RECOMMENDATION ENGINE FOR SUGGESTING INTEGRATION PROCESSES USING ARTIFICIAL INTELLIGENCE

Application No. 18/756,656

• Filed: Jun 27, 2024
• Examiner: BROWN, SARA GRACE
• Art Unit: 3625
• Tech Center: 3600 — Transportation & Electronic Commerce
• Assignee: BOOMI, INC.
• OA Round: 1 (Non-Final)

Office 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 .
Information Disclosure Statement
The information disclosure statement (IDS) filed on 06/27/2024 has been fully considered.
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 1-18 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-16 are directed to a method, claim 17 is directed to a system, and claim 18 is directed to a non-transitory computer readable medium. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1, 17, and 18 are related to identifying processes, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Claim 1 recites limitations, similarly recited in claims 17 and 18, including “retrieve organization-specific data for an organization, wherein the organization-specific data comprise integration data, representing one or more integration processes on an integration platform of the organization, and profile data, representing one or more attributes of the organization; derive an input feature vector from the organization-specific data, wherein the input feature vector comprises a value for each of a plurality of features; determines at least one cluster of one or more other feature vectors, from among a plurality of clusters, to which the input feature vector is similar according to a similarity metric; identify one or more pre-built integration processes associated with the at least one cluster.” These limitations, as drafted, but for the recitation of the preamble language, is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the preamble language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the preamble language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 3-5 and 9-14 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 2, 6-8, and 15-16 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 17, and 18 do not integrate the judicial exception into a practical application. Independent claim 1 recites “a method comprising using at least one hardware processor to, in a recommendation process” within the preamble of the claim. Independent claim 17 recites a system comprising “at least one hardware processor; and software that is configured to, when executed by the at least one hardware processor.” Independent claim 18 recites “a non-transitory computer-readable medium having instructions stored therein, wherein the instructions, when executed by a processor, cause the processor to,” which is recited in the preamble of the claim. Claims 1, 17, and 18 further recite “apply a recommendation engine to the input feature vector, wherein the recommendation engine determine…” Claims 1, 17, and 18 further recite “and generate a screen of a graphical user interface, wherein the screen comprises a visual representation of each of the one or more pre-built integration processes, and wherein each visual representation is associated with an input for installing the respective pre-built integration process on the integration platform of the organization.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 3-5 and 9-14 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not prove integration into a practical application
Dependent claim 2 introduces the additional element of “wherein the method further comprises using the at least one hardware processor to, in response to selection of the input for installing one of the one or more pre-built integration processes, redirect the graphical user interface to a screen comprising a virtual canvas on which shapes, representing components of the one pre-built integration process, are arranged according to a design of the one pre-built integration process, and wherein the shapes are configured to be dragged and dropped, so as to enable rearrangement of the components of the one pre-built integration process on the virtual canvas.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. This limitation does not integrate the judicial exception into a practical application because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 6 introduces the additional element of “further comprising using the at least one hardware processor to apply an explanation model to the input feature vector to produce an explanation for each of the one or more pre-built integration processes, wherein the screen further comprises, for each visual representation of one of the one or more pre-built integration processes, a visual representation of the explanation for the respective pre-built integration process.” Dependent claim 7 introduces the additional element of “wherein the explanation model is a Local Interpretable Model-agnostic Explanations (LIME) model.” Dependent claim 8 introduces the additional element of “wherein the visual representation of each explanation comprises a natural-language expression.” The limitations of utilizing the explanation model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 15 introduces the additional element of “wherein the recommendation process is automatically executed when the screen is requested.” Dependent claim 16 introduces the additional element of “wherein the screen is a homepage of the graphical user interface for an authenticated user.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims above, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 17, and 18 do not comprise anything significantly more than the judicial exception. Independent claim 1 recites “a method comprising using at least one hardware processor to, in a recommendation process” within the preamble of the claim. Independent claim 17 recites a system comprising “at least one hardware processor; and software that is configured to, when executed by the at least one hardware processor.” Independent claim 18 recites “a non-transitory computer-readable medium having instructions stored therein, wherein the instructions, when executed by a processor, cause the processor to,” which is recited in the preamble of the claim. Claims 1, 17, and 18 further recite “apply a recommendation engine to the input feature vector, wherein the recommendation engine determine…” Claims 1, 17, and 18 further recite “and generate a screen of a graphical user interface, wherein the screen comprises a visual representation of each of the one or more pre-built integration processes, and wherein each visual representation is associated with an input for installing the respective pre-built integration process on the integration platform of the organization.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) are not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 3-5 and 9-14 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 2 introduces the additional element of “wherein the method further comprises using the at least one hardware processor to, in response to selection of the input for installing one of the one or more pre-built integration processes, redirect the graphical user interface to a screen comprising a virtual canvas on which shapes, representing components of the one pre-built integration process, are arranged according to a design of the one pre-built integration process, and wherein the shapes are configured to be dragged and dropped, so as to enable rearrangement of the components of the one pre-built integration process on the virtual canvas.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This limitation is not anything significantly more than the judicial exception because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 6 introduces the additional element of “further comprising using the at least one hardware processor to apply an explanation model to the input feature vector to produce an explanation for each of the one or more pre-built integration processes, wherein the screen further comprises, for each visual representation of one of the one or more pre-built integration processes, a visual representation of the explanation for the respective pre-built integration process.” Dependent claim 7 introduces the additional element of “wherein the explanation model is a Local Interpretable Model-agnostic Explanations (LIME) model.” Dependent claim 8 introduces the additional element of “wherein the visual representation of each explanation comprises a natural-language expression.” The limitations of utilizing the explanation model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 15 introduces the additional element of “wherein the recommendation process is automatically executed when the screen is requested.” Dependent claim 16 introduces the additional element of “wherein the screen is a homepage of the graphical user interface for an authenticated user.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims above, are not anything significantly more than the judicial exception.
Accordingly, claims 1-18 are rejected under 35 USC 101.

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 (i.e., changing from AIA to pre-AIA ) 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 6, 8, 10-12, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ali et al. (US 20250165849 A1) in view of Garg et al. (US 12353436 B1).

Regarding claim 1, Ali teaches a method comprising using at least one hardware processor to, in a recommendation process (Figs. 2-3):
retrieve organization-specific data for an organization ([0063] teaches a data acquisition layer that is tasked with collecting data from the various sources within the entity’s IT ecosystem, wherein the collected data is then passed to a data pre-processing module where it is cleansed, normalized, and transformed into a uniform format suitable for analysis, wherein a feature extraction process is applied to the pre-processed data to identify the most relevant attributes for the machine learning model, wherein [0070] teaches on the backend, each element of the predefined process elements are backed by a data model that captures its properties and behaviors, which are stored in a format that the system interprets to execute the workflow logic, wherein [0071] teaches the AI’s training data is sourced from the organization’s historical workflow executions which are logged and stored in the database system, wherein [0020] teaches the present system presents a solution to the pervasive challenges by consolidating all organizational actions into a single point of entry, thereby streamlining all tasks across various systems, wherein [0021] teaches a dynamic flow designer within the system allows for visual creation and definition of workflows that provide workflows for specific needs within the organization; see also: [0011, 0069, 0072]),
wherein the organization-specific data comprise integration data ([0063] teaches a data acquisition layer that is tasked with collecting data from the various sources within the entity’s IT ecosystem, wherein the collected data is then passed to a data pre-processing module where it is cleansed, normalized, and transformed into a uniform format suitable for analysis, wherein a feature extraction process is applied to the pre-processed data to identify the most relevant attributes for the machine learning model, wherein [0070] teaches on the backend, each element of the predefined process elements are backed by a data model that captures its properties and behaviors, which are stored in a format that the system interprets to execute the workflow logic, wherein [0071] teaches the AI’s training data is sourced from the organization’s historical workflow executions which are logged and stored in the database system, wherein [0020] teaches the present system presents a solution to the pervasive challenges by consolidating all organizational actions into a single point of entry, thereby streamlining all tasks across various systems, wherein [0021] teaches a dynamic flow designer within the system allows for visual creation and definition of workflows that provide workflows for specific needs within the organization; see also: [0011, 0069, 0072]),
representing one or more integration processes on an integration platform of the organization ([0063] teaches a data acquisition layer that is tasked with collecting data from the various sources within the entity’s IT ecosystem, wherein the collected data is then passed to a data pre-processing module where it is cleansed, normalized, and transformed into a uniform format suitable for analysis, wherein a feature extraction process is applied to the pre-processed data to identify the most relevant attributes for the machine learning model, wherein [0070] teaches on the backend, each element of the predefined process elements are backed by a data model that captures its properties and behaviors, which are stored in a format that the system interprets to execute the workflow logic, wherein [0071] teaches the AI’s training data is sourced from the organization’s historical workflow executions which are logged and stored in the database system, wherein [0020] teaches the present system presents a solution to the pervasive challenges by consolidating all organizational actions into a single point of entry, thereby streamlining all tasks across various systems, wherein [0021] teaches a dynamic flow designer within the system allows for visual creation and definition of workflows that provide workflows for specific needs within the organization; see also: [0011, 0069, 0072]),
and profile data, representing one or more attributes of the organization ([0063] teaches after the system is initialized, a data acquisition layer is established that is tasked with collecting data from the various sources within the entity’s IT ecosystem, wherein the collected data is pre-processed in order to perform a feature extraction process that identifies the most relevant attributes for the machine learning model, wherein [0052] teaches performing data transformation in order to consolidate data in order to perform attribute selection, wherein [0068-0069] teach integration of the workflow elements can be performed using database connections using vendor-specific protocols, wherein the process elements used to design workflows that meet their specific needs, wherein [0071] teaches the AI training data is sourced from the organization’s historical workflow executions, which are logged and stored in the database system in order to provide insights into the organization’s evolving operational landscape; see also: [0011, 0027, 0064]);
derive an input feature vector from the organization-specific data ([0055] teaches the machine learning algorithms can utilize an instance-based method, such as learning vector quantization, or support vector machine in order to enable the system to learn from past incidents to adapt to new previously unseen, wherein [0057] teaches the trained machine learning model can be deployed into an existing production environment to make practical business decisions based on live data, wherein the machine learning subsystem uses the inference engine to make decisions, wherein the inference engine can be applied to the live data, wherein [0058] teaches the machine learning model can utilize categorized outputs, such as groups or clusters, that are then presented to the user input system, wherein [0053] teaches the feature extraction or selection is a process of dimensionality reduction by which an initial set of data is reduced to more manageable groups for processing, wherein the feature extraction and selection may be used to select and combine variables into features, wherein [0063] teaches after the system is initialized, a data acquisition layer is established that is tasked with collecting data from the various sources within the entity’s IT ecosystem, wherein the collected data is pre-processed in order to perform a feature extraction process that identifies the most relevant attributes for the machine learning model; see also: [0052, 0063-0065]; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language.),
apply a recommendation engine to the input feature vector (Fig. 2C and [0057-0058] teach the trained machine learning model can be deployed to operationalize the anticipatory and actionable routing of incidents, wherein the model can be deployed into an existing production environment, wherein the machine learning model may receive live data based on that can be used to generate inferences for the user, wherein the machine learning subsystem uses an inference engine to apply the model to live data in order to facilitate responses, as well as in [0087] teaches the AI models are part of this monitoring phase using clustering to analyze the collected data, wherein the models are built and maintained using machine learning frameworks, wherein the model can suggest new optimization opportunities, as well as in [0089-0090] teach the feedback of the system’s functionality can be utilized to adjust to new data, user behavior, and feedback to deliver a refined, efficient workflow experience continuously; see also: [0069-0072, 0092]),
wherein the recommendation engine determines at least one cluster of one or more other feature vectors (Fig. 2C and [0057-0058] teach the trained machine learning model can be deployed to operationalize the anticipatory and actionable routing of incidents, wherein the model can be deployed into an existing production environment, wherein the machine learning model may receive live data based on that can be used to generate inferences for the user, wherein the machine learning subsystem uses an inference engine to apply the model to live data in order to facilitate responses, as well as in [0087] teaches the AI models are part of this monitoring phase using clustering to analyze the collected data, wherein the models are built and maintained using machine learning frameworks, wherein the model can suggest new optimization opportunities, as well as in [0089-0090] teach the feedback of the system’s functionality can be utilized to adjust to new data, user behavior, and feedback to deliver a refined, efficient workflow experience continuously; see also: [0069-0072, 0092]),
identify one or more pre-built integration processes associated with the at least one cluster (Fig. 2C and [0057-0058] teach the trained machine learning model can be deployed to operationalize the anticipatory and actionable routing of incidents, wherein the model can be deployed into an existing production environment, wherein the machine learning model may receive live data based on that can be used to generate inferences for the user, wherein the machine learning subsystem uses an inference engine to apply the model to live data in order to facilitate responses, wherein [0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0070] teaches the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein the dynamic flow designer is an AI-powered recommendation engine, wherein this engine can analyze historical workflow data and efficiency metrics, wherein by applying machine learning models, the engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flower designer as intelligent prompts, offering options for improving the efficiency of the workflow based on the analysis of past performance and common organization practices, wherein [0071] teaches the AI training data is sourced from the organization’s historical workflow executions, which are logged and stored in the database, wherein [0072] teaches the system’s machine learning component begins an ongoing process of training and refinement, wherein the AI analyzes the effectiveness of different workflows and interactions to optimize task prioritization and workflow design, wherein [0073] teaches data collection is facilitated by a data acquisition module that continuously gathers workflow execution data, which provides raw material that the AI uses to learn and adapt by extracting relevant features and feeding them into the AI model to recognize patterns in workflow executions and user behaviors; see also: [0059-0061]);
and generate a screen of a graphical user interface ([0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0070] teaches the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein the dynamic flow designer is an AI-powered recommendation engine, wherein this engine can analyze historical workflow data and efficiency metrics, wherein by applying machine learning models, the engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flower designer as intelligent prompts, offering options for improving the efficiency of the workflow based on the analysis of past performance and common organization practices, wherein [0071] teaches the AI training data is sourced from the organization’s historical workflow executions, which are logged and stored in the database, wherein [0072] teaches the system’s machine learning component begins an ongoing process of training and refinement, wherein the AI analyzes the effectiveness of different workflows and interactions to optimize task prioritization and workflow design, wherein [0073] teaches data collection is facilitated by a data acquisition module that continuously gathers workflow execution data, which provides raw material that the AI uses to learn and adapt by extracting relevant features and feeding them into the AI model to recognize patterns in workflow executions and user behaviors; see also: [0057-0061]),
wherein the screen comprises a visual representation of each of the one or more pre-built integration processes ([0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0070] teaches the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein the dynamic flow designer is an AI-powered recommendation engine, wherein this engine can analyze historical workflow data and efficiency metrics, wherein by applying machine learning models, the engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flower designer as intelligent prompts, offering options for improving the efficiency of the workflow based on the analysis of past performance and common organization practices, wherein [0071] teaches the AI training data is sourced from the organization’s historical workflow executions, which are logged and stored in the database, wherein [0072] teaches the system’s machine learning component begins an ongoing process of training and refinement, wherein the AI analyzes the effectiveness of different workflows and interactions to optimize task prioritization and workflow design, wherein [0073] teaches data collection is facilitated by a data acquisition module that continuously gathers workflow execution data, which provides raw material that the AI uses to learn and adapt by extracting relevant features and feeding them into the AI model to recognize patterns in workflow executions and user behaviors; see also: [0057-0061]),
and wherein each visual representation is associated with an input for installing the respective pre-built integration process on the integration platform of the organization ([0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0070] teaches the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein the dynamic flow designer is an AI-powered recommendation engine, wherein this engine can analyze historical workflow data and efficiency metrics, wherein by applying machine learning models, the engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flower designer as intelligent prompts, offering options for improving the efficiency of the workflow based on the analysis of past performance and common organization practices, wherein [0071] teaches the AI training data is sourced from the organization’s historical workflow executions, which are logged and stored in the database, wherein [0072] teaches the system’s machine learning component begins an ongoing process of training and refinement, wherein the AI analyzes the effectiveness of different workflows and interactions to optimize task prioritization and workflow design, wherein [0073] teaches data collection is facilitated by a data acquisition module that continuously gathers workflow execution data, which provides raw material that the AI uses to learn and adapt by extracting relevant features and feeding them into the AI model to recognize patterns in workflow executions and user behaviors; see also: [0057-0061]).
While Ali teaches deriving input features from organization specific data, Ali does not explicitly teach derive an input feature vector, wherein the input feature vector comprises a value for each of a plurality of features; apply a recommendation engine to the input feature vector, wherein the recommendation engine determines at least one cluster of one or more other feature vectors, from among a plurality of clusters, to which the input feature vector is similar according to a similarity metric.
From the same or similar field of endeavor, Garg teaches derive an input feature vector (Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 25 lines 3-33 teach vector embeddings comprise features, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records; see also: Col 27 lines 59-63, Col 30 lines 35-45, Col 32 lines 14-34),
wherein the input feature vector comprises a value for each of a plurality of features (Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 25 lines 3-33 teach vector embeddings comprise features, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records; see also: Col 27 lines 59-63, Col 30 lines 35-45, Col 32 lines 14-34);
apply a recommendation engine to the input feature vector, wherein the recommendation engine determines at least one cluster of one or more other feature vectors, from among a plurality of clusters (Col 30 line 58 to Col 31 line 3 teaches the vector embeddings of existing records are clustered so that a single distance may be determined between similar existing records and the first record, without determining a distance between the first record and all existing records, wherein clustering may group together the subset of records having a same connection to the target dimension, wherein Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 25 lines 3-33 teach the artificial intelligence system can recommend fields for use in rules by generating vector embeddings of nodes and clustering the nodes mapped to the same or similar values in the target dimension, wherein the aggregate vector embeddings may be determined for each of the clusters based on an aggregate combination of the vector embeddings in the cluster, wherein the embedded features having a lowest predictive impact on the mapping value may be excluded, wherein the use of clusters of vector embeddings finds the features with the highest predictive impact, wherein the proposed rules are recommended to a user, wherein the rules are reviewed and modified in a rules configuration interface depending on user preference, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records; see also: Col 27 lines 59-63, Col 30 lines 35-45),
to which the input feature vector is similar according to a similarity metric (Col 30 line 58 to Col 31 line 3 teaches the vector embeddings of existing records are clustered so that a single distance may be determined between similar existing records and the first record, without determining a distance between the first record and all existing records, wherein clustering may group together the subset of records having a same connection to the target dimension, wherein Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 25 lines 3-33 teach the artificial intelligence system can recommend fields for use in rules by generating vector embeddings of nodes and clustering the nodes mapped to the same or similar values in the target dimension, wherein the aggregate vector embeddings may be determined for each of the clusters based on an aggregate combination of the vector embeddings in the cluster, wherein the embedded features having a lowest predictive impact on the mapping value may be excluded, wherein the use of clusters of vector embeddings finds the features with the highest predictive impact, wherein the proposed rules are recommended to a user, wherein the rules are reviewed and modified in a rules configuration interface depending on user preference, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records, and wherein Col 27 lines 59-63 teach the vector embeddings are determined using a cosine distance between two vectors using a Pearson correlation coefficient, using a Euclidean distance, or other vector distance metric; see also: Col 30 lines 35-45).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Ali to incorporate the teachings of Garg to include derive an input feature vector, wherein the input feature vector comprises a value for each of a plurality of features; apply a recommendation engine to the input feature vector, wherein the recommendation engine determines at least one cluster of one or more other feature vectors, from among a plurality of clusters, to which the input feature vector is similar according to a similarity metric. One would have been motivated to do so in order to find the best-matching templates by relying on vector embeddings and clustering (Garg, Col 2 lines 17-35). By incorporating the teachings of Garg, one would have been able to score the matching of templates utilizing vector embeddings by how accurately the selections align with the source dimension (Garg, Col 35 line 50 to Col 36 line 11).
Regarding claims 17 and 18, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 17, Ali teaches a system comprising (Fig. 1b and [0035-0036] teach a system): at least one hardware processor (Fig. 1b and [0035-0036] teach a system comprising a processor); and software that is configured to, when executed by the at least one hardware processor (Fig. 1b and [0035-0036] teach a system comprising a processor that can process instructions stored in a memory). Regarding claim 18, Ali teaches a non-transitory computer-readable medium having instructions stored therein, wherein the instructions, when executed by a processor, cause the processor to (Fig. 1b and [0035-0036] teach a system comprising a computer-readable medium storing instructions performed by a processor). Accordingly, claims 17 and 18 are rejected as being unpatentable over Ali in view of Garg.

Regarding claim 2, the combination of Ali and Garg teaches all the limitations of claim 1 above.
Ali further teaches wherein the method further comprises using the at least one hardware processor to, in response to selection of the input for installing one of the one or more pre-built integration processes, redirect the graphical user interface to a screen comprising a virtual canvas on which shapes (Fig. 3 and [0060] teach optimizing workflow management including a first step of installing the software system on the organization’s servers and configuring it to interface with the preexisting IT infrastructure, wherein [0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0070] teaches the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein the dynamic flow designer is an AI-powered recommendation engine, wherein this engine can analyze historical workflow data and efficiency metrics, wherein by applying machine learning models, the engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flower designer as intelligent prompts, offering options for improving the efficiency of the workflow based on the analysis of past performance and common organization practices; see also: [0071-0072]),
representing components of the one pre-built integration process, are arranged according to a design of the one pre-built integration process (Fig. 3 and [0060] teach optimizing workflow management including a first step of installing the software system on the organization’s servers and configuring it to interface with the preexisting IT infrastructure, wherein [0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0070] teaches the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein the dynamic flow designer is an AI-powered recommendation engine, wherein this engine can analyze historical workflow data and efficiency metrics, wherein by applying machine learning models, the engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flower designer as intelligent prompts, offering options for improving the efficiency of the workflow based on the analysis of past performance and common organization practices; see also: [0071-0072]), and wherein the shapes are configured to be dragged and dropped, so as to enable rearrangement of the components of the one pre-built integration process on the virtual canvas (Fig. 3 and [0060] teach optimizing workflow management including a first step of installing the software system on the organization’s servers and configuring it to interface with the preexisting IT infrastructure, wherein [0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0070] teaches the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein the dynamic flow designer is an AI-powered recommendation engine, wherein this engine can analyze historical workflow data and efficiency metrics, wherein by applying machine learning models, the engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flower designer as intelligent prompts, offering options for improving the efficiency of the workflow based on the analysis of past performance and common organization practices; see also: [0071-0072]).

Regarding claim 3, the combination of Ali and Garg teaches all the limitations of claim 1 above.
However, Ali does not explicitly teach further comprising using the at least one hardware processor to: retrieve global data for a plurality of organizations, wherein the global data comprise the integration data and the profile data for each of the plurality of organizations; derive a plurality of feature vectors from the global data, wherein each of the plurality of feature vectors comprises a value for each of the plurality of features; and group the plurality of feature vectors into the plurality of clusters using a clustering algorithm.
From the same or similar field of endeavor, Garg further teaches further comprising using the at least one hardware processor to: retrieve global data for a plurality of organizations (Col 25 lines 3-33 teach generating vector embeddings of nodes that have been mapped to a target dimension, wherein the vectors have features, wherein Col 26 line 54 to Col 27 line 26 teach generating vector embeddings that represent the records in the source dimension, wherein the source comprises features, wherein Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records, wherein Col 2 lines 1-15 teach the aligning applications for an organization during a merger or acquisition, wherein Col 26 lines 41-52 teach all dimensions may be maintained on a global level with respect to specific blacklist/whitelist options; see also: Col 27 lines 59-63, Col 30 lines 35-45, Col 32 lines 14-34),
wherein the global data comprise the integration data and the profile data for each of the plurality of organizations (Col 25 lines 3-33 teach generating vector embeddings of nodes that have been mapped to a target dimension, wherein the vectors have features, wherein Col 26 line 54 to Col 27 line 26 teach generating vector embeddings that represent the records in the source dimension, wherein the source comprises features, wherein Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records, wherein Col 2 lines 1-15 teach the aligning applications for an organization during a merger or acquisition, wherein Col 26 lines 41-52 teach all dimensions may be maintained on a global level with respect to specific blacklist/whitelist options; see also: Col 27 lines 59-63, Col 30 lines 35-45, Col 32 lines 14-34);
derive a plurality of feature vectors from the global data, wherein each of the plurality of feature vectors comprises a value for each of the plurality of features (Col 25 lines 3-33 teach generating vector embeddings of nodes that have been mapped to a target dimension, wherein the vectors have features, wherein Col 26 line 54 to Col 27 line 26 teach generating vector embeddings that represent the records in the source dimension, wherein the source comprises features, wherein Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records, wherein Col 2 lines 1-15 teach the aligning applications for an organization during a merger or acquisition, wherein Col 26 lines 41-52 teach all dimensions may be maintained on a global level with respect to specific blacklist/whitelist options; see also: Col 27 lines 59-63, Col 30 lines 35-45, Col 32 lines 14-34); and
group the plurality of feature vectors into the plurality of clusters using a clustering algorithm (Col 30 line 58 to Col 31 line 3 teaches the vector embeddings of existing records are clustered so that a single distance may be determined between similar existing records and the first record, without determining a distance between the first record and all existing records, wherein clustering may group together the subset of records having a same connection to the target dimension, wherein Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 25 lines 3-33 teach the artificial intelligence system can recommend fields for use in rules by generating vector embeddings of nodes and clustering the nodes mapped to the same or similar values in the target dimension, wherein the aggregate vector embeddings may be determined for each of the clusters based on an aggregate combination of the vector embeddings in the cluster, wherein the embedded features having a lowest predictive impact on the mapping value may be excluded, wherein the use of clusters of vector embeddings finds the features with the highest predictive impact, wherein the proposed rules are recommended to a user, wherein the rules are reviewed and modified in a rules configuration interface depending on user preference, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records, and wherein Col 27 lines 59-63 teach the vector embeddings are determined using a cosine distance between two vectors using a Pearson correlation coefficient, using a Euclidean distance, or other vector distance metric; see also: Col 2 lines 1-15, Col 26 lines 41-52, Col 30 lines 35-45).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the further teachings of Garg to include further comprising using the at least one hardware processor to: retrieve global data for a plurality of organizations, wherein the global data comprise the integration data and the profile data for each of the plurality of organizations; derive a plurality of feature vectors from the global data, wherein each of the plurality of feature vectors comprises a value for each of the plurality of features; and group the plurality of feature vectors into the plurality of clusters using a clustering algorithm. One would have been motivated to do so in order to find the best-matching templates by relying on vector embeddings and clustering (Garg, Col 2 lines 17-35). By incorporating the teachings of Garg, one would have been able to score the matching of templates utilizing vector embeddings by how accurately the selections align with the source dimension (Garg, Col 35 line 50 to Col 36 line 11).

Regarding claim 4, the combination of Ali and Garg teaches all the limitations of claim 3 above.
Ali further teaches wherein the clustering algorithm is a K-Means algorithm ([0055] teaches the machine learning algorithms include using a clustering method, such as k-means clustering, to learn new patterns; see also: [0056-0057]).

Regarding claim 6, the combination of Ali and Garg teaches all the limitations of claim 1 above.
Ali further teaches further comprising using the at least one hardware processor to apply an explanation model to the input feature vector to produce an explanation for each of the one or more pre-built integration processes ([0070] teaches the AI powered recommendations engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flow designer as intelligent prompts, offering options to improve the efficiency of the workflow based on the analysis of past performance and common organizational practices, wherein the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein [0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0020] teaches artificial intelligence can enable assignors to specify and tailor workflows for different processes and assignees, wherein the centralized system is intuitive and generates an interface with AI prioritization, which provides a consolidated dashboard view, wherein [0021] teaches a dynamic flow designer within the system allows for the visual creation and definition of workflows, wherein the AI can analyze historical data and organizational patterns to recommend the most effective workflow designs, wherein the AI system can proactively suggest delegation of tasks to prevent workflow interruptions; see also: [0022, 0057-0061, 0071-0073]),
wherein the screen further comprises, for each visual representation of one of the one or more pre-built integration processes, a visual representation of the explanation for the respective pre-built integration process ([0070] teaches the AI powered recommendations engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flow designer as intelligent prompts, offering options to improve the efficiency of the workflow based on the analysis of past performance and common organizational practices, wherein the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein [0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0020] teaches artificial intelligence can enable assignors to specify and tailor workflows for different processes and assignees, wherein the centralized system is intuitive and generates an interface with AI prioritization, which provides a consolidated dashboard view, wherein [0021] teaches a dynamic flow designer within the system allows for the visual creation and definition of workflows, wherein the AI can analyze historical data and organizational patterns to recommend the most effective workflow designs, wherein the AI system can proactively suggest delegation of tasks to prevent workflow interruptions; see also: [0022, 0057-0061, 0071-0073]).
While Ali teaches further comprising using the at least one hardware processor to apply an explanation model to the input feature vector to produce an explanation for each of the one or more pre-built integration processes, Ali does not explicitly teach the input feature vector.
From the same or similar field of endeavor, Garg further teaches the input feature vector (Col 31 line 61 to Col 32 line 13 teach the incoming node used to search for other nodes that are similar to the incoming node and that have already been connected to the target dimension, wherein the search may find a template node or cluster of candidate template nodes, wherein the matching can be performed for clusters of candidate template nodes that are the closest neighbor, wherein Col 32 lines 14-34 teach a vector embedding is generated for the new or updated node in the source dimension, wherein the vector embedding is fed into the trained model, wherein the trained model is used to determine a distance between the vector embedding and other embeddings, such as an aggregate vector embedding of a closest cluster of vector embeddings that have a connection with the target dimension in order to identify the most closely matching vector embedding, wherein Col 25 lines 3-33 teach vector embeddings comprise features, wherein Col 2 lines 16-35 teach the processes rely on matching field relates to template records and relies upon vector embeddings and clustering, which can be used to identify template records; see also: Col 27 lines 59-63, Col 30 lines 35-45, Col 32 lines 14-34).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the further teachings of Garg to include the input feature vector. One would have been motivated to do so in order to find the best-matching templates by relying on vector embeddings and clustering (Garg, Col 2 lines 17-35). By incorporating the teachings of Garg, one would have been able to score the matching of templates utilizing vector embeddings by how accurately the selections align with the source dimension (Garg, Col 35 line 50 to Col 36 line 11).

Regarding claim 8, the combination of Ali and Garg teaches all the limitations of claim 6 above.
Ali further teaches wherein the visual representation of each explanation comprises a natural-language expression ([0070] teaches the AI powered recommendations engine can identify patterns and suggest optimal workflow configurations, wherein the AI suggestions are presented to the user within the flow designer as intelligent prompts, offering options to improve the efficiency of the workflow based on the analysis of past performance and common organizational practices, wherein the designer interface presents a canvas where users can drag and drop predefined process elements, which could include tasks, decision points, and various action nodes, wherein the elements are represented using visually distinct icons that make it easier for users to identify and organize them into a coherent workflow sequence, wherein each element is backed by a data model that captures its properties and behaviors, wherein [0069] teaches the system’s dynamic flow designer allows manager and IT staff to visually create and customize workflows, wherein this interface lets users drag and drop different process elements to design workflows that meet their specific needs, wherein the AI utilizes supervised learning techniques to suggest workflow designs based on historical efficiency data and common organizational patterns, wherein the system’s dynamic flow designer is an integral component that provides the interface enabling creation and customization of workflow, wherein [0020] teaches artificial intelligence can enable assignors to specify and tailor workflows for different processes and assignees, wherein the centralized system is intuitive and generates an interface with AI prioritization, which provides a consolidated dashboard view, wherein [0021] teaches a dynamic flow designer within the system allows for the visual creation and definition of workflows, wherein the AI can analyze historical data and organizational patterns to recommend the most effective workflow designs, wherein the AI system can proactively suggest delegation of tasks to prevent workflow interruptions; see also: [0022, 0057-0061, 0071-0073]).

Regarding claim 10, the combination of Ali and Garg teaches all the limitations of claim 1 above.
Ali further teaches wherein the plurality of features comprises a size of the organization [0055] teaches the machine learning algorithms can utilize an instance-based method, such as learning vector quantization, or support vector machine in order to enable the system to learn from past incidents to adapt to new previously unseen, wherein [0057] teaches the trained machine learning model can be deployed into an existing production environment to make practical business decisions based on live data, wherein the machine learning subsystem uses the inference engine to make decisions, wherein the inference engine can be applied to the live data, wherein [0092] teaches the feedback may be received for a smaller organization, as well as in [0080] teaches utilizing a different algorithm for a smaller organization; see also: [0083, 0091]).

Regarding claim 11, the combination of Ali and Garg teaches all the limitations of claim 1 above.
However, Ali does not explicitly teach wherein the plurality of features comprises a location of the organization.
From the same or similar field of endeavor, Garg teaches wherein the plurality of features comprises a location of the organization (Col 10 lines 2-16 teach the template record may have one or more location values, and the template record may be mapped to a specific record, such as “West” region, in a location dimension, wherein the template records are discovered using rules-driven processes, wherein Col 10 lines 17-41 teach each application in an organization’s ecosystem may have a different representation based on different levels of specificity, such as larger regions or smaller regions, wherein these values in the different data structures may also roll up to other records of varying specificity, as well as in Col 12 lines 38-62 teach the location may be stored in the record, wherein the location may be the location of the employee; see also: Col 10 line 57 to Col 11 line 9, Col 13 lines 17-40).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the further teachings of Garg to include wherein the plurality of features comprises a location of the organization. One would have been motivated to do so in order to find the best-matching templates by relying on vector embeddings and clustering (Garg, Col 2 lines 17-35). By incorporating the teachings of Garg, one would have been able to score the matching of templates utilizing vector embeddings by how accurately the selections align with the source dimension (Garg, Col 35 line 50 to Col 36 line 11).

Regarding claim 12, the combination of Ali and Garg teaches all the limitations of claim 1 above.
Ali further teaches wherein the plurality of features comprises an industry of the organization ([0055] teaches the machine learning algorithms can utilize an instance-based method, such as learning vector quantization, or support vector machine in order to enable the system to learn from past incidents to adapt to new previously unseen, wherein [0057] teaches the trained machine learning model can be deployed into an existing production environment to make practical business decisions based on live data, wherein the machine learning subsystem uses the inference engine to make decisions, wherein the inference engine can be applied to the live data, wherein [0092] teaches in a highly regulated industry, feedback and data analysis might be subject to stricter compliance checks before being used to alter system behavior, wherein the feedback loop ensures that the workflow management and response system remains dynamic and responsive to user needs, wherein the iterative process of collecting feedback can be used to adjust the AI models and workflow configurations to adapt to the evolving demands of the organization; see also: [0091]).

Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ali et al. (US 20250165849 A1) in view of Garg et al. (US 12353436 B1) in view of Osman (US 20250390921 A1).

Regarding claim 5, the combination of Ali and Garg teaches all the limitations of claim 3 above.
However, Ali does not explicitly teach wherein the clustering algorithm is a K-Prototype algorithm.
From the same or similar field of endeavor, Osman teaches wherein the clustering algorithm is a K-Prototype algorithm ([0025] teaches providing a modular and flexible architecture that allows for seamless integration with existing products and order processing workflows, wherein [0041] teaches the machine learning model may utilize unsupervised approaches including clustering, such as k-prototypes or k-means; see also: [0039, 0058]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the teachings of Osman to include wherein the clustering algorithm is a K-Prototype algorithm. One would have been motivated to do so in order to enhance accessibility and user experience by extracting data for further processing and integration into the order management workflow (Osman, [0058]). By incorporating the teachings of Osman, one would have been able to easily adapt to changes without requiring extensive intervention or downtime by providing seamless integration with existing processing workflows (Osman, [0025]).

Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ali et al. (US 20250165849 A1) in view of Garg et al. (US 12353436 B1) in view of Fani Sani et al. (US 20240028996 A1).

Regarding claim 7, the combination of Ali and Garg teaches all the limitations of claim 6 above.
However, Ali does not explicitly teach wherein the explanation model is a Local Interpretable Model-agnostic Explanations (LIME) model.
From the same or similar field of endeavor, Fani Sani teaches wherein the explanation model is a Local Interpretable Model-agnostic Explanations (LIME) model ([0019] teaches the system can perform a root cause analysis in process mining, wherein a visualizer can generate a visualization of the RCA rules, wherein the visualizations may be interactive, wherein the users may select a condition to view actions triggered by the conditions, wherein the rules may be developed by modifying feature values using Local Interpretable Model agnostic Explanations, or LIME, wherein the users may provide target goals to improve a process before and/or after visualization, wherein the RCA engine may perform an RCA based on modified feature values to achieve the target goal and present target modifications, wherein [0020] teaches the analysis performed on processes, such as tasks, workflows, to determine which processes are best suited for a task; see also: [0003, 0060]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the teachings of Fani Sani to include wherein the explanation model is a Local Interpretable Model-agnostic Explanations (LIME) model. One would have been motivated to do so in order to improve a process after visualization by developing rules by modifying feature values using LIME (Fani Sani, Abstract). By incorporating the teachings of Fani Sani, one would have been able to improve the interactive visualization of a process by viewing actions triggered by the condition using LIME (Fani Sani, [0019]).

Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ali et al. (US 20250165849 A1) in view of Garg et al. (US 12353436 B1) in view of Eldan et al. (US 12169802 B1).

Regarding claim 9, the combination of Ali and Garg teaches all the limitations of claim 1 above.
However, Ali does not explicitly teach wherein the plurality of features comprises an indication of one or more data endpoints in the integration data.
From the same or similar field of endeavor, Eldan teaches wherein the plurality of features comprises an indication of one or more data endpoints in the integration data (Fig. 2 and Col 20 lines 1-21 teach a workflow diagram that includes three distinct workflow branches comprising a number of blocks, wherein Col 22 lines 23 to Col 23 line 3 teach the workflow blocks may be categorized as a function of the at least one received trigger and implemented action, wherein the workflow may include a start block and at least one end block depending on the number of branches included in the workflow, wherein the workflows blocks are placed at the beginning and end of the workflow, wherein the blocks implement decision actions and may be triggered by an event corresponding to the end of execution of a connected upstream workflow block; see also: Col 17 lines 28-58, Col 43 lines 36-56).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the teachings of Eldan to include wherein the plurality of features comprises an indication of one or more data endpoints in the integration data. One would have been motivated to do so in order to enhance the efficiency and convenience of the workflow construction by leveraging pre-defined functionalities and configurations including enabling dragging of workflow blocks (Eldan, Col 43 lines 36-56). By incorporating the teachings of Eldan, one would have been able to increase efficiency by allowing multiple paths or branches within a workflow to execute independently and in parallel (Eldan, Col 87 line 63 to Col 88 line 3).

Claim(s) 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ali et al. (US 20250165849 A1) in view of Garg et al. (US 12353436 B1) in view of Eidelman et al. (US 20230214754 A1).

Regarding claim 13, the combination of Ali and Garg teaches all the limitations of claim 1 above.
However, Ali does not explicitly teach wherein the plurality of features comprise an indication of one or more trade associations to which the organization belongs.
From the same or similar field of endeavor, Eidelman teaches wherein the plurality of features comprise an indication of one or more trade associations to which the organization belongs ([0097] teaches commissions or regulatory agencies comprise governmental units that produce regulations and/or enforcement actions, such as the federal trade commission, the federal institute for drugs and medical devices, etc., wherein the regulatory rules comprise rules or guidelines that may have the force of law and may implement one or more pieces of regulation, wherein [0456] teaches features from the training data for training the prediction model include an agency, as well as in [0464] teaches calculating metrics based on trade associations being one of the attributes of the user, wherein the individual may receive a calculation indicating their interaction; see also: [0094, 0108, 0178]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the teachings of Eidelman teaches wherein the plurality of features comprise an indication of one or more trade associations to which the organization belongs. One would have been motivated to do so in order to best strategize how to promote an organizational interest within a governmental unit (Eidelman, [0002]). By incorporating the teachings of Eidelman, one would have been able to understand the impact of the effects of a policy from a trade association (Eidelman, [0108]).

Regarding claim 14, the combination of Ali and Garg teaches all the limitations of claim 1 above.
However, Ali does not explicitly teach wherein the plurality of features comprise an indication of one or more government agencies with which the organization interacts.
From the same or similar field of endeavor, Eidelman teaches wherein the plurality of features comprise an indication of one or more government agencies with which the organization interacts ([0268] teaches receiving, at the user input module, policymaker data including regulation information of the regulators or government officials and party affiliation of each of the regulators or government officials, wherein [0492] teaches the server maintains a list of user-selectable issues including issues related to one or more government bodies; see also: [0094, 0261, 0277, 0464]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the teachings of Eidelman teaches wherein the plurality of features comprise an indication of one or more government agencies with which the organization interacts. One would have been motivated to do so in order to best strategize how to promote an organizational interest within a governmental unit (Eidelman, [0002]). By incorporating the teachings of Eidelman, one would have been able to understand the impact of the effects of a policy from a trade association (Eidelman, [0108]).

Claim(s) 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ali et al. (US 20250165849 A1) in view of Garg et al. (US 12353436 B1) in view of Williams et al. (US 20240281410 A1).

Regarding claim 15, the combination of Ali and Garg teaches all the limitations of claim 1 above.
However, Ali does not explicitly teach wherein the recommendation process is automatically executed when the screen is requested.
From the same or similar field of endeavor, Williams teaches wherein the recommendation process is automatically executed when the screen is requested ([0601] teaches the workflow system may operate with the GUI of the platform 100 for displaying a workflow tool that maybe be found under a workflows section in a top navigation set tool, wherein this may provide a home where a visual automation builder may be used, which may allow for processes such as triggers and actions to be executed, wherein the GUI of the platform displays a custom code action using custom workflow actions system, wherein [0613] teaches the custom workflow actions system and custom workflow actions processes may allow for running this code internally with respect to the platform, wherein this may allow for any use to immediately run workflow actions by the integrated platform, as well as in [0361] teaches human read-able suggestions may be automatically generated by the system and provided as outputs, wherein the suggestions include suitable and relevant recommendations, wherein [0793] teaches use of registration patterns may facilitate accessing the CRM and configuring the accessed CRM data for use, wherein once granted access to the CRM through a customer portal, an integrator may create new objects; see also: [0087, 0471, 0603]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali and Garg to incorporate the teachings of Williams to include wherein the recommendation process is automatically executed when the screen is requested. One would have been motivated to do so in order to avoid requiring access to multiple log ins by providing one customer portal that can integrate all applications of the user, thus keeping processes running smoothly (Williams, [0793]). By incorporating the teachings of Williams, one would have been able to allow for improved and faster development of new custom object types by clients and developers of the framework by providing a customization system including a multi-service business platform (Williams, [0087]).

Regarding claim 16, the combination of Ali, Garg, and Williams teaches all the limitations of claim 15 above.
However, Ali does not explicitly teach wherein the screen is a homepage of the graphical user interface for an authenticated user.
From the same or similar field of endeavor, Williams further teaches wherein the screen is a homepage of the graphical user interface for an authenticated user ([0601] teaches the workflow system may operate with the GUI of the platform 100 for displaying a workflow tool that maybe be found under a workflows section in a top navigation set tool, wherein this may provide a home where a visual automation builder may be used, which may allow for processes such as triggers and actions to be executed, wherein the GUI of the platform displays a custom code action using custom workflow actions system, wherein [0613] teaches the custom workflow actions system and custom workflow actions processes may allow for running this code internally with respect to the platform, wherein this may allow for any use to immediately run workflow actions by the integrated platform, as well as in [0361] teaches human read-able suggestions may be automatically generated by the system and provided as outputs, wherein the suggestions include suitable and relevant recommendations, wherein [0793] teaches use of registration patterns may facilitate accessing the CRM and configuring the accessed CRM data for use, wherein once granted access to the CRM through a customer portal, an integrator may create new objects; see also: [0471, 0603]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ali, Garg, and Williams to incorporate the teachings of Williams to include wherein the screen is a homepage of the graphical user interface for an authenticated user. One would have been motivated to do so in order to avoid requiring access to multiple log ins by providing one customer portal that can integrate all applications of the user, thus keeping processes running smoothly (Williams, [0793]). By incorporating the teachings of Williams, one would have been able to allow for improved and faster development of new custom object types by clients and developers of the framework by providing a customization system including a multi-service business platform (Williams, [0087]).

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Pringle et al. (US 20240031367 A1) discloses an integration process including a homepage
Krishnan et al. (US 20220138345 A1) discloses recommending aspects of an integration process

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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625

Prosecution Timeline

• Jun 27, 2024: Application Filed
• Mar 21, 2026: Non-Final Rejection — §101, §103 (current)