DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 1/8/26, Applicant, on 4/6/26, amended claims. Claims 1, 4-11, and 14-24 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/6/26 has been entered.
Response to Amendment
Applicant’s amendments are acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 8 recites the limitation "implementing the graph-based onotology and a first subset of triples". There is insufficient antecedent basis for this limitation in the claim, as claim 1 now recites “graph data representing a knowledge graph including a plurality of triples.” It is unclear if it is the same subset in claim 8, or a further limiting one. For purpose of applying prior art only, Examiner interprets claim 8 as referring to the same triples as claim 1. Claim 18 recites similar limitations and is rejected for the same reasons.
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, 4-11, and 14-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
“A method comprising:
forming… a plurality of files representing automated workflow templates associated with graph data, the graph data representing a knowledge graph including a plurality of triples;
receiving… configuration data indicating one or more parameters;
linking… the automated workflow template to executable instructions;
executing… a process workflow based on the configuration data and the executable instructions;
retrieving… execution data associated with execution of the process workflow,
analyzing, …, the execution data and the graph data to identify an omission comprising a missing ownership relationship between a data asset node in the knowledge graph and a user account node in the knowledge graph;
based on identifying the omission, generating, by the data processing hardware, an updated triple comprising a subject corresponding to the data asset node, a predicate representing an ownership relationship, and an object corresponding to the user account node and updating the knowledge graph to include the updated triple.”
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “certain methods of organizing human activity” – managing personal behavior or interactions between people (including following rules or instructions), as here we are having a template of workflow [series of tasks, that can be e.g. “approval” between people; assigning tasks to people – see e.g. Applicant’s FIG. 8], with a graph (e.g. See FIG. 1B, 146 - people are approving different requests); receiving configuration data (e.g. see [0043] as published – can be identity of users that can provide approval), and then linking the template with the type of workflow, and then identifying an omission (Applicant’s [0045] as published – omission of data example- “omit an assigned role of “data steward”, finding similar users with similar skills and recommending them) and assigning ownership of an asset/task to a user based on user skills/attributes. Accordingly, claim 1 is directed to an abstract idea because it is generating a series of rules or instructions people follow for tasks/approvals/workflows, and assigning ownership for omitted/missing information based on “relationship” in terms of similarity of user attributes to the task in the business process.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. A computer is not explicitly recited at this time. It appears a computer may be intended; for purposes of compact prosecution, Examiner recommends as starting point to recite a computer performing the steps here. In particular, the claim 1 recites additional elements that are:
A method comprising:
forming, by data processing hardware, a plurality of files representing automated workflow templates associated with graph data;
[each step by data processing hardware]
…
linking, by data processing hardware, the automated workflow template to executable instructions;
executing the process workflow based on the configuration data and the executable instructions…”
The claim’s “automated” and “executable instructions” are interpreted as a “computer” performing the steps; and this is treated as “apply it [abstract idea] on a computer - MPEP 2106.05f applies –the claim involves a computer in only some of the steps; and is considered “apply it [the abstract idea] on a computer”; merely uses a computer as a tool to perform an abstract idea.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing system [if recited in future, or based on limitation of “automation” and “executable instructions”, is treated as MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent claim 11 is directed to a system at step 1, which is a statutory category. Claim 11 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. The additional limitations, of processor, memory, processor executing instructions, are all part of “apply it on a computer” (MPEP 2106.05f) at step 2a, prong 2 and step 2b. The claim is not patent eligible.
Claims 4, 7, 12-14, 17 narrow the abstract idea by having various further data determined, related to tasks, workflows, templates.
Claims 5 and 15 narrow the abstract idea for similar reasons as above; but further states “object-oriented programming language.” This is viewed as an additional element and treated as MPEP 2106.05f “apply it [abstract idea] on a computer” and MPEP 2106.05h “field of use” at step 2a, prong two and step 2B.
Claims 6 and 16 narrow the abstract idea for similar reasons as above; but further states “application programming interface.” This is viewed as an additional element and treated as MPEP 2106.05f “apply it [abstract idea] on a computer” and MPEP 2106.05h “field of use” at step 2a, prong two and step 2B.
Claims 8, 18 narrow the abstract idea by having a graph-based ontology; generating a template based on the ontology, and “a subset of triples,” which based on Applicant’s [0003, 0048, 0085] as published are “metadata or statements”; and are viewed as some description of data. Any additional elements in amended claim 8, which still does not require a computer, at this time or once the claim is amended to clarify steps are performed “by a computer”, are also viewed as MPEP 2106.05f “apply it [abstract idea] on a computer.”
Claims 9 and 19 narrow the abstract idea for similar reasons as above; but further states “storing the automated workflow templates in a template repository.” This is viewed as an additional element and treated as MPEP 2106.05f “apply it [abstract idea] on a computer” and MPEP 2106.05h “field of use” at step 2a, prong two and step 2B. At step 2B, it is also considered a conventional computer function (See MPEP 2106.05d(II) iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306.
Claims 10 and 20 narrow the abstract idea by describing different data that are in a template in the alternative; one alternative is just “delegate ownership to a dataset” which is viewed as assigning responsibility.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-7, 9-11, 14-17, 19-20, 22, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US 2021/0004711) and Jiang (CN 116128257) and Makhija (US 20220327006).
Concerning claim 1, Gupta discloses:
A method (Gupta – see par 3 – computer-implemented method generating data structure that stores a knowledge graph for a decision making process that is to be automated) comprising:
forming, by data processing hardware, a plurality of files representing automated workflow templates associated with graph data (Gupta – See par 22 – RPA (robotic process automation) automates repetitive human tasks; see par 63 - FIG. 3 depicts a block diagram of a system for automating a process cognitively according to one or more embodiments of the present invention. The system 100 facilitates not only automation of a process, but also facilitates optimizes the process and decision-making that is part of executing the method using artificial intelligence (AI). The system 100 derives actionable, real-time insights from operations intelligence to augment the formulation, orchestration, and automation of an adaptive process. The system 100 further facilitates a cognitive RPA (Robotic process automation) that formulates and orchestrates processes that reshape themselves as they run; see par 64 - The system includes a knowledge graph generator 115 that automatically generates a knowledge graph 120 using machine learning and deep learning techniques to identify the changes that happen using one or more of records, time series data, raw events. In one or more examples, such data representing a process (process-representation 105) that is to be automated is stored using a structured format such as a metalanguage, for example, extendable markup language (XML), business process execution language (BPEL), a Business Process Model and Notation (BPMN), etc.; knowledge graph generates a process execution model 130 (decision tree), which includes “context”, which includes values of the entities at the time of process execution).
To any extent Gupta doesn’t disclose a “template”, Makhija discloses:
forming, by data processing hardware, a plurality of files representing “automated workflow templates” associated with graph data (Makhija – see par 56 - Data modeling is done through template definitions and API calls to the shared frameworks platform layer. See par 116 - Further, the platform as a product advantageously enables citizen developer user to create end to end application with additional customized microservices and API deployed and configured in CDS. The developer creates application templates based on the operational verticals with predefined rules and workflows and database schema for out of box implementation. Third party implementation entities can select the application, customize and extend schema based on specific needs of customer region and line of operation. The platform has an ability create microservices and schema dynamic based on standard templates and customize layer; see par 95 - the AI flow Orchestrator engine 201 takes a record centric view of all transactions applied to different records for the approval process).
Gupta discloses:
the graph data representing a knowledge graph … (Gupta – see par 64 – knowledge graph; data representing a process (process-representation 105) that is to be automated is stored using a structured format such as a metalanguage, for example, extendable markup language (XML), business process execution language (BPEL), a Business Process Model and Notation (BPMN), etc.))
Gupta discloses that data representing a process that is to be automated is stored using a structured format such as BPMN (See par 64). Gupta discloses having a knowledge graph 120 using entities, attributes, and relationships extracted by a process miner (See FIG. 1) (see par 66) and that its robotic process automation system that creates decision models is trained on domain knowledge (See par 119).
Jiang discloses:
the graph data representing a knowledge graph “including a first subset of triples” (Jiang see page 7, 2nd paragraph - present invention provides a task personnel matching method based on knowledge graph, comprising the following steps: S1, constructing staff knowledge graph the staff knowledge graph multiple triad groups; the triple group takes staff as head entity, the head entity is associated with several attributes, each attribute has corresponding attribute value as the tail entity. Firstly, employee information is constructed by enterprise staff to knowledge graph staff. FIG. 1 shows a structure of a triple in the staff knowledge graph which is a triple ( 1) head entity, 2) relation (attribute), 3) tail entity).
Gupta, Makhija, and Jiang disclose:
receiving, by the data processing hardware, configuration data indicating one or more parameters ([0043] as published - Rather, configuration data, as variables, may be used to define workflow functionalities, whereby persons or users of any role may be capable to configure workflow processes. - Gupta discloses the limitations based on broadest reasonable interpretation in light of the specification– see par 71 - the knowledge graph generator 115 enhances the process-specific knowledge graph 120 with “entity source”, “states”, “conditions”, and “actions”—by analyzing the static process definitions (workflow and rules for decisions); and by analyzing the historical data (using machine learning algorithms) generated by process execution engines in the RPA system 100 when executing the process with manual intervention. Such information can be derived by evaluation of a process logs, audit trail logs, and other such information associated with the process);
linking, by the data processing hardware, the automated workflow template to executable instructions (Applicant’s [0035] as published states “Template generator 127 may generate an automated workflow template linked to a process model. In some examples, a process model may be compliant with a Business Process Model and Notation (“BPMN”) standard, such as that maintained by Object Management Group, Inc., or OMG™, Standards Development Organization® at http//www(dot)bpmn(dot)org. Also, template generator 127 may be configured to generate an automated workflow template linked to executable objects of an object-oriented programming language, such a Java®, Python™, or the like, to, for example, provide functionality of an automated workflow based on a BPMN model. “
Gupta discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 64 - In one or more examples, such data representing a process (process-representation 105) that is to be automated is stored using a structured format such as a metalanguage, for example, extendable markup language (XML), business process execution language (BPEL), a Business Process Model and Notation (BPMN), etc. The data representing the process can include one or more entities present in the process and relationships between entities that influence the process. Such data can be electronically/digitally stored in the form of BPEL/BPMN, web service description language (WSDL), java connector architecture (JCA) files, etc. see par 70 - It should be noted that the “entities” as described herein include computer data structures (e.g. objects) that are automatically instantiated and attributes populated by the knowledge graph generator 115. see par 119 - One or more embodiments of the present invention accordingly facilitate a robotic process automation system that can automatically create a decision and process model, which is trained on domain knowledge and can formulate rules and workflow to implement a process using artificial intelligence; see par 124 – carrying out operations… using object oriented programming language;
see also Makhija – See par 49, FIG. 1 – foundation layer 102 enables creation and management of smart forms (and templates), framework to define UI (user interface) screens, controls, etc. through use of templates; see par 56 - Data modeling is done through template definitions and API calls to the shared frameworks platform layer. See par 62 - The entity machine 106A with a citizen developer user UI is configured for sending, receiving, modifying or triggering processes and data object for creation of one or more of a SCM application over a network 107);
executing, by the data processing hardware, a process workflow based on the configuration data and the executable instructions (Gupta – see par 74 - the process-specific knowledge graph 105 is further enhanced with “internal factors” and “external factors” that influence the outcomes. In one or more embodiments of the present invention, such factors are determined by analyzing historic process data, events and logs, various systems-of-records (databases and documents) like policies, regulation, streaming events, feeds etc.); see par 83 - The derivations of the knowledge graph 120 are consumed for the process execution depending on the context, content, and configuration. The RPA system 100 accordingly provides a dynamic behavior where the process execution assimilates the external factors and influences the decision making automatically. see par 84 - Referring back to the RPA system 100 in FIG. 3, the knowledge graph 120 that is generated is used by a decision tree maker 125 to generate a process execution model 130 (decision tree). An execution engine 150 uses the decision tree 130 to execute the process. The decision tree 130 includes “content”, which includes the entities associated with the process; “context”, which includes values of the entities at the time of process execution, and “contract”, which includes the factors/values that the entities hold or the condition and actions present in the process specific knowledge graph 120.
see also Makhija – see par 99 - The AI task orchestrator engine 202 further determine tradeoff between tasks that require user approval vs. automated approval to enable automation of process flows as shown by flow diagram 200D of FIG. 2D. See par 116 - Further, the platform as a product advantageously enables citizen developer user to create end to end application with additional customized microservices and API deployed and configured in CDS. The developer creates application templates based on the operational verticals with predefined rules and workflows and database schema for out of box implementation. Third party implementation entities can select the application, customize and extend schema based on specific needs of customer region and line of operation. The platform has an ability create microservices and schema dynamic based on standard templates and customize layer); and
retrieving, by the data processing hardware, execution data associated with the execution of the process workflow, (Gupta – See par 63 - The system 100 derives actionable, real-time insights from operations intelligence to augment the formulation, orchestration, and automation of an adaptive process. The system 100 further facilitates a cognitive RPA that formulates and orchestrates processes that reshape themselves as they run. see par 77 - The knowledge graph generator 115 automatically maps the process flow execution results against input variables, values obtained during process flow, the values of “internal factors” and the values of “external factors”. For the mapping, the process-specific knowledge graph 120 is enhanced with “entity source”, “states”, “conditions” and “actions” by analysing the static process definitions (workflow and rules for decisions); and by analysing the historical data (using machine learning algorithms) generated by process execution engines. See par 83 - the RPA system 100 is facilitate to determine actionable insights from the knowledge graph 120, the insights being used as an addendum for executing the process. The derivations (based on influencing factors) from the knowledge graph 120 include both, implicit information as well as the explicit data. The derivations of the knowledge graph 120 are consumed for the process execution depending on the context, content, and configuration. The RPA system 100 accordingly provides a dynamic behavior where the process execution assimilates the external factors and influences the decision making automatically;
see also Makhija - see par 84 - Further, the workflow includes compliance process with validation of end to end flows and is responsible to verify and simulate the flow as per the data model. The compliance process identifies outliers and generates alerts along with replaying the events. Furthermore, the workflow block 100B includes a user process block that generates notations, identifies blocks requiring user intervention, defines start and end of the flow, and connects user action to business events);
analyzing, by the data processing hardware, the execution data and the graph data to identify an omission comprising a missing ownership relationship between a data asset node in the knowledge graph and a user account node in the knowledge graph (Gupta see par 75 - based on the evaluating the process flow logs, event logs, parsing the process-representation 105, the rules that are part of the process execution are classified as internal factors. For example, these internal factors can be either from rules (decision nodes): if local skills exist, is travel request for maintenance or new deal, is maintenance valid and fields from systems of records like database, employeeRole, employeeBand. These internal factors are evaluated based on the process flow and values for the conditions that can be determined directly from electronic data sources such as databases and the condition(s) to make a decision based on these can also be determined from the electronic data sources. see par 79 - In the example, it is determined whether local skills are available (e.g. person at the destination) to handle the problem (situation), which is noted as the reason for the travel, at 215. If local skill is not available, and if traveling is permitted at this time of year per organization's policies, the travel request is approved, at 220 and 225. These steps can be performed automatically by the RPA system 100, without any manual intervention once the input data for the travel request is received).
Gupta discloses that the RPA (Robotic Process Automation) results in orchestrating processes that reshape themselves as they run (See par 63) and determining insights used as an addendum for executing a process (See par 63, 83). Gupta also discloses that internal factors include decision nodes and parsing a process representation to look at local skills, and “employeeRole” (See par 75) and that there are reasons why a person requests travel, such as “whether local skills are available (e.g. person at the destination) to handle the problem” because when “local skill is not available,” a travel request is permitted to handle the problem at the other location (See par 79).
Jiang discloses:
analyzing, by the data processing hardware, the execution data and the graph data to identify an omission comprising a missing ownership relationship between a data asset node in the knowledge graph and a user account node in the knowledge graph (Applicant’s [0045] as published states “In another example, governance logic module 123 may be configured to identify a status from any number of statuses (e.g., a status of one of “unassigned,” “pending,” “complete,” etc.) of an automated workflow, and may be configured further to automatically take action to resolve certain status conditions to ensure an automated workflow may operate optimally.” In some cases, governance logic module 123 may be configured to automatically obviate (e.g., using algorithms configured by machine learning and the like) the issue or determine a recommended action. For example, if a dataset, resource data, data products, data assets, and the like are determined to omit an assigned role of ‘data steward,’ governance logic module 123 may automatically generate a recommend data steward based on, for instance, characteristics of data steward user and the attributes of dataset data or metadata.
Jiang – see page 2, 1st paragraph – choosing suitable people to do suitable work is … also an important role in the development of enterprises. the traditional task personnel matching is generally enterprise management personnel or project manager according to the past experience to roughly evaluate, the evaluation first presence and reality deviation risk, such as once the task change, time, cost, human resource and other factors will be changed; see page 2 - the technical solution of the invention is to use a task personnel matching method based on knowledge graph, comprising: constructing staff knowledge graph the staff knowledge graph multiple triad groups; the triple group uses staff as head entity, the head entity is associated with several attributes, each attribute has corresponding attribute value as tail entity; obtaining the triple low-dimensional vector by learning the staff knowledge graph coding the task name to obtain the vector of the task name; performing similarity calculation to the ternary group vector and the task name vector, and matching the task with the highest similarity of the staff. As an improvement, the attributes in the three-element group include academic qualifications, working years, skills, and the tasks that have been completed. see page 9 - S43 selects the task with the highest similarity to match to the staff. after finishing the model training, inputting the task name vector and the low-dimensional vector of the staff, knowledge graph the minimum distance and the highest similarity of staff and task to match. As shown in FIG. 3, the present invention further provides a task personnel matching system based on knowledge graph).
Gupta, Makhija, and Jiang disclose:
based on identifying the omission, generating, by the data processing hardware, an updated triple comprising a subject corresponding to the data asset node, a predicate representing an ownership relationship (Gupta - If local skill is not available, and if traveling is permitted at this time of year per organization's policies, the travel request is approved, at 220 and 225), and an object corresponding to the user account node and updating the knowledge graph to include the updated triple (Applicant’s [0039] as published states “A fifth automated workflow template may be configured to trigger a workflow responsive of detecting no identified owner (e.g., a user terminates employment with an enterprise or changes roles), whereby the fifth automated workflow template may be configured to automatically delegate ownership to another user that has similar characteristics or attributes (e.g., similar roles, similar permissions, etc.).”;
Jiang see page 7, 2nd paragraph - present invention provides a task personnel matching method based on knowledge graph, comprising the following steps: S1, constructing staff knowledge graph the staff knowledge graph multiple triad groups; the triple group takes staff as head entity, the head entity is associated with several attributes, each attribute has corresponding attribute value as the tail entity. Firstly, employee information is constructed by enterprise staff to knowledge graph staff. FIG. 1 shows a structure of a triple in the staff knowledge graph which is a triple (head entity, relation (attribute), tail entity (attribute value)} of a pair of N, such as {staff Li certain (entity), academic (attribute), research (attribute value)}, and {staff Li certain (entity)).
Gupta, Makhija, and Jiang are analogous art as they are directed to business process workflows with tasks for people (see Gupta Abstract, FIG. 4, par 80; Makhija Abstract, see par 52, 86, 104, FIG. 2A-2F – approvals/tasks with user intervention;; Jiang Abstract). 1) Gupta discloses mapping processes, knowledge graphs, and then having a structure based on “context” (See par 84-85). Makhija improves upon Gupta by having “templates” with “predefined rules and workflows” that are then customized (See par 56, 116). One of ordinary skill in the art would be motivated to further include “templates” to efficiently improve upon the customization for workflows in Gupta. 2) Gupta discloses that data representing a process that is to be automated is stored using a structured format such as BPMN (See par 64). Gupta discloses having a knowledge graph 120 using entities, attributes, and relationships extracted by a process miner (See FIG. 1) (see par 66) and that its robotic process automation system that creates decision models is trained on domain knowledge (See par 119). Gupta also discloses that internal factors include decision nodes and parsing a process representation to look at local skills, and “employeeRole” (See par 75) and that there are reasons why a person requests travel, such as “whether local skills are available (e.g. person at the destination) to handle the problem” because when “local skill is not available,” a travel request is permitted to handle the problem at the other location (See par 79). Jiang improves upon Gupta by having a matching between task personnel attributes and tasks based on similarity and having a “triple” related to a knowledge graph for staffing (see page 7). One of ordinary skill in the art would be motivated to further include matching of tasks to skills/attributes of personnel and “triple” information in a knowledge graph to efficiently improve upon the customization for workflows and knowledge graph in Gupta and the assigning of workers from other locations when local skills not available in Gupta.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for having business processes using knowledge graphs that are for different contexts in Gupta (See Abstract, par 64, 84-85, FIG. 3), to further include templates for customization in Makhija, and to further include matching tasks and personnel by similarity along with triples in RDF in Jiang, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning independent claim 11, Gupta and Makhija and Jiang disclose:
A system (Gupta – see par 4 – system includes a memory, and a processor coupled with the memory. The processor performs a method for automating a decision making process.) comprising:
a memory including executable instructions (Gupta – see par 5 - a computer program product includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processing circuit to cause the processing circuit to perform a method for automating a decision making process); and
a processor, responsive to executing the instructions, is configured to (Gupta – see par 5 - a computer program product includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processing circuit to cause the processing circuit to perform a method for automating a decision making process).
The remaining limitations are similar to claim 1 above. Claim 11 is rejected for the same reasons.
Concerning claim 4 and 14, Gupta discloses:
The method of claim 1 wherein linking the automated workflow template to the executable instructions comprises:
linking the automated workflow template to a process model (Gupta discloses the limitations based on broadest reasonable interpretation in light of the specification –see par 70 - It should be noted that the “entities” as described herein include computer data structures (e.g. objects) that are automatically instantiated and attributes populated by the knowledge graph generator 115. see par 84 - ] Referring back to the RPA system 100 in FIG. 3, the knowledge graph 120 that is generated is used by a decision tree maker 125 to generate a process execution model 130 (decision tree). see par 119 - One or more embodiments of the present invention accordingly facilitate a robotic process automation system that can automatically create a decision and process model, which is trained on domain knowledge and can formulate rules and workflow to implement a process using artificial intelligence; see par 124 – carrying out operations… using object oriented programming language;
see also Makhija – see par 43 - the configurable components enable an application developer user/citizen developer, a platform developer user and a SCM application user working with the SCM application to execute the operations to code the elements of the SCM through configurable components. see par 56 - a developer user or admin user will structure one or more SCM application and associated functionality by the application layer of microservices, either by leveraging the shared frameworks platform layer; data modeling is done through template definitions and API calls to the shared frameworks platform layer; See par 62 - The entity machine 106A with a citizen developer user UI is configured for sending, receiving, modifying or triggering processes and data object for creation of one or more of a SCM application over a network 107. see par 84 - the workflow visualization block 100B includes a process modeler that is responsible to generate notations and defines process as per trained models saved in the repository. The process modeler manages various processes, responsible to integrate these processes and use tools repository to structure the workflow. Further, the workflow includes compliance process with validation of end to end flows and is responsible to verify and simulate the flow as per the data model) compliant with a Business Process Model and Notation (“BPMN”) standard (Gupta see par 64 - In one or more examples, such data representing a process (process-representation 105) that is to be automated is stored using a structured format such as a metalanguage, for example, extendable markup language (XML), business process execution language (BPEL), a Business Process Model and Notation (BPMN), etc. The data representing the process can include one or more entities present in the process and relationships between entities that influence the process. Such data can be electronically/digitally stored in the form of BPEL/BPMN, web service description language (WSDL), java connector architecture (JCA) files, etc.)
Obvious to combine Gupta, Makhija, and Jiang for the same reasons as claim 1 above.
Concerning claim 5 and 15, Gupta discloses:
The method of claim 1 …executable objects of an object-oriented programming language (Gupta – See par 64 – data representing a process stored in structured format… such as java connector architecture (JCA) files; see par 70 - It should be noted that the “entities” as described herein include computer data structures (e.g. objects) that are automatically instantiated and attributes populated by the knowledge graph generator 115; see par 124 – computer readable instructions to carry out operations include… an object oriented programming language;)
Makhija discloses:
The method of claim 1 wherein linking the automated workflow template to the executable instructions comprises linking the automated workflow template to executable objects … to implement a process model (Makhija – see par 56 - Data modeling is done through template definitions and API calls to the shared frameworks platform layer. See par 62 - The entity machine 106A with a citizen developer user UI is configured for sending, receiving, modifying or triggering processes and data object for creation of one or more of a SCM application over a network 107. See par 84 - the workflow visualization block 100B includes a process modeler that is responsible to generate notations and defines process as per trained models saved in the repository. The process modeler manages various processes, responsible to integrate these processes and use tools repository to structure the workflow. See par 116 - Further, the platform as a product advantageously enables citizen developer user to create end to end application with additional customized microservices and API deployed and configured in CDS. The developer creates application templates based on the operational verticals with predefined rules and workflows and database schema for out of box implementation. Third party implementation entities can select the application, customize and extend schema based on specific needs of customer region and line of operation. The platform has an ability create microservices and schema dynamic based on standard templates and customize layer. See par 120 - In step 403 interacting by an AI based orchestration engine with one or more configurable components in a codeless platform architecture for executing the SCM operations wherein the AI based orchestration engine drives execution of SCM operations through one or more data objects mapped to the API for structuring the workflow of the SCM operations by a process modeler of the AI based orchestration engine.).
Obvious to combine Gupta and Makhija for the same reasons as claim 1 above. In addition, Gupta discloses using java (see par 64), instantiating objects (See par 70), and using object oriented programming language (See par 124). Makhija improves upon Gupta by further having a template [see reasons for combining claim 1] and executing data objects (See par 120).
Concerning claim 6 and 16, Gupta and Makhija disclose:
The method of claim 1 wherein…
linking the automated workflow template to executable objects of an object-oriented programming language (Gupta – See par 64 – data representing a process stored in structured format… such as java connector architecture (JCA) files; see par 124 – computer readable instructions to carry out operations include… an object oriented programming language; see par 70 - It should be noted that the “entities” as described herein include computer data structures (e.g. objects) that are automatically instantiated and attributes populated by the knowledge graph generator 115) via an application programming interface (“API”) to implement a process model (Gupta – see par 103 - Here, an “adapter” refers to technique used to access an external system. As the process can be interacting with different external (third party) systems, the adapters provide an abstraction in terms of connectivity, access, retrieval, updating of the external system during the process flow. For example, an application programming interface, a protocol, or any other specific access mechanism used for such access can be included in, or referred to as the adapter.
Makhija discloses:
The method of claim 1 wherein linking the automated workflow template to the executable instructions comprises:
linking the automated workflow template to executable objects …via an application programming interface (“API”) to implement a process model (Makhija – see par 56 - Data modeling is done through template definitions and API calls to the shared frameworks platform layer. See par 62 - The entity machine 106A with a citizen developer user UI is configured for sending, receiving, modifying or triggering processes and data object for creation of one or more of a SCM application over a network 107. See par 84 - the workflow visualization block 100B includes a process modeler that is responsible to generate notations and defines process as per trained models saved in the repository. The process modeler manages various processes, responsible to integrate these processes and use tools repository to structure the workflow. See par 116 - Further, the platform as a product advantageously enables citizen developer user to create end to end application with additional customized microservices and API deployed and configured in CDS. The developer creates application templates based on the operational verticals with predefined rules and workflows and database schema for out of box implementation. Third party implementation entities can select the application, customize and extend schema based on specific needs of customer region and line of operation. The platform has an ability create microservices and schema dynamic based on standard templates and customize layer. See par 120 - In step 403 interacting by an AI based orchestration engine with one or more configurable components in a codeless platform architecture for executing the SCM operations wherein the AI based orchestration engine drives execution of SCM operations through one or more data objects mapped to the API for structuring the workflow of the SCM operations by a process modeler of the AI based orchestration engine.).
It would be obvious to combine Gupta and Makhija for the same reasons as claim 1 and 5. In addition, Gupta and Makhija disclose use of APIs.
Concerning claim 7 and 17, Gupta and Makhija disclose:
The method of claim 1 further comprising implementing an instance of the automated workflow (Gupta – see par 63 - The system 100 further facilitates a cognitive RPA that formulates and orchestrates processes that reshape themselves as they run. These processes are data driven, adaptive, and intelligent, determining and executing a next action based on context formation from data, instead of the same repeatable sequence of actions. In other words, using the cognitive RPA, the system 100 automatically determines a sequence of operations in the process that is to be executed based on one or more data input from the user along with several contextual restrictions that the system 100 automatically detects; see par 79 - FIG. 4 depicts a flowchart of a process execution by an RPA system according to one or more embodiments of the present invention. Here, execution of the specific process of travel request approval is shown) template (Makhija – see par 49 – foundation layer 102 provides a set of microservices that execute the tasks of managing code deployment, supporting code versioning, deployment etc. The layer collectively enables creation and management of smart forms (and templates), framework to define UI screens, controls etc. through use of templates. Seamless theming support is built to enable specific form instances (created at runtime) to have personalized themes, extensive customization of the user experience (UX) for each client entity and or document; see par 76 - the orchestrator UI 109C provides details through graphical representation to customize data flow, workflow, manage run, settings and configuration to execute the workflow; see par 90 - The release manager component 111C is responsible for managing, planning, scheduling, and controlling delivery throughout the release lifecycle using other subcomponents and for Orchestrating entire pipeline with automation. The deployment manager component 111D configures and run delivery workflows for applications and platforms. It Creates standardized deployment process to deploy predictable, high-quality releases. The component automates workflows).
It would be obvious to combine Gupta and Makhija for the same reasons as claim 1.
Concerning claim 9 and 19, Gupta and Makhija disclose:
The method of claim 1 further comprising storing the automated workflow templates in a template repository, the automated workflow template associated with a link to a user input at an interface. (Makhija – see par 49 – foundation layer 102 enables creation and management of smart forms (and templates), framework to define UI screens, controls etc. through use of templates. See par 62 - The entity machine 106A with a citizen developer user UI is configured for sending, receiving, modifying or triggering processes and data object for creation of one or more of a SCM application over a network 107. see par 84 - the workflow visualization block 100B includes a process modeler that is responsible to generate notations and defines process as per trained models saved in the repository. The process modeler manages various processes, responsible to integrate these processes and use tools repository to structure the workflow. Further, the workflow includes compliance process with validation of end to end flows and is responsible to verify and simulate the flow as per the data model. The compliance process identifies outliers and generates alerts along with replaying the events. Furthermore, the workflow block 100B includes a user process block that generates notations, identifies blocks requiring user intervention, defines start and end of the flow, and connects user action to business events).
It would be obvious to combine Gupta and Makhija for the same reasons as in claim 1 above.
Concerning claim 10 and 20, Gupta and Makhija disclose:
The method of claim 1 wherein forming the plurality of files representing automated workflow templates comprises forming one or more of:
a data access template configured to perform a process workflow to provide approval responsive to a request to access data (Examiner notes that the claim as constructed only “represents” workflow, and is viewed as “describing” the names of different data in the templates, and as of 10/31/25, in the alternative. The various descriptions are viewed as “names”, printed matter in MPEP 2111.05 and since they do not have a functional relationship with the computer, they are not entitled to patentable weight. Nonetheless for purposes of compact prosecution, art will be applied –
Gupta discloses the alternative - see par 25 - consider an example scenario of a travel request approval process in an organization; see also Makhija – par 110 – Process Orchestrator has Access control policies; par 119 - the platform of the present invention provides creation of Seed project that includes common repository and template project containing all metadata information and applications can be built on top using these templates. Additional Features required to implement as part of Seed project include project repository as seed for all entity/end user implementations, manage all the entities from single manage interface to decide which entity is going to view what specific data, setting the permission and access control to the application development team);
a data enrichment template configured to identify types of data requiring action (Gerber discloses the alternative – see par 222 - The modeling task can be facilitated by providing features which assist users during modeling and make recommendations on how to complete a being edited business process model. Ideally, the assistance approach is context-aware, which means that it takes the current progress of modeling as a context for the recommendation into account. The basis for such a recommendation feature could be a repository of completed business process models.
See also Makhija – see par 84 - Further, the workflow includes compliance process with validation of end to end flows and is responsible to verify and simulate the flow as per the data model. The compliance process identifies outliers and generates alerts along with replaying the events. Furthermore, the workflow block 100B includes a user process block that generates notations, identifies blocks requiring user intervention, defines start and end of the flow, and connects user action to business events.);
a metadata completeness template configured to modify metadata to establish a substantially complete set of metadata for a dataset, a metadata freshness template configured to assign a task to review a dataset periodically (Makhija discloses the alternative– see par 104 - Depending on the criticality of required approvals, the ML classification model described in “AI Task orchestrator” section can also be extended to classify the level of User intervention. For example, the ML classification model can leverage the organizational hierarchy to route high-value invoices to managers for approvals and simple invoices get auto-approved but general invoices requiring attention get routed to financial analysts as depicted by flow diagram 200F in FIG. 2F. ),
an automatic delegation template configured to delegate ownership to a dataset, or
a status-based template configured to assign a status to a dataset.
It would be obvious to combine Gupta and Makhija for the same reasons as in claim 1 above.
Concerning claim 22 and 24, Gupta and Makhija disclose:
The method of claim 1, wherein:
the automated workflow templates and the graph data are logically linked as entities originating from one or more networked data stores (Gupta – see par 47 - In a related embodiment, the plurality of configurable components includes one or more data layer configurable components including but not limited to Query builder, graph database parser, data service connector, transaction handler, document structure parser, event store parser and tenant access manager. The data layer provides abstracted layers to the SCM service to perform data operations like Query, insert, update, delete and Join on various types of data stores document database (DB) structure, relational structure, key value structure and hierarchical structure. see par 70 - external system which form the interactions is captured along with the operations invoked on the systems and the input/output values for such interactions. It should be noted that the “entities” as described herein include computer data structures (e.g. objects) that are automatically instantiated and attributes populated by the knowledge graph generator 115. see par 104 - The knowledge graph generator 115 accordingly determines and stores in the knowledge graph 120, which is specific to the process being automated, at least a. Sequence of operations, b. Data flow across the process, c. External operations/invocations (performed by third party systems), d. Identification of the states in the system during execution of the process,
see also for “templates” Makhija – see par 119 - the platform of the present invention provides creation of Seed project that includes common repository and template project containing all metadata information and applications can be built on top using these templates. This will enable faster development of applications and structuring of workflow by multiple application development teams. Additional Features required to implement as part of Seed project include project repository as seed for all entity/end user implementations, manage all the entities from single manage interface to decide which entity is going to view what specific data, setting the permission and access control to the application development team to help them maintain and deploy their features and fixes faster and safer for Version control. see FIG. 6, par 134 - CLM Basic Service Invokes Document Shared which internally transforms the view model in data Entity and invokes Platform Query Provider Service; h) Query Provider service construct the Query in GQL (Gep Query language) and invokes Data service for Saving Data in Data Storage); and
executing the process workflow comprises transmitting execution data via federated data access to extend the knowledge graph (Gupta – see par 69 - Further, interaction of process-representation 105 with system of records like databases, Java message services (JMS), packaged applications etc., is done through one or more adapters exposed as wsdl/JCA compliant resource adapters. The information can be retrieved from a BPEL file, a JCA file, and SCA file or any other data representation that includes at least the information of table name, queried column info, and schema details. see par 70 - It should be noted that the “entities” as described herein include computer data structures (e.g. objects) that are automatically instantiated and attributes populated by the knowledge graph generator 115. see par 103 - discovery of system of records such as database and the entities that are being used in the process is performed by the knowledge graph generator 115 using one or more adapters. Here, an “adapter” refers to technique used to access an external system. As the process can be interacting with different external (third party) systems, the adapters provide an abstraction in terms of connectivity, access, retrieval, updating of the external system during the process flow. ).
Obvious to combine for the same reasons as claim 1 above.
Claims 8, 18 and 21, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US 2021/0004711), and Makhija (US 2022/0327006), as applied above to claims 1, 4-7, 9-11, 14-17, 19-20, 22, and 24, and further in view of Bachhofner, “Automated Process Knowledge Graph Construction from BPMN Models,” 2022, In International Conference on Database and Expert Systems Applications, pages 32-47.
Concerning claim 8 and 18, Gupta discloses that data representing a process that is to be automated is stored using a structured format such as BPMN (See par 64).
Gupta discloses having a knowledge graph 120 using entities, attributes, and relationships extracted by a process miner (See FIG. 1) (see par 66) and that its robotic process automation system that creates decision models is trained on domain knowledge (See par 119). Makhija discloses having a workflow knowledge database that includes building model-driven flows incorporating application process within supply chain (See par 74). Jiang discloses having knowledge graphs
Bachhofner discloses:
The method of claim 1 further comprising:
implementing a graph-based ontology (Bachhofner –see page 34, 1st paragraph – transform business process models in BPMN into a KG (knowledge graph) representation in RDF (resource description framework) based on and extending an existing ontology for process representation);
generating the automated workflow template based on implementing the graph-based ontology and a first subset of triples (Bachhofner –see page 34, 1st paragraph – transform business process models in BPMN into a KG representation in RDF (resource description framework) based on tonology for process representation; supports BPMN 2.0 elements, including “most used ones”; see page 35, 2nd paragraph - For modeling KGs (knowledge graphs), Resource Description Framework (RDF) [13] is a widely used language recommended by the World Wide Web Consortium (W3C). KGs in RDF are formed from triples, each of which consists of a subject, a predicate, and an object; see page 35, last paragraph - BPMN-based Ontology (BBO) [5] is an ontology to represent business processes modeled in BPMN 2.0 in a KG. An ontology is necessary as it for example allows us to sub-class it’s concepts, or use their properties); and
implementing the configuration data associated with a second subset of triples, based on the automated workflow template (Bachhofner – page 35 - Figure 2 illustrates two triples in RDFs from our motivating use case - the two triples belong to the first task of our mass production process. The first triple encodes the statement “Activity 10ruka0 is a subclass of bbo:ManualTask”, and the second “Activity 10ruka0 has the bbo name of Set machine to auto mode),
wherein a function of the automated workflow template is configured to extend at least one of a data catalog or a knowledge graph (Bachhofner – see page 39, 2nd paragraph - To transform BPMN models into a knowledge graph representation, we use RDF Mapping Language (RML) as a declarative mapping language. RML allows for a similar abstraction for the relationship of heterogeneous data structures to RDF. RML by definition is a “a generic mapping language, based on and extending” the Relational data base to Resource description framework Mapping Language (R2RML) standard; see page 41, 2nd paragraph - software layer on top of the RML adds a number of convenient features. First, the user can specify a folder instead of only a single file at a time for the transformation. Second, the URI templates need to be set only once and do not need to be manually exchanged each time. Third, it allows the user to easily change the target ontology. And finally, it encapsulates the complexity of RML into simple command line calls. As an engine for the RML transformations, we decided to use RMLMapper5 as it offers a command line as well as a library interface which enables us to change to a different architecture in the future without changing the technology behind the transformation).
It would be obvious to combine Gupta and Makhija and Jiang for the same reasons as in claim 1 above. Gupta, Makhija, Jiang, and Bachhofner are analogous art as they are directed to business process workflows with tasks for people (see Gupta Abstract, FIG. 4, par 80; Makhija Abstract, see par 52, 86, 104, FIG. 2A-2F – approvals/tasks with user intervention; Jiang Abstract, par 35; Bachhofner, page 34, 1st paragraph; page 38, Section 4.1). Gupta discloses that data representing a process that is to be automated is stored using a structured format such as BPMN (See par 64). Gupta discloses having a knowledge graph 120 using entities, attributes, and relationships extracted by a process miner (See FIG. 1) (see par 66) and that its robotic process automation system that creates decision models is trained on domain knowledge (See par 119). Makhija discloses having a workflow knowledge database that includes building model-driven flows incorporating application process within supply chain (See par 74). Jiang discloses having a triple for matching staff to a task. Bachhofner improves upon Gupta and Makhija and Jiang by using triples with knowledge graphs and ontologies. One of ordinary skill in the art would be motivated to further include triples with knowledge graphs and ontologies to efficiently improve upon the customization for workflows in BPMN in Gupta and the data modeling done through template definitions in Makhija (See par 56), and the use of triples in knowledge graphs in Jiang.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for having business processes using knowledge graphs that are customized in Gupta (See Abstract, par 64, 84-85, FIG. 3), to further include templates for customization in Makhija, the use of triples in knowledge graphs in Jiang, and to further include triples with knowledge graphs and ontologies as disclosed in Bachhofner, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 21 and 23, Gupta discloses:
The method of claim 1, wherein:
the automated workflow templates are stored as triples within the knowledge graph (Bachhofner –see page 34, 1st paragraph – transform business process models in BPMN into a KG representation in RDF (resource description framework) based on tonology for process representation; supports BPMN 2.0 elements, including “most used ones”; see page 35, 2nd paragraph - For modeling KGs (knowledge graphs), Resource Description Framework (RDF) [13] is a widely used language recommended by the World Wide Web Consortium (W3C). KGs in RDF are formed from triples, each of which consists of a subject, a predicate, and an object; see page 35, last paragraph - BPMN-based Ontology (BBO) [5] is an ontology to represent business processes modeled in BPMN 2.0 in a KG. An ontology is necessary as it for example allows us to sub-class it’s concepts, or use their properties); and
forming the plurality of files comprises generating respective triples that define relationships between template nodes and data asset nodes in the knowledge graph (Bachhofner – page 35 - Figure 2 illustrates two triples in RDFs from our motivating use case - the two triples belong to the first task of our mass production process. The first triple encodes the statement “Activity 10ruka0 is a subclass of bbo:ManualTask”, and the second “Activity 10ruka0 has the bbo name of Set machine to auto mode; see page 41, 2nd paragraph - software layer on top of the RML adds a number of convenient features. First, the user can specify a folder instead of only a single file at a time for the transformation. Second, the URI templates need to be set only once and do not need to be manually exchanged each time. Third, it allows the user to easily change the target ontology. And finally, it encapsulates the complexity of RML into simple command line calls. As an engine for the RML transformations, we decided to use RMLMapper5 as it offers a command line as well as a library interface which enables us to change to a different architecture in the future without changing the technology behind the transformation).
Obvious to combine for the same reasons as claim 8 above.
Response to Arguments
Applicant's arguments filed 4/6/26 have been fully considered but they are not persuasive and/or are moot in view of the new rejections.
With regards to 101, Applicant argues that the claim is “maintaining the integrity of a complex graph data structure” by use of “updated triple comprising a subject corresponding to the data asset node, a predicate representing an ownership relationship (e.g. Applicant’s [0039] – characterizing similarity/fitness for a person to a task “ownership to another user that has similar characteristics or attributes (e.g., similar roles, similar permissions, etc.).”), and an object corresponding to the user account node” (e.g. current person assigned to task), and is tied to technical operation of a computerized data governance system. Remarks, pages 10-11. In response, Examiner respectfully disagrees. First, the “integrity” in the specification appears to be related to governance/policies [see Applicant’s 0045-0046], which would be part of the abstract idea; however, it is not helpful as this is not even required by the claim. Second, as explained in the revised rejection in response to the amendment, the “omission” and assigning ownership is viewed as assigning ownership/responsibility for processes/tasks, which is part of the abstract idea. Third, the “updated triple” is viewed as an additional element to extent it’s stored data. However, it just represents three piece of data of the business process of having an ownership relationship for tasks/jobs by people (e.g. Applicant’s [0039] – characterizing similarity/fitness for a person to a task “ownership to another user that has similar characteristics or attributes (e.g., similar roles, similar permissions, etc.).”), and an object corresponding to the user account node” (e.g. current person assigned to task).
Applicant further argues that the claim is not directed to the abstract idea of “certain methods of organizing human activity” and workflows to involve human approvals” but rather, programmatic operations on a knowledge graph data structure and generating triples to “remediate” omissions. Remarks, page 10. In response, Examiner respectfully disagrees. Applicant’s FIG. 8 gives an example where “tasks” are ones that people perform including “data request approval” and status of tasks can be “unassigned, pending, complete.” As explained in the revised rejection above, the triple is just looking at other people’s attributes (Applicant’s example is “skills”), and recommending/assigning a person who is most similar to a task be responsible or have ownership. This is part of the abstract idea of certain methods of organizing human activity and having user fulfill different roles and follow rules/instructions.
Applicant then argues that the “analyzing the execution data and the graph data to identify an omission including a missing ownership relationship” requires “programmatic correlation” with “vast numbers of interconnected nodes and relationships (e.g. triples). Remarks, page 11. In response, Examiner respectfully disagrees. First, the claims do not require “vast numbers of interconnected nodes and relationships.” Second, arguing what is or isn’t in the “human mind” is for the abstract idea grouping of “mental processes.” See MPEP 2106.04(a). Applicant’s only example of “omission” is in [0045] of data “omit an assigned role of “data steward”. Scoring users to fulfill the missing responsibility for the task is part of the abstract. Applicant then points to example in paragraph 39 of “automatically delegate ownership to another user” which also supports the same conclusion. Moreover, even if “vast numbers” of candidate users are analyzed, this argument is not persuasive. See MPEP 2106.05f – “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.” See MPEP 2106.05(f), citing Intellectual Ventures I LLC v. Capital One Bank (USA), 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
However, here the abstract idea grouping is “certain methods of organizing human activity.” Even if it was, Examiner notes “examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept.“ See MPEP 2106.04(a)(2)(III)(C).
Applicant then argues that “generating an updated triple… and updating the knowledge graph” is eligible based on Desjardins for improvement to “how the machine learning model itself operates.” Remark, page 12. Examiner respectfully disagrees. First, there is not “learning” or “training” or “neural networks.” This is not a similar situation. Second, unlike Desjardins where details on technical improvement were in the specification, Applicant’s argument that the “triple” itself “improves quality and integrity of the knowledge graph” is not persuasive as this relates to improving the abstract idea. Here, the triple represents a user’s fitness for an unassigned task. See also MPEP 2106.04(d)(1) “Conversely, if the specification explicitly sets forth an improvement only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field.”
The 103 arguments are moot in view of the new rejections necessitated by the amendments.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IVAN R GOLDBERG whose telephone number is (571)270-7949. The examiner can normally be reached 830AM - 430PM.
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
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