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 .
Response to Amendment
1. Claims 1 and 3-9 are currently pending.
2. Claims 2 and 10 are canceled.
3. Claims 1 and 3-8 are currently amended.
4. The 112(b) rejections to Claims 1 and 3-9 have been overcome unless otherwise indicated below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
5. Claims 1 and 3-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Any claim not specifically mentioned, including Claims 3-9, have been included based on its dependency.
6. Claim 1 recites the limitation "each customer" in Line 4. There is insufficient antecedent basis for this limitation in the claim.
7. Claim 1 recites the limitation "each neighborhood operator" in Lines 20-21. There is insufficient antecedent basis for this limitation in the claim. More specifically, it is unclear if the “preset neighborhood operator” is the same as “each neighborhood operator. Under the broadest reasonable interpretation, the neighborhood operators are interpreted as the same.
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.
8. Subject Matter Eligibility Analysis of Claim 1 (see MPEP §2106.03):
As a method, the claim is directed to a statutory category (Step 1).
Claim 1 is rejected under 35 U.S.C 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 is directed to constructing a mission planning with constraints, initializing a mission sequence with each customer, and updating the mission sequence to determine an optimal UAV mission sequence. This limitation akin to a mental process as a human mind can schedule a plurality of UAVs to be assigned to a specific customer and be optimized so the UAVs are assigned the closest customer. For example, a human mind can identify that there are 3 UAVs and 5 customers and determine a schedule by assigning the 3 UAVs to the closest customers and repeat the process based on the current resources of available UAVs and customers needing assistance. (Step 2A, Prong 1).
The applicant does not recite additional elements that integrate the judicial exception into a practical application. There is no tangible outcome to the claim as nothing is controlled based on the determined optimal UAV mission. The applicant has recited a claim in which determines a schedule to complete missions for a plurality of UAVs (Step 2A, Prong 2).
The claim does not provide an inventive concept and the claim recites no additional elements. Accordingly, the lack of additional elements does not integrate the abstract idea into a practical application because there are no meaningful limits imposed on practicing the abstract idea (see MPEP §2106.05(i)(a)) (Step 2B).
In conclusion, Claim 1 is directed toward non-subject matter eligible material and is thus rejected under 35 U.S.C 101 as being patent ineligible.
9. Subject Matter Eligibility Analysis of Claim 5 (see MPEP §2106.03):
As a method, the claim is directed to a statutory category (Step 1).
Claim 5 is rejected under 35 U.S.C §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 5 is directed to the following formula:
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This limitation is a mathematical concept. “A mathematical formula as such is not accorded the protection of our patent laws” (see MPEP §2106.04(a)(2)) (Step 2A, Prong 1).
The applicant has not recited improvement to any technology or technical field. The applicant has recited a claim in which, is a mathematical concept, and has not presented any improvement to the instantly applicable technology (Step 2A, Prong 2).
The claim does not provide an inventive concept and the claim recites no additional elements (Step 2B).
In conclusion, Claim 5 is directed toward non-subject matter eligible material and is thus rejected under 35 U.S.C §101 as being patent ineligible.
10. Subject Matter Eligibility Analysis of Claim 8 (see MPEP §2106.03):
As a method, the claim is directed to a statutory category (Step 1).
Claim 8 is rejected under 35 U.S.C §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 5 is directed to the following formula:
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This limitation is a mathematical concept. “A mathematical formula as such is not accorded the protection of our patent laws” (see MPEP §2106.04(a)(2)) (Step 2A, Prong 1).
The applicant has not recited improvement to any technology or technical field. The applicant has recited a claim in which, is a mathematical concept, and has not presented any improvement to the instantly applicable technology (Step 2A, Prong 2).
The claim does not provide an inventive concept and the claim recites no additional elements (Step 2B).
In conclusion, Claim 8 is directed toward non-subject matter eligible material and is thus rejected under 35 U.S.C §101 as being patent ineligible.
Claim Rejections - 35 USC § 103
11. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
12. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
13. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
14. Claims 1 and 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Kothari (US 20240249218 A1) in view of Spjuth (US 20210304343 A1).
15. Regarding Claim 1, Kothari teaches a method for large-scale unmanned aerial vehicle (UAV) mission planning, comprising (Kothari: [0023] and [0025]):
Constructing an objective function for UAV mission planning and its constraints (Kothari: [0042] and [0044] Note that the objective function is equivalent to the request 302 and the constraints of the mission planning are equivalent to the prioritization engines 312 and rules engines 310.);
Initializing a mission sequence and inserting each customer into a respective mission sequence with a smallest objective function value according to a change value of the objective function value after the insertion of a customer in the mission sequence to obtain a plurality of initial mission sequences (Kothari: [0036] Note that under the broadest reasonable interpretation, inserting each customer into the mission sequence with the smallest objective function value according to the change value is equivalent to scheduling AVs missions based on the assigned priorities of each mission.);
Iteratively performing the following update operations on the plurality of initial mission sequences (Kothari: [0045] and [0046]):
Using a... algorithm to divide the plurality of initial mission sequences into a plurality of groups; …using a... algorithm to optimize the mission sequences of each group of the plurality of groups in turn and then merging them to obtain a plurality of global mission sequences (Kothari: [0036] Note that dividing the initial mission sequences into a plurality of groups is equivalent to matching the prioritized mission list to available resources (e.g., readily available AVs). Also, note that optimizing the mission sequence to merge into a global mission sequence is equivalent to scheduling the AV missions to make efficient use of the available resources.);
If a total objective function value of the plurality of global mission sequences is smaller than a total objective function value of the plurality of initial mission sequences, then updating the plurality of initial mission sequences using the plurality of global mission sequences; performing a next round of update operation until an end condition is reached, and forming an optimal UAV mission with updated plurality of initial mission sequences (Kothari: [0022], [0046], and [0051] Note that the total objective function value of the global mission sequence being smaller than the total objective function value of the initial mission sequence is equivalent to the AV completing the initial mission request and being assigned a next (different) mission). The initial mission request is updated because a different mission is assigned next.);
Wherein after merging to obtain the plurality of global mission sequences, a local search algorithm is used to iteratively repair each global mission sequence in turn using each neighborhood operator, and the repaired mission sequence with the smallest objective function value is taken to update the corresponding global mission sequence; the repaired plurality of global mission sequences are obtained (Kothari: [0036] and [0046] Note that repairing the global mission sequence with the smallest objective function value to update corresponding mission sequence is equivalent to efficient scheduling AV missions based on mission requests (and resource availability changes).).
Kothari fails to explicitly teach using a spectral clustering algorithm to divide the plurality of initial mission sequences into a plurality of groups; and according to a preset neighborhood operator, using an adaptive variable neighborhood search algorithm to optimize the mission sequences of each group of the plurality of groups in turn and then merging them to obtain a plurality of global mission sequences.
However, in the same field of endeavor, Spjuth teaches using a spectral clustering algorithm to divide the plurality of initial mission sequences into a plurality of groups; and according to a preset neighborhood operator, using an adaptive variable neighborhood search algorithm to optimize the mission sequences of each group of the plurality of groups in turn and then merging them to obtain a plurality of global mission sequences (Spjuth: [0214] and [0216] Note that the clustering algorithm includes adaptive variable neighborhood search algorithm (see [0215] nearest neighbor algorithm) because the mission sequences are optimized.).
Kothari and Spjuth are considered to be analogous to the claim invention because they are in the same field of vehicle fleet management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Kothari to incorporate the teachings of Spjuth to use a spectral clustering algorithm to divide the initial mission sequences and to use an adaptive variable neighborhood search algorithm to optimize the mission sequences because it provides the benefit of improving scheduling efficiency for maximizing utilization of the UAVs. Clustering the initial mission sequences in groups provides the added benefit of decreasing the total flight path of the UAVs.
16. Regarding Claim 3, Kothari and Spjuth remains as applied above in Claim 1, and further, Kothari teaches constructing an objective function with a cumulative time for each UAV to reach a customer and a minimum total cumulative time for each UAV to reach each customer in the mission sequence from a warehouse as the objective (Kothari: [0038] Note that constructing an objective function with a cumulative time is equivalent to estimating the duration time of the missions.);
Constructing constraints based on a number of times each customer is visited, a time for a drone to reach a customer in the same mission sequence… (Kothari: [0042], [0043], and [0051] Note that the constraint of the time for a drone to reach a customer in the same mission sequence is equivalent to assigning a high priority to a mission (so the AV is assigned to that mission first). Also, note that the number of times the customer is visited is equivalent to the number of AVs assigned to the mission.);
When calculating the objective function value of any mission sequence, if the current mission sequence satisfies all the constraints, calculating the objective function value according to the objective function, otherwise, setting the objective function value to the pre-set maximum function value (Kothari: [0043] and [0044]).
Kothari fails to explicitly teach a maximum capacity of the supplies carried by the drone, and a remaining power of the drone to reach each customer.
However, in the same field of endeavor, Spjuth teaches a maximum capacity of the supplies carried by the drone, and a remaining power of the drone to reach each customer (Spjuth: [0212], [0217], and [0270] Note that the maximum capacity of the supplies is equivalent to the energy consumption (also see [0192, a heavy payload has considerable energy consumption).).
Kothari and Spjuth are considered to be analogous to the claim invention because they are in the same field of vehicle fleet management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Kothari to incorporate the teachings of Spjuth to construct constraints based on the maximum capacity of supplies carried and the remaining power of the drone because it provides the benefit of additional constraints to ensure the UAV can complete the mission. This improves scheduling efficiency for maximizing utilization of the UAVs.
17. Regarding Claim 4, Kothari and Spjuth remains as applied above in Claim 3, and further, Kothari teaches constructing, for each UAV, a plurality of first mission sequences using a warehouse to which it belongs as a starting point of the mission sequence (Kothari: [0036] and [0070] Note that the warehouse to which the UAV belongs as the starting point is equivalent to the base of operations.);
Placing all customers into a set to be inserted (Kothari: [0042] and [0051]);
Iteratively performing the following update operations on the plurality of first mission sequences: inserting each customer in the set to be inserted into each first mission sequence in turn, calculating the change value of the objective function value of each first mission sequence before and after the insertion, and obtaining a difference between a second minimum change value and a minimum change value; comparing the difference value corresponding to each customer, inserting the customer with a largest difference value into the first mission sequence corresponding to the minimum change value of that customer and deleting the customer from the set to be inserted, and performing a next update operation until the set to be inserted is empty (Kothari: [0036] and [0044] Note that calculating the change value of the objective function value before and after insertion, obtaining a difference between, and comparing the difference value is equivalent to matching the prioritized mission list to the available AV resources. Assigning the AVs to the highest priority missions corresponds to the minimum change value of that customer because the highest priority missions are to be completed first.);
Screening the first mission sequence containing the customer from the updated plurality of first mission sequences, and inserting a warehouse nearest to a last customer into a tail of a screened plurality of the first mission sequences as the end of the mission sequence, respectively, to obtain the plurality of initial mission sequences (Kothari: [0037] and [0046]).
18. Regarding Claim 5, Kothari and Spjuth remains as applied above in Claim 1, and further, Kothari teaches calculating, as input samples, the coordinates of a center of gravity of each initial mission sequence based on the coordinates of a latitude and longitude where each node in each initial mission sequence is located, and a number of customers and warehouses, by means of the following formula
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where (bmx, bmy,) denotes a longitude coordinates and a latitude coordinates of a center of gravity bm of an m-th mission sequence Vm;|Vm|denotes a number of nodes in the m-th mission sequence, said nodes including customers and warehouses; xi denotes longitude coordinates of the i-th node; y denotes latitude coordinates of the i-th node (Spjuth: [0214] and [0217] Note that one of ordinary skill in the art would recognize that Spjuth teaches the coordinates of the center of gravity of the initial mission sequence is calculated based on the coordinates of each node, the number of customers, and the number of warehouses (also see Fig. 23A). Therefore, the calculation of the geometric centers is equivalent to the formula.);
Inputting the input samples, the pre-defined number of feature dimensions and the number of customers in each group into the spectral clustering algorithm to cluster the plurality of initial mission sequences into the plurality of groups (Spjuth: [0215]).
19. Claims 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kothari (US 20240249218 A1), in view of Spjuth (US 20210304343 A1), and in further view of Pandit (US 20200130828 A1).
20. Regarding Claim 6, Kothari and Spjuth remains as applied above in Claim 1. Kothari and Spjuth fail to explicitly teach the limitations recited in Claim 6.
However, in the same field of endeavor, Pandit teaches performing the following iterative optimization operations on the mission sequence of each group: initializing a global optimal solution, an optimal solution of a previous round iteration and neighborhood operator weights, and setting a maximum number of executions (Pandit: [0019] and [0054]);
In each execution, selecting a neighborhood operator by use of a roulette wheel selection algorithm based on weights of each neighborhood operator (Pandit: [0046]);
Calculating a first solution for each group based on the selected neighborhood operator (Pandit: [0019] and [0047]);
Obtaining a second solution by applying a variable neighborhood descent algorithm to the first solution (Pandit: [0053]);
Updating performance record values of the selected neighborhood operator, the global optimal solution and a current round iteration optimal solution based on the global optimal solution, the current round iteration optimal solution, a previous round iteration optimal solution and the second solution, updating each group with the global optimal solution for the next execution until the maximum number of executions is reached, updating the previous round iterative optimal solution for each group with the current round iterative optimal solution, updating the weights of each neighborhood operator according to a preset weight update rate and the performance record value of each neighborhood operator, and carrying out a next round of iterative optimization until the maximum number of group iterations is reached, and obtaining the global optimal solution for each group, which is the mission sequence of each group after optimization (Pandit: [0051], [0052], and [0054]).
Kothari, Spjuth, and Pandit are considered to be analogous to the claim invention because they are in the same field of vehicle fleet management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Kothari and Spjuth to incorporate the teachings of Pandit to sequentially optimize the mission sequences of each group by performing iterative optimization operations on the mission sequence because it provides the benefit of determining the most efficient assignments of each UAV by adjusting the clusters to be the most optimal. Using feedback-based machine learning improves the efficiency of the UAV assignments by reassigning UAV clusters.
21. Regarding Claim 7, Kothari, Spjuth, and Pandit remains as applied above in Claim 6, and further, Pandit teaches updating the current-round iteration optimal solution using the second solution if the current-round iteration optimal solution is empty (Pandit: [0053]);
If the objective function value of the second solution is less than the objective function value of the global optimal solution, updating the global optimal solution using the second solution, by increasing the performance record value of the selected neighborhood operator based on a value of a first weight update factor; if the objective function value of the second solution is smaller than the objective function value of the optimal solution of the current round iteration, updating the optimal solution of the current round iteration using the second solution, by increasing the performance record value of the selected neighborhood operator according to a value of a second weight update factor; if the objective function value of the second solution is smaller than the objective function value of the optimal solution of the previous round iteration, increasing the performance record value of the selected neighborhood operator according to a value of a third weight update factor; wherein the value of the first weight update factor is greater than the value of the second weight update factor, and the value of the second weight update factor is greater than the value of the third weight update factor (Pandit: [0051], [0053], and [0058] Note that the objective function value of the second solution being less than the objective function value of the global optimal solution, current round iteration, and previous round iteration is equivalent to completing the process in Fig. 5 a plurality of times. Under the broadest reasonable interpretation, the objective function value of the second solution being less than the objective function value of the global optimal solution, current round iteration, or previous round iteration is interpreted as the desire to re-cluster the UAV into another cluster based on the parameters. Also, note that increasing the performance record value of the selected neighborhood operator based on the value of the first weight update factor is equivalent to refining the cluster parameters that define the clusters. The first weight update factor is greater than the second weight update factor, which is greater than the third weight update factor because the process continually refines the UAV clusters.).
22. Regarding Claim 8, Kothari, Spjuth, and Pandit remains as applied above in Claim 6, and further, Pandit teaches the weights of each neighborhood operator are updated according to a preset weight update rate and a recorded performance value of each neighborhood operator by the following formula:
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Where wp denotes a weight of a p-th neighborhood operator, σp denotes a performance record value of the p-th neighborhood operator; p denotes a weight update rate, 0 < p < 1 (Pandit: [0051] and [0053] Note that one of ordinary skill in the art would recognize that Pandit teaches updating the weights of each neighborhood operator by receiving feedback analysis and determining if the UAV is still suited for the current cluster. Therefore, the determination that the UAV needs to be re-clustered based on the characteristics and parameters is equivalent to the formula.).
23. Regarding Claim 9, Kothari and Spjuth remains as applied above in Claim 1. Kothari and Spjuth fail to explicitly teach the limitations recited in Claim 9.
However, in the same field of endeavor, Pandit teaches an Or-Opt operator for randomly selecting segments containing three consecutive customers and reinserting the segments into other positions; a fragment swapping operator for exchanging fragments of any length in a sequence of two missions; a three-node position swap operator for randomly selecting 2 customers c1 and c2 on one mission sequence and one customer c3 on another mission sequence, inserting customer c3 into the position of customer c1, inserting customer c1 into the position of customer c2, and inserting customer c2 into the position of customer c3 (Pandit: [0022], [0051], and [0058] Note that reinserting segments into other positions and exchanging fragments is equivalent to reassigning UAVs to different clusters. Also, note that the three-node position swap is equivalent to completing the process of Fig. 5 three times for separate UAVs.).
Kothari, Spjuth, and Pandit are considered to be analogous to the claim invention because they are in the same field of vehicle fleet management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Kothari and Spjuth to incorporate the teachings of Pandit to reinsert segments into other positions and exchange fragments because it provides the benefit of adjusting the UAV assignments to be the most optimal. Using feedback-based machine learning improves the efficiency of the UAV assignments by reassigning UAV clusters.
Response to Argument
24. Applicant's arguments filed 11/2/2025 regarding the U.S.C. 101 rejection have been fully considered but they are not persuasive.
25. First, the Applicant has alleged "according to the above additional element 1) claimed in the above amended Claim 1, the following technical problem can be solved: "how to iteratively repair each global mission sequence." The Examiner disagrees.
Claim 1 is rejected under U.S.C. 101 because it is directed to an abstract idea without significantly more. The claim is akin to a mental process as a human mind can schedule a plurality of UAVs to be assigned to a specific customer and be optimized so the UAVs are assigned to the closest customer. Further, the human mind can also repeat the process to determine a different, more optimized schedule for the UAVs.
As currently claimed, there are no additional elements that integrate the judicial exception into a practical application. There is no tangible outcome as nothing is controlled based on the determination of the UAV mission planning. To overcome the abstract idea, the Applicant may amend the independent claim to incorporate a control step of the UAV based on the mission planning.
26. Applicant's arguments filed 11/2/2025 regarding the U.S.C. 103 rejections have been fully considered but they are not persuasive.
27. First, the Applicant has alleged "Spjuth refers to clustering algorithm, but does not refer to spectral clustering algorithm, and the spectral clustering algorithm is not disclosed in Spjuth." The Examiner disagrees.
Spjuth teaches in [0214] that a clustering algorithm is used to determine a number of clusters and the launch locations for each cluster. This is equivalent to using a spectral clustering algorithm to divide the plurality of initial mission sequences into a plurality of groups. Further, Spjuth teaches in [0215] that the clustering algorithm may use a partitional clustering algorithm or any alternative. Therefore, Spjuth teaches to use any clustering algorithm to divide initial mission sequences into a plurality of groups. It would have been well within the skill level of one of ordinary skill in the art for any alternate clustering algorithm to include a spectral clustering algorithm absent a showing to the contrary. Also, one of ordinary skill in the art would recognize that the partitional clustering algorithm is an integral component of the spectral cluttering algorithm and, as a result, relies on the output of the partitional cluster to finalize the grouping of the mission sequences.
28. Second, the Applicant disagrees the clustering algorithm includes the adaptive variable neighborhood search algorithm "because spectral clustering algorithm is a clustering algorithm, but adaptive variable neighborhood search algorithm is a search algorithm, they are totally different." The Examiner disagrees.
Under the broadest reasonable interpretation of the claims, the adaptive variable neighborhood search algorithm optimizes mission sequences for each of the plurality of groups. In [0214] and [0216] of Spjuth, it is explained that clusters are determined for launch locations of the UAVs in the plurality of groups. The number of clusters may be adjusted to search and achieve a desired balance between distance traveled by stations versus UAVs. This is equivalent to optimizing the mission sequences of each group of the plurality of groups to obtain a global mission sequence. Therefore, the clustering algorithm in Spjuth is equivalent to the adaptive variable neighborhood search algorithm because there is no indication that the algorithms result in different outcomes. For both the current claims and in Spjuth do the algorithms optimize the mission sequences. Just because the algorithms have different names does not make them distinct. As a result, Spjuth teaches the adaptive variable neighborhood search algorithm under the broadest reasonable interpretation.
29. Third, the Applicant has alleged "paragraphs [0036] and [0046] of Kothari are silent of 'a local search algorithm is used to iteratively repair each global mission sequence in turn using each neighborhood operator' in the distinguishing technical feature 2)." The Examiner disagrees.
Kothari teaches in [0036] to use fleet managers (test managers) approve scheduling of AV mission for efficiently operating the fleet. The system in Kothari uses scheduling algorithms that are applied to information to schedule the fleet of vehicles (see [0023]). Further, it is explained in [0038] that the information and fleet performance are continually monitored for refining the schedule algorithms and [0046] revises the schedule based on changes. This is equivalent to iteratively repairing each global mission sequence because the UAV missions are updated based on the resource availability of the UAV fleet. Under the broadest reasonable interpretation, the local search algorithm is equivalent to the scheduling algorithms in Kothari because the global mission sequences are updated during each shift of the UAVs.
30. Kothari (US 20240249218 A1), in view of Spjuth (US 20210304343 A1), and in further view of Pandit (US 20200130828 A1) teaches all aspects of the invention. The rejection is modified according to the newly amended language but still maintained with the current prior art of record.
31. Claims 1 and 3-9 remain rejected under their respective grounds and rational as cited above, and as stated in the prior office action which is incorporated herein. Also, although not specifically argued, all remaining claims remain rejected under their respective grounds, rationales, and applicable prior art for these reasons cited above, and those mentioned in the prior office action which is incorporated herein.
Conclusion
32. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
33. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL T SILVA whose telephone number is (571)272-6506. The examiner can normally be reached Mon-Tues: 7AM - 4:30PM ET; Wed-Thurs: 7AM-6PM ET; Fri: 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, Angela Ortiz can be reached at 571-272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL T SILVA/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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