12096246DETAILED 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 office action is in reply communication filed on 07/17/2024.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement/s (IDS/s) submitted on 07/08/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement/s is/are being considered by the examiner.
Double Patenting
Non-Statutory
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Note that the applicant filing of the continuing application is voluntary and not the direct, unmodified result of restriction requirement under 35 U.S.C. 121 (i.e. without a restriction requirement by the examiner) and the claims of the second application are drawn to the “same invention” as patent.
It has been held that the omission an element and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 136 USPQ 184 (CCPA). Also note Ex parte Rainu, 168 USPQ 375 (Bd.App.1969); omission of a reference element whose function is not needed would be obvious to one skilled in the art. Moreover, the doctrine of double patenting seeks to prevent the unjustified extension of patent exclusivity beyond the term of a patent.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of Wang et al. (US Patent No 12,096,246) (referred as Wang’s 246).
Note that the applicant filing of the continuing application is voluntary and not the direct, unmodified result of restriction requirement under 35 U.S.C. 121 (i.e. without a restriction requirement by the examiner) and the claims of the second application are drawn to the “same invention” as the first application or patent. Moreover, although the conflicting claims are not identical, they are not patentably distinct from each other because claims of the instant application are the same scope of the claims of Wang et al. (US Patent No 12,096,246) by adding the well-known elements and functions as set forth below.
Regarding claim 1, Wang’s 246 discloses a method for a network-optimization controller, the method comprising the network-optimization controller:
determining a performance metric to optimize for a cellular network (see Claim 1, lines 1-4);
determining at least one network-configuration parameter that affects the performance metric (see Claim 1, lines 5-6);
sending a gradient-request message to multiple base stations that directs multiple wireless transceivers to respectively evaluate gradients of the performance metric relative to the at least one network-configuration parameter (see Claim 1, lines 7-11);
receiving, from the multiple base stations, gradient-report messages generated by the multiple wireless transceivers, the gradient-report messages respectively including the gradients (see Claim 1, lines 19-22);
analyzing the gradients using machine learning to determine at least one optimized network-configuration parameter (see Claim 1, lines 26-28); and
sending an optimization message to at least one of the multiple base stations that directs at least one of the multiple wireless transceivers to use the at least one optimized network-configuration parameter (see Claim 1, lines 32-35).
Regarding claim 2, Wang’s 246 discloses the method of claim 1, wherein: the multiple wireless transceivers comprise the multiple base stations, and wherein: the at least one network-configuration parameter comprises at least one of the following: a downlink transmit power configuration; an antenna array configuration; a phase-code interval; a time-multiplexed pilot pattern; a data tone power; a data-to-pilot power ratio; a downlink time-slot allocation percentage; a subframe configuration; a handover configuration; or a multi-user scheduling configuration (see Claim 2).
Regarding claim 3, Wang’s 246 discloses the method of claim 2, wherein: the performance metric comprises at least one of the following: spectrum efficiency; network capacity; cell-edge capacity; packet latency; total network interference; signal-to-interference-plus-noise ratio; received signal strength indication; reference signal received power; reference signal received quality; bit-error rate; packet-error rate; jitter; transmit-power headroom; or transmit power (see Claim 3).
Regarding claim 4, Wang’s 246 discloses the method of claim 1, wherein: the multiple wireless transceivers comprise multiple user equipments that are in communication with the multiple base stations; the sending of the gradient-request message to the multiple base stations directs the multiple base stations to pass the gradient-request message to the multiple user equipments; and the sending of the optimization message to the at least one of the multiple base stations directs the at least one of the multiple base stations to pass the optimization message to the at least one of the multiple user equipments (see Claim 4).
Regarding claim 5, Wang’s 246 discloses the method of claim 4, wherein: the sending of the gradient-request message to the multiple base stations directs the multiple base stations to: individually forward the gradient-request message to the multiple user equipments; broadcast the gradient-request message to the multiple user equipments; or multicast the gradient-request message to the multiple user equipments (see Claim 5).
Regarding claim 6, Wang’s 246 discloses the method of claim 4, wherein: the at least one network-configuration parameter comprises at least one of the following: an uplink transmit power configuration; a time-multiplexed pilot pattern; a data tone power; an uplink time-slot allocation percentage; a subframe configuration; a multi-user scheduling configuration; or a random-access configuration (see Claim 2).
Regarding claim 7, Wang’s 246 discloses the method of claim 4, wherein: the performance metric comprises at least one of the following: spectrum efficiency; network capacity; packet latency; signal-to-interference-plus-noise ratio; received signal strength indication; reference signal received power; reference signal received quality; bit-error rate; packet-error rate; jitter; transmit-power headroom; or transmit power (see Claim 3).
Regarding claim 8, Wang’s 246 discloses the method of claim 1, wherein: the multiple wireless transceivers comprise at least one first base station of the multiple base stations and at least one user equipment that is attached to at least one second base station of the multiple base stations; the sending of the gradient-request message to the multiple base stations directs the at least one second base station to pass the gradient-request message to the at least one user equipment; and the sending of the optimization message to the at least one of the multiple base stations directs the at least one second base station to pass the optimization message to the at least one user equipment (see Claim 4).
Regarding claim 9, Wang’s 246 discloses the method of claim 8, wherein: the determining of the at least one network-configuration parameter comprises: determining a first network-configuration parameter that affects the performance metric, the first network-configuration parameter associated with the at least one user equipment; and determining a second network-configuration parameter that affects the performance metric, the second network-configuration parameter associated with the at least one first base station; the sending of the gradient-request message comprises: sending, to the at least one second base station, a first gradient-request message that directs the at least one second base station to pass the first gradient-request message to the at least one user equipment and directs the at least one user equipment to evaluate a first gradient of the performance metric relative to the first network-configuration parameter and generate a first gradient-report message of the gradient-report messages; and sending a second gradient-request message to the at least one first base station that directs the at least one first base station to evaluate a second gradient of the performance metric relative to the second network-configuration parameter and generate a second gradient-report message of the gradient-report messages; the analyzing of the gradients comprises analyzing the first gradient and the second gradient together using machine learning to determine a first optimized network-configuration parameter associated with the at least one user equipment and a second optimized network-configuration parameter associated with the at least one first base station; and the sending of the optimization message comprises: sending a first optimization message to the at least one second base station that directs the at least one second base station to pass the first optimization message to the at least one user equipment and directs the at least one user equipment to use the first optimized network-configuration parameter; and sending a second optimization message to the at least one first base station that directs the at least one first base station to use the second optimized network-configuration parameter (see Claim 6).
Regarding claim 10, Wang’s 246 discloses the method of claim 1, wherein: the at least one optimized network-configuration parameter comprises multiple optimized network-configuration parameters respectively associated with the multiple wireless transceivers (see Claim 8).
Regarding claim 11, Wang’s 246 discloses the method of claim 1, wherein: the at least one network-configuration parameters specifies a delta change to current network-configuration parameters that are used by the multiple wireless transceivers prior to the sending of the gradient-request message (see Claim 9).
Regarding claim 12, Wang’s 246 discloses the method of claim 1, wherein: the gradients comprise a first amount of change in the performance metric relative to a second amount of change in the at least one network-configuration parameter (see Claim 2).
Regarding claim 13, Wang’s 246 discloses the method of claim 1, wherein: the analyzing of the gradients using machine learning comprises: employing gradient descent to determine the at least one optimized network-configuration parameter that minimizes a cost function; or employing gradient ascent to determine the at least one optimized network-configuration parameter that maximizes a utility function (see Claim 10).
Regarding claim 14, Wang’s 246 discloses the method of claim 13, wherein: the at least one optimized network-configuration parameter is associated with a local optima of the cost function or the utility function (see Claim 11).
Regarding claim 15, Wang’s 246 discloses the method of claim 14, further comprising: determining at least one second network-configuration parameter that differs from the at least one optimized network-configuration parameter; sending a second gradient-request message to the multiple base stations that directs the multiple wireless transceivers to respectively evaluate second gradients of the performance metric relative to the at least one second network-configuration parameter; receiving, from the multiple base stations, second gradient-report messages generated by the multiple wireless transceivers, the second gradient-report messages respectively including the second gradients; analyzing the second gradients using machine learning to determine at least one second optimized network-configuration parameter; and sending a second optimization message to the at least one of the multiple base stations that directs the at least one of the multiple wireless transceivers to use the at least one second optimized network-configuration parameter (see Claim 12).
Regarding claim 16, Wang’s 246 discloses the method of claim 1, further comprising: storing network topology data of the cellular network; and determining the at least one optimized network-configuration parameter based on the network topology data (see Claim 14).
Regarding claim 17, Wang’s 246 discloses a network-optimization controller comprising: a processor and memory system configured to: determine a performance metric to optimize for a cellular network; determine at least one network-configuration parameter that affects the performance metric; send a gradient-request message to multiple base stations that directs multiple wireless transceivers to respectively evaluate gradients of the performance metric relative to the at least one network-configuration parameter; receive, from the multiple base stations, gradient-report messages generated by the multiple wireless transceivers, the gradient-report messages respectively including the gradients; analyze the gradients using machine learning to determine at least one optimized network-configuration parameter; and send an optimization message to at least one of the multiple base stations that directs at least one of the multiple wireless transceivers to use the at least one optimized network-configuration parameter (see Claim 15).
Regarding claim 18, Wang’s 246 discloses the network-optimization controller of claim 17, wherein: the multiple wireless transceivers comprise multiple user equipments that are in communication with the multiple base stations; wherein to send the gradient-request message to the multiple base stations directs the multiple base stations to pass the gradient-request message to the multiple user equipments; and wherein to send the optimization message to the at least one of the multiple base stations directs the at least one of the multiple base stations to pass the optimization message to the at least one of the multiple user equipments (see Claim 16).
Regarding claim 19, Wang’s 246 discloses the network-optimization controller of claim 17, wherein: to send the gradient-request message to the multiple base stations directs the multiple base stations to: individually forward the gradient-request message to the multiple user equipments; broadcast the gradient-request message to the multiple user equipments; or multicast the gradient-request message to the multiple user equipments (see Claim 4).
Regarding claim 20, Wang’s 246 discloses a non-transitory computer-readable medium storing computer executable code, the code when executed by a processor causes the processor to: determine a performance metric to optimize for a cellular network; determine at least one network-configuration parameter that affects the performance metric; send a gradient-request message to multiple base stations that directs multiple wireless transceivers to respectively evaluate gradients of the performance metric relative to the at least one network-configuration parameter; receive, from the multiple base stations, gradient-report messages generated by the multiple wireless transceivers, the gradient-report messages respectively including the gradients; analyze the gradients using machine learning to determine at least one optimized network-configuration parameter; and send an optimization message to at least one of the multiple base stations that directs at least one of the multiple wireless transceivers to use the at least one optimized network-configuration parameter (see Claim 1).
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 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 of this title, 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 1-8, 10-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Guey et al. (US 20130109421) in view of OKTAY et al. (US 2019/0141580).
Regarding claim 1, Guey discloses a method for a network-optimization controller [see Fig. 2, 3, ¶ 23; a distributed parameter update procedure 50 of a network node], the method comprising the network-optimization controller:
determining a performance metric to optimize for a cellular network [see ¶¶ 8, 28; computing a new parameter value that minimizes a summed cost function];
determining at least one network-configuration parameter that affects the performance metric [see ¶¶ 8, 28; when a parameter value is changed, a search is conducted of a parameter space to find a new parameter value that minimizes some cost function];
sending a gradient-request message to multiple base stations that directs multiple wireless transceivers to respectively evaluate gradients of the performance metric relative to the at least one network-configuration parameter [see ¶¶ 8, 28; exchanges current parameter settings with the neighboring nodes in a local network of the network node; wherein the network node then computes a partial derivative of a local cost function with respect to each of its neighboring nodes];
receiving, from the multiple base stations, gradient-report messages generated by the multiple wireless transceivers, the gradient-report messages respectively including the gradients, the gradients specifying an amount of change in the performance metric relative to an amount of change in the at least one network-configuration parameter [see ¶¶ 22, 24; receives gradients computed by each of its neighboring nodes. After the exchange, the network node computes a summed impact function as a weighted sum of the impact functions of the network node and its neighboring nodes; the network node determines a new parameter setting based on the summed impact function and the gradient of the summed cost function];
determining at least one optimized network configuration parameter [see ¶ 22; computes a summed impact function as a weighted sum of the impact functions of the network node and its neighboring nodes. The network node determines a new parameter setting (= one optimized network- configuration parameter) based on the summed impact function and the gradient of the summed cost function]; and
sending an optimization message to at least one of the multiple base stations that directs at least one of the multiple wireless transceivers to use the at least one optimized network- configuration parameter [see ¶¶ 22-23; the new parameter value (= one optimized network- configuration parameter) for the network node is then sent to the neighboring nodes. After exchanging new parameter settings with its neighbor nodes, the process may be repeated].
Guey does not explicitly disclose analyzing the gradients using machine learning to determine at least one optimized network-configuration parameter.
However, OKTAY discloses analyzing the gradients using machine learning to determine at least one optimized network-configuration parameter [see ¶¶ 16; Claim 1, determining a new set of access network parameters improving the service performance for the plurality of subscribers; and (d) sending instructions to systems controlling access network parameters to change access network configurations according to the new set of access network parameters].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention was made to provide “analyzing the gradients using machine learning to determine at least one optimized network-configuration parameter” as taught by OKTAY in the system of Guey, so that it would to increase the use of already allocated frequencies, there will also be an increasing need to unlock new spectrum bands [see OKTAY; ¶ 12].
Regarding claim 2, the combined system of Guey and OKTAY disclose the method of claim 1.
Guey further discloses wherein: the at least one network-configuration parameter comprises at least one of the following:
an uplink transmit power configuration; an uplink time-slot allocation percentage [see ¶ 24; Examples of continuous value parameters include the transmit power of a node 20 and antenna weights for nodes 20 in a multiple-input, multiple output (MIMO) system with multiple antennas].
Regarding claim 3, the combined system of Guey and OKTAY disclose the method of claim 1.
Guey further discloses wherein: the performance metric comprises at least one of the following: spectrum efficiency; network capacity; cell-edge capacity; transmit-power headroom; or transmit power [see ¶¶ 21-22; a distributed parameter update procedure is used to coordinate the parameter settings between cells 12 in order to optimize some aspect of network performance. For example, the distributed parameter update procedure could be used to determine transmit power settings for the nodes 20 to maximize system capacity].
Regarding claim 4, the combined system of Guey and OKTAY disclose the method of claim 1.
Guey further discloses wherein: the multiple wireless transceivers include the user equipment and a second user equipment [see ¶ 20; and Fig. 1: The nodes 20 may comprise base stations that provide service to user terminals 30 in their respective cells 12. In some instances, the nodes 20 may comprise user terminals 30 capable of peer-to-peer communications (indicating at least two user terminals)); and
sending of the gradient-request message to the multiple base stations [see ¶¶ 11, 30; The new parameter value (= one optimized network- configuration parameter) for the network node is then sent to the neighboring nodes. After exchanging new parameter settings with its neighbor nodes, the process may be repeated].
Although Guy and OKTAY do not disclose “the sending of the gradient-request message to the multiple base stations directs the multiple base stations to: individually forward the gradient-request message to the user equipment and the second user equipment; broadcast the gradient-request message to the user equipment and the second user equipment; or multicast the gradient-request message to the user equipment and the second user equipment”, this is simply a design implementation choice that can be easily selected by a person of ordinary skill in the art based on the above teaching from Guey in order to achieve optimum performance in communicating with the user equipments.
Regarding claim 5, the combined system of Guey and OKTAY disclose the method of claim 4.
Guey further discloses wherein: the sending of the gradient-request message to the multiple base stations directs the multiple base stations to: individually forward the gradient-request message to the multiple user equipments; broadcast the gradient-request message to the multiple user equipments; or multicast the gradient-request message to the multiple user equipments [¶¶ 20, 42; the nodes 20 may comprise base stations that provide service to user terminals 30, each node if can communicate with its neighboring nodes in .sub.i to exchange parametric information required to evaluate a cost function defined for its local network .sub.i.].
Regarding claim 6, the combined system of Guey and OKTAY disclose the method of claim 4.
Guey further discloses wherein: the at least one network-configuration parameter comprises at least one of the following: an uplink transmit power configuration; a time-multiplexed pilot pattern; a data tone power; an uplink time-slot allocation percentage; a subframe configuration; a multi-user scheduling configuration; or a random-access configuration [see ¶ 46; configured to connect with a signaling network and enable the exchange of cost functions and parameter settings with other nodes 20].
Regarding claim 7, the combined system of Guey and OKTAY disclose the method of claim 4.
Guey further discloses wherein: the performance metric comprises at least one of the following: spectrum efficiency; network capacity; packet latency; or transmit power [see ¶ 24; wherein: the performance metric the distributed parameter update procedure to parameters that can have a continuous value include bandwidth, latency, and transmit power)]
Regarding claim 8, the combined system of Guey and OKTAY disclose the method of claim 1.
Guey further discloses wherein: the multiple base stations comprise a first base station and a second base station, the multiple wireless transceivers comprise the user equipment and the base station; the user equipment is attached to the second base station [see ¶ 20; and Fig. 1: the nodes 20 may comprise base stations (indicates at least two base stations) that provide service to user terminals 30 in their respective cells 12 (the user terminal may be connected to the second base station)); and
sending of the gradient-request message to the multiple base stations [see ¶ 11; the new parameter value (= one optimized network- configuration parameter) for the network node is then sent to the neighboring nodes and exchanging new parameter settings with its neighbor nodes, the process may be repeated].
Although Guy and OKTAY do not disclose “the sending of the gradient-request message to the multiple base stations directs the second base station to pass the gradient-request message to the user equipment; and the sending of the optimization message to the at least one of the multiple base stations directs the second base station to pass the optimization message to the user equipment”, this is simply a design implementation choice that can be easily selected by a person of ordinary skill in the art based on the above teaching from Guey in order to achieve optimum performance in communicating with the user equipments.
Regarding claim 10, the combined system of Guey and OKTAY disclose the method of claim 1.
Although Guey and OKTAY do not explicitly disclose wherein: the at least one optimized network-configuration parameter comprises multiple optimized network-configuration parameters respectively associated with the multiple wireless transceivers, this is simply a design implementation choice that can be easily selected by a person of ordinary skill in the art because multiple parameters may be grouped for optimizing performance.
Regarding claim 11, the combined system of Guey and OKTAY disclose the method of claim 1.
Guey further discloses wherein: the at least one network-configuration parameter specifies a delta change to current network-configuration parameters that are used by the multiple wireless transceivers prior to the sending of the gradient-request message [see ¶ 21; in the communication network 10, the nodes 20 may need to adapt a parameter, such as transmit power, responsive to short-term changes (indicating a delta change to a current network-configuration parameter) in the communication network 10. The parameter setting in each cell 12 will affect neighboring cells].
Regarding claim 12, the combined system of Guey and OKTAY disclose the method of claim 1.
Guey further discloses wherein: the gradients comprise a first amount of change in the performance metric relative to a second amount of change in the at least one network-configuration parameter [see ¶ 32; wherein the gradient of the cost function can be evaluated at the current parameter value, the distributed parameter update procedure updates the parameter value by searching for a better solution than the current one along the direction of a line segment defined by p.sub.i.sup.n-.gamma..sub.i.sup.n.alpha].
Regarding claim 13, the combined system of Guey and OKTAY disclose the method of claim 1.
Guey further discloses wherein: the analyzing of the gradients using machine learning comprises:
employing gradient ascent to determine the at least one optimized network- configuration parameter that maximizes a utility function [see ¶¶ 8, 11; when a parameter value is changed, a search is conducted of a parameter space to find a new parameter value that minimizes some cost function; the network computes a partial derivative of a local cost function with respect to each of its neighboring nodes. A separate instance of the local cost function is computed for each neighboring node. Once the partial derivatives are computed, they are exchanged with the neighboring nodes].
Regarding claim 14, the combined system of Guey and OKTAY disclose the method of claim 13.
Guey further discloses wherein: the at least one second optimized network-configuration parameter is associated with a global optima of the cost function or the utility function [see ¶¶ 11, 29; wherein the at least one second optimized network-configuration parameter is associated with a global optima of the cost function or the utility function (continuous parameters, such as transmit power, a search over the entire parameter space for the optimum solution of p.sub.i would be computationally complex and time-consuming)].
Regarding claim 16, the combined system of Guey and OKTAY disclose the method of claim 1.
Guey further discloses storing network topology data of the cellular network [see ¶ 3; storing network topology data of the cellular network]; and determining the at least one optimized network-configuration parameter based on the network topology data [see ¶ 3; determining the at least one optimized network-configuration parameter based on the network topology data (new cells are added or subtracted, a different set of neighbor cell relationships is created)].
Regarding claims 17, 18, and 19, the claims recite network-optimization controller comprising: a processor and memory system [see Fig. 6, ¶ 48; node 20 is a base station in a cell 12 comprising a configuration processor 26 and memory 28] configured to perform the method for a network-optimization controller recited as in claims 1, 4, and 5 respectively; therefore, claims 17, 18, and 19 are rejected along the same rationale that rejected in claims 1, 4, and 5 respectively.
Regarding claim 20, the claim recites a non-transitory computer-readable medium storing computer executable code, the code when executed by a processor causes the processor to perform the method for a network-optimization controller recited as in claim 1; therefore, claim 20 is rejected along the same rationale that rejected in claims 1.
Allowable Subject Matter
Claims 9 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
SHIRAZIPOUR et al. (US 2021/0119881) – a first network node determines a configuration of one or more parameters in the RAN based on one or more machine-implemented reinforcement learning (RL) procedures to optimize the performance of the RAN based on the one or more parameters.
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/PHONG LA/Primary Examiner, Art Unit 2469
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