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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 9, 2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3-4, 10-12, 16-20, 23, 25, 47-49, and 52 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1, 3, 10, 12, 17-19, 23, 25, 47-48, and 52 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (hereinafter “Ma”, US 2021/0160149) in view of Xue et al. (hereinafter “Xue”, US 2021/0376895).
Regarding claim 1, Ma discloses a method performed by a wireless communication device for machine-learned optimization of wireless networks (i.e., UE as shown in Fig. 12), the method comprising:
sending, to a network node (i.e., BS as shown in Fig. 12), information that indicates one or more capabilities of the wireless communication device for reporting of predicted values that are predicted by the wireless communication device using one or more machine learning capabilities of the wireless communication device (i.e., UE sends information indicating an AI/ML capability of the UE (e.g., reinforcement learning, neural network (NN), deep neural network (DNN)) to BS at 1010 as described in paragraphs 0113-0114. The UE has a capability to send training response to NW at 1118 as described in paragraph 0144);
receiving, from the network node, a request, the request comprising: (a) a request to start reporting predicted values based on a machine learning model, (b) a request to start training the machine learning model for generating predicted values, or (c) both (a) and (b) (i.e., the UE receives a training request from the BS at 1012 as described in paragraph 0121); and
performing one or more actions in response to receiving the request (i.e., the UE sends a response to the training request at 1014 as described in paragraph 0122).
Ma, however, does not expressly disclose:
wherein performing the one or more actions comprises: generating one or more reports comprising one or more predicted values based on the machine learning model and sending the one or more reports to the network node,
wherein generating and sending the one or more reports is activated when a triggering criterion is satisfied,
wherein the triggering criterion is a required accuracy level for the one or more predicted values, a required confidence level for the one or more predicted values, a time-based triggering criterion or a prediction performance-based triggering criterion.
In a similar endeavor, Xue discloses qualifying machine learning-based CSI prediction. Xue also discloses:
wherein performing the one or more actions comprises: generating one or more reports comprising one or more predicted values based on the machine learning model (i.e., the UE uses the trained NN to predict CSI as described in paragraphs 0050-0051, and 0069) and sending the one or more reports to the network node (i.e., the UE reports to the gNB the calculated CSI and the quantized CS difference value as described in paragraphs 0023, 0050-0053, 0066-0067, 0070, and 0080),
wherein generating and sending the one or more reports is activated when a triggering criterion is satisfied (i.e., as described in paragraph 0053, and 0066-0067),
wherein the triggering criterion is a required accuracy level for the one or more predicted values, a required confidence level for the one or more predicted values, a time-based triggering criterion or a prediction performance-based triggering criterion (i.e., the UE reports the calculated CSI and the quantized CSI difference value based on a qualifying session as described in paragraphs 0051-0053, and 0066-0069. Also, the CSI measurement and prediction is based on an expected time as described in paragraph 0074-0076).
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references, and arrive at the present invention.
The motivation/suggestion for doing so would have been to improve reliability of connections between a network entity and a user equipment.
With further regard to claim 23, Ma also discloses a wireless communication device for machine-learned optimization of wireless networks (i.e., UE 110 as shown in Fig. 2), comprising:
one or more transmitters (i.e., transceiver 202);
one or more receivers (i.e., transceiver 202); and
processing circuitry associated with the one or more transmitters and the one or more receivers (i.e., processing unit 200 associated with transceiver 202).
Regarding claim 3, Ma and Xue disclose all limitations recited within claims as described above. Xue also discloses wherein performing the one or more actions further comprises training the machine learning model for generating the predicted values (i.e., training model is used to predict CSI as described in paragraphs 0050-0053).
Regarding claim 10, Ma and Xue disclose all limitations recited within claims as described above. Ma also discloses wherein the triggering criterion is based on:
availability of network capabilities at the network node;
subscription to one or more services at the network node;
configuration at the wireless communication device for:
(a) Guaranteed Flow Bit Rate, GFBR, for Upload and Download;
(b) Maximum Packet Loss Rate for Upload and Download;
(c) reporting of Quality of Experience, QoE, measurements for at least one application; or
(d) any two or more of (a)-(c);
detection of a change of Quality of Service, QoS, parameters associated with the wireless communication device;
the wireless communication device being served by a certain slice;
the wireless communication device being static;
the wireless communication device being located within a geographic area;
the wireless communication device having a specific Service Profile Identifier, SPID; or
a movement pattern of the wireless communication device (i.e., physical speed/velocity at which the wireless device is moving, QoS requirements, etc. as described in paragraph 0070).
Regarding claim 12, Ma and Xue all limitations recited within claims as described above. Ma also discloses wherein the predicted values comprise predicted Radio Resource Management, RRM, related values, predicted beam related values, predicted values for future traffic needs of the wireless communication device, or predicted measurement values for:
(a) one or more frequencies;
(b) traffic steering;
(c) serving cell selection;
(d) Quality of Service, QoS, prediction;
(e) Radio Resource Management, RRM; or
(f) any two or more of (a)-(e) (i.e., predict CSI as described in paragraphs 0050-0053).
Regarding claim 17, Ma and Xue disclose all limitations recited within claims as described above. Ma also discloses wherein the information that indicates the one or more capabilities of the wireless communication device for reporting of predicted values comprises physical characteristic data for the wireless communication device descriptive of:
(a) battery power;
(b) available memory;
(c) computational capacity;
(d) sensor capabilities;
(e) parameters descriptive of a physical environment of the wireless communication device;
(f) acceleration or velocity of the wireless communication device;
(g) nearby network infrastructure; or
(h) any two or more of (a)-(g) (i.e., UE indicates AI/ML capability of the UE including whether or not the UE supports AI/ML for optimization of an air interface, type and/or level of complexity of AI/ML the UE is capable of supporting as described in paragraph 0113-0114).
Regarding claim 18, Ma and Xue disclose all limitations recited within claims as described above. Ma also discloses wherein, prior to sending the information that indicates the one or more capabilities, the method further comprising:
receiving a request from the network node for the one or more capabilities of the wireless communication device for reporting of predicted values (i.e., information is sent by the UE in response to a capability enquiry from the BS as described in paragraph 0120); and
wherein sending the information that indicates the one or more capabilities of the wireless communication device for reporting of predicted values comprises sending the information that indicates the one or more capabilities of the wireless communication device for reporting of predicted values responsive to receiving the request from the network node for the one or more capabilities of the wireless communication device for reporting of predicted values (i.e., UE sends information indicating an AI/ML capability of the UE (e.g., reinforcement learning, neural network (NN), deep neural network (DNN)) to BS at 1010 as described in paragraphs 0113-0114. The UE has a capability to send training response to NW at 1118 as described in paragraph 0144).
Regarding claim 19, Ma and Xue disclose all limitations recited within claims as described above. Xue also discloses wherein performing the one or more actions in response to receiving the request comprises activating one or more procedures that replace measurements with predicted values (i.e., predict CSI at a future time using prediction model at step 814).
Regarding claim 25, Ma discloses a method performed by a network node for machine-learned optimization of wireless networks, the method comprising:
receiving, from a plurality of wireless communication devices, information that indicates one or more capabilities of the plurality of wireless communication devices for reporting of predicted values (i.e., UE sends information indicating an AI/ML capability of the UE (e.g., reinforcement learning, neural network (NN), deep neural network (DNN)) to BS at 1010 as described in paragraphs 0113-0114. The UE has a capability to send training response to NW at 1118 as described in paragraph 0144);
either or both of:
determining one or more wireless communication devices from which to request reporting of predicted values from the plurality of wireless communication devices based on the received information;
determining one or more reports to request from one or more wireless communication devices from among the plurality of wireless communication devices based on the received information (i.e., the UE receives a training request from the BS at 1012 as described in paragraph 0121); and
sending, to the one or more wireless communication devices, one or more messages, the one or more messages comprising: (a) a request to start reporting predicted values, (b) a request to start training a machine learning model for generating predicted values, or (c) both (a) and (b) (i.e., the UE sends a response to the training request at 1014 as described in paragraph 0122).
Ma, however, does not expressly disclose:
wherein the reporting the predicted values comprises generating one or more reports comprising one or more predicted values based on the machine learning model and sending the one or more reports to the network node,
wherein generating and sending the one or more reports is activated when a triggering criterion is satisfied,
wherein the triggering criterion is a required accuracy level for the one or more predicted values, a required confidence level for the one or more predicted values, a time-based triggering criterion or a prediction performance-based triggering criterion.
In a similar endeavor, Xue discloses qualifying machine learning-based CSI prediction. Xue also discloses:
wherein the reporting the predicted values comprises generating one or more reports comprising one or more predicted values based on the machine learning model (i.e., the UE uses the trained NN to predict CSI as described in paragraphs 0050-0051, and 0069) and sending the one or more reports to the network node (i.e., the UE reports to the gNB the calculated CSI and the quantized CS difference value as described in paragraphs 0023, 0050-0053, 0066-0067, 0070, and 0080),
wherein generating and sending the one or more reports is activated when a triggering criterion is satisfied (i.e., as described in paragraph 0053, and 0066-0067),
wherein the triggering criterion is a required accuracy level for the one or more predicted values, a required confidence level for the one or more predicted values, a time-based triggering criterion or a prediction performance-based triggering criterion (i.e., the UE reports the calculated CSI and the quantized CSI difference value based on a qualifying session as described in paragraphs 0051-0053, and 0066-0069. Also, the CSI measurement and prediction is based on an expected time as described in paragraph 0074-0076).
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references, and arrive at the present invention.
The motivation/suggestion for doing so would have been to improve reliability of connections between a network entity and a user equipment.
With further regard to claim 52, Ma also discloses a network node (i.e., a base station 170 as shown in Fig. 3) for machine-learned optimization of wireless networks comprising:
one or more transmitters (i.e., transmitter 252);
one or more receivers (i.e., receiver 254); and
processing circuitry (i.e., processing unit 250).
Regarding claim 47, Ma and Xue disclose all limitations recited within claims as described above. Ma also discloses wherein, prior to receiving the information that indicates the one or more capabilities of the plurality of wireless communication devices, the method comprises:
receiving, from a supervised node, data indicative of a request to configure the one or more wireless communication devices for reporting of the predicted values (i.e., the UE receives from the base station a training request at 1012 as shown in Fig. 12).
Regarding claim 48, Ma and Xue disclose all limitations recited within claims as described above. Xue also discloses receiving one or more reports from the one or more wireless communication devices, the one or more reports comprising predicted values (i.e., the predicted CSI as described in paragraphs 0050-0053); and
sending the one or more reports from the one or more wireless communication devices to the supervised node (i.e., the UE sends the predicted CSI as described in paragraphs 0050-0053).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Xue and further in view of Ryu et al. (hereinafter “Ryu”, US 2021/0328630).
Regarding claim 4, Ma and Xue disclose all limitations recited within claims as described above, but do not expressly disclose features of this claim.
In a similar endeavor, Ryu discloses machine learning model selection in beamformed communications. Ryu also discloses wherein the one or more reports further comprise information that indicates an accuracy or confidence level of the one or more predicted values (i.e., the UE provides feedback to the base station related to the accuracy of the prediction of the model indicated by the base station as described in paragraph 0054).
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references, and arrive at the present invention.
The motivation/suggestion for doing so would have been to update recommendations for future indications of which model to select.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Xue and further in view of Zeng et al. (hereinafter “Zeng”, US 2021/0211912).
Regarding claim 11, Ma and Xue disclose all limitations recited within claims as described above, but do not expressly disclose features of this claim.
In a similar endeavor, Zeng discloses forward-looking channel state information prediction and reporting method. Zeng also discloses wherein the request comprises a request to start reporting predicted values at a particular time(s) or during a particular time window(s) (i.e., requesting the UE to estimate future channel state information expected to occur a number of subframes or slots in the future as described in paragraph 0004).
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references, and arrive at the present invention.
The motivation/suggestion for doing so would have been to improve the communication links in wireless communications systems.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Xue and further in view of Vitthaladevuni et al. (hereinafter “Vitthaladevuni”, US 2023/0188302).
Regarding claim 16, Ma and Xue disclose all limitations recited within claims as described above, but do not expressly disclose features of this claim.
In a similar endeavor, Vitthaladevuni discloses configurable metrics for channel state compression and feedback. Vitthaladevuni also discloses wherein the information that indicates one or more capabilities of the wireless communication device for reporting of predicted values further comprises a performance metric indicative of an accuracy of the wireless communication device for performance of the one or more capabilities (i.e., the UE reports the CSI feedback at an appropriate level of accuracy as described in paragraphs 0011 and 0014).
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references, and arrive at the present invention.
The motivation/suggestion for doing so would have been to minimize unnecessary overhead.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Xue, and further in view of Yang (US 2022/0104060).
Regarding claim 20, Ma and Xue disclose all limitations recited within claims as described above, but do not expressly disclose features of this claim.
In a similar endeavor, Yang discloses measurement control method, electronic device, and storage medium. Yang also discloses wherein performing the one or more actions comprises, after transitioning from a connected state to an inactive state and subsequently transitioning back to the connected state in association to a second network node, providing data resulting from performing the one or more actions to the second network node (i.e., the target network device receives the RRC resume request message sent by the terminal device, and determine that there is data to transfer as described in paragraph 0110).
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references, and arrive at the present invention.
The motivation/suggestion for doing so would have been to enable the terminal device to get connected to the network and transmit data.
Claim(s) 49 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Xue and further in view of Gao et al. (hereinafter “Gao”, US 2022/0271805).
Regarding claim 49, Ma and Xue disclose all limitations recited within claims as described above, but does not expressly disclose features of this claim.
In a similar endeavor, Gao discloses channel information obtaining method. Gao also discloses wherein the network node comprises a gNB-Centralized Unit, CU, and the supervised node comprises a gNB-Distributed Unit, DU (i.e., gNB DU and CU as described in paragraph 0115).
Therefore, it would have been obvious to one of ordinary skilled in the art to modify the teachings of the cited references, and arrive at the present invention.
The motivation/suggestion for doing so would have been to improve flexibility, scalability and performance of the network.
Conclusion
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/Wayne H Cai/Primary Examiner, Art Unit 2644