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 responsive to the application filed 05/15/2024. Claims 1-20 are presented for examination.
Priority
Applicant’s claim for the benefit of a prior filed application US 18/373,918, filed 05/16/2023, is acknowledged.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 12 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12455743 in view of Achin et al. (US 10496927 B2, published 12/03/2019), hereafter Achin, in view of Vu et al. (US 11966340 B2, filed 03/15/2022), hereafter Vu, and further in view of Moon et al. (US 10649794 B2, published 05/12/2020), hereafter Moon. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the present invention are similar in scope to the claims of U.S. Patent No. 12455743. For example, the table below shows similarities and differences between the instant application and the U.S. Patent No. 12455743.
18/665,211 (Instant Application)
US 12455743 B2
1. An apparatus, comprising: a memory storing instructions; a communications interface; and at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to: obtain a dataset comprising a plurality of indexed data elements, each of the indexed data elements comprising a sample identifier and a temporal identifier associated with a corresponding temporal interval; based on the sample and temporal identifiers, perform operations that partition the dataset into corresponding ones of a plurality of partitioned datasets in accordance with first configuration data, each of the partitioned datasets comprising a subset of the indexed data elements; generate feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements, the feature vectors being generated in accordance with second configuration data; and perform operations, in accordance with third configuration data, that train a machine-learning process to predict an occurrence of a target event during a future temporal interval based on the feature vectors associated with each of the partitioned datasets.
12. The apparatus of claim 1, wherein the at least one processor is further configured to: obtain, from the memory, pipelining data characterizing a sequential execution of a plurality of application engines, each of the application engines being associated with a corresponding one of the first, second, and third configuration data; based on the pipelining data, execute sequentially the application engines based on the corresponding ones of the first, second, and third configuration data, the executed application engines causing the at least one processor to obtain the dataset comprising the plurality of indexed data elements; perform the operations that partition the dataset into the corresponding ones of the partitioned datasets based on the sample and temporal identifiers; generate the feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements; and perform the operations that train the machine-learning process based on the feature vectors associated with each of the partitioned datasets; obtain artifact data generated by the executed application engines and store the artifact data within a portion of the memory; and transmit at least a portion of the artifact data to a computing system via the communications interface.
1. An apparatus, comprising: a memory storing instructions; a communications interface; and at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to: obtain, from the memory, elements of configuration data associated with a plurality of application engines and pipelining data characterizing a sequential execution of at least a subset of the application engines, at least one of the elements of configuration data being generated by a computing system; based on the pipelining data, execute sequentially each of the subset of the application engines in accordance with corresponding ones of the elements of configuration data, the executed subset of the application engines causing the at least one processor to perform operations that at least one of (i) train a machine-learning or artificial-intelligence process or (ii) apply the trained machine-learning or artificial-intelligence process to an input dataset; perform operations that obtain artifact data generated by the executed subset of the application engines and that store the artifact data within a portion of the memory; and transmit at least a portion of the artifact data to the computing system via the communications interface.
The U.S. Patent No. 12455743 did not teach to obtain a dataset comprising a plurality of indexed data elements, each of the indexed data elements comprising a sample identifier and a temporal identifier associated with a corresponding temporal interval; based on the sample and temporal identifiers, perform operations that partition the dataset into corresponding ones of a plurality of partitioned datasets in accordance with first configuration data, each of the partitioned datasets comprising a subset of the indexed data elements; generate feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements, the feature vectors being generated in accordance with second configuration data; and perform operations, in accordance with third configuration data, that train a machine-learning process to predict an occurrence of a target event during a future temporal interval based on the feature vectors associated with each of the partitioned datasets as recited in claims 1 and 12 of the instant application.
However, Achin teaches: obtain a dataset comprising a plurality of indexed data elements, each of the indexed data elements comprising a temporal identifier associated with a corresponding temporal interval ([Col. 3, Lines 53-59] discusses receiving a set of indexed data comprising a time indicator and value of one or more variables, which constitutes a sample identifier); based on the sample and temporal identifiers, perform operations that partition the dataset into corresponding ones of a plurality of partitioned datasets in accordance with first configuration data, each of the partitioned datasets comprising a subset of the indexed data elements; ([Col. 3-4, Lines 60-27] discusses performing operations that split the indexed data elements into subsets based on the information, the identifiers, in the time-series data); generate feature vectors ([Col. 21-22, Lines 65-15] discusses generating parameterized features to be used on a dataset); and perform operations, in accordance with third configuration data, that train a machine-learning process to predict an occurrence of a target event during a future temporal interval based on the feature vectors associated with each of the partitioned datasets ([Col. 3-4, Lines 65-9] discusses training a predictive model to generate predictions of future target values, the prediction concerning the occurrence of a target variable or event; the forecast range indicates a duration of a period for which the prediction occurs; thus, a temporal interval based on the feature vectors associated with the partitioned dataset).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated obtaining a dataset comprising indexed elements comprising a temporal identifier, partitioning the dataset into subsets of indexed elements, generating feature vectors, and training a predictive model to predict the occurrence of a target event as suggested by Achin into U.S. Patent No. 12455743 because both of these systems are addressing applying a machine-learning process to an input dataset. Doing so would be motivated by the desire to systematically and cost-effectively evaluate the space of potential predictive modeling solutions for prediction problems (Achin [Col. 3]).
The combination of U.S. Patent No. 12455743 and Achin did not teach each of the indexed data elements comprising a sample identifier; generate feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements, the feature vectors being generated in accordance with second configuration data as recited in claims 1 and 12 of the instant application.
However, Vu teaches to generate feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements, the feature vectors being generated in accordance with second configuration data ([Col. 4, Lines 63-64 & Col. 14, Lines 30-35] discusses generating feature vectors for the series data that has been partitioned).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated generating feature vectors associated with each of the partitioned datasets in accordance with second configuration data as suggested by Vu into the combination of U.S. Patent No. 12455743 and Achin because these systems are addressing applying a machine-learning process to an input dataset. Doing so would be motivated by the desire to improve runtime by generating feature vectors to identify data configurations (Vu [Col. 4 & 15]).
The combination of U.S. Patent No. 12455743, Achin, and Moon did not teach each of the indexed data elements comprising a sample identifier as recited in claims 1 and 12 of the instant application.
However, Moon teaches each of the indexed data elements comprising a sample identifier ([Col. 10, Lines 40-49 & Col. 13, Lines 54-62] discusses obtaining an input containing sample identifier in the form of a key value and a per-record time stamp).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated each of the indexed data elements comprising a sample identifier as suggested by Moon into the combination of U.S. Patent No. 12455743, Achin, and Vu because these systems are addressing applying a machine-learning process to an input dataset. Doing so would be motivated by the desire to implement an identifier to indicate the type of data being obtained (Moon [Col. 10]).
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: The claim recites “An apparatus comprising:”; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
generate feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements, the feature vectors being generated in accordance with second configuration data: These limitations recite mathematical concepts similar to organizing information and manipulating information through mathematical correlation per MPEP 2106.04(a)(2)(I)(A)(iv).
based on the sample and temporal identifiers, perform operations that partition the dataset into corresponding ones of a plurality of partitioned datasets in accordance with first configuration data, each of the partitioned datasets comprising a subset of the indexed data elements: These limitations recite a mentally performable process with the aid of pen and paper of using observation and judgement to perform operations that partition the dataset into corresponding ones of a plurality of partitioned datasets in accordance with first configuration data, each of the partitioned datasets comprising a subset of the indexed data elements.
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
a memory storing instructions; a communications interface; and at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to: These additional elements are recited at a high level of generality and amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception. See MPEP § 2106.05(f).
obtain a dataset comprising a plurality of indexed data elements, each of the indexed data elements comprising a sample identifier and a temporal identifier associated with a corresponding temporal interval: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
and perform operations, in accordance with third configuration data, that train a machine-learning process to predict an occurrence of a target event during a future temporal interval based on the feature vectors associated with each of the partitioned datasets: These additional elements recite only the idea of performing operations that train a machine-learning process to predict an occurrence of a target event and attempts to cover any implementation of performing operations that train machine-learning processes without any restriction as to the specific operations that are performed, or how these respective operations train the machine-learning process to predict future occurrences. Thus, these additional elements do not meaningfully limit the claim and do not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking generic computer components to apply the underlying judicial exception, adding words equivalent to "apply it" with the judicial exception, and insignificant extra-solution activity of data gathering recited by “obtain a dataset comprising a plurality of indexed data elements, each of the indexed data elements comprising a sample identifier and a temporal identifier associated with a corresponding temporal interval” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
the machine-learning process comprises a forward-in-time machine learning process: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the machine-learning process, to a particular technological environment or field of use, e.g. comprises a forward-in-time machine learning process. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
and the at least one processor is further configured to perform the operations that partition the dataset into corresponding ones of the plurality of partitioned datasets, and to generate the feature vectors associated with each of the partitioned datasets, without data leakage: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the at least one processor, to a particular technological environment or field of use, e.g. is further configured to perform the operations that partition the dataset into corresponding ones of the plurality of partitioned datasets, and to generate the feature vectors associated with each of the partitioned datasets, without data leakage. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
the first configuration data comprises sampling data and temporal splitting data: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the first configuration data, to a particular technological environment or field of use, e.g. comprises sampling data and temporal splitting data. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
the partitioned datasets comprise a training dataset, a validation dataset, and a testing dataset: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the partitioned datasets, to a particular technological environment or field of use, e.g. comprise a training dataset, a validation dataset, and a testing dataset. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
and the at least one processor is further configured to execute the instructions to partition the dataset into corresponding ones of the training dataset, the validation dataset, and the testing dataset in accordance with the sampling data and the temporal splitting data, each of the training dataset, the validation dataset, and the testing dataset comprising the corresponding subset of the indexed data elements and ground-truth labels associated with corresponding ones of the indexed data elements: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the at least one processor, to a particular technological environment or field of use, e.g. is further configured to execute the instructions to partition the dataset into corresponding ones of the training dataset, the validation dataset, and the testing dataset in accordance with the sampling data and the temporal splitting data, each of the training dataset, the validation dataset, and the testing dataset comprising the corresponding subset of the indexed data elements and ground-truth labels associated with corresponding ones of the indexed data elements. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 1, as well as:
generate the feature values based on an application of the at least one of the aggregation operation or the post-processing operation to elements of source data associated with the prior temporal interval: These limitations recite mathematical concepts similar to organizing information and manipulating information through mathematical correlation per MPEP 2106.04(a)(2)(I)(A)(iv).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
each of the feature vectors comprise values of a plurality of features: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. each of the feature vectors, to a particular technological environment or field of use, e.g. comprise values of a plurality of features. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
the second configuration data specifies, for each of the feature values, at least one of an aggregation operation or a post-processing operation, source data associated with the feature values, and a prior temporal interval: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the second configuration data, to a particular technological environment or field of use, e.g. specifies, for each of the feature values, at least one of an aggregation operation or a post-processing operation, source data associated with the feature values, and a prior temporal interval. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 4, as well as:
and for each of the subset of the indexed data elements of the corresponding one of the partitioned datasets, generate each of the feature values based on an application of the at least one of the aggregation operation or the post-processing operation to elements of source data: These limitations recite mathematical concepts similar to organizing information and manipulating information through mathematical correlation per MPEP 2106.04(a)(2)(I)(A)(iv).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
obtain the sample identifier and the temporal identifier associated with each of the subset of the indexed data elements of the corresponding one of the partitioned datasets; for each of the subset of the indexed data elements of the corresponding one of the partitioned datasets, perform operations that obtain the elements of source data based on the second configuration data, and determine that the elements of source data are (i) associated with the prior temporal interval based on the corresponding temporal identifier and (ii) associated with the sample identifier: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “obtain the sample identifier and the temporal identifier associated with each of the subset of the indexed data elements of the corresponding one of the partitioned datasets; for each of the subset of the indexed data elements of the corresponding one of the partitioned datasets, perform operations that obtain the elements of source data based on the second configuration data, and determine that the elements of source data are (i) associated with the prior temporal interval based on the corresponding temporal identifier and (ii) associated with the sample identifier” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 4.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
the feature values of at least one of the feature vectors are associated with a feature group, and the at least one of the aggregation operation or the post-processing operation is associated with the feature group: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the feature values of at least one of the feature vectors, to a particular technological environment or field of use, e.g. are associated with a feature group, and the at least one of the aggregation operation or the post-processing operation is associated with the feature group. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
and the second configuration data comprises a group identifier of the feature group, feature identifiers of the feature values, and an operation identifier of the at least one of the aggregation operation or the post-processing operation, the second configuration data associating the group identifier with each of the feature identifiers and with the operation identifier: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the second configuration data, to a particular technological environment or field of use, e.g. comprises a group identifier of the feature group, feature identifiers of the feature values, and an operation identifier of the at least one of the aggregation operation or the post-processing operation, the second configuration data associating the group identifier with each of the feature identifiers and with the operation identifier. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 1, as well as:
based on the application the machine-learning process to the feature vectors, generate elements of predictive output associated with corresponding ones of the indexed data elements of each of the partitioned datasets: These limitations recite mathematical concepts similar to organizing information and manipulating information through mathematical correlation per MPEP 2106.04(a)(2)(I)(A)(iv).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
the third configuration data comprises an identifier of the machine-learning process and a value of a process parameter of the machine- learning process: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the third configuration data, to a particular technological environment or field of use, e.g. comprises an identifier of the machine-learning process and a value of a process parameter of the machine- learning process. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
and the at least one processor is further configured to execute the instructions to: apply the machine-learning process to the feature vectors associated with each of the partitioned datasets in accordance with the process parameter value: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the at least one processor, to a particular technological environment or field of use, e.g. is further configured to execute the instructions to: apply the machine-learning process to the feature vectors associated with each of the partitioned datasets in accordance with the process parameter value. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the at least one processor is further configured to execute the instructions to generate elements of explainability data characterizing the training of the machine-learning process in accordance with fourth configuration data, the elements of explainability data comprising at least one of (i) a first value characterizing an importance of one or more features on an output of the trained machine-learning process or (ii) a second value characterizing a performance of the trained machine-learning process: These additional elements amount to insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein the at least one processor is further configured to execute the instructions to generate elements of explainability data characterizing the training of the machine-learning process in accordance with fourth configuration data, the elements of explainability data comprising at least one of (i) a first value characterizing an importance of one or more features on an output of the trained machine-learning process or (ii) a second value characterizing a performance of the trained machine-learning process” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics, or performing repetitive calculations per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 8, as well as:
based on at least a portion of the explainability data, determine a modified composition of the feature vectors, and generate modified second configuration data that specifies the modified composition; in accordance with the modified second configuration data, generate additional feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements: These limitations recite mathematical concepts similar to organizing information and manipulating information through mathematical correlation per MPEP 2106.04(a)(2)(I)(A)(iv).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
and perform additional operations, in accordance with the third configuration data, that train the machine-learning process to predict the occurrence of the target event during the future temporal interval based on the additional feature vectors associated with each of the partitioned datasets: These additional elements recite only the idea of performing operations that train a machine-learning process to predict an occurrence of a target event and attempts to cover any implementation of performing operations that train machine-learning processes without any restriction as to the specific operations that are performed, or how these respective operations train the machine-learning process to predict future occurrences. Thus, these additional elements do not meaningfully limit the claim and do not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 10
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 1, as well as:
for each of the subset of the indexed data elements of corresponding ones of the partitioned datasets, generate each of the additional feature vectors based on a corresponding one of the elements of the second configuration data: These limitations recite mathematical concepts similar to organizing information and manipulating information through mathematical correlation per MPEP 2106.04(a)(2)(I)(A)(iv).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
obtain a plurality of elements of the second configuration data, each of the elements of the second configuration data being associated with an additional feature vector and specifying a composition of the additional feature vector: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
perform operations, in accordance with the third configuration data, that train the machine-learning process to predict the occurrence of the target event during the future temporal interval based on the additional feature vectors associated with each of the partitioned datasets: These additional elements recite only the idea of performing operations that train a machine-learning process to predict an occurrence of a target event and attempts to cover any implementation of performing operations that train machine-learning processes without any restriction as to the specific operations that are performed, or how these respective operations train the machine-learning process to predict future occurrences. Thus, these additional elements do not meaningfully limit the claim and do not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f).
and generate elements of explainability data characterizing the training of the machine-learning process based on the additional feature vectors, the elements of explainability data comprising ranked values characterizing an importance of each feature within the additional feature vectors on an output of the trained machine-learning process: These additional elements amount to insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception, and insignificant extra-solution activity of data gathering recited by “obtain a plurality of elements of the second configuration data, each of the elements of the second configuration data being associated with an additional feature vector and specifying a composition of the additional feature vector” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics, and “and generate elements of explainability data characterizing the training of the machine-learning process based on the additional feature vectors, the elements of explainability data comprising ranked values characterizing an importance of each feature within the additional feature vectors on an output of the trained machine-learning process” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics, and performing repetitive calculations per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 11
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the at least one processor is further configured to receive, via the communications interface, at least one of the first configuration data, the second configuration data, or the third configuration data from a computing system, the computing system being configured to generate the at least one of the first configuration data, the second configuration data, or the third configuration data: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein the at least one processor is further configured to receive, via the communications interface, at least one of the first configuration data, the second configuration data, or the third configuration data from a computing system, the computing system being configured to generate the at least one of the first configuration data, the second configuration data, or the third configuration data.” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 12
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of claim 1, as well as:
based on the pipelining data, execute sequentially the application engines based on the corresponding ones of the first, second, and third configuration data, the executed application engines causing the at least one processor to obtain the dataset comprising the plurality of indexed data elements: These limitations recite mathematical concepts similar to organizing information and manipulating information through mathematical correlation per MPEP 2106.04(a)(2)(I)(A)(iv).
generate the feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements: These limitations recite mathematical concepts similar to organizing information and manipulating information through mathematical correlation per MPEP 2106.04(a)(2)(I)(A)(iv).
perform the operations that partition the dataset into the corresponding ones of the partitioned datasets based on the sample and temporal identifiers: These limitations recite a mentally performable process with the aid of pen and paper of using observation and judgement to perform operations that partition the dataset into the corresponding ones of the partitioned datasets based on the sample and temporal identifiers.
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
obtain, from the memory, pipelining data characterizing a sequential execution of a plurality of application engines, each of the application engines being associated with a corresponding one of the first, second, and third configuration data: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
and perform the operations that train the machine-learning process based on the feature vectors associated with each of the partitioned datasets: These additional elements recite only the idea of performing operations that train a machine-learning process based on the feature vectors and attempts to cover any implementation of performing operations that train machine-learning processes without any restriction as to the specific operations that are performed, or how these respective operations train the machine-learning process based on the feature vectors. Thus, these additional elements do not meaningfully limit the claim and do not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f).
obtain artifact data generated by the executed application engines and store the artifact data within a portion of the memory: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
and transmit at least a portion of the artifact data to a computing system via the communications interface: These additional elements amount to insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception, and insignificant extra-solution activity of data gathering recited by “obtain, from the memory, pipelining data characterizing a sequential execution of a plurality of application engines, each of the application engines being associated with a corresponding one of the first, second, and third configuration data” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics, “obtain artifact data generated by the executed application engines and store the artifact data within a portion of the memory” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics, and “transmit at least a portion of the artifact data to a computing system via the communications interface” which are well-understood routine and conventional activities similar to receiving or transmitting data over a network per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claims 13-19
Step 1: These claims are directed to “A computer-implemented method, comprising:”; therefore, it is directed the statutory category of a process.
Step 2A Prong 1: Claims 13-19 recite the same judicial exceptions as Claims 1-12, respectively.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The analysis at this step for Claims 13-19 mirrors that of Claims 1-12, respectively.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception for these claims. The analysis at this step for Claims 13-19 mirrors that of Claims 1-12, respectively.
Claim 20
Step 1: This claim recites "A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising:"; therefore, it is directed to the statutory category of an article of manufacture.
Step 2A Prong 1: Claim 20 recite the same judicial exception as Claim 1.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The only difference between Claim 20 and Claim 1, is that Claim 20 is directed to "A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step for Claim 20 mirrors that of Claim 1.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception for these claims. The only difference between Claim 20 and Claim 1, is that Claim 20 is directed to "A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception, the analysis at this step for Claim 20 mirrors that of Claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-10 & 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Achin et al. (US 10496927 B2, published 12/03/2019), hereafter Achin, in view of Moon et al. (US 10649794 B2, published 05/12/2020), hereafter Moon, and further in view of Vu et al. (US 11966340 B2, filed 03/15/2022), hereafter Vu.
Regarding independent claim 1, Achin teaches an apparatus comprising:
a memory storing instructions; a communications interface; and at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions ([Col. 20, Lines 15-31 & Col. 57, Lines 13-14] discusses a memory storing instructions, a communications interface, and a processor coupled to the memory and communications interface) to:
obtain a dataset comprising a plurality of indexed data elements, each of the indexed data elements comprising a temporal identifier associated with a corresponding temporal interval ([Col. 3, Lines 53-59] discusses receiving a set of indexed data comprising a time indicator and value of one or more variables, which constitutes a sample identifier);
based on the sample and temporal identifiers, perform operations that partition the dataset into corresponding ones of a plurality of partitioned datasets in accordance with first configuration data, each of the partitioned datasets comprising a subset of the indexed data elements ([Col. 3-4, Lines 60-27] discusses performing operations that split the indexed data elements into subsets based on the information, the identifiers, in the time-series data);
generate feature vectors ([Col. 21-22, Lines 65-15] discusses generating parameterized features to be used on a dataset);
and perform operations, in accordance with third configuration data, that train a machine-learning process to predict an occurrence of a target event during a future temporal interval based on the feature vectors associated with each of the partitioned datasets ([Col. 3-4, Lines 65-9] discusses training a predictive model to generate predictions of future target values, the prediction concerning the occurrence of a target variable or event; the forecast range indicates a duration of a period for which the prediction occurs; thus, a temporal interval based on the feature vectors associated with the partitioned dataset).
Achin does not explicitly teach each of the indexed data elements comprising a sample identifier; generate feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements, the feature vectors being generated in accordance with second configuration data.
However, in a similar field of endeavor, Moon teaches a system for aggregating features for machine learning wherein indexed input comprises a sample identifier and temporal identifier ([Col. 10, Lines 40-49 & Col. 13, Lines 54-62] discusses obtaining an input containing sample identifier in the form of a key value and a per-record time stamp).
Because Achin teaches obtaining a dataset comprising indexed elements comprising a temporal identifier, partitioning the dataset into subsets of indexed elements, generating feature vectors, and training a predictive model to predict the occurrence of a target event; and Moon teaches a system for aggregating features for machine learning wherein indexed input comprises a sample identifier and temporal identifier, accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate indexed input comprising a sample identifier as taught by Moon into Achin’s apparatus, with a reasonable expectation of success, to teach an apparatus configured to obtain a dataset comprising a plurality of indexed data elements, comprising a sample identifier and a temporal identifier associated with a corresponding temporal interval; based on the sample and temporal identifiers, perform operations that partition the dataset into corresponding ones of a plurality of partitioned datasets in accordance with first configuration data, each of the partitioned datasets comprising a subset of the indexed data elements; generate feature vectors; and perform operations, in accordance with third configuration data, that train a machine-learning process to predict an occurrence of a target event during a future temporal interval based on the feature vectors associated with each of the partitioned datasets. This combination would have been motivated by the desire to implement an identifier to indicate the type of data being obtained (Moon [Col. 10]).
The combination of Achin and Moon does not explicitly teach generate feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements, the feature vectors being generated in accordance with second configuration data.
However, in a similar field of endeavor, Vu teaches a system for time series pipeline generation wherein datasets are partitioned then further used to generate associated feature vectors ([Col. 14, Lines 6-12] discusses the machine learning component partitioning the datasets into subsets of indexed data elements; [Col. 4, Lines 63-64 & Col. 14, Lines 30-35] discusses generating feature vectors for the series data that has been partitioned).
Because the combination of Achin and Moon teaches obtaining a dataset comprising indexed elements comprising a sample and temporal identifier, partitioning the dataset into subsets of indexed elements, generating feature vectors, and training a predictive model to predict the occurrence of a target event; and Vu teaches a system wherein datasets are partitioned then further used to generate associated feature vectors, accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate datasets are partitioned then further used to generate associated feature vectors as taught by Vu into the combination of Achin and Moon’s apparatus, with a reasonable expectation of success, to teach an apparatus to obtain a dataset comprising a plurality of indexed data elements, each of the indexed data elements comprising a sample identifier and a temporal identifier associated with a corresponding temporal interval; based on the sample and temporal identifiers, perform operations that partition the dataset into corresponding ones of a plurality of partitioned datasets in accordance with first configuration data, each of the partitioned datasets comprising a subset of the indexed data elements; generate feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements, the feature vectors being generated in accordance with second configuration data; and perform operations, in accordance with third configuration data, that train a machine-learning process to predict an occurrence of a target event during a future temporal interval based on the feature vectors associated with each of the partitioned dataset. This combination would have been motivated by the desire to improve runtime by generating feature vectors to identify data configurations (Vu [Col. 4 & 15]).
Regarding dependent claim 2, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 1, including wherein:
the machine-learning process comprises a forward-in-time machine learning process (Achin [Col. 4, Lines 2-13] discusses the machine learning process wherein the subsets are generated in time order given the time series data; thus, the process is forward-in-time);
and the at least one processor is further configured to perform the operations that partition the dataset into corresponding ones of the plurality of partitioned datasets, and to generate the feature vectors associated with each of the partitioned datasets, without data leakage (Achin [Abstract] discusses including a skip range indicating temporal lag between time periods which works to prevent leakage).
Regarding dependent claim 3, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 1, including wherein:
the first configuration data comprises sampling data and temporal splitting data (Achin [Col. 3-4, Lines 60-27] discusses performing operations that split the indexed data elements into subsets based on the information, the identifiers, in the time-series data);
the partitioned datasets comprise a training dataset, a validation dataset, and a testing dataset (Vu [Col. 5, Lines 9-13] discusses partitioning the full dataset into a training dataset, a testing dataset, and a holdout dataset, which constitutes a validation dataset);
and the at least one processor is further configured to execute the instructions to partition the dataset into corresponding ones of the training dataset, the validation dataset, and the testing dataset in accordance with the sampling data and the temporal splitting data, each of the training dataset, the validation dataset, and the testing dataset comprising the corresponding subset of the indexed data elements and ground-truth labels associated with corresponding ones of the indexed data elements (Vu [Col. 5, Lines 9-13] discusses partitioning the full dataset into a training dataset, a testing dataset, and a holdout dataset, which constitutes a validation dataset; Achin [Col. 10, Lines 38-39] discusses use of training-output collections of the observations, which constitutes ground-truth labels associated with corresponding indexed data elements).
Regarding dependent claim 4, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 1, including wherein:
each of the feature vectors comprise values of a plurality of features (Vu [Col. 4, Lines 63-64 & Col. 14, Lines 30-35] discusses multiple transformer feature vectors that each comprise a distinct feature);
the second configuration data specifies, for each of the feature values, at least one of an aggregation operation or a post-processing operation, source data associated with the feature values, and a prior temporal interval (Vu [Col. 4, Lines 63-64 & Col. 14, Lines 30-35] discusses a window transformer that is an aggregation operation defined over a bounded historical span; Vu [Col. 11, Lines 49-52] discusses these transformers use historical data of a prior temporal interval);
and the at least one processor is further configured to execute the instructions to, for each of the subset of the indexed data elements of a corresponding one of the partitioned datasets, generate the feature values based on an application of the at least one of the aggregation operation or the post-processing operation to elements of source data associated with the prior temporal interval (Vu [Col. 4, Lines 63-64 & Col. 14, Lines 30-35] discusses a window transformer that is an aggregation operation defined over a bounded historical span; Vu [Col. 11, Lines 49-52] discusses these transformers use historical data of a prior temporal interval).
Regarding dependent claim 5, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 4, including wherein the at least one processor is further configured to execute the instructions to:
obtain the sample identifier and the temporal identifier associated with each of the subset of the indexed data elements of the corresponding one of the partitioned datasets (Achin [Col. 3, Lines 53-59] discusses receiving a time indicator and value of one or more variables, which constitutes a temporal and sample identifier);
for each of the subset of the indexed data elements of the corresponding one of the partitioned datasets, perform operations that obtain the elements of source data based on the second configuration data, and determine that the elements of source data are (i) associated with the prior temporal interval based on the corresponding temporal identifier and (ii) associated with the sample identifier (Achin [Col. 6, Lines 48-54] discusses fitting a predictive model to the data to obtain the source data that is bounded by a starting and ending time; thus the elements that are associated with the prior temporal interval would also be associated with the sample identifier);
and for each of the subset of the indexed data elements of the corresponding one of the partitioned datasets, generate each of the feature values based on an application of the at least one of the aggregation operation or the post-processing operation to elements of source data (Achin [Col. 5, Lines 20-45] discusses generating features by application of an aggregation operation to elements of source data; Vu [Col. 4, Lines 63-64 & Col. 14, Lines 30-35] discusses a window transformer that is an aggregation operation defined over a bounded historical span).
Regarding dependent claim 6, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 4, including wherein:
the feature values of at least one of the feature vectors are associated with a feature group, and the at least one of the aggregation operation or the post-processing operation is associated with the feature group (Moon [Col. 4, Lines 52-55 & Col. 6, Lines 39-43] discusses features are associated with a group, and aggregation operations are associated with the whole feature group);
and the second configuration data comprises a group identifier of the feature group, feature identifiers of the feature values, and an operation identifier of the at least one of the aggregation operation or the post-processing operation, the second configuration data associating the group identifier with each of the feature identifiers and with the operation identifier (Moon [Col. 4, Lines 62-65] discusses a prefix that operates as a group identifier; Moon [Col. 6, Lines 1-4] discusses a features element that operates as a feature identifier of the feature values; Moon [Col. 6, Lines 39-43] discusses the use of an operator element that operates as an operation identifier for the specific aggregation operation to be performed; thus, the data is associated with the group, feature, and operation identifiers).
Regarding dependent claim 7, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 1, including wherein:
the third configuration data comprises an identifier of the machine-learning process and a value of a process parameter of the machine- learning process (Achin [Col. 23, Lines 18-20 & Col. 23, Lines 32-35] discusses the algorithm may be parameterized, and this parameter may be tuned; thus, the data comprises an identifier and a value of a process parameter is tuned);
and the at least one processor is further configured to execute the instructions to: apply the machine-learning process to the feature vectors associated with each of the partitioned datasets in accordance with the process parameter value (Achin [Col. 6-7, Lines 58-23] discusses fitting the predictive machine-learning model to the features associated with each dataset in accordance with the parameter values);
and based on the application the machine-learning process to the feature vectors, generate elements of predictive output associated with corresponding ones of the indexed data elements of each of the partitioned datasets (Achin [Col. 4, Lines 26-27] discusses post fitting of the machine-learning model, wherein the model is tested on the data to generate predictive output).
Regarding dependent claim 8, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 1, including wherein the at least one processor is further configured to execute the instructions to generate elements of explainability data characterizing the training of the machine-learning process in accordance with fourth configuration data, the elements of explainability data comprising at least one of (i) a first value characterizing an importance of one or more features on an output of the trained machine-learning process or (ii) a second value characterizing a performance of the trained machine-learning process (Achin [Col. 15, Lines 15-31] discusses generating an accuracy value characterizing the performance of the trained machine-learning model).
Regarding dependent claim 9, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 8, including wherein the at least one processor is further configured to:
based on at least a portion of the explainability data, determine a modified composition of the feature vectors, and generate modified second configuration data that specifies the modified composition (Achin [Col. 13, Lines 24-49] discusses performing feature engineering to modify the composition of feature vectors by removing a particular feature, and would therefore generate modified configuration data);
in accordance with the modified second configuration data, generate additional feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements (Achin [Col. 13, Lines 36-49] discusses generating additional features associated with the dataset, adding the features to the dataset);
and perform additional operations, in accordance with the third configuration data, that train the machine-learning process to predict the occurrence of the target event during the future temporal interval based on the additional feature vectors associated with each of the partitioned datasets (Achin [Col. 13-14, Lines 50-4] discusses further training the machine-learning model to predict occurrences of a target event based on the additional features that were generated for the predictive model).
Regarding dependent claim 10, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 1, including wherein the at least one processor is further configured to execute the instructions to:
obtain a plurality of elements of the second configuration data, each of the elements of the second configuration data being associated with an additional feature vector and specifying a composition of the additional feature vector (Moon [Col. 3, Lines 1-14] discusses the ability to add new features that are associated with configuration data, and the composition of these features is specified when added; thus, the system obtains configuration data associated with an additional feature vector);
for each of the subset of the indexed data elements of corresponding ones of the partitioned datasets, generate each of the additional feature vectors based on a corresponding one of the elements of the second configuration data (Moon [Col. 2-3, Lines 60-14] discusses the additional features can be generated or added in order to correspond to the elements of the configuration data);
perform operations, in accordance with the third configuration data, that train the machine-learning process to predict the occurrence of the target event during the future temporal interval based on the additional feature vectors associated with each of the partitioned datasets (Achin [Col. 13, Lines 50-65] discusses fitting a machine-learning model to predict a target event for a second dataset; thus, a dataset containing the additional feature vectors);
and generate elements of explainability data characterizing the training of the machine-learning process based on the additional feature vectors, the elements of explainability data comprising ranked values characterizing an importance of each feature within the additional feature vectors on an output of the trained machine-learning process (Achin [Col. 13, Lines 27-49] discusses comparing features for their ranking of their respective importances on an output of the trained machine-learning process).
Regarding claims 13-19, claims 13-19 are method claims that are substantially the same as the system of claims 1, 3-4, & 7-11, respectively. Therefore, claims 13-19 are rejected for the same reasons as claims 1, 3-4, & 7-11, respectively.
Regarding claim 20, claim 20 is a computer-readable storage medium claim that is
substantially the same as the system of claim 1. Therefore, claim 20 is rejected for the same reasons as claim 1.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Achin, in view of Moon, in view of Vu, as applied in claim 1, and further in view of Dirac et al. (US 9886670 B2, published 02/06/2018), hereafter Dirac.
Regarding dependent claim 11, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 1, including wherein the at least one processor is further configured to receive, via the communications interface, at least one of the first configuration data, the second configuration data, or the third configuration data from a computing system (Vu [Col. 9, Lines 18-37] discusses receiving configuration data from one or more external services of a computing system).
The combination of Achin, Moon, and Vu does not explicitly teach the computing system being configured to generate the at least one of the first configuration data, the second configuration data, or the third configuration data.
However, in a similar field of endeavor, Dirac teaches a pipeline system for processing input for machine learning, wherein a computing system generates configuration data ([Col. 25, Lines 5-19] discusses generating configuration data by processing input from a computing system or interface, pre-processing it and providing it to the machine-learning server).
Because the combination of Achin, Moon, and Vu teaches receiving configuration data from a computing system; and Dirac teaches a computing system generates configuration data, accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate a computing system generating configuration data as taught by Dirac into the combination of Achin, Moon, and Vu’s apparatus, with a reasonable expectation of success, to teach wherein the at least one processor is further configured to receive, via the communications interface, at least one of the first configuration data, the second configuration data, or the third configuration data from a computing system, the computing system being configured to generate the at least one of the first configuration data, the second configuration data, or the third configuration data. This combination would have been motivated by the desire to include the ability to generate configuration data such as functions or methods defined in one or more libraries (Dirac [Col. 25]).
Regarding dependent claim 12, the combination of Achin, Moon, and Vu teaches the claimed invention as claimed in claim 1, including:
obtain the dataset comprising the plurality of indexed data elements (Achin [Col. 3, Lines 53-59] discusses receiving a set of indexed data comprising a time indicator and value of one or more variables, which constitutes a sample identifier; Moon [Col. 10, Lines 40-49 & Col. 13, Lines 54-62] discusses obtaining an input containing sample identifier in the form of a key value and a per-record time stamp);
perform the operations that partition the dataset into the corresponding ones of the partitioned datasets based on the sample and temporal identifiers (Achin [Col. 3-4, Lines 60-27] discusses performing operations that split the indexed data elements into subsets based on the information, the identifiers, in the time-series data; Vu [Col. 14, Lines 6-12] discusses the machine learning component partitioning the datasets into subsets of indexed data elements);
generate the feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements (Achin [Col. 21-22, Lines 65-15] discusses generating parameterized features to be used on a dataset; Vu [Col. 4, Lines 63-64 & Col. 14, Lines 30-35] discusses generating feature vectors for the series data that has been partitioned);
and perform the operations that train the machine-learning process based on the feature vectors associated with each of the partitioned datasets (Achin [Col. 3-4, Lines 65-9] discusses training a predictive model to generate predictions of future target values, the prediction concerning the occurrence of a target variable or event; the forecast range indicates a duration of a period for which the prediction occurs; thus, a temporal interval based on the feature vectors associated with the partitioned dataset).
The combination of Achin, Moon, and Vu does not explicitly teach to obtain, from the memory, pipelining data characterizing a sequential execution of a plurality of application engines, each of the application engines being associated with a corresponding one of the first, second, and third configuration data; obtain artifact data generated by the executed application engines and store the artifact data within a portion of the memory; and transmit at least a portion of the artifact data to a computing system via the communications interface.
However, in a similar field of endeavor, Dirac teaches a pipeline system for processing input for machine learning, wherein memory is obtained for a sequential execution of application engines ([Col. 8-9, Lines 48-26] discusses three engines in sequence wherein the engines are chained with configuration data and the output is used as the next engine’s input; thus, this data flow operates as the pipeline of data characterizing a sequential execution); artifacts are obtained as a result of application engine generation and stored in memory ([Col. 15, Lines 25-36 & Col. 8, Lines 26-28] discusses obtaining an artifact generated by the application engines and storing the artifact within a repository in memory); and the artifact data is transmitted to a computing system via a communications interface ([Col. 8, Lines 37-40] discusses that clients are able to view at least a subset of artifacts and thus, the subset of artifacts are returned to the client computing system through the same interface used to submit the configuration).
Because the combination of Achin, Moon, and Vu teaches obtaining a dataset comprising indexed elements, partitioning the dataset based on sample and temporal identifiers, generating feature vectors associated with the partitioned datasets, and training the machine-learning process based on the feature vectors; and Dirac teaches memory is obtained for a sequential execution of application engines, artifacts are obtained as a result of application engine generation and stored in memory, and the artifact data is transmitted to a computing system via a communications interface, accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate memory obtained for a sequential execution of application engines, artifacts obtained as a result of application engine generation and stored in memory, and the artifact data is transmitted to a computing system via a communications interface as taught by Dirac into the combination of Achin, Moon, and Vu’s apparatus, with a reasonable expectation of success, to teach an apparatus to obtain, from the memory, pipelining data characterizing a sequential execution of a plurality of application engines, each of the application engines being associated with a corresponding one of the first, second, and third configuration data; based on the pipelining data, execute sequentially the application engines based on the corresponding ones of the first, second, and third configuration data, the executed application engines causing the at least one processor to obtain the dataset comprising the plurality of indexed data elements; perform the operations that partition the dataset into the corresponding ones of the partitioned datasets based on the sample and temporal identifiers; generate the feature vectors associated with each of the partitioned datasets based on the corresponding subset of the indexed data elements; and perform the operations that train the machine-learning process based on the feature vectors associated with each of the partitioned datasets; obtain artifact data generated by the executed application engines and store the artifact data within a portion of the memory; and transmit at least a portion of the artifact data to a computing system via the communications interface. This combination would have been motivated by the desire to implement the apparatus within a larger workflow, including receiving specific requests from clients or memory (Dirac [Col. 8]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gambhir (US 11853312 B1, filed 06/22/2022) ([Abstract] A method for generating a feature store comprises receiving an indication of user input indicating a database management system and a server system hosting a target database; and in response to receiving the indication of user input: determining a connection string for the target database; using the connection string to connect to the target database via the database management system; after connecting to the target database, generating a structure of the feature store in the target database; and populating the feature store with predefined feature values).
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/RILEY S ACOSTA/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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