DETAILED ACTION
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 .
This action is made non-final.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/13/2026 and 04/15/2026 has been entered. Claims 1, 3-17 and 19-20 are pending. Claims 1, 9 and 17 are independent claims.
Response to Arguments
Applicant’s arguments, dated 3/13/2026, regarding the 35 U.S.C. 112(a) rejections of the previous office action have been fully considered, but are not persuasive. The applicant argues that a person skilled in the art who has knowledge related to the binomial distribution can easily determine the size of each subset using the parameters provided in the as-filed specification and in the relevant claims. Examiner argues that the recited line “a size of each subset, of the set of feature vectors, is determined by using a binomial distribution” is unclear, as the size of each subset would be a single value for each subset, while the binomial distribution provides a range of values and corresponding probabilities. The examiner further argues that a full, clear, concise and exact description of subset size determination using a binomial distribution is not provided by the specification in the paragraphs listed by the applicant or clarified in the arguments.
Applicant’s arguments, dated 3/13/2026, regarding the 35 U.S.C. 112(b) rejections of the previous office action have been fully considered. In light of the amendments, the 112(b) rejection for claim 17 has been withdrawn.
Applicant’s arguments, dated 3/13/2026, regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered, but are not persuasive. Due to the claim amendments, the scope of the claims has changed and new grounds of rejection are applied – see the updated rejection below.
Applicant argues that claim 1 recites operations that are performed by a machine and are not performable by the human mind. Examiner argues that many of the listed limitations, when given their broadest reasonable interpretation, are mentally performable, or mathematical calculations. For example, examiner argues that as claimed, generating a distribution vector by: (1) splitting a subset of an outcomes vector, (2) initializing (i.e., creating) the distribution vector with a specific size, and (3) assigning a value to one of the distribution vector dimensions are mentally performable steps.
Applicant argues that claim 1 integrates any alleged abstract idea into a practical application. Examiner argues that many of the claim limitations recited in the arguments as providing a technical benefit are mental processes or mathematical calculations (i.e., calculating a Bregman divergence between vectors is a mathematical calculation or formula, and updating weights “based on” the calculated divergence is a mental process and/or mathematical calculation). As stated in MPEP 2106.05(a), paragraph 6, the improvement in technology cannot be provided by the judicial exception, but can be provided by one or more additional elements considered alone or in combination with judicial exception. Examiner argues that the additional elements in claim 1 only amount to insignificant extra-solution activity, and therefore do not provide significantly more than the judicial exception.
Applicant’s arguments, dated 3/13/2026, regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered, and are persuasive. Due to the claim amendments, all 103 rejections have been withdrawn.
Claim Objections
Claims 7, 8, 15, 16 and 17 are objected to because of the following informalities:
Claim 7 recites “2n subsets of D… wherein n is equal to a population of D…” but should read “2n subsets of the set of feature vectors… wherein n is equal to a population of the set of feature vectors”, or the like.
Claim 8 recites “a collection of random subsets of D” but should read “a collection of random subsets of the set of feature vectors”, or the like.
Claim 15 recites “2n subsets of the feature vector… wherein n is equal to a population of the feature vector…” but should read “2n subsets of the set of feature vectors… wherein n is equal to a population of the set of feature vectors…”, or the like.
Claim 16 recites “a collection of random subsets of the feature vector…” but should read “a collection of random subsets of the set of feature vectors…”, or the like.
Claim 17 recites “into number of buckets…” in line 19 but should read “into a number of buckets…” or the like.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contain subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Specifically, both claims recite "wherein a size of each subset, of the set of feature vectors, is determined by using a binomial distribution with parameters n and p, wherein n is equal to the total population of D and p is equal to 0.5". It is not clear how the binomial distribution is used to determine sizes of each subset, as the binomial distribution is a probability model and does not have a clear connection to determining the size of a subset.
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, 3-17, 19 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory
category. See MPEP 2106.03. Claim 1 recites: A computer-implemented method for improving performance of a machine learning system by training a neural network to perform an analysis that satisfies average calibration, the computer-implemented method comprising… Claim 1 is directed to a process (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 1 recites: and repeatedly… selecting a subset of the set of feature vectors (selecting a subset from a set is a mental process); generating a distribution vector for a subset of the outcomes vector, wherein the subset of the outcomes vector corresponds to the subset of the set of feature vectors (selecting corresponding output vector values is mental process), and the generating of the distribution vector comprises: splitting the subset of the outcomes vector into a number of buckets (splitting data into groups is a mental process); initializing the distribution vector with a number of dimensions corresponding to the number of buckets plus an extra dimension (creating vectors of a certain size is a mental process); and assigning, to the extra dimension of the distribution vector, a value equal to a total population of the subset of the set of feature vectors minus a total number of data points in the subset of the outcomes vector (subtraction of two values is a mathematical calculation, plus a mental process of assigning the calculation result to the vector)… calculating a Bregman divergence between the distribution vector and a scoring distribution vector of the prediction vector (calculating a Bregman divergence between two vectors is a mathematical calculation); and updating weights of the neural network based on the Bregman divergence (updating weights of a neural network based on the calculated Bregman divergence is a mental process or mathematical calculation)… These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements,
considered individually and in combination, integrate the judicial exception into a practical application? Claim 1 recites: obtaining, using at least one hardware processor, an outcomes vector and a set of feature vectors, wherein each feature vector of the set of feature vectors corresponds to an outcome of outcomes in the outcomes vector… using the at least one hardware processor… producing a prediction vector by running the neural network on the subset of the set of feature vectors… to enable the performance of the analysis that satisfies the average calibration. Obtaining the outcomes vector and feature vectors is extra-solution activity of data gathering that does not add a meaningful limitation to the process of improving the machine learning system. Using a hardware processor is an attempt to apply the abstract idea(s) on generic computing components. Producing a prediction vector by using a neural network model is an attempt to use the neural network model by merely applying the abstract idea without placing any limits on how the neural network model operates. Further, the claim omits any details as to how the neural network model solves a technical problem and instead recites only the idea of a solution or outcome, equivalent to adding the words “apply it” to the judicial exception – see MPEP 2106.05(f). Specifying that the updating enables performance of analysis that satisfies the average calibration without describing specific steps of the updating process recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished, equivalent to adding the words “apply it” to the judicial exception – see MPEP 2106.05(f)(Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they only amount to data gathering without significantly more (MPEP 2106.05(g)) or provide nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)). These limitations, taken either alone or in combination, fails to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Regarding claims 3-8, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 3, using a Kaplan-Meier estimator is a mathematical formula; Claim 4, specifying an equipment outcomes vector and performing maintenance is specifying a field of use without significantly more; Claim 5, using squared loss is a mathematical formula; Claim 6, using a Kullback-Leibler divergence is a mathematical formula; Claim 7, drawing a subset from other subsets is a mental process, and the use of a binomial distribution is a mathematical formula; Claim 8, drawing a subset randomly from a collection of subsets is a mental process).
Regarding claim 9:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory
category. See MPEP 2106.03. Claim 9 recites: A computer-implemented method for improving performance of a machine learning system by training a neural network to perform an analysis that satisfies average calibration, the computer-implemented method comprising… Claim 9 is directed to a process (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 9 recites: selecting… a subset of a set of feature vectors; selecting… a subset of an outcomes vector, wherein each outcome in the subset of the outcomes vector corresponds to a feature vector of the feature vectors in the subset of the set of feature vectors (selecting a subset of vectors, and then selecting a corresponding subset from a vector is a mental process); splitting… the subset of the outcomes vector into a number of buckets (splitting data into buckets is a mental process); initializing… a distribution vector with a number of dimensions corresponding to the number of buckets plus an extra dimension (creating a vector of a certain size is a mental process); assigning to each dimension of the distribution vector… a value equal to a number of data points in a corresponding bucket (assigning a value to a vector is a mental process, and counting the number of data points in a bucket is a mental process); assigning to the extra dimension of the distribution vector… a value equal to a total population of the subset of the set of feature vectors minus a total number of data points in the subset of the outcomes vector (subtracting two values is a mathematical calculation, and assigning the result of that mathematical calculation to a vector is a mental process)… and updating… weights of the neural network based on a Bregman divergence between the distribution vector and a scoring distribution vector of the prediction vector (determining a Bregman divergence between two vectors is a mathematical calculation, and updating weights based on a Bregman divergence is a mental process or mathematical calculation). These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, considered individually and in combination, integrate the judicial exception into a practical application? Claim 9 recites: using at least one hardware processor… using the at least one hardware processor… using the at least one hardware processor… using the at least one hardware processor… using the at least one hardware processor… using the at least one hardware processor… producing, using the at least one hardware processor, a prediction vector by running the neural network on the subset of the set of feature vectors… using the at least one hardware processor… Using a hardware processor is an attempt to apply the abstract idea(s) on generic computing components. Producing a prediction vector by using a neural network model is an attempt to use the neural network model by merely applying the abstract idea without placing any limits on how the neural network model operates. Further, the claim omits any details as to how the neural network model solves a technical problem and instead recites only the idea of a solution or outcome, equivalent to adding the words “apply it” to the judicial exception – see MPEP 2106.05(f) (Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they amount to limiting the field of use without significantly more (MPEP 2106.05(h)). These limitations, taken either alone or in combination, fails to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Regarding claims 10-16, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 10, selecting a different subset of a set of feature vectors and the outcomes vector is a mental process, repeating the series of mental processes/mathematical calculations [i.e., assigning values to a vector, calculating a divergence, and updating weights], is still reciting the abstract idea, and outputting of a vector by a model is still an attempt to apply the judicial exception; Claim 11, using squared loss is a mathematical formula; Claim 12, using a Kullback-Leibler divergence is a mathematical formula; Claim 13, using a Kaplan-Meier estimator is a mathematical formula; Claim 14, using a Nelson-Aalen estimator is a mathematical formula; Claim 15, drawing a subset from other subsets is a mental process, and the use of a binomial distribution is a mathematical formula; Claim 16, drawing a subset randomly from a collection of subsets is a mental process and having a specific number of subsets that is derived from the result of a division operation is a mathematical calculation).
Regarding claim 17, it is an apparatus implementing the method of claim 1 and is rejected on the same grounds – see above.
Regarding claims 19 and 20, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occur within the apparatus (Claim 19, using a Kullback-Leibler divergence is a mathematical formula; Claim 20, receiving a feature vector is insignificant extra-solution activity of data inputting/gathering that does not add a meaningful limitation to the machine learning apparatus, and producing a new outcomes vector by running the neural network on the received feature vector is again an attempt to use the neural network model by merely applying the abstract idea without placing any limits on how the neural network model operates, equivalent to adding the words “apply it” to the judicial exception).
Allowable Subject Matter
Claims 1, 3-17, 19 and 20 are allowable over prior art. However, they are only allowable if the claims are amended to overcome the 101 rejections and 112 rejections.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARRISON CHAN YOUNG KIM whose telephone number is (571)272-0713. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HARRISON C KIM/Examiner, Art Unit 2145
/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
Prosecution Insights