DETAILED ACTION
Claims 1-20 are presented for examination.
This office action is in response to submission of application on 05-MAY-2023.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is direction to an abstract idea without significantly more.
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide run) to perform the claim limitation.
MPEP 2106.04(a)(2)(I) “The mathematical concepts grouping is defined as mathematical
relationships, mathematical formulas or equations, and mathematical calculations.”
Regarding claim 1:
Step 2A, Prong 1 will now be evaluated for this claim:
A judicial exception is recited in this claim as it recites a mental process:
wherein the set of data object references are predicted as required input to the processing operation by the machine learning engine
Prediction of required input may be performed in the human mind by comparing previously required input to other input sources.
A judicial exception is recited in this claim as it recites a mathematical concept:
Step 2A, Prong 2 will now be evaluated for this claim:
Furthermore, the additional elements:
generating, as output from the machine learning engine, a set of data object references
This describe the generic application of a machine learning model, which is a generic computer function.
are interpreted as a general purpose computer under MPEP 2106.05(f)
Furthermore, MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post-solution activity to be insignificant extra-solution activity.
The following steps are mere data gathering:
receiving, as input to a machine learning engine, a processing operation identifier, and an initial data object reference, wherein the processing operation identifier identifies a processing operation
Receiving data is data gathering, as is the inputting of data in a machine learning model.
executing a batch retrieval process, wherein the batch retrieval process retrieves a set of data objects that corresponds to the set of data object references from a datastore
Batch retrieval is further data retrieval.
The following steps are merely post solution activity:
loading the set of data objects in a cache; and executing the processing operation using the set of data objects loaded in the cache
The execution of the processing operation and the placement of the set of generated data objects are not integrated into a claim as a whole.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed practicing the abstract idea.
Therefore, the claim is related to an abstract idea.
Step 2B will now be discussed with regards to this claim:
The claim does not provide an inventive concept. There is no additional Insignificant Extra- Solution Activity, as identified in Step 2A Prong Two, that provides an inventive concept.
Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) does not overcome a rejection.
Generally linking the use of the judicial exception to computer environments, e.g., a claim describing how the abstract idea of creating a contractual relationship that guarantees performance of a transaction be performed using a computer that receives and sends information over a network, as discussed in buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014). (MPEP § 2106.05(h)) does not overcome a rejection.
The additional elements have been considered both individually and as an ordered combination as to whether they whether they warrant significantly more consideration.
The claim is ineligible.
Regarding claim 2, which depends upon claim 1:
This claim further limits the initial data object reference of claim 1, but this does not provide a further advantage. Further specifying the initial data object reference does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 3, which depends upon claim 1:
This claim further limits the processing operation identifier of claim 1, but this does not provide a further advantage. Further specifying the processing operation identifier does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 4, which depends upon claim 1:
The following is considered a post-solution activity:
saving a training data set, wherein the training data set comprises a set of data object references from a historical execution of the processing operation
Saving the training data set would be storing it and hence output, which is considered a form of post-solution activity.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 5, which depends upon claim 4:
This claim further limits the training data set of claim 4, but this does not provide a further advantage. Further specifying the training data set does not overcome the parent claim’s rejection.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 6, which depends upon claim 5:
The following is considered a generic computer function:
processing the training data set with a machine learning algorithm
This is a broad application of a generic machine learning algorithm.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 7, which depends upon claim 6:
The following is considered a generic computer function:
generating, based on processing the training data set with a machine learning algorithm, a machine learning model
Generating a generic machine learning model based on training data is a generic computer function.
The following would be an evaluation:
wherein the machine learning model is executed by the machine learning engine to predict the set of data object references
The prediction of a set of data object references is, as in claim 1, considered to be performable in the human mind. The machine learning model would be mere instructions to apply by a computer.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Claims 8-14 recite a system that parallels the method of claims 1-7 respectively. Therefore, the analysis discussed above with respect to claims 1-7 also applies to claims 8-14 respectively. Accordingly, claims 8-14 are rejected based on substantially the same rationale as set forth above with respect to claims 1-7 respectively.
Claims 15-17 recite a non-transitory computer readable storage medium that parallels the method of claims 1-3 respectively. Therefore, the analysis discussed above with respect to claims 1-3 also applies to claims 15-17 respectively. Accordingly, claims 15-17 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3 respectively.
Regarding claim 18, which depends upon claim 15:
The following is considered a post-solution activity:
saving a training data set, wherein the training data set comprises a set of data object references from a historical execution of the processing operation, the processing operation identifier, and the initial data object reference.
Saving the training data set would be storing it and hence output, which is considered a form of post-solution activity.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 19, which depends upon claim 18:
The following is considered a generic computer function:
processing the training data set with a machine learning algorithm
This is a broad application of a generic machine learning algorithm.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Regarding claim 20, which depends upon claim 19:
The following is considered a generic computer function:
generating, based on processing the training data set with a machine learning algorithm, a machine learning model
Generating a generic machine learning model based on training data is a generic computer function.
The following would be an evaluation:
wherein the machine learning model is executed by the machine learning engine to predict the set of data object references
The prediction of a set of data object references is, as in claim 1, considered to be performable in the human mind. The machine learning model would be mere instructions to apply by a computer.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Claim Rejections - 35 USC § 103
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zang et al. (Pub. No. US 20180314975 A1, filed April 27th 2017, hereinafter Zang) in view of Mozes et al. (Pub. No. US 11176480 B2, filed August 2nd 2016, hereinafter Mozes).
Regarding claim 1:
Claim 1 recites:
A method comprising: receiving, as input to a machine learning engine, a processing operation identifier, and an initial data object reference, wherein the processing operation identifier identifies a processing operation; generating, as output from the machine learning engine, a set of data object references, wherein the set of data object references are predicted as required input to the processing operation by the machine learning engine; executing a batch retrieval process, wherein the batch retrieval process retrieves a set of data objects that corresponds to the set of data object references from a datastore; loading the set of data objects in a cache; and executing the processing operation using the set of data objects loaded in the cache.
Zang discloses receiving, as input to a machine learning engine, a processing operation identifier, and an initial data object reference, wherein the processing operation identifier identifies a processing operation:
Zang teaches the identification of first machine learning projects that are similar to a secondary machine learning project through a variety of methods, wherein a machine learning project would be a processing operation identifier as it identifies a particular application of the process without itself being the machine learning model, which would be the processing operation (Paragraph 3). Furthermore, one of the methods that may be used to determine similarity is the comparison of training datasets which would be an initial data object reference as the training data itself is used a reference for the features which are contained within, which may be compared in order to determine common features (Paragraph 6).
This process of selection is performed by an ensemble transfer learning engine, which would be a machine learning engine (Paragraph 41). Therefore, the ensemble transfer learning engine, having performed this process, receives the processing operation identifier and the initial data object reference.
Zang discloses generating, as output from the machine learning engine, a set of data object references, wherein the set of data object references are predicted as required input to the processing operation by the machine learning engine:
Zang teaches that upon identifying similar projects, training data is created for the processing operation i.e. the second machine learning project by applying the selected machine learning models to the input data, therefore created output data for the machine learning engine in the form of a set of data object references wherein the training data is predicted as required input to the process operation by way of the selection of similar machine leaning models (Paragraph 3).
Mozes in the same field of endeavor of machine learning teaches executing a batch retrieval process, wherein the batch retrieval process retrieves a set of data objects that corresponds to the set of data object references from a datastore:
Mozes teaches retrieving sections of data using a partition key that identifies a data partition in a data set (Column 7, lines 1-10) wherein a data partition would be a data object that correspond to a set of data object references, wherein the references would be the overall datasets. Likewise, this would be a batch retrieval process as the partitions are batches of data.
Mozes and the present application are analogous art because they are in the same field of endeavor.
Mozes discloses loading the set of data objects in a cache; and executing the processing operation using the set of data objects loaded in the cache
Mozes teaches a data cache that is configured to persist a data mining model (Column 5, lines 45-50) which then loads a set of partitioned training data or the data objects and trains a data mining model using the training data thereby executing it (Column 6, lines 5-15)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Zang and Mozes. This would have provided the improvement of better identification of appropriate machine learning models for specific sets of data (Mozes, Column 1, lines 35-50).
Regarding claim 2, which depends upon claim 1:
Claim 2 recites:
The method of claim 1, wherein the initial data object reference is a key value, and wherein the datastore is a key-value pair datastore.
Zang in view of Mozes disclose the method of claim 1 upon which claim 2 depends. Furthermore, Mozes discloses the limitations of claim 2:
Mozes teaches that the data partition are created and references based on the partition keys (Column 7, lines 1-10) wherein the partition key would therefore be the key value and the data partition a value, making the datastore a key-value pair datastore.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Zang and Mozes. This would have provided the improvement of better identification of appropriate machine learning models for specific sets of data (Mozes, Column 1, lines 35-50).
Regarding claim 3, which depends upon claim 1:
Claim 3 recites:
The method of claim 1, wherein the processing operation identifier identifies a computer application.
Zang in view of Mozes disclose the method of claim 1 upon which claim 3 depends. Furthermore, Zang discloses the limitations of claim 3:
Zang teaches that the machine learning projects act as the processing operation identifier for the associated machine learning models as in claim 1 (Paragraph 3), wherein machine learning models may be a type of computer application.
Regarding claim 4, which depends upon claim 1:
Claim 4 recites:
The method of claim 1, comprising: saving a training data set, wherein the training data set comprises a set of data object references from a historical execution of the processing operation.
Zang in view of Mozes disclose the method of claim 1 upon which claim 4 depends. Furthermore, Mozes discloses the limitations of claim 4:
Mozes teaches a training data set that may represent historical information of a business, including business operations such as sales, which may be considered a historical execution of the processing operation as it refers to a particular action that was completed. Furthermore, the training data may include multiple data partitions identified by partition keys, wherein the partition keys are data object references for the data partitions. Furthermore, the presence of the training data set within the database system indicates the training data set was saved (Column 6-7, lines 60-67 and lines 1-10)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Zang and Mozes. This would have provided the improvement of better identification of appropriate machine learning models for specific sets of data (Mozes, Column 1, lines 35-50).
Regarding claim 5, which depends upon claim 4:
Claim 5 recites:
The method of claim 4, wherein the training data set includes the processing operation identifier and the initial data object reference.
Zang in view of Mozes disclose the method of claim 4 upon which claim 5 depends. Furthermore, Mozes discloses the limitations of claim 5:
Mozes teaches a training data set that may represent historical information of a business, including business operations such as sales, which may be considered a processing operation as it refers to a particular action that was completed. The sales therefore would be the processing operation identifier for a historical execution of the processing operation and the initial data object reference may refer to the data object i.e. the data partition in which the particular sales information is stored (Column 6-7, lines 60-67 and lines 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Zang and Mozes. This would have provided the improvement of better identification of appropriate machine learning models for specific sets of data (Mozes, Column 1, lines 35-50).
Regarding claim 6, which depends upon claim 5:
Claim 6 recites:
The method of claim 5, comprising: processing the training data set with a machine learning algorithm.
Zang in view of Mozes disclose the method of claim 5 upon which claim 6 depends. Furthermore, Zang discloses the limitations of claim 6:
Zang teaches that accuracy of the model using the training set may be measured, which indicates that the training data set is processed by a machine learning algorithm (Paragraph 31).
Regarding claim 7, which depends upon claim 6:
Claim 7 recites:
The method of claim 6, comprising: generating, based on processing the training data set with a machine learning algorithm, a machine learning model, wherein the machine learning model is executed by the machine learning engine to predict the set of data object references.
Zang in view of Mozes disclose the method of claim 6 upon which claim 7 depends. Furthermore, Zang discloses the limitations of claim 7:
Zang teaches an ensemble transfer learning engine that identified common features in both an existing machine learning project and a new machine learning project, thereby processing the training data in order to determine those shared features (Paragraph 48). Furthermore, the machine learning models are then retrained using the common feature set, which generates a new machine learning model wherein the machine learning model is then applied to the input data (Paragraph 9) wherein these retrained machine learning models may then be used to predict the set of data object references i.e. the appropriate training set and set of data object references for another machine learning model by generating said set as output (Paragraph 6).
Claims 8-14 recite a system that parallels the method of claims 1-7 respectively. Therefore, the analysis discussed above with respect to claims 1-7 also applies to claims 8-14 respectively. Accordingly, claims 8-14 are rejected based on substantially the same rationale as set forth above with respect to claims 1-7 respectively.
Claims 15-17 recite a non-transitory computer readable storage medium that parallels the method of claims 1-3 respectively. Therefore, the analysis discussed above with respect to claims 1-3 also applies to claims 15-17 respectively. Accordingly, claims 15-17 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3 respectively.
Regarding claim 18, which depends upon claim 15:
Claim 18 recites:
The non-transitory computer readable storage medium of claim 15, comprising: saving a training data set, wherein the training data set comprises a set of data object references from a historical execution of the processing operation, the processing operation identifier, and the initial data object reference.
Zang in view of Mozes disclose the method of claim 15 upon which claim 18 depends. Furthermore, Mozes discloses the limitations of claim 18:
Mozes teaches a training data set that may represent historical information of a business, including business operations such as sales, which may be considered a historical execution of the processing operation as it refers to a particular action that was completed. Furthermore, the training data may include multiple data partitions identified by partition keys, wherein the partition keys are data object references for the data partitions. Furthermore, the presence of the training data set within the database system indicates the training data set was saved (Column 6-7, lines 60-67 and lines 1-10).
Furthermore, the sales therefore would be the processing operation identifier for a historical execution of the processing operation and the initial data object reference may refer to the data object i.e. the data partition in which the particular sales information is stored (Column 6-7, lines 60-67 and lines 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Zang and Mozes. This would have provided the improvement of better identification of appropriate machine learning models for specific sets of data (Mozes, Column 1, lines 35-50).
Regarding claim 19, which depends upon claim 18:
Claim 19 recites:
The non-transitory computer readable storage medium of claim 18, comprising: processing the training data set with a machine learning algorithm.
Zang in view of Mozes disclose the method of claim 18 upon which claim 19 depends. Furthermore, Zang discloses the limitations of claim 19:
Zang teaches that accuracy of the model using the training set may be measured, which indicates that the training data set is processed by a machine learning algorithm (Paragraph 31).
Regarding claim 20, which depends upon claim 19:
Claim 20 recites:
The non-transitory computer readable storage medium of claim 19, comprising: generating, based on processing the training data set with a machine learning algorithm, a machine learning model, wherein the machine learning model is executed by the machine learning engine to predict the set of data object references.
Zang in view of Mozes disclose the method of claim 19 upon which claim 20 depends. Furthermore, Zang discloses the limitations of claim 20:
Zang teaches an ensemble transfer learning engine that identified common features in both an existing machine learning project and a new machine learning project, thereby processing the training data in order to determine those shared features (Paragraph 48). Furthermore, the machine learning models are then retrained using the common feature set, which generates a new machine learning model wherein the machine learning model is then applied to the input data (Paragraph 9) wherein these retrained machine learning models may then be used to predict the set of data object references i.e. the appropriate training set and set of data object references for another machine learning model by generating said set as output (Paragraph 6).
Conclusion
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/A.J.M./Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142