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 .
Summary
This Final Office Action is in response to the communication received on July 7, 2026.
Claims 1, 18, and 21-22 have been amended.
Claims 17 have been cancelled.
Claims 1-16 and 18-22 are pending.
The earliest priority date of the claimed invention is October 15, 2021.
Response to Amendment
Amendments to Claims 1, 18, and 21-22 are acknowledged. Amendments to Claims 1 and 21-22 are sufficient to overcome the 35 USC 103 rejection of Claims 1-16 and 18-22.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 and 18-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 – Statutory Categories
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter.(Claim 21 is a process and Claims 1-16, 18-20 and 22 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 1 (and similarly Claims 21-22) recites the following abstract concepts that are found to include “abstract idea.” Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
a server configured to receive one or more data objects from at least one data source; one or more data element nodes configured to create one or more sub-network through a graphical data structure wherein one or more data elements are extracted from the one or more data objects for analysis to identify the one or more data elements to be ingested as one or more data element node of the data network (See MPEP 2106.04(a)(2)(III) Mental Processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011));
one or more data connectors of the graphical data structure configured for connecting the one or more data element node to form the data network wherein the one or more data connectors include at least one identifier configured to identify the one or more data element node of the data network based on at least one relationship between one or more data attributes associated with the one or more data object and one or more historical data element (See MPEP 2106.04(a)(2)(III) Mental Processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011)); and
a processor coupled to an Artificial intelligence (AI) engine configured for processing the one or more data objects based on ensemble of one or more data models including graphical data model, document data model and relationship data model wherein a data analyzer configured for enabling the processor to link the one or more data attribute of the one or more data objects to the one or more historical data elements and assign the identifier based on the at least one relationship to the one or more processed data elements of the data object associated with the data attribute before ingesting the data element in the data network as a data element node to create the data network, wherein a data extraction process for extracting one or more data attributes associated with the one or more data objects includes the steps of (See MPEP 2106.04(a)(2)(III) Mental Processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011) and PEG Example 47, Claim 2, The claimed discretizing, detecting, and analyzing steps encompass mental choices or evaluations):
identifying a type of data object (See MPEP 2106.04(a)(2)(III) Mental Processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011) and PEG Example 47, Claim 2, The claimed discretizing, detecting, and analyzing steps encompass mental choices or evaluations); and
sending the data object to at least one data recognition training model for identification of the one or more data attribute wherein the data recognition training model processes the data object based on prediction analysis by a bot for obtaining the data attribute with a confidence score, wherein the data network is a graphical data network configured to process a document data model, a relationship data model, and a graphical data model structured data objects wherein the Al engine coupled to the processor is configured to enable the network to assign the identifier for the one or more data elements based on the data attributes associated with the data objects (See MPEP 2106.04(a)(2)(III) Mental Processes, a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011) and PEG Example 47, Claim 2, The claimed discretizing, detecting, and analyzing steps encompass mental choices or evaluations).
Claim 1 (and similarly Claims 21-22) is directed to a series of steps for processing and linking data to be ingested in a data network, which are concepts that can be performed in the human mind and thus grouped as mental processes. The mere nominal recitation of a server, data source, data element nodes, data object, graphical data structure, data network, processor, bot, and graphical data network, does not take the claim out of the mental processes. Thus, Claim 1 (and similarly Claims 21-22) recites an abstract idea.
Step 2A – Prong 2: Can the Judicial Exception Recited be integrated into a practical application
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 1 (and similarly Claims 21-22) is not integrated into a practical application. The additional elements are: a server, data source, data element nodes, data object, graphical data structure, data network, processor, bot, and graphical data network, that implements the underlying abstract idea. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 1 (and similarly Claims 21-22) is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 1 (and similarly Claims 21-22) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, steps a) receive data objects, b) create sub-network, c) connecting data element node to form the data network, and d) processing the data objects by identifying a type of data object and sending the data object to data recognition training model for identification of the data attribute, do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Claim 1 (and similarly Claims 21-22) is ineligible.
Claim 2 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 3 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 4 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 5 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 6 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 7 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 8 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 9 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 10 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 11 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 12 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 13 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 14 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 15 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 16 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 18 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 19 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 20 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Prior Art
Claims 1-16 and 18-22 in the instant application are allowable over the prior art because the prior arts of record fail to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 1 recites the following:
A data network comprises:
a server configured to receive one or more data objects from at least one data source;
one or more data element nodes configured to create one or more sub-network through a graphical data structure wherein one or more data elements are extracted from the one or more data objects for analysis to identify the one or more data elements to be ingested as one or more data element node of the data network;
one or more data connectors of the graphical data structure configured for connecting the one or more data element node to form the data network wherein the one or more data connectors include at least one identifier configured to identify the one or more data element node of the data network based on at least one relationship between one or more data attributes associated with the one or more data object and one or more historical data element; and
a processor coupled to an Artificial intelligence (Al) engine configured for processing the one or more data objects based on ensemble of one or more data models including graphical data model, document data model and relationship data model wherein a data analyzer configured for enabling the processor to link the one or more data attribute of the one or more data objects to the one or more historical data elements and assign the identifier based on the at least one relationship to the one or more processed data elements of the data object associated with the data attribute before ingesting the data element in the data network as a data element node to create the data network, wherein a data extraction process for extracting one or more data attributes associated with the one or more data objects includes the steps of:
identifying a type of data object; and
sending the data object to at least one data recognition training model for identification of the one or more data attribute wherein the data recognition training model processes the data object based on prediction analysis by a bot for obtaining the data attribute with a confidence score, wherein the data network is a graphical data network configured to process a document data model, a relationship data model, and a graphical data model structured data objects wherein the Al engine coupled to the processor is configured to enable the network to assign the identifier for the one or more data elements based on the data attributes associated with the data objects. (Emphasis added to highlight features that distinguish over the prior art).
As further explained below, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the Applicant’s claimed invention.
US Pat Pub 2023/0052533 “Furtak” discloses identifying access anomalies using network graphs, obtaining access data for an entity, generating a network graph baseline profile based on the plurality of data elements, generating a network graph current profile based on the plurality of data elements, generating comparison data based on comparing the plurality of baseline network graphs with the one or more current network graphs and comparing the plurality of baseline nodes and the plurality of baseline edges with the plurality of current nodes and the plurality of current edges, determining, based on the comparison data, anomaly data comprising one or more flagged network accesses to the enterprise system, and providing the anomaly data indicating the flagged network accesses to an authentication system.. Furtak fails to disclose a graphical data network configured to process a document data model, a relationship data model, and a graphical data model structured data objects wherein the AI engine coupled to the processor is configured to enable the network to assign the identifier for the one or more data elements based on the data attributes associated with the data objects.
US Pat Pub 2023/0067069 “Ghatage” teaches an Artificial Intelligence (AI) based document digitization, transformation and validation system extracts fields from digital documents via different document digitization processes. A document packet with a plurality of documents is initially accessed and any non-digital documents in the document packet are digitized. The errors in the digitized documents are corrected and non-English documents are translated into English. Each of the documents is provided to a plurality of digitization services for the extraction of fields by a plurality of field extraction models. If a field has multiple field instances extracted by more than one digitization service, then a field instance with the highest confidence score is selected for inclusion into the consolidated results. Ghatage fails to teach a graphical data network configured to process a document data model, a relationship data model, and a graphical data model structured data objects wherein the AI engine coupled to the processor is configured to enable the network to assign the identifier for the one or more data elements based on the data attributes associated with the data objects.
US Pat Pub 2021/0182996 Cell teaches a cloud-based management platform with a micro-services architecture having a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application. The microservices layers can include a data collection layer that can collect information from a set of Internet of Things resources that collect information with respect to supply chain entities and demand management entities related to the value chain network entities of the platform. Cella fails to teach a graphical data network configured to process a document data model, a relationship data model, and a graphical data model structured data objects wherein the AI engine coupled to the processor is configured to enable the network to assign the identifier for the one or more data elements based on the data attributes associated with the data objects.
Response to Arguments
35 USC 101
Applicant's arguments filed July 7, 2026 have been fully considered but they are not persuasive.
First, Applicant argues that the operations recited in claim 1 is not directed to an abstract idea of mental processes because the operations recited in claim 1 of: processing an ensemble of three distinct data models (graphical, document, and relationship data models) simultaneously against incoming data objects from at least one data source; identifier assignment based on relationship analysis across a historical data elements database before ingestion; data recognition training models producing confidence scores via a bot, and structuring a graphical data network with data element nodes, sub-networks, and connectors that update as new data objects are ingested, require computational processing, require specific machine implemented processing logic, and require structural machine operation. However, the Examiner finds that while the claims may include these elements, the claim is directed to steps for processing and linking data to be ingested in a data network by linking data attributes of a data object historical data elements and assigning an identifier based on the relationship. This linking and assigning an identifier to a data object amounts to collecting and comparing known information, and is thus classified as the abstract idea of mental processes.
Next, Applicant argues that Claim 1 recites a practical application of the judicial exception. Applicant argues that a technical solution is provided to the technical problems of efficiently processing data from enterprise applications with distinct data structures and implementation architectures, microservices architectures with multiple isolated data stores of structured and unstructured data make it difficult to provide a centralized network, and data objects from disparate sources (IoT, blockchain/linkedchain, APIs, codeless platforms) cannot be connected without specific technical implementation. While the arguments and the specification point to a clearer solution, the claims as currently written are directed to high level data processing (mental processes) with technical jargon that is unclear about the specific uses or implementations of the technical features to achieve the results. As such, the claims are found to be merely implemented using generic computing functions.
35 USC 103
Applicant’s arguments, see Applicant Arguments/Remarks Made in an Amendment, filed July 7, 2026, with respect to 35 USC 103 have been fully considered and are persuasive. The 35 USC 103 rejection of Claims 1-16 and 18-22 has been withdrawn.
Conclusion
THIS ACTION IS MADE FINAL. 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 REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
