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 .
DETAILED ACTION
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a method (claims 1 - 6), non-transitory computer readable medium (claims 7 - 12 ), and an apparatus with structural components (13 – 18). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claim 7,
Step 2A, Prong One
The claim recites in part:
identify a plurality of first factors from the disentangled low-dimensional representation of the obtained data that affect an output of the artificial intelligence model;
determine a generative mapping from the disentangled low-dimensional representation between the identified plurality of first factors and the output of the artificial intelligence model, using causal reasoning, the generative mapping learned through an optimization program that maximizes causal influence of the identified plurality of first factors on an output of the artificial intelligence model while ensuring the disentangled low-dimensional representation faithfully represents a data distribution;
generate explanation data using the determined generative mapping, the generated explanation data providing a description of an operation leading to the output of the artificial intelligence model using the identified plurality of first factors;
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 easily identify a plurality of first factors by writing them down using a pencil and paper of highlighting them on the screen of a generic computer. (2) A human can easily transform or summarize (generative mapping) the first factors to create some type of meaning. (3) A human can easily generate a diagram (generate explanation data) that clearly visualizes the relationships, descriptions, and insights of the first factors
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:
obtain a dataset as an input for an artificial intelligence model, wherein the obtained dataset is filtered to a disentangled low-dimensional representation;
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:
provide the generated explanation data via a graphical user interface.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. 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)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The non-transitory machine readable medium, at least one machine, and user interface 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)).
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:
obtain a dataset as an input for an artificial intelligence model, wherein the obtained dataset is filtered to a disentangled low-dimensional representation;
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").
The claim further recites:
provide the generated explanation data via a graphical user interface.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
05(f))
The non-transitory machine readable medium, at least one machine, and user interface 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)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim recites in part:
learn the generated generative mapping data to generate the explanation data;
identify a plurality of second factors within the obtained data, wherein the identified plurality of second factors have lesser impact on the output of the artificial intelligence model when compared to the identified plurality of first factors.
As drafted and under its broadest reasonable interpretation, this 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 would naturally learn from transforming or summarizing (generative mapping) the first factors to create some type of meaning. (2) A human can easily identify a plurality of second factors by writing them down using a pencil and paper of highlighting them on the screen of a generic computer. A human can also prioritize the first factors because they have a higher impact over the second factors.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claim 9,
Step 2A, Prong One
The claim recites in part:
wherein to learn the generated generative mapping data to generate the explanation data
define a causal model representing a relationship between the identified first factors, second factors, and the output of the artificial intelligence model;
define a quantifying metric to quantify the causal influence of the identified first factors on the output of the artificial intelligence model; and
define a learning framework.
As drafted and under its broadest reasonable interpretation, this 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 learn mapping data as humans have been learning long before computers where invented+ (2) A human can easily apply an independent first variable to the first factor and the second factor and compare the before and after outputs to see how the independent variable directly causes a change in the first factor and the second factor (4) A human can easily determine a quantifying metric which is just the strength or impact the independent variable has on changing the output associated with the first factor and the second factor (4) A human would naturally learn how the cause and effect that an independent variable can have.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claim 10,
Step 2A, Prong One
The claim recites in part:
describe a functional causal structure of the dataset;
derive an explanation from an indirect causal link from the identified plurality of first factors and the output of the artificial intelligence model.
As drafted and under its broadest reasonable interpretation, this 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 describe anything including a functional causal structure of the dataset. (2) A human can observe and analyze the indirect causal link and easily derive an explanation to understand the “why” the or “how” the indirect causal link has a causal effect o on the first factors or the second factors.
Humans have been describing and deriving explanations before computers where even created.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claim 11,
Step 2A, Prong One
The claim recites in part:
define the quantifying metric considering a factor to capture functional dependencies and quantify indirect causal relationship between the identified plurality of first factors and the output of the artificial intelligence model.
As drafted and under its broadest reasonable interpretation, this 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, a human can easily determine a quantifying metric which is just the strength or impact the independent variable has on changing the output associated with the first factor and the second factor
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claim 12,
Step 2A, Prong One
The claim recites in part:
the identified plurality of second factors does not affect the output of the artificial intelligence model.
As drafted and under its broadest reasonable interpretation, this 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, a human can easily identify a plurality of first factors by writing them down using a pencil and paper of highlighting them on the screen of a generic computer.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
Claim 1 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 2 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 3 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
As to claims 4,
Step 2A, Prong One
The claim recites in part:
wherein the identifying, determining and generating are each by the causal explanation computing apparatus.
As drafted and under its broadest reasonable interpretation, this 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, humans have been identifying, determining, and generating before computers where even invented.
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:
obtaining, by a causal explanation computing apparatus, the dataset as an input for the artificial intelligence model, wherein the obtained dataset is filtered to the disentangled low-dimensional representation;
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:
providing, by the causal explanation computing apparatus, the generated explanation data via a graphical user interface;
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. 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)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The computing apparatus and graphical user interface 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)).
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:
obtaining, by a causal explanation computing apparatus, the dataset as an input for the artificial intelligence model, wherein the obtained dataset is filtered to the disentangled low-dimensional representation;
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").
The claim further recites:
providing, by the causal explanation computing apparatus, the generated explanation data via a graphical user interface;
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
05(f))
The computing apparatus and graphical user interface 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)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 5,
Step 2A, Prong One
The claim recites in part:
learning, by the causal explanation computing apparatus, the generated generative mapping to generate the explanation data comprising:
defining, by the causal explanation computing apparatus, a causal model representing a relationship between the identified first factors, the second factors, and the output of the artificial intelligence model;
defining, by the causal explanation computing apparatus, a quantifying metric to quantify the causal influence of the identified first factors on the output of the artificial intelligence model; and
defining, by the causal explanation computing apparatus, a learning framework;
identifying, by the causal explanation computing apparatus, second factors within the obtained dataset, wherein the identified second factors have a lesser impact on the output of the artificial intelligence model when compared to the identified first factors;
wherein the quantifying metric is defined considering a factor to capture functional dependencies and quantify indirect causal relationship between the identified first factors and the output of the artificial intelligence model;
wherein the defining the causal model comprises:
escribing a functional causal structure of the dataset; and
deriving an explanation from an indirect causal link from the identified first factors and the output of the artificial intelligence model; and
wherein the quantifying metric is defined considering a factor to capture functional dependencies and quantify indirect causal relationship between the identified first factors and the output of the artificial intelligence model.
As drafted and under its broadest reasonable interpretation, this 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 would naturally learn how the cause and effect that an independent variable can have. (2) A human can easily circle data with a pencil on a sheet of paper to “identify” data within a dataset. (3) A human can easily determine a quantifying metric which is just the strength or impact the independent variable has on changing the output associated with the first factor and the second factor. (4) A human can define a causal model as simply cause and effect relationships.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
Claim 6 has similar limitations as claim 12. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
The computing apparatus, memory, machine readable medium, storage system, processor, and a graphical user interface 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)).
Claim 14 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 11. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 12. Therefore, the claim is rejected for the same reasons as above.
As to claims 19,
Step 2A, Prong One
The claim recites in part:
learning the generative mapping through an optimization program that maximizes causal influence of the identified first factors on the output of the artificial intelligence model while ensuring the disentangled low-dimensional representation faithfully represents a data distribution.
As drafted and under its broadest reasonable interpretation, this 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, a person can compare factors, determine which most influence an outcome, and create a simplified representation of those relationships
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
As to claims 20,
Step 2A, Prong One
The claim recites in part:
learn the generative mapping through an optimization program that maximizes causal influence of the identified plurality of first factors on an output of the artificial intelligence model while ensuring the disentangled low-dimensional representation faithfully represents a data distribution.
As drafted and under its broadest reasonable interpretation, this 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, a person can compare factors, determine which most influence an outcome, and create a simplified representation of those relationships
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception
Response to Arguments
Applicant's arguments filed 5/18/2026 have been fully considered but they are not persuasive.
Claim Objections
Newly added amendments overcome the 101 Rejection and the 101 Rejection has been withdrawn
Claim Rejections - 35 USC § 103
If the newly added limitations of claim 7 are added to all the independent claim it would overcome the art rejections going forward.
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 Claims Do Not Recite a Mental Process
Applicant respectfully submits that the Primary Examiner errs at Step 2A, Prong One because the claimed steps cannot practically be performed in the human mind. The USPTO's Subject Matter Eligibility Examples provide instructive guidance on this issue. In Example 39 (Facial Recognition), the USPTO found that a Claim to training a neural network for facial detection did not recite a judicial exception because "the claim does not recite a mental process because the steps are not practically performed in the human mind." USPTO Subject Matter Eligibility Examples, Example 39.
The present Claims are analogous. The Claims require operations on a "disentangled low-dimensional representation" of a dataset - a specific technical transformation involving latent factors learned through optimization, not something performable mentally. As disclosed in the Specification, the causal explanation computing apparatus "constructs a generative model consisting of a disentangled representation of the data and a generative mapping from this representation to the data space" and "learns the disentangled representation in such a way that each factor controls a different aspect of the data, and a subset of the factors have a large causal influence on the classifier output." See, as-filed (PCT) Specification, [0046]. This involves defining "a structural causal model (SCM) that relates independent latent factors defining data aspects, the data samples that are input to the classifier, and the classifier outputs." Id.
Similarly, the claimed step of "determining a generative mapping using causal reasoning" involves learning a mapping through an optimization program. As disclosed in the Specification, "the approach is an optimization program for learning a mapping from the latent factors to the data space" where "[t]he objective of the optimization program ensures that the learned disentangled representation represents the data distribution while simultaneously encouraging a subset of latent factors to have a large causal influence on the classifier output." See, Specification, [0046].
The Specification further explains that learning the generative mapping requires defining "(i) a model of the causal relationship between α, ß, X, and Y, (ii) a metric to quantify the causal influence of α on Y, and (iii) a learning framework that maximizes this influence while ensuring that p(g(a, β)) ≈ p(X)." See, Specification, [[0053]. These operations involving structural causal models, information-theoretic measures of causal influence, and optimization programs cannot be performed with pencil and paper.
The examiner disagrees. The applicant mentions Example 39 of the ‘USPTO July 2024 Subject Matter Eligibility Examples’ as an example but the Applicant does not explain how the cited example is relevant to the presently claimed invention. The example is not tied to the claimed features, nor is any comparison provided demonstrating how it supports patent eligibility. It is unclear why the Applicant relies on this example.
Applicant’s argues that the claimed operations cannot be performed mentally because they involve a disentangled low-dimensional representation, structural causal models, causal influence metrics, and an optimization program. However, applicant relies primarily on the complexity of the implementation described in the Specification rather than what is actually required by the claim.
The claimed invention involves identifying factors associated with data, evaluating relationships between the factors and an outcome, determining which factors have the greatest causal influence, and generating mapping based on those relationships. A person can perform these activities through observation, evaluation, and judgement. For example, a person can identify factors affecting an outcome, determine which factors have the greatest influence, and mentally establish a relationship between those factors and the expected outcome
Just implementing such evaluations using a structural causal model, mathematical metric, or optimization program does not remove the claims from being a mental process.
As per MPEP 2106.04(a)(2)(III)(C)), a claim that requires a computer may still recite a mental process. In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
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The applicant argues:
The Claims Integrate Any Alleged Abstract Idea Into a Practical Application
Alternatively, assuming arguendo that the Primary Examiner maintains that the pending Claims recite an abstract idea, they integrate it into a practical application by improving AI explainability technology. Under MPEP § 2106.04(d)(1), a Claim that improves the functioning of a computer or improves another technology or technical field integrates a judicial exception into a practical application.
The Specification discloses how the invention provides specific technical improvements over prior AI explanation methods. The disclosed technology provides "causal, not correlational" explanations, and addresses "two common challenges in counterfactual explanation: a computationally infeasible search in input space can be avoided because a low-dimensional set of latent factors that can be optimized, and ensuring that perturbations result in a valid data point." See, Specification, [0027].
The Specification further explains that "the generative model enables the causal explanation computing apparatus to construct explanations that respect the data distribution" because "an explanation is only meaningful if it describes combinations of data aspects that naturally occur in the dataset." See, Specification, [[0047].
These are concrete technical improvements to AI explainability technology, analogous to the improvements found sufficient in USPTO Example 47, Claim 3, which was found eligible because it improved network security technology.
The examiner disagrees with the Applicant’s statement that the claimed invention integrates the abstract idea into a practical application by improving AI explainability technology.
The examiner respectfully 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:
"causal, not correlational" explanations, and addresses "two common challenges in counterfactual explanation: a computationally infeasible search in input space can be avoided because a low-dimensional set of latent factors that can be optimized, and ensuring that perturbations result in a valid data point."
"the generative model enables the causal explanation computing apparatus to construct explanations that respect the data distribution" because "an explanation is only meaningful if it describes combinations of data aspects that naturally occur in the dataset."
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.
Applicant argues that the claimed generative model provides “causal, not correlational” explanations, avoids computationally infeasible searches, and produces explanations that respect the data distribution, these so-called benefits just reflect the improvements to the underlying abstract analysis itself (i.e. how information is evaluated or optimized). The claims do not recite a specific improvement to the functioning of a computer or other technology, but rather use generic computing components to perform the claimed analysis more efficiently or accurately.
Improving the quality, validity, or computational efficiency of the resulting explanation does not, by itself, constitute a technological improvement under MPEP 2106.04(d)(1). Rather, the additional elements merely facilitate the performance of the abstract idea on a computer. Therefore, the claimed judicial exception is not integrated into a practical application.
The applicant mentions Example 47 of the ‘USPTO July 2024 Subject Matter Eligibility Examples’ as an example but the Applicant does not explain how the cited example is relevant to the presently claimed invention. The example is not tied to the claimed features, nor is any comparison provided demonstrating how it supports patent eligibility. It is unclear why the Applicant relies on this example.
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).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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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