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 § 112
2. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claim 1 is rejected as failing to define the invention in the manner required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
4. Claims 1 is rejected under 112 (b) since it’s not clear where the body of the claim begins (or where the preamble ends), that the scope of the claim is not clear here.
Claim Rejections - 35 USC § 103
1. 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.
2. 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.
3. Claims 1-4, 6-8, 11, 14-15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No US 2021/0158151 to Wang et al. (hereinafter Wang) in view of U.S. Publication No US 2022/0108014 to Schiffman et al. (hereinafter Schiffman)
As to claims 1 and 18, Wang discloses a method of artificial intelligence (AI) setting within a communication system with a base station and several user equipments (UEs), wherein the user equipments each report an artificial intelligence parameter for universal communications identifier (UCI) capabilities to the base station, wherein the user equipments are grouped by the base station based on the artificial intelligence parameter and wherein the base station multicasts artificial intelligence configuration to each group of user equipments separately (Wang; [0168]; Fig.13:1305 shows and discloses plurality of UEs reports metric and capability information to a base station using machine-learning architectures. [0158] discloses machine-learning architectures for broadcast and multicast communications. In implementations, a network entity determines a configuration of a deep neural network (DNN) for processing broadcast or multicast communications transmitted over a wireless communication system, where the communications are directed to a targeted group of user equipments (UEs)).
Wang discloses wherein the user equipments are grouped by the base station, but fails to disclose wherein UEs report universal communications identifier capabilities. However, Schiffman discloses
wherein the user equipments are grouped by the base station based on the universal communications identifier capabilities (Schiffman; [0044] discloses the remote party 302 may obtain information regarding the capabilities of each of the computing devices 300a-d from the attestation, as well as potentially being able to obtain the identity of each of the computing devices 300a-d and their individual capabilities from the attestation. Information regarding the capabilities may be conveyed by the attestation in the form of an indicator or Universal Resource Identifier (URI) to more complete information about the capability).
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings in order to report more complete information about the capability.
As to claims 2, the rejection of claim 1 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein the base station multicasts the respective artificial intelligence configuration in dependency of the artificial intelligence parameter for the universal communications identifier capabilities (Schiffman; [0044] there is a plurality of computing devices 300a-d. Upon receiving the request as described above, the individual computing devices 300a-d may individually attest to their capabilities and provide an indication of the group of devices of which they are a member. In the figure, the individual attestations are depicted as individual arrows 304a-d from the respective computing devices 300a-d. A remote party 302 communicatively coupled to each of the computing devices 300a-d may then implement a procedure based on the plurality of received attestations 304a-d, which in this example, are individual attestations from each of the computing devices 300a-d).
As to claims 3, the rejection of claim 1 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein the user equipments each receive the artificial intelligence configuration to be used (Wang; [0158]-[0159] discloses the network entity forms a network-entity DNN based on the determined configuration of the DNN and processes the broadcast or multicast communications using the network-entity DNN. In implementations, the network entity forms a common DNN to process and/or propagate the broadcast or multicast communications to the targeted group of UEs).
As to claims 4, the rejection of claim 1 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein the artificial intelligence configuration is submitted via at least one specific multicast message (Wang; [0158]-[0159] discloses the network entity forms a network-entity DNN based on the determined configuration of the DNN and processes the broadcast or multicast communications using the network-entity DNN. In implementations, the network entity forms a common DNN to process and/or propagate the broadcast or multicast communications to the targeted group of UEs).
As to claims 6, the rejection of claim 4 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein the at least one specific multicast message encompasses information concerning the user equipments of the respective group which are addressed by the respective specific multicast message (Wang; [0158]-[0159] discloses the network entity forms a network-entity DNN based on the determined configuration of the DNN and processes the broadcast or multicast communications using the network-entity DNN. In implementations, the network entity forms a common DNN to process and/or propagate the broadcast or multicast communications to the targeted group of UEs)
As to claims 7, the rejection of claim 1 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein artificial intelligence configuration is added to a message to be transmitted (Wang; [0026]; [0056] discloses machine-learning architectures for broadcast and multicast communications. In implementations, a network entity associated with a wireless communication system determines a configuration of a deep neural network (DNN) for processing broadcast or multicast communications transmitted over a wireless communication system to a targeted group of UEs).
As to claims 8, the rejection of claim 7 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein the artificial intelligence configuration is added to the message to be transmitted by means of radio resource control signaling (Wang; [0094] discloses the base station 120, as one example, processes downlink control channel information using a first DNN of the DNNs 604, processes downlink data channel information using a second DNN of the DNNs 604, and so forth).
As to claims 11, the rejection of claim 1 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein the artificial intelligence parameter and/or the artificial intelligence configuration is an artificial intelligence model type, a number of layers, a number of neurons of each layer, a trained weight values, an artificial intelligence model maturity and/or a duration of artificial intelligence model application (Wang; [0056] discloses The E2E ML controller 318 determines an end-to-end machine-learning configuration (E2E ML configuration) for processing information exchanged through an E2E communication, such as a QoS flow. In implementations, the E2E ML controller analyzes any combination 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) of devices. Here Wang is applied for 2nd alternative number of layers)
As to claims 14, the rejection of claim 1 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein the base station requests the user equipments to report the artificial intelligence parameter for the universal communications identifier capabilities (Wang; [0168] discloses the base station 120 receives UE capabilities from one or more UEs in the targeted group of UEs in response to sending a request for UE capabilities).
As to claims 15, the rejection of claim 1 as listed above is incorporated herein. In addition, Wang- Schiffman discloses wherein the base station is a gNodeB base station (Wang; [0031])
4. Claims 5 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No US 2021/0158151 to Wang et al. (hereinafter Wang) in view of U.S. Publication No US 2022/0108014 to Schiffman et al. (hereinafter Schiffman) in view of U.S. Publication No US 2023/0345448 to Zhang et al. (hereinafter Zhang)
As to claims 5, Wang- Schiffman discloses multicast message, but fails to disclose of using multicast radio network temporary identifier. However, Zhang discloses
wherein the at least one specific multicast message is scrambled by multicast radio network temporary identifier (Zhang; [0018] discloses receiving configuration information related to hybrid automatic repeat request-acknowledgement (HARQ-ACK) feedback through a radio resource control (RRC) message, where the configuration information includes HARQ-ACK feedback related information for each multicast radio network temporary identifier (RNTI) of one or more multicast RNTIs; determining an indication related to enabling/disabling the HARQ-ACK feedback in an associated downlink control information (DCI); and performing the HARQ-ACK feedback corresponding to the multicast RNTI based on the determined indication related to enabling/disabling the HARQ-ACK feedback) .
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings in order to include HARQ-ACK feedback related information for each multicast radio network temporary identifier (RNTI)
As to claims 10, Wang- Schiffman discloses machine learning based communication, but fails to disclose of using multicast radio network temporary identifier. However, Zhang discloses
wherein at least one of a radio network temporary identifier (RNTI) list or a user equipment identification list is provided, which indicates which user equipments apply for artificial intelligence based compression (Zhang; [0018] discloses receiving configuration information related to hybrid automatic repeat request-acknowledgement (HARQ-ACK) feedback through a radio resource control (RRC) message, where the configuration information includes HARQ-ACK feedback related information for each multicast radio network temporary identifier (RNTI) of one or more multicast RNTIs; determining an indication related to enabling/disabling the HARQ-ACK feedback in an associated downlink control information (DCI); and performing the HARQ-ACK feedback corresponding to the multicast RNTI based on the determined indication related to enabling/disabling the HARQ-ACK feedback).
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings in order to include HARQ-ACK feedback related information for each multicast radio network temporary identifier (RNTI)
5. Claims 9, 12-13 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No US 2021/0158151 to Wang et al. (hereinafter Wang) in view of U.S. Publication No US 2022/0108014 to Schiffman et al. (hereinafter Schiffman) in view of U.S. Publication No US 2026/0012398 to Matsuda et al. (hereinafter Matsuda)
As to claims 9, Wang- Schiffman discloses of sending capability information, but fails to disclose CSI feedback. However, Matsuda discloses
wherein the universal communications identifier capabilities comprise artificial intelligence capability of channel state information (CSI) feedback and/or hybrid automatic repeat request (HARQ) (Matsuda; [0227] discloses the terminal apparatus B feeds back the encoded downlink-channel-state information to the base station. Here Matsuda is applied for the 1st alternative).
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings in order to determine downlink control signal on the basis of the downlink-channel-state information fed back.
As to claims 12, Wang- Schiffman discloses of sending capability information, but fails to artificial intelligence model type. However, Matsuda discloses
wherein information about which channel uses which artificial intelligence model type is taken into account by the base station (Matsuda; [0186] discloses a difference in processing capability between terminal apparatuses, and there may be a terminal apparatus that is not capable of setting and training an AI/ML model properly. In order to deal with this issue, the base station may transmit, to the terminal apparatus, information on which to base learning of an AI/ML model (such as the number of input nodes, the number of output nodes, a layer configuration, and a value of weighting coefficient). Further, the terminal apparatus may transmit, to the base station, information regarding the capability of the terminal apparatus with respect to an AI/ML model (such as the type of a supported AI/ML model, the number of input nodes, the number of output nodes, a layer configuration, and information regarding a held CPU or GPU).
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings in order to determine type of a supported AI/ML model).
As to claims 13, the rejection of claim 12 as listed above is incorporated herein. In addition, Wang- Schiffman-Matsuda discloses wherein the artificial intelligence configuration is chosen by the base station based on priorities, sizes and/or latency requirements of the respective channels (Wang; [0168] discloses the base station 120 receives UE metrics from one or more UE of the UE(s) 110, such as power measurements (e.g., RSS), error metrics, timing metrics, QoS, latency, a Reference Signal Receive Power (RSRP), SINR information, CQI, CSI, Doppler feedback, QoS, latency, etc. Here Wang is applied for the 2nd alternative latency)
As to claims 16, Wang- Schiffman discloses of sending capability information, but fails to auto encoder. However, Matsuda discloses
wherein an auto-encoder used in the respective user equipment is configured based on the artificial intelligence configuration (Matsuda; Fig.11:S309; [0227]).
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings in order to encode the downlink-channel-state information.
As to claims 17, Wang- Schiffman discloses of sending capability information, but fails to auto decoder. However, Matsuda discloses
wherein an auto-decoder used in the base station is also configured based on the artificial intelligence configuration (Matsuda; Fig.11:S311; [0229]).
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings in order to decode the encoded downlink-channel-state information received from the terminal apparatus B, and acquires the downlink-channel-state information.
Conclusion
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/FAISAL CHOUDHURY/Primary Examiner, Art Unit 2478