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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 7, 9-11,17-22 and 29-30 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al. (US 2023/0155702 A1). Regarding claims 1, 17, Wang et al. discloses a method of wireless communication for a user equipment (UE) vendor, comprising: communicating a training data request to initiate a model training for the UE vendor and a network entity vendor (See Fig 16 S710 Para 865 teaches “The terminal device sends channel learning training request signaling (an example of a first request message) to the network device.”); receiving a training data report in response to communicating the training data request (See Fig 16 S720 Para 868 teaches “The network device sends channel learning training signaling (an example of first indication information). Correspondingly, in S720 , the terminal device receives the channel learning training signaling.”); performing the model training for channel status information (CSI) feedback (CSF) models (See Fig 16 S730 Para 869 “S730. “The terminal device performs channel learning model training based on the channel learning training signaling.”); and communicating a model training report to the network entity vendor (See Fig 16 S740 Para 870 “The terminal device sends channel learning feedback signaling (an example of a first message).). Regarding claim 2, Wang et al. discloses a method, wherein communicating the training data request further comprises communicating the training data request to the network entity vendor to initiate model training. (See Fig 16 S710 Para 865 teaches “The terminal device sends channel learning training request signaling (an example of a first request message) to the network device.”) Regarding claim 4, Wang et al. discloses a method wherein communicating the training data request further comprises communicating the training data request to a UE to collect data for the model training (Fig 16, S730. Para 869 The terminal device performs channel learning model training based on the channel learning training signaling.) Regarding claim 7, Wang et al. discloses a method , wherein the training data report includes at least one of a timestamp, location information, and metadata (Para 460-461 teaches of using the location of the terminal) Regarding claim 9, Wang et al. discloses a method, wherein the location information includes at least one of a Global Navigation Satellite System (GNSS) and a radio fingerprint corresponding to a Reference Signal Receive Power (RSRP)/Reference Signal Received Quality (RSRQ) measurement of a serving cell and neighbor cells. (Para 460-461 teaches of using the location of the terminal) Regarding claim 10, Wang et al. discloses a method, wherein the metadata includes at least one of a reference signal (RS) type identification (ID) and NM ID (Para 419-420 teaches of identifying the reference signal and CRI value associated with it) Regarding claim 11, Wang et al. discloses a method wherein communicating the model training further comprises either directly uploading the model training or distilling data for a network entity to derive the model training. (Para 270-276 teaches of directly uploading the model training data). Regarding claims 29, 30 Wang et al. discloses an apparatus for wireless communication for a user equipment (UE) vendor, comprising: a memory storing computer-executable instructions; and at least one processor coupled to the memory (See Fig 19 items 3200 and 3100) and configured to execute the computer-executable instructions to:, comprising: communicate a training data request to initiate a model training for the UE vendor and a network entity vendor (See Fig 16 S710 Para 865 teaches “The terminal device sends channel learning training request signaling (an example of a first request message) to the network device.”); receive a training data report in response to communicating the training data request (See Fig 16 S720 Para 868 teaches “The network device sends channel learning training signaling (an example of first indication information). Correspondingly, in S720 , the terminal device receives the channel learning training signaling.”); perform the model training for channel status information (CSI) feedback (CSF) models (See Fig 16 S730 Para 869 “S730. “The terminal device performs channel learning model training based on the channel learning training signaling.”); and communicate a model training report to the network entity vendor (See Fig 16 S740 Para 870 “The terminal device sends channel learning feedback signaling (an example of a first message).). Regarding claim 18, Wang et al. discloses a method, wherein communicating the training data request further comprises communicating the training data request to a model manager to forward to a network entity for performing a UE selection procedure.. (See Fig 16 S710 Para 865 teaches “The terminal device sends channel learning training request signaling (an example of a first request message) to the network device.”) Regarding claim 19, Wang et al. discloses a method, wherein communicating the training data request further comprises communicating the training data request to model manager to for performing a UE selection procedure. (See Fig 16 S710 Para 865 teaches “The terminal device sends channel learning training request signaling (an example of a first request message) to the network device.”)
Claim Rejections - 35 USC § 103
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2023/0155702 A1) in view of Freda et al. (US 2012/0057872 A1).
Regarding claim 3, Wang et al. discloses the claimed invention as set forth in claim 2 above. Wang et al. does not explicitly disclose a method, further comprising receiving a training data request acknowledgement (ACK) from the network entity vendor in response to communicating the training data request. However Freda et al. disclose a method, further comprising receiving a training data request acknowledgement (ACK) from the network entity vendor in response to communicating the training data request (Para 175 teaches of sending an ACK message for requesting training data). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use the method of sending an ACK message for requesting training data of Freda et al. with the system of Wang et al. in order to provide a system that provides fault tolerant data ingestion wherein assuring checkpoint synchronization while providing optimized pipeline flow.
Claim(s) 5, 6, 8, and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2023/0155702 A1) in view of Huawei et al .( Update to further clarify ML model sharing between NWDAF instances) ( As provided in the IDS). Regarding claims 5, 20, Wang et al. discloses the claimed invention as set forth in claim 2 above. Wang et al. does not explicitly disclose a method, further comprising registering one or more CSF models with a network associated with the UE vendor and the network entity vendor. However Huawei et al. discloses a method, further comprising registering one or more CSF models with a network associated with the UE vendor and the network entity vendor. ( Fig 6.56.2 teaches procedure for trained model registration, discovery, update and consumption). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use the method of registering CFS models of Huawei et al. with the system of Wang et al. in order to provide a system that allows separate vendors to interoperate seamlessly without mismatch. Regarding claims 6, 21 Wang et al. discloses a method, wherein registering the one or more CSF models includes registering one or more model identifications (IDs) or model structure (MS) IDs, list of parameter set (PS) IDs, and applicable scenarios for each PS including an area, configuration and UE type (Para 829 teaches of model identifiers). Regarding claim 8, Wang et al. discloses the claimed invention as set forth in claim 7 above. Wang et al. does not explicitly disclose a method, wherein the timestamp corresponds to at least one of an absolute time, relative time, or a combination of a system frame number (SFN), timeslot, and an optional symbol.. However Huawei et al. discloses a method, wherein the timestamp corresponds to at least one of an absolute time, relative time, or a combination of a system frame number (SFN), timeslot, and an optional symbol ( Page 5 item 12 teaches “The ML Model Consumer NWDAF locally collects training data information when conducting inference operation as well as related situational information during inferencing. e.g. inference timestamp, UE types, UE location, application ID. time, etc”). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use the method of using timestamps of Huawei et al. with the system of Wang et al. in order to provide a system that allows separate vendors to interoperate seamlessly without mismatch.
Allowable Subject Matter
Claims 12-16 and 23-28 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AJAY P CATTUNGAL whose telephone number is (571)270-7525. The examiner can normally be reached M-F 9:00-5:00 PM.
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/AJAY CATTUNGAL/Primary Examiner, Art Unit 2467
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