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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/26/26, to enter the claims of 5/27/26, has been entered.
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 4/1/26, Applicant, on 5/27/26, amended claims. Claims 1, 3-8, 10-15, and 17-20 are pending in this application and have been rejected below.
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, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
A method for assessing a quality of a data fabric, …, the method comprising:
receiving, …, a plurality of input data products from at least one data source into the data fabric;
transmitting, …, the plurality of input data products to a quality scoring engine installed within the data fabric; and
assessing, … using the quality scoring engine, the quality of the data fabric based on an analysis of each of the plurality of input data products during a lifecycle of each corresponding input data product within the data fabric,
wherein the analysis of each of the plurality of input data products comprises:
receiving…, a plurality of scoring parameters, a plurality of rule definitions, and a metadata for each of the plurality of input data products;
calculating, …, a respective data offering quality score against each of the plurality of scoring parameters during the lifecycle of each of the plurality of input data products within the data fabric, wherein each respective data offering quality score is calculated based on an application of the plurality of rule definitions against each of the plurality of input data products;
generating…, a data fabric quality scoreboard based on an aggregation of the respective data offering quality scores calculated for each of the plurality of input data products; and
displaying, …, the data fabric quality scoreboard … for evaluating the quality of the data fabric, wherein the plurality of input data products comprises data owning system details, a first data product, and data offering details;
wherein the data fabric comprises…; a raw data products stage where metadata that specifies data types and relationships for raw data sources is defined (this stage just states metadata is giving a description to a human reader); a curated data product offerings stage where an output of the raw data products stage is transformed into insights via at least one from among a transformation, a normalization, and a standardization (This just refers to normalizing/standardizing the data from different sources); and a data discovery channel stage that provides access to authorized users (Appears to be stating the data is accessible), and
wherein the calculating of each respective data offering quality score is performed across each stage of the data fabric.”
As drafted, this is, under its broadest reasonable interpretation, directed to the Abstract idea groupings of “mathematical relationships” and “certain methods of organizing human activity” (commercial interactions, sales activities or behaviors; business relations) as here we have scoring data, and aggregating the scores of the data, and Certain Methods of Organizing Human Activity, as Applicant’s [0004-0005] as filed gives background that the data is referring to different organizations, banks, financial institutions discovering data products from a data marketplace, for which data offerings are better than others for an organization/business entity, based on the scores. Claim 2 is now in claim 1, where the data have “details,” narrowing the abstract idea, by describing/details the data for a person’s understanding. Many of the steps recite scoring during different phases with different rules. Newly added limitations describe metadata to “specify” data types and relationships for raw data sources, normalize/standardize data, and make the it available to a user, which further narrow the abstract idea. Examiner notes the “metadata” that “specifies” data types and relationships, has no functional relationship with the computer as claimed, and the metadata representing/specifying types/relationships only conveys a message to a human reader, and are not entitled to patentable weight. See MPEP 2111.05.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements that are:
A method for assessing a quality of a data fabric, the method being implemented by at least one processor, the method comprising:
receiving, by the at least one processor, a plurality of input data products from at least one data source into the data fabric;
transmitting, by the at least one processor, the plurality of input data products to a quality scoring engine installed within the data fabric; and
assessing, by the at least one processor using the quality scoring engine, the quality of the data fabric based on an analysis of…,
wherein the analysis of each of the plurality of input data products comprises:
receiving, by the at least one processor, a plurality of scoring parameters, a plurality of rule definitions, and a metadata for each of the plurality of input data products;
calculating, by the at least one processor, a respective data offering quality score …;
generating, by the at least one processor, a data fabric quality scoreboard based on an aggregation of the respective data offering quality scores calculated for each of the plurality of input data products; and
displaying, by the at least one processor, the data fabric quality scoreboard via a user interface (UI) for evaluating the quality of the data fabric, wherein the plurality of input data products comprises data owning system details, a first data product, and data offering details;
wherein the data fabric a data integration stage where disparate data sources are integrated (Applicant’s [0005] states “banking technology has started implementing a logical data fabric through data virtualization that integrates multiple data sources and provides a data market place for users to discover data products and query data”; [0114] as published states “Starting from a data integration stage 618 where different disparate data sources (for example data source or data producer 602) are integrated’.
Examiner notes, the claim is written in a way to “describe” the data. Examiner does suggest at least refraining from placing the limitations here in the “wherein” clause, and positively reciting “integrating” the data from the multiple sources. For compact prosecution, even if it is positively recited, it just refers to having multiple data sources be combined in some way. There is also confusion because it appears this may just be referring to the very 1st step in the claim – “receiving… a plurality of input data products from at least one data source into the data fabric.”)
(Additional elements involve computer processor performing the steps and displaying on a user interface the results), which are viewed as MPEP 2106.05f (apply it [abstract idea] by a computer) and MPEP 2106.05h field of use for a GUI and a processor). To extent “access” to authorized users involves receiving or transmitting data, this is considered “apply it [abstract idea] on a computer” (MPEP 2106.05f) and field of use (MPEP 2106.05h) at step 2a, prong two and step 2B.
These elements of processor and GUI, amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and individually or in combination is consideration “field of use” (MPEP 2106.05h). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. The claim is directed to an abstract idea.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer system are MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and MPEP 2106.05h (field of use). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In addition, at step 2B, to extent new limitation of “provides access to authorized users” is referring to “access” and receiving or transmitting data, it is also considered a conventional computer function to receive and transmit data – see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent claim 8 is directed to an apparatus at step 1, which is a statutory category. Claim 8 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2 and step 2b. Claim 8 recites “computing device comprising a processor, memory, communication interface coupled to each of the processor and the memory, wherein the processor is configured to.” This is the same as claim 1 above, where the bolded portion is considered to be executed by a computer and in combination/individually at step 2a, prong two and step 2b - is considered MPEP 2106.05f – apply it [abstract idea] on a computer and field of use (MPEP 2106.05h). The other portions of claim 8 are similar to claim 1.
Independent claim 15 is directed to an article of manufacture at step 1, which is a statutory category. Claim 15 recites similar limitations as claim 1 and claim 8 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. Claim 15 recites “non-transitory computer readable storage medium storing instructions … comprising executable code which, when executed by a processor”. At step 2a, prong two and step 2B, claim 15 considered to be executed by a computer and in combination/individually at step 2a, prong two and step 2b - is considered MPEP 2106.05f – apply it [abstract idea] on a computer and field of use (MPEP 2106.05h).
Claims 3, 10, 17 narrow the abstract idea by analyzing the data products in a sequence or an order.
Claims 4, 11 narrow the abstract idea by having categories and subcategories and providing names for categories. Examiner notes the names have no functional relationship with the computer, and only conveys a message to a human reader, and are not entitled to patentable weight. See MPEP 2111.05.
Claims 5, 12 narrow the abstract idea by naming subcategories. Examiner notes the names have no functional relationship with the computer, and only conveys a message to a human reader, and are not entitled to patentable weight. See MPEP 2111.05.
Claims 6, 13, 19 narrow the abstract idea by specifying at least one name of the metadata. Examiner notes the names have no functional relationship with the computer, and only conveys a message to a human reader, and are not entitled to patentable weight. See MPEP 2111.05.
Claims 7, 14, 20 narrow the abstract idea by having customized rules that are used in claim 1 for scoring.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Suggestions? Examiner is not sure which areas to focus on that have “technical details” for improving the claim for 101 purposes; based on disclosure here, perhaps [0047, 0085, 0094] as published, FIG. 3, FIG. 6.
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.
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 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, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sira (US 2019/0258981) and Apuri (US 2024/0386000).
Concerning claim 1, Sira discloses:
A method for assessing a quality of a data fabric (Sira – see par 15 - The data fabric module 114 compares the KPIs to predetermined thresholds to determine compliant, and non-compliant operations), the method being implemented by at least one processor (Sira – see par 39-40, FIG. 5 - The computing device 500 includes one or more non-transitory computer-readable media for storing one or more computer-executable instructions or software for implementing exemplary embodiments.), the method comprising:
receiving, by the at least one processor, a plurality of input data products from at least one data source into the data fabric (Sira – see par 15 - The data fabric module 114 may be implemented as a software module that receives as input the output feeds of the compliance systems 102A, 102B, 102C, 102D, the external data sources 106A, 106B, and entity data sources 107);
transmitting, by the at least one processor, the plurality of input data products to a quality scoring engine installed within the data fabric (Sira – see par 11 - Compliance systems 102A, 102B, 102C, 102D may include systems for audits, annual employee assessments, regulatory visits, licenses, anti-corruption systems, supply chain, employee training, and food safety systems. See par 12 - External data sources 106A, 106B may provide relevant information to augment data from the compliance systems 102A, 102B, 102C, 102D. For example, a compliance system 102A, 102B, 102C, 102D tracking food safety on a per store basis, may be augmented by an external data source 106A, 106B, such as Facebook, where a user of the social network posted a video, image, or text showing a food safety violation at a particular store run by the organization.; see par 13 - Entity data sources 107 include entity data that includes facility-specific data for a multiple facilities for the organization that may be located in multiple locations. The entity data may include, but is not limited to, facility format and size data, human resources data and inventory data for the different facilities in the organization; see par 15 - The data fabric module 114 transforms the input from compliance systems 102A, 102B, 102C, 102D, the external data sources 106A, 106B, and entity data sources 107 into key performance indicators (KPIs).); and
assessing, by the at least one processor using the quality scoring engine the quality of the data fabric based on an analysis of each of the plurality of input data products… (Sira – see par 15- external data sources 106A, 106B, and entity data sources 107 into key performance indicators (KPIs). The KPIs are determined in part from relevant data from the compliance systems 102A, 102B, 102C, 102D. The data fabric module 114 compares the KPIs to predetermined thresholds to determine compliant, and non-compliant operations. Additionally, with historical data, the data fabric module 114 can identify trends in compliant operations and additionally predict future compliance based on past compliance; see par 18 - A “push” configuration receives data and KPIs from the server 104 indicative of a current state of the organization's compliance efforts. The server 104 continuously “pushes” data to the dashboard user interface 112 in the “push” configuration. A “pull” configuration corresponds to a granular view whereby the dashboard user interface 112 receives an input request to find more information about a particular item on the display.)
Sira discloses analyzing compliance outputs and having different metrics (See par 11) and looking at KPIs (key performance indicators) of compliance system sources (See par 15). However, it is unclear if there is a lifecycle, as best understood.
Apuri discloses:
the quality of the data fabric based on an analysis of each of the plurality of input data products “during a lifecycle of each corresponding input data product within the data fabric” (Applicant’s [0112] as filed states “The plurality of scoring parameters includes a set of categories (of measures) and a set of subcategories. In an exemplary implementation, the set of categories of the plurality of scoring parameters includes at least one from among a data product development lifecycle category 610, a data product maturity category 612, a performance optimization category 614, and a usage category 616. The DFQS module allocates scores for each of the categories (e.g., the data product development lifecycle category 610, the data product maturity category 612, the performance optimization category 614, and the usage category 616) within the data fabric to enhance the reliability of measurement of the data fabric quality for the plurality of data products.”
Apuri – see par 21 - Rules may be automatically inferred based on data of data elements in one or more datasets and an identified significance of data in data points to extract common characteristics of data to create, teach and train one or more data quality (DQ) models for data elements across all data stores of the enterprise network. see par 38 - The data fabric 220 may include a data integration and cataloging module 222, a data cleansing and masking module 224, a data transformation module 226, and a data governance module 228, and the like. Each module may be used for importing and formatting data for use within the network and may utilize one or more data quality models to format raw data for use by the one or more applications 108. The cognitive ML model engine may process one or more DQ models within the datastore to ensure data quality of the raw data set before importing the raw data into the application data store(s) 285 such as by generating different data quality rules from raw data and/or feedback from existing system data use and DQ models. see par 41 - These applications may change or be modified over time. Additionally, technology bases or frameworks utilized by the applications may change over time. New applications may be added to the enterprise network, and older applications may be decommissioned. As such, the DQ (data quality) models may be required to be continuously trained and/or otherwise modified over time, such as by the feature engineering and training engine 240, at 231. see par 43 - the DQ schema consumption engine 260 may provide DQ rules to be used by the application data stores 285 and/or to provide data consumption reports, alerts, and/or other feedback from the data consumption reporting engine 280 to provide data accuracy validations. The data confidence information may provide confidence information regarding the data quality of the stored data, such as a confidence score regarding data accuracy information, data format information, data quality rule effectiveness, and/or the like.),
Sira and Apuri disclose:
wherein the analysis of each of the plurality of input data products comprises:
receiving, by the at least one processor, a plurality of scoring parameters, a plurality of rule definitions, and a metadata for each of the plurality of input data products (claim 6 recites “metadata comprises at least one from among a data owning system identifier, a data owning system name, a data owning system description, a data domain name, a data domain description, a data offering identifier, and a data offering name.”
Sira – see par 12 - External data sources 106A, 106B may provide relevant information to augment data from the compliance systems 102A, 102B, 102C, 102D. For example, a compliance system 102A, 102B, 102C, 102D tracking food safety on a per store basis, may be augmented by an external data source 106A, 106B, such as Facebook, where a user of the social network posted a video, image, or text showing a food safety violation at a particular store run by the organization. See par 13 - Entity data sources 107 include entity data that includes facility-specific data for a multiple facilities for the organization that may be located in multiple locations. see par 15 - The data fabric module 114 compares the KPIs to predetermined thresholds to determine compliant, and non-compliant operations. See par 18 - “push” configuration receives data and KPIs from the server 104 indicative of a current state of the organization's compliance efforts. see par 24 - Entity data 212 may be provided to the data fabric module 114. Entity data 212 contains relevant information to identify a sub components within the organization. As noted above, it may include facility format and size data, human resources data, and inventory data. For example, entity data 212 can include store numbers, distribution center identifiers, departments within those larger entities, as well as employee numbers;
See also Apuri – see par 23 - By using AI/ML, the inline data quality schema management system builds an algorithm which can learn and extract DQ metadata rules from different datasets by identifying relevance, relations, data domain of data fields and identifying different patterns of data to generate proprietary and standardized DQ data models. By integrating the built algorithm with a data fabric, the inline data quality schema management system creates a data schema pipeline for intelligent building, training, and evolution of DQ data models);
calculating, by the at least one processor, a respective data offering quality score against each of the plurality of scoring parameters during the lifecycle of each of the plurality of input data products within the data fabric, wherein each respective data offering quality score is calculated based on an application of the plurality of rule definitions against each of the plurality of input data products (Sira – see par 9 - The system evaluates the received data from all the compliance, internal, and external systems and determines key performance indicators. The system utilizes the key performance indicators to indicate status of, and trends and predictions related to, compliance areas within the organization. The statuses, trends, and predictions populate a dashboard user interface that intuitively and efficiently provides a snapshot view of the output of the disparate systems. see par 15 - The data fabric module 114 transforms the input from compliance systems 102A, 102B, 102C, 102D, the external data sources 106A, 106B, and entity data sources 107 into key performance indicators (KPIs). The KPIs are determined in part from relevant data from the compliance systems 102A, 102B, 102C, 102D. see par 26 - A dashboard user interface 112 interfaces with the data fabric module 114 to provide relevant real time displays of the underlying international compliance system 218 data, entity data 212, and external data 216. The dashboard user interface 112 may be implemented with differing elements designed to show an organization's compliance performance. Dashboard user interface 112 elements can include a status elements 204, trending elements 206 and predictive elements 208. Status elements 204 utilize the data identified and/or generated by data fabric module 114 to visually represent the current state of the organization in relation to compliance.
see also Apuri par 34 - The cognitive machine learning engine 150-1 may have instructions that direct and/or cause the inline data quality schema management system 104 to perform one or more operations associated with identifying DQ metadata rules from different datasets by identifying relevance, relations, data domain of data fields and identifying different patterns of data, and the like. The data quality rule model training engine 150-2 may have instructions that may cause the inline data quality schema management system 104 to perform train one or more DQ models to identify different data quality rules for each data element from each particular dataset. see par 43 - At 281, the application data store 285 (metadata, usage data, error data, and the like) and/or reporting may utilize the data consumption reporting engine 280 to retrieve DQ confidence information from the DQ confidence data store 295. The data confidence information may provide confidence information regarding the data quality of the stored data, such as a confidence score regarding data accuracy information, data format information, data quality rule effectiveness, and/or the like.);
generating, by the at least one processor, a data fabric quality scoreboard based on an aggregation of the respective data offering quality scores calculated for each of the plurality of input data products (Sira –see par 18 - The visualization of the compliance information is presented in the dashboard user interface 112. See par 21 - The data fabric module 114 provides computer software function, routines, and subroutines for the processing of the data from the different sources within the international compliance systems 218. For example, the data fabric module 114 provides support for the parsing and processing of data presented in comma separated value (CSV) files, as well as other spreadsheet formats. Likewise the data fabric module 114 is configured to extract information directly from the independent components of the international compliance systems 218. see par 37 - The key performance indicators corresponding to the more granular data points (e.g. single store) may aggregate to form the key performance indicators on a more macro level (e.g. global set).
see also Apuri par 42 - The cognitive ML model engine 230 may synthesize data quality rules while weighing context of data values by identifying data groups based on relevance and semantics of data to generate multiple data value patterns. For example, as seen in FIG. 4, an illustrative neuron 400 may be used as part of an intelligent algorithm to generate DQ rules for the DQ models by performing multiple stages of evaluation for each neuron type. Neuron types may include atomic value(s), ranges, groups, overall domains, relevance factors, relationships to other fields, and the like. The illustrative neuron 400 may process a plurality of inputs (e.g., x.sub.1, x.sub.2, x.sub.3, and the like) to determine a weighted sum of the inputs (e.g., w.sub.1*x.sub.1, w.sub.2*x.sub.2, w.sub.3*x.sub.3, and the like) to be used as an input to an activation function (f) to produce the output (y)); and
displaying, by the at least one processor, the data fabric scoreboard via a user interface (UI) for evaluating the quality of the data fabric (Sira – see par 26 - A dashboard user interface 112 interfaces with the data fabric module 114 to provide relevant real time displays of the underlying international compliance system 218 data, entity data 212, and external data 216. The dashboard user interface 112 may be implemented with differing elements designed to show an organization's compliance performance. Dashboard user interface 112 elements can include a status elements 204, trending elements 206 and predictive elements 208. Status elements 204 utilize the data identified and/or generated by data fabric module 114 to visually represent the current state of the organization in relation to compliance;
see also Apuri – see par 43 - the DQ schema consumption engine 260 may provide DQ rules to be used by the application data stores 285 and/or to provide data consumption reports, alerts, and/or other feedback from the data consumption reporting engine 280 to provide data accuracy validations).
wherein the plurality of input data products comprises data owning system details, a first data product, and data offering details (Sira – see par 12- external data sources 106A, 106B may provide relevant information to augment data from the compliance systems 102A, 102B, 102C, 102D. For example, a compliance system 102A, 102B, 102C, 102D tracking food safety on a per store basis, may be augmented by an external data source 106A, 106B; See par 13 - Entity data sources 107 include entity data that includes facility-specific data for a multiple facilities for the organization that may be located in multiple locations; see par 18 - The predictive configuration of the dashboard user interface 112 provides extrapolated data determined by the data fabric module 114 from the raw data received from compliance systems 102A, 102B, 102C, 102D, and the external data sources 106A, 106B. The dashboard user interface 112 interpolates past trends in the raw data and the KPIs and projects future trends based on the historical data;
See also Apuri par 26 - The client computing system 122 and/or the application computing systems 108 may comprise various servers and/or databases that store and/or otherwise maintain account information, such as financial account information including account balances, transaction history, account owner information, client details; see par 37 - The data fabric 220 may be a single, unified architecture with an integrated set of technologies and services, designed to deliver integrated and enriched data at the right time using right method. It may be used to build intelligent data integration and data pipelining solutions by connecting data of different forms coming from multiple sources. see par 41 - The data quality model may be defined as standard and/or proprietary formats to be used across business or organization and hence providing single location of data quality rule for data element. Illustrative data quality metadata for different data elements (e.g., a party name data element, a tax identifier data element) and the like);
wherein the data fabric comprises a data integration stage where disparate data sources are integrated (Apuri – see par 38 - The data fabric 220 may include a data integration and cataloging module 222. The data integration and cataloging module 222 receives raw data from the one or more data sources and processes the input data to catalog the data to identify one or more application systems associated with the data. see par 39 - The data integration and cataloging module 222 receives raw data from the one or more data sources and processes the input data to catalog the data to identify one or more application systems associated with the data. In some cases, the raw input data may include an indication of an associated application computing system.); a raw data products stage where metadata that specifies data types and relationships for raw data sources is defined (Sira – see par 11 - The raw data for the system may come in part from a multitude of disparate compliance systems 102A, 102B, 102C, 102D.
Apuri – see par 34 - The cognitive machine learning engine 150-1 may have instructions that direct and/or cause the inline data quality schema management system 104 to perform one or more operations associated with identifying DQ metadata rules from different datasets by identifying relevance, relations, data domain of data fields and identifying different patterns of data, and the like; see par 42 - The cognitive ML model engine 230 may process one or more cognitive ML algorithms using a deep learning and/or a neural network technique to identify DQ metadata rules from different datasets (e.g., raw data sets, application data sets, and the like) by identifying relevance, relations, data domain of data fields and identifying different patterns of data); a curated data product offerings stage where an output of the raw data products stage is transformed into insights via at least one from among a transformation, a normalization, and a standardization (Sira – see par 16 - The databases 110A, 110B index and store raw data from the compliance systems 102A, 102B, 102C, 102D, the external data sources 106A, 106B. Additionally the databases 110A, 110B can index and store transformed data from data fabric module 114 that is based on the raw data from the compliance systems 102A, 102B, 102C, 102D, and the external data sources 106A, 106B. KPIs and thresholds may be stored in the databases 110A, 110B for retrieval by the data fabric module 114. Trending information, including threshold violations, may be indexed and stored in the databases 110A, 110B. see par 18 - if a user is viewing a high level geographic map view of a region and desires more detailed information about a specific location, the user may select the location and the dashboard user interface 112 requests or “pulls” the data from the data fabric module 114 executing on the server 104. The predictive configuration of the dashboard user interface 112 provides extrapolated data determined by the data fabric module 114 from the raw data received from compliance systems 102A, 102B, 102C, 102D, and the external data sources 106A, 106B. The dashboard user interface 112 interpolates past trends in the raw data and the KPIs and projects future trends based on the historical data;
see also Apuri – see par 37 - At 211, the data fabric 220 may import raw data from the raw data source 205 and, at 221, process the raw data via one or more data processing algorithms. The data fabric is a network-based architecture that facilitates data integration into a network environment via data pipelines to enable algorithms for analytics, insight generation, orchestration, and application of the data. Often, the data fabric 220 may be used to provide a layer of abstraction over underlying data components, such as via metadata, to make information and insights available to business users without duplication or mandatory data science efforts; see par 40 - e, the data transformation module 226 may transform a numerical data record from a first numerical data type (e.g., integer) to a second numerical data type (e.g., floating point). Other data types that may be utilized with the transformations may include textual data type, string data types combining data types, splitting raw data elements into two or more data elements, combining portions of different data elements into a combined data element, translation or otherwise mapping of data, generalizing of lower level data types into higher level categorizations, integration of data into different data sets or categorizations, discretization of data sets, manipulating data, and the like. ); and a data discovery channel stage that provides access to authorized users (Apuri –see par 24, FIG. 1A - The computing environment 100 may comprise, for example, an inline data quality schema management system 104, one or more application computing systems 108, one or more client computing systems 122; see par 25 - In some cases, the client computing systems 122 may use DQ rules received from the inline data quality schema management system 104 to validate data accuracy for processing day to day operations such as generating transactions, validating transactions, generating reports, and the like. see par 27 - In addition, an application computing systems 108 may be linked to and/or operated by a specific enterprise user (who may, for example, be an employee or other affiliate of the enterprise organization) who may have administrative privileges to perform various operations within the private network 125), and
wherein the calculating of each respective data offering quality score is performed across each stage of the data fabric (Apuri – see par 22 - . An AI/ML algorithm may be based on a cognitive solution to discover unique, business-relevant relationships between the available data points and generate data quality (DQ) metadata models on data points. DQ metadata models is based on a proprietary format that can be utilized across the enterprise network, such as in databases, applications, reports, extract, transform, and load (ETL) processes and the like, as additional validations to ensure data integrity and accuracy. These models can be embedded with data to enrich accuracy and correctness of data as it is received by the enterprise network and/or ingested by relevant data repositories. see par 32 - In some cases, inline data quality schema management system 104 may comprise components (such as those discussed above) that may be distributed across multiple computing devices such as to enable operation of different computing engines such as a cognitive machine learning model, systems used in different stages of data fabric pipelines, systems to store one or more data quality models, and/or the like. see par 43 - the DQ schema consumption engine 260 may provide DQ rules to be used by the application data stores 285 and/or to provide data consumption reports, alerts, and/or other feedback from the data consumption reporting engine 280 to provide data accuracy validations. At 281, the application data store 285 (metadata, usage data, error data, and the like) and/or reporting may utilize the data consumption reporting engine 280 to retrieve DQ confidence information from the DQ confidence data store 295. The data confidence information may provide confidence information regarding the data quality of the stored data, such as a confidence score regarding data accuracy information, data format information, data quality rule effectiveness, and/or the like.).
Both Sira and Apuri are analogous art as they are directed to assessing information from different data sources and integrating software (see Sira Abstract, par 15; Apuri Abstract, par 5, 22). Sira discloses analyzing compliance outputs and having different metrics (See par 11) and looking at KPIs (key performance indicators) from compliance systems (See par 15). Apuri improves upon Sira by disclosing using different quality rules and feedback, where applications and data quality change over time as applications get decommissioned eventually and also considers Data Governance (par 21, 38, 41, 43, FIG. 2 – 221-228). One of ordinary skill in the art would be motivated to further include having quality rules and effectiveness for data fabric that change over time and consider Data Governance to efficiently improve upon the KPIs/metrics calculated in Sira.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the use of KPI and compliance analysis in Sira to further assess quality rules and effectiveness for data fabric that change over time and consider Data Governance as disclosed in Apuri, since the claimed invention is merely a combination of old elements, and in 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 and there is a reasonable expectation of success.
Concerning independent claim 8, Sira and Apuri disclose:
A computing device configured to implement an execution of a method for assessing a quality of a data fabric (Sira – see par 39-40, FIG. 5 - The computing device 500 includes one or more non-transitory computer-readable media for storing one or more computer-executable instructions or software for implementing exemplary embodiments), the computing device comprising:
a processor (Sira – see par 39-41 – computing device 500);
a memory (Sira – see par 40 - for example, volatile memory 504 included in the computing device 500 can store computer-readable and computer-executable instructions or software for implementing exemplary operations of the computing device 500.); and
a communication interface coupled to each of the processor and the memory (Sira – see par 44 - or example, volatile memory 504 included in the computing device 500 can store computer-readable and computer-executable instructions or software for implementing exemplary operations of the computing device 500.), wherein the processor is configured to.
It would have been obvious to combine Sira and Apuri for the same reasons as claim 1 above.
Concerning independent claim 15, Sira and Apuri disclose:
A non-transitory computer readable storage medium storing instructions for assessing a quality of a data fabric, the storage medium comprising executable code which, when executed by a processor, causes the processor to (Sira – see par 40 - for example, volatile memory 504 included in the computing device 500 can store computer-readable and computer-executable instructions or software for implementing exemplary operations of the computing device 500).
It would have been obvious to combine Sira and Apuri for the same reasons as claim 1 above.
Concerning claims 3, 10, and 17, Sira and Apuri disclose:
The method as claimed in claim 1, wherein the analysis of each of the plurality of input data products within the data fabric is performed in a sequential manner (Sira – see par 26 - The dashboard user interface 112 is updated to display the necessary sub-elements comprising the status elements 204. The trending elements 206 utilize the data provided by the data fabric module 114 to visually represent a transition of the organization over time in relation to compliance;
Apuri – see par 26 - In some cases, the client computing systems 122 may use DQ rules received from the inline data quality schema management system 104 to validate data accuracy for processing day to day operations such as generating transactions, validating transactions, generating reports, and the like. see par 37 - The data fabric is a network-based architecture that facilitates data integration into a network environment via data pipelines to enable algorithms for analytics, insight generation, orchestration, and application of the data. The data fabric 220 may be a single, unified architecture with an integrated set of technologies and services, designed to deliver integrated and enriched data at the right time using right method. It may be used to build intelligent data integration and data pipelining solutions by connecting data of different forms coming from multiple sources see par 43 - Each DQ model stored in the DQ model datastore 235 may be associated with a particular application or set of applications using at least a same partial data set. The DQ schema consumption engine 260, at 261, may provide DQ rules to be used by a data integration engine utilizing ETL functionality to use one or more sets of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML). Similarly, the DQ schema consumption engine 260 may provide DQ rules to be used by the application data stores 285 and/or to provide data consumption reports, alerts, and/or other feedback from the data consumption reporting engine 280 to provide data accuracy validations. ).
It would have been obvious to combine Sira and Apuri for the same reasons as claim 1 above.
Concerning claims 4 and 11, Sira and Apuri disclose:
The method as claimed in claim 1, wherein the plurality of scoring parameters comprises a set of categories and a set of subcategories (Sira – see par 13 - Entity data sources 107 include entity data that includes facility-specific data for a multiple facilities for the organization that may be located in multiple locations. see par 24 - Entity data 212 may be provided to the data fabric module 114. Entity data 212 contains relevant information to identify a sub components within the organization. As noted above, it may include facility format and size data, human resources data, and inventory data. For example, entity data 212 can include store numbers, distribution center identifiers, departments within those larger entities, as well as employee numbers. see par 29 - The diagram 300A includes relevant information for the display of a status element including a compliance category 302 and a status indicator 308A. Additionally the view provides view filtering, which is displayed as a no filter view 304. The compliance category 302 provides a textual description of the compliance category from the international compliance systems 218l;
See also Apuri par 41 - For example, the DQ model may include SQL language commands, statements and/or other characteristics and semantics associated with a particular data repository type and/or category of data repositories. In some cases, the DQ model may include data query language and/or syntax combinations that may be used to organize or otherwise format data based on one or more data quality rules. This data quality model can be utilized in multiple destination applications to ensure consistent data quality and accuracy of data elements in the data across the enterprise network. ), and
wherein the set of categories of the plurality of scoring parameters comprises at least one from among a data product maturity category, a data product development lifecycle category, a performance optimization category, and a usage category (Examiner notes the names have no functional relationship with the computer, and only conveys a message to a human reader, and are not entitled to patentable weight. See MPEP 2111.05. Nonetheless, for purposes of compact prosecution, art will be applied –
Sira – see par 29 - The diagram 300A includes relevant information for the display of a status element including a compliance category 302 and a status indicator 308A. Additionally the view provides view filtering, which is displayed as a no filter view 304. The compliance category 302 provides a textual description of the compliance category from the international compliance systems 218.
Apuri – see par 21 - These Data Quality rules may be added as additional metadata for a data schema for particular relational database management system and/or noSQL databases to improve and add validations for data to ensure data consistency and/or to identify invalid data. see par 43 - . At 281, the application data store 285 (metadata, usage data, error data, and the like) and/or reporting may utilize the data consumption reporting engine 280 to retrieve DQ confidence information from the DQ confidence data store 295. ).
It would have been obvious to combine Sira and Apuri for the same reasons as claim 1 above.
Concerning claims 5 and 12, Sira and Apuri disclose:
The method as claimed in claim 4, wherein the set of subcategories of the plurality of scoring parameters comprises at least one from among an onboarding data subcategory, a raw data exposure subcategory, and a data product offering subcategory (Examiner notes the names have no functional relationship with the computer, and only conveys a message to a human reader, and are not entitled to patentable weight. See MPEP 2111.05. Nonetheless, for purposes of compact prosecution, art will be applied - Sira – see par 20 - Additional compliance systems may be included in the international compliance system 218. Audits, assessments, regulatory visits 220 system provides the entry point for data relating to regulatory systems. Regulatory systems may include both governmental and organizational. Audit systems may provide data related to internal and external audits for different groups within the organization. Assessment systems may include data relating to human resource performance assessment. A license manager 222 provides data related to licensing requirements with which an organization may be required to conform. License manager 222 may aggregate licensing data across various disciplines including software agreements and cross branding.
Apuri – see par 26 - . In an arrangement where the private network 125 is associated with a financial institution (e.g., a bank), the application computing systems 108 may be configured, for example, to host, execute, and/or otherwise provide one or more transaction processing programs, such as an online banking application, fund transfer applications, client onboarding applications; see par 37 - The raw data source 205 may include a relational database, a noSQL database, a spreadsheet, a text file, and/or other data storage format. At 211, the data fabric 220 may import raw data from the raw data source 205 and, at 221, process the raw data via one or more data processing algorithms. The data fabric is a network-based architecture that facilitates data integration into a network environment via data pipelines to enable algorithms for analytics, insight generation, orchestration, and application of the data; see par 39 - In some cases, the raw input data may be cataloged to reflect a security level associated with the data, such as public information, private information, secret information and the like. The data cleansing and masking module 224 may process business rules and/or may operate based on regulations to maintain a proper level of security based on a category associated with the data. see par 43 - . The data confidence information may provide confidence information regarding the data quality of the stored data, such as a confidence score regarding data accuracy information, data format information, data quality rule effectiveness, and/or the like.).
It would have been obvious to combine Sira and Apuri for the same reasons as claim 1 above.
Concerning claims 6, 13, and 19, Sira and Apuri disclose:
The method as claimed in claim 1, wherein the metadata comprises at least one from among a data owning system identifier, a data owning system name, a data owning system description, a data domain name, a data domain description, a data offering identifier, and a data offering name (Examiner notes the names have no functional relationship with the computer, and only conveys a message to a human reader, and are not entitled to patentable weight. See MPEP 2111.05. Nonetheless, for purposes of compact prosecution, art will be applied -
Sira - see par 25 - Utilizing the text following a hash symbol in a “hashtag,” a system can identify relevant information matching that text. The relevant information matching that text may then be input by the data fabric module 114 whereby the search term that yielded the match, correlates the data to the respective compliance system. Alternatively, the relevant information matching the text may be parsed by a natural language processor and given context thereby correlating the relevant information to the respective compliance system.
Apuri – see par 26 - The client computing system 122 and/or the application computing systems 108 may comprise various servers and/or databases that store and/or otherwise maintain account information, such as financial account information including account balances, transaction history, account owner information, client details, client agreements, and/or other information. see par 34 - The cognitive machine learning engine 150-1 may have instructions that direct and/or cause the inline data quality schema management system 104 to perform one or more operations associated with identifying DQ metadata rules from different datasets by identifying relevance, relations, data domain of data fields and identifying different patterns of data, and the like. see par 44 - For example, data element 310 shows an illustrative data element associated with party name data types and is associated with a client data domain. ).
It would have been obvious to combine Sira and Apuri for the same reasons as claim 1 above.
Concerning claims 7, 14, and 20, Sira and Apuri disclose:
The method as claimed in claim 1, wherein each of the plurality of rule definitions is customized based on a type of the plurality of input data products (Sira – see par 11 - Compliance systems 102A, 102B, 102C, 102D may operate under the control of an organization and/or may be independent systems configured to track an organization's compliance with laws, regulations, and rules promulgated by one or more governmental or other authorities exercising oversight on the organization. Compliance systems 102A, 102B, 102C, 102D may include systems for audits, annual employee assessments; see par 20 - A license manager 222 provides data related to licensing requirements with which an organization may be required to conform. License manager 222 may aggregate licensing data across various disciplines including software agreements and cross branding. The license manager 222 may provide information indicative of a percentage of valid required licenses and permits held by the organization across the organization's facilities
Apuri – see par 2 - As such, customized validation techniques based on business rules are difficult to define across the enterprise network due to disparate data storage types, schemas, and usage requirements and cannot be easily standardized for the different database technologies. see par 40 - The data transformation module 226 may process instructions to transform data based on one or more rules and/or a category associated with the data. see par 43 - The DQ schema consumption engine 260, at 261, may provide DQ rules to be used by a data integration engine utilizing ETL functionality to use one or more sets of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML).).
It would have been obvious to combine Sira and Apuri for the same reasons as claim 1 above.
Response to Arguments
Applicant's arguments filed 1/29/26 have been fully considered but they are not persuasive and/or are moot in view of the new rejections.
With regards to 101, Applicant argues that claim 1 is a practical application (step 2a, prong two) because new limitations include 1) metadata that specifies data types and relationships; 2) disparate data sources are integrated…; 3) insights via at least one from… transformation/ normalization/ standardization; 4) access to authorized users; and quality score being performed across each stage of the data fabric.” Remarks, pages 10-11. In response, Examiner respectfully disagrees. First, the new limitations are addressed in the revised rejection. Second, the limitations are in “wherein” statements and many of the limitations are just “describing” the data. For example, the 1) metadata just “specifies” what data represents and the “data types and relationships” specified are not functionally involved in the claims; there is no positive of 2) “integration” – rather the claim is “assessing… quality of the data fabric” and one of the stages scores the fact that the data is already “integrated”; the 3) insights are part of the abstract idea, as it is just normalizing/standardizing data. In addition, MPEP 2106.05(c) states “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366.” With regards to 4), to extent “access” to authorized users involves receiving or transmitting data, this is considered “apply it [abstract idea] on a computer” (MPEP 2106.05f) and field of use (MPEP 2106.05h) at step 2a, prong two and step 2B. At step 2B, it is also considered a conventional computer function to receive and transmit data – see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321.
Applicant then argues that by scoring across each stage, and new limitations, as well as “monitoring and tracking ability of the maturity level of data integration and consumption with the data fabric” is provided and as a result “disparate data systems are unified, security and privacy messages are strengthened, and end users are provided with more data accessibility.” Remarks, pages 11-12. In response, Examiner respectfully disagrees. First, scoring for maturity level is directed to the abstract idea of “mathematical relationships and certain methods of organizing human activity.” As Applicant even points to in paragraph 4-5, and as claimed for “respective data offering quality score” and “data offering details,” the claims are directed to “users to discover data products” offered that have different scores/maturities which is all part of the abstract idea as identified. Second, with regards to “disparate data systems are unified, security and privacy measures are strengthened, and end users are provided with more data accessibility” this is based on one mention in the specification paragraph 5 as published with no details. This is viewed as MPEP 2106.04(d)(1) “Conversely, if the specification explicitly sets forth an improvement only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field.” With regards to “security and privacy measures are strengthened” this is not persuasive – as a list of authorized people alone is not considered a practical application nor improving technology.
Applicant then argues that based on Desjardins there is a practical application here, similar to providing “technical improvements” by “addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance sequential training” and “reduced system complexity.” Remarks, page 12-13. In response, Examiner respectfully disagrees. Examiner respectfully disagrees as this claim is not similar to Desjardins. First, the USPTO 12/5/25 Desjardins Memo, on page 2, explains the details leading to eligibility as “In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems” and that the claims reflected the improvement. Here, we do not have a similar situation. All we have is argument of “unified” disparate data systems and strengthening security and privacy measures. There are no technical details on the “unified” – it can just be a set of banking information combined from “multiple sources”. Accordingly, the arguments for Desjardins are not persuasive at this time. Examiner notes, Desjardins reflected an improvement in Machine learning and training. Examiner instead suggests Applicant consider arguing something related to data structures, databases, querying, if that is indeed the technical focus.
With regards to 103, Applicant argues that Sira and Sailer do not disclose “calculating, by the at least one processor, a respective data offering quality score against each of the plurality of scoring parameters during the lifecycle of each of the plurality of input data products within the data fabric, wherein each respective data offering quality score is calculated based on an application of the plurality of rule definitions against each of the plurality of input data products” [Feature C] as the citations from Sira and Sailer do not relate to “scoring parameters,” “rule definitions,” and “metadata.” Remarks, pages 15-16. In response, Examiner respectfully disagrees. Sira discloses showing an organization’s compliance performance (See par 26, FIG. 2). additional citations now applied in par 9, 15 to also show “evaluation” of data and taking data and forming “key performance indicators.” Newly cited Apuri also discloses “The data confidence information may provide confidence information regarding the data quality of the stored data, such as a confidence score regarding data accuracy information, data format information, data quality rule effectiveness, and/or the like” in paragraph 43.
The remaining arguments are moot over the new rejections.
Conclusion
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
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