DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement filed on 10/5/2023 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because no copy of the foreign patent document was submitted. The IDS has been placed in the application file, but the information referred to therein concerning the foreign reference has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Title
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Examiner believes that the title of the invention is imprecise. A descriptive title indicative of the invention will help in proper indexing, classifying, searching, etc. See MPEP 606.01. However, the title of the invention should be limited to 500 characters. Examiner suggests including the aspect(s) of the claims which Applicant believes to be novel or nonobvious over the prior art.
Specification
The disclosure is objected to because of the following informalities:
Specification paragraph 57 recites, “…an input data set comprising at least two different data types contained in a single container (e.g. first data relating to income data; second data relating to spend data in a set of transactions communicated across a computing environment.” However, an end parenthesis is missing.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Specifically, claim 1 recites, in part, a set of training models applying machine learning and operable by the processor each trained independently and separately for being trained based on historical values for the input data set to predict future outcomes for each of the data types from the data preparation software container. The phrase each trained independently and separately for being trained is unclear and should be rephrased to clarify the scope of the limitation. Additionally, in claim 1 line 11, another plurality does not clearly indicate what it is a plurality of. Additionally, in claim 1 line 15, the model train container is recited however the claim previously recited at least one model train container as well as another model train container. It is unclear if these are the same elements, different elements, or related elements.
Specifically, claim 4 recites the inferences without clear antecedent basis.
Specifically, claim 7 recites multiple models however multiple input trained models was introduced in claim 1. It is unclear if this is the same element, a different element, or related elements. Additionally, claim 7 recites the model train container however claim 1 recited at least one model train container as well as another model train container. It is unclear if these are the same elements, different elements, or related elements. Lastly, the claim recites generate model inference providing multiple predictions. The scope is unclear since it appears the claim should recite generate a model inference providing multiple predictions or generate model inferences providing multiple predictions.
For this reason, the above listed claims are rejected for containing this language or being dependent on a claim that contains this language.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a program per se (i.e. software per se) which is non-statutory subject matter according to MPEP 2106, which states that a computer program per se is not directed to one of the four categories of statutory subject matter listed in 35 U.S.C. 101. The claimed computer architecture comprises no more than data in containers, which when given their broadest reasonable interpretation in light of the specification (see paragraph 3 – “A container is a unit of software that packages code and its dependencies so the application runs quickly and reliably across computing environments.”), can be no more than software per se.
Additionally, Examiner notes that an explanation of the exact term “machine-readable medium” of claim 15 is not found in the specification (paragraph 73 uses terms such as “data storage media,” “computer-readable media,” “computer-readable medium,” and “computer-readable storage media”). Therefore, “non-transitory machine-readable medium” is interpreted to be non-transitory and excluding signals.
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.
Claim(s) 1-2, 4-9, 11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaushik et al. (hereinafter Kaushik), U.S. Patent Application Publication 2021/0034278 in view of Shah et al. (hereinafter Shah), Building Modern Clouds: Using Docker, Kubernetes & Google Cloud Platform.
Regarding claim 1, Kaushik discloses a multi-model machine learning train and inference pipeline computing architecture in a cloud computing environment, comprising:
a data preparation software container operable by a processor containing an input data set comprising at least two different data types [“The process begins with step 200, obtaining historical storage resource utilization data for a given set of storage resources of one or more storage systems. The given set of storage resources in some embodiments comprises a storage pool allocated for a given user, the storage pool comprising a first set of storage resources allocated from a first one of the one or more storage systems and a second set of storage resources allocated from a second one of the one or more storage systems.” ¶40] contained in a single container;
a set of training models applying machine learning and operable by the processor each trained independently and separately for being trained based on historical values for the input data set to predict future outcomes for each of the data types [“The plurality of time series capacity prediction forecasting models comprises at least a first time series capacity prediction forecasting model that takes into account a first type of seasonality and trend factors, and at least a second time series capacity prediction forecasting model that takes into account a second type of seasonality and trend factors.” ¶41] from the data preparation software container, a plurality of the set of training models grouped into at least one model train container for storing the trained models based on a type of data being predicted [“at least a first time series capacity prediction forecasting model that takes into account a first type of seasonality and trend factors” ¶41] and another plurality grouped into another model train container having a different type of data contained therein [“at least a second time series capacity prediction forecasting model that takes into account a second type of seasonality and trend factors” ¶41]; and
a single inference model operable by the processor for each data type performing joint nested inference based on multiple input trained models received from the model train container for a particular type of data [“determine or select appropriate models for generating an "ensemble" forecast or storage resource capacity prediction” ¶53; “The ensemble forecast uses the selected models to generate a set of individual or model-specific storage resource capacity predictions (e.g., one from each of the selected models). The model-specific storage resource capacity predictions are combined (e.g., using a weighted average) to calculate the overall or ensemble storage resource capacity prediction” ¶57] held within one container, the inference model for predicting, in a single inference component, multiple inferences for future values of the particular type of data having various subcategories [“forecast storage resource utilization (e.g., forecast amounts of free or available storage resources 410/510, forecast amounts of storage resources used 420/520)” ¶61; “output also illustrates the cone of uncertainty, with lines showing a mean value (e.g., 403/503), an upper bound (e.g., 404/504) and a lower bound (e.g., 405/505) on the forecasts.” ¶61].
However, Kaushik fails to explicitly disclose a data preparation software container operable by a processor containing an input data set comprising at least two different data types contained in a single container;
a set of training models applying machine learning and operable by the processor each trained independently and separately for being trained based on historical values for the input data set to predict future outcomes for each of the data types from the data preparation software container, a plurality of the set of training models grouped into at least one model train container for storing the trained models based on a type of data being predicted and another plurality grouped into another model train container having a different type of data contained therein; and
a single inference model operable by the processor for each data type performing joint nested inference based on multiple input trained models received from the model train container for a particular type of data held within one container, the inference model for predicting, in a single inference component, multiple inferences for future values of the particular type of data having various subcategories.
Shah discloses a data preparation software container operable by a processor containing [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1] an input data set comprising at least two different data types contained in a single container [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1];
a set of training models applying machine learning and operable by the processor each trained independently and separately for being trained based on historical values for the input data set to predict future outcomes for each of the data types from the data preparation software container [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1], a plurality of the set of training models grouped into at least one model train container for storing the trained models [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1] based on a type of data being predicted and another plurality grouped into another model train container [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1] having a different type of data contained therein; and
a single inference model operable by the processor for each data type performing joint nested inference based on multiple input trained models received from the model train container [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1] for a particular type of data held within one container [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1], the inference model for predicting, in a single inference component, multiple inferences for future values of the particular type of data having various subcategories.
It would have been obvious to one having ordinary skill in the art, having the teachings of Kaushik and Shah before him before the effective filing date of the claimed invention, to modify the data, models, and ensemble of Kaushik to incorporate the containers of Shah.
Given the advantage of shared resources, versatility, and rapid development and management, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding claim 2, Kaushik and Shah disclose the multi-model machine learning model architecture of claim 1. Kaushik further discloses wherein each single inference model performs nested inferences comprising a future point estimate, an upper bound of distribution and a lower bound of distribution for one of the input data types [“forecast storage resource utilization (e.g., forecast amounts of free or available storage resources 410/510, forecast amounts of storage resources used 420/520)” ¶61; “output also illustrates the cone of uncertainty, with lines showing a mean value (e.g., 403/503), an upper bound (e.g., 404/504) and a lower bound (e.g., 405/505) on the forecasts.” ¶61].
Regarding claim 4, Kaushik and Shah disclose the multi-model machine learning model architecture of claim 1. Kaushik further discloses further comprising a monitoring component operable by the processor that combines the inferences and ground truths at each single inference model such that each of the inferences are used differently [“the output includes a waveform showing historical storage resource utilization ( e.g., actual amounts of free or available storage resources 401/501, actual amounts of storage resources used 402/502) and forecast storage resource utilization (e.g., forecast amounts of free or available storage resources 410/510, forecast amounts of storage resources used 420/ 520). The output also illustrates the cone of uncertainty, with lines showing a mean value (e.g., 403/503), an upper bound (e.g., 404/504) and a lower bound (e.g., 405/505) on the forecasts.” ¶61; “calculating the overall storage resource capacity prediction as a weighted average of the selected subset of the model-specific storage resource capacity predictions. Weights for the selected subset of the model-specific storage resource capacity predictions may be based at least in part on historical performance of respective ones of the time series capacity prediction forecasting models used to generate the selected subset of the model-specific storage resource capacity predictions” ¶43].
Regarding claim 5, Kaushik and Shah disclose the multi-model machine learning model architecture of claim 1. Kaushik further discloses wherein the single inference model has a single ground truth for multiple subcategories of output inferences [“the output includes a waveform showing historical storage resource utilization ( e.g., actual amounts of free or available storage resources 401/501, actual amounts of storage resources used 402/502) and forecast storage resource utilization (e.g., forecast amounts of free or available storage resources 410/510, forecast amounts of storage resources used 420/ 520). The output also illustrates the cone of uncertainty, with lines showing a mean value (e.g., 403/503), an upper bound (e.g., 404/504) and a lower bound (e.g., 405/505) on the forecasts.” ¶61].
Regarding claim 6, Kaushik and Shah disclose the multi-model machine learning model architecture of claim 1. Kaushik further discloses wherein, in an inference stage, various incoming data types received for inference of future values in a data preparation stage [“The process begins with step 200, obtaining historical storage resource utilization data for a given set of storage resources of one or more storage systems. The given set of storage resources in some embodiments comprises a storage pool allocated for a given user, the storage pool comprising a first set of storage resources allocated from a first one of the one or more storage systems and a second set of storage resources allocated from a second one of the one or more storage systems.” ¶40] are combined and written into a single data preparation container.
However, Kaushik fails to explicitly disclose are combined and written into a single data preparation container.
Shah discloses are combined and written into a single data preparation container [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kaushik and Shah before him before the effective filing date of the claimed invention, to modify the combination to incorporate the containers of Shah.
Given the advantage of shared resources, versatility, and rapid development and management, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 7, Kaushik and Shah disclose the multi-model machine learning model architecture of claim 1. Kaushik further discloses wherein the single inference model is configured to read from multiple models contained in the model train container at a same time and generate model inference providing multiple predictions in a single inference run for each of the multiple models [“The ensemble forecast uses the selected models to generate a set of individual or model-specific storage resource capacity predictions (e.g., one from each of the selected models). The model-specific storage resource capacity predictions are combined (e.g., using a weighted average) to calculate the overall or ensemble storage resource capacity prediction” ¶57].
However, Kaushik fails to explicitly disclose contained in the model train container.
Shah discloses contained in the model train container [“A container is a closed environment for the software. It bundles all the files and libraries that the application needs to function correctly. Multiple containers can be deployed on the same machine and share the resources.” §II.A ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kaushik and Shah before him before the effective filing date of the claimed invention, to modify the combination to incorporate the containers of Shah.
Given the advantage of shared resources, versatility, and rapid development and management, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 8-9, 11-14 are rejected on the same grounds as claims 1-2, 4-7 respectively.
Claim 15 is rejected on the same grounds as claim 1.
Claim(s) 3 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaushik and Shah, in view of Chen et al. (hereinafter Chen), XGBoost: A Scalable Tree Boosting System.
Regarding Claim 3, Kaushik and Shah disclose the multi-model machine learning model architecture of claim 1.
However, Kaushik fails to explicitly disclose wherein the single inference model is an XGBoost model.
Chen discloses wherein the single inference model is an XGBoost model [“a scalable end-to-end tree boosting system called XGBoost” Abstract].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kaushik, Shah, and Chen before him before the effective filing date of the claimed invention, to modify the combination to incorporate the XGBoost model of Chen.
Given the advantage of scaling beyond billions of examples while using far fewer resources, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 10 is rejected on the same grounds as claim 3.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Conclusion
Any prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c). Additionally when amending, in their remarks Applicant should particularly cite to the supporting paragraphs in the original disclosure for the amendments.
The following references were found during the examination of this patent application and were found to be relevant to patentability. Applicant is advised to review these references prior to responding to this Office action.
Hugh et al. (Ensure consistency in data processing code between training and inference in Amazon SageMaker) discloses Inference Pipelines, a new feature in Amazon SageMaker that enables you to specify a sequence of steps that are executed in order for each inference request.
Rana et al. (Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power) discloses a method called NNE2D that combines variable selection based on mutual information and an ensemble of neural networks, to compute 2D-interval forecasts, where the two interval boundaries are expressed in terms of percentiles.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T. Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.B./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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