DETAILED ACTION
This action is in response to the original filing of 4-24-2024. Claims 1-7 are pending and have been considered below:
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 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 1-7 is/are rejected under 35 U.S.C. 103 as being
unpatentable over Xu et al. (“Xu” 20210295168 A1) in view of
Zhou et al. (“Zhou” 20240096076 A1) and Pezeshki et al. (“Pezeshki” 20230084883 A1).
Claim 1: Xu discloses a method for training a machine learning model, comprising the following steps:
ascertaining, for each of a plurality of training data elements, a gradient of a target function, wherein each of the gradients includes a component for each of a plurality of parameters of the machine learning model (Paragraphs 23 (data (component) gradients provided), 41 and 44-45; loss gradient provides a target function);
and adjusting the machine learning model in a direction given by the overall gradient (Paragraphs 23 and 41 and 44-45; adjustment of weighting (up/down)).
Xu may not explicitly disclose every feature of generating an overall gradient by averaging the ascertained gradients component-wise by summing, for each component, values of the ascertained gradients for the component
Zhou is provided because it discloses a system for determining the gradient of a function (abstract: loss) and further provides gradients through component-wise functionality and a summing of terms (components) (Paragraphs 17 and 91-92).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in order to implement a component wise operation in Xu. Xu and Zhou are found to operate in the same field of artificial intelligence including neural networks. One would have been motivated to provide the functionality because it allows the system to better identify and label training data for improved updating capabilities (Zhou: Paragraphs 19-20).
Nor disclosed is dividing a resulting sum for the component by a number of the ascertained gradients for which the component is above a specified threshold value.
Pezeshki is provided because it discloses a system for determining the gradient and further discloses utilizing a summing and division functionality of a designated group in order to update the model (Paragraphs 147 and 149).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in order to implement summing and dividing of gradients in Xu. Xu and Pezeshki are found to operate in the same field of artificial intelligence including neural networks. One would have been motivated to provide the functionality because the readings provide data which can improve performance and efficiency of learning (Pezeshki: Paragraph 26).
Claim 2: Xu, Zhou and Pezeshki disclose a method according to claim 1, wherein the target function depends on rewards that are contained in the training data elements and specify rewards for state transitions caused by outputs of the machine learning model (Zhou: abstract and Paragraphs 92-93; by penalizing the network aims to adjust away (rewarded)).
Claim 3: Xu, Zhou and Pezeshki disclose a method according to claim 1, wherein the specified threshold value is zero (Xu: Paragraphs 29 and 61; the gradient can specified threshold ).
Claim 4: Xu, Zhou and Pezeshki disclose a method according to claim 1, wherein the machine learning model is configured and is being trained to receive, as input, information about a kinematic state of a vehicle and to output control information for the vehicle for a driving stabilization program (Zhou: Paragraph 3-4, 11, 13 and 30 (apply brakes/stabilization)).
Claim 5 is similar in scope to claim 1 and therefore rejected under the same rationale.
Further, supplying information about states of the technical system to the machine learning model; and controlling the technical system according to outputs of the trained machine learning model in responses to the supplied information (Zhou: Paragraphs 30-31 and 39-40; supplied data characterized and action applied (i.e. braking)).
Claims 6-7 are similar in scope to claim 1 and rejected under the same rationale.
Device (Xu: Paragraph 51)
Medium (Xu: Paragraph 125)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20180075347 A1 Alistarh et al.
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148
8-17-2026
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