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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/09/2026 has been entered.
Status of the Claims
Claims 1, 12, and 20 have been amended. Claims 9 and 18 have been cancelled. Claims 1-8, 10-17, and 19-20 are pending and have been considered by the Examiner.
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, 10-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8, 10-11 and 20 each recites a device comprising a processor (a product), and claims 12-17 and 19 each recites a method. A product and a method each falls within one of the four statutory categories of patent eligible subject matter.
CLAIM 1
Step 2A Prong 1: A corresponding plurality of training objective function values computed at the exact objective function from the training input states is a mathematical calculation. Instant specification paragraph [0035], lines 3-7 discloses performing the mathematical calculation.
Compute an estimated optimal state of the exact objective function using the trained
Starting at an initial state, computing a preliminary estimated optimal state by performing a plurality of fast-step iterations of a Monte Carlo algorithm with respective fast-step acceptance probabilities that are determined based at least in part on the approximated objective function is a mathematical calculation. Instant specification paragraph [0027] discloses a mathematical formula for the fast-step acceptance probability.
Performing a correction iteration that has a correction-step acceptance probability determined based at least in part on respective values of the approximated objective function and the exact objective function computed at the preliminary estimated optimal state is a mathematical calculation. Instant specification paragraph [0030] discloses a mathematical formula for the correction-step acceptance probability. The claim recites abstract ideas.
Step 2A Prong 2: A computing device comprising: a processor configured to perform operations amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Train a machine learning model using a training dataset that includes: a plurality of training input states of an exact objective function, and a corresponding plurality of training objective function values amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Using the trained machine learning model amounts to invoking the model merely as a tool to perform an existing process under MPEP 2106.05(f).
Output programmatical control instructions to one or more hardware devices based at least in part on the estimated optimal state amounts to mere data-gathering, an insignificant post-solution activity under MPEP 2106.05(g). The feature amounts to transmitting programmatic control instructions to a hardware device.
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere generic computer functions as disclosed in combination with an insignificant post-solution activity that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A computing device comprising: a processor configured to perform operations amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Train a machine learning model using a training dataset that includes: a plurality of training input states of an exact objective function, and a corresponding plurality of training objective function values amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Using the trained machine learning model amounts to invoking the model merely as a tool to perform an existing process under MPEP 2106.05(f).
Output programmatical control instructions to one or more hardware devices based at least in part on the estimated optimal state amounts to transmitting programmatic control instructions to a hardware device. This is analogous to transmitting data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are mere generic computer functions as disclosed in combination with a well-understood, routine, conventional activity that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
CLAIM 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The Monte Carlo algorithm is a Markov chain Monte Carlo (MCMC) algorithm selected from the group consisting of a Metropolis-Hastings algorithm, a simulated annealing algorithm, a simulated quantum annealing algorithm, a parallel tempering algorithm, and a population annealing algorithm is a mathematical calculation. Specification paragraphs [0024]-[0025] disclose formulas for a Metropolis-Hastings algorithm.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas.
CLAIM 3 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated. Each of the fast-step iterations of the MCMC algorithm has a fast-step acceptance probability given by
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where x is a current state, x' is an updated state, β is an inverse temperature, and
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is a change in a value of the approximated objective function between the current state and the updated state is a mathematical calculation.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas.
CLAIM 4 incorporates the rejection of claim 3.
Step 2A Prong 1: The abstract ideas of claim 3 are incorporated. The correction-step acceptance probability of the correction iteration is given by
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458
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where ΔE is a change in a value of the exact objective function between the initial state and the preliminary estimated optimal state is a mathematical calculation.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas.
CLAIM 5 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated. The respective fast-step acceptance probabilities of the plurality of fast-step iterations are determined based at least in part on a constraint function in addition to the approximated objective function is a mathematical calculation. Specification paragraph [0032] discloses a formula for computing a fast-step acceptance probabilities based on a constraint function.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas.
CLAIM 6 incorporates the rejection of claim 5.
Step 2A Prong 1: The abstract ideas of claim 5 are incorporated. Each of the fast-step iterations of the MCMC algorithm has a fast-step acceptance probability given by
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434
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where x is a current state, x' is an updated state, β is an inverse temperature,
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38
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is a change in a value of the approximated objective function between the current state and the updated state, γ is a constraint function weighting parameter, and ΔC is a change in a value of the constraint function between the current state and the updated state is a mathematical calculation.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas.
CLAIM 7 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The limitation “repeat an estimation loop that includes the plurality of fast-step iterations and the correction iteration until the correction iteration is accepted” amounts to repeating mathematical calculations.
Step 2A Prong 2 and Step 2B: A processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
CLAIM 8 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The approximated objective function has a reduced number of variables relative to the exact objective function amounts to a field of use and technological environment under MPEP 2106.05(h).
CLAIM 10 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. During each of the fast-step iterations of the Monte Carlo algorithm, the processor is configured to sample from a Gibbs distribution over an approximated state space of the approximated objective function is a mathematical calculation. Instant specification paragraph [0023] discloses a mathematical formula for the Gibbs distribution.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas.
CLAIM 11 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The Monte Carlo algorithm is a non-Markovian Monte Carlo algorithm in which the processor is configured to compute the preliminary estimated optimal state based at least in part on a sequence of one or more prior states is a mathematical calculation. Instant specification paragraph [0039] discloses a mathematical formula for a non-Markovian Monte Carlo algorithm.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas.
Claims 12-15 each recites a method which implements the same features as product claims 1-4, respectively, and are therefore rejected for at least the same reasons.
Claim 16 recites a method which implements the same features as product claims 5 and 6 and are therefore rejected for at least the same reasons.
Claims 17 and 19 each recites a method which implements the same features as product claims 7 and 11, respectively, and are therefore rejected for at least the same reasons.
CLAIM 20
Step 2A Prong 1: A corresponding plurality of training objective function values computed at the exact objective function from the training input states is a mathematical calculation. Instant specification paragraph [0035], lines 3-7 discloses performing the mathematical calculation.
Compute an estimated optimal state of the exact objective function using the trained
Starting at an initial state, computing a preliminary estimated optimal state by performing the plurality of fast-step iterations, wherein: each of the fast-step iterations is an iteration of a Markov chain Monte Carlo (MCMC) algorithm with a respective fast-step acceptance probability that is determined based at least in part on the approximated objective function is a mathematical calculation. Instant specification paragraph [0027] discloses a mathematical formula for the fast-step acceptance probability.
The MCMC algorithm is selected from the group consisting of a Metropolis-Hastings algorithm, a simulated annealing algorithm, a simulated quantum annealing algorithm, a parallel tempering algorithm, and a population annealing algorithm is a mathematical calculation. Specification paragraphs [0024]-[0025] disclose formulas for a Metropolis-Hastings algorithm.
Performing the correction iteration, wherein the correction iteration is an iteration of the MCMC algorithm that has a correction-step acceptance probability determined based at least in part on respective values of the approximated objective function and the exact objective function computed at the preliminary estimated optimal state is a mathematical calculation. Instant specification paragraph [0030] discloses a mathematical formula for the correction-step acceptance probability. The claim recites abstract ideas.
Step 2A Prong 2: A computing device comprising: a processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Train a machine learning model using a training dataset that includes: a plurality of training input states of an exact objective function, and a corresponding plurality of training objective function values amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Using the trained machine learning model amounts to invoking the model merely as a tool to perform an existing process under MPEP 2106.05(f).
Output programmatical control instructions to one or more hardware devices based at least in part on the estimated optimal state amounts to mere data-gathering, an insignificant post-solution activity under MPEP 2106.05(g). The feature amounts to transmitting programmatic control instructions to a hardware device.
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere generic computer functions as disclosed in combination with an insignificant post-solution activity that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A computing device comprising: a processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Train a machine learning model using a training dataset that includes: a plurality of training input states of an exact objective function, and a corresponding plurality of training objective function values amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Using the trained machine learning model amounts to invoking the model merely as a tool to perform an existing process under MPEP 2106.05(f).
Output programmatical control instructions to one or more hardware devices based at least in part on the estimated optimal state amounts to transmitting programmatic control instructions to a hardware device. This is analogous to transmitting data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are mere generic computer functions in combination with a well-understood, routine, conventional activity as disclosed that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Response to Arguments
The following is the examiner’s response to the applicant’s arguments filed 01/09/2026.
Applicant’s First Arguments: On pages 10-11, the Applicant argues claim 1 is integrated into the practical application of programmatic hardware device control (e.g., in electric grid control or computing resource load balancing) instead of being directed to a mathematical concept. Applicant argues the features of claim 1 allow the computing system to control the hardware device in order to bring some operating parameter of the hardware device closer to an optimal value, according to the exact objective function; they allow the computing device to efficiently obtain the estimated optimal state even in examples in which evaluating the exact objective function is computationally expensive; and they allow the computing device to control hardware device with a faster control loop.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive. Examiner respectfully disagrees that claim 1 is integrated into a practical application. In Step 2A Prong 1, the limitation “compute an estimated optimal state of the exact objective function using the trained… model as an approximated objective function” is a mathematical calculation. Instant specification paragraphs [0027] and [0030] disclose mathematical formulas for a fast-step acceptance probability and a correction-step acceptance probability, respectively. Computing an estimated optimal state cannot provide a technical improvement. Any purported improvement is merely an improvement to the mathematical calculation itself. MPEP 2106.05(a) states, “It is important to note, the judicial exception alone cannot provide the improvement.” MPEP 2106.05(a), II. states, “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
In Step 2A Prong 2, the limitation of “using the trained machine learning model” amounts to invoking the processor merely as a tool to perform an existing process. MPEP 2106.05(f) states, “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.”
The limitation “output programmatical control instructions to one or more hardware devices based at least in part on the estimated optimal state” amounts to transmitting programmatic control instructions to a hardware device, which is an insignificant post-solution activity. MPEP 2106.05(g) states, “ Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term ‘extra-solution activity’ can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.” The primary process recited by claim 1 is a series of mathematical calculations. The feature of outputting programmatical control instructions based on the estimated optimal state is incidental to the mathematical calculations and are merely a nominal or tangential addition to the claim. It is further noted that claim 1 does not positively recite applying the instructions to control the hardware device nor explain how the instructions might control the hardware device in order to bring some operating parameter of the hardware device closer to an optimal value, according to the exact objective function.
Applicant’s Second Arguments: On page 11, the Applicant argues claim 1 is directed to eligible subject matter for reasons analogous to those provided for SME Example 47 claim 3.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive. Examiner respectfully disagrees that the final limitation of pending claim 1 is similar to steps (d)-(f) of SME Example 47 claim 3. Page 12 of the USPTO’s July 2024 Subject Matter Eligibility Examples states: “Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets.” In contrast, the final limitation of pending claim 1 amounts to transmitting programmatic control instructions to a hardware device, which is an insignificant post-solution activity. As stated above, claim 1 does not positively recite applying the instructions to control the hardware device nor explain how the instructions might control the hardware device in order to bring some operating parameter of the hardware device closer to an optimal value, according to the exact objective function. The argument that this limitation is similar to steps (d)-(f) amounts to a mere allegation that pending claim 1 is patent eligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127