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 .
Claim Rejections - 35 USC § 102
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 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.
Claims 1-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al. (WO 2022/060748).
Regarding claims 1 and 7, Wang discloses a method and apparatus comprising: at least one processor; and at least one memory including program code; the at least one memory and the computer program code configured to, with the at least one processor (paragraph 211, lines 1-3: “A user equipment comprising: a processor; and computer-readable storage media comprising instructions, responsive to execution by the processor, for directing the user equipment to perform … methods”), cause the apparatus at least to perform:
receiving, by a first user device (coordinating UE 111) from a network node (base station 120), an indication (“command”) for the first user device to act as a user device-network relay (“coordinating UE”) (paragraph 72, lines 1-5: “At 615 …the base station 120 communicating directly with each UE (e.g., a command to join a UECS, a command to act as a coordinating UE for the UECS)”); and
in response to the receiving: carrying out a discovery procedure for determining at least one second user device (“subset of UEs” = “UE 112” and/or “UE 113”) that is capable of sidelink communications and utilizes machine learning (paragraph 76 …: “In some aspects, the coordinating UE 111 optionally determines one or more subsets of UEs at 630. For example, the coordinating UE 111 determines the subset of UEs based on UE capabilities of UES within the UECS and/or common channel conditions of UEs within the UECS (indicated by signal or link quality parameters). To illustrate, the coordinating UE 111 requests and receives an indication of UE hardware configurations (not illustrated) and selects ... a subset of UEs based upon common hardware capabilities”; since the coordinating UE selects a subset of UEs among the UEs in the UECS and the UEs in the UECS are UEs which are capable of sidelink communication and which utilize machine learning, the selected subset of UEs are consequently UEs which are capable of sidelink communications and which utilize machine learning);
receiving, by the first user device from the network node, machine learning configuration information (paragraph 74, line 2: “baseline ML configuration(s)”); and
transmitting, by the first user device to the at least one second user device (UE 112, UE 113, “subset of UEs included in the UECS 108”) via sidelink communications, the machine learning configuration information for carrying out a machine learning related operation (645, 655, 650, 660) (paragraph 74, lines 1-4: “As part of initializing the UECS DNNs at 620, the base station 120 sometimes communicates the baseline ML configuration(s) determined at 610 to the coordinating UE 111 and directs the UE 111 to communicate the baseline ML configuration(s) to all UEs included in the UECS 108, or a subset of UEs included in the UECS 108”).
Regarding claims 2 and 8, Wang discloses prior to the transmitting, modifying, by the first user device, the machine learning configuration information (paragraph 68, lines 1-5: “Some implementations iteratively train the DNN 402 using the same set of training data and/or additional training data that has the same input characteristics to improve the accuracy of the machine-learning module. During training, the machine-learning module modifies some or all of the architecture and/or parameter configurations of a neural network included in the machine-learning module, such as node connections, coefficients, kernel sizes, etc”).
Regarding claims 3 and 10, Wang discloses that the carrying out the discovery procedure for determining at least one second user device in proximity of the first user device that is capable of sidelink communications and utilizes machine learning comprises: carrying out a discovery procedure for determining at least one second user device that is capable of using or performing a machine learning-enabled function for which the machine learning configuration information is transmitted or provided, or will be transmitted or provided, by the first user device to the at least one second user device (paragraph 69, lines 5-8: “At times, the coordinating UE 111, the UE 112, and/or the UE 113 transmit an indication of ML capabilities (e.g., supported ML architectures, supported number of layers, available processing power, memory limitations, available power budget, fixed-point processing vs. floating-point processing, maximum kernel size capability, computation capability).”).
Regarding claims 4 and 11, Wang discloses notifying, by the first user device, the at least one second user device that the first user device has a capability to provide machine learning configuration information via sidelink communications (paragraph 65, lines 6-10: “the UE 113 communicates respective updated ML configuration information to the UE 112 using the side link 133 (and without communicating the updated ML configuration information to the coordinating UE). Alternatively, or additionally, the UE 112 communicates respective updated ML configuration information to the UE 113 using the side link 133.”; paragraph 76 …: “In some aspects, the coordinating UE 111 optionally determines one or more subsets of UEs at 630. For example, the coordinating UE 111 determines the subset of UEs based on UE capabilities of UES within the UECS and/or common channel conditions of UEs within the UECS (indicated by signal or link quality parameters). To illustrate, the coordinating UE 111 requests and receives an indication of UE hardware configurations (not illustrated) and selects ... a subset of UEs based upon common hardware capabilities”; since the coordinating UE selects a subset of UEs among the UEs in the UECS and the UEs in the UECS are UEs which are capable of sidelink communication and which utilize machine learning, the selected subset of UEs are consequently UEs which are capable of sidelink communications and which utilize machine learning).
Regarding claims 5 and 12, Wang discloses that the transmitting the machine learning configuration information comprises at least one of the following: transmitting, by the first user device to the at least one second user device via sidelink communications, the machine learning configuration information via unicast communications; or transmitting, by the first user device to the at least one second user device via sidelink communications, the machine learning configuration information via groupcast or broadcast communications (paragraph 52, lines 6-11: “Each UE transmits the EQ samples to a coordinating UE over a side link and/or a local wireless connection. In aspects, the UEs transmit timing information with the EQ samples. Using the timing information, the coordinating UE time-aligns and combines the EQ samples and processes the combined EQ samples to decode the user-plane data for the target UE. The coordinating UE then transmits the data packets to the target UE over the side link and/or local wireless connection.”).
Regarding claims 6 and 13, Wang discloses that the transmitting the machine learning configuration information comprises: determining whether a number of user devices within an area or a user device density is greater than a threshold; transmitting, by the first user device to the at least one second user device via sidelink communications, the machine learning configuration information via unicast communications if the number of the other user devices within an area or the user device density is not greater than the threshold; and transmitting, by the first user device to the at least one second user device via sidelink communications, the machine learning configuration information via groupcast or broadcast communications if the number of the other user devices within an area or the user device density is greater than the threshold (paragraph 83, lines 5-11: “the UE 112 and/or UE 113 determine that an ML parameter has changed more than a first threshold value by periodically comparing the ML parameter to the first threshold value, that the DNN architecture has changed through a reconfiguration request, or that a signal or link quality parameter has changed by a second threshold value by comparing the quality parameters to the second threshold value (or a difference from a prior value) each time the quality parameters are generated. In some aspects, the UE 112 and/or UE 113 detect a UE location change by a third threshold value.”; Paragraph 51, lines 10-16: “.the neural network manager selects one or more NN formation configurations from the neural network table 412 by matching the input characteristics to a current operating environment and/or configuration, such as by matching the input characteristics to current channel conditions, the number of UEs participating in a UECS or a number of UEs in a subset of UEs from the UECS, an estimated location of a target UE in the UECS, an estimated location of a coordinating UE in the UECS, a type of side link used by the UECS, UE capabilities, UE characteristics (e.g., velocity, location, etc.) and so forth.”)
9. The apparatus of claim 7, wherein the modifying the machine learning configuration information is performed by the user device based on an indication received by the user device from the network node.
Claims 14-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pezeshki et al. (US 2022/0180251).
Regarding claims 14 and 18, Pezeshki discloses a method and apparatus comprising: at least one processor; and at least one memory including program code; the at least one memory and the computer program code configured to, with the at least one processor (paragraph 51, lines 9-11: “the processor components … and the memory components … may be operatively coupled, communicatively coupled, electronically coupled, electrically coupled”), cause the apparatus to at least perform:
receiving, by a second user device (UE 610 in FIG. 6) from a first user device (UE 605), a notification (assistance request 635) that the first user device has a capability to provide machine learning configuration information via sidelink communications, wherein the second user device utilizes machine learning to perform one or more machine learning related operations (paragraph 100, lines 1-3: “As shown by reference number 635, the UE 605 may transmit, and the UE 610 may receive, a request for local update uploading assistance (shown as an “assistance request”). The UE 605 may transmit the request via a sidelink connection.”; Paragraph 103, lines 1-3: “As shown by reference number 650, the UE 610 may generate an aggregated local update. In some aspects, the UE 610 may generate the aggregated local update by aggregating the first local update and the second local update. In some aspects, the UE 610 may aggregate any number of other local updates.”);
receiving, by the second user device from the first user device via sidelink communications, the machine learning configuration information (paragraph 101, lines 1-4: “As shown by reference number 645, the UE 605 may transmit, and the UE 610 may receive, the first local update. The UE 605 may transmit the first local update by transmitting a sidelink communication that includes the first local update. In some aspects, the UE 605 may transmit the sidelink communication to the UE 610 based at least in part on receiving the assistance confirmation.”); and
using, by the second user device, the received machine learning configuration information to configure and/or operate a machine learning model at the second user device to carry out a machine learning related operation (paragraph 104, lines 1-5: “As shown by reference number 655, the UE 610 may transmit the aggregated local update to the base station 615. In some aspects, the UE 610 may transmit the aggregated local update to another UE (not shown). The UE 610 may transmit an assistance notification to the base station 615 that indicates that the aggregated update comprises an aggregation of the first local update and the second local update. In some aspects, the assistance notification may indicate a first identifier associated with the UE 605 and a second identifier associated with the UE 610.”).
Regarding claims 15 and 19, Pezeshki discloses that the machine learning configuration received by the second user device has been modified by the first user device before being received the second user device (paragraph 101, lines 1-4: “As shown by reference number 645, the UE 605 may transmit, and the UE 610 may receive, the first local update. The UE 605 may transmit the first local update by transmitting a sidelink communication that includes the first local update. In some aspects, the UE 605 may transmit the sidelink communication to the UE 610 based at least in part on receiving the assistance confirmation.”).
Regarding claims 16 and 20, Pezeshki discloses that the receiving a notification comprises at least one of: receiving, by the second user device, an announcement message indicating that the first user device has a capability to provide or relay machine learning configuration information via sidelink communications to the second user device; or performing the following: transmitting, by the second user device to the first user device, a solicitation message requesting information as to relay capabilities of the first user device; and receiving, by the second user device from the first user device, a response message indicating that the first user device has the capability to provide machine learning configuration information via sidelink communications to the at least one second user device (paragraph 100, lines 1-7: “As shown by reference number 635, the UE 605 may transmit, and the UE 610 may receive, a request for local update uploading assistance (shown as an “assistance request”). The UE 605 may transmit the request via a sidelink connection. In some aspects, the request may include a request to forward the first local update to the base station 615 either directly or via another UE. In some aspects, the request may include a request to perform an aggregation of the first local update and the second local update. As shown by reference number 640, the UE 610 may transmit, and the UE 605 may receive, an assistance confirmation. In some aspects, this initial request and confirmation exchange may be used to avoid the case in which the UE 610 had already sent the second local update to the base station 615.”).
Regarding claims 17 and 21, Pezeshki discloses that the receiving the machine learning configuration information comprises at least one of the following: receiving, by the second user device from the first user device via sidelink communications, the machine learning configuration information via unicast communications; or receiving, by the second user device from the first user device via sidelink communications, the machine learning configuration information via groupcast or broadcast communications (paragraph 98, lines 1-5: “As shown by reference number 620, the base station 610 may transmit, and the UE 605 may receive, a federated learning participant indication. The UE 610 also may receive the federated learning participant indication. The federated learning participant indication may identify one or more UEs of a set of UEs participating in a federated learning round. For example, the federated learning participant indication may identify the UE 605 and the UE 610. The federated learning participant indication may be multicast to the UEs of the set of participating UEs.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shuman et al. (US 2023/0184870) discloses a UE that uses a machine learning algorithm to determine whether to transmit a sidelink synchronization signal.
Diwahar et al. (US 2020/0204961) discloses sending a request message carried out by a first network node when it is determined that the status of a learning capability is enabled for both the first network node and a second network node.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAM BHATTACHARYA whose telephone number is (571)272-7917. The examiner can normally be reached weekdays, 9-5:30.
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/SAM BHATTACHARYA/Primary Examiner, Art Unit 2646