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 .
Response to Arguments
Applicant's arguments filed 9-12-2025 have been fully considered but they are not persuasive.
In re pgs. 8-9, applicant argues claimed feature cannot be practically be performed by the human mind.
In response, the Examiner respectfully disagrees.
The claim does include features (high level model training, reward function selection) that can be done mentally.
In re pgs. 9-10, applicant argues claim is integrated into a practical application.
In response, the Examiner respectfully disagrees.
The claim limitation of “determine automatic driving operations based on processing the input state and observation information through the selected reward function” amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of presentation of offer or statistics under MPEP 2106.05(d)).
In re pg. 10, applicant argues technical improvement.
In response, the Examiner respectfully disagrees.
Improving estimation accuracy of a model amounts to improvement in abstract idea, not technical improvement.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the following limitations:
train a hierarchical mixtures of experts (HME) model by inverse reinforcement learning based on the decision-making history (training a hierarchical mixtures of experts in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper); wherein the training comprises
training the HME model using an EM algorithm, and when a learning result using the EM algorithm satisfies a predetermined condition, learn the hierarchical mixtures of experts by factorized asymptotic Bayesian inference using gate functions that calculate branching probabilities for input data indicating driving state and observation information (EM algorithm and Bayesian are mathematical concepts),
to select, based on receiving state and observation information collected during automatic driving, a reward function from among a plurality of reward functions of the HME model (selecting a reward function in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
The claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites the following additional elements:
A learning device for automatic driving applications, the learning device comprising: a memory storing instructions; and one or more processors configured to execute the instructions to (amounts to a generic computer component to perform a computer function as discussed in MPEP 2106.05(f));
receive input of a decision-making history of a subject (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)), wherein the decision- making history comprises driving history data based on complex intentions of drivers (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h));
wherein the HME model comprises a plurality of driving expert models for automatic driving (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)), and
wherein the trained HME model is configured to, and to determine automatic driving operations based on processing the input state and observation information through the selected reward function (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
A learning device for automatic driving applications, the learning device comprising: a memory storing instructions; and one or more processors configured to execute the instructions to (amounts to a generic computer component to perform a computer function as discussed in MPEP 2106.05(f));
receive input of a decision-making history of a subject (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)), wherein the decision- making history comprises driving history data based on complex intentions of drivers (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h));
wherein the HME model comprises a plurality of driving expert models for automatic driving (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is well understood, routine and convention activity of receiving or gathering data as identified by the court in MPEP 2106.05(d)), and
wherein the trained HME model is configured to, and to determine automatic driving operations based on processing the input state and observation information through the selected reward function (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of presentation of offer or statistics under MPEP 2106.05(d)).
The claim is not patent eligible.
Claim 2: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
train the HME model using the EM algorithm and calculate a log likelihood of the decision-making history; and when it is determined that the log likelihood is monotonically increasing, switch the training method from EM algorithm to the factorized asymptotic Bayesian inference, and train the HME model by the factorized asymptotic Bayesian inference using an approximate value of the lower limit of a factorized information criterion (EM algorithm, log likelihood and Bayesian are mathematical concepts).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 3: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
repeat training the HME model by the EM algorithm until it is determined that the log likelihood is monotonically increasing (EM algorithm, log likelihood are mathematical concepts).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 4: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
train a model by the EM algorithm using an equation excluding terms that represents regularization effect of the factorized asymptotic Bayesian inference from equations used when updating the variational probabilities of hidden variables used in the factorized asymptotic Bayesian inference (EM algorithm, log likelihood, variational probabilities are mathematical concepts).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claims 5-6 are method claims having similar limitation as claims 1-2 and are rejected under the same rationale.
Claims 7-8 are non-transitory computer readable information medium claims having similar limitation as claims 1-2 and are rejected under the same rationale. The additional elements in claim 7 is A non-transitory computer readable information recording medium storing a learning program, when executed by a processor, that performs a method for automatic driving applications: (amounts to performing generic function of execution of stored instructions (MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract into practical application and are not sufficient to amount to significant more than the abstract idea. Therefore, the claims are an abstract idea.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choi et al (“Hierarchical Bayesian Inverse Reinforcement Learning” 2015) disclose Hierarchical Bayesian Inverse Reinforcement Learning.
Li et al (“Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models” 2015) disclose Factorized Asymptotic Bayesian Inference.
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.
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/LUT WONG/Primary Examiner, Art Unit 2127