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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/9/2025, 7/11/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 15-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 15 recites a processor comprising at least one controller coupled with at least one memory....The claims processor could be a hardware element or a software element. A memory is cited but it is unclear that the cited memory is part of the claimed processor. Therefore, the processor being claimed is software per se which does not fall under any of the statutory categories defined under § 101. Software per se is not a useful process, a machine, a manufacture, or a composition of matter.
Therefore claim 15 and its dependent claims are directed towards non- statutory subject matter.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-4, 6-19 are rejected under 35 U.S.C. 103 as being unpatentable over Vaze et al (us 2025/0193778) (hereinafter Vaze) in view of Sampaio et al (us 2021/0374614) (hereinafter Sampaio) and further in view of Li et al (us 2023/0316085) (hereinafter Li).
As regarding claim 1, Vaze discloses a first device for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the first device to (see Vaze 0216-0218 wireless device with processor, memory…): generate a set of probability values based at least in part on inputting unlabeled data in a machine learning model (see Vaze 0137 performance of the model is evaluated; 0181-0182 further discloses provide output of AI model to evaluate accuracy of output of AI model).
Vaze is silent in regard to the concept of each probability value of the set of probability values indicates a probability that a respective label value is a label of at least a portion of the unlabeled data.
Sampaio teaches the concept of each probability value of the set of probability values indicates a probability that a respective label value is a label of at least a portion of the unlabeled data (see Sampaio 0015, using unlabeled data for training and evaluating ML model; 0039-0041, identify a portion of the unlabeled data to label…and 0081 teaches determine the outlier for the unlabeled pool of data to find greatest outlier relative to label data).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Sampaio to Vaze because they're analogous art. A person would have been motivated to modify Vaze with Sampaio’s teaching for the purpose of efficiently and quickly train the model (see Sampaio 0014).
The combination of Vaze-Sampaio is silent in regard to the concept of generate a performance metric for the machine learning model based at least in part on the set of probability values and communicate in accordance with the performance metric.
Li teaches the concept of generate a performance metric for the machine learning model based at least in part on the set of probability values (see Li 0113-0115, generate the likelihood of sample data is calculated and calculate entropy value and compared to the threshold value to determine if the entropy value is high or low as compared to the threshold) and communicate in accordance with the performance metric (see Li, 0117-0118, output the prediction of the entropy).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Li to Vaze-Sampaio because they're analogous art. A person would have been motivated to modify Vaze-Sampaio with Li’s teaching for the purpose of efficiently improving the training of model (see Li 0004-0005).
As regarding claim 2, Vaze-Sampaio-Li discloses generate a respective subset of probability values for each respective portion of the unlabeled data based at least in part on inputting the respective portions of the unlabeled data in the machine learning model and generate an entropy value for each respective subset of probability values (see Li 0113-0115, generate the likelihood of sample data is calculated and calculate entropy value and compared to the threshold value to determine if the entropy value is high or low as compared to the threshold), wherein the performance metric is based at least in part on an average of the entropy values (see Li 0115, mean and variance of entropy value). The same motivation was utilized in claim 1 applied equally well to claim 2.
As regarding claim 3, Vaze-Sampaio-Li discloses scale the entropy values by one or more probability values associated with the unlabeled data, wherein the one or more probability values associated with the unlabeled data indicate a likelihood of the unlabeled data; or weight the entropy values by the one or more probability values associated with the unlabeled data (see Li 0113-0115, generate the likelihood of sample data is calculated and calculate entropy value and compared to the threshold value to determine if the entropy value is high or low as compared to the threshold). The same motivation was utilized in claim 1 applied equally well to claim 3.
As regarding claim 4, Vaze-Sampaio-Li discloses receive, from a second device, signaling indicating the one or more probability values associated with the unlabeled data (see Vaze 0135, training data regard signal from other UE).
As regarding claim 6, Vaze-Sampaio-Li discloses receive, from a second device, signaling indicating the unlabeled data (see Vaze 0135, training data regard signal from other UE).
As regarding claim 7, Vaze-Sampaio-Li discloses reference signaling (see Vaze 0071,0086,0178, reference signal).
As regarding claim 8, Vaze-Sampaio-Li discloses receive, from a second device, reference signaling; and measure the received reference signaling, wherein the unlabeled data is obtained based at least in part on the measurement (see Vaze 0071,0086,0178, receive reference signal).
As regarding claim 9, Vaze-Sampaio-Li discloses one or more of a reference signal receive power (RSRP) value, a reference signal receive quality (RSRQ) value, a signal-to-noise ratio (SNR) value, a channel quality indicator (CQI) value, a modulation coding scheme (MCS) value, a precoding matrix indicator (PMI) value, or a rank value (see Vaze 0178, RSRP).
As regarding claim 10, Vaze-Sampaio-Li discloses detect a trigger event, wherein the performance metric is generated based at least in part on detecting the trigger event (see Li 0113-0115, generate the likelihood of sample data is calculated and calculate entropy value and compared to the threshold value to determine if the entropy value is high or low as compared to the threshold). The same motivation was utilized in claim 1 applied equally well to claim 10.
As regarding claim 11, Vaze-Sampaio-Li discloses transmit, to a second device, a report indicating the performance metric (see Li, 0117-0118, output the prediction of the entropy). The same motivation was utilized in claim 1 applied equally well to claim 11.
As regarding claim 12, Vaze-Sampaio-Li discloses compare the performance metric to a threshold, wherein the report is transmitted based at least in part on the performance metric satisfying the threshold (see Li 0113-0115, generate the likelihood of sample data is calculated and calculate entropy value and compared to the threshold value to determine if the entropy value is high or low as compared to the threshold; see Li, 0117-0118, output the prediction of the entropy). The same motivation was utilized in claim 1 applied equally well to claim 12.
As regarding claim 13, Vaze-Sampaio-Li discloses the machine learning model comprises a classifier machine learning model that generates labels for the unlabeled data, the labels included in a set of one or more labels (see Sampaio 0015, using unlabeled data for training and evaluating ML model; 0039-0041, identify a portion of the unlabeled data to label…and 0081 teaches determine the outlier for the unlabeled pool of data to find greatest outlier relative to label data). The same motivation was utilized in claim 1 applied equally well to claim 13.
As regarding claim 14, Vaze-Sampaio-Li discloses the unlabeled data comprises one or more of a scalar, a one-dimensional vector, or a matrix having two or more dimensions (see Vaze 0165, two dimensional neural network).
As regarding claims 15-19, limitations of claims 15-19 are similar to limitations of rejected claims 1-4,6-14
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Vaze-Sampaio-Li as applied to claim 2 above, and further in view of Woo (us 2023/0142131).
As regarding claim 5, Vaze-Sampaio-Li discloses the invention as claims in claim 2 above, however Vaze-Sampaio-Li is silent in regard to the concept of the entropy values are generated via Shannon entropy.
Woo teaches the concept of the entropy values are generated via Shannon entropy (see Woo 0071, Shannon entropy).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Woo to Vaze-Sampaio-Li because they're analogous art. A person would have been motivated to modify Vaze-Sampaio-Li with Woo’s teaching for the purpose of enabling efficient information processing, decision-making, and predictive modeling across multiple domains.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Vaze et al (us 2025/0193778) (hereinafter Vaze) in view of Li et al (us 2023/0316085) (hereinafter Li).
As regarding claim 20, Vaze discloses first device for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the first device to (see Vaze 0216-0218 wireless device with processor, memory…): generate a set of probability values based at least in part on inputting unlabeled data in a machine learning model (see Vaze 0137 performance of the model is evaluated; 0181-0182 further discloses provide output of AI model to evaluate accuracy of output of AI model).
Vaze is silent in regard to the concept of generate an entropy value for unlabeled data; generate a performance metric for the machine learning model based at least in part on the entropy value; and communicate in accordance with the performance metric.
Li teaches the concept of generate an entropy value for unlabeled data; generate a performance metric for the machine learning model based at least in part on the entropy value (see Li 0113-0115, generate the likelihood of sample data is calculated and calculate entropy value and compared to the threshold value to determine if the entropy value is high or low as compared to the threshold) and communicate in accordance with the performance metric (see Li, 0117-0118, output the prediction of the entropy).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Li to Vaze because they're analogous art. A person would have been motivated to modify Vaze with Li’s teaching for the purpose of efficiently improving the training of model (see Li 0004-0005).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUYEN MY DOAN whose telephone number is (571)272-4226. The examiner can normally be reached (571)272-4226.
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/DUYEN M DOAN/Primary Examiner, Art Unit 2459
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