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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 11/14/25, Applicant, on 1/29/26, 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”
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.
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.
(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).
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.
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, 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 Sailer (US 2023/0177435).
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.
Sailer 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 [0094] as filed discusses categories and talks about “continuous integration and continuous delivery/deployment (CI/CD) pipeline”
Sailer discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 41 - the DevOps lifecycle (sometimes called the continuous delivery pipeline) is a series of iterative, automated development processes, or workflows, executed within a larger, automated and iterative development lifecycle designed to optimize the rapid delivery of high-quality software. The workflows typically includes planning, development, integration, deployment, operations and learning. see par 44 - During integration (or build, or continuous Integration and continuous delivery (Cl/CD), the new code is integrated into the existing code base, then tested and packaged into an executable for deployment; see par 45 - In deployment (usually called continuous deployment), the runtime build output (from integration) is deployed to a runtime environment—usually a development environment where runtime tests are executed for quality, compliance and security. If errors or defects are found, developers have a chance to intercept and remediate any problems before any end users see them),
Sira and Sailer 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 Sailer – see par 38 - . This policy uses subject attributes provided from a user repository 408, as well runtime and environment data received from policy information point (PIP) 410. The policy decision point (PDP) 404 receives similar information and responds to an XACML policy query received from the policy enforcement point (PEP) 406 to enforce the policy on a subject and with respect to a particular action initiated by the subject. See par 52 - The compliance management tier 504 preferably sits atop the infrastructure tier 502 and functions to encapsulate one or more security and compliance policies, preferably in a standardized manner, and that are implemented via the primitives (the controls 503) in the infrastructure tier 502; see par 54 - the security/compliance requirements may vary and include one or more of: the customer's own controls, profiles, assessment tools, remediation, and GRC (Governance/Risk/Compliance) requirements. Each such requirement is characterized by one or more compliance artifacts. Thus, e.g., the customer may specify its own controls, which have their associated controls artifacts, its own profiles, which have their associated profiles artifacts, and so forth. As will be described, the platform of this disclosure enables these user-specified requirements to be integrated into the continuous compliance processing provided by the platform in a seamless and automated manner.);
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 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.
Sailer – discloses entire limitation and “lifecycle” – see par 45 - In deployment (usually called continuous deployment), the runtime build output (from integration) is deployed to a runtime environment—usually a development environment where runtime tests are executed for quality, compliance and security. see par 50 - regulatory compliance (governance and risk) are also best addressed early and throughout the development lifecycle. As noted above, regulated industries are often mandated to provide a certain level of observability, traceability and access of how features are delivered and managed in their runtime operational environment. See par 55 - Control-based information expressed using OSCAL formats enables easy access of control information from security and privacy control catalogs, the sharing of machine-readable control baselines, maintaining and sharing actionable, up-to-date information about how controls are implemented in the customer's systems, and automating monitoring and assessment of the effectiveness of the customer s controls.);
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). Sailer – see par 56 - A fifth module 616 within the compliance management tier 604 aggregates results 615 for each of the target environments and reports those aggregated results 617 back to the governance tier 606. The fifth module 616 also stores raw data results in an associated archive (not shown). Finally, a sixth module 618 within the governance tier 606 collects the aggregated results and reports back in an automated manner to the customer or regulator against one or more specified requirements. The reporting provided by the sixth module may be customized according to a customer's BYO reporting requirements.); 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 Sailer – see par 56 - a sixth module 618 within the governance tier 606 collects the aggregated results and reports back in an automated manner to the customer or regulator against one or more specified requirements. The reporting provided by the sixth module may be customized according to a customer's BYO reporting requirements.).
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 Sailer – see par 58 - At step (1), vendors or service owners declare to the SCC one or more product compliance definitions (e.g., properties, checks, policy parameters, etc.), and one or more mappings to SCC regulation catalogs. At step (2), customers select a profile to describe their compliance intent and to declare their policy parameter(s) values. This enables the compliance hub to observe and manage the profile of interest. At step (3), and using one or more Policy Validation Points (PVPs), one or more assessment tools validate the profile and actively manage security and compliance against selected inventory components).
Both Sira and Sailer are analogous art as they are directed to assessing information from different data sources and integrating software (see Sira Abstract, par 15; Sailer Abstract, par 34, 36, 44-45). 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). Sailer improves upon Sira by disclosing analyzing software in a lifecycle (e.g. development, integration, deployment, etc) and during a continuous delivery (CI/CD) and looking at quality/defects (See par 41-45). One of ordinary skill in the art would be motivated to further include having a lifecycle/stage component 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 lifecycle, stages, and quality as disclosed in Sailer, 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 Sailer 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 Sailer for the same reasons as claim 1 above.
Concerning independent claim 15, Sira and Sailer 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 Sailer for the same reasons as claim 1 above.
Concerning claims 3, 10, and 17, Sira and Sailer 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;
See also Sailer – see par 42 - In planning, teams scope out new features and functionality in a next release, typically drawing from prioritized end-user feedback and case studies, as well as inputs from all internal stakeholders. The goal in the planning stage is to maximize the business value of the product by producing a backlog of features that when delivered produce a desired outcome that has value. See par 60 - If step 806 determines that the finding object is not present in the customer's parsed artifact, the alignment operation automatically augments to the customer's parsed artifact to include it (e.g., using a vendor product mapping). For the customer's control artifact, augmentation may involve adding a parsed parameter, a sub-control or a section. For the customer's profile artifact, augmentation may involve adding a parsed parameter value, or a control priority).
It would have been obvious to combine Sira and Sailer for the same reasons as claim 1 above.
Concerning claims 4 and 11, Sira and Sailer 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 Sailer par 60 - At step 806, the translated parsed BYO artifacts are then subject to a compliance ontology alignment, which aligns the module concepts with existing industry artifacts. The nature of the alignment will vary depending on the implementation (and the artifact), but typically this operation augments the parsed artifact with one or more predefined parsed parameters or parameter values, sub-controls or control priorities, findings, etc. ), 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. Sailer – see par 51 - in one embodiment security and compliance is provided by a cloud-based security and compliance platform where, for example, customers define controls, assess posture, monitor security and compliance, remediate issues, and collect audit evidence. To this end, the cloud platform provides a Security and Compliance Center (SCC) network-accessible dashboard that enables a user (customer) to view and automate its security and compliance postures, to enable configuration governance, and to detect vulnerabilities and threats. See par 58 - assessment tools validate the profile and actively manage security and compliance against selected inventory components, and in the process collect evidence, providing updated inventory and posture results, and the like;
Sailer – see par 46 - In the operations workflow, feature performance, behavior, and availability are monitored to ensure that the features are able to provide value add to end users. Operations ensures that features are running smoothly and that there are no interruptions in service—by making sure the network, storage, platform, compute and security posture are all healthy. If something goes wrong, operations ensures incidents are identified, the proper personnel are alerted, problems are determined, and fixes are applied.).
It would have been obvious to combine Sira and Sailer for the same reasons as claim 1 above.
Concerning claims 5 and 12, Sira and Sailer 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.
Sailer – see par 45 - disclosing “onboarding data” - In deployment (usually called continuous deployment), the runtime build output (from integration) is deployed to a runtime environment—usually a development environment where runtime tests are executed for quality, compliance and security. If errors or defects are found, developers have a chance to intercept and remediate any problems before any end users see them. There are typically environments for development, test, and production, with each environment requiring progressively “stricter” quality gates. A good practice for deployment to a production environment is typically to deploy first to a subset of end users, and then eventually to all users once stability is established. See par 51 - the cloud platform provides a Security and Compliance Center (SCC) network-accessible dashboard that enables a user (customer) to view and automate its security and compliance postures, to enable configuration governance, and to detect vulnerabilities and threats).
It would have been obvious to combine Sira and Sailer for the same reasons as claim 1 above.
Concerning claims 6, 13, and 19, Sira and Sailer 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.
See also Sailer – see par 60 - At step 804, the output of the compliance parser (namely, the parsed artifacts) are translated into the OSCAL format. Preferably, the translator function operates based on the #D ontology concept classes, and it preserves original endpoint namespaces. Thus, and at step 804, the customer's BYO controls artifacts are translated into a catalog format, the customer's BYO profile artifacts are translated into a profile format, the customer's BYO assessment artifacts are translated to a result format, and the customer's BYO GRC artifacts are translated to GRC formats. At step 806, the translated parsed BYO artifacts are then subject to a compliance ontology alignment, which aligns the module concepts with existing industry artifacts. The nature of the alignment will vary depending on the implementation (and the artifact), but typically this operation augments the parsed artifact with one or more predefined parsed parameters or parameter values, sub-controls or control priorities, findings, etc. As an example, typically an assessment result object checks if an observation object (i.e., a pass/fail policy) has an associated finding object (i.e., the impacted regulation control that will pass/fail).).
It would have been obvious to combine Sira and Sailer for the same reasons as claim 1 above.
Concerning claims 7, 14, and 20, Sira and Sailer 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
See also Sailer – see par 5- User-specified (“bring-your-won” (BYO)) requirements include one or more of: the customer's own controls, profiles, assessment tools, remediation, and GRC (Governance/Risk/Compliance) requirements. Each such requirement is characterized by one or more BYO compliance artifacts. The user-specified or regulation-specified requirements are translated into a machine-readable format. Translation may include transforming the user-specified or regulation-specified requirements from an unstructured format into a set of codified requirements, and mapping the codified requirements to a set of standardized policies supported by enterprise computing environment. see par 58 - . As noted above, the compliance posture may be set forth in an audit report, e.g., supporting evidence, possibly customized according to a BYO requirement. ).
It would have been obvious to combine Sira and Sailer 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 “by virtue of the assessing of the quality of the data fabric based on analysis of each of the plurality of input data products during a lifecycle of each corresponding input data product within the data fabric, a monitoring and tracking ability of the maturity level” 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 9-11. 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.” Notably, the claim does not even reflect this mention from paragraph 5 as it has no limitations related to “disparate data systems are unified,” nor “security,” nor “privacy,” nor “accessibility.” Examiner does not recommend importing paragraph 5 into the claim though, as there do not appear to be details related to it. Instead, Applicant should focus on “technical details” if possible; Examiner is not sure which areas to focus on based on disclosure here, perhaps [0047, 0094] as published, FIG. 3, FIG. 6.
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 11. 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 a bare assertion of “unified” disparate data systems and strengthening security and privacy measures, based on one mention in the specification, with no reflection in the claims of technical aspects even relating to these aspects. Accordingly, the arguments for Desjardins are not persuasive at this time.
With regards to 103, Applicant argues that Sira does not disclose “transmitting, by the at least one processor, the plurality of input data products to a quality scoring engine installed within the data fabric” [Feature A] as the citations of paragraphs 11-15 do not include “destination” and do not include “quality scoring engine.” Remarks, pages 13-14. In response, Examiner respectfully disagrees. Sira discloses having audits and compliance. The data can come from external data sources 106 which is transmitted to the compliance systems 102 and the “data fabric module 114” which in paragraph 15, transforms the input from compliance systems 102, data sources 106, and entity data sources 107 into KPI. The data is going to a quality scoring engine 114 within the data fabric of a server in the same manner as Applicant’s disclosure and claims where for example, Applicant has a “data fabric quality score module 608 installed in a device/computer. Sira also discloses storing “one or more” computer-executable instructions or software” for implementing the embodiments, as cited in paragraphs 39-40 and FIG. 5, so if Applicant is arguing there are “two modules” versus “one” module, it is also not persuasive as non-obvious in view of the prior art.
Applicant argues that Sira and Sailer do not disclose “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” [Feature B] as the citations from Sira and Sailer do not relate to “scoring parameters,” “rule definitions,” and “metadata.” Remarks, pages 14. In response, Examiner respectfully disagrees. Specific citations in the prior art were not addressed. Sira discloses comparing KPIs to predetermined thresholds to determine compliant and non-compliant operations (disclosing scoring parameters and rule definitions). Many of Sira disclosures cited disclose metadata (e.g. par 13 facility-specific data; par 24 facility format, size, human resources, inventory, distribution center identifiers, employee numbers). Sailer discloses having subject attributes (e.g. par 38 – disclosing metadata). Sailer also discloses having a compliance where it has controls, artifacts, and requirements (par 52, 54 – disclosing scoring, rule definitions, and metadata).
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. In response, Examiner respectfully disagrees. Specific citations in the prior art were not addressed. Sira discloses showing an organization’s compliance performance (See par 26, FIG. 2). Sailer discloses having compliance in how a “level” of observability, traceability, and access of how features are delivered and managed in their runtime operational environment (See par 50). Sailer further discloses having information on baselines, maintaining, and sharing actionable, up-to-date information about how controls are implemented and assessment of the effectiveness of the customer’s controls (See par 55).
Applicant argues that Sira and Sailer do not disclose “the plurality of input data products comprises data owning system details, a first data product, and data offering details” [Feature D – was claim 2 previously] as the citations from Sira and Sailer because [page 15 of Remarks] 1) “facility-specific data” requires a facility [in Sira] as opposed to details relating to “data owning system, a first data product, and data offering details” [as claimed] and argues on pages 15-16 that 2) “raw data received from compliance systems” [Sira par 18] and “product compliance definitions” & “actively manage security and compliance” [both in Sailer par 58] are each referring to “raw data associated with compliance to some standard, as opposed to details relating to a “data owning system, a data product, or a data offering” [as claimed]. Remarks, pages 15. In response, Examiner respectfully disagrees with this analysis. The second argument, that somehow the claimed “details” are unrelated to compliance to some standard, is not a limitation required by the claim. Furthermore, Applicant’s own examples in a table on page 8 of the publication [after paragraph 96 as published/filed] state details of data offering types can be “raw” and/or “derived.” Returning back to Applicant’s first argument on page 15 of the Remarks, Sira discloses having extrapolated data determined from “raw data” in 102a, 102b, 102c, 102d. The claim only requires some kind of description with regards to “data owning system” and Sira discloses description/details where the it is for an organization in different locations in paragraph 13. Sira also discloses “first data products” in that, similar to Applicant’s examples in the table on page 8 of the publication [after paragraph 96 as published/filed] where data offering type can be “raw” and/or derived, Sira also discloses having extrapolated data determined from “raw data” in 102a, 102b, 102c, 102d. Sailer paragraph 58 was also applied as it has various compliance definitions and includes properties, policy parameters, and selected inventory components which all disclose “details” as well. Examiner notes that “details”, at the end of the claim, are not even used in any of the other limitations in the claim, so this is not very helpful at this time, as it is only describing the data.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
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