The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 4 April 2024. THIS ACTION IS NON-FINAL.
Status of Claims
Claims 1-20 are pending.
Claims 2, 5, 8, 9, 12, 15, 18 and 19 are rejected under 35 U.S.C. 112(b) as indefinite.
Claim 1-20 are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 8, 9, and 19 are objected to for depending on rejected claims.
Claim Objections
Claims 8, 9 and 19 are objected to because of the following informalities:
(a) Claim 8 recites “from the plurality of confidence interval”; it appears this should read “from the plurality of confidence intervals.”
(b) Claims 9 and 19 recite “a minimum number of implementations of the of the trained machine learning model”; the duplicated “of the” should be deleted.
(c) Claim 19 recites “configured execute the instructions”; it appears this should read “configured to execute the instructions.”
Appropriate correction is required.
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 2, 5, 8, 9, 12, 15, 18 and 19 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims depending from a rejected claim are not separately rejected unless noted.
Claims 2 and 12 recite “wherein the training data is based on implementing the trained machine learning model in one or more environments that are different from the new environment.” Parent claims 1 and 11 require the machine learning model to be trained on (“based on”) the set of training data. It is unclear how the training data used to produce the trained model can itself be based on implementing that same already-trained model, i.e., the claim is circular as to sequence, and one of ordinary skill in the art could not determine whether the claim requires an iterative/pre-trained model generating the data, a different (earlier) model, or something else, the claim is therefore indefinite. For examination purposes, the limitation is interpreted as requiring that the training data be collected from execution of a (preliminary or earlier) version of the model in one or more environments other than the new environment.
Claims 5 and 15 recite “a minimum number of times the trained machine learning model to implement the machine learning model” (claim 5) and a “confidence set” that “comprises a confidence value … and at least one of: a coverage …, a tightness …, and a minimum number ….” The quoted phrase of claim 5 is grammatically incomplete, so the metes and bounds of the “minimum number” element cannot be ascertained. Further, it is unclear whether “at least one of: A, B, and C” requires at least one of each of A, B and C, or at least one item selected from the group, the claim is therefore indefinite. For examination purposes, the claims are interpreted as requiring the confidence value plus any one of the listed items, and the phrase in claim 5 as “a minimum number of times to implement the trained machine learning model.”
Claim 15 is further indefinite because it depends from system claim 11 but recites “further comprising: determining a confidence set …,” i.e., a method step. A single claim that recites both an apparatus and a method step of using the apparatus is indefinite because it is unclear whether infringement occurs when the system is created or when the step is performed, the claim is therefore indefinite. For examination purposes, claim 15 is interpreted as reciting that the at least one processor is configured to determine the confidence set.
Claims 8 and 18 recite “determining an optimal confidence interval from the plurality of confidence intervals.” The term “optimal” is a relative term that renders the claims indefinite. The specification does not provide a standard for ascertaining the requisite degree; to the contrary, [0029] states that “optimal” may mean “as good or effective as possible or practical under the given circumstances,” and [0062], [0065] and [0072] alternately describe the “optimal” interval as the one with the highest lower limit, the smallest tightness, or a lower limit exceeding another model’s upper limit. One of ordinary skill in the art would not be reasonably apprised of the scope of the invention, the claim is therefore indefinite. For examination purposes, “optimal confidence interval” is interpreted as the confidence interval that is best under any selected criterion.
Claims 9 and 19 recite “wherein the determined confidence interval comprises the input and the minimum number of implementations of the trained machine learning model in the new environment.” A confidence interval is a range of values of a parameter (here, a success rate). It is unclear how a range of success-rate values can “comprise” a user input (a confidence value or tightness) and a count of implementations. It is also unclear whether “a success rate” in the input clause of claims 9 and 19 refers to the success rate introduced in claims 1 and 11 or to a different success rate, the claim is therefore indefinite. For examination purposes, the wherein clause is interpreted as requiring that the confidence interval be determined using the input and the minimum number of implementations, and “a success rate” is interpreted as “the success rate.”
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.
Judicial Exception
Claims 1-20 of the claimed invention are directed to a judicial exception, an abstract idea, without significantly more.
Regarding claims 1-20:
(Independent Claims)
With regards to claim 1,
Step 1: The claim recites a method (process), which falls into one of the statutory categories.
Step 2A – Prong 1: the claim, in part, recites:
(a) “for each implementation of the trained machine learning model in the new environment, measuring a performance metric associated with performance of the machine learning model in the new environment” (mental process and/or math concept), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting generic computer elements, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “measuring” a performance metric encompasses a person observing each trial and judging it a success or failure (the specification states that “an operator may determine whether a trial was successful or not and assign a label to each trial,” [0038]), which is observation, evaluation and judgment that can be performed by a human using pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas;
(b) “determining a confidence interval on a success rate of the machine learning model in the new environment based on the performance metric” (mathematical concept and/or mental process), as drafted, is a statistical calculation: computing an interval estimate of a binomial proportion from observed successes and failures (e.g., Clopper-Pearson, Wald, or Dvoretzky-Kiefer-Wolfowitz bounds, see [0020], [0041]-[0051]). This falls within the “Mathematical Concepts” grouping (mathematical relationships, formulas, and calculations). For a small number of trials (e.g., 7 successes in 10 trials, [0019]), the interval can also practically be computed by a human with pen, paper and a calculator or a statistical table (Mental Processes);
(c) “… based on the confidence interval”, i.e., deciding whether to deploy based on the confidence interval (mental process and/or math concept), which, under its BRI, is a judgment/decision a person can make by comparing the interval to an acceptable level of performance (see [0062]: “the lower limit of the confidence interval computed at operation 208 can be compared to a threshold”).
Accordingly, the claim recites an abstract idea.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: (a) “training a machine learning model based on a set of training data,” recited at a high level of generality with no particular model, architecture, or training technique, which is mere instructions to implement an abstract idea using a generic computing technique, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); (b) “implementing the trained machine learning model a number of times in a new environment, wherein the new environment is not part of the training data,” which is mere instructions to implement an abstract idea using a generic computing technique, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); (c) “deploying the machine learning model on machines in the new environment,” which is recited without any particular machine, any particular manner of deployment, or any particular resulting control, and so amounts to post-solution activity and/or mere instructions to “apply it” (see MPEP 2106.05(f), 2106.05(g)); and (d) the “new environment”/“machines” context, which merely links the abstract idea to a field of use and technological environment (see MPEP 2106.05(h)). The claim does not recite any improvement to the functioning of a computer, to machine learning technology itself, or to the machines (see MPEP 2106.04(d)(1), 2106.05(a)); the asserted improvement is in the statistical evaluation, which is itself part of the abstract idea. Accordingly, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claim recites the additional elements of: (a) “training a machine learning model …,” which is mere instructions to implement an abstract idea using generic computing, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); (b) “implementing the trained machine learning model a number of times in a new environment,” which is mere instructions to implement an abstract idea using a generic computing technique, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); (c) “deploying the machine learning model on machines,” which is insignificant post-solution activity / “apply it” (see MPEP 2106.05(f), (g)); and (d) the environment/machines context, which is field of use and technological environment (see MPEP 2106.05(h)). Accordingly, the additional elements individually or in combination do not amount to significantly more than the judicial exception. The claim is not patent eligible.
(Independent Claims)
With regards to claim 11,
Step 1: The claim recites a system comprising a memory and at least one processor, i.e., a machine, which falls into one of the statutory categories.
Step 2A – Prong 1: the claim, in part, recites: (a) “for each implementation … measure a performance metric associated with performance of the machine learning model in the new environment” (mental process); (b) “determine a confidence interval on a success rate of the machine learning model in the new environment based on the performance metric” (mathematical concept and/or mental process); and (c) the decision to deploy “based on the confidence interval” (mental process). These limitations are abstract for the same reasons set forth for the corresponding limitations of claim 1, which are incorporated herein. Accordingly, the claim recites an abstract idea.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: (a) “A system comprising: a memory configured to store instructions; and at least one processor communicatively coupled to the memory and configured to execute the instructions,” which is mere instructions to implement an abstract idea using a generic computing device, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); (b) “obtain a machine learning model trained on a set of training data” and “implement the trained machine learning model a number of times in a new environment,” which are extra-solution activity of pre-solution data gathering (see MPEP 2106.05(g)); (c) “deploy the machine learning model on machines in the new environment,” which is post-solution activity / “apply it” (see MPEP 2106.05(f), (g)); and (d) the environment/machines context, which is field of use and technological environment (see MPEP 2106.05(h)). Accordingly, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements are: (a) a generic memory and processor (MPEP 2106.05(f)); (b) obtaining a trained model and implementing it a number of times to collect outcomes, which is pre-solution data gathering (MPEP 2106.05(g)) and well-understood, routine and conventional (MPEP 2106.05(d)(II), “receiving or transmitting data over a network,” “storing and retrieving information in memory”; (c) deploying, which is post-solution activity / “apply it” (MPEP 2106.05(f), (g)); and (d) field of use (MPEP 2106.05(h)). Accordingly, the additional elements individually or in combination do not amount to significantly more than the judicial exception. The claim is not patent eligible.
(Dependent claims)
Claims 2-10 are dependent on claim 1 and include all the limitations of claim 1. Claims 12-20 are dependent on claim 11 and include all the limitations of claim 11. Therefore, claims 2-10 and 12-20 recite the same abstract ideas.
With regards to claims 2 and 12, the claim recites further limitation of “wherein the training data is based on implementing the trained machine learning model in one or more environments that are different from the new environment”, which merely specifies the source of the data on which the generic training is performed.
The claim recites the additional element that the training data derives from running a model in other environments, which is extra-solution data gathering (see MPEP 2106.05(g)) and generic training of a model with data (“apply it,” see MPEP 2106.05(f)). Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element is data gathering and generic training (MPEP 2106.05(f), (g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claims 3 and 13, the claim recites further limitation of “wherein implementing the trained machine learning model comprises: deploying the trained machine learning model on a machine in the new environment”, which merely specifies how the trial data is gathered.
The claim recites the additional element of “deploying the trained machine learning model on a machine in the new environment” to run the trials, which is extra-solution activity of pre-solution data gathering (see MPEP 2106.05(g)) and generic use of a machine as a tool (see MPEP 2106.05(f)). Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element is data gathering by running the model on a machine, which is extra-solution activity of pre-solution data gathering / post-solution data output (see MPEP.2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claims 4 and 14, the claim recites further limitation of “wherein the performance metric comprises one of: (i) a binary success or failure of each implementation … and (ii) a continuous total reward of each implementation …” (mental process and/or math concept), which is a further process of judging each trial as a success or failure, or totaling a reward, i.e., observation/evaluation and simple arithmetic. This is based on observation, evaluation, judgement, and/or opinion that could be performed by a human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claims 5 and 15, the claim recites further limitation of “determining a confidence set based on the confidence interval, wherein the confidence set comprises a confidence value representative of an acceptable probability that the confidence interval does not contain a true unknown success rate and at least one of: a coverage …, a tightness …, and a minimum number of times …” (mental process and/or math concept), which is a further mathematical calculation (statistical parameters of a binomial / empirical distribution), which falls within the “Mathematical Concepts” grouping of abstract ideas, and which for small sample sizes can also be performed by a human using paper / pen / calculator (“Mental Processes”).
Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claims 6 and 16, the claim recites further limitation of “determining a lower limit of the confidence interval; determining if the lower limit of the confidence interval exceeds a threshold; and deploying the machine learning model on machines in the new environment when the lower limit of the confidence interval exceeds or is equal to the threshold” (mental process and/or math concept), wherein determining the lower limit is a mathematical concept and comparing it to a threshold and deciding accordingly is a mental process (evaluation/judgment).
The claim recites the additional element of “deploying the machine learning model on machines … when the lower limit … exceeds or is equal to the threshold,” which is conditional post-solution activity recited at a high level of generality, i.e., mere instructions to apply the result of the judgment (see MPEP 2106.05(f), (g)). Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of deploying when a threshold is met is “apply it” / post-solution activity (MPEP 2106.05(f), (g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claims 7 and 17, the claim recites further limitation of “retraining the machine learning model when the lower limit of the confidence interval is below the threshold”, wherein the decision to retrain based on the comparison is a mental process (evaluation/judgment).
The claim recites the additional element of “retraining the machine learning model,” which is recited at a high level of generality without any particular training technique and is merely using a computer as a tool to perform the abstract idea / generic training (see MPEP 2106.05(f)). Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, generic retraining is “apply it” (MPEP 2106.05(f)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claims 8 and 18, the claim recites further limitation of “determining a plurality of confidence intervals for the new environment based on implementing a plurality of machine learning models in the new environment; determining an optimal confidence interval from the plurality of confidence intervals; and deploying … a machine learning model … corresponding to the optimal confidence interval” (mental process and/or math concept), wherein computing the confidence intervals is a mathematical concept, and comparing them to select the best is a mental process (evaluation/judgment).
The claim recites the additional elements of “implementing a plurality of machine learning models in the new environment” (pre-solution data gathering, MPEP 2106.05(g)) and “deploying … a machine learning model … corresponding to the optimal confidence interval” (post-solution activity / “apply it,” MPEP 2106.05(f), (g)). Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements are data gathering and “apply it” (MPEP 2106.05(f), (g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claims 9 and 19, the claim recites further limitation of “receiving an input of one or more of: (i) a confidence value … and (ii) tightness …; and computing a minimum number of implementations … based on the input and a cumulative distribution function of the performance metric …”, wherein computing a required sample size from a confidence level and a cumulative distribution function is a mathematical concept (“Mathematical Concepts” grouping).
The claim recites the additional element of “receiving an input,” which is extra-solution activity of pre-solution data gathering (see MPEP 2106.05(g)). Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, receiving an input is well-understood, routine and conventional data gathering (MPEP 2106.05(d)(II), “receiving or transmitting data over a network”). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claims 10 and 20, the claim recites further limitation of “wherein the machines comprise vehicles”, which does not change the abstract idea identified above.
The claim recites the additional element of “vehicles,” which merely limits the deployment to a particular field of use and technological environment (see MPEP 2106.05(h)); no particular vehicle control or operation is recited. Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element is field of use (MPEP 2106.05(h)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
Allowable Subject Matter
Claims 1-20 include allowable subject matter since when reading the claims in light of the specification, as per, MPEP §2111.01 or Toro Co. v. White Consolidated Industries Inc., 199F.3d 1295, 1301, 53 USPQ2d 1065, 1069, 1069 (Fed.Cir. 1999), none of the references of record alone or in combination disclose or suggest the combination of limitations specified in claims 1-20.
In interpreting the claims, in light of the specification filed on 4 April 2024, the Examiner finds the claimed invention to be patentably distinct from the prior arts of record.
Regarding the independent claims, the primary reason for the allowance is the inclusion of the specific process / system of repeatedly executing a trained model in an unseen environment, deriving a confidence interval on its success rate from per-run performance, and deploying the model to machines based on the interval.
None of the cited prior art references, singly or in combination, fully teaches all limitations of independent claims 1 and 11.
Regarding the dependent claims, which include all the limitations of the independent claims, are also allowed.
The followings are references close to the invention claimed:
Thomas et al., US-PGPUB NO. 20160148251A1 [hereafter Thomas] teaches using concentration inequalities on historical data to compute high-confidence lower bounds on a new policy's performance, deploying it only when it statistically outperforms the current policy. However Thomas does not teach the specific claimed elements combination of executing the model repeatedly in a new, unseen environment to measure per-trial success and deploying on machines based on the success-rate confidence interval.
Kislovskiy et al., US-PGPUB NO. 20180341571A1 [hereafter Kislovskiy] teaches distributing autonomous-vehicle software versions to safety-driven vehicles in a region, analyzing trip log anomalies, and deploying to fully autonomous vehicles once a safety standard is met. However Kislovskiy does not teach the specific claimed elements combination of determining a confidence interval on a trained model's success rate from per-trial performance metrics and gating deployment on that interval.
Usatov et al., US-PGPUB NO. 20230186175A1 [hereafter Usatov] teaches comparing a deployed primary machine learning model with challenger models on performance metrics, and retraining or replacing the deployed model when a challenger performs better. However Usatov does not teach the specific claimed elements combination of repeated trials in a new environment absent from training, a confidence interval on success rate, and deployment on machines based on that interval.
Vincent, Feldman & Schwager, "Guarantees on Robot System Performance Using Stochastic Simulation Rollouts," arXiv:2309.10874, Sept. 2023 [hereafter Vincent] teaches distribution-free, finite-sample bounds on expected cost, VaR, CVaR and failure probability from stochastic simulation rollouts, with multi-hypothesis correction for comparing policies. However Vincent does not teach the specific claimed elements combination of training a model, executing it in a new real environment excluded from training, and deploying it on machines based on the success-rate confidence interval.
Kalra & Paddock, "Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?", Transportation Research Part A, Vol. 94, pp. 182–193, 2016 [hereafter Kalra] teaches statistically computing confidence bounds on autonomous vehicle failure rates and the number of test miles needed to demonstrate reliability at a given confidence. However Kalra does not teach the specific claimed elements combination of a trained machine learning model evaluated in a new environment excluded from training, and deployment on machines based on the success-rate confidence interval.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSU-CHANG LEE whose telephone number is 571-272-3567. The fax number is 571-273-3567.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas, can be reached 571-272-2589.
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/TSU-CHANG LEE/
Primary Examiner, Art Unit 2128
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