DETAILED ACTION
This Office Action is in response to Application# 18/742,883 filed on June 13, 2024 in which claims 1-20 are presented for examination.
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 claims
Claims 1-20 are pending, of which claims 1-20 are rejected under 35 U.S.C. 101.
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.
Claim 1-20 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claim 1-20 recite a method and apparatus respectively.
The analysis of claim 1 is as follows:
Step 2A, prong one:
The claim recites the following limitations which are drawn towards an abstract idea:
wherein the set of thermodynamic chips comprises at least:
a first thermodynamic chip comprising:
oscillators representing a first set of visible neurons, at least some of which are clamped to input data (recites an algorithmic process of an oscillators representing a first set of visible neurons as input data and recites at a high-level of generality);
oscillators representing bias values for the first set of visible neurons (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality); and
oscillators representing weighting values for interactions between the first set of visible neurons (recites an algorithmic process of an oscillators representing weighting values a first set of visible neurons as input data and recites at a high-level of generality);
a second thermodynamic chip comprising:
oscillators representing a second set of visible neurons, wherein the visible neurons of the second set are not clamped to the input data (recites an algorithmic process of an oscillators representing a first set of visible neurons as input data and recites at a high-level of generality);
oscillators representing bias values for the second set of visible neurons (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality); and
oscillators representing weighting values for interactions between the second set of visible neurons (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality); and
generate one or more inferences based on test data, wherein:
at least some of the oscillators of the first thermodynamic chip corresponding to the visible neurons of the first set are clamped to the test data, while other oscillators of the first thermodynamic chip corresponding to visible neurons for which values are to be inferred are left un-clamped (recites an algorithmic process of an oscillators representing values to be inferred visible neurons as input data and recites at a high-level of generality),
the oscillators of the first and second thermodynamic chip coupled via the server thermodynamic chip evolve to generate inference values for visible neurons of the first thermodynamic chip that are to be inferred based on the test data (recites an algorithmic process of an oscillators representing values to be inferred visible neurons as input data and recites at a high-level of generality); and
the inference values are generated by sampling the visible neurons of the first thermodynamic chip corresponding to the inference values to be inferred based on the test data (recites an algorithmic process of an oscillators representing values to be inferred visible neurons as input data and recites at a high-level of generality).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
a server thermodynamic chip comprising:
weighting value coordination oscillators and bias value coordination oscillators coupled, via position and momentum coupling, to the oscillators of the first and second thermodynamic chips representing the respective weighting values and bias values (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract weighting value and bias values on oscillators, see MPEP 2106.05(f)),
wherein the set of thermodynamic chips are configured to:
learn values for the respective weighting values and bias values while the visible neurons of the first set are clamped, at least in part, to training data used as the input data, wherein evolution of the first and second thermodynamic chip coupled via the server thermodynamic chip learns the values for the respective weighting values and bias values (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract weighting value and bias values on oscillators, see MPEP 2106.05(f)).
This judicial exception is not integrated into a practical application because, as seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). The additional elements merely recite, at a high-level of generality, training on thermodynamics chips.
Step 2B:
Below is the analysis of the claims:
A system, a set of thermodynamic chips, wherein respective ones of the oscillators are coupled with one another in a configuration that corresponds to an engineered Hamiltonian (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the thermodynamic chips as a tool to implement the abstract idea of training portions of generative model, see MPEP 2106.05(f)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements merely recite, at a high-level of generality, dynamic threshold mechanism of similarity score distribution.
The analysis of claim 9 is as follows:
Step 2A, prong one:
The claim recites the following limitations which are drawn towards an abstract idea:
clamping oscillators of a first thermodynamic chip to training data values, wherein the oscillators of the first thermodynamic chip clamped to the training data values represent visible neurons, and wherein the first thermodynamic chip comprises other oscillators representing weights and biases (recites an algorithmic process of an oscillators representing a first set of visible neurons as input data and recites at a high-level of generality);
causing a set of thermodynamic chips to evolve while clamped to the training data values, the set of thermodynamic chips comprising: the first thermodynamic chip with oscillators clamped to the training data values (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality); and
a second thermodynamic chip comprising oscillators representing visible neurons that are not clamped to the training data values and other oscillators representing weights and biases (recites an algorithmic process of an oscillators representing weighting values a first set of visible neurons as input data and recites at a high-level of generality);
wherein the evolution of the set of thermodynamic chips learns updated weights and biases that reflect relationships in the training data;
clamping at least some of the oscillators of the first thermodynamic chip to test data values (recites an algorithmic process of an oscillators representing a first set of visible neurons as input data and recites at a high-level of generality);
sampling other ones of the oscillators of the first thermodynamic chip corresponding to visible neurons that were not clamped to the test data to generate inference values (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
a third thermodynamic chip that functions as a server thermodynamic chip and couples, via position and momentum coupling, oscillators of the first and second thermodynamic chips that represent complimentary weights and complimentary biases (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract weighting value and bias values on oscillators, see MPEP 2106.05(f)),
causing the set of thermodynamic chips to further evolve while clamped to the test data values, wherein the learned weights and biases are maintained during the further evolution (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract weighting value and bias values on oscillators, see MPEP 2106.05(f)).
This judicial exception is not integrated into a practical application because, as seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). The additional elements merely recite, at a high-level of generality, training on thermodynamics chips.
Step 2B:
Below is the analysis of the claims:
A system, a set of thermodynamic chips, wherein respective ones of the oscillators are coupled with one another in a configuration that corresponds to an engineered Hamiltonian (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the thermodynamic chips as a tool to implement the abstract idea of training portions of generative model, see MPEP 2106.05(f)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements merely recite, at a high-level of generality, dynamic threshold mechanism of similarity score distribution.
The analysis of claim 19 is as follows:
Step 2A, prong one:
The claim recites the following limitations which are drawn towards an abstract idea:
oscillators, wherein respective ones of the oscillators are coupled with one another in a configuration that corresponds to an engineered Hamiltonian (recites an algorithmic process of an oscillators representing a first set of visible neurons as input data and recites at a high-level of generality);
wherein the set of thermodynamic chips comprises at least:
a first thermodynamic chip comprising:
oscillators representing visible neurons for the first thermodynamic chip, at least some of which are clamped to input data (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality);
oscillators representing bias values, in the engineered Hamiltonian, for the visible neurons for the first thermodynamic chip (recites an algorithmic process of an oscillators representing weighting values a first set of visible neurons as input data and recites at a high-level of generality);
oscillators representing weighting values for interactions between the visible neurons of the first thermodynamic chip (recites an algorithmic process of an oscillators representing a first set of visible neurons as input data and recites at a high-level of generality);
a second thermodynamic chip comprising:
oscillators representing visible neurons for the second thermodynamic chip, wherein the visible neurons for the second thermodynamic chip are not clamped to the input data (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality); and
oscillators representing bias values for the visible neurons for the second thermodynamic chip (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality); and
oscillators representing weighting values for interactions between the visible neurons of the second thermodynamic chip (recites an algorithmic process of an oscillators representing values to be inferred visible neurons as input data and recites at a high-level of generality).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
a server thermodynamic chip comprising:
weighting value coordination oscillators and bias value coordination oscillators coupled, via position and momentum coupling, to the oscillators of the first and second thermodynamic chips representing the respective weighting values and bias values (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract weighting value and bias values on oscillators, see MPEP 2106.05(f)),
This judicial exception is not integrated into a practical application because, as seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). The additional elements merely recite, at a high-level of generality, training on thermodynamics chips.
Step 2B:
Below is the analysis of the claims:
A system, a set of thermodynamic chips, wherein respective ones of the oscillators are coupled with one another in a configuration that corresponds to an engineered Hamiltonian (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the thermodynamic chips as a tool to implement the abstract idea of training portions of generative model, see MPEP 2106.05(f)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements merely recite, at a high-level of generality, dynamic threshold mechanism of similarity score distribution.
The analysis of claims 2-8, 10-18 and 20 are as follows:
Step 2A, prong one:
The claim recites the following limitations which are drawn towards an abstract idea:
Claim 3 recites wherein the evolution of the oscillators of the first and second thermodynamic chip coupled via the server thermodynamic chip is an evolution according to Langevin dynamics (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality).
Claim 4 recites wherein the first thermodynamic chip, the server thermodynamic chip and the second thermodynamic chip are arranged in a stacked configuration with the server thermodynamic chip positioned between the first thermodynamic chip and the second thermodynamic chip (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality).
Claim 5 recites wherein the engineered Hamiltonian comprises a three-body coupling term that couples, for a respective one of the thermodynamic chips, the visible neurons, the weight values, and the bias values (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 6 recites wherein: the system further comprises a pulse drive; and the pulse drive is configured to initialize respective hyperparameters of the engineered Hamiltonian (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 7 recites wherein the oscillators are implemented using single-well or double-well protentional resonators (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality).
Claim 8 recites wherein the inference values represent distributional values (recites an algorithmic process of oscillators representing bias values for the visible neurons and recites at a high-level of generality).
Claim 10 recites wherein respective ones of the oscillators of the first, second, and third thermodynamic chips are coupled with one another in a configuration that corresponds to an engineered Hamiltonian (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 11 recites wherein positive and negative phase terms of the engineered Hamiltonian are used for the position and momentum couplings between the server thermodynamic chip and the first thermodynamic chip and between the server thermodynamic chip and the second thermodynamic chip (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 12 recites wherein the positive and negative phase terms cause: the weighting values for the first and second thermodynamic chip to be same values within a threshold amount of difference, and the bias values for the first and second thermodynamic chip to be same values within a threshold amount of difference (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 13 recites wherein the evolution of the oscillators of the first and second thermodynamic chip coupled via the server thermodynamic chip is an evolution according to Langevin dynamics (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 14 recites wherein the first thermodynamic chip, the server thermodynamic chip, and the second thermodynamic chip are arranged in a stacked configuration with the server thermodynamic chip positioned between the first thermodynamic chip and the second thermodynamic chip (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 16 recites wherein the engineered Hamiltonian comprises a three-body coupling term that couples, for a respective one of the thermodynamic chips, the visible neurons, the weight values, and the bias values (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 17 recites wherein the oscillators are implemented using single-well protentional resonators (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
Claim 18 recites wherein the oscillators are implemented using double-well protentional resonators (recites an algorithmic process of an oscillators representing weighting values a second set of visible neurons as input data and recites at a high-level of generality).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
Claim 2 recites wherein positive and negative phase terms of the engineered Hamiltonian are used for the position and momentum couplings between the server thermodynamic chip and the first thermodynamic chip and between the server thermodynamic chip and the second thermodynamic chip, wherein the positive and negative phase terms cause: the weighting values for the first and second thermodynamic chip to be same values within a threshold amount of difference, and the bias values for the first and second thermodynamic chip to be same values within a threshold amount of difference (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of training portions of generative model, see MPEP 2106.05(f)).
Claim 15 recites wherein the thermodynamic chips used in performing the training and inference generation, further comprise: one or more additional thermodynamic chips comprising oscillators representing visible neurons that are clamped to training data values; or another set of one or more additional thermodynamic chips comprising oscillators representing visible neurons that are not clamped to the training data values, wherein the server thermodynamic chip couples, via position or momentum coupling, oscillators of the one or more additional thermodynamic chips or oscillators of the other set of additional thermodynamic chips to oscillators of the first or second thermodynamic chips (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of training portions of generative model, see MPEP 2106.05(f)).
Claim 20 recites wherein positive and negative phase terms of an overall engineered Hamiltonian are used for the position and momentum couplings between the server thermodynamic chip and the first thermodynamic chip and between the server thermodynamic chip and the second thermodynamic chip, wherein the positive and negative phase terms cause: the weighting values for the first and second thermodynamic chip to be same values within a threshold amount of difference, and the bias values for the first and second thermodynamic chip to be same values within a threshold amount of difference (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of training portions of generative model, see MPEP 2106.05(f)).
This judicial exception is not integrated into a practical application because, as seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). The additional elements merely recite, at a high-level of generality, training portions of neural networks.
Step 2B:
Below is the analysis of the claims:
A computer implemented system comprising: a communication interface; a memory storing instructions; one or more processors coupled to the communication interface and to the memory, the one or more processors configured to execute the instructions to perform operations comprising (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of training portions of data for fraudulent transactions, see MPEP 2106.05(f)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements merely recite, at a high-level of generality, training portions of neural networks.
Allowable Subject Matter
Claims 1-20 are allowed over prior art. However, the claims need to overcome 35 U.S.C. 101 rejection for allowability.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159
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