DETAILED ACTION
Notice of 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 .
Response to Amendment
Applicant’s Amendment and remarks dated 7/8/2026 have been considered. Claims 2, 7, 10, 13, 15-16, and 19-20 are cancelled. Claims 1, 3-6, 8-9, 11-12, 14, and 17-18 are pending.
Response to Arguments
On page 6 of Applicant’s 7/8/2026 Amendment and remarks, Applicant asserts that no new matter is added via the amendments to the claims.
The examiner agrees that at least original claims 2 and 7 and para. 0019 provide sufficient written description support for the claim amendments.
On page 8 of Applicant’s 7/8/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 1, Applicant argues:
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The examiner respectfully disagrees, and notes that Applicant has not provided any rebuttal to any of the mental processes identified in the office action. The new amendments to the independent claims merely recite training a machine learning model using the recited “re-labeled training data” to ultimately “predict[] a failure state of the system using operational data of the system,” where such prediction it itself a mental process. The machine learning model is not a mental process, and is therefore considered with respect to Step 2A, Prong 2 and Step 2B.
The examiner agrees that the recited “processor set” is not a mental process, but as explained in the detailed rejections, such “processor set” is recited at a high-level of generality and is not sufficient to impart eligibility under Step 2A, Prong 2 and Step 2B. The processor set is used to perform mental processes of analyzing time-series data, and again, Applicant has not rebutted any of the specific findings of mental processes in the office action.
On page 9 of Applicant’s 7/8/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 1, Applicant argues:
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The examiner respectfully disagrees. As explained above, the “computer-implemented processor set” is used to perform mental processes of analyzing time-series data, and again, Applicant has not rebutted any of the specific findings of mental processes in the office action.
On page 9 of Applicant’s 7/8/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 2, Applicant argues:
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The examiner respectfully disagrees. The new amendments to the independent claims merely recite training a machine learning model using the recited “re-labeled training data” to ultimately “predict[] a failure state of the system using operational data of the system,” where such prediction it itself a mental process. The machine learning model is not a mental process, and is therefore considered with respect to Step 2A, Prong 2 and Step 2B. But because the machine learning model is recited at a high-level, without any actual improvements to machine learning technologies, the use of such machine learning, by itself, does not impart subject matter eligibility.
On page 10 of Applicant’s 7/8/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 2, Applicant argues:
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The examiner respectfully disagrees. MPEP 2106.04(d)(1) explains that one way to show integration of the judicial exception into a practical application is to demonstrate that the “claimed invention improves the functioning of a computer or improves another technology or technical field.” Here, the examiner respectfully disagrees that “machine-learning-based failure prediction analysis” is an “another technology or technical field.” Failure prediction analysis is a mental process, where a human such as a skilled technician, can analyze time-series data from an industrial asset (such as a wind turbine or pump) and predict why the asset failed. The use of a machine learning model merely improves the efficiency of the mental process of failure prediction analysis.
Moreover, MPEP 2106.04(d)(1) explains that the claims must reflect the claimed improvement, and the claims do not reflect any improvements with respect to failure prediction analysis.
On page 11 of Applicant’s 7/8/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2B, Applicant argues:
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The examiner respectfully disagrees for the same reasons explained above with respect to Step 2A, Prong 1 and Step 2A, Prong 2.
On page 12 of Applicant’s 7/8/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant requests such rejections to be withdrawn in view of incorporating the subject matter of dependent claim 7 into the dependent claims, which was previously found to include allowable subject matter.
The examiner agrees. All rejections under 35 U.S.C. 103 are hereby withdrawn.
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, 3-6, 8-9, 11-12, 14, and 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1, 3-6, 8-9, and 11 are directed to a method (a process), Claims 12 and 14 are directed to a computer program product comprising one or more computer readable storage media (an article of manufacture), and Claims 17-18 are directed to a system (a machine), which each fall within one of the four statutory categories of inventions.
In particular, with respect to claims 12 and 14, the examiner notes that para. 0021 of the instant specification states that: “A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media” (emphasis added) and therefore under the broadest reasonable interpretation in view of the specification, the “one or more computer readable storage media” recited in claims 12 and 14 is interpreted to exclude transitory signals per se.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, 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 (e.g., “processor set”).
identifying, ... a first region and a second region in the labeled training data, wherein ... the second region is exclusive of the first region; and (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can review labeled training data in the form of time series data plotted on paper, where the human can mentally identify first and second regions as recited in this limitation)
creating, ... re-labeled training data by altering one or more labels of the labeled training data in the first region based on data in the second region. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally create labeled training data in the form of time series data plotted on paper, and then mentally alter the labels of the training data)
wherein the creating re-labeled training data comprises solving an optimization framework using feature values and initial labels of the labeled training data (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally create labeled training data in the form of time series data plotted on paper, by solving an optimization framework comprising a set of simple equations representing bounding boxes and/or loss metrics using features values and initial labels of the labeled training data as variables for such simple equations)
wherein the optimization framework is configured to maintain label temporal consistency for noisy data by minimizing event label switches (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally create labeled training data by solving an optimization framework configured as set forth by this limitation)
predicting a failure state of the system using operational data of the system ... (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally predict a failure state of the system using operational data of the system, such as by reviewing operational data of the system and mentally predicting a system failure if a value exceeds a particular threshold for a threshold period of time)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element (e.g., “processor set”) which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “obtaining, by a processor set, labeled training data associated with a system” limitation, such additional element of a data gathering step is 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 (see MPEP 2106.05(g)).
Regarding the “the first region is associated with a failure of the system” limitation, this limitation merely describes the data being processed, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Regarding the “by the processor set” limitation, such limitation is 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. In particular, the claim only recites the additional element of a processor set. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a processor set). 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)).
Regarding the “training a machine learning model using the re-labeled training data” limitation, such limitation is 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. In particular, the claim only recites the additional element of generic machine learning training. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning training). 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)).
Regarding the “with the machine learning model” limitation, such limitation is 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. In particular, the claim only recites the additional element of using a machine learning model for a particular purpose. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (using a machine learning model for a particular purpose). 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)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
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 element (e.g., “processor set”) is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “obtaining, by a processor set, labeled training data associated with a system” limitation, as discussed above, the additional element of a data gathering step is 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").
Regarding the “by the processor set” limitation, such limitation is 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, because the limitation merely provides 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 add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “training a machine learning model using the re-labeled training data” limitation, such limitation is 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, because the limitation merely provides 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 add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “with the machine learning model” limitation, such limitation is 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, because the limitation merely provides 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 add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 3
Step 2A, Prong 1
wherein the optimization framework comprises tensor classification with canonical polyadic (CP) decomposition (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally create labeled training data in the form of time series data plotted on paper, by solving an optimization framework comprising a set of simple equations representing bounding boxes and/or loss metrics using features values and initial labels of the labeled training data as variables for such simple equations, where the optimization framework further represents data in a tensor and then decomposes such tensor using rank-1 tensors during CP decomposition)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 4
Step 2A, Prong 1
wherein the optimization framework comprises a symmetric cross-entropy loss and a Gaussian kernel function (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally create labeled training data in the form of time series data plotted on paper, by solving an optimization framework comprising a set of simple equations representing bounding boxes and/or loss metrics using features values and initial labels of the labeled training data as variables for such simple equations, and then further optimizes such simple equations using symmetric-cross entropy loss and Gaussian kernel functions which are mathematical concepts)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 1
wherein the optimization framework comprises feature selection based on group sparsity (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally create labeled training data in the form of time series data plotted on paper, by solving an optimization framework comprising a set of simple equations representing bounding boxes and/or loss metrics using features values and initial labels of the labeled training data as variables for such simple equations, where the feature selection groups labels and selects labels having sparser densities) limitation, this limitation merely describes a type of data processing, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the optimization framework comprises feature selection based on group sparsity” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 6
Step 2A, Prong 1
wherein the optimization framework comprises: a rank-one tensors approximation multi-class classification with symmetric cross-entropy loss; a group sparsity for selecting relevant features; and a Gaussian kernel function for measuring data similarity between two tensors (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally create labeled training data in the form of time series data plotted on paper, by solving an optimization framework comprising a set of simple equations representing bounding boxes and/or loss metrics using features values and initial labels of the labeled training data as variables for such simple equations, where the optimization framework as claimed utilizes mathematical concepts, including representing data as rank-one tensors and classifying according to the mathematical concept of symmetric cross-entropy loss, feature selection grouping labels and selecting labels having sparser densities, and using a Gaussian kernel function to measure data similarity between two tensors, which also utilizes mathematical concepts)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 8
Step 2A, Prong 1
training the optimization framework using a decomposition algorithm (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally create labeled training data in the form of time series data plotted on paper, by solving an optimization framework comprising a set of simple equations representing bounding boxes and/or loss metrics using features values and initial labels of the labeled training data as variables for such simple equations, and where the simple equations are derived using a decomposition algorithm which is a mathematical concept)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 9
Step 2A, Prong 1
wherein the altering one or more labels of the labeled training data in the first region based on data in the second region is further based on the user input (under the broadest reasonable interpretation, a human can mentally consider user input from another human when altering training data labels)
Step 2A, Prong 2
Regarding the “further comprising receiving user input regarding initial labels of the labeled training data” limitation, such additional element of a data gathering step is 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 (see MPEP 2106.05(g)).
Step 2B
Regarding the “further comprising receiving user input regarding initial labels of the labeled training data” limitation, as discussed above, the additional element of a data gathering step is 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").
Regarding Claim 11
Step 2A, Prong 2
Regarding the “the system comprises an industrial asset equipped with one or more sensors” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (industrial assets equipped with one or more sensors). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Regarding the “the labeled training data includes time series data or tensor data obtained from the one or more sensors” limitation, such additional element of a data gathering step is 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 (see MPEP 2106.05(g)).
Step 2B
Regarding the “the system comprises an industrial asset equipped with one or more sensors” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding the “the labeled training data includes time series data or tensor data obtained from the one or more sensors” limitation, as discussed above, the additional element of a data gathering step is 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").
Regarding Claim 12
Step 2A, Prong 1
Claim 12 recites a computer program product comprising one or more computer readable storage media that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 12. While claim 12 recites additional generic computing components (“computer program product comprising one or more computer readable storage media”, “program instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 12 recites a computer program product comprising one or more computer readable storage media that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 12. While claim 12 recites additional generic computing components (“computer program product comprising one or more computer readable storage media”, “program instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2A, Prong 2. Such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional elements of (“computer program product comprising one or more computer readable storage media”, “program instructions”, and “machine learning model”). These additional elements 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. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Claim 12 recites a computer program product comprising one or more computer readable storage media that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 12. While claim 12 recites additional generic computing components (“computer program product comprising one or more computer readable storage media”, “program instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2B
Claim 14 depends from claim 13 and recites a computer program product that corresponds to the method of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 13.
Regarding Claim 17
Step 2A, Prong 1
Claim 17 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 17. While claim 17 recites additional generic computing components (“processor set”, “computer readable storage media”, “program instructions” and “machine learning model”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 17 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 17. While claim 17 recites additional generic computing components (“processor set”, “computer readable storage media”, “program instructions” and “machine learning model”), such additional generic computing components do not change the analysis under Step 2A, Prong 2. Such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional elements (“processor set”, “computer readable storage media”, “program instructions” and “machine learning model”). These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Claim 17 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 17. While claim 17 recites additional generic computing components (“processor set”, and “computer readable storage media” and “program instructions”), such additional generic computing components do not change the analysis under Step 2B.
Claim 18 depends from claim 17 and recites a system that corresponds to the methods of claims 2 and 6, and is therefore rejected for the same reasons explained above with respect to claims 2, 6, and 17.
Allowable Subject Matter
Claims 1, 3-6, 8-9, 11-12, 14, and 17-18 would be allowed over the prior art, provided that the rejections under 35 U.S.C. 101 are overcome.
The following is an examiner’s statement of reasons for allowance:
Independent claims 1, 12, and 17 would be considered allowable, provided that the rejections under 35 U.S.C. 101 are overcome, because none of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specified in such claims, including at least:
wherein the optimization framework is further configured to maintain label temporal consistency for noisy data by minimizing event label switches.
The closest prior art of record discloses:
US 20230290336 A1, hereinafter referenced as CHANG, discloses techniques for correcting labels with respect to time-series speech data. (para. 0031).
20190379589 A1, hereinafter referenced as RYAN, discloses identifying anomalies in different time windows, where windows are exclusive from one another. (para. 0062).
US 20240203108 A1, hereinafter referenced as WANG I, discloses a loss function that takes into account feature values and original labels. (para. 0062).
However, the examiner has found that the distinct feature of the Applicant's claimed invention over the prior art is the explicit claiming of the aforementioned limitations in combination with all the other limitations as specified in independent claims 1, 12, and 17. To the extent that these features are not found in the prior art cited by Examiner, claims 1, 12, and 17 would be considered allowable, provided that the rejections under 35 U.S.C. 101 are overcome.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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 MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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 Rivas 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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/MICHAEL C. LEE/Examiner, Art Unit 2128
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