aligned with the style of the digital content 126. The operator associated with the online platform 120 can also provide a prompt to the fine-tuned pre-trained LLM for generating question-answer pairs ([0026]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Kumbi into the teachings of Bhat and Khosla for the purpose of validating the question using a textual entailment model to ensure that the answers corresponding to the questions are accurately derived.
Response to Arguments
7. Applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of the new ground of rejection(s).
With respect to the 101 rejection(s), Applicant argues that claim 1 should not be found to recite human activity or a mental process. Examiner respectfully disagrees. Each of the independent claims recites steps that result in generating a synthetic question and synthetic answer pairs from source text. This appears to be a general purpose computer, for example a processor, processing units, a system, with no significantly more specialized elements. All of the recited steps are processes that, under its broadest reasonable interpretation, covers the limitations under the organized human activity. The claims recite language models and machine learning models but lack of detail in the claims as to the form of the machine learning model (e.g., layers, nodes, etc. and what they do) and how the language models applied in generating question and answer.
Applicant further argues that “The pending claims amounts to an improvement in the fields of synthetic data generation and machine learning…”. Examiner respectfully submits that the claims recite language models and machine learning models but lack of detail in the claims as to the form of the machine learning model (e.g., layers, nodes, etc. and what they do) and how the language models applied in generating question and answer pairs.
Applicant further argues that “The claimed process creates a synthetic question and answer from a repository of textual data and uses training data including one or more synthetic question and synthetic answer pairs to execute one or more iterations of training a machine learning model technique should substantially improve the quality of synthetic training data over techniques apply a single prompt to generate multiple data points, thereby improving the accuracy of and reducing hallucinations generated by the models trained using that dataset.” Examiner respectfully submits that the claims read as a process for generating a synthetic question and synthetic answer pairs from source text. There is no technical details as to the generating question-answer pairs of the machine learning models and there is no step as to how the language models applied in generating question and answer pairs. The one or more iterations of training a machine learning model just suggests an idea of a solution that lacks details per 2106.05(f) and would not constitute a technical improvement or practical application in step 2A prong 2.
Conclusion
8. 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.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUYNH H NGUYEN whose telephone number is (571)272-7489. The examiner can normally be reached Monday-Thursday 7:30AM-5:30PM.
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/QUYNH H NGUYEN/Primary Examiner, Art Unit 2693