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 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 1-2, 4-14, 16-18 and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite mental processes of observation, judgement and evaluation. This judicial exception is not integrated into a practical application because nor amount to significantly more than the judicial exception because the additional elements of the claims alone or in combination are mere insignificant extra solution activity in combination of generic computer hardware that are implemented to perform the disclosed abstract idea above. See the analysis below for further details.
Claims 1, 14 and 18
Step 1: The claim recites a method, apparatus and non-transitory storage medium, therefore the claims fall the statutory categories.
Step 2A Prong 1: The claim recites, inter alia:
Analyzing the logistic operation order data and predicting, based at least in part on the analyzing, a logistic provider to perform the on logistic operations at a given level of performance (This amounts to a mental process of observation, judgment and evaluation. For example, it is a user reviewing logistic order data from a customer (wherein to deliver a product) and picking a logistic provider (a delivery company) that should be used to deliver the product in a given amount of time or time window.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Receiving logistic operation order data, wherein the logistic operation order data identifies at least one logistics operation to be performed (This amount to receiving data, which is data collection, and as such extra-solution activity, see MPEP 2106.05(g).)
wherein the steps of the method are executed by a processing device operatively coupled to a memory. (This amount to using generic computer hardware (process and memory) to executed the abstract idea, see MPEP 2106.05(f).)
a processing device operatively coupled to memory; (claim 11) (This amount to using generic computer hardware (process and memory) to executed the abstract idea, see MPEP 2106.05(f).)
training the one or more machine learning algorithms with historical logistics data; (This claim is cited a high level of generality and results in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “Receiving logistic operation order data, wherein the logistic operation order data identifies at least one logistics operation to be performed” amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The use of the process, processing device, memory and training a machine learning algorithm amounts to using generic computer hardware apply an abstract idea, see MPEP 2106.05(f). When viewing the claim as a whole it does not amount to significantly more than the abstract idea.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer hardware that are implemented to perform the disclosed abstract idea above.
2. The method of claim 1 further comprising generating, based at least in part on the predicting, a request for the logistics provider to perform the at least one logistics operation, wherein the request is transmitted to the logistics provider. (This amount to transmitting data which is extra-solution activity, see MPEP 2106.05(g) and it is well-understood, routine and conventional. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.)
4. The method of claim 1 wherein the historical logistics data specifies a plurality of logistics operations associated with respective ones of a plurality of logistics providers, and whether there were any issues with respective ones of the plurality of logistics operations. (This amount to a particular type of data to used and manipulated with is extra-solution activity, see MPEP 2106.05(g).)
5. The method of claim 4 wherein the historical logistics data specifies one or more features associated with the respective ones of the plurality of logistics operations, and wherein the one or more features include at least one of date, customer, product, product part, logistics operation type, location and cost level. (This amount to a particular type of data to used and manipulated with is extra-solution activity, see MPEP 2106.05(g).)
6. The method of claim 1 wherein: the historical logistics data specifies one or more features associated with respective ones of a plurality of logistics operations; (This amount to a particular type of data to used and manipulated with is extra-solution activity, see MPEP 2106.05(g).) and the one or more machine learning algorithms automatically encode the one or more features for use in a training dataset. (This is recited a high level of generality which results using the machine learning model and algorithm as a tool to execute the abstract idea, see MPEP 2106.05(f).)
7. The method of claim 1 wherein: the historical logistics data specifies one or more features associated with respective ones of a plurality of logistics operations; (This amount to a particular type of data to used and manipulated with is extra-solution activity, see MPEP 2106.05(g).) and the method further comprises extracting one or more sub-features from the one or more features to be used during the training. (This is recited a high level of generality which results using the machine learning model and algorithm as a tool to execute the abstract idea, see MPEP 2106.05(f).)
Claims 8, 16 and 20
8. The method of claim 1 wherein the one or more machine learning algorithms comprise a plurality of decision trees, and the plurality of decision trees are respectively trained with different portions of the historical logistics data. (This limitation is cited at a high level of generality result in the training and decision trees being used a tool to execute the abstract idea, see MPEP 2106.05(f).)
Claims 16 and 20 are the apparatus and non-transitory computer readable storage medium embodiment of claim 8 with similar limitation to that of 8 and thus are rejected using the same reasoning.
Claims 9 and 17
9. The method of claim 8 wherein the analyzing comprises: sequentially analyzing the logistics operation order data with respective ones of the plurality of decision trees to generate respective predictions; and aggregating the respective predictions to determine the logistics provider to perform the at least one logistics operation. (These limitation is cited at a high level of generality result in the d decision trees being used a tool to execute the abstract idea, see MPEP 2106.05(f).)
Claim 17 is the apparatus embodiment of claim 9 with similar limitation to that of 9 and thus are rejected using the same reasoning.
10. The method of claim 1 further comprising harvesting the historical logistics data from at least one of a customer relationship management system, an enterprise resource planning system, a sales system and an order fulfillment system. (This limitation amounts to data collection which is extra-solution activity, see MPEP 2106.06(g). Also, it well-understood, routine and conventional. . See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.)
11. The method of claim I wherein the one or more machine learning algorithms comprise an ensemble decision tree-based boosting algorithm. (This limitation is cited a high level of generality and result using the machine learning model and algorithm as a tool to execute the abstract idea, see MPEP 2106.05(f).)
12. The method of claim 1 wherein the one or more machine learning algorithms comprise a categorical boosting algorithm. (This limitation is cited a high level of generality and result using the machine learning model and algorithm as a tool to execute the abstract idea, see MPEP 2106.05(f).)
13. The method of claim 1 wherein the one or more machine learning algorithms comprise a shallow learning algorithm. (This limitation is cited a high level of generality and result using the machine learning model and algorithm as a tool to execute the abstract idea, see MPEP 2106.05(f).)
Claim 21. The apparatus of claim 14 wherein the one or more machine learning algorithms comprise an ensemble decision tree-based boosting algorithm. (This limitation is cited a high level of generality and result using the machine learning model and algorithm as a tool to execute the abstract idea, see MPEP 2106.05(f).)
22. The apparatus of claim 14 wherein the one or more machine learning algorithms comprise a categorical boosting algorithm. (This limitation is cited a high level of generality and result using the machine learning model and algorithm as a tool to execute the abstract idea, see MPEP 2106.05(f).)
23. The apparatus of claim 14 wherein the one or more machine learning algorithms comprise a shallow learning algorithm. (This limitation is cited a high level of generality and result using the machine learning model and algorithm as a tool to execute the abstract idea, see MPEP 2106.05(f).)
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1-3, 10, 13-14, 18 and 23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Spector et al. (US2018/0315319 A1 – hereinafter Spector).
In regards to claim 1, Spector discloses a method comprising:
receiving logistics operation order data, wherein the logistics operation order data identifies at least one logistics operation to be performed; (Spector fig. 2a element 200 receives an order and para. [0055] cites “The received orders 182 may be assigned an order identifier, stored in the order storage of the routing system 120 and may include the data associated with the order, including, for example, the origination location and destination location and any order criteria or data such as any pick-up time window or delivery time window, an order status (e.g., new, assigned, picked up or not, in-transit, delivered or completed, etc.)…”. There is logistic data associated each order and operation to be performed, delivery.)
analyzing the logistics operation order data using one or more machine learning algorithms; (Spector para. [0014] teaches using machine learning by delivery system and para. [0017] teaches training the machine learning model based historical order data and para. [0076] teaches machine learning model using order data to determine delivery person.)
training the one or more machine learning algorithms with historical logistics data; (Spector para. [0014] teaches using machine learning by delivery system and para. [0017] teaches training the machine learning model based historical order data and para. [0055] teach order has logistic data associated with it.)
predicting, based at least in part on the analyzing, a logistics provider to perform the at least one logistics operation at a given level of performance (Spector fig. 2B element 252, 254, 256, 259, and 260 disclose selecting a driver (logistics provider) to perform the one logistic operation (delivering order), wherein a user is selected, notified of order deliver wherein they can accept or rejected it, and if accepted the order is assigned to the driver for delivery. Also, Spector para. [0015] teaches removing drivers would have low possibility of being a good and delivering the package and para. [0020] teaches selecting the highest rank driver who has the best likelihood of delivery, wherein given performance level is delivering the package.)
wherein the steps of the method are executed by a processing device operatively coupled to a memory. (Spector fig. 1 discloses a system that performs the operations above and had memory in driver data storage, model storage, rule storage, order storage and so on.)
In regards to claim 2, Spector discloses the method of claim 1 further comprising generating, based at least in part on the predicting, a request for the logistics provider to perform the at least one logistics operation, wherein the request is transmitted to the logistics provider. (Spector fig. 2B element 252, 254, 256, 259, and 260 disclose selecting a driver (logistics provider) to perform the one logistic operation (delivering order), wherein a user is selected, notified of order deliver wherein they can accept or rejected it, and if accepted the order is assigned to the driver for delivery.)
In regards to claim 10, Spector discloses the method of claim 3 further comprising harvesting the historical logistics data from at least one of a customer relationship management system, an enterprise resource planning system, a sales system and an order fulfillment system. (Spector para. [0024] teaches “In one embodiment, filtering the set of drivers to determine a reduced set of drivers comprises applying a set of filtering rules before applying the filtering model and wherein the filtering model is a recurrent neural network model trained on a set of historical snapshot of previously completed orders.” As the historical data is based on previously completed order the examiner interprets this to be an order fulfillment system.)
In regards to claim 13, Spector discloses the method of claim 1 wherein the one or more machine learning algorithms comprise a shallow learning algorithm. (Spector para. [0095] teaches the machine learning model by a decision tree which a type of shallow learning algorithm.)
In regards to claim 14, it is the apparatus embodiment of claim 1 with similar limitations and thus rejected using the same reasoning found in claim 1. It includes the limitations of a process and memory being used which is disclosed in Spector fig. 1 and para. [0117].
In regards to claim 18, it is the non-transitory process-readable storage medium embodiment of claim 1 with similar limitations and thus rejected using the same reasoning found in claim 1.
In regards to claim 23, it is the non-transitory process-readable storage medium embodiment of claim 13 with similar limitations and thus rejected using the same reasoning found in claim 13.
Claim Rejections - 35 USC § 103
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.
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.
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.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Spector et al. (US2018/0315319 A1 – hereinafter Spector) as applied to claim 3 above, and further in view of Kuettner (US 2020/0160263 A1).
In regards to claim 4, Spener discloses the method of claim 3 bit does disclose wherein the historical logistics data specifies a plurality of logistics operations associated with respective ones of a plurality of logistics providers, and whether there were any issues with respective ones of the plurality of logistics operations.
Keuttner discloses historical logistic data associated with a logistic provider and whether there were any issues with respective one of logistic operations. (Keuttner para. [0095] discloses wherein a record of each deliver driver is maintain and show issues of how many food order that not validate, took too long, or if the driver received complaints.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify the teachings of Spencer with that of Keuttner as both references deal with delivery things and it provides the benefit of keeping a record of driver performance to avoid using drivers with a history of bad performance which could hurt the company’s reputation as suggested in paragraph [0095] of Keuttner.
In regards to claim 5, Spencer in view of Keuttner disclose the method of claim 4 wherein the historical logistics data specifies one or more features associated with the respective ones of the plurality of logistics operations, and wherein the one or more features include at least one of date, customer, product, product part, logistics operation type, location and cost level. (Spector fig. 2a element 200 receives an order and para. [0055] cites “The received orders 182 may be assigned an order identifier, stored in the order storage of the routing system 120 and may include the data associated with the order, including, for example, the origination location and destination location and any order criteria or data such as any pick-up time window or delivery time window, an order status (e.g., new, assigned, picked up or not, in-transit, delivered or completed, etc.)…”.)
Claims 6-9, 11-12, 16-17 and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Spector et al. (US2018/0315319 A1 – hereinafter Spector) as applied to claim 3 above, and further in view of Prokhorenkova et al. (“CatBoost: Unbiased Boosting with Categorical Features” – hereinafter Prokhorenkova).
In regards to claim 6, Spector discloses the method of claim 3 wherein: the historical logistics data specifies one or more features associated with respective ones of a plurality of logistics operations; and (Spector para. [0055] cites “The received orders 182 may be assigned an order identifier, stored in the order storage of the routing system 120 and may include the data associated with the order, including, for example, the origination location and destination location and any order criteria or data such as any pick-up time window or delivery time window, an order status (e.g., new, assigned, picked up or not, in-transit, delivered or completed, etc.)…”. Spector para. [0014] teaches using machine learning by delivery system and para. [0017] teaches training the machine learning model based historical order data. In para. [0055] order have feature data associated logistic operations, destination location, origin location , order status and so on. These are categorical data or feature data associated logistic operation.)
However, Spector does not explicitly disclose the one or more machine learning algorithms automatically encode the one or more features for use in a training dataset.
Prokhorenkova discloses automatically encoding the one or more features for use in training dataset. (Prokhorenkova section 3.1 teaches using CatBoost that includes encoding features of training data using one-hot coding.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Spector with that of Prokhorenkova as both references deal with the user of decision trees. The motivation to do with based on Spector disclose in para. [0095] that machine learning model may be a decision and the Prokhorenkova discloses on page 1 last paragraph and on page 2 fourth paragraph that CatBoost is a form of gradient boosting using binary decision trees that outperforms other gradient boosted decision trees, thus the user would be motivated to do so in order to create the best performing system possible.
In regards to claim 7, Spector discloses the method of claim 3 wherein: the historical logistics data specifies one or more features associated with respective ones of a plurality of logistics operations; and (Spector para. [0055] cites “The received orders 182 may be assigned an order identifier, stored in the order storage of the routing system 120 and may include the data associated with the order, including, for example, the origination location and destination location and any order criteria or data such as any pick-up time window or delivery time window, an order status (e.g., new, assigned, picked up or not, in-transit, delivered or completed, etc.)…”. Spector para. [0014] teaches using machine learning by delivery system and para. [0017] teaches training the machine learning model based historical order data. In para. [0055] order have feature data associated logistic operations, destination location, origin location , order status and so on. These are categorical data or feature data associated logistic operation.)
However, Spector does not explicitly disclose extracting one or more sub-features from the one or more features to be used during the training.
Prokhorenkova discloses extracting one or more sub-features from the one or more features to be used during the training. (Prokhorenkova page 2-3 section 3.1 teaches category is feature which is represent by a node of a decision tree and it splits into sub features as it progresses down the decision tree and the category is split in sub features.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Spector with that of Prokhorenkova as both references deal with the user of decision trees and the benefit of doing so it creating an efficient and more accurate decision tree.
In regards to claim 8, Spector discloses the method of claim 3 wherein the one or more machine learning algorithms comprise a decision tree (Spector para. [0095]) trained on historical data (Spector para. [0014] teaches using machine learning by delivery system and para. [0017] teaches training the machine learning model based historical order data and para. [0055] teach order has logistic data associated with it.) but does not explicitly disclose a plurality of decision trees are respectively trained with different portions of the historical data.
Prokhorenkova disclose a plurality of decision trees are respectively trained with different portions of the historical data. (Prokhorenkova fig. 1 shows an algorithm 1 shows where in a first model (decision tree) is trained using examples 1-5, and second model is trained with examples 1-6 and third with be examples 1-7. Also see section 4.2 on page 5 that explains this further. )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Spector with that of Prokhorenkova in order to allow a plurality of decision tree as both references deal with the use of decision trees and the benefit of doing so it creating a model is able doing ordered boosting creating a more efficient model.
In regards to claim 9, Spector in view of Prokhorenkova discloses the method of claim 8 wherein the analyzing comprises:
sequentially analyzing the logistics operation order data with respective ones of the plurality of decision trees to generate respective predictions; and (Prokhorenkova section 4.2 teaches sequential decision trees and Spector teaches using logistic operation order data.)
aggregating the respective predictions to determine the logistics provider to perform the at least one logistics operation.(Prokhorenkova page 1 section 1 first paragraph teaches combining weak learner to create a strong prediction, this would be aggregating the respected predictions for the weak learners.)
In regards to claim 11, Spector discloses the method of claim 1 wherein the one or more machine learning algorithms comprise decision tree-based algorithm. (Spector para. [0095] disclose the machine learning model is a decision tree.)
However, Spector does not explicitly disclose wherein the machine learning algorithm is an ensemble decision tree boosting algorithm.
Prokhorenkova disclose wherein the machine learning algorithm is an ensemble decision tree boosting algorithm. (Prokhorenkova abstract and page 1 section 1 teaches using CatBoost which is a form gradient categorical boosting using decision trees that out performs existing state of the art gradient boosted decision trees. It also teaches gradient boosting is an ensemble of predictions, wherein the predictions are weak base predictions or decision trees. Thus, it teaches using an ensemble decision tree boosting algorithm.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Spector with that of Prokhorenkova as both references deal with the user of decision trees. The motivation to do with based on Spector disclose in para. [0095] that machine learning model may be a decision and the Prokhorenkova discloses on page 1 last paragraph and on page 2 fourth paragraph that CatBoost is a form of gradient boosting using binary decision trees that outperforms other gradient boosted decision trees, thus the user would be motivated to use the best performing system.
In regards to claim 12, Spector in view of Prokhorenkova disclose the method of claim 11 wherein the one or more machine learning algorithms comprise a categorical boosting algorithm. (Prokhorenkova page 1 last paragraph teaches using CatBoost which categorial boosting, thus it a categorical boosting algorithm.)
In regards to claim 16, it is the apparatus embodiment of claim 8 with similar limitations and thus rejected using the same reasoning found in claim 8.
In regards to claim 17, it is the apparatus embodiment of claim 9 with similar limitations and thus rejected using the same reasoning found in claim 9.
In regards to claim 20, it is the non-transitory process-readable storage medium embodiment of claim 8 with similar limitations and thus rejected using the same reasoning found in claim 8.
In regards to claim 21, it is the non-transitory process-readable storage medium embodiment of claim 11 with similar limitations and thus rejected using the same reasoning found in claim 11.
In regards to claim 22, it is the non-transitory process-readable storage medium embodiment of claim 12 with similar limitations and thus rejected using the same reasoning found in claim 12.
Response to Arguments
Applicant's arguments filed 26 November have been fully considered but they are not persuasive. The applicant argues the claims do contain a mental process and can not be performed mentally. The examiner respectfully traverses the applicant arguments as the claims contain the mental process of observation, judgment and evaluation. For example, it is a user reviewing logistic order data from a customer (wherein to deliver a product) and picking a logistic provider (a delivery company) that should be used to deliver the product in a given amount of time or time window. Also, the AI elements of the claims are cited a high level of generality resulting using machine learning as tool to execute the abstract idea, see MPEP 2106.05(f). As such the rejection under 35 USC 101 for being abstract idea is maintained. Additionally, the applicant argues that Spector fails to discloses training a machine learning model using historical logistical data, and predicting a logistic provider to perform the at least one logistic operation at a given level of performance. The examiner respectfully traverses the applicant arguments as Spector does disclose the limitations. Spector para. [0014] teaches using machine learning by delivery system and para. [0017] teaches training the machine learning model based historical order data and para. [0055] teaches order data has logistic data associated with it. Thus, the system is training historical logistic data. Further Spector fig. 2B element 252, 254, 256, 259, and 260 disclose selecting a driver (logistics provider) to perform the one logistic operation (delivering order), wherein a user is selected, notified of order deliver wherein they can accept or rejected it, and if accepted the order is assigned to the driver for delivery. Also, Spector para. [0015] teaches removing drivers who would have low possibility of being a good and delivering the package and para. [0020] teaches selecting the highest ranked driver who has the best likelihood of delivery, wherein given performance level is delivering the package. As such Spector does disclose all the limitations of claim 1 and the rejection is maintained.
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 PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at 571-270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127