CTNF 18/530,313 CTNF 81430 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections 07-29-01 AIA Claim s 9, 17 and 20 are objected to because of the following informalities: Claim 9 The claim recites “…comprising a processor configure to:” which should be “…comprising a processor configured to:”. Claim 17 The claim recites: “…wherein the set of scoring rules are based on the plurality of metrics, characteristics of the input data set, the type of the problem…” which should be “…wherein the set of scoring rules are based on the plurality of metrics, characteristics of the input data set and the type of the problem”. Claim 20 The claim recites “…clustering each of the groups based on the generated features, identifying a cluster having a highest average performance metric” which should be “…clustering each of the groups based on the generated features and identifying a cluster having a highest average performance metric” . Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea. This judicial exception is not integrated into a practical application and the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Step 1 for all claims Claims 1-8 and 17-20 are directed to a method and claims 9-16 are directed to a computing system comprising a processor. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). Claims 1 and 9. Step 2A, prong 1 Using claim 1 as the representative claim, the claims recite in part: identifying, based at least in part on the input data set and the problem, a set of machine learning pipelines from a database comprising a plurality of machine learning pipelines. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses analyzing input data and a problem to be solved to determine a machine learning pipeline capable of solving the problem. Step 2A, prong 2 The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: receiving, from a user, an input data set and a type of problem to solve based on the input data set which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)). recommending, to the user, a first machine learning pipeline of the set of machine learning pipelines from the set of machine learning to the user, wherein each of the plurality of machine learning pipelines includes a pipeline score and wherein the first machine learning pipeline has a highest pipeline score of the set of machine learning pipelines which amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). providing, to the user, a rule set associated with the first machine learning pipeline, wherein the rule set includes one or more suggested settings associated with the first machine learning pipeline which amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). Claim 9 further recites the additional element of a processor which is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: receiving, from a user, an input data set and a type of problem to solve based on the input data set is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). recommending, to the user, a first machine learning pipeline of the set of machine learning pipelines from the set of machine learning to the user, wherein each of the plurality of machine learning pipelines includes a pipeline score and wherein the first machine learning pipeline has a highest pipeline score of the set of machine learning pipelines which amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price” providing, to the user, a rule set associated with the first machine learning pipeline, wherein the rule set includes one or more suggested settings associated with the first machine learning pipeline amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”. Claim 9 further recites the additional element of a processor which is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. Claims 2 and 10. Step 2A, prong 1 The claim recites: wherein each of the plurality of machine learning pipelines includes a sequence of stages that include one or more of data ingestion, data validation, feature extraction, machine learning model/version selection, training data selection/preparation, model training, model evaluation, and model validation. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses determining the workflow or processing stages that the identified machine learning pipeline needs to include in order to solve the problem. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claims 3 and 11. Step 2A, Prong 2 The claim recites: “wherein each of the plurality of machine learning pipelines includes a machine learning module associated with each of the sequence of stages” which is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element individually or in combination does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) Step 2B As set forth above, the claim recites: “wherein each of the plurality of machine learning pipelines includes a machine learning module associated with each of the sequence of stages” which is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claims 4 and 12. Step 2A, prong 1 The claim recites “wherein each of the plurality of machine learning pipelines are created based on an analysis of previously executed machine learning experiments and wherein each of the previously executed machine learning experiments is associated with a previous type of problem and a previous input data set”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses analyzing how previous machine learning experiments performed when processing previous data similar to the current data to determine if the machine learning pipeline could be able to solve the current problem. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claims 5 and 13. The claim recites: “wherein the set of machine learning pipelines is identified based on the previous type of problem being the same as the type of problem and a similarity between the previous input data set and the input data set exceeding a threshold value”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses identifying the machine learning pipelines that can solve the problem based on how similar the current problem and the data is to problems previously solved and the data processed. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claims 6 and 14. Step 2A, prong 1 The claim recites: “wherein each of the previously executed machine learning experiments includes a sequence of stages, a machine learning module associated with each of the sequence of stages, and one or more metrics associated with the previously executed machine learning experiments. This limitation further describes the previously executed machine learning experiments and is therefore part of the mental process of how these machine learning experiments are analyzed. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claims 7 and 15. The claim recites “wherein the pipeline score of each of the plurality of machine learning pipelines is created by applying a set of scoring rules to the one or more metrics associated with the previously executed machine learning experiments”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses using some evaluation criteria to assign different scores to each machine learning pipeline. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claims 8 and 16. The claim recites: wherein the one or more metrics associated with the previously executed machine learning experiments, include performance metrics for the machine learning modules associated with each of the sequence of stages. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses using some criteria to measure how well the machine learning modules performed when executed. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claim 17 Step 2A, prong 1 creating a machine learning pipeline for each of plurality of the machine learning experiments, the machine learning pipeline including a sequence of stages including one or more of data ingestion, data validation, feature extraction, machine learning model/version selection, training data selection/preparation, model training, model evaluation, and model validation performed during the machine learning experiments. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses determining the workflow or processing stages that the identified machine learning pipeline needs to include in order to solve the problem. calculate a pipeline score for each of the machine learning pipelines by applying the set of scoring rules to the plurality of metrics, characteristics of the input data set, the type of the problem. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses evaluating the metrics, the input data set and the type of problem using some criteria to determine a value that reflects the degree to which they satisfy the criteria. Step 2A, prong 2 The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: obtaining a plurality of machine learning experiments performed by users, wherein each of the plurality of the machine learning experiment includes an input data set and a type of problem to solve based on the input data set; obtaining a plurality of metrics for each of the machine learning pipelines; obtaining a set of scoring rules for scoring the machine learning pipeline, wherein the set of scoring rules are based on the plurality of metrics, characteristics of the input data set, the type of the problem These limitations are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: obtaining a plurality of machine learning experiments performed by users, wherein each of the plurality of the machine learning experiment includes an input data set and a type of problem to solve based on the input data set; obtaining a plurality of metrics for each of the machine learning pipelines; obtaining a set of scoring rules for scoring the machine learning pipeline, wherein the set of scoring rules are based on the plurality of metrics, characteristics of the input data set, the type of the problem These limitations are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. Claim 18 Step 2A, prong 1 The claim recites “wherein obtaining a plurality of metrics for each of the machine learning pipelines includes performing a statistical analysis on the input data set. As drafted, this limitation is directed to a mathematical concept”. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claim 19 The claim recites wherein obtaining a plurality of metrics for each of the machine learning pipelines includes grouping the plurality of machine learning experiments based on the type of problem and generating features and performance metrics for each group. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses grouping machine learning experiments that correctly solved similar problems and identifying similar features between the machine learning experiments within each group. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Claim 20 The claim recites wherein obtaining a plurality of metrics for each of the machine learning pipelines further includes clustering each of the groups based on the generated features, identifying a cluster having a highest average performance metric. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) with the aid of pencil and paper. For example, this limitation encompasses making clusters of the groups by analyzing the features within the groups and identifying which cluster has the highest average performance metric. Step 2A, prong 2 The claim does not include any additional elements that individually or in combination integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that individually or in combination amount to significantly more than the judicial exception. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gao et al. US PGPUB 2022/0383183 Lopes et al. US PGPUB 2024/0168855 Marinescu et al. US PGPUB 2023/0114013 Fusco et al. US PGPUB 2022/0207349 Rawat et al. US PGPUB 2022/019822 Kishimoto et al. US PGPUB 2021/0326736 None of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specified in the independent claims, including at least: Claims 1 and 9 recommending, to the user, a first machine learning pipeline of the set of machine learning pipelines from the set of machine learning to the user, wherein each of the plurality of machine learning pipelines includes a pipeline score and wherein the first machine learning pipeline has a highest pipeline score of the set of machine learning pipelines and providing, to the user, a rule set associated with the first machine learning pipeline, wherein the rule set includes one or more suggested settings associated with the first machine learning pipeline. Claim 17 obtaining a set of scoring rules for scoring the machine learning pipeline, wherein the set of scoring rules are based on the plurality of metrics, characteristics of the input data set, the type of the problem and calculate a pipeline score for each of the machine learning pipelines by applying the set of scoring rules to the plurality of metrics, characteristics of the input data set, the type of the problem. Claims 1-20 are rejected. CORRESPONDENCE INFORMATION Any inquiry concerning this communication or earlier communications from the examiner should be directed to Omar F Fernandez Rivas whose telephone number is (571)272-2589. The examiner can normally be reached on Mon-Fri 5:30-3:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Wiley can be reached on (571) 272-4150. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128 Application/Control Number: 18/530,313 Page 2 Art Unit: 2128 Application/Control Number: 18/530,313 Page 3 Art Unit: 2128 Application/Control Number: 18/530,313 Page 4 Art Unit: 2128 Application/Control Number: 18/530,313 Page 5 Art Unit: 2128 Application/Control Number: 18/530,313 Page 6 Art Unit: 2128 Application/Control Number: 18/530,313 Page 7 Art Unit: 2128 Application/Control Number: 18/530,313 Page 8 Art Unit: 2128 Application/Control Number: 18/530,313 Page 9 Art Unit: 2128 Application/Control Number: 18/530,313 Page 10 Art Unit: 2128 Application/Control Number: 18/530,313 Page 11 Art Unit: 2128 Application/Control Number: 18/530,313 Page 12 Art Unit: 2128 Application/Control Number: 18/530,313 Page 13 Art Unit: 2128 Application/Control Number: 18/530,313 Page 14 Art Unit: 2128 Application/Control Number: 18/530,313 Page 15 Art Unit: 2128 Application/Control Number: 18/530,313 Page 16 Art Unit: 2128 Application/Control Number: 18/530,313 Page 17 Art Unit: 2128 Application/Control Number: 18/530,313 Page 18 Art Unit: 2128
Prosecution Insights