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 .
Response to Arguments
101 Rejection
With respect to Applicant’s argument that “The Applicant now submits that amended independent claim 1 recites subject matter that is not directed towards an abstract idea….. wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data;…. and adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow”, Examiner respectfully disagrees.
Examiner cites that the limitation amended limitation “adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow” recites a mental process because human mind can adjust one or more steps of the workflow based on a result of the preceding steps in the workflow by evaluation and judgement/observation of data. For example, human mind can observe the result of a previous step, then mentally decide to change the order, content, or selection of the next step by evaluation and judgment of the workflow steps.
The amended limitation “wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
With respect to Applicant’s argument that “The Applicant respectfully submits that one or more features of amended independent claim 1 cannot be performed/executed by human mind. These steps are inextricably tied to a machine/device, specifically, context-aware data ingestion, enterprise object model population, and dynamic workflow adjustments based on real-time execution outcomes…..enterprise object population, and workflow optimization”, Examiner respectfully disagrees.
Examiner cites that “adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow” recites a mental process because human mind can adjust one or more steps of the workflow based on a result of the preceding steps in the workflow by evaluation and judgement/observation of data. For example, human mind can observe the result of a previous step, then mentally decide to change the order, content, or selection of the next step by evaluation and judgment of the workflow steps. The claimed “adjusting” process does not require any specific machine, hardware, specialized data structure, or technological implementation that would preclude mental performance.
The limitation “mapping the ingested raw data to a plurality of unpopulated enterprise object models to standardize the ingested raw data, wherein the ingested raw data is mapped to the enterprise object models based on a common lexicon” recites a mental process because human mind can map the ingested raw data to populate the enterprise object model based on a common lexicon by evaluating and judging the ingested raw data.
The argument is focused on the limitations reciting the abstract idea. However, as stated in MPEP 2106.04(d) and 2106.04(d)(1) the focus has to be improvements to technology to computer functionality, not improvements solely to an abstract idea. Or as stated in MPEP 2106.05(a), “the judicial exception alone cannot provide the improvement.”
The limitation “ingesting raw data from the plurality of systems of record based on the received request to ingest raw data” and “to populate the enterprise object models” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g) and is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
The limitation “wherein one or more data relationships of the ingested raw data are in the common lexicon” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitations do not recite any improvement to the technology.
With respect to Applicant’s argument that “The Applicant respectfully submits that the subject matter of amended independent claim 1 is not directed to an abstract idea but rather to a technological improvement in data ingestion, enterprise object modeling, and dynamic workflow execution, which could not be performed in the absence of the claimed computing system….. Therefore, the subject matter of independent claim 1 is not similar to the alleged abstract idea but rather presents a technologically advanced, structured, and automated approach to enterprise data processing and workflow execution, ensuring real-time adaptability and operational efficiency”, Examiner respectfully disagrees.
Examiner cites that the following limitations in the independent claims are directed to abstract ideas:
-“mapping the ingested raw data to a plurality of unpopulated enterprise object models to standardize the ingested raw data, wherein the ingested raw data is mapped to the enterprise object models based on a common lexicon” recites a mental process because human mind can map the ingested raw data to the enterprise object model based on a common lexicon by evaluating and judging the ingested raw data.
-“creating one or more objectified view of a workflow by using the standardized data based on the request” recites a mental process because human mind can create a objectified view of a workflow by using the standardized data by evaluating and judging the data.
-“adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow” recites a mental process because human mind can adjust one or more steps of the workflow based on a result of the preceding steps in the workflow by evaluation and judgement/observation of data. For example, human mind can observe the result of a previous step, then mentally decide to change the order, content, or selection of the next step by evaluation and judgment of the workflow steps.
The argument is focused on the limitations reciting the abstract idea. However, as stated in MPEP 2106.04(d) and 2106.04(d)(1) the focus has to be improvements to technology to computer functionality, not improvements solely to an abstract idea. Or as stated in MPEP 2106.05(a), “the judicial exception alone cannot provide the improvement.”
The claims do not recite any specific algorithm, hardware improvement, or technical implementation that would amount to a technological improvement or integrate the abstract idea into a practical application.
With respect to Applicant’s argument that “Regarding Prong Two of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance, even if one were to arrive at a conclusion satisfying the Prong One of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits that the alleged abstract idea is integrated into a practical implementation….. …This also provides an objectified view of a workflow based on standardized data, allowing users to define operational parameters during runtime. This dynamically adjustable workflow enables adaptive and automated process execution without manual reconfiguration. This context-aware machine-driven workflow execution mechanism reduces operational inefficiencies, enhances data-driven automation, and enables real-time adaptability. (See at least paragraphs [0002], [0003], [0014], [0033], [0043], [0046]-[0048], [0050], [0057]-[0059], [0061]-[0065], and [0068] of the published Specification)”, Examiner respectfully disagrees.
Examiner cites that the additional elements are the following:
-“a processor”, “a memory including one or more instructions that when executed by the processor cause the system” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“disparate data sources”, “data access layer”, “external systems” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“receiving a request to ingest raw data from a plurality of external systems of record” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“ingesting raw data from the plurality of systems of record based on the received request to ingest raw data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“wherein one or more data relationships of the ingested raw data are in the common lexicon” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“storing the standardized data and the plurality of populated enterprise object models” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“providing the plurality of populated enterprise object models to the data access layer for accessing the standardized data” recites an insignificant extra solution activity as recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“wherein the workflow comprises of one or more customizable features defined by one or more users during run time of the workflow, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“populating a plurality of enterprise object models with the integrated data based on a user request” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
The claims do not recite any specific algorithm, hardware improvement, or technical implementation that would amount to a technological improvement or integrate the abstract idea into a practical application.
With respect to Applicant’s argument that “Regarding Step 2B, even if one were to arrive at a conclusion satisfying the Step 2A of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits that elements of amended independent claim 1 provide an inventive concept and amounts to significantly more than the exception itself. ….Accordingly, based at least on the above, the subject matter of independent claim 1 provides the following technical advantages. The claimed subject matter enables automated and intelligent ingestion of raw data from multiple external systems of record by utilizing contextual metadata identifiers, ensuring that data is accurately categorized and mapped upon ingestion”, Examiner respectfully disagrees.
Examiner cites that the following limitations are well-known, routine and conventional activities:
-“receiving a request to ingest raw data from a plurality of external systems of record” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“ingesting raw data from the plurality of systems of record based on the received request to ingest raw data” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“storing the standardized data and the plurality of populated enterprise object models” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“populating a plurality of enterprise object models with the integrated data based on a user request” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“providing the plurality of populated enterprise object models to the data access layer for accessing the standardized data” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Moreover, Applicant’s arguments rely on unclaimed advantages and descriptions from the specification, rather than limitations actually recited in the claim. The asserted improvements—such as intelligent ingestion, contextual metadata-driven categorization, dynamic workflow execution, automated mapping, data-driven decision-making, reduced human error, and enhanced agility—are not in the claims. The claim does not specify how any of these asserted improvements are achieved nor does it include any technological mechanism that effects these improvements.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
103 Rejections:
With respect to Applicant’s argument that Chaudhry does not teach the amended limitation “wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data”, Examiner respectfully disagrees.
Examiner cites that Chaudhry teaches in fig. 16, [0109, Context information 1106 may also include one or more identifiers of software application 124A (e.g., the name, version, build number, or any other information suitable for identifying software application 124A)]; which describes that the request comprises context information and the context information context information identifier (contextual metadata identifier). Therefore, Chaudhry teaches the above cited limitation.
For the amended limitation “adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow” new reference Vohra et al. (US 9,595,014) is cited.
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, 4, 8-11, 13, 14, 16-28 are rejected under 35 U.S.C. 101 because of the following reasons:
Claim 1:
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“mapping the ingested raw data to a plurality of unpopulated enterprise object models to standardize the ingested raw data, wherein the ingested raw data is mapped to the enterprise object models based on a common lexicon” recites a mental process because human mind can map the ingested raw data to the enterprise object model based on a common lexicon by evaluating and judging the ingested raw data.
-“creating one or more objectified view of a workflow by using the standardized data based on the request” recites a mental process because human mind can create a objectified view of a workflow by using the standardized data by evaluating and judging the data.
-“adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow” recites a mental process because human mind can adjust one or more steps of the workflow based on a result of the preceding steps in the workflow by evaluation and judgement/observation of data. For example, human mind can observe the result of a previous step, then mentally decide to change the order, content, or selection of the next step by evaluation and judgment of the workflow steps.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“disparate data sources”, “data access layer”, “external systems” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“receiving a request to ingest raw data from a plurality of external systems of record” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“ingesting raw data from the plurality of systems of record based on the received request to ingest raw data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“and to populate the enterprise object models” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“wherein one or more data relationships of the ingested raw data are in the common lexicon” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“storing the standardized data and the plurality of populated enterprise object models” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“providing the plurality of populated enterprise object models to the data access layer for accessing the standardized data” recites an insignificant extra solution activity as recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“wherein the workflow comprises of one or more customizable features defined by one or more users during run time of the workflow, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“receiving a request to ingest raw data from a plurality of external systems of record” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“ingesting raw data from the plurality of systems of record based on the received request to ingest raw data” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“and to populate the enterprise object models” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“storing the standardized data and the plurality of populated enterprise object models” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“providing the plurality of populated enterprise object models to the data access layer for accessing the standardized data” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 4:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“transforming the collected raw data before the data is mapped to the plurality of enterprise object models” recites a mental process because human mind can transform/format the data before mapping it to an object model by evaluating and judging the data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“and wherein the collected and transformed raw data is deleted at a given time frame after a project completion” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 8:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the collected raw data is mapped to the plurality of enterprise object models within a data lake” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 9:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the plurality of external systems of record are the systems of record of a client and each of the plurality of external systems of record is accessible through an application programming interface, and wherein the plurality of external systems of record include one or more of a manufacturing execution system, an enterprise resource planning system, an electronic batch record system, and a quality management system” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 10:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the data access layer is configured to receive one or more electronic validations to validate the provided plurality of enterprise object models” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the data access layer is configured to receive one or more electronic validations to validate the provided plurality of enterprise object models” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 11:
At Step 1:
The claim is directed to a “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating one or more reports based on the integrated data” recites a mental process because human mind can generate a report by evaluating and judging the integrated data.
-“creating one or more objectified view of a workflow by using the standardized data based on the request” recites a mental process because human mind can create a objectified view of a workflow by using the standardized data by evaluating and judging the data.
-“adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow” recites a mental process because human mind can adjust one or more steps of the workflow based on a result of the preceding steps in the workflow by evaluation and judgement/observation of data. For example, human mind can observe the result of a previous step, then mentally decide to change the order, content, or selection of the next step by evaluation and judgment of the workflow steps.
At Step 2A, Prong Two:
The claim recites the following additional elements:
“data access layer”, “external systems”, “integration framework”, “common framework” are generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“ingesting raw data from a plurality of external systems of record based on a received request to ingest raw data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“providing the ingested raw data to an integration framework for integration of the raw data to a plurality of enterprise object models as integrated data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“storing the integrated data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“populating a plurality of enterprise object models with the integrated data based on a user request” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“providing the plurality of populated enterprise object models to a data access layer for accessing the integrated data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“wherein one or more data relationships of the ingested raw data are in the common wherein the integration framework includes a common lexicon for converting raw data to a common framework, wherein one or more data relationships of the ingested raw data are in the common lexicon” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“wherein the workflow comprises of one or more customizable features defined by one or more users during run time of the workflow, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ingesting raw data from a plurality of external systems of record based on a received request to ingest raw data” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“providing the ingested raw data to an integration framework for integration of the raw data to a plurality of enterprise object models as integrated data” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“storing the integrated data” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“populating a plurality of enterprise object models with the integrated data based on a user request” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“providing the plurality of populated enterprise object models to a data access layer for accessing the integrated data” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the plurality of external systems of record include one or more of a manufacturing execution system, an enterprise resource planning system, an electronic batch record system, and a quality management system” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 14:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the ingested raw data is ingested over a particular time frame” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 16:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the ingested raw data is real-time data that is provided to the integration framework for real-time integration of the raw data to the plurality of enterprise object models to provide real time enterprise object models to the data access layer, wherein the data access layer determines how the enterprise object models maps to a database automatically based on one or more rules stored in the data access layer” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 17:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the real time enterprise object models are used to generate a real time report through the data access layer” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 18:
At Step 1:
The claim is directed to a “system” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating one or more reports based on the integrated data” recites a mental process because human mind can generate a report by evaluating and judging the integrated data.
-“creating one or more objectified view of a workflow by using the standardized data based on the request” recites a mental process because human mind can create a objectified view of a workflow by using the standardized data by evaluating and judging the data.
-“adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow” recites a mental process because human mind can adjust one or more steps of the workflow based on a result of the preceding steps in the workflow by evaluation and judgement/observation of data. For example, human mind can observe the result of a previous step, then mentally decide to change the order, content, or selection of the next step by evaluation and judgment of the workflow steps.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“a processor”, “a memory including one or more instructions that when executed by the processor cause the system” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“data access layer”, “external systems”, “integration framework”, “common framework” are generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“ingest raw data from at least one of the plurality of systems of record based on a received request to ingest raw data, wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“provide the ingested raw data to an integration framework for integration of the raw data to a plurality of enterprise object models as integrated data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“store the integrated data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“populate a plurality of enterprise object models with the integrated data based on a user request” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“provide the plurality of populated enterprise object models to a data access layer for accessing the integrated data and generating one or more reports based on the integrated data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“wherein one or more data relationships of the ingested raw data are in the common wherein the integration framework includes a common lexicon for converting raw data to a common framework, wherein one or more data relationships of the ingested raw data are in the common lexicon” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“wherein the workflow comprises of one or more customizable features defined by one or more users during run time of the workflow, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ingest raw data from at least one of the plurality of systems of record based on a received request to ingest raw data, wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“provide the ingested raw data to an integration framework for integration of the raw data to a plurality of enterprise object models as integrated data” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“store the integrated data” i is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“provide the plurality of populated enterprise object models to a data access layer for accessing the integrated data and generating one or more reports based on the integrated data” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 19:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the data access layer is configured to receive one or more electronic validations to validate the provided plurality of enterprise object models” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the data access layer is configured to receive one or more electronic validations to validate the provided plurality of enterprise object models” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 20:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the ingested raw data is integrated to the plurality of enterprise object models within a data lake and the ingestion of the raw data is based on a multi-factor authentication” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Claim 21:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“auditing the raw data to monitor one or more services performed by the plurality of external systems to locate a source of an error associated with the raw data” recites a mental process because human mind can audit the raw data to monitor and find an error associated with the raw data by evaluating and judging the data.
-“allocating one or more tasks to generate one or more schedules and assign one or more tasks” recites a mental process because human mind can allocate, generate schedule and assigning one or more tasks by evaluating and judging the data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“providing access to the workflow to define customizable features” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’.
-“and wherein the customizable features are at least one of: charts, graphs, and texts” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“autopopulating the one or more enterprise object models for viewing in a report, wherein the enterprise object models are autopopulated based on one or more intents associated with the ingested raw data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“providing access to the workflow to define customizable features” is well-understood, routine and conventional activities (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“autopopulating the one or more enterprise object models for viewing in a report, wherein the enterprise object models are autopopulated based on one or more intents associated with the ingested raw data” is well-understood, routine and conventional activities (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 22:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein creating the one or more objectified view of the workflow comprises creating at least one of: a work process, work products, behaviors, outcomes, and other data” recites a mental process because human mind can create a workflow process by evaluating and judging the data.
Claim 23:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the plurality of external systems of record includes one or more of a manufacturing execution system, an enterprise resource planning system, an electronic batch record system, and a quality management system” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 24:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“track one or more services performed by the one or more users of the system” recites a mental process because human mind can track one or more services by the users by observing and evaluating the data.
-“translate the raw data into a standardize format” recites a mental process because human mind can analyze raw data and translate the raw data into a standardize format by evaluate and judging the raw data.
-“and determine whether the one or more users are configured to access one or more parts of the integrated data” recites a mental process because human mind can determine whether the users can access one or more parts of the integrated data by evaluating the users data and judging the users data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“retrieve the stored integrated data” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“provide one or more identity services” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“store the one or more relationships of the raw data in the common lexicon” recites an insignificant extra solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“retrieve the stored integrated data” is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“provide one or more identity services” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“store the one or more relationships of the raw data in the common lexicon” is well-understood, routine and conventional, (WURC), see MPEP 2106.05(d)(II) "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 25:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“allocate one or more tasks, generate one or more schedules, and assign one or more tasks” recites a mental process because human mind can allocate tasks, generate schedules and assign one or more tasks by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the system further comprises a workflow engine” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 26:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the one or more instructions which when executed by the processor further cause the system to remove the ingested raw data after the generation of the one or more reports” recites an insignificant extra solution activity as “Selecting a particular data source or type of data to be manipulated”. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the one or more instructions which when executed by the processor further cause the system to remove the ingested raw data after the generation of the one or more reports” recites an insignificant extra solution activity as “Selecting a particular data source or type of data to be manipulated” similar to iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 27:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“at least one of map, sort, and label the ingested raw data after the generation of the one or more reports” recites a mental process because human mind can map/sort/label data based on evaluating and judging the data after the report is generated by looking at the report.
Claim 28:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining, based on metadata associated with the received request, whether the ingested raw data is in the plurality of external systems of record” recites a mental process because human mind can determine whether the ingested raw data is in the plurality of external systems of record by evaluation and judgement.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 11, 18, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014).
With respect to claim 1, Beymer teaches a method of accessing data from disparate data sources at a data access layer, the method comprising ([0054, Search and access may be supported through a small set of APIs], [0055, patient data is ingested from a plurality of data sources]; examiner’s note: the data is accessed from multiple sources via API (access layer)):
ingesting raw data from the plurality of external system of record ([0032, including PatientMeasurementsModel (holds any structured measurements from external sources)], [0042, As part of the ingestion process, cross-references among models are filled-up], [0055, a plurality of data models is populated based on the ingested patient data]; examiner’s note: ingesting data (raw data because the data is standardized later) from plurality of systems/sources which includes external system of record);
mapping the ingested raw data to a plurality of unpopulated enterprise object models [0032, each analytic knows which models to populate in the current analytics pipelines], [0043, ALPR models are populated in the order given, filling up foreign key references using search keys]; examiner’s note: the system knows which model to populate (map) to standardize ([0011, which shred the abstract data type elements according to their data types, conforming to corresponding schemas 106 in corresponding data models], [0041, The models described herein are populated from incoming data]; examiner’s note: the data is corrected to conform to a specific schema) the ingested raw data and to populate the enterprise object models ([0054, desired model may be populated as much as one knows, and all models that satisfy those criteria are retrieved], [0055, a plurality of data models is populated based on the ingested patient data, each data model comprising an abstract data type]; examiner’s note: the healthcare system model is enterprise object models and the models are populated by the ingested raw data, the ingested data is reviewed to conform to a specific format (standardized));
storing the standardized data and the plurality of populated enterprise object models ([0008, a generalized distributed framework for parallel search and retrieval of unstructured and structured patient data], [0011, the information to be modeled is represented programmatically through abstract data types and stored in the banks as indexed documents 105], [0015, Each storable data model is stored as a single document], [0048, information is stored in each document per model], [0055, patient data is ingested from a plurality of data sources. At 402, a plurality of data models is populated based on the ingested patient data]; examiner’s note: the data and models are stored); and
providing the plurality of populated enterprise object models to the data access layer for accessing the standardized data ([0054, Search and access may be supported through a small set of APIs. Search may be achieved through a common API for accessing abstract data types of KnowledgeModel class. In various embodiments, a common search engine is leveraged across all banks]; examiner’s note: the models are provided to the user via visualization module/API (access layer)).
Beymer does not explicitly teach receiving a request to ingest raw data from a plurality of external systems of record, ingesting raw data from the plurality of systems of record based on the received request to ingest raw data, wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data; creating one or more objectified view of a workflow by using the standardized data based on the request; wherein the ingested raw data is mapped to the enterprise object models based on a common lexicon, wherein one or more data relationships of the ingested raw data are in the common lexicon; wherein the workflow comprises of one or more customizable features, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions and adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow.
However, Parker teaches wherein the ingested raw data is mapped to the enterprise object models based on a common lexicon, wherein one or more data relationships of the ingested raw data are in the common lexicon ([0027, first mapping the different nomenclature used by various providers into a common lexicon for those offerings, a structure that defines and organizes the offerings in a comprehensible way according to relationships defined within the common lexicon]; examiner’s note: the data (ingested raw data) is mapped to a model (enterprise data model) based on a common lexicon and the relationships are defined in the common lexicons).
One of ordinary skill in the art would recognize that incorporating mapping of raw data to an enterprise object model based on a common lexicon and relationships of data of the common lexicons of Parker into the invention of Beymer to create the object models using the common lexicons and relationships of the common lexicons. Beymer, Parker are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate functionalities of Parker into the system of Beymer to have a system to which will map data according to a common lexicons and relationships. The motivation would be to have data consistency and clarity that ensures that the terms and definitions are used uniformly across the model, reducing ambiguity and also to speeds up the system because commonly used terms and relationships can be readily adapted and also to reduces model error ([0027, a model to provide a structure that defines and organizes the offerings in a comprehensible way]).
Beymer, Parker do not explicitly teach receiving a request to ingest raw data from a plurality of systems of record; ingesting raw data from the plurality of external systems of record based on the received request to ingest raw data, wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data; creating one or more objectified view of a workflow by using the standardized data based on the request, wherein the workflow comprises of one or more customizable features, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions.
However, Chaudhry teaches receiving a request to ingest raw data from a plurality of systems of record ([0058, a request for data from a social networking application, etc.)], [0069, providing requested input data to executing workflows]; examiner’s note: receiving a request to input data (ingest)), wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data (fig. 16, [0109, Context information 1106 may also include one or more identifiers of software application 124A (e.g., the name, version, build number, or any other information suitable for identifying software application 124A)]; examiner’s note: the request comprises context information and the context information context information identifier (contextual metadata identifier));
ingesting raw data from the plurality of external systems of record based on the received request to ingest raw data ([0058, a request for data from a social networking application, etc.)], [0069, providing requested input data to executing workflows]; examiner’s note: receiving a request to input data (ingest) from multiple sources, Beymer also teaches external systems in [0032]);
creating one or more objectified view of a workflow by using the standardized data based on the request ([0055, workflow definition information 316 may be generated to contain information in the format of a JSON], [0058, a developer is enabled to select workflow step 502 from a list or library of workflow steps], [0094, Create a new flow” within software application GUI 1122], [0122, workflow templates and/or workflow steps and create workflows than she otherwise would be]; examiner’s note: the workflow includes multiple workflow steps and presented in the GUI (objectified view) and the workflow is created with the JSON format standardized data based on the request to create a workflow);
wherein the workflow comprises of one or more customizable features defined by one or more users during run time of the workflow ([0038, provide data 132 to workflows developed using workflow designer 106 when such workflows are executed at runtime], [0063, the credentials may be requested of a user during runtime], [0084, workflow customizer 1102 can select workflow templates and/or workflow steps for presentation to the user that are more likely to be useful and/or of interest to the user]; examiner’s note: the workflow steps (features) are customizable during the runtime of the workflow by the user), and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions ([0049, saved workflow selector 304 may display a list of saved workflows, may enable navigation to a saved workflow, and/or may provide another mechanism for selecting a saved workflow for editing], [0055, Workflow definition information 316 includes information that defines the sequence and operation of the workflow of workflow logic (e.g., lists the workflow step operations and their ordering/sequencing) and includes the parameter values for the workflow]; examiner’s note: the workflow steps (customizable features) which are defined using workflow definitions (base workflow definitions) and modified workflow templates are generated (workflow definitions))
One of ordinary skill in the art would recognize that incorporating a request to ingest data from external system, customizable workflow features using workflow definitions defined by user during runtime of the workflow of Chaudhry into the invention of Beymer/Parker to create workflows with customizable features according to workflow definitions and ingest data when the request is added to ingest data. Beymer, Parker, Chaudhry are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate functionalities of Chaudhry into the system of Beymer/Parker to have a system which will have the ability to customize workflow features and ingest raw data in response to a request. The motivation would be to have efficiency of the workflow, consistency of a business process, flexibility of changing data and improved maintenance (Chaudhury, [0122, workflow step(s) provide a technical effect in that they can improve the user experience when using the workflow development system because the user is enabled to more quickly locate desired workflow templates and/or workflow steps and create workflows than she otherwise would be]).
Beymer, Parker and Chaudhry do not explicitly teach adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow.
However, Vohra teaches adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow ([col. 5, lines 25-30, “if the successor tasks are dependent upon the predecessor tasks, the data and/or parameters of the dependent successor tasks are updated accordingly. The predecessor tasks are then removed from the new (or modified) workflow”], modifying (adjusting) the workflow sequence based on preceding steps of the workflow).
One of ordinary skill in the art would recognize that incorporating adjusting the workflow steps based on the preceding results of the workflow steps of Vohra into the invention of Beymer/Parker/Chaudhry to create efficient workflows. Beymer, Parker, Chaudhry, Vohra are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate functionalities of Vohra into the system of Beymer/Parker/Chaudhry to have a system which will have the ability to adjust workflow steps based on a results of a preceding workflow steps. The motivation would be to have increase efficiency of the workflow, reduce processing time of the flow, dynamic adaption and improved maintenance of the system (Vohra, [col. 6, lines 1-5, “The workflow instance in execution does not need to be interrupted while changes are made to the workflow. The execution of the workflow instance is not suspended or made idle while changes are made to the workflow”]).
With respect to claim 11, Beymer teaches a system for providing processed data to a data access layer for the generation of a data-based product, the system comprising ([0054, Search and access may be supported through a small set of APIs], [0055, patient data is ingested from a plurality of data sources]; examiner’s note: the data is accessed from multiple sources via API (access layer)):
a processor communicatively coupled to a plurality of external systems of record ([0055, patient data is ingested from a plurality of data sources], [0060, one or more processors or processing units 16]; examiner’s note: the plurality of external systems coupled to memory and processor);
a memory including one or more instructions that when executed by the processor cause the system to ([0053, remote computer storage media including memory storage devices], [0054, operatively connected to a memory device]; examiner’s note: the plurality of external systems coupled to memory and processor):
ingest raw data from at least one of the plurality of external systems of record ([0032, including PatientMeasurementsModel (holds any structured measurements from external sources)], [0042, As part of the ingestion process, cross-references among models are filled-up], [0055, a plurality of data models is populated based on the ingested patient data]; examiner’s note: ingesting data (raw data because the data is standardized later) from plurality of systems/sources which includes external system of record);
provide the ingested raw data to an integration framework for integration of the raw data to a plurality of enterprise object models as integrated data ([0027, The results returned are merged], [0054, desired model may be populated as much as one knows, and all models that satisfy those criteria are retrieved], [0055, a plurality of data models is populated based on the ingested patient data, each data model comprising an abstract data type]; examiner’s note: the healthcare system model is enterprise object models and the models are populated by the ingested raw data, data from multiple sources are the integration data, the ingesting module is the integration framework);
store the integrated data ([0008, a generalized distributed framework for parallel search and retrieval of unstructured and structured patient data], [0011, the information to be modeled is represented programmatically through abstract data types and stored in the banks as indexed documents 105], [0015, Each storable data model is stored as a single document], [0048, information is stored in each document per model], [0055, patient data is ingested from a plurality of data sources. At 402, a plurality of data models is populated based on the ingested patient data]; examiner’s note: the data and models are stored);
populate a plurality of enterprise object models with the integrated data ([0024, including fully populated data models], [0032, each analytic knows which models to populate in the current analytics pipelines], [0041, The models described herein are populated from incoming data], [0043, ALPR models are populated in the order given]; examiner’s note: the system knows which model to populate);
provide the plurality of populated enterprise object models to a data access layer for accessing the integrated data and generating one or more reports based on the integrated data ([0038, Reports (e.g., Diagnostic reports including pathology, cardiology, etc., Clinical history notes, admission, discharge summaries), Extracted measurements (e.g., from Labs, analysis of reports, imaging, etc.)], [0054, Search may be achieved through a common API for accessing abstract data types of KnowledgeModel class. In various embodiments, a common search engine is leveraged across all banks]; examiner’s note: the models are provided to the user via visualization module/API (access layer) and reports are generated from the data that is integrated).
Beymer does not explicitly teach ingest data based on a received request to ingest raw data, wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data; populate models based on a user request; wherein the integration framework includes a common lexicon for converting raw data to a common framework, wherein one or more data relationships of the ingested raw data are in the common lexicon; creating one or more objectified view of a workflow by using the standardized data based on the request, wherein the workflow comprises of one or more customizable features defined by one or more users during run time of the workflow, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions; adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow.
However, Parker teaches wherein the integration framework includes a common lexicon for converting raw data to a common framework, wherein one or more data relationships of the ingested raw data are in the common lexicon ([0027, first mapping the different nomenclature used by various providers into a common lexicon for those offerings, a structure that defines and organizes the offerings in a comprehensible way according to relationships defined within the common lexicon]; examiner’s note: the data (ingested raw data) is mapped to a model (enterprise data model) based on a common lexicon and the relationships are defined in the common lexicons).
One of ordinary skill in the art would recognize that incorporating converting raw data to an enterprise object model based on a common lexicon and relationships of data of the common lexicons of Parker into the invention of Beymer to create the object models using the common lexicons and relationships of the common lexicons. Beymer, Parker are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate functionalities of Parker into the system of Beymer to have a system to which will map data according to a common lexicons and relationships. The motivation would be to have data consistency and clarity that ensures that the terms and definitions are used uniformly across the model, reducing ambiguity and also to speeds up the system because commonly used terms and relationships can be readily adapted and also to reduces model error ([0027, a model to provide a structure that defines and organizes the offerings in a comprehensible way]).
Beymer, Parker do not explicitly teach ingest data based on a received request to ingest raw data, wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data; populate models based on a user request; creating one or more objectified view of a workflow by using the standardized data based on the request, wherein the workflow comprises of one or more customizable features, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions; and adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow.
However, Chaudhry teaches ingest data based on a received request to ingest raw data ([0058, a request for data from a social networking application, etc.)], [0069, providing requested input data to executing workflows]; examiner’s note: receiving a request to input data (ingest)), wherein the request comprises of at least one contextual metadata identifier associated with the ingested raw data (fig. 16, [0109, Context information 1106 may also include one or more identifiers of software application 124A (e.g., the name, version, build number, or any other information suitable for identifying software application 124A)]; examiner’s note: the request comprises context information and the context information context information identifier (contextual metadata identifier));
populate models based on a user request ([0058, a request for data from a social networking application, etc.)], [0069, providing requested input data to executing workflows], [0086, Workflow customizer 1102 may also be configured to pre-populate (e.g., automatically populate)]; examiner’s note: receiving a request to populate models based on a request);
creating one or more objectified view of a workflow by using the standardized data based on the request ([0055, workflow definition information 316 may be generated to contain information in the format of a JSON], [0058, a developer is enabled to select workflow step 502 from a list or library of workflow steps], [0094, Create a new flow” within software application GUI 1122], [0122, workflow templates and/or workflow steps and create workflows than she otherwise would be]; examiner’s note: the workflow includes multiple workflow steps and presented in the GUI (objectified view) and the workflow is created with the JSON format standardized data based on the request to create a workflow);
wherein the workflow comprises of one or more customizable features, and wherein the customizable features of the workflow is defined using a base definition of the workflow for generating one or more workflow definitions ([0049, saved workflow selector 304 may display a list of saved workflows, may enable navigation to a saved workflow, and/or may provide another mechanism for selecting a saved workflow for editing], [0055, Workflow definition information 316 includes information that defines the sequence and operation of the workflow of workflow logic (e.g., lists the workflow step operations and their ordering/sequencing) and includes the parameter values for the workflow]; examiner’s note: the workflow steps (customizable features) which are defined using workflow definitions (base workflow definitions) and modified workflow templates are generated (workflow definitions)).
One of ordinary skill in the art would recognize that incorporating a request to ingest data from external system, customizable workflow features using workflow definitions defined by user during runtime of the workflow of Chaudhry into the invention of Beymer/Parker to create workflows with customizable features according to workflow definitions and ingest data when the request is added to ingest data. Beymer, Parker, Chaudhry are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate functionalities of Chaudhry into the system of Beymer/Parker to have a system which will have the ability to customize workflow features and ingest raw data in response to a request. The motivation would be to have efficiency of the workflow, consistency of a business process, flexibility of changing data and improved maintenance (Chaudhury, [0122, workflow step(s) provide a technical effect in that they can improve the user experience when using the workflow development system because the user is enabled to more quickly locate desired workflow templates and/or workflow steps and create workflows than she otherwise would be]).
Beymer, Parker and Chaudhury do not explicitly teach adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow.
However, Vohra teaches adjusting one or more steps of the workflow in a sequence based on a result of one or more preceding steps in the workflow ([col. 5, lines 25-30, “if the successor tasks are dependent upon the predecessor tasks, the data and/or parameters of the dependent successor tasks are updated accordingly. The predecessor tasks are then removed from the new (or modified) workflow”], modifying (adjusting) the workflow sequence based on preceding steps of the workflow).
One of ordinary skill in the art would recognize that incorporating adjusting the workflow steps based on the preceding results of the workflow steps of Vohra into the invention of Beymer/Parker/Chaudhry to create efficient workflows. Beymer, Parker, Chaudhry, Vohra are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate functionalities of Vohra into the system of Beymer/Parker/Chaudhry to have a system which will have the ability to adjust workflow steps based on a results of a preceding workflow steps. The motivation would be to have increase efficiency of the workflow, reduce processing time of the flow, dynamic adaption and improved maintenance of the system (Vohra, [col. 6, lines 1-5, “The workflow instance in execution does not need to be interrupted while changes are made to the workflow. The execution of the workflow instance is not suspended or made idle while changes are made to the workflow”]).
Claim 18 encompasses the same scope of invention of claim 11, in additions of a processor, a memory storing instructions (fig. 1). Therefore, claim 18 is rejection on the same basis of rejection of claim 11.
With respect to claim 22, Beymer, Parker, Chaudhry, Vohra in combination teach the method of claim 1, but does not explicitly teach wherein creating the one or more objectified view of the workflow comprises creating at least one of: a work process, work products, behaviors, outcomes, and other data.
However, Chaudhry further teaches wherein creating the one or more objectified view of the workflow comprises creating at least one of: a work process, work products, behaviors, outcomes, and other data ([0076, workflow logic 120 performs its functions, such as processing orders, tracking information, generating messages, processing documents to generate tasks or information, collecting feedback, and/or any other functions]; examiner’s note: the workflow includes steps/feedback/other information (other data/outcomes/work process)). One of ordinary skill in the art would recognize that incorporating creating a work process/other data of Chaudhry into the ingestion and populating models of Beymer/Parker/Vohra to create a work process/other data to understand a workflow better. Beymer, Parker, Chaudhry, Vohra are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate feature of Chaudhry into the system of Beymer, Parker, Vohra to have a system to create work process. The motivation to have a clear structure of the workflow for better understanding and visualization of the process to detect any error faster and update the system faster (Chaudhry, [0122, This feature can also provide a technical effect in that the performance of the underlying computers upon which the workflow development system is implemented is improved]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Boggs (US 2007/0036077).
With respect to claim 4, Beymer, Parker, Chaudhry in combination teach the method of claim 1, Beymer further teaches further comprising transforming the collected raw data before the data is mapped to the plurality of enterprise object models ([0011, conforming to corresponding schemas 106 in corresponding data models]; examiner’s note: the transforming the data before it is mapped to the models) but does not explicitly teach wherein the collected and transformed raw data is deleted at a given time frame after a project completion.
However, Boggs teaches wherein the collected and transformed raw data is deleted at a given time frame after a project completion ([0123, Once processing of the data has been completed, the data is stored for a second time period before it is deleted from the system]; examiner’s note: the deletion of data after a time period when the data is no longer needed (after a project completion)). One of ordinary skill in the art would recognize that incorporating deleting record of Boggs into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to delete records when no longer needed. Beymer, Parker, Chaudhry, Boggs are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Boggs into the system of Beymer/Parker/Chaudhry/Vohra to have a system which will delete records after use. The motivation to delete records after the completion of a project to save space of the system and also to save cost of the system to store data (Boggs, [0009, improve economic efficiency by more accurately matching network equipment deployments to meet the service level requirements]).
Claim(s) 8, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Jorasch et. al. (US 2021/0373676).
With respect to claim 8, Beymer, Parker, Chaudhry, Vohra in combination teach the system of claim 1, but do not explicitly teach wherein the collected raw data is mapped to the plurality of enterprise object models within a data lake.
However, Jorasch teaches wherein the collected raw data is mapped to the plurality of enterprise object models within a data lake ([0073, such as the Amazon® Simple Storage Service (Amazon® S3™) available from Amazon®]; examiner’s note: the amazon 3 is a datalake).
One of ordinary skill in the art would recognize that incorporating a data lake to storage data of Jorasch into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to have to store vast amount of data. Beymer, Parker, Chaudhry, Vohra, Jorasch are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Jorasch into the system of Beymer/Parker/Chaudhry/Vohra to have a robust system. The motivation would be to have to store large amount of data to make the system more efficient (Jorasch, [0004, Various embodiments allow for improved control of presentation content, and/or enhanced engagement of presentation participants]).
With respect to claim 20, Beymer, Parker, Chaudhry, Vohra in combination teach the system of claim 18, but do not explicitly teach wherein the ingested raw data is integrated to the plurality of enterprise object models within a data lake; the ingestion of the raw data is based on a multi-factor authentication.
However, Jorasch teaches wherein the ingested raw data is integrated to the plurality of enterprise object models within a data lake ([0073, such as the Amazon® Simple Storage Service (Amazon® S3™) available from Amazon®]; examiner’s note: the amazon 3 is a datalake); the ingestion of the raw data is based on a multi-factor authentication ([1208, as part of a multi-factor authentication process]; examiner’s note: multi-factor authentication for data).
One of ordinary skill in the art would recognize that incorporating a data lake to storage data and have a multi-factor authentication of Jorasch into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to have data security and also to store vast amount of data. Beymer, Parker, Chaudhry, Vohra, Jorasch are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Jorasch into the system of Beymer/Parker/Chaudhry/Vohra to have a robust system. The motivation would be to have a secure system to protect data integrity and also to store large amount of data to make the system more efficient (Jorasch, [0004, Various embodiments allow for improved control of presentation content, and/or enhanced engagement of presentation participants]).
Claim(s) 10, 19, 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Rao et al. (US 2020/0028691)
With respect to claim 10, Beymer, Parker, Chaudhry, Vohra in combination teach the method of claim 1, but do not explicitly teach wherein the data access layer is configured to receive one or more electronic validations to validate the provided plurality of enterprise object models.
However, Rao teaches wherein the data access layer is configured to receive one or more electronic validations to validate the provided plurality of enterprise object models ([0006, associated validation rules to be performed by at least selected ones of the plurality of different entities], [0007, a validation of an approval by the trusted agent for each set of actions wherein the validation requires at least one level of approval by the trusted agent]; examiner’s note: validating the entities (models)).
One of ordinary skill in the art would recognize that incorporating validating models of Rao into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to have data security. Beymer, Parker, Chaudhry, Vohra, Rao are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Rao into the system of Beymer/Parker/Chaudhry/Vohra to have a robust system. The motivation would be to have a secure system to protect data models and also to have more system more efficient (Rao, [0003, ensure validations, reliability, efficiency and safety at all times], [0060, workflow contracts will ensure the secure loading of LSAPs from a remote system]).
Claim 19 is rejected on the same basis of rejection of claim 10.
With respect to claim 25, Beymer, Parker, Chaudhry, Vohra in combination teach the system of claim 18, but do not in combination teach a workflow engine, wherein the workflow engine is further configured to: allocate one or more tasks, generate one or more schedules, generate one or more schedules, assign one or more tasks.
However, Rao teaches wherein the system further comprises a workflow engine, wherein the workflow engine is further configured to: allocate one or more tasks, generate one or more schedules ([0060, the list of tasks but it is contemplated that the particular list of tasks can be changed], [0061, A request at task 610 may include the maintenance manager creating 612 the work order for the LSAP], [0068, stores intra-task functions for scheduling activities, scheduling of software uploads]; examiner’s note: the allocation of tasks and scheduling tasks), and assign one or more tasks ([0052, work procedures relating to maintenance tasks which can be distributed across specialized a set of TrustFlow network entities designated as trusted agents], [0061, A request at task 610 may include the maintenance manager creating 612 the work order for the LSAP module installation and a selected work procedure is executed for the maintenance technician]; examiner’s note: assigning tasks to agents). One of ordinary skill in the art would recognize that incorporating allocating, assigning tasks and generating one or more tasks of Rao into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to assign tasks/schedules and generate schedule for users. Beymer, Parker, Chaudhry, Vohra, Rao are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate feature of Rao into the system of Beymer, Parker, Chaudhry, Vohra to have a system to make the workflow process more efficient. The motivation would be to improve efficiency, better organization, enhance time management and improved communication of a system (Rao, [0003, ensure validations, reliability, efficiency and safety at all times], [0060, workflow contracts will ensure the secure loading of LSAPs from a remote system]).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et.al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Shoroff et al. (US 6,381,602).
With respect to claim 24, Beymer, Parker, Chaudhry, Vohra in combination teach the system of claim 18, Beymer further teaches wherein the one or more instructions which when executed by the processor further cause the system to: retrieve the stored integrated data ([0008, search and retrieval across such data sources], [0024, Search operations on a single index may include various options]; examiner’s note: the storage module can store data to retrieve data), Parker teaches store the one or more relationships of the raw data in the common lexicon ([0027, first mapping the different nomenclature used by various providers into a common lexicon for those offerings]; examiner’s note: relationships of data in a lexicon), Chaudhry teaches track one or more services performed by the users of the system ([0109, monitoring a user's actions when interacting with software application 124A]; examiner’s note: tracks users activities/services) .
Beymer, Parker, Chaudhry, Vohra in combination do not explicitly teach provide one or more identity services; translate the raw data into a standardize format, determine whether the one or more users are configured to access one or more parts of the integrated data.
However, Shoroff teaches provide one or more identity services ([col. 10, lines 10-15, “ Since user contexts and security identifiers compatible with access control system 72 consist of the user's mailbox name and any distribution lists that contain the mailbox, data server B 64 determines which mailbox has as its primary user "accounting.backslash.tammy"]; examiner’s note: the access control system provides identity services); translate the raw data into a standardize format ([col. 3, lines 17-20, “translates the user context that identifies the user to a format that is compatible with the security provider"]; examiner’s note the translate data into a standard format), determine whether the one or more users are configured to access one or more parts of the integrated data ([col. 10, lines 10-15, “col. 10, lines 10-15, “ Since user contexts and security identifiers compatible with access control system 72 consist of the user's mailbox name and any distribution lists that contain the mailbox, data server B 64 determines which mailbox has as its primary user "accounting.backslash.tammy"]; examiner’s note: the access control system provides identity services). Beymer, Parker, Chaudhry, Vohra, Shoroff are analogous art because each art teaches data models.
One of ordinary skill in the art would recognize that incorporating translating the data into a standardize format and providing identity services and determining which uses can access data of Soroff into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to have data security and also to translate data into a standard format to access the data faster. Beymer, Parker, Chaudhry, Vohra, Shoroff are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Soroff into the system of Beymer/Parker/Chaudhry/Vohra to have a system which will provide better data security and also provide faster readability and accessibility of the data. The motivation would be to have a secure system to protect data integrity and also to have standard data format for seamless integration (Shoroff; col. 11, lines 62-63, “The caching feature is particularly beneficial when access control information 98 is associated with a folder and applies to multiple documents”]).
Claim(s) 9, 13, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Brooks et al. (US 2021/0335467).
With respect to claim 9, Beymer, Parker, Chaudhry, Vohra in combination teach the method of claim 1, Beymer further teaches wherein the plurality of systems of record are the systems of record of a client and each of the plurality of external system systems of record is accessible through an application programming interface ([0054, Search may be achieved through a common API for accessing abstract data types of KnowledgeModel class. In various embodiments, a common search engine is leveraged across all banks]; examiner’s note: the API to access records) but does not explicitly teach wherein the external systems of record include one or more of a manufacturing execution system, an enterprise resource planning system, an electronic batch record system, and a quality management system.
However, Brooks teaches wherein the external systems of record include one or more of a manufacturing execution system, an enterprise resource planning system, an electronic batch record system, and a quality management system ([1103, a framework program that integrates an enterprise resource planning (ERP)], [1109, electronic batch records]; examiner’s note: electronic batch data record system/ERP). One of ordinary skill in the art would recognize that incorporating different types of record systems of Brooks into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to have multiple data record systems. Beymer, Parker, Chaudhry, Vohra, Brooks are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Brooks into the system of Beymer/Parker/Chaudhry/Vohra to have an efficient system. The motivation to have multiple data records system to access and process data from multiple systems to integrate various business functions to a unified platform to have data consistency, easy access and also to reduce cost of a system (Brooks, [1103, an enterprise resource planning (ERP) system that enables automation of logistical tasks and a manufacturing execution system (MES) that enables automation of manufacturing tasks]).
Claim 13 is rejected on the same basis of rejection of claim 9.
Claim 23 is rejected on the same basis of rejection of claim 9.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et,al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Kelly et. al. (US 2022/0114491).
With respect to claim 14, Beymer, Parker, Chaudhry, Vohra in combination teach the method of claim 1, but do not explicitly teach wherein the collected raw data is collected over a specified time frame.
However, Kelly teaches wherein the collected raw data is collected over a specified time frame ([0094, the predetermined amount of time may be any time length that allows the collection of data found useful for training of the machine learning data], [0107, predetermined amount of time may be any time length that allows the collection of data for training the machine learning model, e.g., one day, one week, one month, one year, etc.]; examiner’s note: the data is collected over a specified period of time). One of ordinary skill in the art would recognize that incorporating specified time frames to collected data of Kelly into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to have a specified time frame for data collection. Beymer, Parker, Chaudhry, Vohra, Kelly are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Kelly into the system of Beymer/Parker/Chaudhry/Vohra to have a robust system. The motivation would be to collect data over a specified time period to process the data to identify multiple business strategies i.e. seasonal trends, for risk assessments and improvements of the system (Kelly, [0033, the enhanced operation of machine learning systems], [0094, the first three weeks may be used as the test dataset and the last week used as the validation dataset, or other arrangement that can be used for a test dataset and a validation dataset]).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et,al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Allan et al. (US 2018/0052898).
With respect to claim 16, Beymer, Parker, Chaudhry, Vohra in combination teach the method of claim 11, but do not explicitly teach wherein the ingested raw data is real-time data that is provided to the integration framework for real-time integration of the raw data to the plurality of enterprise object models to provide real time enterprise object models to the data access layer.
However, Allan teaches wherein the ingested raw data is real-time data that is provided to the integration framework for real-time integration of the raw data to the plurality of enterprise object models to provide real time enterprise object models to the data access layer ([0085, processing of real time data and batch data within distinct batch and real time layers], [0376, Data flows can be decomposed into a model describing transformations of data], [0384, a model can be generated for the process]; examiner’s note: the data is provided for integration in real-time to provide real time enterprise object models to the data access layer), wherein the data access layer determines how the enterprise object models maps to a database automatically based on one or more rules stored in the data access layer ([0302, auto-mapping can be driven by a metadata, schema, and statistical profiling of a dataset; and used to map a source dataset or entity associated with an input HUB], the mapping is based on schema, metadata, statistical profiling (rules)). Beymer, Parker, Chaudhry, Vohra,Allan are analogous art because each art teaches workflow models.
One of ordinary skill in the art would recognize that incorporating real-time data into the integration framework for real time integration of object models to the data access layer of Allan into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to have real time integration of data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Allan into the system of Beymer/Parker/Chaudhry/Vohra to have a robust system. The motivation would be to have real-time integration to have consistent and updated data all the times to increase customer experience, enhance operational performance (Allan, [0304, enable a user to focus on simplification of a dataflow application]).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et,al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Bingham et al. (US 2017/0286499).
With respect to claim 17, Beymer, Parker, Chaudhry, Vohra in combination teach the method of claim 16, but do not explicitly teach wherein the real time enterprise object models are used to generate a real time report through the data access layer.
However, Bingham teaches wherein the real time enterprise object models are used to generate a real time report through the data access layer ([0081, real-time system loads], [0228, a time window for which the report is to provide real-time data], [0230, a reviewer may request a real-time report that is generated based on the time window entered by the reviewer]; examiner’s note: generating a report through the GUI (access layer) with the real time enterprise object models). One of ordinary skill in the art would recognize that incorporating real-time report generation with the real time enterprise object models through the data access layer of Bingham into the ingestion and populating models of Beymer/Parker/Chaudhry to have real time report of real-time enterprise object models. Beymer, Parker, Chaudhry, Vohra, Bingham are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Bingham into the system of Beymer/Parker/Chaudhry/Vohra to have a system which will provide real-time reports. The motivation would be to have real-time integration to have consistent and updated reports all the times to increase customer experience, enhance operational performance (Bingham, [0185, such operation (e.g., improve an overall efficiency or improve an efficiency pertaining to a particular type of event)]).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view of Parker et al. (US 2020/0106697) and in view of Chaudhry et.al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of McBride et al. (US 2018/0210781).
With respect to claim 21, Beymer, Parker, Chaudhry, Vohra in combination teach the system of claim 1, Beymer teaches autopopulating the one or more enterprise object models for viewing in a report ([0024, Various desired outputs are available, including fully populated data models]; [0041, the models described herein are populated from incoming data. Data may be drawn from custom adapters, HL7 ingestion, DICOM ingestion, or Patient ID Merge], examiner’s note: auto populating fields (models) to view), wherein the enterprise object models are autopopulated based on one or more intents associated with the ingested raw data ([0032],[0036, patient-specific knowledge is captured through a data model that models a Patient's longitudinal clinical history, Admission and administration information regarding patients, Derived clinical features from analytical processing of patient data, and Metadata about unstructured patient data. This patient information is represented through structured data model captured as searchable documents], [0055, At 401, patient data is ingested from a plurality of data sources. At 402, a plurality of data models is populated based on the ingested patient data, each data model comprising an abstract data type], the based on the ingested type of patient data (intent), the models are populated and the model includes an abstract data type); Chaudhry further teaches providing access to the workflow to define customizable features, and wherein the customizable features are at least one of: charts, graphs, and texts ([0053, enter input data into a text input box or other data entry element to configure (e.g., specify an input parameter of) a workflow step]; examiner’s note: workflow step (customizable features) includes text).
Vohra teaches allocating one or more tasks to generate one or more schedules ([col. 2, lines 20-21, “the system 10 may include one or more other schedulers/load balancers”], [col. 3, lines 40-50, “At any given time during the execution of the workflow instance, tasks that are currently being executed may be referred to as current tasks, tasks that have already been completed during the execution of the workflow instance may be referred to as predecessor tasks, and tasks that have yet to be completed during the execution of the workflow instance may be referred to as successor tasks. Each of the tasks may be independent of any other task, or may be dependent upon one or more other tasks”]; examiner’s note: the allocation of tasks for workers to generate schedule) and assign one or more tasks ([col. 8, lines 65-67, “the workflow instances may be directed to assigning airline flights for crew members, routing aircraft, generating airline flight numbers, and assigning airline flights for equipment”]; examiner’s note: assigning tasks).
One of ordinary skill in the art would recognize that incorporating allocating, assigning tasks and generating one or more tasks of Vohra into the ingestion and populating models of Beymer/Parker/Chaudhry to assign tasks/schedules and generate schedule for users. Beymer, Parker, Chaudhry, Vohra are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate feature of Vohra into the system of Beymer, Parker, Chaudhry to have a system to make the workflow process more efficient. The motivation would be to improve efficiency, better organization, enhance time management and improved communication of a system (Vohra, [col. 6, lines 1-5, “The workflow instance in execution does not need to be interrupted while changes are made to the workflow. The execution of the workflow instance is not suspended or made idle while changes are made to the workflow”]).
Beymer, Parker, Chaudhry, Vohra in combination do not explicitly teach auditing the raw data to monitor one or more services performed by the plurality of external systems to locate a source of an error associated with the raw data; providing access to the workflow to define customizable features, and wherein the customizable features are at least one of: charts, graphs, and texts; allocating one or more tasks to generate one or more schedules and assign one or more tasks.
However, McBride teaches an error associated with the raw data ([0044, initially determines 802 whether an error has been detected on a target volume 302b]; examiner’s note: error data with the raw data); auditing the raw data to monitor one or more services performed by the plurality of external systems to locate a source of an error associated with the raw data ([0041, a method 700 for auditing data consistency across source], [0044, audits 806 the source volume 302a and target volume 302b for other errors]; examiner’s note: auditing raw data to identify the source of the error and audit includes services used by systems). One of ordinary skill in the art would recognize that incorporating audit tracking to identify error and locating source of the error of McBride into the ingestion and populating models of Beymer/Parker/Chaudhry/Vohra to have audit data to identify error and also auditing raw data to locate source of an error. Beymer, Parker, Chaudhry, Vohra, McBride are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of McBride into the system of Beymer/Parker/Chaudhry/Vohra to a system which will track errors. The motivation would be to track audit to identify error and source of an error to fix the error to have data accuracy and enhance quality control of the system (McBride, [0037, methods to more efficiently correct errors in consistency when discovered]).
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view Parker et al. (US 2020/0106697) and in view of Chaudhry et.al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Wright et al. (US 7,072,881).
With respect to claim 26, Beymer, Parker, Chaudhry, Vohra in combination teach the system of claim 18, but do not explicitly teach wherein the one or more instructions which when executed by the processor further cause the system to remove the ingested raw data after the generation of the one or more reports.
However, Wright teaches wherein the one or more instructions which when executed by the processor further cause the system to remove the ingested raw data after the generation of the one or more reports ([col. 3, lines 1-5, “The customer data may be deleted from the mainframe database system in a further embodiment”]; examiner’s note: the data (ingested raw data) is deleted after the generation of the reports).
One of ordinary skill in the art would recognize that incorporating deleting raw data after the generation of the reports of Wright into the invention of Beymer/Parker/Chaudhry/Vohra to delete data after generating the necessary reports. Beymer, Parker, Chaudhry, Vohra, Wright are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Wright into the system of Beymer/Parker/Chaudhry/Vohra to have a system which will delete records. The motivation would be to save space of the system to make the system more efficient (Wright, [col. 3, lines 1-5, “advantage of the present invention is to reduce the amount of time and personnel required to manage telecommunications reports”]).
Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view Parker et al. (US 2020/0106697) and in view of Chaudhry et.al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Carlson et al. (US 2006/0294069).
With respect to claim 27, Beymer, Parker, Chaudhry, Vohra in combination teach the system of claim 18, but do not explicitly teach wherein the one or more instructions which when executed by the processor further cause the system to at least one of map, sort, and label the ingested raw data after the generation of the one or more reports.
However, Carlson teaches wherein the one or more instructions which when executed by the processor further cause the system to at least one of map, sort, and label the ingested raw data after the generation of the one or more reports ([0015, Sorting and/or filtering of the report data is available at a detail], examiner’s note: sorting the data (ingested raw data) after the report is generated).
One of ordinary skill in the art would recognize that incorporating sorting raw data after the generation of the reports of Carlson into the invention of Beymer/Parker/Chaudhry/Vohra to sort data after generating the necessary reports. Beymer, Parker, Chaudhry, Vohra, Carlson are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Carlson into the system of Beymer/Parker/Chaudhry to have a system which will sort data. The motivation would be to sort data after the generation of the report to view/find data faster (Carlson, [0015, Flexible sorting and filtering of the data in the report is available]).
Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beymer et al. (US 2020/0218738) and in view Parker et al. (US 2020/0106697) and in view of Chaudhry et.al. (US 2017/0315782) and in view of Vohra et al. (US 9,595,014) and in view of Yan et al. (US 2017/0220685).
With respect to claim 28, Beymer, Parker, Chaudhry, Vohra in combination teach the method of claim 11, but do not explicitly teach further comprising determining, based on metadata associated with the received request, whether the ingested raw data is in the plurality of external systems of record.
However, Yan teaches determining, based on metadata associated with the received request, whether the ingested raw data is in the plurality of external systems of record ([0268, a search command is received at the search head 1806 of the core engine 1802. The search head 1806 may determine whether the data to be searched resides at internal indexers 1808 or an external data system 1814]; examiner’s note: the search request includes search head (metadata) to determine which system to retrieve data from i.e. external systems).
One of ordinary skill in the art would recognize that incorporating determine from the request which system the data is in of Yan into the invention of Beymer/Parker/Chaudhry/Vohra to retrieve data from the systems. Beymer, Parker, Chaudhry, Vohra, Yan are analogous art because each art teaches workflow models.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate the functionalities of Yan into the system of Beymer/Parker/Chaudhry/Vohra to have a system which will locate data from multiple systems. The motivation would be to locate data faster to save time of a system (Yan, [0061, stored in a database to facilitate efficient retrieval and analysis of those data items at search time]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FATIMA P MINA whose telephone number is (571)270-3556. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann Lo can be reached on 571-272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FATIMA P MINA/ Examiner, Art Unit 2159
/ANN J LO/ Supervisory Patent Examiner, Art Unit 2159
Prosecution Insights