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
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 1-2, 5-6, 9-10, 13-14 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fakoorian et al (US 2024/0406036 A1).
Regarding Claim 1, 9, Fakoorian teaches a method/apparatus of a first device in a communication system, the method comprising:
determining whether to train an artificial intelligence (AI) model ([0064], “the network indicates to the UE that retuning is required”; [0040], “For AI/ML models, retuning is essential to maintaining system performance. For example, a trained model may perform poorly based on any number of reasons and require a new training phase. Thus, new training data may be gathered to re-train the model”);
transmitting, based on the determination, control information to a second device, comprising at least one of a first indicator indicating whether to train the Al model or a second indicator indicating a reference signal to be used for training the Al model ([0066], “the UE is indicated to retune its AI/ML network by a group common (GC) indication, e.g. a GC-DCI. A group of UEs may be indicated to enter (or exit) a specific ML/AI mode, for example, all indicated UEs are signaled to enter AI/ML training mode and receive high density DMRS or CSI-RS”); and
transmitting data to the second device ([0066], “receive high density DMRS or CSI-RS”, it’s noted that network side is the one sending the high density DMRS or CSI-RS).
Regarding Claim 2, 10, Fakoorian further teaches that the transmitting of the control information comprises:
in case that the Al model is determined to be trained, transmitting, to the second device, the control information comprising the first indicator indicating to train the Al model and the second indicator ([0066], “the UE is indicated to retune its AI/ML network by a group common (GC) indication, e.g. a GC-DCI. A group of UEs may be indicated to enter (or exit) a specific ML/AI mode, for example, all indicated UEs are signaled to enter AI/ML training mode and receive high density DMRS or CSI-RS”); and
in case that a training of the Al model is determined to be omitted, transmitting, to the second device, the control information comprising the first indicator indicating to omit the training of the Al model ([0068, “a similar mechanism may be used to request and/or indicate a switch from the training phase to the inference phase”, it’s noted that inference phase means training of the AI model is omitted), and
wherein the control information is transmitted through one of a downlink control information (DCI), uplink control information (UCI), a medium access control (MAC) control element (CE), or a radio resource control (RRC) message ([0066], “GC-DCI”).
Regarding claim 5, 13, Fakoorian teaches a method/apparatus of a second device in a communication system, the method comprising:
receiving, from a first device, control information comprising at least one of a first indicator indicating whether to train an artificial intelligence (Al) model or a second indicator indicating a reference signal to be used for training the Al model ([0064], “the network indicates to the UE that retuning is required”; [0040], “For AI/ML models, retuning is essential to maintaining system performance. For example, a trained model may perform poorly based on any number of reasons and require a new training phase. Thus, new training data may be gathered to re-train the model”, [0066]); identifying, based on the control information, the Al model to be used for receiving data; and receiving, based on the identified Al model, the data from the first device ([0077], “When the UE is activated by a user, the UE may enter the inference phase as a default state until a retuning of the channel estimation model is determined to be needed. The re-tuning comprises a further training phase in which additional training data is input to the AI model and the hyperparameters of the model are adjusted. The retuning phase may require significantly less input data and processing than the initial training and may be performed regularly to improve the performance of the AI model in light of updated training information”).
Regarding claim 6, 14, Fakoorian teaches the identifying of the Al model comprises:
in case that the first indicator indicates to train the Al model, training the Al model based on the reference signal indicated by the second indicator ([0066], “the UE is indicated to retune its AI/ML network by a group common (GC) indication, e.g. a GC-DCI. A group of UEs may be indicated to enter (or exit) a specific ML/AI mode, for example, all indicated UEs are signaled to enter AI/ML training mode and receive high density DMRS or CSI-RS”); and in case that the first indicator indicates to omit a training of the Al model, omitting the training of the Al model ([0068, “a similar mechanism may be used to request and/or indicate a switch from the training phase to the inference phase”, it’s noted that inference phase means training of the AI model is omitted).
Allowable Subject Matter
Claim 3-4, 7-8, 11-12 and 15 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.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art of record, alone or in combination, does not teach or suggest further comprising a third indicator indicating a coherence time, and wherein the training of the AI model is omitted during the coherence time.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIMING LIU whose telephone number is (571)270-3859. The examiner can normally be reached M-F, 8:30am-5:00pm.
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/SIMING LIU/Primary Examiner, Art Unit 2411
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