DETAILED ACTION
Claims 1-15 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Notes
Examiner has cited particular columns and line numbers, paragraph numbers, or figures in the references as applied to the claims below 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 from the applicant, in preparing the responses, to fully consider the references in 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 or disclosed by the examiner.
Claim Objections
Claims 1-11 are objected to because of the following informalities: Claims refer to reference character(s) which makes it unclear. Appropriate correction is required.
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, and 4-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Statutory Category: Claims 1, 13, 14 and 15 are directed to a method, computer-readable medium, data processing apparatus and a data processing system, respectively. Therefore, the claims are directed to one of the four statutory categories of inventions.
Step 2A – Prong 1: Claims 1, 13, 14 and 15 recites, automatically generating a data transformation graph (100) based on the data transformations (200), wherein the data transformation graph (100) links the data transformations (200) by way of their input datasets (104, 108) and output datasets (108, 110). These limitations as drafted, is a process that, under their broadest reasonable interpretation, covers an abstract idea such as performance of the limitation in the mind. That is, other than a generic computer, nothing in the claim elements precludes the steps from practically being performed mentally. Specifically, automatically generating a data transformation graph (100) based on the data transformations (200), wherein the data transformation graph (100) links the data transformations (200) by way of their input datasets (104, 108) and output datasets (108, 110) can be performed mentally through observation, evaluation, judgement, opinion by a developer to draw a graph based on the observed data transformations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the category of abstract idea within the mental process grouping. Accordingly, the claim recites an abstract idea under step 2A prong 1.
Step 2A, Prong 2: The additional elements do not integrate the judicial exception into a practical application. The limitations obtaining electronic data defining a plurality of data transformations (200), wherein each data transformation (200) defines a step function (106) and at least one of a set of input datasets (104, 108) and a set of output datasets (108, 110) add insignificant extra solution activity, such as data gathering and transmission, see MPEP 2106.05(g). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application.
Step 2B: As discussed with respect to step 2A prong 2, the additional elements obtaining electronic data defining a plurality of data transformations (200), wherein each data transformation (200) defines a step function (106) and at least one of a set of input datasets (104, 108) and a set of output datasets (108, 110) amount to well-understood, routine conventional activities as seen in court cases storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 and receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Accordingly, the claim does not amount to significantly more than the judicial exception, thus lack an inventive concept for patent eligibility under 35 USC 101.
Regarding claim 4, the additional elements wherein the definition of a given data transformation (200) comprises at least one of: a definition of the set of input datasets (104, 108) and/or the set of output datasets (108, 110), in particular by way of one or more pointers to corresponding input and/or output storage locations; and computer code, or a reference to computer code, for defining the step function (106), in particular for defining how the step function (106) transforms the input datasets (104, 108) into the output datasets (108, 110), add insignificant extra solution activity, such as data gathering and transmission, see MPEP 2106.05(g). . Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 5, the additional elements wherein the data transformations are defined in one or more computer files, such as in a dedicated computer file per data transformation (200) or in a computer file for multiple data transformations (200), recites field of use, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 6, the additional elements further comprising executing the data transformation graph (100), comprising: determining an execution environment for executing the data transformation graph (100), in particular whether the execution environment comprises a local data processing apparatus and/or a central and/or remote, in particular cloud-based, data processing apparatus; and executing the plurality of data transformations (200) in an order indicated by the data transformation graph (100); wherein, preferably, the step of executing the plurality of data transformations (200) in an order indicated by the data transformation graph (100) comprises: determining an initial data transformation (200) which does not depend on any other data transformation (200), and executing said initial data transformation (200); and traversing the data transformation graph (100) to determine a next data transformation (200) which does not depend on any other data transformation (200), and executing said next data transformation (200), recites field of use, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 7, the additional elements wherein executing a given data transformation (200) comprises: executing a pre-step function, if present; executing the step function (106); and executing a post-step function, if present, recites field of use, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 8, the additional elements wherein executing the step function (106) comprises: on-demand loading of data associated with the input datasets (104, 108), preferably from one or more input storage locations defined by the data transformation (200); wherein, optionally, the loading comprises downloading the data into a local execution environment; and writing data associated with the output datasets (108, 110), preferably to one or more output storage locations defined by the data transformation (200), recites field of use, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 9, the additional elements wherein a given data transformation (200) is only executed if an access privilege level of a user associated with the executing is sufficient, recites field of use, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 10, the additional elements wherein the step of obtaining electronic data defining a plurality of data transformations (200) comprises receiving user input defining the plurality of data transformations (200); and wherein the method further comprises: executing the data transformation graph (100) in the local execution environment associated with the user; and receiving user input for publishing at least some of the data transformations (200) to a central execution environment, in particular a production environment, add insignificant extra solution activity, such as data gathering and transmission, see MPEP 2106.05(g). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 11, the additional elements being executed in a central execution environment, in particular a production environment; wherein the step of obtaining electronic data defining a plurality of data transformations (200) comprises receiving the data transformations (200) from one or more execution environments associated with one or more users; and wherein the method further comprises executing the data transformation graph (100) n the central execution environment, add insignificant extra solution activity, such as data gathering and transmission, see MPEP 2106.05(g). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 12, the additional elements further comprising providing a definition of one or more execution environments, the definition indicating at least one of: at least one configuration variable of the execution environment; it least one data access method supported by the execution environment; a reference to a file system; one or more pre-step functions and/or post-step functions; and error handling functionality, recites field of use, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Claim 13 recites a “computer program or computer-readable medium” comprising “instructions”. Examiner is to apply the term “computer-readable medium” to its broadest reasonable interpretation which would be any medium usable by a computer which would include carrier waves and signals see specification page 20 lines 15-21. Thus, the claim is directed to non-statutory matter. See MPEP § 2106. To overcome this type of rejection applicant may amend claims to include “non-transitory computer-readable medium.”
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 10-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nandakumar (US-PAT-NO: 11,074,107 B1).
As per claim 1, Nandakumar teaches a computer-implemented method with at least the following steps: obtaining electronic data defining a plurality of data transformations (200), wherein each data transformation (200) defines a step function (106) and at least one of a set of input datasets (104,108) (see Nandakumar [column 13, lines 21-36], “2. Data Processing. In this step, a data scientist processes the data extracted from different sources to a format which can be used in the modeling process. Once the data is extracted, the data needs to be converted into a form that can be consumed by the model. In the data processing step, multiple steps may be utilized to cleanse the data and bring it to a format which can be fed to the model. Some of the basic steps involved in data processing may include: (1) cleanse the data that is extracted from the data sources are filtered. In this process, certain columns and rows are removed from the dataset based on the requirements of the use case; (2) normalize the data within a range of values using the statistical features of the dataset; and (3) encode the data to a format that can be fed to the model for processing. There are different types of encoding depending on the use case and statistical features of the dataset”) and a set of output datasets (108, 110) (see Nandakumar [column 13, lines 58-67 and column 14, lines 1-17], “Certain aspects and exemplary embodiments of the present disclosure provide for a computer system, comprising at least one computer processor configured to execute computer program instructions; and a non-transitory computer-readable memory device communicably engaged with the at least one computer processor and having computer program instructions stored thereon that, when executed, cause the at least one computer processor to perform operations comprising storing at least one software component comprising a block, the block comprising at least one function for encapsulating at least one computing task as an object within an object-oriented programming framework; receiving a user-generated input comprising at least one declarative specification for one or more operations of a data processing pipeline or workflow; processing the user-generated input according to at least one artificial intelligence framework to identify one or more computing tasks associated with the one or more operations; configuring one or more blocks to encapsulate the one or more computing tasks as objects within the one or more blocks; and configuring one or more functional relationships between the one or more blocks according to the at least one declarative specification, wherein the one or more functional relationships comprise at least one data transport function between the one or more blocks, wherein the one or more one or more blocks, when configured according to the declarative specification, comprise a topology of the data processing pipeline or workflow”); and automatically generating data transforming graph (100) based on the data transformations (200), wherein the data transformation graph (100) links the data transformations (200) by way of their input datasets (104, 108) and output datasets (108, 110) (see FIG. 1 and FIG. 2, also see Nandakumar [column 16,lines 33-67 and column 17, lines 1-32], “Turning now descriptively to the drawings, in which the same reference characters denote the same elements throughout the several views, FIG. 1 depicts a functional block diagram 100 of an artificial intelligence operating system (“AiOS”) 102. In accordance with various embodiments, AiOS 102 comprises an integrated environment comprising one or more software component 104 each pre-loaded with an AI OS intelligent functionality. Software component 104 contains a built and tested code block 106 that provides a mechanism 108 to encapsulate one or more operations of a ML product lifecycle. AiOS 102 may comprise a library 110 of reusable or customizable components. In accordance with various embodiments, one or more component 104 may be linked in a sequential connection 114 and/or parallel connection 116 comprising a topology of pipeline 118 for building an analytic ML model. Software component 104 may be configured to accept one or more streaming data sources 120. An analytic model created from pipeline 118 consumes data source 120, typically in the form of a data stream. An analytic data provide may utilize one or more data sources including, for example, APACHE SPARK, HADOOP, AMAZON REDSHIFT, AZURE SQL Data Warehouse, MICROSOFT SQL Server, and/or TERADATA. The analytic data provider or source may utilize one or more example infrastructure systems including: on-premises hardware, such as in-office computing and/or proprietary datacenter computing; or off-premises hardware, such as cloud infrastructure including AMAZON WEB SERVICES, MICROSOFT AZURE, IBM BLUEMIX, and/or GOOGLE Cloud Platform. In accordance with various embodiments, AiOS 102 is configured to enable user-friendly data science experimentation, exploration, analytic model execution, prototyping, pipeline 118 construction, to establish a complete end-to-end, transparent, AI pipeline building process for the development, production, and deployment of reproducible, scalable, and interoperable ML models and AI applications with governance”) which discloses a graph wherein the nodes are related with tasks and functions for data processing, wherein data processing comprises data conversion and cleansing as further elaborated in Nandakumar [column 13,lines 21-36] and FIG. 3 disclosing the implementation of a CSVReader).
As per claim 2, Nandakumar teaches wherein the data transformations (200) are defined using constructs of a programming language; wherein the step of generating the data transformation graph (100) comprises creating objects in the programming language to represent the data transformations (200); and wherein, optionally, the programming language is Python, and the step function (106) is indicated by a Python decorator (see Nandakumar [column 12, lines 18-26], “The AI OS may comprise an integrated environment comprising one or more software components call blocks, each block comprising programming code configured to enable various function and operations within an AI OS intelligent framework. According to certain embodiments, a block may comprise one or more integrated and tested code segments to provide a mechanism to encapsulate one or more operations of a ML product lifecycle; for example, a Python decorator.”).
As per claim 3, Nandakumar teaches wherein all objects that constitute the data transformation graph (100) are stored simultaneously in a working memory of a data processing apparatus (see Nandakumar FIG. 7 and [column 26,lines 18-67], “Referring now to FIG. 7, a process flow diagram for the pipeline intelligence process 700 is shown. According to certain aspects of the present disclosure, pipeline intelligence process 700 may comprise one or more steps 704-710 for the intelligent management of a pipeline 702. 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. In accordance with certain exemplary embodiments, step 704 may comprise one or more operations for ensuring that socket transport blocks are instantiated concurrently, and real-time executing pipelines are functioning operatively. In accordance with certain alternative embodiments, step 704 may comprise one or more operations for ensuring that transport mode blocks are instantiated concurrently, and real-time executing pipelines are functioning operatively. In accordance with certain exemplary embodiments, step 706 may comprise one or more operations for optimally allocating one or more computing memory resources according to one or more algorithm, rules engine or derived heuristic. In various embodiments, one or more streaming, cache, combinations thereof, or the like data set, data flow, data frame, data source (e.g., Kafka, disk, etc.) may be allocated to one or more memory space. In accordance with certain exemplary embodiments, step 708 may comprise one or more operations for monitoring computing resource consumption and modulating the activities of one or more blocks to manage peak utilization optimally. In accordance with certain exemplary embodiments, step 710 may comprise one or more operations for dynamically managing fault tolerance and ensuring operation robustness. In various embodiments, block operations are monitored to mitigate one or more faults including, but not limited to, block failover, transport failover, or combinations thereof and the like. In various embodiments, one or more configurable transport mechanism enables the transfer of streaming, or file, or data memory between two blocks arranged within a pipeline. In various embodiments, the transport mechanism provides access to live data sources including, but not limited to, chat, streaming video, or combinations thereof and the like. In various embodiments, an adapter enables the conversion of one or more data frame to satisfy the input requirements of a block arranged within a pipeline. In various embodiments, the output of a block is adapted or modified as batch-by-batch or atomic, contiguous streaming dataset. In various embodiments, pipeline intelligence process 700 enables a service block to interact with an external resource or application in real-time, via streaming data, for receiving and processing one or more service requests”).
As per claim 4, Nandakumar teaches wherein the definition of a given data transformation (200) comprises at least one of: a definition of the set of input datasets (104, 108) and/or the set of output datasets (108, 110), in particular by way of one or more pointers to corresponding input and/or output storage locations; and computer code, or a reference to computer code, for defining the step function (106), in particular for defining how the step function (106) transforms the input datasets (104, 108) into the output datasets (108, 110) (see Nandakumar FIG. 3, showing the CSVReader comprising parameters such as filepath for the input data, wherein said filepath is a pointer to said input).
As per claim 5, Nandakumar teaches wherein the data transformations are defined in one or more computer files, such as in a dedicated computer file per data transformation (200) or in a computer file for multiple data transformations (200) (see Nandakumar FIG. 3 and [column 17, lines 64-67 and column 18,lines 1-39], “Referring now to FIG. 3, a diagram 300 illustrating a code sequence of a block 302 is shown. Block 302 may comprise software component 104 of FIG. 1. According to certain aspects of the present disclosure, block 302 comprises at least one computing instruction 304 or code that enables a user to define one or more computing tasks. In various embodiments, the task includes, but is not limited to a function, an instance, class 306, or the like. In a preferred embodiment, the said function, instance, or class are objects within an object-oriented programming language (e.g. Python) that can be configured declaratively by chaining together functions with defined input and output objects without explicit execution description by a user. In various embodiments, one or more instances of block 304 comprise Python class objects containing one or more function, instance, or class annotation decorator 308. In various embodiments, one or more instances of block 304 comprise class objects of additional object-oriented programming technologies or languages, including PYSPARK, JAVA, JJAVASCRIPT, C++, VISUAL BASIC, .NET, RUBY, SCALA, LISP, MATLAB and the like, containing one or more function, instance, or class annotation decorator 308. In various embodiments, decorator 308 enables one or more computing code to be evaluated at one or more function definition or function invocation. In accordance with certain aspects of the present disclosure, AiOS 102 of FIG. 1 enables a user to declaratively specify one or more tasks within block 302 including, but not limited to, one or more data transport mechanism between two blocks, computing infrastructure, local or distributed computing resources, and execution mode. Block 302 may comprise one or more import function 310 configured to import one or more internal or external AI or ML libraries, standard block library, deployment technology (e.g., container, etc.) within block 302. In various embodiments, block 302 can be configured with one or more task to enable the monitoring of real-time code, instruction execution, resource utilization (e.g., CPU usage), model performance metrics (e.g., accuracy, precision, recall, etc.). In various embodiments, one or more blocks 302 are connected, via code command lines or instructions, to form an AI or ML pipeline, for example, pipeline 118 of FIG. 1, or complex scientific, engineering, or business analytical, scalable workflow”).
As per claim 6, Nandakumar teaches further comprising executing the data transformation graph (100), comprising: determining an execution environment for executing the data transformation graph (100), in particular whether the execution environment comprises a local data processing apparatus and/or a central and/or remote, in particular cloud-based, data processing apparatus; and executing the plurality of data transformations (200) in an order indicated by the data transformation graph (100); wherein, the step of executing the plurality of data transformations (200) in an order indicated by the data transformation graph (100) comprises: determining an initial data transformation (200) which does not depend on any other data transformation (200), and executing said initial data transformation (200); and traversing the data transformation graph (100) to determine a next data transformation (200) which does not depend on any other data transformation (200), and executing said next data transformation (200) (see Nandakumar [column 7,lines 22-62], “An object of the present disclosure is a general-purpose computer system configured to execute or otherwise embody various aspects of the said artificial intelligence (AI) operating system (OS) for managing the full lifecycle of AI and/or ML application development, from product creation through deployment and operation. The computer system in accordance with the present disclosure may comprise systems and/or sub-systems, including at least one microprocessor, memory unit (e.g., ROM), removable storage device (e.g., RAM), fix/removable storage device(s), input-output (I/O) device, network interface, display, and keyboard. In various embodiments, the general-purpose computing system may serve as a client enabling user access to the OS system and methods, locally or as a client, of a distributed computing platform or back-end server. In accordance with various embodiments, one or more code, script, or declaration may be executed locally or remotely. In various embodiments, the OS may provide user-access to a free standing CLI. In accordance with various embodiments, the CLI allows users to specify, declare, and run tasks, execute pipelines locally, sync data and code, sync run and result output, track local experiments, publish a block or pipeline into a database, or the like. In various embodiments, the CLI enables a secured connection to a cloud platform to execute one or more tasks using a secure shell (“SSH”) network protocol. In accordance with various embodiments, the OS may provide a GUI to create a pipeline by dragging and dropping blocks onto a canvas and connecting them to rapidly create pipelines, manage executors, transport mechanisms, and adaptors. In accordance with various embodiments, the computing system of the present disclosure may execute instructions encapsulated within a block including, but not limited to, annotated decorator function, instance, class, or object. In accordance with certain aspects of the present disclosure, the instructions encapsulated within the block may be configured to enable sequential, parallel, synchronous, asynchronous, or concurrent completion of one or more said tasks of an end-to-end AI solution, application, ML model development, deployment, product lifecycle, combinations thereof, and the like” showing preferable features being optional features and the optional features related with initial data not being dependent on other transformations, is a non-technical feature void of any technical effect, considering that the transformed data may at most be either used for a general machine training or presented to a user).
As per claim 7, Nandakumar teaches wherein executing a given data transformation (200) comprises: executing a pre-step function, if present; executing the step function (106); and executing a post-step function, if present (see Nandakumar [column 18, lines 4-9], “In a preferred embodiment, the said function, instance, or class are objects within an object-oriented programming language (e.g. Python) that can be configured declaratively by chaining together functions with defined input and output objects without explicit execution description by a user”).
As per claim 10, Nandakumar teaches wherein the step of obtaining electronic data defining a plurality of data transformations (200) comprises receiving user input defining the plurality of data transformations (200); and wherein the method further comprises: executing the data transformation graph (100) in the local execution environment associated with the user (see Nandakumar FIG. 3 and [column 7,lines 22-62], “An object of the present disclosure is a general-purpose computer system configured to execute or otherwise embody various aspects of the said artificial intelligence (AI) operating system (OS) for managing the full lifecycle of AI and/or ML application development, from product creation through deployment and operation. The computer system in accordance with the present disclosure may comprise systems and/or sub-systems, including at least one microprocessor, memory unit (e.g., ROM), removable storage device (e.g., RAM), fix/removable storage device(s), input-output (I/O) device, network interface, display, and keyboard. In various embodiments, the general-purpose computing system may serve as a client enabling user access to the OS system and methods, locally or as a client, of a distributed computing platform or back-end server. In accordance with various embodiments, one or more code, script, or declaration may be executed locally or remotely. In various embodiments, the OS may provide user-access to a free standing CLI. In accordance with various embodiments, the CLI allows users to specify, declare, and run tasks, execute pipelines locally, sync data and code, sync run and result output, track local experiments, publish a block or pipeline into a database, or the like. In various embodiments, the CLI enables a secured connection to a cloud platform to execute one or more tasks using a secure shell (“SSH”) network protocol. In accordance with various embodiments, the OS may provide a GUI to create a pipeline by dragging and dropping blocks onto a canvas and connecting them to rapidly create pipelines, manage executors, transport mechanisms, and adaptors. In accordance with various embodiments, the computing system of the present disclosure may execute instructions encapsulated within a block including, but not limited to, annotated decorator function, instance, class, or object. In accordance with certain aspects of the present disclosure, the instructions encapsulated within the block may be configured to enable sequential, parallel, synchronous, asynchronous, or concurrent completion of one or more said tasks of an end-to-end AI solution, application, ML model development, deployment, product lifecycle, combinations thereof, and the like”); and receiving user input for publishing at least some of the data transformations (200) to a central execution environment, in particular a production environment (see Nandakumar [column 17, lines 64-67 and column 18, lines 1-39], “Referring now to FIG. 3, a diagram 300 illustrating a code sequence of a block 302 is shown. Block 302 may comprise software component 104 of FIG. 1. According to certain aspects of the present disclosure, block 302 comprises at least one computing instruction 304 or code that enables a user to define one or more computing tasks. In various embodiments, the task includes, but is not limited to a function, an instance, class 306, or the like. In a preferred embodiment, the said function, instance, or class are objects within an object-oriented programming language (e.g. Python) that can be configured declaratively by chaining together functions with defined input and output objects without explicit execution description by a user. In various embodiments, one or more instances of block 304 comprise Python class objects containing one or more function, instance, or class annotation decorator 308. In various embodiments, one or more instances of block 304 comprise class objects of additional object-oriented programming technologies or languages, including PYSPARK, JAVA, JJAVASCRIPT, C++, VISUAL BASIC, .NET, RUBY, SCALA, LISP, MATLAB and the like, containing one or more function, instance, or class annotation decorator 308. In various embodiments, decorator 308 enables one or more computing code to be evaluated at one or more function definition or function invocation. In accordance with certain aspects of the present disclosure, AiOS 102 of FIG. 1 enables a user to declaratively specify one or more tasks within block 302 including, but not limited to, one or more data transport mechanism between two blocks, computing infrastructure, local or distributed computing resources, and execution mode. Block 302 may comprise one or more import function 310 configured to import one or more internal or external AI or ML libraries, standard block library, deployment technology (e.g., container, etc.) within block 302. In various embodiments, block 302 can be configured with one or more task to enable the monitoring of real-time code, instruction execution, resource utilization (e.g., CPU usage), model performance metrics (e.g., accuracy, precision, recall, etc.). In various embodiments, one or more blocks 302 are connected, via code command lines or instructions, to form an AI or ML pipeline, for example, pipeline 118 of FIG. 1, or complex scientific, engineering, or business analytical, scalable workflow”).
As per claim 11, Nandakumar teaches being executed in a central execution environment, in particular a production environment; wherein the step of obtaining electronic data defining a plurality of data transformations (200) comprises receiving the data transformations (200) from one or more execution environments associated with one or more users; and wherein the method further comprises executing the data transformation graph (100) n the central execution environment (see Nandakumar FIG. 3 and [column 7,lines 22-62], “An object of the present disclosure is a general-purpose computer system configured to execute or otherwise embody various aspects of the said artificial intelligence (AI) operating system (OS) for managing the full lifecycle of AI and/or ML application development, from product creation through deployment and operation. The computer system in accordance with the present disclosure may comprise systems and/or sub-systems, including at least one microprocessor, memory unit (e.g., ROM), removable storage device (e.g., RAM), fix/removable storage device(s), input-output (I/O) device, network interface, display, and keyboard. In various embodiments, the general-purpose computing system may serve as a client enabling user access to the OS system and methods, locally or as a client, of a distributed computing platform or back-end server. In accordance with various embodiments, one or more code, script, or declaration may be executed locally or remotely. In various embodiments, the OS may provide user-access to a free standing CLI. In accordance with various embodiments, the CLI allows users to specify, declare, and run tasks, execute pipelines locally, sync data and code, sync run and result output, track local experiments, publish a block or pipeline into a database, or the like. In various embodiments, the CLI enables a secured connection to a cloud platform to execute one or more tasks using a secure shell (“SSH”) network protocol. In accordance with various embodiments, the OS may provide a GUI to create a pipeline by dragging and dropping blocks onto a canvas and connecting them to rapidly create pipelines, manage executors, transport mechanisms, and adaptors. In accordance with various embodiments, the computing system of the present disclosure may execute instructions encapsulated within a block including, but not limited to, annotated decorator function, instance, class, or object. In accordance with certain aspects of the present disclosure, the instructions encapsulated within the block may be configured to enable sequential, parallel, synchronous, asynchronous, or concurrent completion of one or more said tasks of an end-to-end AI solution, application, ML model development, deployment, product lifecycle, combinations thereof, and the like”).
As per claim 12, Nandakumar teaches further comprising providing a definition of one or more execution environments, the definition indicating at least one of: at least one configuration variable of the execution environment; it least one data access method supported by the execution environment; a reference to a file system; one or more pre-step functions and/or post-step functions; and error handling functionality (see Nandakumar FIG. 3 and [column 6, lines 15-63], “An object of the present disclosure is 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. In accordance with certain exemplary embodiments, task execution may be conducted on one or more target resources including, but not limited to, one or more cloud/remote server, multi-core workstations, distributed computing systems, supercomputers, or combinations thereof and the like. In accordance with certain exemplary embodiments, an executor engine may enable user-defined scaling or configuration for pipeline execution. A configuration may comprise the specifications of an external computing system, connection channel, communication protocol, technology binding, memory allocation, queues, computing duration, and data transport/management options. In accordance with certain exemplary embodiments, an executor engine may automatically configure and execute tasks based on derived heuristics, meta-heuristics and one or more historical test cases tuned to common execution patterns, to determine an optimal resource allocation strategy for a user-defined ML pipeline. In accordance with various embodiments, an execution engine may construct and orchestrate execution using a task or task dependency graph comprising all the states for a pipeline program mapped to available execution resources. In certain exemplary embodiments, a task graph is represented as a directed acyclic graph (DAG), preferably a dynamic task graph. In certain exemplary embodiments, an execution engine may perform a variety of functions including, but not limited to, tracking information in a data structure, deriving and resolving dependencies, storing-receiving metadata and/or future data or results from asynchronous operations or call backs, performing fault-tolerant, processing exceptions and execution errors, and combinations thereof and/or the like. In certain exemplary embodiments, an execution engine control logic may be derived from one or more annotated decorators of one or more blocks enabling asynchronous, parallel, and portable execution of heterogenous pipeline workloads independent of resource allocations or constraints”).
As per claim 13, this is the computer-readable medium (see Nandakumar [column 7, lines 63-67 and column 8, lines 1-7], “Certain aspects of the present disclosure provide for a computer system, comprising at least one computer processor configured to execute computer program instructions; and a non-transitory computer-readable memory device communicably engaged with the at least one computer processor and having computer program instructions stored thereon that, when executed, cause the at least one computer processor to perform operations comprising storing at least one software component comprising a block, the block comprising at least one function for encapsulating at least one computing task as an object within an object-oriented programming framework”) claim to method claim 1. Therefore, it is rejected for the same reasons as above.
As per claim 14, this is the data processing apparatus to method claim 1. Therefore, it is rejected for the same reasons as above.
As per claim 15, this is the data processing system, comprising: a plurality of local execution environments, each being executable on a data processing apparatus associated with a user, and configured for executing the method claim 1; and a central execution environment, being executable on a data processing apparatus, in particular a cloud-based data processing apparatus, and configured for executing the method of claim 1 (see Nandakumar (see Nandakumar [column 7,lines 22-62], “An object of the present disclosure is a general-purpose computer system configured to execute or otherwise embody various aspects of the said artificial intelligence (AI) operating system (OS) for managing the full lifecycle of AI and/or ML application development, from product creation through deployment and operation. The computer system in accordance with the present disclosure may comprise systems and/or sub-systems, including at least one microprocessor, memory unit (e.g., ROM), removable storage device (e.g., RAM), fix/removable storage device(s), input-output (I/O) device, network interface, display, and keyboard. In various embodiments, the general-purpose computing system may serve as a client enabling user access to the OS system and methods, locally or as a client, of a distributed computing platform or back-end server. In accordance with various embodiments, one or more code, script, or declaration may be executed locally or remotely. In various embodiments, the OS may provide user-access to a free standing CLI. In accordance with various embodiments, the CLI allows users to specify, declare, and run tasks, execute pipelines locally, sync data and code, sync run and result output, track local experiments, publish a block or pipeline into a database, or the like. In various embodiments, the CLI enables a secured connection to a cloud platform to execute one or more tasks using a secure shell (“SSH”) network protocol. In accordance with various embodiments, the OS may provide a GUI to create a pipeline by dragging and dropping blocks onto a canvas and connecting them to rapidly create pipelines, manage executors, transport mechanisms, and adaptors. In accordance with various embodiments, the computing system of the present disclosure may execute instructions encapsulated within a block including, but not limited to, annotated decorator function, instance, class, or object. In accordance with certain aspects of the present disclosure, the instructions encapsulated within the block may be configured to enable sequential, parallel, synchronous, asynchronous, or concurrent completion of one or more said tasks of an end-to-end AI solution, application, ML model development, deployment, product lifecycle, combinations thereof, and the like”). Therefore, it is rejected for the same reasons as 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.
Claim(s) 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Nandakumar (US-PAT-NO: 11,074,107 B1), in further view of Lerman (US-PGPUB-NO: 2023/0108808 A1).
As per claim 8, Nandakumar does not explicitly teach wherein executing the step function (106) comprises: on-demand loading of data associated with the input datasets (104, 108), preferably from one or more input storage locations defined by the data transformation (200); wherein, optionally, the loading comprises downloading the data into a local execution environment; and writing data associated with the output datasets (108, 110), preferably to one or more output storage locations defined by the data transformation (200). However, Lerman teaches wherein executing the step function (106) comprises: on-demand loading of data associated with the input datasets (104, 108), preferably from one or more input storage locations defined by the data transformation (200); wherein, optionally, the loading comprises downloading the data into a local execution environment; and writing data associated with the output datasets (108, 110), preferably to one or more output storage locations defined by the data transformation (200) (see Lerman paragraph [0189], “The term “cloud computing” is generally used herein to describe a computing model which enables on-demand access to a shared pool of computing resources, such as computer networks, servers, software applications, and services, and which allows for rapid provisioning and release of resources with minimal management effort or service provider interaction”).
Nandakumar and Lerman are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective date to modify Nandakumar’s teaching of data processing for managing AI solutions development lifecycles with Lerman’s teaching of data science workflow execution platform with automatically managed code and graph-based data job management to incorporate an on-demand system such as cloud computing to provide greater flexibility to data processing taught in Nandakumar.
As per claim 9, Nandakumar modified with Lerman teaches wherein a given data transformation (200) is only executed if an access privilege level of a user associated with the executing is sufficient (see Lerman paragraph [0054], “In an embodiment, the disclosed system is programmed to implement data access controls, working from the assumption that many organizations will have a variety of data, with different data potentially having its own level of sensitivity. In an embodiment, the disclosed system is programmed to implement IAM abstractions, such as: users, teams, organizations, and accounts. In an embodiment, the disclosed system is programmed to implement permissions for schemas and tables for any of the foregoing abstraction layers. In an embodiment, the disclosed system is programmed to implement IAM policies so that organizations can use the policies that exist for cloud computing providers. In an embodiment, the disclosed system is programmed to integrate with existing Active Directory setups”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fink et al. (US-PGPUB-NO: 2019/0243619 A1) teaches extensible data transformation authoring and validation system.
Dang et al. (US-PGPUB-NO: 2019/0114289 A1) teaches dynamically performing data processing in a data pipeline system.
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/LENIN PAULINO/Examiner, Art Unit 2197
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