DETAILED ACTION
Claims 11-13 and 16-20 are presented for examination.
This office action is in response to submission of application on 28-SEPTEMBER-2022.
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 28-SEPTEMBER-2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The amendment filed 12-NOVEMBER-2025 in response to the non-final office action mailed 15-AUGUST-2025 has been entered. Claims 11-13 and 16-20 remain pending in the application.
With regards to the non-final office action’s rejection under 101, the amendments to the claims are not sufficient to overcome the original rejection with regards to the claims being directed towards an abstract idea.
With regards to the non-final office action’s rejection under 112, the amendment to the claims have overcome the original rejection.
With regards to the non-final office action’s rejection under 103, the amendment to the claims have overcome the original rejection. Furthermore, although a new search was performed for the amended limitations, the amended limitations are additionally not taught by the prior art.
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 11-13 and 16-20 rejected under 35 U.S.C. 101 because the claimed invention is direction to an abstract idea without significantly more.
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide run) to perform the claim limitation.
MPEP 2106.04(a)(2)(I) “The mathematical concepts grouping is defined as mathematical
relationships, mathematical formulas or equations, and mathematical calculations.”
Regarding claim 11:
Step 2A, Prong 1 will now be evaluated for this claim:
A judicial exception is recited in this claim as it recites a mental process:
predict future sequential time series (xt+i to xt+i) in time steps (t+1 to t+h) as a function of past sequential time series (xl to xt) to control an engineering system, using training data sets (xl to xt+h)
Prediction a future time series would be a form of evaluation as a human would be able to look at past time series and evaluate from them a possible sequence of future data.
A judicial exception is recited in this claim as it recites a mathematical concept:
adapting a parameter of the artificial neural network as a function of a loss function, the loss function including a first term, which includes an estimate of a lower bound (ELBO) of distances between a prior probability distribution (prior) over at least one latent variable and a posterior probability distribution (inference) over the at least one latent variable; wherein the prior probability distribution (prior) is independent of a future sequential time series (xt+1 to xt+h).
This limitation describes a specific mathematical equation and process that forms the loss function.
Predict the future sequential time series (xt+1 to xt+h) in time steps (t+1 to t+h) as a function of the past sequential time series (x1 to xt)
Prediction in the form of a function would be a mathematical calculation.
Wherein the lower bound (ELBO) is estimated according to the following rule, using the loss function:
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These limitations describe a specific mathematical relationship and as such are a mathematical concept. Furthermore, the estimate of the lower bound using a loss function is a mathematical calculation.
Step 2A, Prong 2 will now be evaluated for this claim:
Furthermore, the additional elements:
training an artificial neural network
training an artificial neural network with the adapted parameter
are interpreted as a general purpose computer under MPEP 2106.05(f)
Furthermore, MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post-solution activity to be insignificant extra-solution activity.
The following steps are merely post solution activity:
control an engineering system
Controlling a engineering system is not discussed in further detail in the claim and is considered an insignificant application.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed practicing the abstract idea.
Therefore, the claim is related to an abstract idea.
Step 2B will now be discussed with regards to this claim:
The claim does not provide an inventive concept. There is no additional Insignificant Extra- Solution Activity, as identified in Step 2A Prong Two, that provides an inventive concept.
Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) does not overcome a rejection.
Generally linking the use of the judicial exception to computer environments, e.g., a claim describing how the abstract idea of creating a contractual relationship that guarantees performance of a transaction be performed using a computer that receives and sends information over a network, as discussed in buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014). (MPEP § 2106.05(h)) does not overcome a rejection.
The additional elements have been considered both individually and as an ordered combination as to whether they whether they warrant significantly more consideration.
The claim is ineligible.
Regarding claim 12, which depends upon claim 11.
This claim further limits the artificial neural network of claim 11. Further specifying the artificial neural network in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 13, which depends upon claim 11.
This claim further limits the artificial neural network of claim 11. Further specifying the artificial neural network in this manner does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Claim 16 recites a non-transitory machine-readable storage medium that parallels the method of claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 16. Accordingly, claim 16 is rejected based on substantially the same rationale as set forth above with respect to claim 1.
Regarding claim 17:
The following would be a mental process:
Predict future sequential time series (xt+1 to xt+h) in time steps (t+1 to t+h) as a function of past sequential time series (x1 to xt) to control an engineering system, using training data sets (x1 to xt+h), the artificial neural network being trained by
Prediction a future time series would be a form of evaluation as a human would be able to look at past time series and evaluate from them a possible sequence of future data.
The following would be a mathematical concept:
adapting a parameter of the artificial neural network as a function of a loss function, the loss function including a first term, which includes an estimate of a lower bound (ELBO) of distances between a prior probability distribution (prior) over at least one latent variable and a posterior probability distribution (inference) over the at least one latent variable; wherein the prior probability distribution (prior) is independent of future sequential time series (xt+l to xt+h)
This limitation describes a specific mathematical equation and process that forms the loss function.
The following would be a generic computer function:
an artificial neural network including Bayesian neural network, the artificial neural network being trained
The following would be post-solution activity:
To control an engineering system
Controlling an engineering system and its ties to the application is not discussed in further detail in the claim and is considered an insignificant application.
This claim is ineligible.
Regarding claim 18:
The following would be a mental process:
predict future sequential time series (xt+1 to xt+h) in time steps (t+1 to t+h) as a function of past sequential time series (xl to xt) to control an engineering system, using training data sets (xI to xt+h)
Prediction a future time series would be a form of evaluation as a human would be able to look at past time series and evaluate from them a possible sequence of future data.
The following would be a mathematical concept:
adapting a parameter of the artificial neural network as a function of a loss function, the loss function including a first term, which includes an estimate of a lower bound (ELBO) of distances between a prior probability distribution (prior) over at least one latent variable and a posterior probability distribution (inference) over the at least one latent variable; wherein the prior probability distribution (prior) is independent of future sequential time series (xt+l to xt+h)
This limitation describes a specific mathematical equation and process that forms the loss function.
The following would be a generic computer function:
using an artificial neural network including a Bayesian neural network, the method comprising: providing a trained artificial neural network, the artificial neural network being trained
The following would be post-solution activity:
To control an engineering system
controlling, using the trained artificial neural network, the engineering system, the engineering system including a robot or a vehicle or a tool or a machine tool
Controlling an engineering system and its ties to the application is not discussed in further detail in the claim and is considered an insignificant application.
This claim is ineligible.
Claim 19 recites a non-transitory machine-readable storage medium that parallels the method of claim 18. Therefore, the analysis discussed above with respect to claim 18 also applies to claim 19. Accordingly, claim 19 is rejected based on substantially the same rationale as set forth above with respect to claim 18.
Claim 20 recites a device that parallels the method of claim 18. Therefore, the analysis discussed above with respect to claim 18 also applies to claim 20. Accordingly, claim 20 is rejected based on substantially the same rationale as set forth above with respect to claim 18.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter for claims 11-13 and 16-20:
Regarding claim 11:
The references of record (Han et al. (Pub. No. US 20190180882 A1, filed July 10th 2018, hereinafter Han) in view of Menick et al. (Pub. No. WO 2019155064 A1, filed February 11th, 2019, hereinafter Menick) further in view of Mehta et al. (Pub. No. US 20200207375 A1, filed December 26th 2019, hereinafter Mehta)) alone or in combination do not disclose or suggest the limitations found within claim 11.
This claim recites a specific mathematical formula for estimating the lower bound, which is not found within the prior art. In contrast, Mehta discusses the minimization of a cost function (Paragraph 135), wherein the minimization may be considered to be a form of estimating the lower bound of the function, but it does not recite the precise formula that the present application uses for its estimate. Therefore, Mehta does not teach this limitation, nor do Han or Menick, alone or in combination.
Regarding claims 12-13:
These claims fully incorporate the allowable elements of their parent claim, and as such contain allowable subject matter for the same reasons as listed above.
Regarding claims 16-20:
Each of these claims contains an analogous limitation as claim 11’s, and as such contain allowable subject matter for the same reasons as listed above.
Response to Arguments
Applicant’s arguments filed 12-NOVEMBER-2025 have been fully considered, but the examiner believes that not all are fully persuasive.
Regarding the applicant’s remarks on non-final office action’s 101 rejection of the claims as an abstract idea, the applicant argues that regardless of the original claims, the amended claims are not directed towards an abstract idea. The examiner respectfully requests the applicant’s consideration of the following:
The applicant argues that the amended limitation training the artificial neural network with the adapted parameter to predict the future sequential time series (xt+1 to xt+h) in time steps (t+1 to t+h) as a function of the past sequential time series (x1 to xt) to control the engineering system for the following reasons:
The applicant alleges that the training of the artificial neural network with the adapted parameter provides an improvement to the control of an engineer system.
However, the alleged improvements are believed by the examiner to result from the abstract idea of predicting the future sequential time series. For example, in the cited section of the present application’s specification (Page 4, line 21 – Page 5, line 1), the improvement is from the prediction of the future probability distribution. MPEP 2106.05(a) states that “the judicial exception alone cannot provide the improvement”. While the improvement “can be provided by one or more additional elements” or “the additional element(s) in combination with the recited judicial exception” there is no indication that the training of the artificial neural network specifically is instrumental in providing this improvement.
Likewise, the applicant’s arguments that the limitation provides improved prediction quality and marked improvement in control result from the estimate of the lower bound, which is math and hence a judicial exception. (Present specification, Page 5, line 21 – Page 6, line 3).
The analogous elements of claims 16-20 are likewise not viewed as sufficient to overcome the 101 rejection for similar reasoning.
The applicant argues that the controlling, using the trained artificial neural network with the adapted parameter, the engineering system, the engineer system including a robot or a vehicle or a tool or a machine tool of claims 18 and 19, and the analogous limitation of claim 20, recite elements that amount to practical application. Controlling an engineering system as described is an existing process, wherein the computer elements are invoked only as a tool to perform it. For this reason, this limitation is viewed as extra-solution activity as mere instructions to apply an exception (MPEP 2106.05(f)).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDRIA JOSEPHINE MILLER whose telephone number is (703)756-5684. The examiner can normally be reached Monday-Thursday: 7:30 - 5:00 pm, every other Friday 7:30 - 4:00.
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/A.J.M./Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142