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 .
Claim Objections
Claims 1 and 11 are objected to because of the following informalities: Amendments for both claims recite “… configured to extra one or more phrases …”. “Extra” should be “extract”. Appropriate correction is required.
Response to Arguments
Regarding the arguments against the rejection of claims under 35 USC 101, the Examiner respectfully disagrees. Applicant argues that the claim is expressly tied to a concrete machine implementation and does not merely describe a result that could be achieved mentally. Examiner asserts that the use of these generic computing components recites mere computer implementation and does not demonstrate a practical application nor a technology improvement as described in the Step 2A Prong 2 analysis in the below rejection. Further, the use of the “structured database” recites the use of a generic storage system to store the data that is indexed, with no indication of a technological improvement or a particular machine to carry out the steps of the abstract idea. Further, use of the “parsing module” recites a generic computing device to perform the parsing, which is part of the abstract idea. The generation of the machine-readable vectors recites using the generic computing components to carry out the vectors, where vector structuring and the data transformation can be performed mentally, where the “machine readable” structure is merely the result of the generic computer implementation. Because of the BRI of the feature vector and comparing it to the indexed data, such a comparison could be performed mentally where the use of the indexed data in the database and the machine-readable nature of the vectors are the results of the mere computer implementation. As described further in the Step 2A Prong 2 analysis below, the use of the machine learning and iterative training amount to nothing more than an instruction to apply the abstract idea using generic computing components. Further, use of the GUI recites merely displaying the results in insignificant post solution activity. Again, use of the processor and computing components to calculate the abstract index value score is insignificant.
Applicant further argues that the claims recite specific computing architecture that imposes a meaningful limit to any alleged abstract idea. Examiner asserts, as noted above and further below in the rejection, the use of the additional elements do not demonstrate a technical improvement nor a practical application. The alleged improvement of the efficiency and accuracy of data set selection and matching is merely the result of the use of generic computing components for the automation of tasks, see MPEP 2106.05(f), specifically” "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).” As mentioned previously and below, the generation of the machine-readable vectors recites using the generic computing components to carry out the vectors, where vector structuring and the data transformation can be performed mentally, where the “machine readable” structure is merely the result of the generic computer implementation. Because of the BRI of the feature vector and comparing it to the indexed data, such a comparison could be performed mentally where the use of the indexed data in the database and the machine-readable nature of the vectors are the results of the mere computer implementation. As described further in the Step 2A Prong 2 analysis below, the use of the machine learning and iterative training amount to nothing more than an instruction to apply the abstract idea using generic computing components. The iterative training of the models as further described in the Applicant’s Specification recites generic training steps for the algorithm and does not recite more than the use of “off the shelf” training algorithms for a ML model that would inherently improve the accuracy and adaptation of a generic ML model. When the elements are considered in an ordered combination, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to generate a vibrant compatibility plan, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
Applicant further argues that the arrangement of the limitations demonstrates a particularized technical solution implemented through defined machine operations, aligning with the principles articulated in Berkheimer. Examiner further asserts that there is no indication of a technology improvement nor do the claims recites elements that are significantly more than the judicial exception. As recited in the below rejection, the use of the generic, non-specific computing components to improve the manner in which the compatibility plans are generated using AI recites an improvement to the abstract idea, not a technology improvement, See MPEP 2106.05(a)II, particularly “Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
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 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
Claims 1-10 are related to a system, and claims 11-20 are also related to a method (i.e., a process). Accordingly, these claims are all within at least one of the four statutory categories.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Representative independent claim 1 includes limitations that recite at least one abstract idea as underlined in the following limitations. Specifically, independent claim 1 recites:
An apparatus for generating a vibrant compatibility plan using artificial intelligence, the apparatus comprising:
at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive at least a composition datum from a user client device, generated as a function of at least a user conclusive label and at least a user dietary response, wherein the at least a composition datum comprises:
at least a biological extraction;
at least an element of user body data; and
at least an element of desired dietary state data;
select at least a correlated dataset containing a plurality of data entries as a function of the at least a composition datum, wherein the at least a correlated dataset is stored in a structured database comprising indexed physiological trait fields and corresponding food element records;
extract at least a physiological trait from the at least a composition datum using a parsing module wherein the parsing module is configured to extra one or more phrases by performing dependency parsing processes, wherein the parsing module converts the composition datum into a structured machine-readable feature vector representing the at least a physiological trait;
match the at least a physiological trait to the at least a correlated dataset containing at least an element of the at least a physiological trait using the parsing module, wherein matching comprises computing a similarity value between the structured machine-readable feature vector and indexed physiological trait fields of the correlated dataset;
generate a vibrant compatibility plan containing a plurality of second food elements as a function of the at least a physiological trait, wherein generating the vibrant compatibility plan comprises:
receiving a training data set, wherein the training data set comprises outputs correlated to inputs, where the inputs comprise a plurality of physiological traits and the outputs comprise a plurality of first food elements;
training, iteratively, a machine-learning model using the training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; and
determining the vibrant compatibility plan as a function of the at least a physiological trait using the trained machine-learning model; and
compare each first food element of the plurality of first food elements with a second food element, wherein comparing each first food element of the plurality of first food elements with the second food element comprises:
generating at least a food element compatibility index value score as a function of each comparison; and
display the vibrant compatibility plan through a graphical user interface (GUI) of the user client device of the comparison.
The Examiner submits that the foregoing underlined limitations constitute a “mental process” as the following abstract limitations are related to abstract steps of analysis and determinations for generating a vibrant compatibility plan which can practically be performed in the human mind:
“select” at least a correlated dataset containing a plurality of data entries as a function of the at least a composition datum, which is an abstract limitation of analysis of the composition datum to then determine the correlated dataset,
“extract” at least a physiological trait from the at least a composition datum by extracting one or more phrases by performing dependency parsing processes, which is an abstract limitation of analysis of the composition datum to then determine the physiological traits,
“converting” the composition datum into a structured feature vector representing the physiological trait, which recites the abstract limitation of changing data into a abstract model related to a feature vector, where under broadest reasonable interpretation, the vector is an abstract construction,
“match” the at least a physiological trait to the at least a correlated dataset containing at least an element of the at least a physiological trait wherein matching comprises computing a similarity value between the structured vector and indexed physiological trait fields of the correlated dataset, which is an abstract limitation of analysis of the trait to the correlated dataset and the previously generated abstract vector with the index trait fields, and where the similarity value is an abstract number generated to determine similarity,
“generate” and “determine” a vibrant compatibility plan containing a plurality of second food elements as a function of the at least a physiological trait, which is an abstract limitation of determination of the vibrant compatibility plan from the analysis of the physiological trait,
“compare” each first food element of the plurality of first food elements with a second food element, wherein comparing each first food element of the plurality of first food elements with the second food element comprises: “generating” at least a food element compatibility index value score as a function of each comparison, which are abstract limitations of analysis of the food elements with a second food element, further analysis using comparing of the elements to then determine the compatibility index value score.
The claim limitations as a whole recite steps for generate a vibrant compatibility plan using abstract steps of analysis and determinations which can practically be performed in the human mind, and therefore recite a mental process.
The abstract idea of claim 11 is the same as claim 1.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., processor) and will be discussed in further detail below.
Accordingly, the claim as a whole recites at least one abstract idea.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claims 2 and 12 recite further abstract detail of the generated plan comprising data related to a user that is linked to a particular body dimension, further describing the abstract idea. Claims 3 and 13 recite further abstract detail of the generated plan comprising recommended restaurants determined through badge technology, further describing the abstract idea. Claims 4 and 14 recite further abstract detail of the generated plan comprising recommended exercise routines, further describing the abstract idea. Claims 5 and 15 recite further abstract detail of the generated plan comprising incorporating user feedback to update the plan, further describing the abstract idea. Claims 6 and 16 recite further abstract detail of the generated plan by it being updated based on the profession of the user, further describing the abstract idea. Claims 9 and 19 recite further abstract limitations of “ranking” the index value scores of each comparison where the rank comprises a hierarchical rank, further describing the abstract idea.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case for claim 1, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
An apparatus for generating a vibrant compatibility plan using artificial intelligence, the apparatus comprising:
at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
receive at least a composition datum from a user client device, generated as a function of at least a user conclusive label and at least a user dietary response, wherein the at least a composition datum comprises:
at least a biological extraction;
at least an element of user body data; and
at least an element of desired dietary state data (merely data gathering steps as noted below, see MPEP 2106.05(g) and Versata Dev. Group, Inc. v. SAP Am., Inc.);
select at least a correlated dataset containing a plurality of data entries as a function of the at least a composition datum wherein the at least a correlated dataset is stored in a structured database comprising indexed physiological trait fields and corresponding food element records (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
extract at least a physiological trait from the at least a composition datum using a parsing module wherein the parsing module is configured to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) extra one or more phrases by performing dependency parsing processes, wherein the parsing module converts the composition datum into a structured machine-readable (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) feature vector representing the at least a physiological trait;
match the at least a physiological trait to the at least a correlated dataset containing at least an element of the at least a physiological trait using the parsing module, wherein matching comprises computing a similarity value between the structured machine-readable feature vector and indexed physiological trait fields of the correlated dataset;
generate a vibrant compatibility plan containing a plurality of second food elements as a function of the at least a physiological trait, wherein generating the vibrant compatibility plan comprises:
receiving a training data set, wherein the training data set comprises outputs correlated to inputs, where the inputs comprise a plurality of physiological traits and the outputs comprise a plurality of first food elements;
training, iteratively, a machine-learning model using the training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; and (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f))
determining the vibrant compatibility plan as a function of the at least a physiological trait using the trained machine-learning model; and (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f))
compare each first food element of the plurality of first food elements with a second food element, wherein comparing each first food element of the plurality of first food elements with the second food element comprises:
generating at least a food element compatibility index value score as a function of each comparison; and
display the vibrant compatibility plan through a graphical user interface (GUI) of the user client device of the comparison (merely post solution activity as noted below, see MPEP 2106.05(g) and Symantec).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of:
the overall apparatus comprising at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to perform steps,
a correlated dataset is stored in a structured database comprising indexed physiological trait fields and corresponding food element records,
using a parsing module,
converting the composition datum into machine readable feature vectors,
receiving a training data set, wherein the training data set comprises outputs correlated to inputs, where the inputs comprise a plurality of physiological traits and the outputs comprise a plurality of first food elements; training, iteratively, a machine-learning model using the training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; and
using the trained machine-learning model,
the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0009] of the Applicant’s Specification recites the overall generic computing system for the apparatus comprising the processor, memory and storage. [0022] recites the use of the structured database storing the indexed fields and food element records, however this recites merely storing the data in a generic storage device. [0033] recites the use of the parsing module, however the recitation of this computing device is generic. [0030, 0034] recites the generation of the computer-readable vectors, however there is no indication of a particular configuration of a computerized vector to demonstrate a particular technology environment. [0042] recites further of the generic training sets that are generated and [0029] recites the generic training and iterative retraining of the models without providing specifics of the configuration and use of the training/iterative re-training algorithms. [0030, 0042] recites the use of the trained model and how it recites “apply it” to the abstract idea. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Regarding the additional limitations of receive at least a composition datum from a user client device, generated as a function of at least a user conclusive label and at least a user dietary response, wherein the at least a composition datum comprises: at least a biological extraction; at least an element of user body data; and at least an element of desired dietary state data, this is merely a pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [0025, 0058] of the Applicant’s Specification recites the receiving of the composition datum from the user client device and [0014, 0069] recites further detail of the use of the conclusive label and dietary response. [0010] recites how the received, gathered data is a biological extraction, element of user body data, and a desired dietary state data. The receiving of data from the user client device are used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activity.
Regarding the additional limitation of display the vibrant compatibility plan through a graphical user interface (GUI) of the user client device of the comparison, this is merely post-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of insignificant application to the at least one abstract idea in a manner that does not meaningfully limit the at least on abstract idea (see MPEP § 2106.05(g)). [0019] of Applicant’s specification recites the output of the plan (which is part of the abstract idea) as merely carried out in a GUI of the user client device, and therefore recites impractical application.
Claim 11 recites similar additional elements as claim 1.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to generate a vibrant compatibility plan, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claims 5 and 15 recites additional elements of receiving the suer feedback from a wearable device, however the use of this device is for the insignificant pre-solution activity of data gathering for the abstract idea. Claims 7 and 17 recites further detail of the insignificant data gathering for the received user body data comprising a nutritional biomarker, and is thus insignificant pre-solution activity. Claims 8 and 18 recites further detail of the insignificant data gathering for the received user conclusive label comprising data describing a current medical condition, and is thus insignificant pre-solution activity. Claims 10 and 20 recite further additional elements of generating a generic unsupervised machine learning model as a function of the dataset and where the generation of the model includes a generic hierarchical clustering model to output the second food element, however these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components.
Thus, taken alone and in ordered combination, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding the additional limitations of:
the overall apparatus comprising at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to perform steps,
a correlated dataset is stored in a structured database comprising indexed physiological trait fields and corresponding food element records,
using a parsing module,
converting the composition datum into machine readable feature vectors,
receiving a training data set, wherein the training data set comprises outputs correlated to inputs, where the inputs comprise a plurality of physiological traits and the outputs comprise a plurality of first food elements; training, iteratively, a machine-learning model using the training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; and
using the trained machine-learning model,
the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). [0009] of the Applicant’s Specification recites the overall generic computing system for the apparatus comprising the processor, memory and storage. [0022] recites the use of the structured database storing the indexed fields and food element records, however this recites merely storing the data in a generic storage device. [0033] recites the use of the parsing module, however the recitation of this computing device is generic. [0030, 0034] recites the generation of the computer-readable vectors, however there is no indication of a particular configuration of a computerized vector to demonstrate a particular technology environment. [0042] recites further of the generic training sets that are generated and [0029] recites the generic training and iterative retraining of the models without providing specifics of the configuration and use of the training/iterative re-training algorithms. [0030, 0042] recites the use of the trained model and how it recites “apply it” to the abstract idea. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception. The storage of the data in the structured database recites well understood, routine, and conventional activity.
Regarding the additional limitations of receive at least a composition datum from a user client device, generated as a function of at least a user conclusive label and at least a user dietary response, wherein the at least a composition datum comprises: at least a biological extraction; at least an element of user body data; and at least an element of desired dietary state data, this is merely a pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”). [0025, 0058] of the Applicant’s Specification recites the receiving of the composition datum from the user client device and [0014, 0069] recites further detail of the use of the conclusive label and dietary response. [0010] recites how the received, gathered data is a biological extraction, element of user body data, and a desired dietary state data. The receiving of data from the user client device are used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activity and does not recite significantly more than the judicial exception. The extraction and receiving of composition datum from storage devices for example as recited in [0033, 0035] recites well understood, routine, and conventional activities.
Regarding the additional limitation of display the vibrant compatibility plan through a graphical user interface (GUI) of the user client device of the comparison, this is merely post-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of insignificant application to the at least one abstract idea in a manner that does not meaningfully limit the at least on abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)”). [0019] of Applicant’s specification recites the output of the plan (which is part of the abstract idea) as merely carried out in a GUI of the user client device, and therefore recites impractical application and does not recite significantly more than the judicial exception. The transmission of the abstract generated plan to another computing device for the insignificant post solution activity recites well understood, routine, and conventional activity.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exceptions for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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.
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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686
Prosecution Insights