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 .
This action is in response to the Application filed on 07/27/2026. Claims 1 and 4-24 are pending in the case.
Applicant Response
In Applicant’s response dated 07/27/2026, Applicant amended Claims 1, 4-11, and 18-22, added new claims 23-24 and argued against all objections and rejections previously set forth in the Office Action dated 03/30/2026.
Examiner Comments
4. 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 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.
Claim Rejections - 35 USC § 103
5. 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.
6. Claims 1 and 4-24 are rejected under 35 U.S.C. 103 as being unpatentable over Munawar (US 20190385091 A1, 2019-12-19) in view of Agarwal ( NPL: Title: Making Contextual Decisions with Low Technical Debt ; Date: 9 May 2017, arXiv:1606.03966v2 [cs.LG]) in further view of TERRA (Pub. No.: US 20240119300 A1, Pub. Date: 2024-04-11)
Regarding independent Claim 1,
Munawar teaches computer implemented method comprising:
obtaining an event log of events representing reactions of an environment to first actions taken by an agent (see Munawar: Fig.3, [0004], “obtaining, from an environment, a given experience that includes an action, a state and a reward (i.e. event log of events.) The method further includes storing the given experience in an experience buffer responsive to a value of the reward included in the given experience exceeding a first threshold experience into an event buffer (i.e. event log).”), the agent having selected the first actions based at least on context associated with the events and a first reward function employed by the agent (see Munawar: Fig.3, [0041], “Every experience {q, a, r} is inserted into the experience buffer 381, except the experiences where the agent dies (e.g., falling in the hole). If a similar event (same q) exist in the event buffer 382 with different action or reward, then this experience is also inserted into the event buffer 382. “extract a local state q from the environment 385 to obtain {q, s, r}.”).
Munawar does not teach the computer implemented method wherein:
the agent assigned first action probabilities to the first actions based at least on the context and the first reward function;
processing the events in the event log to determine different second action probabilities that the agent would have assigned to the first actions by evaluating the context with different alternative reward functions;
based at least on the first action probabilities and the different second action probabilities, predicting performance of the different alternative reward functions for an evaluation metric;
based at least on the predicted performance of the different alternative reward functions for the evaluation metric, selecting a second reward function from the different alternative reward functions;
configuring the agent according to the second reward function; and
deploying the agent configured with the second reward function for execution in the environment, wherein the second reward function causes the agent to adapt internal parameters of the agent according to the second reward function and take second actions in the environment based at least on the second reward function.
However, Agarwal teach the system wherein:
the agent assigned first action probabilities to the first actions based at least on the context and the first reward function (see Agarwal: Pg.4, Sec.3, Left Col., Para.2, “Each interaction is logged as a tuple (x,a,r,p), where p is the exploration policy’s probability of choosing a given x. These datapoints are called exploration data. Recording p enables unbiased policy learning, which addresses (F1).”)
processing the events in the event log to determine different second action probabilities that the agent would have assigned to the first actions by evaluating the context with different alternative reward functions (see Agarwal: Pg.4, Sec.3, Left Col., Para.2, “olicy learning. Given N exploration datapoints, we can evaluate any policy π (i.e., estimate its average reward) regardless of how the data was collected. The simplest approach is to use inverse propensity scoring (ips): ips(π) = 1N N t=1 1{π(xt) = at} rt/pt, (1), where the indicator is 1 when π’s action matches the exploration data and 0 otherwise. This estimator has three important properties.”)
based at least on the first action probabilities and the different second action probabilities, predicting performance of the different alternative reward functions for an evaluation metric (see Agarwal: Pg.4, Sec.3, Left Col., Para.2, “This estimator has three important properties. First, it is data-efficient: each interaction can be used to evaluate any π that has a matching action, regardless of the policy collecting the data. In contrast, A/B testing only uses data collected using π to evaluate π. Second, the importance weighting by pt makes it statistically unbiased: it converges to the true reward as N → ∞. Third, the estimator can be computed incrementally as new data arrives.”)
based at least on the predicted performance of the different alternative reward functions for the evaluation metric, selecting a second reward function from the different alternative reward functions (see Agarwal: Pg.4, Sec.3, Right Col., Para.2, “Policy evaluation allows us to search a policy class Π to find the best policy, with accuracy similar to the above bounds (replace K with |Π|). This is called policy training. Typically Π is defined by a tunable template, such as linear vectors, decision trees, or neural nets. Note that we need not test every policy in Π; instead, we can use a reduction to cost-sensitive classification [19], for which many practical algorithms exist.”)
Because both Munawar and Agarwal are in the same/similar field of endeavor of inverse reinforcement learning using agents, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to modify teaching of Munawar to include the system that determine respective probabilities being determined based at least on different alternative reward functions of the different alternative agent configurations as taught by Agarwal. One would be motivated to make such a combination in order to allows testing and optimization over exponentially more policies for the same amount of data and policies being evaluated need not be deployed or even known ahead of time, saving vast business and engineering effort. (see Agarwal: Pg.2, Sec.1, Right Col., Para.3)
Munawar and Agarwal does not teach the system wherein:
configuring the agent according to the second reward function; and
deploying the agent configured with the second reward function for execution in the environment,
wherein the second reward function causes the agent to adapt internal parameters of the agent according to the second reward function and take second actions in the environment based at least on the second reward function.
However, TERRA teach the system wherein:
configuring the agent according to the second reward function (see TERRA: Fig.2, [0034], “configuring a reinforcement learning agent to perform an efficient reinforcement learning procedure, wherein the reinforcement learning agent comprises a model trained using a machine learning process (i.e. selected agent configuration) to determine actions to be performed by the reinforcement learning agent.”); and
deploying the agent configured with the second reward function for execution in the environment (see TERRA: Fig.4, [0095], “the RL agent performs step 204 described above and uses the NN to determine an action to perform in the communications network, based on the values of the sensor data (i.e. the set of features). The action is selected based on the policy that is applied to it and the RL agent performs the determined action. The action affects the environment.”)
wherein the second reward function causes the agent to adapt internal parameters of the agent according to the second reward function (see TERRA: Fig.2, [0035], “The reinforcement learning agent receives a reward based on whether the action changes the system in compliance with the objective (e.g. towards the preferred state), or against the objective (e.g. further away from the preferred state). The reinforcement learning agent therefore adjusts parameters in the system with the goal of maximizing the rewards received.”) and take second actions in the environment based at least on the second reward function (see TERRA: Fig.4, 0095], “, the RL agent performs step 204 described above and uses the NN to determine an action to perform in the communications network, based on the values of the sensor data (i.e. the set of features). The action is selected based on the policy that is applied to it and the RL agent performs the determined action. The action affects the environment.”)
Because Munawar, Agarwal and TERRA are in the same/similar field of endeavor of reinforcement learning using agents, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to modify teaching of Munawar to include the system that configure the agent according to the selected agent configuration and adapt internal parameters of the agent according to the selected reward function and deploy the agent configured with the second reward function for execution in the environment as taught by TERRA. After modification of Munawar, the reinforcement learning exploration that exploit past experiences for critical events can incorporate the configuration configuring a reinforcement learning agent to perform an efficient reinforcement learning procedure teaching of as taught by TERRA. One would be motivated to make such a combination in order to improve adaptability and scalability of RL model training. (see TERRA [0003].
Regarding Claim 4,
Munawar, Agarwal and TERRA teach all the limitations of Claim 1. Munawar further teaches the system wherein:
predicting the performance of the different alternative agent configurations (see Munawar: Fig.3, [0047], “] At block 325, insert the similar events {q, a, r} where the agent has died into the event buffer 382. In this way, the system can use the information in a future scenario to help the agent or another agent survive the similar events.” i.e. predicting performance),: determining, from the event log, predicted aggregate values of the evaluation metric for the plurality of alternative reward functions (see Munawar: Fig.3, [0045], “or a given experience (denoted by {local state, action, reward}, that is, {q, a, r}), determine if the agent has died. If so, then proceed to block 320. Otherwise, proceed to block 330. The condition of the agent dying can be considered a first threshold on the reward for the given experience, where if the agent has died, then the first threshold is considered to be exceeded.”).
Regarding Claim 5,
Munawar, Agarwal and TERRA teach all the limitations of Claim 4. Munawar further teach the method wherein:
determining the predicted aggregate values of the evaluation metric (see Munawar: Fig.1, [0032], “The exploitation is performed in a usual manner. During exploration, if the current state is found in the event buffer, then the actions in the event buffer are sampled with a fixed probability (ρ). However, after a given number of steps (β), the use of the event buffer stops but the random exploration continues. Therefore, during training, the next action is determined either by using the learned model (exploitation), or random exploration, or by sampling the actions for similar events in the event buffer.”), comprises: for each particular first event in the event log:
determining a value of the particular event for the evaluation metric, the value being determined based on a particular reaction of the environment to a particular action taken in a particular context by the agent (see Munawar: Fig.3, [0045], “or a given experience (denoted by {local state, action, reward}, that is, {q, a, r}), determine if the agent has died. If so, then proceed to block 320. Otherwise, proceed to block 330. The condition of the agent dying can be considered a first threshold on the reward for the given experience, where if the agent has died, then the first threshold is considered to be exceeded.”)
weighting the value of the particular event to obtain weighted values of the evaluation metric for the different alternative reward functions, the weighting being based on corresponding second action probabilities that the agent would have given to the particular first action with the different alternative reward functions relative to a particular first action probability that the agent actually gave to the particular first action according to the first reward function (see Munawar: Fig.3, [0042], “a probability of the sampling event buffer during exploration, (β) denotes when to stop using the event buffer (in this case, it is the maximum number of steps, ϵ.sub.min denotes a minimum probability of exploration, ϵ.sub.max denotes a maximum probability of exploration, N denotes a size of the experience buffer, and M denotes a size of the event buffer.”); and
aggregating the weighted values of each particular event for each of the different alternative reward functions, the aggregating resulting in the predicted aggregate values of the evaluation metric (see TERRA: Fig.4, [0100], “The rewards modelling module performs step 206 whereby the feature importance information is used adjust the reward given to the RL agent for the action. In this embodiment a scaling is calculated based on whether the feature is a correct feature with which to have determined the action and the normal reward modelling is then multiplied by the scaling value.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to modify teaching of Munawar to include the system that aggregating the weighted values of each particular event for each alternative agent configuration to obtain the predicted aggregate values of the evaluation metric as taught by TERRA. One would be motivated to make such a combination in order to improve increase model optimization by automating reinforcement learning optimization with less human intervention.
Regarding Claim 6,
Munawar, Agarwal and TERRA all the limitations of Claim 5. Munawar further teaches the system wherein:
determining the value of the particular event based on a function for the evaluation metric (see Munawar: Fig.3, [0042], “provide next action {a} to the environment 385. Thus, during training, the next action {a} is determined by using (i) the learned model (exploitation), or (ii) random exploration, or (iii) by sampling the actions for similar events in the event buffer 382.”)
Regarding Claim 7,
Munawar, Agarwal and TERRA all the limitations of Claim 6. Munawar further teaches the system wherein:
the function maps the context to the values of the evaluation metric (see Munawar: Fig.4, [0059], “during inference, store all experiences and their respective frequencies of occurrence (with reward).”)
Regarding Claim 8,
Munawar, Agarwal and TERRA all the limitations of Claim 4. Munawar further teaches the system wherein:
populating a data structure with predicted aggregate values of a plurality of evaluation metrics for the different alternative reward function (see Munawar: Fig.10, [0106], “workloads and functions which may be provided from this layer include: mapping and navigation 1091; software development and lifecycle management 1092; virtual classroom education delivery 1093; data analytics processing 1094; transaction processing 1095; and improved reinforcement learning exploration by exploiting past experiences for critical events 1096.”
Regarding Claim 9,
Munawar, Agarwal and TERRA all the limitations of Claim 8. Munawar further teaches the system wherein:
the data structure comprises a table with rows representing the different alternative agent configurations associate with the different alternative reward functions and columns representing different evaluation metrics (see Munawar: Fig.10, [0103], “Hardware and software layer 1060 includes hardware and software components. Examples of hardware components include: mainframes 1061; RISC (Reduced Instruction Set Computer) architecture-based servers 1062; servers 1063; blade servers 1064; storage devices 1065; and networks and networking components 1066. In some embodiments, software components include network application server software 1067 and database software 1068.”) Examiner notes that tabular format with rows and columns is a standard data structure used to store data.
Regarding Claim 10,
Munawar, Agarwal and TERRA all the limitations of Claim 1. Munawar further teaches the system wherein:
outputting a graphical representation of the data structure (see Munawar: Fig.4, [0061]. generate and display a visualization by plotting one or more experiences using the respective rewards corresponding to the experiences as weights for visualization.”); and
identifying second reward function based at least on user input directed to the graphical representation of the data structure (see Munawar: Fig.11, [0105]. “Metering and Pricing 1082 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 1083 provides access to the cloud computing environment for consumers and system administrators.
Regarding independent Claim 11,
Claim 11 is a system claim and has similar/same claim limitation as Claim 1 and is rejected under the same rationale.
Regarding Claim 12,
Munawar, Agarwal and TERRA all the limitations of Claim 11. Munawar further teaches the system wherein:
adapt the internal parameters of the agent by using the selected reward function to evaluate reactions of the environment to the further actions taken by the agent based on context describing the environment (see Munawar: Fig.3, [0053], “sample the replay buffer 383 and update model parameters for the model 384.”)
Regarding Claim 13,
Munawar, Agarwal and TERRA all the limitations of Claim 12. Munawar further teaches the system wherein:
the agent comprises a linear model that determines a probability density function of expected rewards for different actions based on the selected reward function (see Munawar: Fig.1, [0031], “The exploitation is performed in a usual manner. During exploration, if the current state is found in the event buffer, then the actions in the event buffer are sampled with a fixed probability (ρ).”)
Regarding Claim 14,
Munawar, Agarwal and TERRA all the limitations of Claim 13. Munawar further teaches the system wherein:
the agent randomly samples from the probability density function and, in at least some instances, chooses an action that does not have the highest expected reward (see Munawar: Fig.1, [0031], “(ρ) denotes a probability of the sampling event buffer during exploration, (β) denotes when to stop using the event buffer (in this case, it is the maximum number of steps, ϵ.sub.min denotes a minimum probability of exploration, ϵ.sub.max denotes a maximum probability of exploration, N denotes a size of 312
the experience buffer, and M denotes a size of the event buffer.”
Regarding Claim 15,
Munawar, Agarwal and TERRA all the limitations of Claim 14. However, Munawar further teaches the system wherein:
the agent comprising a contextual bandit (see Munawar: Fig.3, [0038], “an exploration stage 392, and a learning loop 393. The populating buffers stage 391 involves blocks 305 through 345 described below. The exploration stage 392 involves blocks 340 through 350 described below. The learning loop 393 involves block 355 described below.”)
Regarding Claim 16,
Munawar, Agarwal and TERRA all the limitations of Claim 12. Munawar further teaches the system wherein:
the actions comprise recommending electronic items, the reactions indicate whether users selected the recommended electronic items, and the context comprises information about the users (see Munawar: Fig.3, [0082], “present invention can be applied to robotic applications (e.g., warehouse robots, manipulators, etc.), dialog applications (e.g., customer service, etc.), plant or data center optimization, gaming applications, and so forth.”)
Regarding Claim 17,
Munawar, Agarwal and TERRA all the limitations of Claim 12. Munawar further teaches the system wherein:
changing a size of a playout buffer that stores voice over internet protocol packets for video calls (see Munawar: Fig.1, [0031], “(ρ) denotes a probability of the sampling event buffer during exploration, (β) denotes when to stop using the event buffer (in this case, it is the maximum number of steps, ϵ.sub.min denotes a minimum probability of exploration, ϵ.sub.max denotes a maximum probability of exploration, N denotes a size of the experience buffer, and M denotes a size of the event buffer.”)
Regarding Claim 18,
Munawar, Agarwal and TERRA all the limitations of Claim 17. TERRA further teaches the system wherein:
the reactions indicate whether a playout buffer became empty during the video calls and the context indicates network jitter during the video calls (see Munawar: Fig.1, [0031], “(ρ) denotes a probability of the sampling event buffer during exploration, (β) denotes when to stop using the event buffer (in this case, it is the maximum number of steps, ϵ.sub.min denotes a minimum probability of exploration, ϵ.sub.max denotes a maximum probability of exploration, N denotes a size of the experience buffer, and M denotes a size of the event buffer.”)
Regarding Claim independent 19,
Claim 19 is a computer-readable storage medium and has similar/same claim limitation as Claim 1 and is rejected under the same rationale.
Regarding Claim 20,
Munawar, Agarwal and TERRA all the limitations of Claim 19. Munawar further teaches the system wherein:
receiving, from the application, a reaction of the environment to the selected action (see Munawar: Fig.3, [0037], “The method/system 300 involves an experience buffer 381, an event buffer 382, a replay buffer 383, a model 384, and a target environment (hereinafter “environment” in short) 385. The replay buffer 383 is a commonly used construct in reinforcement learning algorithms. The replay buffer 383 stores <state, action, next state, reward and episode end flag> for all the actions.”)
determining a reward value for the selected action based at least on the reaction and the selected reward function (see Munawar: Fig.3, [0050], “determine if exploration is to be performed. If so, then proceed to block 345. Otherwise, proceed to block 350. and
updating internal parameters of the agent based at least on the reward value (see Munawar: Fig.3, [0056], “sample the replay buffer 383 and update model parameters for the model 384.”)
Regarding Claim 21,
Munawar, Faust and TERRA all the limitations of Claim 19. Munawar further teaches the system wherein:
replaying the events in the event log using the different alternative reward functions to determine the different second action probabilities that the agent would have assigned to the first actions taken by the agent using the different alternative reward functions (see Munawar: Fig.3, [0037], “The replay buffer 383 is a commonly used construct in reinforcement learning algorithms. The replay buffer 383 stores <state, action, next state, reward and episode end flag> for all the actions.”)
weighting the events in the event log based at least on ratios between the different second action probabilities and the first action probabilities (see Munawar: Fig.4 [0061], “generate and display a visualization by plotting one or more experiences using the respective rewards corresponding to the experiences as weights for visualization.”); and
predicting the performance of the different alternative reward functions based at least on the weighting (see Munawar: Fig.3, [0055], “provide next action {a} to the environment 385. Thus, during training, the next action {a} is determined by using (i) the learned model (exploitation), or (ii) random exploration, or (iii) by sampling the actions for similar events in the event buffer 382.”)
Regarding Claim 22,
Munawar, Faust and TERRA all the limitations of Claim 21. Munawar further teaches the system wherein:
the different second action probabilities are determined and the second reward function is selected prior to executing the agent with the second reward function (see Munawar: Fig.3, [0032], “The exploitation is performed in a usual manner. During exploration, if the current state is found in the event buffer, then the actions in the event buffer are sampled with a fixed probability (ρ). However, after a given number of steps (β), the use of the event buffer stops but the random exploration continues. Therefore, during training, the next action is determined either by using the learned model (exploitation), or random exploration, or by sampling the actions for similar events in the event buffer.”)
Regarding Claim 23,
Munawar, Faust and TERRA all the limitations of Claim 1. Munawar further teaches the system wherein:
the second reward function is selected based entirely on the first actions taken by the agent when configured with the first reward function and without executing the agent when configured with the second reward function (see Agarwal: Pg.4, Sec.3, Left Col., Para.2, “olicy learning. Given N exploration datapoints, we can evaluate any policy π (i.e., estimate its average reward) regardless of how the data was collected. The simplest approach is to use inverse propensity scoring (ips): ips(π) = 1N N t=1 1{π(xt) = at} rt/pt, (1), where the indicator is 1 when π’s action matches the exploration data and 0 otherwise. This estimator has three important properties.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to modify teaching of Munawar to include the system that select second reward function without executing the agent when configured with the second reward function as taught by Agarwal. One would be motivated to make such a combination in order to allows testing and optimization over exponentially more policies for the same amount of data and policies being evaluated need not be deployed or even known ahead of time, saving vast business and engineering effort. (see Agarwal: Pg.2, Sec.1, Right Col., Para.3)
Regarding Claim 24,
Munawar, Faust and TERRA all the limitations of Claim 1. Munawar further teaches the system wherein:
the different second action probabilities for the different alternative reward functions are computed from the event log (see Agarwal: Pg.6, Sec.4.2, Left Col., Para.7, “The Store implements the Deploy abstraction to provide model and data storage. This data is also used by the Offline Learner for offline experimentation, such as tuning hyper parameters, evaluating other learning algorithms or policy classes, changing the reward metric, etc.”), without the agent executing in the environment when configured with the different alternative reward functions (see Agarwal: Pg.4, Sec.3, Left Col., Para.3, “Thus, using a fixed exploration dataset, we can compute accurate counterfactual estimates of how arbitrary policies would have performed without actually running them, in real-time. We use this in our system design to enable advanced monitoring and safeguards addressing (F4). In contrast, A/B testing would have to run a live experiment to test each policy.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to modify teaching of Munawar to include the system that compute different second action probabilities for the different alternative reward functions without the agent executing in the environment when configured with the different alternative reward functions as taught by Agarwal. One would be motivated to make such a combination in order to allows testing and optimization over exponentially more policies for the same amount of data and policies being evaluated need not be deployed or even known ahead of time, saving vast business and engineering effort. (see Agarwal: Pg.2, Sec.1, Right Col., Para.3)
Response to Arguments
Claim Rejections - 35 U.S.C. § 101,
Regarding the 35 U.S.C. 101 rejection for being directed non-statutory subject matter has been updated based on applicant amendments and. Therefore, the 35 U.S.C. 101 rejection has been withdrawn.
Claim Rejections - 35 U.S.C. § 103,
Applicant’s arguments with respect to claim amendments have been considered but are moot considering the new combination of references being used in the current rejection. The new combination of references was necessitated by Applicant’s claim amendments. Therefore, the claims are rejected under the new combination of references as indicated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 11153375 B2
Friedrich; Michael
Title: Using Reinforcement Learning To Scale Queue-based Services
Description: This disclosure relates to queue-based computing services. More specifically, but not by way of limitation, this disclosure involves using reinforcement learning to adjust a capacity of a queue-based computing service in real-time
US 20180260700 A1
NAGARAJA; NAGENDRA
Title: METHOD AND SYSTEM FOR IMPLEMENTING REINFORCEMENT LEARNING AGENT USING REINFORCEMENT LEARNING PROCESSOR
Description: The embodiments herein relates to the field of reinforcement learning. Particularly, the embodiments herein relates to a processor specifically configured for implementing reinforcement learning operations, and an application-domain specific instruction set (ASI) comprising instructions corresponding to reinforcement learning operations
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
