Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is sent in response to Applicant’s Communication received on 07/21/2026 for application number 18/482,801.
Response to Amendments
3. The Amendment filed 07/21/2026 has been entered. Claim 1 has been amended. Claims 1-5 remain pending in the application.
4. Applicant’s amendment to claim has been fully considered and is persuasive. The objection to this claim is respectfully withdrawn.
Response to Arguments
Applicant argues that the cited references do not disclose the amended feature as recited in claim 1. However, the argument is moot since this is a newly presented limitation, thus changing the scope of the claim. However, a newly found reference, Tang, is applied.
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 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.
6. Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over Saber et al. (U.S. Patent Application Pub. No. US 20230131694 A1) in view of Butchko et al. (U.S. Patent Application Pub. No. US 20150103685 A1), and further in view of Tang et al. (U.S. Patent Application Pub. No. US 20240106508 A1).
Claim 1: Saber teaches a machine learning network (i.e. A feedback scheme in accordance with the disclosure may use artificial intelligence (AI), machine learning (ML), deep learning, and/or the like (any or all of which may be referred to individually and/or collectively as machine learning or ML) to generate a representation of physical layer information for a wireless communication system; para. [0026]), comprising:
a plurality of measurement devices (i.e. A UE may use the CSI-RS to measure downlink channel conditions and generate CSI, for example, by performing a channel estimation and/or a noise variance estimation based on measurements of the CSI-RS signal; para. [0024, 0074]);
one or more of the measurement devices (i.e. A node may refer to a base station, a UE, or any other apparatus that may use one or more ML models as disclosed herein; para. [0030, 0074]) comprising:
one or more communication interfaces configured to allow the device to receive (i.e. The Physical (PHY) layer of the UE may receive the physical signal received on the PDSCH; para. [0005, 0071]) and process physical layer signals (i.e. The physical layer information 105 may include any information relating to the operation of a physical layer of a wireless communication apparatus. For example, the physical layer information 105 may include information (e.g., status information, precoding information, etc.) relating to one or more physical layer channels, signals, beams, and/or the like; para. [0052]);
a memory (i.e. figs. 13, 14, memory; para. [0173]); and
one or more processors (i.e. figs. 13, 14, processor; para. [0005]) configured to execute code to cause the one or more processors to receive physical layer data (i.e. FIG. 1 illustrates an embodiment of a wireless communication apparatus according to the disclosure. The apparatus 101 may include a machine learning model 103 that may receive physical layer information 105 as an input and generate a representation 107 of the physical layer information as an output; para. [0049]);
perform one or more operations on the physical layer data according to a machine learning model to produce changed physical layer data (i.e. The representation 107 of the physical layer information may be a compressed, encoded, encrypted, mapped, or otherwise modified form of the physical layer information 105. Depending on the implementation details, the modification of the physical layer information 105 by the machine learning model 103 to generate the representation 107 of the physical layer information may reduce the resources involved in transmitting the physical layer information 105 between apparatus; para. [0050, 0068]), operating on physical layer/channel information with an ML model to produce a changed form of that information; and
transmit the changed physical layer data to at least one other node (i.e. A node may refer to a base station, a UE, or any other apparatus that may use one or more ML models as disclosed herein; para. [0030]) in the machine learning network (i.e. the apparatus 101 may transmit the representation 107 of the physical layer information to one or more other apparatus as shown by arrow 109; para. [0049, 0068]), sending the ML-generated/changed physical layer representation to another node/apparatus.
Saber does not explicitly teach a device is a test device, changed physical layer data containing changed parameters.
However, Butchko teaches a plurality of test (i.e. The system 20 includes at least one wireless instrument(s) 26, a master controller 28 in communication with the at least one wireless instrument(s) 26 and a user interface device 29, which together form a distributed wireless network testing solution. The wireless network testing solution may provide an outside-in view of the wireless network (client side) versus an inside-out view provided through the existing network access points or gateways; para. [0024]) and measurement devices; one or more of the test and measurement devices (i.e. The system of such embodiments may include a wireless instrument configured to send wireless signals in a monitored area and to measure received wireless signals. The system may further include a master controller configured to connect with the wireless instrument to form a distributed wireless network testing solution; para. [0006, 0025, 0031]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Saber to include the feature of Butchko. One would have been motivated to make this modification because it provides distributed test and measurement devices.
However, Tang teaches perform one or more operations on the physical layer data according to a machine learning model (i.e. the ML module 500 of the TRP 452 is trained using uplink channel state information UL H 904 as an ML module input and the one or more modulation and coding scheme parameters MCS 906 as an ML module output; para. [0152-0153]) to produce changed physical layer data (i.e. the output MCS 906 of the ML module 500 is the optimal MCS or modulation order or coding rate; para. [0131, 0138, 0154]) containing changed parameters (i.e. once the ML modules 500 and 510 have been trained, the TRP 452 uses the trained ML module 500 to predict, based on using the uplink channel state information UL H′904 at slot n1 as an input to the trained ML module 500, compressed MCS parameters MCS′906 corresponding to optimal modulation order and/or coding rate for RBs scheduled at slot n1+m; para. [0165-0169]); and transmit the changed physical layer data to at least one other node in the machine learning network (i.e. the TRP 452 does send the compressed MCS parameters MCS′906 to the UE 402; para. [0132, 0172]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Saber and Butchko to include the feature of Tang. One would have been motivated to make this modification because it improves link adaptation while reducing signaling/feedback overhead.
Claim 2: Saber, Tang, and Butchko teach the machine learning network as claimed in claim 1. Saber further teaches wherein the measurement devices comprise one or more of measurement instruments, sensors, antennas, reconfigurable intelligent surfaces, general purpose computing devices, and servers (i.e. in systems with larger numbers of antenna ports; para. [0033, 0074, 0076]).
However, Butchko further teaches wherein the test and measurement devices comprise one or more of test and measurement instruments, sensors, antennas, reconfigurable intelligent surfaces, general purpose computing devices, and servers (i.e. instruments; para. [0006, 0025, 0033, 0075]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Saber to include the feature of Butchko. One would have been motivated to make this modification because it provides distributed test and measurement devices.
Claim 3: Saber, Tang, and Butchko teach the machine learning network as claimed in claim 1. Saber further teaches wherein the physical layer signals comprise transmission rate (i.e. The at least one processor may be configured to train the machine learning model using a processing allowance. The processing allowance may include a processing time; para. [0005, 0041, 0045]), encoding (i.e. The representation 107 of the physical layer information may be a compressed, encoded, encrypted, mapped, or otherwise modified form of the physical layer information 105; para. [0050]), transmission media (i.e. the inventive principles are not limited to these details and/or applications and may be applied in any other context in which physical layer information may be processed and/or sent between wireless apparatus regardless of whether any of the apparatus may be base stations, UEs, peer devices, and/or the like, and regardless of whether a channel may be a UL channel, a DL channel, a peer channel, and/or the like; para. [0048, 0067]), and interface (i.e. the Physical (PHY) layer of the UE may receive the physical signal received on the PDSCH and apply it as an input to a PDSCH processing chain; para. [0005,0071, 0072]).
Claim 4: Saber, Tang, and Butchko teach the machine learning network as claimed in claim 1. Saber further teaches wherein the code that causes the one or more processors to perform operations comprises code that causes the one or more processors to perform at least one of determining change parameters for returning data to a node that sent the physical layer data (i.e. the first wireless apparatus 401 may provide feedback to the second wireless apparatus 402 in the form of channel information 405 that may be obtained; para. [0067]), determining beam alignment (i.e. The UE may then use the dominant eigenvectors or singular vectors to derive a PMI that may be fed back to the gNB which may use the PMI for beamforming in the DL channel; para. [0075]), canceling interference, and channel estimation (i.e. CSI generation may be performed based on a CSI reference signal (CSI-RS) transmitted by the gNB. A UE may use the CSI-RS to measure downlink channel conditions and generate CSI, for example, by performing a channel estimation and/or a noise variance estimation based on measurements of the CSI-RS signal; para. [0074]).
7. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Saber in view of Butchko, and further in view of Zhao (U.S. Patent Application Pub. No. US 20070127503 A1).
Claim 5: Saber, Tang, and Butchko teach the machine learning network as claimed in claim 1. Saber further teaches wherein signals in the network use signaling for signals sent between nodes (i.e. A UE may send UL signals to the gNB to convey user data and control information using a Physical Uplink Shared Channel (PUSCH) and a Physical Uplink Control Channel (PUCCH), respectively; para. [0072]).
Saber does not explicitly teach User Datagram Protocol (UDP).
However, Zhao teaches wherein signals in the network use User Datagram Protocol (UDP) signaling for signals sent between nodes (i.e. The present method and system for adaptive wireless routing is based on a user datagram protocol (UDP). Each router (such as routers 101-104, and 200) that uses adaptive wireless routing has a routing process that sends and receives datagrams on a UDP port number (obtained from IETF); para. [0041]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Saber and Butchko to include the feature of Zhao. One would have been motivated to make this modification because it provides lightweight internode messaging using established wireless network transport mechanisms.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
D’Oro et al. (Pub. No. US 20220255775 A1), the ML agent may then update the physical signal modifier to implement the second set of signal modification parameters to produce a subsequent modified physical layer signal to be transmitted across the communications channel.
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 extension fee 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 date of this final action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAN TRAN whose telephone number is (303)297-4266. The examiner can normally be reached on Monday - Thursday - 8:00 am - 5:00 pm MT.
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/TAN H TRAN/Primary Examiner, Art Unit 2141
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