DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The rejections under 35 U.S.C. §101 of claim 14 is withdrawn in view of the cancelation of claim 14.
Examiner acknowledges the amendments to the claims received on 12/1/2025 have been entered, and that no new matter has been added.
Response to Arguments
Argument 1: Applicant argues on page 9 in the filing on 12/1/2025 that “Burnett does not teach or suggest at least a method that includes "form a pipeline ...being modifiable responsive to changes to the first module and the second module included in the pipeline and configured to learn trends and predictions ... cause a modification of a functionality of the selected module resulting in a modified module, the modified module being a different version of the selected module included in the pipeline, and cause a modification of the learned trends and predictions," as recited in independent claim 1.”
Response to Argument 1: Respectfully, the combination of Burnett and Jacob teaches the above. Burnett teaches forming a pipeline with a “pipeline 3550 depicts a series of interconnected nodes 3510, 3511” [Burnett 0807, Fig. 35A-35B]. This pipeline being modifiable to changes in the modules such as moving and deleting nodes [Burnett 0809], as well as modifying options for the nodes [Burnett 0901, Fig. 50B-50C]. The modification to the node options cause the nodes/modules to be modified, which means the nodes/modules are a different version [Burnett 0901, Fig. 50B], which cause a modification of the programming based upon the modified module [Burnett 0902, Fig. 50B-C]. In combination with Jacob: Jacob teaches a pipeline with a first and second module with 2 connected nodes, functionality module 204 and functionality module 210 [Jacob Col 7 lines 67-Col 8 lines 1-3, Fig. 3]. Jacob is configured to learn trends and predictions, by using AI and ML to find trends in data [Jacob Col 1 lines 21-24, Fig. 3]. Jacob’s AI modules are also used to create recommendations, or predictions [Jacob Col 4 lines 29-30]. Jacob’s AI modules are modifiable with different tuning parameters [Jacob Col 9 lines 26-42], which results in a modified module, which is a updated, or different version of the module. These changes cause a modification of the learned trends and predictions by changing the behavior of the AI/ML functionality, such as learning rate of AI/ML, error tolerance, weight, weight function of perceptrons, and continually train itself to improve its results [Jacob Col 9 lines 26-42].
Applicant argues that “the claim language includes dynamic code modification of modules within the pipeline, which is absent in Burnett.” Burnett Fig. 50B-50C shows modules within a pipeline, and the modification of module parameters (“timestamp” and “keep”). Then Fig. 50C shows that the modifications have shown up in the updated code [Burnett 0901-0902, Fig. 50B-50C].
Argument 2: Applicant argues on page 11 that “Jacob does not teach or suggest a pipeline that is modifiable responsive to changes in included modules and configured to learn trends and predictions based on the integrated modules, with adaptation of learned trends responsive to module modification. Additionally, Jacob does not teach or suggest integration based on selection from distinct categories and forming a pipeline modifiable based on such category-based integration. Therefore, Jacob fails to teach or suggest every feature recited in independent claims 1.”
Response to Argument 2: Respectfully, the combination of Burnett and Jacob teaches the above. See Response to Argument 1.
Applicant argues that “Jacob does not teach or suggest a pipeline that is modifiable responsive to changes in included modules and configured to learn trends and predictions based on the integrated modules, with adaptation of learned trends responsive to module modification.” Jacob teaches a pipeline that is modifiable responsive to changes in included modules and configured to learn trends and predictions based on the integrated modules (see Response to Argument 1). Regarding “adaptation of learned trends responsive to module modification,” Jacob teaches tuning the modules with parameters to modify the behavior, learning rate, error tolerance, weighting, etc., of the AI/ML. The modules can also continually train itself as it is deployed to provide more meaningful results [Jacob Col 9 lines 26-42]. The modified AI/LM modules are what generates/finds the trends [Jacob Col 1 lines 21-24] and recommendations/predictions [Jacob Col 4 lines 29-30].
Applicant argues that “Jacob does not teach or suggest integration based on selection from distinct categories and forming a pipeline modifiable based on such category-based integration.” Burnett teaches forming a pipeline with nodes/modules from categories. Burnett’s nodes are selected by filtering the available nodes by type (categories). The nodes are also filtered by compatibility with existing nodes in the pipeline (another kind of category) [Burnett 0824, Fig. 35A-B]. These node are dragged and dropped to form/modify the pipeline [Burnett 0824].
This meets the claim limitations as currently claimed, and Applicant's Arguments 1 and 2 filed on 11/13/2020 are not persuasive. Applicant’s remaining statements regarding the remaining independent and dependent claims are not persuasive for the reasons stated above.
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.
Claims 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnett et al., Patent Application Publication number US 20210342125 A1, (hereinafter “Burnett”), in view of Jacob et al., Patent Number US 10776686 B1 (hereinafter “Jacob”).
Claim 1: Burnett teaches “A system comprising:
a processor; a storage medium, the storage medium communicatively coupled to the processor (i.e. one or more hardware processors configured to execute the instructions stored in the one or more memories [Burnett 0159]); and…
receive an indication of selection of a first module (i.e. node addition menu 3560 presents selectable icons 3563 corresponding to nodes of particular functions… A user may be able to drag and drop node options into their pipeline, or may be able to select a node and have the display manager 3422 put the node in place as described above [Burnett 0824, Fig. 35B] note: “Data Stream Source” in Fig. 35B as a first module), the first module being selectable from a first category (i.e. filter 3562 the available node options by type… node options presented in this node addition menu 3560 may dynamically change based on automated analysis of nodes that are compatible with existing nodes specified in the processing pipeline 3550 [Burnett 0824, Fig. 35A-B] note: filtering by type displays nodes of a certain category. Note2: additionally, a category of being compatible at location 3540. Note3: “Data Steam Source” would have been selected via this menu),
receive an indication of selection of a second module, the second module being selectable from a second category (i.e. A user may be able to drag and drop node options into their pipeline [Burnett 0824] note: “node options” is plural, indicating that the user may continue to add categorized nodes. Note2: “Deserializer” as a second module, which would have been selected via menu 3560, which filters based on category, similar the first module),
integrate the first module and the second module (i.e. display manager 3422 can also draw interconnections corresponding to the [new] user-specified node [Burnett 0808] note: “Data Stream source” and “Deserializer” are integrated by a line drawn, connecting them) based upon the first category and the second category (i.e. node options presented in this node addition menu 3560 may dynamically change based on automated analysis of nodes that are compatible with existing nodes specified in the processing pipeline 3550 [Burnett 0824, Fig. 35A-B] note: added nodes will only be integrated in the places they are compatible, which is based on the category of compatibility when they were selected),
form a pipeline having the integrated first module and the second module (i.e. visual representation of the processing pipeline 3550 depicts a series of interconnected nodes 3510, 3511, 3512, 3513, 3514, 3520, 3530, 3531 [Burnett 0807, Fig. 35A-35B] note: “Data Stream source” and “Deserializer” are integrated by a line drawn, connecting them), the pipeline being modifiable responsive to changes to the first module and the second module included in the pipeline (i.e. when a user hovers over a node, an “X” or other suitable icon can appear (for example on a corner of the node) that enable the user to delete the node from the pipeline. If the deleted node was in between two nodes, then the canvas can automatically “collapse” around the deleted node so that the upstream input to the deleted node becomes the input for the node that was downstream of the deleted node [Burnett 0809]) and…
receive a selection of one of the first module or the second module included in the pipeline (i.e. By selection of the node 5018, the window 5016 illustratively displays configuration options for the function [Burnett 0901, Fig. 50B] note: Fig. 50B recreates a pipeline with first module “Data stream source” and second module “Change fields.” Fig. 50B further shows element 5018 with thicker borders, which indicates selection. Fields appear on the bottom half of the screen, which allows a user to adjust the parameters of the node, such as “field_list” and “operator”),
receive an input to modify code of the selected module (i.e. By selection of the node 5018, the window 5016 illustratively displays configuration options for the function [Burnett 0901, Fig. 50B-50C] note: parameters as code. Fig. 50C shows code, including the parameters input into Fig. 50B),
responsive to the received input, cause a modification of a functionality of the selected module resulting in a modified module, the modified module being a different version of the selected module included in the pipeline (i.e. By selection of the node 5018, the window 5016 illustratively displays configuration options for the function. For example, the “change fields” function may accept a “field list” parameter, specifying fields, and an “operator” parameter, specifying whether to keep the listed fields or to remove the listed fields [Burnett 0901, Fig. 50B]), and
cause a modification… based upon the modified module (i.e. the portion 5010 includes an updated query, which now includes the “fields+timestamp” command between the “from” and “group_by” commands. This updated query is reflective of the insertion of the “change fields” node via graphically programming. Illustratively, the “fields” command may be text corresponding to the “change fields” function, with “timestamp” being a specified field, and with the “+” operator designated that the specified field should be maintained. The updated query is illustratively generated by the pipeline format converter 4902, to which a graph data structure represented in the portion 5014 may be passed on selection of the control 5004. Thus, a user may for example confirm that visual programming conducted via the view of FIG. 50B results in a correct query [Burnett 0902, Fig. 50B-C]).”
Burnett is silent regarding “an artificial intelligence configuration module (AICM) communicatively coupled to the storage medium and to the processor, the AICM configured to:…
configured to learn trends and predictions based upon the integrated first module and the second module,” and
cause a modification of the “learned trends and predictions.”
Jacob teaches “an artificial intelligence configuration module (AICM) (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62] note: while instant specification discloses what an AICM does, it does not define what an AICM is. The Examiner interprets an AICM to be a graphical programming interface that uses AI in any form. Jacob Fig. 3 discloses an interface 201 with an graphical AI workflow 200, with at least a first module 204 and a second module 214, integrated with each other with lines/arrows) communicatively coupled to the storage medium and to the processor, the AICM configured to:
… a first module (i.e. a first functionality module 204 [Jacob Col 7 lines 66-67, Fig. 3])…
… a second module (i.e. a second functionality module 210 [Jacob Col 8 lines 2-3, Fig. 3])…
form a pipeline having the integrated first module and the second module (i.e. outputs of the first functionality module 204 are translated and passed by a messaging object 206, to the input of a second functionality module 210 [Jacob Col 7 lines 67-Col 8 lines 1-3, Fig. 3] note: Fig. 3 modules 204 and 210 are connected via lines/arrows), the pipeline being modifiable responsive to changes to the first module and the second module included in the pipeline (i.e. functionality modules can accept parameters [Jacob Col 4 lines 33-34]… functionality module 204 can also accept a number of tuning parameters on its input as well, that can help customize and tailor the analysis of the queries [Jacob Col 9 lines 26-28, Fig. 3) and
configured to learn trends and predictions based upon the integrated first module and the second module (i.e. Artificial intelligence (“AI”) and machine learning (“ML”) capabilities… such as driving business efficiency, finding trends in data [Jacob Col 1 lines 21-24, Fig. 3]… Some functionality modules create recommendations [Jacob Col 4 lines 29-30] note: Jacob Fig. 3 has at least 2 modules/containers, and an integrated interconnected pipeline),
cause a modification of the learned trends and predictions based upon the modified module (i.e. Artificial intelligence (“AI”) and machine learning (“ML”) capabilities… such as driving business efficiency, finding trends in data [Jacob Col 1 lines 21-24]… Some functionality modules create recommendations [Jacob Col 4 lines 29-30]… functionality module 204 can also accept a number of tuning parameters on its input as well, that can help customize and tailor the analysis of the queries. Tuning parameters can be used to set up or change the behavior of AI/ML functionality… Tuning parameters can change the learning rate of an AI/ML functionality… Tuning parameters can change the error tolerance of an AI/ML functionality. Tuning parameters can also affect the weight or weight function of perceptrons in a neural network… functionality module 204 can continually train itself as it is deployed to provide more meaningful results [Jacob Col 9 lines 26-42] note: recommendations as predictions).”
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention/combination of Burnett to include the feature of having the ability to create AI workflows, as disclosed by Jacob.
One would have been motivated to do so, before the effective filing date of the invention because it provides the benefit to provide AI/ML functionality to a visual/graphical programming editor, which increases functionality and adding to user customization and flexibility.
Claim 2: Burnett and Jacob teach all the limitations of claim 1, above. Jacob teaches “a display (i.e. display 114 is… to display visual information (e.g., images, graphical user interfaces, videos, notifications, and the like) [Jacob Col 6 lines 64-67, Fig. 3] note: the display communicates information of the AICM, such as the graphical user interface of workflow interface 201 in Fig. 3) communicatively coupled to the AICM (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62]).”
One would have been motivated to combine Burnett and Jacob, before the effective filing date of the invention because it provides the benefit to use a display to visualize the graphical user interface of the AICM, which helps the user keep track of their program, allowing for easier graphical programming.
Claim 3: Burnett and Jacob teach all the limitations of claim 2, above. Jacob teaches “wherein the AICM comprises the AICM configured to (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62]); and Burnett teaches “receive the indication of selection of the first module and selection of the second module via a user interface (i.e. node addition menu 3560 presents selectable icons 3563 corresponding to nodes of particular functions… A user may be able to drag and drop node options into their pipeline, or may be able to select a node and have the display manager 3422 put the node in place as described above [Burnett 0824, Fig. 35B] note: drag and drop of “Data Stream Source” and “Deserializer” from menu 3560 is an indication of selection).”
One would have been motivated to combine Burnett and Jacob, before the effective filing date of the invention because it provides the benefit to create AI workflows, which provide AI/ML functionality to a visual/graphical programming editor, which increases functionality and adding to user customization and flexibility.
Claim 4: Burnett and Jacob teach all the limitations of claim 2, above. Jacob teaches “wherein the AICM comprises the AICM configured to (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62]); and Burnett teaches “cause to display the first module and the second module as portions of a selectable user interface (Burnett Fig. 35A-35B shows at least two modules. These modules have been added to the UI by selection from the “Add new node” menu in Fig. 35B. Drag and drop of “Data Stream Source” and “Deserializer” from menu 3560 indicates displaying a selectable first and second module from the user interface).”
Claim 5: Burnett and Jacob teach all the limitations of claim 2, above. Jacob “wherein the AICM comprises the AICM configured to (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62]); and Burnett teaches “cause to display the first module and the second module as selectable modules within the first category and the second category (i.e. filter 3562 the available node options by type… node options presented in this node addition menu 3560 may dynamically change based on automated analysis of nodes that are compatible with existing nodes specified in the processing pipeline 3550 [Burnett 0824, Fig. 35A-B] note: filtering by type displays nodes of a certain category. Note2: additionally, a category of being compatible at location 3540. Note3: “Data Steam Source” and “Deserializer” would have been selected via this menu).”
Claim 6: Burnett and Jacob teach all the limitations of claim 2, above. Jacob teaches “wherein the AICM comprises the AICM configured to (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62]); and Burnett teaches “cause to display the pipeline as a sequential pipeline having the graphical representations of the first module and the second module (i.e. visual representation of the processing pipeline 3550 depicts a series of interconnected nodes 3510, 3511, 3512, 3513, 3514, 3520, 3530, 3531 [Burnett 0807, Fig. 35A-35B] note: interconnected arrows determine the direction of data flow, which is sequential. Note2: “Data Stream source” and “Deserializer” are integrated by a line drawn, connecting them).”
Claim 7: Burnett and Jacob teach all the limitations of claim 2, above. Jacob teaches “wherein the AICM comprises the AICM configured to (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62]); and Burnett teaches “cause to display code of the selected module on a portion of the display (i.e. By selection of the node 5018, the window 5016 illustratively displays configuration options for the function [Burnett 0901, Fig. 50B] note: parameters as code. Fig. 50C shows code, including the parameters input into Fig. 50B).”
Claim 8: Burnett and Jacob teach all the limitations of claim 2, above. Jacob teaches “wherein the AICM comprises the AICM configured to (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62]);” and “a second portion configured to display the learned trends and predictions (i.e. Artificial intelligence (“AI”) and machine learning (“ML”) capabilities… such as driving business efficiency, finding trends in data [Jacob Col 1 lines 21-24, Fig. 3]… Some functionality modules create recommendations [Jacob Col 4 lines 29-30] note: Jacob Fig. 3 has at least 2 modules/containers, and an integrated interconnected pipeline);” and
Burnett teaches “cause to display on a user interface a first portion configured to receive the indication of selection of the first module (i.e. node addition menu 3560 presents selectable icons 3563 corresponding to nodes of particular functions… A user may be able to drag and drop node options into their pipeline, or may be able to select a node and have the display manager 3422 put the node in place as described above [Burnett 0824, Fig. 35B] note: “Data Stream Source” in Fig. 35B as a first module) and selection of the second module (i.e. A user may be able to drag and drop node options into their pipeline [Burnett 0824] note: “node options” is plural, indicating that the user may continue to add categorized nodes Note2: “Deserializer” as a second module, which would have been selected via menu 3560, which filters based on category, similar the first module) to display as the pipeline (i.e. display manager 3422 can also draw interconnections corresponding to the [new] user-specified node [Burnett 0808] note: “Data Stream source” and “Deserializer” are integrated by a line drawn, connecting them),” and “a third portion configured to display adjustable parameters of the first and second modules, and a third portion configured to display editable code associated with the first module and the second module (i.e. By selection of the node 5018, the window 5016 illustratively displays configuration options for the function [Burnett 0901, Fig. 50B] note: parameters as code. Fig. 50C shows code, including the parameters input into Fig. 50B. Note: Fig. 50B recreates a pipeline with first module “Data stream source” and second module “Change fields.” Fig. 50B further shows element 5018 with thicker borders, which indicates selection. Fields appear on the bottom half of the screen, which allows a user to adjust the parameters of the node, such as “field_list” and “operator”).”
Claim 9: Burnett and Jacob teach a method for machine learning of trends and predictions, the method comprising operations corresponding to the system of claim 1; therefore, it is rejected under the same rationale.
Claim 10: Burnett and Jacob teach all the limitations of claim 9, above. Burnett teaches “wherein receiving the indications of selection of the first module and the second module comprise receiving the indications via a graphical user interface (GUI) (i.e. node addition menu 3560 presents selectable icons 3563 corresponding to nodes of particular functions… A user may be able to drag and drop node options into their pipeline, or may be able to select a node and have the display manager 3422 put the node in place as described above [Burnett 0824, Fig. 35B] note: “Data Stream source” and “Deserializer” would have been selected via this menu).”
Claim 11: Burnett and Jacob teach all the limitations of claim 9, above. Burnett teaches “wherein integrating the first module and the second module based comprises integrating the first module and the second module (i.e. node options presented in this node addition menu 3560 may dynamically change based on automated analysis of nodes that are compatible with existing nodes specified in the processing pipeline 3550 [Burnett 0824, Fig. 35A-B] note: new nodes will only be integrated in the places they are compatible, which is based on the category of compatibility when they were selected. Note2: “Data Stream source” and “Deserializer” are integrated by a line drawn, connecting them) based upon the first category (i.e. GUI pipeline creator 3420 can… identify a filtered subset of possible nodes that are compatible with the upstream nodes, and can cause display of those nodes to a user [Burnett 0870]) and the second category (i.e. GUI pipeline creator 3420 can call the recommendation module 3426 to… determine compatible downstream nodes. The set of all possible nodes can be filtered (see filtering option 4432 for “compatible nodes”) such that only compatible downstream nodes are displayed [Burnett 0864]) being functionally sequential to each other (Burnett’s modules in [0864 and 0870] are categorized as upstream or downstream, and they are also integrated either upstream or downstream, which is sequential to the data flow).”
Claim 12: Burnett and Jacob teach all the limitations of claim 9, above. Burnett teaches “wherein forming the pipeline comprises causing to display a graphical representation of the pipeline as a portion of GUI (i.e. visual representation of the processing pipeline 3550 depicts a series of interconnected nodes 3510, 3511, 3512, 3513, 3514, 3520, 3530, 3531 [Burnett 0807, Fig. 35A-35B] note: “Data Stream source” and “Deserializer” are integrated by a line drawn, connecting them).”
Claim 13: Burnett and Jacob teach all the limitations of claim 9, above. Jacob teaches “causing, by the AICM (i.e. FIG. 3 illustrates a specific example of an AI/ML defined workflow 200, programmed by coupling functionality modules, represented by nodes, within a workflow interface 201 and connecting them with messaging objects [Jacob Col 7 lines 59-62]); and “a third portion having a graphical representation of the learned trends and predictions (i.e. Artificial intelligence (“AI”) and machine learning (“ML”) capabilities… such as driving business efficiency, finding trends in data [Jacob Col 1 lines 21-24, Fig. 3]… Some functionality modules create recommendations [Jacob Col 4 lines 29-30] note: Jacob Fig. 3 has at least 2 modules/containers, and an integrated interconnected pipeline);” and
Burnett teaches “to display on a user interface a first portion having graphical representations of the first module (i.e. node addition menu 3560 presents selectable icons 3563 corresponding to nodes of particular functions… A user may be able to drag and drop node options into their pipeline, or may be able to select a node and have the display manager 3422 put the node in place as described above [Burnett 0824, Fig. 35B] note: “Data Stream Source” in Fig. 35B as a first module) and the second module (i.e. A user may be able to drag and drop node options into their pipeline [Burnett 0824] note: “node options” is plural, indicating that the user may continue to add categorized nodes. Note2: “Deserializer” as a second module),
a second portion having a graphical representation of the pipeline (i.e. visual representation of the processing pipeline 3550 depicts a series of interconnected nodes 3510, 3511, 3512, 3513, 3514, 3520, 3530, 3531 [Burnett 0807, Fig. 35A-35B] note: “Data Stream source” and “Deserializer” are integrated by a line drawn, connecting them),…
a fourth portion having code of the first module and the second module (i.e. By selection of the node 5018, the window 5016 illustratively displays configuration options for the function [Burnett 0901, Fig. 50B] note: parameters as code. Fig. 50C shows code, including the parameters input into Fig. 50B).”
One would have been motivated to combine Burnett and Jacob, before the effective filing date of the invention because it provides the benefit to provide AI/ML functionality to a visual/graphical programming editor, which increases functionality and adding to user customization and flexibility.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lodhia (US 20200097263 A1) listed on 892 is related to visual programming logic, incorporating AI and ML.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.S./Examiner, Art Unit 2179
/TUYETLIEN T TRAN/Primary Examiner, Art Unit 2179