DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 05/06/2026.
• Claims 17-18 have been withdrawn.
• Claims 1-16 and 19-20 are currently pending and have been examined.
Election/Restriction
Applicant’s election without traverse of claims 1-16 and 19-20 in the reply filed 05/06/2026 is acknowledged.
Information Disclosure Statement
Information Disclosure Statement received 12/12/2024 has been reviewed and considered.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of Application No. ZA2022/04760, filed on 04/29/2022 has been received.
The examiner acknowledges that the instant application is a national stage entry of PCT/IB2023/054520, filed 05/01/2023.
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 Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “analytics module…” (claims 1 and 19); “model module…” (claims 1, 10-11, 15); “data transmission module…”; “data module…” (claim 13); “transmission module…” (claim 13); and “interface module…” (claim 16) with the functional language “which allows for”, and “is capable of” which are not preceded by a structural modifier.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Consistent with page 24, lines 14-25 of the Specification, these limitations are interpreted to represent software operating on hardware.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-16 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-16 and 19-20 are directed to a machine. Therefore, claims 1-16 and 19-20 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
having benchmark data stored, receive one or more data sets from one or more data sources, and allows for one or more users to remotely access;
one or more analytics tools which allows for data analysis of the one or more data sets; and
one or more algorithms for performing a set of instructions on the one or more data sets, comparing the one or more data sets to the benchmark data to derive one or more output data sets having predictive information about the one or more data sets.
The above limitations recite the concept of generating predications based on benchmarks. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors and managing personal behavior or relationships or interactions between people. The Specification page 9, lines 20-25 highlights that the predictions pertain to user product offerings, and are therefore sales and marketing behaviors and activities. Further, the claims are similar to managing personal behavior, specifically following rules or instructions. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the analysis of data are observations, evaluations, and judgements. These limitations are similar to the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Independent claim 19 recites similar limitations as claim 1 and, as such, falls within the same identified grouping of abstract ideas. Accordingly, under Prong One of , Step 2A of the Alice/Mayo test, claims 1 and 19 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1 and 19 recite additional elements, such as a data management platform, a centralized database, an analytics module, a model module, being trained by the one or more algorithms, a digital management system, a computing device, a processor, and a memory. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1 and 19 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1 and 19 merely recite a commonplace business method (i.e., generating predications based on benchmarks) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1 and 19 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1 and 19 specifying that the abstract idea of generating predications based on benchmarks is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1 and 19 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1 and 19 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1 and 19 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1 and 19, these claims recite additional elements, such as a data management platform, a centralized database, an analytics module, a model module, being trained by the one or more algorithms, a digital management system, a computing device, a processor, and a memory. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1 and 19 are manual processes, e.g., receiving information, analyzing information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1 and 19 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims specifying that the abstract idea of generating predications based on benchmarks is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1 and 19 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1 and 19 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-16 and 20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they recite an abstract idea, are not integrated into a practical application, and do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-16 and 20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they further recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Further, these claims, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 4, 7-9, and 14-15 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 2 and 11 further identify the additional elements of data warehouses, a network connection, Tableau, Oracle Business Intelligence, IBM Cognos Analytics, SAS, Microsoft Power BI, Amazon Redshift, Google BigQuery, Snowflake, Alteryx, Cloudera, Apache Hadoop, Google Vertex Al, Microsoft Azure Synapse, Microsoft Data Explorer, CosmosDB, Redis, Azure Cognitive Services, Azure Machine Learning, Spark, Databricks, Sqream, Confluent Kafka, Presto, Trino, Flare, HIDS, a machine learning algorithm, an artificial intelligence algorithm, a deep learning algorithm, a data transmission module, a database, a storage medium, non-transitory storage medium, transitory storage medium, an anonymizing module, a data module, a transmission module, an interface module, and one or more user devices. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-16 and 20 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1 and 19, dependent claims 2-16 and 20 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-16 and 19-20 are ineligible.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 10-12, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Azhen et al. (US 20180314533 A1), hereinafter Azhen, in view of Saulys et al. (US 11526524 B1), hereinafter Saulys.
In regards to claim 1, Azhen discloses a data management platform comprising (Azhen: [abstract]):
a centralized database having benchmark data stored thereon, the centralized database configured to receive one or more data sets from one or more data sources, and wherein the centralized database allows for one or more users to remotely access the centralized database (Azhen: [0057] and Fig. 3 – “FIG. 3 shows an example of benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230…we repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0075] and Fig. 9 – “predictor 250 can also communicate with one or more external devices 920 such as a keyboard, a pointing device, a display 922, etc.; one or more devices that enable a user to interact with the predictor 250; and/or any devices (e.g., network card, modem, etc.) that enable the predictor 250 to communicate with one or more other computing devices. Such communication can occur via I/O interfaces 924. Still yet, the predictor 250 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 926, enabling the system 900 to access a knowledge repository such as the Prediction Models 230”; [0070] – “information processing system 900 may be practiced in various computing environments such as conventional and distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network”);
an analytics module communicatively coupled to the centralized database, the analytics module comprising one or more analytics tools which allows for data analysis (Azhen: [0039] – “performance of data analytics applications such as Spark applications”; [0035] – “‘Spark’ is a proprietary term for a large-scale data analytics framework that facilitates the development and acceleration of big data analytics applications”; [0060-0061] and Fig. 2 – “In one embodiment, the triggering is controlled by hooks or software patches inserted into the Spark software stack and then the Application 205 is run on the Spark software stack…In step 530 the Spark job 110 begins, commencing with the first task. When the task is launched, the performance analyzing tool 150 is triggered and begins monitoring the system at the micro architecture level, recording performance data for hardware and software events”; see also [0057]);
a model module communicatively coupled to the centralized database, the model module comprising one or more algorithms for performing a set of instructions on the one or more data sets, wherein the model module is capable of being trained by the one or more algorithms by comparing the one or more data sets to benchmark data to derive an output dataset having predictive information about the one or more data sets (Azhen: [0063] – “Various machine learning algorithms can be used to solve the problem. One such method is the near distance method wherein the SME vector 213 for the current Spark job 110 is matched against a benchmark 300. The hardware configuration associated with that benchmark 300 (or closest to it) is designated as the Predictive Optimal Hardware Configuration 280 for the remaining runs in the current stage. Additionally, we can find a solution using a decision tree such as the trees 410 and 420 shown in FIG. 4, Support Vector Machine (SVM), logistic regression, and other algorithms.”; [0057] – “benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230. In this example, we experiment with three different hardware configurations, labeled as dscr:0, dscr:15, and dscr:71. We calculate the SME vector 213 for the same Spark job 110 set to these three different hardware configurations. For the first Spark job 110, we determine that the configuration for dscr15 yielded the best results; therefore it is the optimal configuration for that Spark job 110. This becomes Benchmark 1 310. We repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0020] – “term ‘benchmark’ means a standard against which to compare system performance. For example, we use training runs to collect benchmarks for different combinations of system states and performance metric”).
Azhen further discloses data analysis (Azhen: [0035]), yet Azhen does not explicitly disclose that the analytics module allows for data analysis of the one or more data sets.
However, Saulys teaches a data analytics system (Saulys: [abstract]), including
that the analytics module allows for data analysis of the one or more data sets (Saulys: Col. 3, Ln. 24-43 – “Time series data can be ingested into the system from sources such as vendor data feeds, on-premises data centers, and/or enterprise data lakes…users can run analysis from interactive development notebooks (e.g., Jupyter notebooks) integrated with dynamically scalable compute clusters (e.g., Spark clusters)…to begin time series analysis, users may simply add a dataset or choose an existing dataset from a catalog, open it in an interactive development notebook (with a supporting managed cluster), and start using the framework”; Col. 14, Ln. 22-40 – “analytics service 150, in some embodiments, is implemented using software executed by one or multiple computing devices at one location or multiple locations. In some embodiments, the analytics service 150 may be a cloud ‘big data’ platform allowing users to process vast amounts of data using open-source tools such as Apache Spark”; Col. 13, Ln. 5-15 – “data management engine 112 may also interact with a data catalog service 132 at circle (4C) to update the metadata of one or more tables 130A-130N corresponding to the one or more dataset views impacted by the changesets at circle (4C). As described herein, each of the tables 130A-130N may correspond to a particular dataset view and include references to the particular files of changeset data 126 that have the records/data that belong to the table. Thus, these tables 130A-130N may be used, e.g., via a data catalog service 132, by other applications (e.g., an analytics service 150 as one example)”).
It would have been obvious to one of ordinary skill in the art to include the data analysis, as taught by Azhen, the dataset analysis, as taught by Saulys, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Azhen, to include the teachings of Saulys, in order to remove the heavy lifting of building and maintaining a data management solution (Saulys: Col. 2, Ln. 39-40).
In regards to claim 2, Azhen/Saulys teaches the system of claim 1. Yet Azhen does not explicitly disclose wherein the one or more data sources are one or more data warehouses locatable in one or more countries.
However, Saulys teaches a data analysis system (Saulys: [abstract]), including
wherein the one or more data sources are one or more data warehouses locatable in one or more countries (Saulys: Col. 1, Ln. 25-33 – “databases or data stores, where customers may utilize various types of databases such as…data warehouses for analytics…for building applications with highly-connected data, time series databases for measuring changes over time”; Col. 5, Ln. 35-41 – “software executed by one or multiple compute instances and/or computing devices, which may be located in a same geographic area (e.g., a same room, data center, city, etc.) or different geographic areas (e.g., in different data centers, cities, regions, countries, or the like)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Saulys with Azhen for the reasons identified above with respect to claim 1.
In regards to claim 3, Azhen/Saulys teaches the system of claim 1. Azhen further discloses allow for the one or more users to remotely access the centralized database through a network connection to transfer one or more data sets from one or more data sources to the centralized database, wherein the network connection allows for the one or more users to use the analytics module comprising analytics tools to analyse, and wherein the network connection allows for the one or more users to train the model module comprising one or more algorithms by comparing the one or more data sets to the benchmark data to derive an output dataset having predictive information about the one or more data sets (Azhen: [0075] – “predictor 250 can also communicate with one or more external devices 920 such as a keyboard, a pointing device, a display 922, etc.; one or more devices that enable a user to interact with the predictor 250; and/or any devices (e.g., network card, modem, etc.) that enable the predictor 250 to communicate with one or more other computing devices. Such communication can occur via I/O interfaces 924. Still yet, the predictor 250 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 926, enabling the system 900 to access a knowledge repository such as the Prediction Models 230”; see also [0057]; [0063]; [0035]; [0039]; [0075]).
Yet Azhen does not explicitly disclose the analytics tools to analyse the one or more data sets.
However, Saulys teaches a data analysis system (Saulys: [abstract]), including
the analytics tools to analyse the one or more data sets (Saulys: Col. 3, Ln. 24-43 – “Time series data can be ingested into the system from sources such as vendor data feeds, on-premises data centers, and/or enterprise data lakes…users can run analysis from interactive development notebooks (e.g., Jupyter notebooks) integrated with dynamically scalable compute clusters (e.g., Spark clusters)…to begin time series analysis, users may simply add a dataset or choose an existing dataset from a catalog, open it in an interactive development notebook (with a supporting managed cluster), and start using the framework”; Col. 14, Ln. 22-40 – “analytics service 150, in some embodiments, is implemented using software executed by one or multiple computing devices at one location or multiple locations. In some embodiments, the analytics service 150 may be a cloud ‘big data’ platform allowing users to process vast amounts of data using open-source tools such as Apache Spark”; Col. 13, Ln. 5-15 – “data management engine 112 may also interact with a data catalog service 132 at circle (4C) to update the metadata of one or more tables 130A-130N corresponding to the one or more dataset views impacted by the changesets at circle (4C). As described herein, each of the tables 130A-130N may correspond to a particular dataset view and include references to the particular files of changeset data 126 that have the records/data that belong to the table. Thus, these tables 130A-130N may be used, e.g., via a data catalog service 132, by other applications (e.g., an analytics service 150 as one example)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Saulys with Azhen for the reasons identified above with respect to claim 1.
In regards to claim 4, Azhen/Saulys teaches the system of claim 1. Azhen further discloses wherein the analytics module comprising one or more analytics tools and the model module comprising one or more algorithms are dynamically updated and evolve with each instance of the one or more analytics tools analysing and each instance of the model module being trained by the one or more algorithms by comparing the one or more data sets to the benchmark data to derive one or more output data sets having predictive information about the one or more data sets (Azhen: [0020] – “we use training runs to collect benchmarks for different combinations of system states and performance metrics” [0063] – “Various machine learning algorithms can be used to solve the problem. One such method is the near distance method wherein the SME vector 213 for the current Spark job 110 is matched against a benchmark 300. The hardware configuration associated with that benchmark 300 (or closest to it) is designated as the Predictive Optimal Hardware Configuration 280 for the remaining runs in the current stage. Additionally, we can find a solution using a decision tree such as the trees 410 and 420 shown in FIG. 4, Support Vector Machine (SVM), logistic regression, and other algorithms.”; [0057] – “benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230. In this example, we experiment with three different hardware configurations, labeled as dscr:0, dscr:15, and dscr:71. We calculate the SME vector 213 for the same Spark job 110 set to these three different hardware configurations. For the first Spark job 110, we determine that the configuration for dscr15 yielded the best results; therefore it is the optimal configuration for that Spark job 110. This becomes Benchmark 1 310. We repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0020] – “term ‘benchmark’ means a standard against which to compare system performance. For example, we use training runs to collect benchmarks for different combinations of system states and performance metric”).
Yet Azhen does not explicitly disclose the analytics tools analysing the one or more data sets.
However, Saulys teaches a data analysis system (Saulys: [abstract]), including
the analytics tools analysing the one or more data sets (Saulys: Col. 3, Ln. 24-43 – “Time series data can be ingested into the system from sources such as vendor data feeds, on-premises data centers, and/or enterprise data lakes…users can run analysis from interactive development notebooks (e.g., Jupyter notebooks) integrated with dynamically scalable compute clusters (e.g., Spark clusters)…to begin time series analysis, users may simply add a dataset or choose an existing dataset from a catalog, open it in an interactive development notebook (with a supporting managed cluster), and start using the framework”; Col. 14, Ln. 22-40 – “analytics service 150, in some embodiments, is implemented using software executed by one or multiple computing devices at one location or multiple locations. In some embodiments, the analytics service 150 may be a cloud ‘big data’ platform allowing users to process vast amounts of data using open-source tools such as Apache Spark”; Col. 13, Ln. 5-15 – “data management engine 112 may also interact with a data catalog service 132 at circle (4C) to update the metadata of one or more tables 130A-130N corresponding to the one or more dataset views impacted by the changesets at circle (4C). As described herein, each of the tables 130A-130N may correspond to a particular dataset view and include references to the particular files of changeset data 126 that have the records/data that belong to the table. Thus, these tables 130A-130N may be used, e.g., via a data catalog service 132, by other applications (e.g., an analytics service 150 as one example)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Saulys with Azhen for the reasons identified above with respect to claim 1.
In regards to claim 5, Azhen/Saulys teaches the system of claim 1. Azhen further discloses wherein the one or more analytics tools are selected from the group consisting of Tableau, Oracle Business Intelligence, IBM Cognos Analytics, SAS, Microsoft Power BI, Amazon Redshift, Google BigQuery, Snowflake, Alteryx, Cloudera, Apache Hadoop, Google Vertex Al, Microsoft Azure Synapse, Microsoft Data Explorer, CosmosDB, Redis, Azure Cognitive Services, Azure Machine Learning, Spark, Databricks, Sqream, Confluent Kafka, Presto, Trino, Flare, HIDS, and combinations thereof (Azhen: [0039] – “performance of data analytics applications such as Spark applications”; [0035] – “‘Spark’ is a proprietary term for a large-scale data analytics framework that facilitates the development and acceleration of big data analytics applications”; [0060-0061] and Fig. 2 – “In one embodiment, the triggering is controlled by hooks or software patches inserted into the Spark software stack and then the Application 205 is run on the Spark software stack…In step 530 the Spark job 110 begins, commencing with the first task. When the task is launched, the performance analyzing tool 150 is triggered and begins monitoring the system at the micro architecture level, recording performance data for hardware and software events”; see also [0057]).
In regards to claim 6, Azhen/Saulys teaches the system of claim 1. Azhen further discloses wherein the one or more algorithms are selected from the group consisting of a machine learning algorithm, an artificial intelligence algorithm, a deep learning algorithm, a heuristic algorithm, and combinations thereof (Azhen: [0063] – “Various machine learning algorithms can be used to solve the problem. One such method is the near distance method wherein the SME vector 213 for the current Spark job 110 is matched against a benchmark 300. The hardware configuration associated with that benchmark 300 (or closest to it) is designated as the Predictive Optimal Hardware Configuration 280 for the remaining runs in the current stage. Additionally, we can find a solution using a decision tree such as the trees 410 and 420 shown in FIG. 4, Support Vector Machine (SVM), logistic regression, and other algorithms.”; [0005] – “Machine learning is used to find optimal hardware configurations”).
In regards to claim 7, Azhen/Saulys teaches the system of claim 1.
Yet Azhen does not explicitly disclose wherein the one or more data sets comprises customer usage information selected from the group consisting of customer voicecall usage, customer screentime usage, customer data usage, customer messaging usage, customer device hardware specifications, customer transactions, customer interactions, customer behaviour, customer revenue, network operations, network usage, network investment, internal operations, sale information, distribution information, agent operations, merchant operation, agent services, merchant operation, business to business products, business to business products services, digital products, over-the-top applications, customer value management, pricing, operations management, portfolio management, customer location, network location, network transport, network configuration, cybersecurity, and combinations thereof.
However, Saulys teaches a data analysis system (Saulys: [abstract]), including
wherein the one or more data sets comprises customer usage information selected from the group consisting of customer voicecall usage, customer screentime usage, customer data usage, customer messaging usage, customer device hardware specifications, customer transactions, customer interactions, customer behaviour, customer revenue, network operations, network usage, network investment, internal operations, sale information, distribution information, agent operations, merchant operation, agent services, merchant operation, business to business products, business to business products services, digital products, over-the-top applications, customer value management, pricing, operations management, portfolio management, customer location, network location, network transport, network configuration, cybersecurity, and combinations thereof (Saulys: Col. 1, Ln. 22-35 – “public data center operators, such as service providers who operate service provider networks, offer their customers a variety of resources as services. For example, one popular set of services involve databases or data stores, where customers may utilize various types of databases…ledger databases to maintain a complete and verifiable record of transactions”; Col. 16, Ln. 40-45 – “provided analytic functions on the stock's historical time-series data to generate features such as average daily price, daily transaction volume, moving average, etc.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Saulys with Azhen for the reasons identified above with respect to claim 1.
In regards to claim 10, Azhen/Saulys teaches the system of claim 1. Azhen further discloses wherein the model module comprising one or more algorithms is capable of being applied on one or more data sets available on the database to derive an output dataset having predictive information about the one or more data sets (Azhen: [0063] – “Various machine learning algorithms can be used to solve the problem. One such method is the near distance method wherein the SME vector 213 for the current Spark job 110 is matched against a benchmark 300. The hardware configuration associated with that benchmark 300 (or closest to it) is designated as the Predictive Optimal Hardware Configuration 280 for the remaining runs in the current stage. Additionally, we can find a solution using a decision tree such as the trees 410 and 420 shown in FIG. 4, Support Vector Machine (SVM), logistic regression, and other algorithms.”; [0057] – “benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230. In this example, we experiment with three different hardware configurations, labeled as dscr:0, dscr:15, and dscr:71. We calculate the SME vector 213 for the same Spark job 110 set to these three different hardware configurations. For the first Spark job 110, we determine that the configuration for dscr15 yielded the best results; therefore it is the optimal configuration for that Spark job 110. This becomes Benchmark 1 310. We repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0020] – “term ‘benchmark’ means a standard against which to compare system performance. For example, we use training runs to collect benchmarks for different combinations of system states and performance metric”).
Yet Azhen does not explicitly disclose a data transmission module for transmission of the model module to a database that is locatable outside of the centralized database.
However, Saulys teaches a data analysis system (Saulys: [abstract]), including
a data transmission module for transmission of the model module to a database that is locatable outside of the centralized database (Saulys: Col. 16, Ln. 35-46 – “the interactive notebook can be used to combine machine learning (ML) to the time-series pipeline without the need to setup any machine learning frameworks or infrastructure. For example, an analyst building a ML model that predicts whether to buy, sell, or hold a US equity stock, can use the provided analytic functions on the stock's historical time-series data to generate features such as average daily price, daily transaction volume, moving average, etc., and use them as inputs to a ML classification model for predictions, which may optionally be hosted by a machine learning service of the provider network”; Col. 23, Ln. 45-55 – “the user (or another user, application, system, etc.) could run the pipeline with a particular dataset view (e.g., a user-indicated view, or a most current view, etc.) and obtain the results of the pipeline's execution, whether visually, via an output file or message, etc. For example, an application could call the pipeline that is configured to generate features, and these outputted features could be obtained and then provided as inputs to a machine learning model (possibly hosted by a machine learning hosting service of the provider network) to generate a prediction/inference”; see also Col. 30, Ln. 49-60; Col. 8, Ln. 15-26 and Fig. 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Saulys with Azhen for the reasons identified above with respect to claim 1.
In regards to claim 11, Azhen/Saulys teaches the system of claim 10. Azhen further discloses wherein the model module is capable of being stored on a storage medium (Azhen: [0008] – “a computer program product includes a non-transitory computer readable storage medium using a predictive time-sequence model to adapt hardware configurations at run-time for an application including multiple stages of execution”).
In regards to claim 12, Azhen/Saulys teaches the system of claim 11. Azhen further discloses wherein the storage medium is selected from the group consisting of a non-transitory storage medium, transitory storage medium, and combinations thereof (Azhen: [0008] – “a computer program product includes a non-transitory computer readable storage medium using a predictive time-sequence model to adapt hardware configurations at run-time for an application including multiple stages of execution”).
In regards to claim 16, Azhen/Saulys teaches the system of claim 1. Azhen further discloses further comprising an interface module which is communicatively coupled to the model module to receive the predictive information about the one or more data sets, and which is further and which is further communicatively coupled to one or more user devices, thereby allowing the user devices to access the predictive information about the one or more data sets (Azhen: [0075] – “predictor 250 can also communicate with one or more external devices 920 such as a keyboard, a pointing device, a display 922, etc.; one or more devices that enable a user to interact with the predictor 250; and/or any devices (e.g., network card, modem, etc.) that enable the predictor 250 to communicate with one or more other computing devices. Such communication can occur via I/O interfaces 924. Still yet, the predictor 250 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 926, enabling the system 900 to access a knowledge repository such as the Prediction Models 230”; see also [0057]; [0063]; [0035]; [0039]; [0075]).
In regards to claim 19, Azhen discloses a digital management system comprising (Azhen: [abstract]):
a computing device comprising a processor communicatively coupled to a memory which is capable of storing one or more data sets obtainable from one or more data sources thereon, the memory having benchmark data stored thereon (Azhen: [0057] and Fig. 3 – “FIG. 3 shows an example of benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230…we repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0075] and Fig. 9 – “predictor 250 can also communicate with one or more external devices 920 such as a keyboard, a pointing device, a display 922, etc.; one or more devices that enable a user to interact with the predictor 250; and/or any devices (e.g., network card, modem, etc.) that enable the predictor 250 to communicate with one or more other computing devices. Such communication can occur via I/O interfaces 924. Still yet, the predictor 250 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 926, enabling the system 900 to access a knowledge repository such as the Prediction Models 230”; [0070-0071] – “information processing system 900 may be practiced in various computing environments such as conventional and distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network…the information processing system 900 includes the predictor 250 in the form of a general-purpose computing device. The components of the predictor 250 can include, but are not limited to, one or more processor devices or processing units 904, a system memory 906, and a bus 908 that couples various system components including the system memory 906 to the processor 904”);
an analytics module comprising analytical tools, the analytics module communicatively coupled to the memory, wherein the processor is capable of carrying out a set of instructions for the analytical tools to perform analytical operations (Azhen: [0039] – “performance of data analytics applications such as Spark applications”; [0035] – “‘Spark’ is a proprietary term for a large-scale data analytics framework that facilitates the development and acceleration of big data analytics applications”; [0060-0061] and Fig. 2 – “In one embodiment, the triggering is controlled by hooks or software patches inserted into the Spark software stack and then the Application 205 is run on the Spark software stack…In step 530 the Spark job 110 begins, commencing with the first task. When the task is launched, the performance analyzing tool 150 is triggered and begins monitoring the system at the micro architecture level, recording performance data for hardware and software events”; see also [0057]; [0070-0071]);
a model module comprising one or more algorithms, the model module communicatively coupled to the memory, wherein the model module is capable of being trained by the processor applying the one or more algorithms by comparing the one or more data sets to benchmark data to derive an output dataset having predictive information about the one or more data sets (Azhen: [0063] – “Various machine learning algorithms can be used to solve the problem. One such method is the near distance method wherein the SME vector 213 for the current Spark job 110 is matched against a benchmark 300. The hardware configuration associated with that benchmark 300 (or closest to it) is designated as the Predictive Optimal Hardware Configuration 280 for the remaining runs in the current stage. Additionally, we can find a solution using a decision tree such as the trees 410 and 420 shown in FIG. 4, Support Vector Machine (SVM), logistic regression, and other algorithms.”; [0057] – “benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230. In this example, we experiment with three different hardware configurations, labeled as dscr:0, dscr:15, and dscr:71. We calculate the SME vector 213 for the same Spark job 110 set to these three different hardware configurations. For the first Spark job 110, we determine that the configuration for dscr15 yielded the best results; therefore it is the optimal configuration for that Spark job 110. This becomes Benchmark 1 310. We repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0020] – “term ‘benchmark’ means a standard against which to compare system performance. For example, we use training runs to collect benchmarks for different combinations of system states and performance metric”; see also [0070-0071]).
Azhen further discloses data analysis (Azhen: [0035]), yet Azhen does not explicitly disclose that the analytics module performs operations on the one or more data sets.
However, Saulys teaches a data analytics system (Saulys: [abstract]), including
that the analytics module performs operations on the one or more data sets (Saulys: Col. 3, Ln. 24-43 – “Time series data can be ingested into the system from sources such as vendor data feeds, on-premises data centers, and/or enterprise data lakes…users can run analysis from interactive development notebooks (e.g., Jupyter notebooks) integrated with dynamically scalable compute clusters (e.g., Spark clusters)…to begin time series analysis, users may simply add a dataset or choose an existing dataset from a catalog, open it in an interactive development notebook (with a supporting managed cluster), and start using the framework”; Col. 14, Ln. 22-40 – “analytics service 150, in some embodiments, is implemented using software executed by one or multiple computing devices at one location or multiple locations. In some embodiments, the analytics service 150 may be a cloud ‘big data’ platform allowing users to process vast amounts of data using open-source tools such as Apache Spark”; Col. 13, Ln. 5-15 – “data management engine 112 may also interact with a data catalog service 132 at circle (4C) to update the metadata of one or more tables 130A-130N corresponding to the one or more dataset views impacted by the changesets at circle (4C). As described herein, each of the tables 130A-130N may correspond to a particular dataset view and include references to the particular files of changeset data 126 that have the records/data that belong to the table. Thus, these tables 130A-130N may be used, e.g., via a data catalog service 132, by other applications (e.g., an analytics service 150 as one example)”).
It would have been obvious to one of ordinary skill in the art to include the data analysis, as taught by Azhen, the dataset analysis, as taught by Saulys, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Azhen, to include the teachings of Saulys, in order to remove the heavy lifting of building and maintaining a data management solution (Saulys: Col. 2, Ln. 39-40).
In regards to claim 20, Azhen/Saulys teaches the system of claim 19. Azhen further discloses wherein the memory is selected from the group consisting of non-transitory storage medium, transitory storage medium, and combinations thereof (Azhen: [0008] – “a computer program product includes a non-transitory computer readable storage medium using a predictive time-sequence model to adapt hardware configurations at run-time for an application including multiple stages of execution”).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Azhen, in view of Saulys, in view of Xu et al. (US 20220253721 A1), hereinafter Xu.
In regards to claim 8, Azhen/Saulys teaches the system of claim 1.
Yet Azhen does not explicitly disclose wherein the predictive information comprises information about customer's behaviour to derive a user-specific product offering.
However, Xu teaches a prediction system (Xu: [0130]), including
wherein the predictive information comprises information about customer's behaviour to derive a user-specific product offering (Xu: [0165] – “data component 1311 can receive raw logging data, such as historical user session data, from database 1316 to prepare training and evaluation data, such as personalized recommendation data and/or item recommendation data”).
It would have been obvious to one of ordinary skill in the art to include in the analytics, as taught by Azhen/Saulys, the product recommendation, as taught by Xu, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Azhen/Saulys, to include the teachings of Xu, in order to improve evaluation (Xu: [0119]).
Claims 9 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Azhen, in view of Saulys, in view of Bloomquist et al. (US 20200137080 A1), hereinafter Bloomquist.
In regards to claim 9, Azhen/Saulys teaches the system of claim 1. Azhen further discloses wherein the centralized database is configured to only receive one or more data sets (Azhen: [0057] and Fig. 3 – “FIG. 3 shows an example of benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230…we repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0075] and Fig. 9 – “predictor 250 can also communicate with one or more external devices 920 such as a keyboard, a pointing device, a display 922, etc.; one or more devices that enable a user to interact with the predictor 250; and/or any devices (e.g., network card, modem, etc.) that enable the predictor 250 to communicate with one or more other computing devices. Such communication can occur via I/O interfaces 924. Still yet, the predictor 250 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 926, enabling the system 900 to access a knowledge repository such as the Prediction Models 230”; [0070] – “information processing system 900 may be practiced in various computing environments such as conventional and distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network”).
Yet Azhen does not explicitly disclose data sets that have been anonymized to derive anonymized data.
However, Bloomquist teaches a data analysis system (Bloomquist: [abstract]), including
data sets that have been anonymized to derive anonymized data (Bloomquist: [0101] – “analytics can be performed on anonymized version of the data in the consumer-status verification repository 120, thereby enforcing privacy restrictions and other access-control measures with respect to the data in the consumer-status verification repository 120. The anonymized data can be extracted from income or employment data, which is gathered at regular intervals from reliable sources”).
It would have been obvious to one of ordinary skill in the art to include in the analytics, as taught by Azhen/Saulys, the anonymized data, as taught by Bloomquist, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Azhen/Saulys, to include the teachings of Bloomquist, in order to enforce privacy restrictions (Bloomquist: [0101]).
In regards to claim 13, Azhen/Saulys teaches the system of claim 1. Azhen further discloses a data module for receiving the one or more data sets from one or more data sources; and a transmission module for transmission of the data to the centralized database (Azhen: [0057] and Fig. 3 – “FIG. 3 shows an example of benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230…we repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0075] and Fig. 9 – “predictor 250 can also communicate with one or more external devices 920 such as a keyboard, a pointing device, a display 922, etc.; one or more devices that enable a user to interact with the predictor 250; and/or any devices (e.g., network card, modem, etc.) that enable the predictor 250 to communicate with one or more other computing devices. Such communication can occur via I/O interfaces 924. Still yet, the predictor 250 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 926, enabling the system 900 to access a knowledge repository such as the Prediction Models 230”; [0070] – “information processing system 900 may be practiced in various computing environments such as conventional and distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network”).
Yet Azhen does not explicitly disclose an anonymizing module locatable outside of the centralized database, the anonymizing module comprising: an anonymizing algorithm for anonymizing the one or more data sets to derive anonymized data, wherein data is anonymized data.
However, Bloomquist teaches a data analysis system (Bloomquist: [abstract]), including
an anonymizing module locatable outside of the centralized database, the anonymizing module comprising: an anonymizing algorithm for anonymizing the one or more data sets to derive anonymized data, wherein data is anonymized data (Bloomquist: [0101] and Fig. 1 – “analytics can be performed on anonymized version of the data in the consumer-status verification repository 120, thereby enforcing privacy restrictions and other access-control measures with respect to the data in the consumer-status verification repository 120. The anonymized data can be extracted from income or employment data, which is gathered at regular intervals from reliable sources”; [0105-0106] – “a computing device, such as the verification server 118, can retrieve a dataset from the repository 122 for analysis. The dataset can be retrieved using one or more standardized descriptors (e.g., all records matching a certain job title, industry, etc.). For analytical operations, the repository 122 can return anonymized data. An example of anonymized data may include a dataset that include job title, income information, and region information and that excludes PII data (e.g., name, social insurance number, street address, etc.). The computing device can execute one or more analytical algorithms that use the anonymized version of the consumer data”).
It would have been obvious to one of ordinary skill in the art to include in the analytics, as taught by Azhen/Saulys, the anonymized data, as taught by Bloomquist, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Azhen/Saulys, to include the teachings of Bloomquist, in order to enforce privacy restrictions (Bloomquist: [0101]).
In regards to claim 14, Azhen/Saulys teaches the system of claim 13. Azhen further discloses wherein the data is used to train the model module by the one or more algorithms comparing the data to benchmark data stored on the centralized database to derive an output dataset having predictive information about the data (Azhen: [0063] – “Various machine learning algorithms can be used to solve the problem. One such method is the near distance method wherein the SME vector 213 for the current Spark job 110 is matched against a benchmark 300. The hardware configuration associated with that benchmark 300 (or closest to it) is designated as the Predictive Optimal Hardware Configuration 280 for the remaining runs in the current stage. Additionally, we can find a solution using a decision tree such as the trees 410 and 420 shown in FIG. 4, Support Vector Machine (SVM), logistic regression, and other algorithms.”; [0057] – “benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230. In this example, we experiment with three different hardware configurations, labeled as dscr:0, dscr:15, and dscr:71. We calculate the SME vector 213 for the same Spark job 110 set to these three different hardware configurations. For the first Spark job 110, we determine that the configuration for dscr15 yielded the best results; therefore it is the optimal configuration for that Spark job 110. This becomes Benchmark 1 310. We repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0020] – “term ‘benchmark’ means a standard against which to compare system performance. For example, we use training runs to collect benchmarks for different combinations of system states and performance metric”).
Yet Azhen does not explicitly disclose that the data is anonymized data.
However, Bloomquist teaches a data analysis system (Bloomquist: [abstract]), including
that the data is anonymized data (Bloomquist: [0101] and Fig. 1 – “analytics can be performed on anonymized version of the data in the consumer-status verification repository 120, thereby enforcing privacy restrictions and other access-control measures with respect to the data in the consumer-status verification repository 120. The anonymized data can be extracted from income or employment data, which is gathered at regular intervals from reliable sources”; [0105-0106] – “a computing device, such as the verification server 118, can retrieve a dataset from the repository 122 for analysis. The dataset can be retrieved using one or more standardized descriptors (e.g., all records matching a certain job title, industry, etc.). For analytical operations, the repository 122 can return anonymized data. An example of anonymized data may include a dataset that include job title, income information, and region information and that excludes PII data (e.g., name, social insurance number, street address, etc.). The computing device can execute one or more analytical algorithms that use the anonymized version of the consumer data”).
It would have been obvious to one of ordinary skill in the art to include in the analytics, as taught by Azhen/Saulys, the anonymized data, as taught by Bloomquist, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Azhen/Saulys, to include the teachings of Bloomquist, in order to enforce privacy restrictions (Bloomquist: [0101]).
In regards to claim 15, Azhen/Saulys teaches the system of claim 14. Azhen further discloses wherein the model module is configured to be trained by data, and wherein the model module is applied on one or more data sets locatable at a selected location (Azhen: [0063] – “Various machine learning algorithms can be used to solve the problem. One such method is the near distance method wherein the SME vector 213 for the current Spark job 110 is matched against a benchmark 300. The hardware configuration associated with that benchmark 300 (or closest to it) is designated as the Predictive Optimal Hardware Configuration 280 for the remaining runs in the current stage. Additionally, we can find a solution using a decision tree such as the trees 410 and 420 shown in FIG. 4, Support Vector Machine (SVM), logistic regression, and other algorithms.”; [0057] – “benchmarks 300 calculated for a sampling of Training Applications 225. The benchmarks 300 can be stored in a Prediction Models Repository 230. In this example, we experiment with three different hardware configurations, labeled as dscr:0, dscr:15, and dscr:71. We calculate the SME vector 213 for the same Spark job 110 set to these three different hardware configurations. For the first Spark job 110, we determine that the configuration for dscr15 yielded the best results; therefore it is the optimal configuration for that Spark job 110. This becomes Benchmark 1 310. We repeat this for each of Training Applications 225, thus populating the Prediction Models Repository 230 with benchmarks Benchmark 1 through Benchmark n 320”; [0020] – “term ‘benchmark’ means a standard against which to compare system performance. For example, we use training runs to collect benchmarks for different combinations of system states and performance metric”).
Yet Azhen does not explicitly disclose that the data is anonymized data and to comply with country-specific data protection and privacy regulation regulations.
However, Bloomquist teaches a data analysis system (Bloomquist: [abstract]), including
that the data is anonymized data and to comply with country-specific data protection and privacy regulation regulations (Bloomquist: [0101] and Fig. 1 – “analytics can be performed on anonymized version of the data in the consumer-status verification repository 120, thereby enforcing privacy restrictions and other access-control measures with respect to the data in the consumer-status verification repository 120. The anonymized data can be extracted from income or employment data, which is gathered at regular intervals from reliable sources”; [0105-0106] – “a computing device, such as the verification server 118, can retrieve a dataset from the repository 122 for analysis. The dataset can be retrieved using one or more standardized descriptors (e.g., all records matching a certain job title, industry, etc.). For analytical operations, the repository 122 can return anonymized data. An example of anonymized data may include a dataset that include job title, income information, and region information and that excludes PII data (e.g., name, social insurance number, street address, etc.). The computing device can execute one or more analytical algorithms that use the anonymized version of the consumer data”; [0086] – “archived versions of employment or income data may be stored to facilitate efficient updates to the consumer-status verification repository 122. For instance, changes in regulatory requirements by a certain jurisdiction (e.g., a country, a province, etc.) may necessitate one or more changes to the structure of the consumer-status verification repository 122”).
It would have been obvious to one of ordinary skill in the art to include in the analytics, as taught by Azhen/Saulys, the anonymized data, as taught by Bloomquist, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Azhen/Saulys, to include the teachings of Bloomquist, in order to enforce privacy restrictions (Bloomquist: [0101]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL reference U teaches centralized business intelligence. It takes a unifying approach to storing and managing all this business data for analysis. Companies extract data from applications and other sources and store it in a single location, typically using a specialized data management system called a data warehouse
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