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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/11/2025 has been entered.
Status of Claims
• This action is in reply to the Application Number 17/657,878 filed on 04/04/2022.
• Claims 1-3, 7-13, 17-19 are currently pending and have been examined.
• This action is made Non-FINAL in response to the “Amendment” and “Remarks” filed on 10/14/2025.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The certified copy has been filed in Application No. 17/657,878, filed on 04/04/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/04/2022,04/12/2023, 07/27/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 11 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.
The claims 1, 11 are rejected as being indefinite because the claim limitation reciting “optimizing the autonomous driving parameters by applying a feedback from a human viewing a driving image of a robot from a trained robot agent to which the autonomous driving parameters are set differently”. It is not clear what this claim limitation of “a human viewing a driving image of a robot from a trained robot agent” is referring to. It can be interpreted as a human taking a look at an image showing a first robot and this image is sent from a second robot which is a trained robot agent. Or it can also be interpreted as a human taking a look at an image showing a first robot and the first robot is a trained robot agent. Appropriate correction and/or clarification is required. For the purposes of examination, the Office will interpret this limitation as “a human viewing a driving image of a robot”.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed functions of
optimizing the autonomous driving parameters by applying a feedback from a human viewing a driving image of a robot from a trained robot agent to which the autonomous driving parameters are set differently.
The specification of para [0050-0054], [0071-74] and [0081] does not demonstrate that applicant has made an invention that achieves the amended claimed functions of “a human viewing a driving image of a robot from a trained robot agent” because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claims 2-3, 7-10 and 12-13, 17-19 are rejected because their dependence on the independent claim 1 and 11.
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.
Claims 1, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Levine (US20190232488A1) in view of Sampedro (US10131053B1), and further in view of Huang (US 20200104650 A1) and Profio (US 20180061045 A1).
Regarding Claims 1 and 11:
Levine teaches:
A robot … learning method based on deep reinforcement learning executed by a computer system having at least one processor configured to execute computer-readable instructions included in a memory, the method comprising (Levine, abstract, “Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state”, and para[0031], “implementation may include a system of one or more computers”)
applying a plurality of different … parameters simultaneously to a plurality of robot agents in a simulation…; (Levine, para[0062], “the policy parameters may be “pushed” by the training engine 114 to the robots”, and para[0043], “assumption that simulation time is inexpensive”, para[74], “simultaneous updating of those parameters ”)
acquiring, …, a sensor value relating to a movement of a robot from a robot agent of the plurality of robot agents … (Levine, para[0069], “sensor data from one or more sensors, such as an inertial measurement unit (“IMU”)”, and para [0054], “Robots 180A and 180B are “robot arms” having multiple degrees of freedom to enable, through movement of the robots”)
a driving image of a robot (Levine, para[54], “ image of the robot…showing two different poses of a set of poses struck by the robot 180A and its end effector in traversing along the path ”)
… which the autonomous driving parameters are set differently. (Levine, para[03], “ parameters obtained can be different”)
the neural network having a plurality of neural network parameters;(Levine, para[05], “parameters of the neural network”)
updating the plurality of neural network parameters to train the robot agent;( Levine, para[61], “the training engine 114 may generate updated policy parameters”, and para[62], “the policy parameters may be “pushed” by the training engine 114 to the robots 180A and 180B”)
and after said updating the plurality of neural network parameters, (Levine, para[03],” after a given number of training iterations”)
…a driving image of a robot from a trained robot agent…(Levine, para[69], “sensor data from the robot…vision sensor data captured by a vision sensor of the robot”, and para[62], “the policy parameters may be “pushed” by the training engine 114 to the robots 180A and 180B”)
Levine does not explicitly teach, but Sampedro teaches:
…autonomous driving parameters…(Sampedro,Col.11, lines 39-43, “…parameters of the sensor data that is to be applied as input to the model”, and Col.8, lines 6-8, “mobile robots such as telepresence robots and other robots capable of autonomous or controlled self-propulsion” )
acquiring, in real time, a sensor value relating to a movement of a robot…( Sampedro,Col.21, lines 4-10, “determine obstacles in an environment of a robot…such as implementations where vision sensor data is utilized, the obstacle detection engine 525 may update information related to obstacles in real-time”)
and inputting the sensor value and an autonomous driving parameter of the different autonomous driving parameters assigned to the robot agent to a neural network for learning robot autonomous driving. (Sampedro,Col.11, lines 39-43, “a neural network model may be utilized in determining a pre-grasp pose based on sensor data from a vision sensor, and the robot instructions may dictate the parameters of the sensor data that is to be applied as input to the model”, and Col.8, lines 6-8, “mobile robots such as telepresence robots and other robots capable of autonomous or controlled self-propulsion” )
to enable the plurality of robot agents to perform autonomous driving (Sampedro, Col.8, lines 6-8, “mobile robots such as telepresence robots and other robots capable of autonomous or controlled self-propulsion” )
…. by randomly assigning setting values of the … parameters to the plurality of robot agents by a system or a manager; (Sampedro, Col.18, lines 62-65,” the robot instructions may be randomly and/or pseudo-randomly generated to cause the robot to perform a sequence of random operations”, Col.6, lines 60-62, “instructions that define the input parameters … to control the operation of the testing robots”, and Col.4, lines 24-25, “a plurality of robots”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine with these above teachings from Sampedro in order to include autonomous driving parameters and acquiring, in real time, a sensor value relating to a movement of a robot applied with the different autonomous driving parameters as well as inputting the sensor value and an autonomous driving parameter of the different autonomous driving parameters assigned to the robot agent to a neural network for learning robot autonomous driving, and randomly assigning setting values of the autonomous driving parameters to the plurality of robot agents by a system or a manager. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine’s deep reinforcement learning for robotic manipulation method with Sampedro’s real time robot collision avoidance method as “prevent damage to the robot caused by collisions” (Sampedro, Description)
Levine in view of Sampedro does not explicitly teach, but Huang teaches:
…fits the assigned … parameters without retraining (Huang, para[63], “neural network-based classification system, without having to adjust or retrain the parameters of the neural network or the classifier weights of the neural network”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine in view of Sampedro with these above teachings from Huang in order to include fitting assigned parameter without retraining the robot neural network model. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine in view of Sampedro’s deep reinforcement learning for robotic manipulation method with Huang’s neural network-based classification system as efficiently reducing the time and cost.
Levine in view of Sampedro does not explicitly teach, but Profio teaches:
and optimizing the …parameters by applying a feedback from a human viewing ... image... (Profio, para[002], “the selection of parameters is properly optimizing”, and para [53], “user feedback is provided (e.g., from a human operator viewing a displayed reconstructed image)”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine in view of Sampedro with these above teachings from Profio in order to include wherein the autonomous driving parameters are optimized by applying feedback on a driving image of a robot to which the autonomous driving parameters are set differently. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine in view of Sampedro’s deep reinforcement learning for robotic manipulation method with Profio’s adaptive imaging systems as “improve the performance” (Profio, Description)
Claims 2, 7, 12, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Levine (US20190232488A1) in view of Sampedro (US10131053B1), and further in view of Huang (US 20200104650 A1), Profio (US 20180061045 A1), and Biyik (“Batch Active Preference-Based Learning of Reward Functions”, Conference on robot learning, 2018).
Regarding Claims 2 and 12:
Levine in view of Sampedro, Huang and PROFIO as shown in the rejection above, discloses the limitations of claims 1 and 11. Levine does not explicitly teach, but Sampedro teaches:
… inputting randomly sampled… (Sampedro, Col.18, lines 62-65,” the robot instructions may be randomly and/or pseudo-randomly generated to cause the robot to perform a sequence of random operations”, Col.6, lines 60-62, “instructions that define the input parameters … to control the operation of the testing robots”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine with these above teachings from Sampedro in order to include randomly assigning setting values of the autonomous driving parameters to the plurality of robot agents by a system or a manager. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine’s deep reinforcement learning for robotic manipulation method with Sampedro’s real time robot collision avoidance method as “prevent damage to the robot caused by collisions” (Sampedro, Description)
Levine in view of Sampedro, Huang and PROFIO does not explicitly teach, but Biyik further teaches:
The robot autonomous driving learning method of claim 1, wherein the learning of the robot autonomous driving comprises simultaneously performing reinforcement learning of inputting …sampled autonomous driving parameters to the plurality of robot agents.( Biyik, page 1, “we propose a new algorithm–batch active preference-based learning”, and page 2, “Showcasing our algorithm… in autonomous driving”, and page 2, “We model these preferences over the actions of an agent in a fully observable dynamical system D. Let fD denote the dynamics of the system that includes one or multiple robots”, and page 2, “This model can be learned through inverse reinforcement learning (IRL)”,and page 3 “. We construct this balance by introducing a batch active learning approach, where b queries are simultaneously synthesized at a time.”, and page 3, “a pair of trajectories is parameterized by the initial state x0, a set of actions for all the other agents”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine in view of Sampedro, Huang and PROFIO with these above teachings from Biyik in order to include wherein the learning of the robot autonomous driving comprises simultaneously performing reinforcement learning of inputting sampled autonomous driving parameters to the plurality of robot agents. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine in view of Sampedro, Huang and PROFIO’s deep reinforcement learning for robotic manipulation method with Biyik’s Batch Active Preference-Based Learning of Reward Functions as “enables efficient learning of reward functions using as few data samples as possible” (Biyik)
Regarding Claims 7 and 17:
Levine in view of Sampedro, Huang and PROFIO as shown in the rejection above, discloses the limitations of claims 1 and 11. Levine in view of Sampedro, Huang and PROFIO does not explicitly teach, but Biyik further teaches:
The robot autonomous driving learning method of claim 1, wherein the optimizing of the autonomous driving parameters comprises assessing preference for the autonomous driving parameter through pairwise comparisons of the autonomous driving parameters. (Biyik, Page 5, “Fig. 2 (d) shows the successive pairwise comparisons between two samples based on their corresponding conditional entropy. In every pairwise comparison, we eliminate the sample shown with gray edge, keeping the point with the orange edge. The numbers show the order of comparisons done before finding b= 5 optimally different sample points shown in orange”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine in view of Sampedro, Huang and PROFIO with these above teachings from Biyik in order to include wherein the optimizing of the autonomous driving parameters comprises assessing preference for the autonomous driving parameter through pairwise comparisons of the autonomous driving parameters. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine’s in view of Sampedro, Huang and PROFIO’s deep reinforcement learning for robotic manipulation method with Biyik’s Batch Active Preference-Based Learning of Reward Functions as “enables efficient learning of reward functions using as few data samples as possible” (Biyik)
Claims 3, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Levine (US20190232488A1) in view of Sampedro (US10131053B1), and further in view of Huang (US 20200104650 A1), Profio (US 20180061045 A1) and Aliper (US 20190392304 A1).
Regarding Claims 3 and 13:
Levine in view of Sampedro, Huang and PROFIO as shown in the rejection above, discloses the limitations of claims 1 and 11. Levine in view of Sampedro, Huang and PROFIO does not explicitly teach, but Aliper teaches:
The robot autonomous driving learning method of claim 1, wherein the neural network that includes a fully-connected layer and a gated recurrent unit (GRU).( Aliper, para [0076], “The model or architecture 100, 200 contains five components that are neural networks…Gated Recurrent Unit (GRU) cells for the object encoder”, and para[0139], “followed by 3 fully-connected layers”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine in view of Sampedro, Huang and PROFIO with these above teachings from Aliper in order to include wherein the learning robot autonomous driving comprises simultaneously learning autonomous driving of the plurality of robot agents using a neural network that includes a fully-connected layer and a gated recurrent unit (GRU). At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine in view of Sampedro, Huang and PROFIO’s deep reinforcement learning for robotic manipulation method with Aliper’s mutual information adversarial autoencoder as “improve performance and predictive ability” (Aliper, Description)
Claims 8, 10, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Levine (US20190232488A1) in view of Sampedro (US10131053B1), and further in view of Huang (US 20200104650 A1) , Profio (US 20180061045 A1) and Wierstra (US20210089834A1).
Regarding Claims 8 and 18:
Levine in view of Sampedro, Huang and PROFIO as shown in the rejection above, discloses the limitations of claims 1 and 11. Levine in view of Sampedro, Huang and PROFIO, does not explicitly teach, but Wierstra teaches:
The robot autonomous driving learning method of claim 1, wherein the optimizing of the autonomous driving parameters comprises modeling the preference for the autonomous driving parameters using a Bayesian neural network model. (Wierstra, para [0025], “This specification generally describes a reinforcement learning system implemented as computer programs on one or more computers in one or more locations that selects actions to be performed by a reinforcement learning agent interacting with an environment by using a neural network”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine’s in view of Sampedro, Huang and PROFIO’s deep reinforcement learning for robotic manipulation method with these above teachings from Wierstra in order to include wherein the optimizing of the autonomous driving parameters comprises modeling the preference for the autonomous driving parameters using a Bayesian neural network model. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine’s in view of Sampedro, Huang and PROFIO’s deep reinforcement learning for robotic manipulation method with Wierstra’s imagination-based agent neural networks as “has an advantage that an agent such as a robot , or autonomous or semi - autonomous vehicle can improve its interaction with a simulated or real - world environment” (Wierstra, Description)
Regarding Claim 10:
Levine in view of Sampedro, Huang and PROFIO as shown in the rejection above, discloses the limitations of claim 1. Levine in view of Sampedro, Huang and PROFIO does not explicitly teach, but Wierstra further teaches:
A non-transitory computer-readable recording medium storing a computer program enabling a computer to implement the autonomous driving learning method according to claim 1. (Wierstra, para [0025], “This specification generally describes a reinforcement learning system implemented as computer programs on one or more computers in one or more locations that selects actions to be performed by a reinforcement learning agent interacting with an environment by using a neural network”, and para [0035], “The manager 11 is a discrete policy which maps a history h obtained from the memory 14 to the route data p”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine in view of Sampedro with these above teachings from Wierstra in order to include an autonomous driving learning method executed by a computer system having at least one processor configured to execute computer-readable instructions included in a memory, the method comprising. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine’s in view of Sampedro’s deep reinforcement learning for robotic manipulation method with Wierstra’s imagination-based agent neural networks as “has an advantage that an agent such as a robot , or autonomous or semi - autonomous vehicle can improve its interaction with a simulated or real - world environment” (Wierstra, Description)
Claims 9, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Levine (US20190232488A1) in view of Sampedro (US10131053B1), and further in view of Huang (US 20200104650 A1) , Profio (US 20180061045 A1), Wierstra (US20210089834A1) and Biyik (“Batch Active Preference-Based Learning of Reward Functions”, Conference on robot learning, 2018).
Regarding Claims 9 and 19:
Levine in view of Sampedro, Huang, PROFIO, Wierstra, as shown in the rejection above, discloses the limitations of claims 8 and 18. Levine in view of Sampedro, Huang, PROFIO, Wierstra does not explicitly teach, but Biyik teaches:
The autonomous driving learning method of claim 8, wherein the optimizing of the autonomous driving parameters comprises generating a query for pairwise comparisons of the autonomous driving parameters based on uncertainty of a preference model.( Biyik, Page 5, “Fig. 2 (d) shows the successive pairwise comparisons between two samples based on their corresponding conditional entropy. In every pairwise comparison, we eliminate the sample shown with gray edge, keeping the point with the orange edge. The numbers show the order of comparisons done before finding b= 5 optimally different sample points shown in orange”, and page 5, “In greedy selection and successive elimination methods, the conditional entropy maximizer query (ξ ∗ A, ξ∗ B) out of K possible queries will always remain in the resulting batch of size b”, and page3, “As in uncertainty sampling, a similar interpretation of equation (2) is to find a set of feasible queries that maximize the conditional entropy”)
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine in view of Sampedro, Huang, PROFIO, Wierstra with these above teachings from Biyik in order to include wherein the optimizing of the autonomous driving parameters comprises generating a query for pairwise comparisons of the autonomous driving parameters based on uncertainty of a preference model. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine’s in view of Sampedro, Huang, PROFIO, Wierstra’s deep reinforcement learning for robotic manipulation method with Biyik’s Batch Active Preference-Based Learning of Reward Functions as “enables efficient learning of reward functions using as few data samples as possible” (Biyik)
RESPONSE TO ARGUMENTS
Applicant's arguments filed on 10/14/2025 have been fully considered but they are not persuasive.
103 rejection. Applicant argues the cited references do not disclose or suggest the amended features of “ the neural network having a plurality of neural network parameters; updating the plurality of neural network parameters to train the robot agent; and after said updating the plurality of neural network parameters, optimizing the autonomous driving parameters by applying a feedback from a human viewing a driving image of a robot from a trained robot agent to which the autonomous driving parameters are set differently” in claims 1 and 11.
In response of A). Examiner respectively disagrees. Levine teaches claim limitation of “the neural network having a plurality of neural network parameters” in para [05], “parameters of the neural network”. Levine also teaches claim limitation of “updating the plurality of neural network parameters to train the robot agent” in para [61] “the training engine 114 may generate updated policy parameters”, and para[62], “the policy parameters may be “pushed” by the training engine 114 to the robots 180A and 180B”. Furthermore, Levine teaches claim limitation of “and after said updating the plurality of neural network parameters” in para [03], ” after a given number of training iterations”. Moreover, Levine teaches claim limitation of “a driving image of a robot from a trained robot agent” in para[69], “sensor data from the robot…vision sensor data captured by a vision sensor of the robot”. Sampedro teaches the claim limitation of “autonomous driving parameters” in Col.11, lines 39-43, “…parameters of the sensor data that is to be applied as input to the model”. Profio teaches the claim limitation of “ optimizing the parameters by applying a feedback from a human viewing image” in para[002], “the selection of parameters is properly optimizing”, and para [53], “user feedback is provided (e.g., from a human operator viewing a displayed reconstructed image)”. It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Levine in view of Sampedro with these above teachings from Profio in order to include wherein the autonomous driving parameters are optimized by applying feedback on a driving image of a robot from a trained robot agent to which the autonomous driving parameters are set differently. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Levine in view of Sampedro’s deep reinforcement learning for robotic manipulation method with Profio’s adaptive imaging systems as “improve the performance” (Profio, Description). Therefore, Levine in view of Sampedro and Profio teach each and every limitation of “the neural network having a plurality of neural network parameters; updating the plurality of neural network parameters to train the robot agent; and after said updating the plurality of neural network parameters, optimizing the autonomous driving parameters by applying a feedback from a human viewing a driving image of a robot from a trained robot agent to which the autonomous driving parameters are set differently” recited in claims 1 and 11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAI NMN WANG whose telephone number is (571)270-5633. The examiner can normally be reached Mon-Fri 0800-1700.
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/KAI NMN WANG/ Examiner, Art Unit 3667
/REDHWAN K MAWARI/ Primary Examiner, Art Unit 3667