DETAILED ACTION
This Action is responsive to Claims filed 09/30/2025.
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 .
Status of the Claims
Claims 1, 3, 5, 11, and 16-17 have been amended. Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed 09/30/2025 regarding the 35 U.S.C. 101 Rejection of Claims 1-20 have been fully considered but they are not persuasive.
The Applicant argues the newly amended limitations recite patent-eligible subject matter. The Examiner respectfully disagrees with the Applicant. Taken as a whole, the independent claims recite a series of abstract idea mental process steps, or a generic computer performing generic computing functions in association with the abstract idea mental process steps, directly resulting in the alleged improvement to the functioning of a computer or other technical field, including the limitations cited by the Applicant on Page 14 of the Arguments.
The first “training…” step, as presently drafted, is mere pre-solution activity, given the pretraining of the model is recited highly generally. The subsequent “generating…”, “performing…”, “performing…”, “normalizing…”, “generating…”, and “generating…” steps are all practically performed within the human mind or with the aid of pen and paper. There is no specific structure or implementation, save for the recitation of a generic computer, precluding the aforementioned limitations from being performed mentally.
The newly amended “determining…” and “updating…” steps, performed as a result of the preceding “generating…” step, also lack specific structure or implementation, save for the recitation of a generic computer, precluding the aforementioned limitations from being performed mentally. Therefore, the “second training…” step, itself defined by a series of abstract idea mental process steps, is merely instructions to apply those abstract ideas. The Examiner points the Applicant to MPEP 2106(I), 2106.04(I), and 2106.05(b)(I) regarding the eligibility of claims made of otherwise ineligible steps or algorithms. The Examiner also contends that, given the “training…” (or second training) steps of the model are recited to such a generic degree, under the broadest reasonable interpretation of the claims, the alleged improvement is a result of the series of abstract idea mental process steps, and per MPEP 2106.05(a), the improvement cannot come from the abstract idea. See the updated 35 U.S.C. 101 Rejection below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; 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:
Claims 1-10 recite a method for training an initial version of a machine learning classification model, which falls under the statutory category of a process. Claims 11-15 recite non-transitory, computer-readable medium having instructions stored thereon that are executable by a computer system for training an initial version of a machine learning classification model, which falls under the statutory category of a manufacture. Claims 16-20 recite system, comprising: at least one processor; a non-transitory, computer-readable medium having instructions stored thereon that are executable by the at least one processor to cause the system to train a machine learning classification model, which falls under the statutory category of a machine.
Step 2A – Prong 1:
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitation of “performing, by the computer system, one or more transformations based on the plurality of model scores to generate, for the plurality of training samples, a corresponding plurality of weighting values…”, “performing a logarithmic function on the first corresponding model score to generate a first logarithmic value;”, “normalizing the first logarithmic value based on a highest one and a lowest one of a plurality of logarithmic values generated based on the plurality of model scores;”, “and generating a first weighting value for the first training sample based on the normalized first logarithmic value;”, “determining, using the plurality of weighting values, loss values for the plurality of model scores generated using the initial version of the machine learning classification model;”, and “and updating, based on the loss values and according to a second, lower learning rate, one or more parameters of the initial version of the machine learning classification model.” under the broadest reasonable interpretation, covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. These limitations therefore fall within the mental process group.
Step 2A – Prong 2:
The additional elements claim 1 do not integrate the abstract idea into a judicial exception. Claim 1 recites the additional elements “a computer system” and “transformations” are recognized as generic computer components recited at a high level of generality. Although it has and executes instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements recited in the limitations “training phase”, “a machine learning classification model”, “a training dataset”, “training samples”, “model scores”, “a plurality of classes”, and “weighting values” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitation “and generating, by the computer system based on the training dataset, an updated version of the machine learning classification model, including, during a second training phase:” merely amounts to instructions to "apply” the preceding abstract idea mental process steps (See MPEP 2106.04(f) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements recited in the limitations “training, by a computer system, in a first training phase using a first learning rate, an initial version of a machine learning classification model based on a training dataset, wherein, during the first training phase, equal weight is applied to a plurality of training samples in the training dataset;” and “generating, by the computer system using the initial version of the machine learning classification model, a plurality of model scores corresponding to the plurality of training samples in the training dataset, wherein, for a given one of the plurality of training samples, a corresponding given model score from the initial version of the machine learning classification model indicates a probability that the given training sample belongs to a particular one of a plurality of classes;” are found to be mere pre- or post-solution activity such as data output or data transmittal (See MPEP 2106.05(g)).
Step 2B:
The only limitation on the performance of the described method is a limitation reciting “a computer system” and “transformations” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements recited in the limitations “training phase”, “a machine learning classification model”, “a training dataset”, “training samples”, “model scores”, “a plurality of classes”, and “weighting values” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitations “and generating, by the computer system based on the training dataset, an updated version of the machine learning classification model, including, during a second training phase:” merely amounts to instructions to "apply it." (See MPEP 2106.04(f) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements recited in the limitations “training, by a computer system, in a first training phase using a first learning rate, an initial version of a machine learning classification model based on a training dataset, wherein, during the first training phase, equal weight is applied to a plurality of training samples in the training dataset;” and “generating, by the computer system using the initial version of the machine learning classification model, a plurality of model scores corresponding to the plurality of training samples in the training dataset, wherein, for a given one of the plurality of training samples, a corresponding given model score from the initial version of the machine learning classification model indicates a probability that the given training sample belongs to a particular one of a plurality of classes;” are found to be well-understood, routine, or conventional activity but for the recitation of generic computer components or additional elements generally linking the abstract idea to a particular technology or field of use. (See WURC examples MPEP2106.05(d)(II)(i)).
Taken alone or in ordered combination, these additional elements do not amount to
significantly more than the above-identified abstract idea. There is no indication that the
combination of elements improves the functioning of a computer or improves any other
technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 11 and 16.
Claim 11 recites “A non-transitory, computer-readable medium having instructions stored thereon that are executable by a computer system to perform operations comprising…” (generic computer components) “…performing a first training phase using a first learning rate to generate an initial version of a machine learning classification model, wherein, during the first training phase, equal weighting is applied to a plurality of training samples in a training dataset;” (extra-solution activity) “generating, for the plurality of training samples, a corresponding plurality of weighting values…” (abstract idea, mental process) “…wherein, for a given one of the plurality of training samples, generating a corresponding weighting value includes: generating a model score for the given training sample using the initial version of the machine learning classification model;” (abstract idea, mental process) “…and generating the corresponding weighting value, for the given training sample, based on the model score;” (abstract idea, mental process) “performing a logarithmic function…” (abstract idea, mental process) “normalizing the first logarithmic value…” (abstract idea, mental process) “generating a first weighting value…” (abstract idea, mental process) “…and based on the training dataset, performing a second training phase to generate an updated version of the machine learning classification model, including by: using values for one or more parameters of the initial version of the machine learning classification model as initial values for one or more parameters of the updated version of the machine learning classification model;” (instructions to apply) “applying an optimization algorithm to the plurality of weighting values; and modifying, based on output of the optimization algorithm and according to a second learning rate, the initial values for the one or more parameters of the updated version of the machine learning classification model;” (abstract idea, mental process). The additional elements found in claim 1 and repeated in claim 11 are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 16 recites “A system, comprising: at least one processor; a non-transitory, computer-readable medium having instructions stored thereon that are executable by the at least one processor to cause the system to:” (generic computer components) “…access information corresponding to an initial version of a machine learning classification model that was trained, during an initial training phase using a first learning rate, with equal weighting applied to a plurality of training samples in a training dataset;” (data transmittal or manipulation) “…generate, for the plurality of training samples, a plurality of model scores using the initial version of the machine learning classification model, wherein, for a given one of the plurality of training samples, a corresponding model score indicates a probability that the given training sample corresponds to a particular one of a plurality of classes;” (abstract idea, mental process) “…based on the plurality of model scores, determine a plurality of weighting values corresponding to the plurality of training samples…” (abstract idea, mental process) “performing a logarithmic function…” (abstract idea, mental process) “normalizing the first logarithmic value…” (abstract idea, mental process) “generating a first weighting value…” (abstract idea, mental process) “…and generate, based on the training dataset, an updated version of the machine learning classification model…” (instructions to apply) “determining, based on the plurality of weight values, loss values for the plurality of model scores generated using the initial version of the machine learning classification model; and updating, based on the loss values and according to a second, lower learning rate, one or more parameters of the initial version of the machine learning classification model.” (abstract idea mental process steps).
Dependent Claims:
Claim 2 recites a mental process abstract idea step (“…generated such that a first training sample with a first model score is given a higher weighting value than a second training sample with a second, lower model score.”), assigning weight values in an order is practically performed within the human mind or with the aid of pencil and paper.
Claim 3 (claim 17) recites abstract ideas “applying an optimization algorithm to modify one or more parameters of the machine learning classification model, wherein the optimization algorithm uses a particular loss function to evaluate a performance of the machine learning classification model for a given one of the plurality of training samples,” (mathematical relationship or calculation), and “for the given training sample, a corresponding loss value generated using the particular loss function is weighted based on a given weighting value associated with the given training sample.” (mental process).
Claim 4 merely recites a refinement of a loss function additional element. The additional elements recited in claim 4 have been found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 5 recites refinements to the “updating…” mental process step of Claim 1.
Claim 6 (claim 14 and claim 18) recites abstract ideas “performing one or more of a logit transformation function or a Box-Cox transformation” (mathematical calculations).
Claim 7 recites refinements to the additional elements.
Claim 8 (claim 15) recites refinements to the additional elements.
Claim 9 (claim 15 and claim 19) recites refinements to the additional elements.
Claim 10 (claim 20) recites data transmittal and extra-solution activity steps (“receiving, by the computer system, an authorization request corresponding to a second electronic transaction, wherein the authorization request specifies one or more attributes associated with the second electronic transaction;” and “applying, by the computer system, information corresponding to the one or more attributes associated with the second electronic transaction as input to the updated version of the machine learning classification model to generate a predicted classification for the second electronic transaction;”) as well as an abstract idea, mental process step (“determining, by the computer system, whether to authorize the second electronic transaction based on the predicted classification.”)
Claim 12 recites refinements to the additional elements and abstract idea mental process steps (“a corresponding loss value generated using the particular loss function is weighted based on a given weighting value associated with the given training sample.”).
Claim 13 recites refinements to the additional elements and abstract idea mental process steps (“and wherein the corresponding plurality of weighting values are generated such that a first training sample with a first model score is given a higher weighting value than a second training sample with a second, lower model score.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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/GRIFFIN TANNER BEAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121