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 .
Claims 1 – 39 are pending.
Examiner’s Note
The prior art rejection below cites particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “application programming interface configured for”, “AI based orchestration engine configured for, orchestrator UI for”, and orchestrator manager configured to” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1 – 39 are rejected under 35 U.S.C. 103 as being unpatentable over Maes et al., (US PUB 2016/0092283 hereinafter Maes) in view of Cella et al., (US PUB 20210182996 hereinafter Cella), in view of Nandakumar, (US PAT 11/074,107), and further in view of Softr, “Build custom business apps – powered by AI, with no code”, 2018, pages 1 – 18.
Maes, Cella, and Nandakumar were cited in previous office action. Softr is a large file over 25 MB and cannot be uploaded; It can be found on Google.
As to claim 1, Maes teaches a system for process Orchestration (“An orchestrator executes an end-to-end process across applications…” abstract and para. 0015 - 0016) in one or more Supply chain management (SCM) application (“…Any given application can be updated or replaced, simply by replacing or modifying the corresponding adapter. For example, if an enterprise wishes to upgrade or replace application 1 in FIG. 1 (with a new application or an updated version of application 1), then the corresponding adapter 106-1 to which application 1 is coupled can be replaced or updated to support the updated or replaced application. In some cases, replacing the application can involve replacing a first application supplied by a first vendor with a second application supplied by a different vendor. In other cases, replacing the application can involving replacing a first application supplied by a vendor with another application supplied by the same vendor…” para. 0053) and (“…multi-supplier case exchange. Para. 0082), the system comprising:
an application programming interface (API) (“…More recently, applications can at least expose well defined application programming interfaces (APIs) that assume that the applications will be interacting with other systems. Such applications are called by their APIs or can call other APIs. Even with such APIs, applications may not readily interact with each other. Different applications may employ different data formats, different languages, different interfaces, different protocols, and so forth.” para. 0010) for providing access to configuration (“…the orchestrator 102 can issue an abstract application call (e.g. a call to a REST API) to application 1. This abstract application call is received by the adapter 106-1, which translates the abstract application call to one or multiple function calls according to the protocol used by application 1, to perform the task(s) requested by the abstract application call made by the orchestrator 102. The adapter 106-1 also adapts the APIs as noted above…” para. 0048; Note: configuration is disclosed as configuration parameters in para. 0089 of the specification) and workflow operations (“…The events (e.g. results, responses, etc.) received by the orchestrator 102 can be provided by applications that are invoked in the workflow or from another source, such as through the API 105 of the message broker…” para. 0024 - 0026) of one or more SCM applications (“…Any given application can be updated or replaced, simply by replacing or modifying the corresponding adapter. For example, if an enterprise wishes to upgrade or replace application 1 in FIG. 1…” para. 0053 and 0082);
[an Al based orchestration engine configured for interacting with one or more configurable components in a codeless platform architecture for] executing one or more SCM operations (‘…A “workflow” can refer to any process that the enterprise can perform, such as a use case. Such a process of the workflow can also be referred to as an “end-to-end process” or an “enterprise process” since the process involves a number of activities of the enterprise from start to finish. A “use case” can refer to any specific business process or other service that an enterprise desires to implement. An “application” can refer to machine-readable instructions (such as software and/or firmware) that are executable. The application can include logic associated with an enterprise process, which can implement or support all or parts of the enterprise process (or processes)…’ para. 0009) [wherein the Al based orchestration engine drives] execution of the SCM operations through one or more data objects mapped to the API for structuring at least one workflow of the SCM operations (“…The design of the application may or may not have taken into account the presence of other applications upstream or downstream (with respect to an end-to-end process). This is especially true for older (legacy) applications. More recently, applications can at least expose well defined application programming interfaces (APIs) that assume that the applications will be interacting with other systems. Such applications are called by their APIs or can call other APIs. Even with such APIs, applications may not readily interact with each other. Different applications may employ different data formats, different languages, different interfaces, different protocols, and so forth…” para. 0010 and 0026) by a process modeler (“… The flow logic can be in the form of program code (e.g. a script or other form of machine-executable instructions), a document according to a specified language or structure (e.g. Business Process Execution Language (BPEL),a Business Process Model…” para. 0020) [of the Al based orchestration engine];
an orchestrator UI (“In some examples, the portal 304 includes a user interface (UI) 306. The portal 304 can include machine-executable instructions or a combination of machine-executable instructions and processing hardware. The portal 304 can be at a computer (e.g. client computer) that can be remote from the service exchange 100. The UI 306 allows a user to interact with the service exchange 100.” para. 0065) [for monitoring] and providing visibility for structuring the at least one workflow (“…A user can perform an action in the UI 306 that triggers the execution of a flow logic 308 (of multiple different flow logic) by the orchestrator 102 to perform a workflow…” para. 0066) [wherein the orchestrator UI monitors the at least one structured workflow and a task executed through the structured workflow of the SCM operations]; and
an orchestrator manager (“…orchestrator…” para. 0019 - 0026) configured to control the structuring of the at least one workflow wherein the orchestrator manager controls the at least one structured workflow and the task executed through the structured workflow of the SCM operations (“…Alternatively, some or all of the components shown in FIG. 1 can be integrated into a system. The platform can also support a pattern that includes executing a workflow by the orchestrator 102, making calls from the workflow 102 to delegate tasks to applications, receiving events (e.g. responses to calls, results produced by applications, or other events) or calls from the applications or from other sources, and reacting, by the c to the events or calls by performing actions.” para. 0019 - 0026).
Maes does not but Cella teaches
for monitoring and wherein the orchestrator UI monitors the at least one structured workflow and a task executed through the structured workflow of the [SCM] operations (“… a user interface, and modules for investigation and discovery and tracking users' experience and engagements…” para. 0341 and 0374. Note: discovering also comprises monitoring in order to discover, and tracking is monitoring user’s experience and engagements which is user’s experience workflow in the supply chain system involving a variety of factors as indicated in the reference overall and para. 0342) and (“...FIG. 6 illustrates a connected value chain network 668 in which a value chain network management platform 604 (referred to herein in some cases as a “value chain control tower,” the “VCNP,” or simply as “the system,” or “the platform”) orchestrates a variety of factors involved in planning, monitoring, controlling, and optimizing various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, logistics and distribution factors, and the like. By virtue of a unified platform 604 for monitoring and managing supply factors and demand factors as well as status information (e.g., quality and status, plan, order and confirm, and/or track and trace) can be shared about and between various entities (e.g., including customers/consumers, suppliers, distribution such as distributors, suppliers, and production such as producers or production facilities) as demand factors are understood and accounted for, as orders are generated and fulfilled, and as products are created and moved through a supply chain.” Para. 0342).
It 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 was made to modify Maes by apply the teachings of Cella because Cella teaches the same field of the invention of unified orchestration supply chain management (“…unified orchestration of supply and demand…” para. 0336); Cella’s orchestration UI can also track all user’s experience to determine and configure relationships and workflows among demand management and supply chain applications based on inputs (para. 0085 - 0088).
Maes, Cella do not but Nandakumar teaches
an Al based orchestration engine (“… the full artificial intelligence (AI) or machine learning (ML) product development lifecycle. Embodiments of the present disclosure provide for an integrated computing environment comprising one or more software components call blocks, each pre-loaded with an AI OS intelligent functionality. In accordance with certain aspects of the present disclosure, blocks may be linked in a sequential, parallel, or complex topology to form a pipeline for enabling user-friendly data science experimentation, exploration, analytic model execution, prototyping, pipeline construction, and deployment using a GUI. The OS may incorporate an execution engine for constructing and/orchestrating the execution of a pipeline…” title, abstract) configured for interacting with one or more configurable components in a [codeless] platform architecture for executing one or more SCM operations wherein the Al based orchestration engine drives structuring at least one workflow of the [SCM] operations (“…an executor engine for constructing and/orchestrating the execution of a pipeline. In accordance with certain aspects of the present disclosure, an executor engine may comprise a module for controlling block and pipeline tasks, functions, and processes. In accordance with certain exemplary embodiments, an execution engine may coordinate pipeline elements, processes, and functions by configuring specifications; allocating elastic provisioning-deprovisioning execution of resources and the control of task transports to local and external resources; enabling a block or pipeline script to be transported to and from computing resources; and accessing and retrieving resources via one or more API…” col. 6 lines 15 - 60).
It 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 was made to modify Maes and Cella by apply the teachings of Nandakumar because Nandakumar would provide AI based execution engine for managing AI solutions by constructing, orchestrating and executing tasks of blocks and computing resources for workflow (abstract and col. 4).
Maes, Cella and Nandakumar do not but Softr teaches wherein the codeless platform architecture enables a user to create an application without having to write code (“Build custom business apps — powered by AI, with no code” title) and (“Build your first app fast, with AI and no code.” Pages 6 – 7).
It 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 was made to modify Maes and Cella and Nandakumar by apply the teachings of Softr because Softr would apply AI to provide no-code app builder for users to build their business apps quickly and no professional skills (title and pages 6 – 7 and 11 - 12). Further, Maes can apply Softr’s ready-made templates to customize and build apps to manage supply chain.
As to claim 2, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1, Maes and Nandakumar do not Cella teaches wherein the orchestrator UI provides details through graphical representation to customize data flow, workflow, manage run, settings, define functional fixtures, events, State Model and configuration to execute the workflow (“…a user may provide input that controls one or more properties of a digital twin via a graphical user interface” para. 0630) and (“…a graphical user interface that the user may interact with to adjust the design of the logistics environment to adjust the design. The design provided (at least in part) by a user may also be represented in a digital twin of a logistics environment, whereby the digital twin system 2020 may perform simulations using the digital twin” para. 0640).
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella teaches the same field of the invention of unified orchestration supply chain management (“…unified orchestration of supply and demand…” para. 0336); Cella’s orchestration graphical user interface can also track all user’s experience to determine and configure relationships and workflows among demand management and supply chain applications based on inputs (para. 0085 – 0088 and 0640).
As to claim 3, Maes modified by Cella, and Softr Nandakumar teaches the System of claim 1, Maes teaches wherein the one or more data objects includes event data object, state data object, action data object and access control data objects, stakeholder types, rule expression data object, and master data objects (“… a current state of the workflow and calls and events received by the orchestrator …” para. 0022).
As to claim 4, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1, Maes, Nandakumar and Softr do not but Cella teaches wherein the one or more data objects are application functional data objects including Taxonomy associated to a document, sub class of the document, document Types, Application Types, Supplier location, Region of business, taxation attributes, Line attributes, clause type, approval type, and document value (“…a set of taxonomies includes at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, and a business development taxonomy” para.
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella teaches the same field of the invention of unified orchestration supply chain management (“…unified orchestration of supply and demand…” para. 0336); Cella’s taxonomy in all categories of services would provide varieties of data supports (para. 1118 and 1140).
As to claim 5, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1, Maes, Nandakumar and Softr do not but Cella teaches further comprising a historical workflow knowledge database configured for storing a historical workflow dataset wherein the workflow of the SCM operation is structured based on the historical workflow dataset by building model driven Al flows incorporating application process within supply chain At 5102, all historical and current data related to the value chain network are received. The data may include information related to various operating parameters of the value chain network over a particular historical time period, say last 12 months. The data may also provide information on the typical values of various operating parameters under normal conditions. Some examples of operating parameters include: product demand, procurement lead time, productivity, inventory level at one or more warehouses, inventory turnover rates, warehousing costs, average time to transport product from warehouse to shipping terminals, overall cost of product delivery, service levels, etc. At 5104, one or more simulation models of value chain network are created based on the data. The simulation models help in visualizing the value chain network as a whole and in predicting how changes in operating parameters affect the operation and performance of the value chain network…” para. 0764).
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella would provide historical database to save help predict and build models (para. 0764).
As to claim 6, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 3, Maes teaches wherein event data objects and action data objects are mapped to endpoints of the API (“…The events (e.g. results, responses, etc.) received by the orchestrator 102 can be provided by applications that are invoked in the workflow or from another source, such as through the API…” para. 0024).
As to claim 7, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 6, Maes teaches wherein event data objects are connected to action data objects and action data objects are associated to state data objects (“… perform tasks specified by the flow logic in response to a current state of the workflow and calls and events received by the orchestrator…” para. 0022).
As to claim 8, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 2, Maes teaches [wherein the Al based orchestration engine coupled to] a processor executes the SCM operation by at least one data model (“… the flow logic according to a data model…” abstract and (“…The flow logic for a respective workflow can be written abstractly using a canonical data model (CDM) 107…” para. 0036 – 0037) [wherein the Al engine transfers processed data to the UI for visibility], exposes SCM operations through the API and assist the manager for orchestration and control (“…More recently, applications can at least expose well defined application programming interfaces (APIs) that assume that the applications will be interacting with other systems. Such applications are called by their APIs or can call other APIs. Even with such APIs, applications may not readily interact with each other. Different applications may employ different data formats, different languages, different interfaces, different protocols, and so forth.” para. 0010) and (“…The events (e.g. results, responses, etc.) received by the orchestrator 102 can be provided by applications that are invoked in the workflow or from another source, such as through the API 105 of the message broker 104…” para. 0024 – 0025).
Mae and Cella and Softr do not but Nandakumar teaches wherein the Al based orchestration engine coupled to a processor and transfers processed data to the UI for visibility (“…user-friendly construction of AI and/or ML pipelines and is configured to: remove or reduce integration barriers; reduce required expertise for the development and deployment of AI products framework; simplify/streamline integration of raw and heterogeneous data; enable high-level and intuitive abstractions that specify pipeline construction requirements; enable an intuitive and/or automated means, such as a graphical user interface (GUI), for a user to specify ML model standards and customize/configure components, data pipelines, data processing, data transport mechanisms, streaming analytics, AI model libraries, pipeline execution methods, orchestration, adapters, and computing resources…” col. 3 lines 55).
It 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 was made to modify Maes, Cella and Softr by apply the teachings of Nandakumar because Nandakumar would provide graphical user interface with windows, icons and audio indicators for users easily to interact with the system.
As to claim 9, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1,
Maes teaches services with the one or more SCM application (para. 0015 – 0016, 0053 and 0082).
Maes, Nandakumar and Softr do not but Cella teaches further comprising a blockchain connector configured for integrating blockchain service (“… In embodiments, the storage layer 624 may include one or more blockchains 1180, such as ones that store identity data, transaction data, historical interaction data, and the like, such as with access control that may be role-based or may be based on credentials associated with a value chain entity 652, a service, or one or more applications 630…” para. 0370) and (“… For example, a transaction indicating a change of ownership of an entity 652 may be stored in a blockchain and used by multiple applications 630, such as to enable role-based access control, role-based permissions for remote control, identity-based event reporting, and the like that may be connected to and shared with the digital twin 1700 such that the digital twin 1700 may be updated accordingly.
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella would provide blockchain architectures used by supply chain applications for access to services (para. 0446).
As to claim 10, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1, Maes teaches wherein the Al based process orchestration engine includes an AI flow Orchestrator engine configured for monitoring and learning a data flow of SCM operations including input transactions data across different stages of SCM application (“A point-to-point integration mechanism can include a component (or multiple components) provided between applications to perform data transformations, messaging services, and other tasks to allow the applications to determine how and when to communicate and interact with each other.” para. 0012).
As to claim 11, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1, Maes, Cella, and Softr do not but Nandakumar teaches wherein the Al based process orchestration engine includes an Al Task orchestrator engine configured for determining relative importance of different business rules (“… rules engine or derived heuristic…” col. 26 lines 35 – 40) to either automate tasks by a bot or determine dependencies to introduce execution optimizations (“…AI includes any algorithms, methods, or technologies that make a system act and/or behave like a human and includes machine learning, computer vision, natural language processing, cognitive, robotics, and related topics” col. 11 lines 53 – 65).
It 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 was made to modify Maes, Cella and Softr by apply the teachings of Nandakumar because Nandakumar would provide rules to optimize resources and therefore execution (col. 26 lines 35 – 40).
As to claim 12, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1, Maes, Cella and Softr do not but Nandakumar teaches wherein the Al based process orchestration engine includes an Al Compliance orchestrator engine configured to determine anomalies, process pattern drifts, approval optimization or community intelligence (“…In accordance with certain exemplary embodiments, pipeline intelligence process 700 may comprise a pipeline scheduling step 704, a resource allocation step 706, a resource execution monitoring step 708, and fault-detection step 710…” col. 26 lines 20 - 30).
It 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 was made to modify Maes, Cella and Softr by apply the teachings of Nandakumar because Nandakumar would provide ability to detect failures to maintain optimal system performance (col. 26 lines 35 – 40).
As to claim 13, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1, Maes, Nandakumar and Softr do not but Cella teaches wherein the Al based process orchestration engine includes an Al resource orchestrator configured to focus on importance-driven QoS (Quality of Service) requirements to optimize resource allocation of the codeless platform (“…edge intelligence 1420 enables adaptation of edge computation (including where computation occurs within various available networking resources, how networking occurs (such as by protocol selection), where data storage occurs, and the like) that is multi-application aware, such as accounting for QoS…” para. 0567).
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella would provide and apply QoS to comply with policy and regulation to optimize various entities for system (para. 0342 and 0356).
As to claim 14, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 10, Maes teaches [wherein the Al flow Orchestrator engine ] define a data processing path based on a predicted task and perform clustering of transactions based on a plurality of data attributes and task metadata to determine an appropriate rule flow (“… the flow logic according to a data model defining arguments to include in interactions between the orchestrator and each of the applications. ….” Abstract) and (“…The orchestrator 102 is able to evaluate (interpret or execute) a flow logic, and perform tasks specified by the flow logic in response to a current state of the workflow and calls and events received by the orchestrator 102…” para. 0022)
Maes, Cella and Softr do not but Nandakumar teaches wherein the Al flow Orchestrator engine (“…execution engine…” abstract).
It 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 was made to modify Maes, Cella and Softr by apply the teachings of Nandakumar because Nandakumar’s machine learning model would provide simulation to predict their performance (abstract).
As to claim 15, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 11, Maes, Nandakumar and Softr do not but Cella teaches wherein the Al task orchestrator is configured to determine weightage of a task to be executed for identifying process to execute the task (“…one or more information routing recommendations may be adapted while the information is routed based on, for example, changes in network resource availability, network resource discovery, network dynamic loading, priority of recommendations that are generated after information for a first recommendation is in-route, and the like…” 0422) by a bot (“…recommend opportunities to improve one or more of the elements of the platform 604, such as via addition of artificial intelligence 1160, automation (including robotic process automation 1442…” para. 0463).
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella would weight tasks by priorities to make sure the more weight or priority task would be executed earlier (para. 0422).
As to claim 16, Maes modified by Cella, Nandakumar and Softr teaches the System of claim 1, Maes, Nandakumar and Softr do not but Cella teaches wherein the process modeler is configured to generate notations (“…the digital twin system outputs a graphical representation of the environment digital twin to a display, whereby a user views the simulation via the display…” para. 0071) and define processes as per trained models stored in a data model repository wherein the modeler integrates one or more processes utilizing one or more tools (“…wherein the machine learning system trains the machine-learned model based on training data sets that define features of logistics systems and outcomes of the logistics systems; an artificial intelligence system that receives a request for logistics system design and determines a logistics system design recommendation based on the machine-learned model…” para. 0070 – 0073).
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella teaches the same field of the invention of unified orchestration supply chain management (“…unified orchestration of supply and demand…” para. 0336) using machine learning model (para. 0068 - 0078).
As to claim 17, this is a method of claim 1. See rejection for claim 1 above.
As to claim 18, Maes modified by Cella, Nandakumar and Softr teaches the method of claim 17, Maes, Cella and Softr do not but Nandakumar teaches further comprising the step of: remodeling a structure of the workflow based on a task performed through the workflow and corrected outliers (“…AI techniques or algorithms using statistical methods that enable computing systems or machines to improve correlations as more data is used in the model, and for models to change over time as new data is correlated…” col. 12 lines 1 – 10).
It 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 was made to modify Maes, Cella and Softr by apply the teachings of Nandakumar because Nandakumar would provide ability to change models to suit with new demands and improvement (col. 12 lines 1 – 10).
As to claim 19, this claim recites similar scope of claim 2. See rejection for claim 2 above.
As to claim 20, see rejection for claim 5 above.
As to claim 21, this claim recites similar scope of claim 16. See rejection for claim 16 above.
As to claim 22, this claim recites similar scope of claim 8. See rejection for claim 8 above.
As to claims 23 - 28, these claim recites similar scope of claims 9 - 14. See rejection for claims 9 - 11 above.
As to claim 29, Maes modified by Cella, Nandakumar and Softr teaches the method of claim 28, Maes teaches further comprising
extracting process identifier (inherent) related to each SCM operation including transaction within a cluster (“Within a portfolio of applications used by an enterprise, many applications may not be able to directly interact with each other. In general, an application implements a particular set of business logic and is not aware of other applications that are responsible for performing other processes. The design of the application may or may not have taken into account the presence of other applications upstream or downstream (with respect to an end-to-end process).” para. 0010);
Maes, Cella and Softr do not but Nandakumar teaches
extracting task sequences by traversing and performing sequential pattern mining to determine common sequence of executed tasks on the cluster of transaction (“…The designer of a machine learning (ML) model solution, referred to herein as a “data science user,” may utilize one of a number of programming languages/tools (e.g., C, Python, Java, R, etc.) to articulate their analytic model that may use, among others, libraries/packages such as NUMPY for scientific and numeric computing and/or TENSORFLOW for ML. Machine learning represents the automated extraction of models (or patterns) from data…” col. 12 lines 52 – 65) for recommending (“…The data scientist may run multiple experiments and chooses the best model from the experiments. The best model may then be utilized within the AI Solution being built by the data scientist…” col. 13 lines 5 – 55).
It 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 was made to modify Maes, Cella and Softr by apply the teachings of Nandakumar because Nandakumar would provide AI based execution engine for managing AI solutions by constructing and selecting a best model for optimal performance (col. 13 lines 5 - 55).
As to claim 30, this claim has similar scope as claim 15. See rejection for claim 15 above.
As to claim 31, Maes modified by Cella, Nandakumar and Softr teaches the method of claim 30, Maes teaches further comprising
extracting process identifier (inherent) related to each SCM operation including transaction (“Within a portfolio of applications used by an enterprise, many applications may not be able to directly interact with each other. In general, an application implements a particular set of business logic and is not aware of other applications that are responsible for performing other processes. The design of the application may or may not have taken into account the presence of other applications upstream or downstream (with respect to an end-to-end process).” para. 0010);
extracting a task identifier operating on the SCM operation (“…executing the logic of sequencing of the tasks of the workflow…” para. 0016);
Maes, Nandakumar and Softr do not but Cella teaches generating a conditional probability matrix (…The training data may be represented in the machine learning model 3000 by a matrix. The machine learning model 3000 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new inputs …” para. 0556) and (“…Examples of machine-learned models include neural networks (e.g., deep neural networks, convolution neural networks, and many others), regression based models, decision trees, hidden forests, Hidden Markov models …” para. 0630) to predict importance of task to a task dependency (“…notifications to users via enterprise digital twins associated with the respective users. In some embodiments, digital twin notifications are an important part of the overall interaction…” para. 1159) and task to approval dependence (“…if and when the new hires are approved…” para. 0125).
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella would provide Markov machine learning model represented by a matrix producing desired predicted outputs for the system (para. 0556).
As to claim 32, this claim has similar scope as claim 12. See rejection for claim 12 above.
As to claim 33, this claim has similar scope as claim 18. See rejection for claim 18 above.
As to claim 34, this claim has similar scope as claim 29. See rejection for claim 29 above.
As to claim 35, Maes modified by Cella, Nandakumar and Softr teaches the method of claim 17, Maes teaches wherein the one or more SCM application operation includes CLM (Contract lifecycle management), Inventory management, warehouse management, Cycle Counting, Material transfer, Pick List, warehouse management, Order Management, invoice management, Good Receipts, Credit Memo, service confirmation and timesheet, Goods Issue, Return Note, requisition, Demand and Supply planning, Vendor Performance and Risk Management, RFX, Auction, Project Management, Quality management, Forecast Management, Supplier Order Collaboration, Control Tower, Budgeting, Quality Management System, Vendor Management, Field Ticketing, Item and Catalog Management (“…Some of the tasks of the workflow are delegated using the orchestration to be performed by the logic of the applications. As an example, a workflow can include an order fulfillment workflow. An order fulfillment workflow can include the following tasks: receive an order from a customer, determine applications that are to be involved in fulfilling the order, invoke the identified applications to fulfill the order, and return a status (e.g. confirmation number or information to further manage the order, such as to view, update, cancel, or repeat the order) to the customer. Note that the foregoing example order fulfillment workflow is a simplified workflow that includes a simple collection of tasks. An actual order fulfillment workflow may involve many more tasks” para. 0016).
As to claim 36, Maes modified by Cella, Nandakumar and Softr teaches the method of claim 35, Maes teaches further comprising generating a plurality of functional fixtures created for performing the one or more SCM operation by utilizing a library of functions (“… the functions that are called to support the interactions, the events (e.g. responses, results, or other events) that can result, any errors that can arise, and states of the use case executed across the applications…” para. 0038) stored on a functional database (“… storage devices…” para. 0075),
Maes, Nandakumar and Softr do not but Cella teaches wherein a controller is encoded with instructions enabling the controller to function as a bot for generating the fixtures (“… robotic process automation (e.g., automation of intelligent agents for various workflows)…” para. 0345).
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella would provide robotic process automation to do human jobs to save a lot of works (para. 0374).
As to claim 37, Maes modified by Cella and Nandakumar teaches the method of claim 36, Maes, Cella and Softr do not but Nandakumar teaches wherein the plurality of functional fixtures are backend scripts (“… pipeline script…” col. 6 lines 20 – 30) created by the bot based on the at least one operation, data objects and Al processing for enabling automation of the operation by to be executed by the orchestration engine (“…AI includes any algorithms, methods, or technologies that make a system act and/or behave like a human and includes machine learning, computer vision, natural language processing, cognitive, robotics, and related topics” col. 11 lines 53 – 65).
It 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 was made to modify Maes, Cella and Softr by apply the teachings of Nandakumar because Nandakumar would provide rules to optimize resources and therefore execution (col. 26 lines 35 – 40).
As to claim 38, Maes modified by Cella, Nandakumar and Softr teaches the method of claim 37, Maes, Nandakumar and Softr do not but Cella teaches wherein the Al processing includes a processing logic that integrates deep learning (“…The data processing, artificial intelligence and computational systems 634 may relate to artificial intelligence (e.g., expert systems, artificial intelligence, neural, supervised, machine learning, deep learning, model-based systems, and the like)…” para. 0345), predictive analysis, information extraction, planning, scheduling, optimization and robotics for processing the at least one operation by the orchestration engine (“…demand management application 824 (such as, without limitation, an application for analyzing, planning, or promoting interest by customers of a category of goods that can be supplied by or with facilities of a value chain product or service, such as a demand planning application, a demand prediction application…” para. 0353).
It 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 was made to modify Maes, Nandakumar and Softr by apply the teachings of Cella because Cella would provide artificial intelligence machine learning model to implement with robotic automation process to apply in all kinds of applications and environment (para. 0353 and 0374).
As to claim 39, this is a computer program product claim of claim 1. See rejection for claim 1 above. Further, Maes teaches a computer readable storage medium (“… storage devices…” para. 0075) readable by a processor (“…a processor…” para. 0018).
Response to Arguments
Claim Rejections under 35 U.S.C. § 103
Applicant's arguments have been fully considered but they are not persuasive.
Applicant argued
“The Office Action, on p. 7, In. 9 -10 asserts Maes discloses an orchestrator UI via Maes' portal 304 and user interface 306. The Office also asserts Maes' orchestrator UI provides visibility for structuring at least one workflow. That said, the Office Action also asserts Maes' alleged orchestrator UI does not monitor the at least one structured workflow and a task executed through the structured workflow of SCM operations. As such, the Office Action acknowledges Maes does not teach, suggest, or disclose, at least, monitors the at least one structured workflow and a task executed through the structured work flow of the SCM operations, as recited in claim 1. To make up for this deficiency, the Examiner turns to Cella who allegedly discloses the above limitations in paragraphs [0341] and [0374] (see Office Action, p. 8, In. 8-11). Applicant respectfully disagrees.
Cella, in paragraph [0341] discloses:
Referring to FIG. 5, a block diagram is presented at 500 showing components and interrelationships of systems and processes of a value chain network technology stack, which may include, but is
not limited to a presentation layer, an intelligence layer, and
serverless functionalities such as platforms (e.g., development and
hosting platforms), data facilities (e.g., relating to data with IoT
and Big Data), and data aggregation facilities. In example
embodiments, the presentation layer may include, but is not limited
to, a user interface, and modules for investigation and discovery
and tracking users' experience and engagements. In example
embodiments, the intelligence layer may include, but is not limited
to, a statistical and computation methods, semantic models, an
analytics library, a development environment for analytics,
algorithms, logic and rules, and machine learning. In example
embodiments, the platforms or the value chain network technology
stack may include a development environment, APIs for
connectivity, cloud and/or hosting applications, and device
discovery. In example embodiments, the data aggregation facilities
or layer may include, but is not limited to, modules for data
normalization for common transmission and heterogeneous data collection from disparate devices. In example embodiments, the data facilities or layer may include, but is not limited to, IoT and
big data access, control, and collection and alternatives. In example
embodiments, the value chain network technology stack may be further associated with additional data sources and/or technology enablers.
Paragraph [0005] refers to a block diagram illustrated in Cella's FIG. 5. The block diagram shows components and interrelationships of systems and processes of a value chain network technology stack. Among these are a presentation layer, an intelligence layer, and serverless functionalities. FIG. 5 and paragraph [0341] show that the presentation layer may include a user interface and modules for investigation and discovery and tracking users' experience and engagements. However, while paragraph [0341] might suggest a system having a user interface, investigation and discovery modules, and tracking users' experience and engagements, it says nothing about monitoring a least one structured workflow and a task executed through the structured workflow as required by claim 1. Therefore, paragraph [0341] clearly does not teach or suggest an orchestration user interface that monitors the at least one structured workflow and a task executed through the structured workflow of the SCM operations, as recited in claim 1.” (pages 10 last paragraph – page 11 of remark).
In response,
CELLA’s system is about cloud-based management platform with a micro-services architecture having a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application (title, abstract). “The microservices layers can include a data collection layer that can collect information from a set of Internet of Things resources that collect information with respect to supply chain entities and demand management entities related to the value chain network entities of the platform” (title, abstract). A system application is a is structure workflow and tasks are executed in the application for the system to run. Here, the workflow of the system described all over the reference and figures comprising supply, warehousing, delivery, customer, etc. The cited paragraph (para. 0341 and 0347) shows the user interface is used to discover and track all activities, executed tasks and workflow as claimed. Therefore, CELLA teaches claimed “wherein the orchestrator UI monitors the at least one structured workflow and a task executed through the structured workflow of the SCM operations” and it is combination of Maes, Cella and Softr do not but Nandakumar teaches claim 1.
Applicant argued
“Cella, in paragraph [0374] discloses value chain monitoring systems and its data collection systems. This Office Action, while citing this paragraph, did not show how this paragraph even remotely discloses, at least, an orchestration user interface that monitors the at least one structured workflow and a task executed through the structured workflow of the SCM operations, as recited in claim 1.
In view of the above, Applicant notes the Office Action has not shown where Maes teaches or suggests, at least, an orchestration user interface that monitors the at least one structured workflow and a task executed through the structured workflow of the SCM operations, as recited in claim 1. The Office Action has also failed to show where such a limitation is taught or suggested by Cella. Thus, the Office Action has failed to show how the combination of Maes and Cella teach the instant limitation.” (pages of remark).
In response,
Examiner refers to response above. Further, applicant fails to point out how the office action failed to combine all references.
Maes teaches overall claimed invention of a system for process Orchestration in one or more Supply chain management (SCM) application, wherein the supply chain management system comprises multi-supplier application (abstract and para. 0015 – 0016, 0053, 0082), wherein the orchestrator for providing access to configuration (para. 0048 and para. 0089). Cella also teaches supply chain management application as responded above (title, abstract and all figures).
It 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 was made to modify Maes by apply the teachings of Cella because Cella teaches the same field of the invention of unified orchestration supply chain management (“…unified orchestration of supply and demand…” para. 0336); Cella’s orchestration UI can also track all user’s experience to determine and configure relationships and workflows among demand management and supply chain applications based on inputs (para. 0085 - 0088).
Maes and Cella do not but Nandakumar teaches an Al based orchestration engine (title, abstract) for executing one or more operations (col. 6 lines 15 - 60).
It 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 was made to modify Maes and Cella by apply the teachings of Nandakumar because Nandakumar would provide AI based execution engine for managing AI solutions by constructing, orchestrating and executing tasks of blocks and computing resources for workflow (abstract and col. 4).
Maes, Cella and Nandakumar do not but Softr teaches Build custom business apps — powered by AI for uses to build business application with no code (title and Pages 6 – 7).
It 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 was made to modify Maes and Cella and Nandakumar by apply the teachings of Softr because Softr would apply AI to provide no-code app builder for users to build their business apps quickly and no professional skills (title and pages 6 – 7 and 11 - 12). Further, Maes can apply Softr’s ready-made templates to customize and build apps to manage supply chain.
Applicant argued
In addition, the Office Action failed to show the instant limitation is taught by either Nandakumar or Softr. In other words, Applicant respectfully submits the Office Action has failed to show how the prior art, either individually or in combination, teaches or suggests all of the limitation of claim 1. For at least this reason, withdrawal of the rejection of claim 1 and its dependent claims is respectfully requested. For somewhat similar reasons, withdrawal of the rejections of claims 17 and 39, and their dependent claims, is respectfully requested” (pages 13 - 14 of remark).
In response,
Examiner refers to response above.
Conclusion
The prior art made of record but not relied upon request is considered to be pertinent to applicant’s disclosure.
Kalaton, “Codeless Testing Tools to Solve the Challenges of Test Automation in 2021”, 11-16-2020, discloses codeless applications accessible to users for automated testing, pages 1 – 9.
Kintone, “Build Custom Business Apps with Kintone”, 2018, discloses method for buiding business applications with no code required (pages 1 – 7).
Zaleski, (US PUB 2022/0269586), discloses a method for automated test using codeless test generation (title, abstract and figures 1 – 17).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUONG N HOANG whose telephone number is (571)272-3763. The examiner can normally be reached 9:5-30.
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/PHUONG N HOANG/Examiner, Art Unit 2194
/KEVIN L YOUNG/Supervisory Patent Examiner, Art Unit 2194
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