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 .
Status of the Claims
Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “poorly” in claims 5, 12, and 19 is a relative term which renders the claim indefinite. The term “poorly” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Specifically, it is not clear how poorly the models must perform in order to infringe the claims.
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, 7-11, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, et al., U.S. Pat. Pub. No. 2025/0298688 (Reference A of the attached PTO-892) in view of Dalli, et al., U.S. Pat. Pub. No. 2022/0398460 (Reference B of the attached PTO-892).
As per claim 1, Zhang teaches a method, by one or more processors of a computing system, comprising:
accessing a set of sensor data captured from one or more sensors configured to detect one or more operational parameters associated with an operation of a real-time or near real-time system (¶¶ 0025, 73);
inputting the set of sensor data into an ensemble machine-learning model trained to generate a prediction of one or more features of the detected one or more operational parameters based at least in part on the set of sensor data, wherein the ensemble machine-learning model comprises a plurality of machine-learning models trained to generate the prediction of the one or more features of the detected one or more operational parameters (¶¶ 0027-28, 53, 58);
outputting, by the ensemble machine-learning model, the prediction of the one or more features of the detected one or more operational parameters (¶¶ 0053, 58, 63);
generating, based at least in part on the prediction of the one or more features of the detected one or more operational parameters, an explainability output associated with each of the plurality of machine-learning models (¶¶ 0064, 72).
Zhang does not explicitly teach generating, based at least in part on the explainability output, one or more relative commonality scores for each of the plurality of machine-learning models; which is taught by Dalli (¶ 0274; Fig. 3A). It would have been prima facie obvious to incorporate this element for the same reason it is useful in Dalli—namely, to provide a quantitative explanation of model quality comparisons. Moreover, this is merely a combination of old elements in the art of evaluating models. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
As per claim 2, Zhang in view of Dalli teaches claim 1 as above. Zhang further teaches causing a user interface (UI) executing on a computing device to display a real-time or near real-time data visual representation of the explainability output and the set of sensor data (¶¶ 0030-31, 75). Even if these sections are interpreted as not fully teaching the limitations, they are also taught by Dalli (¶¶ 0273-275; Fig. 3A; ¶ 0421; see also ¶ 0426—parameters under analysis can include sensor data) and would have been obvious to incorporate for the same reasons as the elements in claim 1 above. Moreover, with respect to the sensor data, this is not manipulated and instead is merely passively displayed on the interface. This merely descriptive data cannot lend patentability to an invention that would have otherwise been anticipated by the prior art. In re Ngai, 367 F.3d 1336, 1339; 70 USPQ2d 1862, 1864 (Fed. Cir. 2004); cf. In re Gulack, 703 F.2d 1381, 1385; 217 USPQ 401, 404 (Fed. Cir. 1983) (when descriptive material is not functionally related to the substrate, the descriptive material will not distinguish the invention from the prior art in terms of patentability).
As per claim 3, Zhang in view of Dalli teaches claim 2 as above. Dalli further teaches causing the UI executing on the computing device to display a visual representation of the one or more relative commonality scores for each of the plurality of machine-learning models (¶¶ 0273-275; Fig. 3A; ¶ 0421), which would have been obvious to incorporate for the same reasons as the elements in claim 1 above.
As per claim 4, Zhang in view of Dalli teaches claim 1 as above. Dalli further teaches generating the one or more relative commonality scores further comprises generating one or more evaluation metrics indicative of how well each respective machine-learning model of the plurality of machine-learning models performed with respect to generating the prediction of the one or more features of the detected one or more operational parameters (¶¶ 0273-275; Fig. 3A).
As per claim 7, Zhang in view of Dalli teaches claim 1 as above. Zhang further teaches the ensemble machine-learning model comprises one or more of a convolutional neural network (CNN), a deep neural network (DNN) (¶ 0028), a deep convolutional neural network (DCNN), a vision transformer (ViT), one or more sequence-to-sequence (Seq2Seq) models, one or more encoder-decoder sequence models, or one or more transformer models.
As per claims 8-11 and 14, Zhang in view of Dalli teaches a computing system, comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the one or more non-transitory computer-readable storage media, the one or more processors configured to execute the instructions to: perform the functions of analogous claims 1-4 and 7 (Zhang ¶ 0074, see also citations and obviousness rationale above).
As per claims 15-18, Zhang in view of Dalli teaches a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to: perform the functions of analogous claims 1-4 (Zhang ¶ 0074, see also citations and obviousness rationale above).
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, et al. in view of Dalli, et al. as applied to claims 1, 8, and 15 above, further in view of Zhang, et al., U.S. Pat. Pub. No. 2024/0078516 (Reference C of the attached PTO-892) (“Zhang II”).
As per claims 5, 12, and 19, Zhang in view of Dalli teaches claims 1, 8, and 15 as above. Dalli further teaches identifying, based at least in part on the one or more relative commonality scores, that one or more machine-learning models of the plurality of machine-learning models performed poorly with respect to generating the prediction of the one or more features of the detected one or more operational parameters (Fig. 3A), which would have been obvious to incorporate for the same reasons as the elements in claim 1 above.
The references do not explicitly teach decommissioning the identified one or more machine-learning models; which is taught by Zhang II (¶ 0065). It would have been prima facie obvious to incorporate this element for the same reason it is useful in Zhang II-namely, to avoid the use of suboptimal models. Moreover, this is merely a combination of old elements in the art of evaluating models. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, et al. in view of Dalli, et al. as applied to claims 1, 8, and 15 above, further in view of Cheng, et al., U.S. Pat. Pub. No. 2022/0405623 (U.S. Pat. Pub. Citation 5 of the IDS filed 6/28/2024).
As per claims 6, 13, and 20, Zhang in view of Dalli teaches claims 1, 8, and 15 as above. The references do not explicitly teach generating, based at least in part on the explainability output, one or more aggregated feature attributions for each of the plurality of machine-learning models; which is taught by Cheng (¶¶ 0042-44; cl. 4). It would have been prima facie obvious to incorporate this element for the same reason it is useful in Cheng—namely, in order to further interpret the relationship between inputs and outputs of a machine learning model. Moreover, this is merely a combination of old elements in the art of evaluating models. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mathews, et al., U.S. Pat. Pub. No. 2025/0315448 (Reference D of the attached PTO-892) relates to controlling machine learning models.
Crabtree, et al., U.S. Pat. Pub. No. 2025/0259075 (Reference E of the attached PTO-892) relates to controlling machine learning models.
Yu, et al., U.S. Pat. Pub. No. 2024/0386295 (Reference F of the attached PTO-892) relates to controlling machine learning models.
Bucklin, et al., U.S. Pat. Pub. No. 2024/0193481 (Reference G of the attached PTO-892) relates to controlling machine learning models.
Brusa, et al., Explainable AI for machine fault diagnosis: understanding features’ contribution in machine learning models for industrial condition monitoring, Applied Sciences, Vol. 13, No. 4, 2023, pg. 2038 (Reference U of the attached PTO-892) relates to controlling machine learning models.
Mansouri, et al., A deep explainable model for fault prediction using IoT sensors, IEEE Access Vol. 10, 2022, pgs. 66933-66942 (Reference V of the attached PTO-892) relates to controlling machine learning models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL VETTER whose telephone number is (571)270-1366. The examiner can normally be reached M-F 9:00-6:00.
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/DANIEL VETTER/Primary Examiner, Art Unit 3628
