DETAILED ACTION
1. This office action is in response to application 18/274,526 filed on 7/27/2023. The preliminary amendment filed on 7/27/2023. Claims 18-21, 24-27 and 29-31 have been cancelled. Accordingly, claims 1-17, 22, 23, and 28 are pending in this office action
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
3. Claim 22 is objected to because of the following informalities: Claim 22 depends on cancelled claim 21. Examiner assumes claim 21 was meant to depend on either claim 1 or 16. Appropriate correction is required.
Allowable Subject Matter
4. Claims 6-12 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.
Claim Rejections - 35 USC § 102
5. 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 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.
Claim(s) 1, 2, 16, 17 and 22 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2022/0179840 (hereinafter Chatterjee).
As for claim 1 Chatterjee discloses: 1. A method for determining bias of machine learning models (See paragraphs 0002-0004 and 0021 note the system determines bias from the remote machine but can also determine bias with respect to the local machine), comprising: forming a training dataset comprising input data samples provided to a remote machine learning model developed using a machine learning process, and corresponding output data samples obtained from the remote machine learning model (See paragraphs 0026, 0029 and 0044 note the data samples are taken from trusted and untrusted and the system trains from the samples); training a local machine learning model which approximates the remote machine learning model using a machine learning process and the training dataset (See paragraphs 006, 0024, 0038 note the system can be trained on both the trusted and untrusted data); and interrogating (204) the trained local machine learning model to determine whether the remote machine learning model is biased with respect to one or more biasing data parameters (See paragraphs 0019-0024 note the bias correction is trained).
As for claim 2 the rejection of claim 1 is incorporated and further discloses: wherein interrogating the local machine learning model comprises: augmenting the training dataset with the one or more biasing data parameters as input features, to create an augmented training dataset (See paragraphs 0007 and 0037, note an updated training dataset based on the features and the bias is an augmented training dataset); retraining the local machine learning model using the machine learning process and the augmented training dataset (See paragraphs 0026, 0037 note the initial training is used to retrain the system based on the dynamically built trusted dataset);
determining an importance of the one or more biasing data parameters to the retrained local machine learning model; and based on the determined importance of the one or more biasing data parameters to the retrained local machine learning model, determining whether the remote machine learning model is biased with respect to the one or more biasing data parameters (See paragraphs 0033 and 0066).
Claims 16 and 17 are apparatus claims substantially corresponding to the method of claims 1 and 2 and are thus rejected for the same reasons as set forth in the rejection of claims 1 and 2.
As for claim 22 the rejection of claim 1 is incorporated and further Chatterjee discloses:, wherein the one or more actions further comprise determining an importance of input data features to the further machine learning model, based on the importance of the one or more input data features to the further machine learning model, removing one or more input data features from the training dataset to obtain an unbiased training dataset, and retraining the local machine learning model using the machine learning process and the unbiased training dataset (See paragraphs 006, 0024, 0038 note the system can be trained on both the trusted and untrusted data).
Claim Rejections - 35 USC § 103
6. 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) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chatterjee as applied to claim 1 above, and further in view of US 2023/0229735 (hereinafter Jain).
As for claim 3 the rejection of claim 1 is incorporated and further Jain renders obvious: wherein the importance of the one or more biasing parameters is quantified by respective Shapley values associated with the one or more biasing data parameters (See paragraphs 0097). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Jain into the system of Chatterjee. The modification would have been obvious because the two references are concerned with the solution to problem of processing data based on bias (See Jain and Chatterjee abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Jain’s teaching would enable users of the Chatterjee system to have more efficient processing.
As for claim 4 the rejection of claim 2 is incorporated and further Jain renders obvious: wherein whether the remote machine learning model is biased with respect to the one or more biasing data parameters is further based on a comparison of an accuracy of the local machine learning model to an accuracy of the retrained local machine learning model (See paragraphs 0006, 0057 and 0084 note the system can compare intermediate/local data to determine accuracy). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Jain into the system of Chatterjee. The modification would have been obvious because the two references are concerned with the solution to problem of processing data based on bias (See Jain and Chatterjee abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Jain’s teaching would enable users of the Chatterjee system to have more efficient processing.
7. Claim(s) 5, 13-15, 23 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chatterjee as applied to claim 1 or 16 above, and further in view of US 2024/0095588 (hereinafter Vandikas).
As for claim 5 the rejection of claim 1 is incorporated and further Vandikas discloses:
further comprising performing one or more actions to mitigate bias with respect
to the one or more biasing parameters (See paragraph 0029 note the system will retrain to mitigate bias). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Vandikas into the system of Chatterjee. The modification would have been obvious because the two references are concerned with the solution to problem of processing data based on bias (See Vandikas and Chatterjee abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Vandikas’s teaching would enable users of the Chatterjee system to have more efficient processing.
As for claims 13 and 28 the rejection of claims 1 and 16 are incorporated respectfully and further Vandikas discloses: wherein the method is performed by a network node in a communications network (See paragraphs 0022 note network node). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Vandikas into the system of Chatterjee. The modification would have been obvious because the two references are concerned with the solution to problem of processing data based on bias (See Vandikas and Chatterjee abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Vandikas’s teaching would enable users of the Chatterjee system to have more efficient processing.
As for claim 14 the rejection of claim 13 is incorporated and further Vandikas discloses: wherein the input data samples and corresponding output data samples are received from one or more wireless devices coupled to the communications network (See paragraphs 0022 note wireless). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Vandikas into the system of Chatterjee. The modification would have been obvious because the two references are concerned with the solution to problem of processing data based on bias (See Vandikas and Chatterjee abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Vandikas’s teaching would enable users of the Chatterjee system to have more efficient processing.
As for claim 15 the rejection of claim 1 is incorporated and further Vandikas discloses:
wherein one or more of the following applies: the remote machine learning model provides
an output on the basis of which a radio access network operation is performed; the remote machine learning model provides an output on the basis of which an action is performed in a smart factory; the remote machine learning model provides an output on the basis of which an autonomous vehicle operation is performed; and the remote mode provides an output on the basis of which a medical procedure is performed (See paragraphs 0022 note radio). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Vandikas into the system of Chatterjee. The modification would have been obvious because the two references are concerned with the solution to problem of processing data based on bias (See Vandikas and Chatterjee abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Vandikas’s teaching would enable users of the Chatterjee system to have more efficient processing.
As for claim 23 the rejection of claim 22 is incorporated and further Vandikas discloses: wherein the apparatus is caused to train the further machine learning model, determine an importance of input data features to the further machine learning model, and remove one or more input data features from the training dataset iteratively (See paragraphs 0052 and 0067 note the system performs iterative removal). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Vandikas into the system of Chatterjee. The modification would have been obvious because the two references are concerned with the solution to problem of processing data based on bias (See Vandikas and Chatterjee abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Vandikas’s teaching would enable users of the Chatterjee system to have more efficient processing.
Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIYAH STONE HARPER whose telephone number is (571)272-0759. The examiner can normally be reached on Monday-Friday 10:00 am - 6:00 pm.
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/Eliyah S. Harper/Primary Examiner, Art Unit 2166 February 24, 2026