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 .
DETAILED ACTION
This is Non-Final Office Action, in responses to Patent Application filed 09/25/2023; Claims Priority from Provisional Application 63379327, filed 10/13/2022 Claim(s) 1-20 are pending. Claim(s) 1 and 17 is/are independent.
In addition, 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). ... comprising: participating, by a processor of an apparatus, in training of a two-sided artificial intelligence (AI)/machine learning (ML) model; and performing, by the processor, a wireless communication by utilizing the two-sided AI/ML model...and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite ... participating, by a processor of an apparatus, in training of a two-sided artificial intelligence (AI)/machine learning (ML) model; and performing, by the processor, a wireless communication by utilizing the two-sided AI/ML model.
These limitation(s) recite mental processes and mathematical calculation....since training of a two-sided artificial intelligence (AI)/machine learning (ML) model... required high level mathematical calculations...(see the current specifications USPGPUB in Para(s) 4 and 5. i.e. both sides need to be trained through a forward pass (FP) and backpropagation (BP)...)
--------------Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “participating, by a processor of an apparatus” in training of a two-sided artificial intelligence (AI)/machine learning (ML) model;... These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) such as ... “participating, by a processor of an apparatus” in training of a two-sided artificial intelligence (AI)/machine learning (ML) model ...These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 2-16 and 18-20, further recite, addition limitation(s) such as, (plurality of types of training involving training of an autoencoder at a single entity... involving a sequence of separate trainings of the one or more encoders and the one or more decoders at the different entities, training entity, trains a matched two-sided AI/ML model in a single training session and through individual forward pass (FP) and backpropagation (BP) loops, downloads a corresponding part of the two-sided AI/ML model, training stage, shares encoded information in a forward pass (FP) and a decoder shares gradient in a backpropagation (BP), a non-training entity being synchronized and sharing a shared dataset and performing gradient exchange and latent output exchange, multiple encoders share latent in a forward pass (FP) and the multiple decoders share gradients in a backpropagation (BP), learn how to work with the trained one or more encoders, combination of datasets, matched encoder-decoder pair, joint training of one or more encoders and one or more decoders at different entities, and at least one of image compression, channel state information (CSI) compression, and peak-to-average-power ratio (PAPR) reduction, etc.,) These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
(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 and 17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gu (“US 20250379796 A1” filed 09/30/2022 [hereinafter “Gul”].
Independent Claim 1, Gu teaches: A method, comprising: participating, by a processor of an apparatus, in training of a two-sided artificial intelligence (AI)/machine learning (ML) model; and performing, by the processor, a wireless communication by utilizing the two-sided AI/ML model; (In Gu Para(s) 1 and 3 discloses the field of wireless communication systems, and more particularly, to communication devices and methods for machine learning (ML) model training, that utilized the joint training of the plurality of ML models with two-sided AI/ML model...)
Regarding Claim 17 is/are fully incorporated similar subject of claim 1 cited above.
Claims Rejection – 35 U.S.C. 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 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.
Claim(s) 2-4, 10-16, and 15-20 rejected under 35 U.S.C. 103 as being unpatentable over Gu (“US 20250379796 A1” filed 09/30/2022 [hereinafter “Gul”], in view of Koyuncu et al., (“US 20240267568 A1” CON to PCT/EP2021/079028 f filed 10/20/2021 [hereinafter “Koyuncu”].
Claim 2, Gu teaches: wherein the participating in training of the two-sided AI/ML model comprises participating in..(In Gu Para(s) 1 and 3); Gu further teaches training of an autoencoder (in Gu Para(s) 17 and 32, teaches several type of ML models of training ML methods including joint training by both UE and gNB wherein a basic auto-encoder model is included.) ... However, Gu does not expressly teach, But the combination of Gu and Koyuncu teach: ... a first type of training involving training of an autoencoder at a single entity; OR
a second type of training involving joint training of one or more encoders and one or more decoders at different entities; OR
a third type of training involving a sequence of separate trainings of the one or more encoders and the one or more decoders at the different entities...(in Koyuncu Para(s) 201-204, i.e., the input image may be processed by an autoencoding convolutional neural network .... Such an autoencoder 310 down-samples the input image by applying multiple convolutions and non-linear transformations, and produces a latent tensor y....)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Gu’s autoencoder method, to include a means said, a first type of training involving training of an autoencoder at a single entity; OR
a second type of training involving joint training of one or more encoders and one or more decoders at different entities; OR
a third type of training involving a sequence of separate trainings of the one or more encoders and the one or more decoders at the different entities... as taught by Koyuncu, that improved compression and decompression techniques that improve compression ratio with little to no sacrifice in picture quality ...[in Koyuncu Para 4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 3, Gu and Koyuncu teach: wherein the first type of training comprises a training stage in which the apparatus, as a training entity, trains a matched two-sided AI/ML model in a single training session (Gu Para(s) 1 and 3 and 49-54, i.e., training stage in which the apparatus, as a training entity, trains a matched two-sided AI/ML model in a single training session...) and through individual forward pass (FP) and backpropagation (BP) loops; (In Gu Para 67 and Fig. 8, teaches through individual forward pass (FP) and backpropagation (BP) loops...)
Claim 4, Gu and Koyuncu teach: wherein the first type of training further comprises an inference stage in which a non-training entity requests the training entity to provide corresponding encoder and decoder models and downloads a corresponding part of the two-sided AI/ML model (Gu Para(s) 1 and 3 and 45-46, i.e., training stage in which the apparatus, as a training entity, trains a matched two-sided AI/ML model in a single training session...wherein the ML models need to be monitored during model inference. A functional framework of RAN intelligence is provided in RAN3. It can be further modified for RAN1. The ML Model will be monitored after deployment to check whether it works properly. Usually, the ML model performance is compared to a criterion. If the ML model does not work properly. The UE will switch to another ML model, or fallback to the non-AI working way. The ML model being monitored will be retrained.... The decoder refers to the CSI reconstruction part and the encoder refers to the CSI generation part. When the gNB configures the encoders of a plurality of UEs (UE1 and UE2 here), the configuration information should be a broadcast-like signal, transmitted downlink...)
Claim 10, Gu and Koyuncu teach: wherein the third type of training comprises an encoder-first sequence of separate trainings such that one or more encoders are trained first and one or more decoders learn how to work with the trained one or more encoders; (Gu Para(s) 37, 40 and 46, i.e., training encoder sequence of separate trainings ...wherein trained decoders to work with the trained encoders...)
Claim 11, Gu and Koyuncu teach: wherein the third type of training comprises a decoder-first sequence of separate trainings such that one or more decoders are trained first and one or more encoders learn how to work with the trained one or more decoders; (Gu Para(s) 37, 40 and 46, i.e., training encoder sequence of separate trainings ...wherein trained decoders to work with the trained encoders...)
Claim 12, Gu and Koyuncu teach: wherein the third type of training comprises an encoder-first sequence of separate trainings involving a first entity that uses an encoder training a respective matched encoder-decoder pair and providing a dataset to a second entity that uses a decoder and trains the decoder with the dataset; (Gu Para(s) 37, 40 and 46, i.e., training encoder/decoder respectively of separate trainings ...wherein trained decoders to work with the trained decoders...data.)
Claim 13, Gu and Koyuncu teach: wherein the third type of training comprises an encoder-first training of multiple encoders and multiple decoders (In Gu Para 40, i.e., during training, a forward propagation and a backward propagation make a loop for encoders of the at least one first node the common decoder of the second node...)... such that: each of one or more first entities having the multiple encoders trains a respective two-sided AI/ML model to provide a respective dataset; and one or more second entities having the multiple decoders receive a combination of datasets from the one or more first entities and train the multiple decoders with the combination of datasets; (Gu Para(s) 37, 40 and 46, i.e., training encoder/decoder respectively of separate trainings ...wherein trained decoders to work with the trained decoders...data.)
Claim 14, Gu and Koyuncu teach: wherein the third type of training comprises a decoder-first sequence of separate trainings involving a first entity that uses a decoder training a respective matched encoder-decoder pair and providing a dataset to a second entity that uses an encoder and trains the encoder with the dataset (In Gu Para 40, i.e., during training, a forward propagation and a backward propagation make a loop for encoders of the at least one first node the common decoder of the second node... Moreover, Gu in Para(s) 37, 40 and 46, i.e., two UEs with corresponding encoder share a common decoder at the gNB side according to an embodiment of the present disclosure. FIG. 6 illustrates that, as an example, there are several first nodes, which can be UEs, and the one second that uses a decoder training...wherein IG. 6 is a schematic diagram illustrating an example of two UEs with corresponding encoder share a common decoder at the gNB side according to an embodiment of the present disclosure. FIG. 6 illustrates that, as an example, there are several first nodes, which can be UEs, and the one second node, which can be gNB. The encoder of UE 1 and encoder of UE2 shares a common decoder at the gNB. The decoder refers to the CSI reconstruction part and the encoder refers to the CSI generation part. When the gNB configures the encoders of a plurality of UEs (UE1 and UE2 here), the configuration information should be a broadcast-like signal, transmitted downlink. The configuration information is training assistant information...(in the BRI is recognized as respective matched encoder-decoder pair and providing a dataset to a second entity that uses an encoder and trains the encoder with the dataset....as claimed.)
Claim 15, Gu and Koyuncu teach: wherein the third type of training comprises a decoder-first training of multiple encoders and multiple decoders such that: each of one or more first entities having the multiple decoders trains a respective two-sided AI/ML model to provide a respective dataset; and one or more second entities having the multiple encoders receive a combination of datasets from the one or more first entities and train the multiple encoders with the combination of datasets (In Gu Para 40, i.e., during training, a forward propagation and a backward propagation make a loop for encoders of the at least one first node the common decoder of the second node... Moreover, Gu in Para(s) 37, 40 and 46, i.e., two UEs with corresponding encoder share a common decoder at the gNB side according to an embodiment of the present disclosure. FIG. 6 illustrates that, as an example, there are several first nodes, which can be UEs, and the one second that uses a decoder training...wherein IG. 6 is a schematic diagram illustrating an example of two UEs with corresponding encoder share a common decoder at the gNB side according to an embodiment of the present disclosure. FIG. 6 illustrates that, as an example, there are several first nodes, which can be UEs, and the one second node, which can be gNB. The encoder of UE 1 and encoder of UE2 shares a common decoder at the gNB. The decoder refers to the CSI reconstruction part and the encoder refers to the CSI generation part. When the gNB configures the encoders of a plurality of UEs (UE1 and UE2 here), the configuration information should be a broadcast-like signal, transmitted downlink. The configuration information is training assistant information...(in the BRI is recognized as multiple encoders receive a combination of datasets....as claimed.)
Claim 16, Gu and Koyuncu teach: wherein the participating in training of the two-sided AI/ML model comprises participating in training of the two-sided AI/ML model with respect to AT LEAST ONE OF image compression, channel state information (CSI) compression, and peak-to-average-power ratio (PAPR) reduction (In Gu Para 40, i.e., during training, a forward propagation and a backward propagation make a loop for encoders of the at least one first node the common decoder of the second node; (Gu Para(s) 2-3, i.e., training of the two-sided AI/ML model with respect to channel state information (CSI) compression...)
Regarding Claim(s) 18-20 (respectively) is/are fully incorporated similar subject of claim(s) 2, 10 and 16 (respectively) cited above.
Allowable Subject Matter
Claim(s) 5-9 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 and amending to remedy the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cricri et al., (“ US 20230269387 A1” filed 06/11/2021, relates to overfit a neural network on each media item, from a batch of media items, for a number of iterations to obtain an overfitted neural network model for the each media item; evaluate the overfitted neural network model on the each media item to obtain evaluation errors; and update parameters of the neural network to be based on the evaluation errors.... [the Abstract].
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/QUOC A TRAN/ Primary Examiner, Art Unit 2145
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