DETAILED ACTION
Claims 1-4, 6, 9, 10-13 and 15-19 are pending in this application.
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 .
Claim Rejections - 35 USC § 103
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, 9, 10, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 11,416,224 B1 issued to Kaitha in view of U.S. Pat. No. 10,803,392 B1 issued to Khanet et al.
As to claim 1, Kaitha teaches a system for intelligent software code optimization, the system comprising:
a computing platform including a memory and one or more computing processor devices in communication with the memory (Storage Unit 406); and
an Artificial Intelligence (AI) engine (AI Module 104) stored in the memory, executable by at least one of the one or more computing processing devices and including an ML model, the ML model (i) trained to generate software code for a specific task (One or More Outputs 114) (“…In a variety of embodiments, the system 100 may be implemented with modules and sub-modules. For example, the system 100 may include an AI module 104. The AI module 104 can implement a machine learning model used to generate the source code 102 based on one or more inputs. How the machine learning model operates will be described further below. The inputs may include variables, parameters, conditional statements, software templates, or a combination thereof, that together represent the constraints of the software application being developed and the rules of the company or institution for developing the software application…In a variety of embodiments, the inputs may include application specific inputs 106, external inputs 108, revisions 110, and production inputs 112. Based on the inputs, the AI module 104 can generate one or more outputs 114. The outputs 114 can include at least the source code 102. The source code 102 can embody some or all of the inputs. For example, the source code 102 can embody or implement the rules or constraints of the software application being developed. How the AI module 104 generates the outputs 114 will be discussed further below…” Col. 3 Ln. 27-49) and (ii) configured to:
receive first inputs (Application Specific Inputs 106) that define a plurality of currently available hardware resources for executing software code, wherein the plurality of currently available hardware resources comprise currently available processing units (Dependencies 124/memory allocation/hardware components) (“…The application specific inputs 106 refer to inputs representing variables, rules, constraints, or other requirements of the software application to be developed. For example, the application specific inputs 106 can include application requirements 122 and dependencies 124. The application requirements 122 refer to application specific features and can include the features and functions that the software application must implement or perform. For example and without limitation, the application requirements 122 can include the descriptions of the specific interfaces the software application must have; the memory allocation and usage requirements for the software application; the classes, methods, or functions the software application must implement or use; safety and security requirements of the software application; what programming languages the software should be implemented in; etc. The dependencies 124 refer to inputs representing what components, systems, or other software or hardware the software application depends on, or must interface with, to perform its functions. For example and without limitation, the dependencies 124 can include descriptions or parameters indicating the hardware components the software application must interface with; what other software applications or application programming interfaces (APIs) the software application must interface with; what external systems the software application must interface with; etc…” Col. 3 Ln. 50-67, Col. 4 Ln. 1-8), and
in response to receiving the first inputs (Application Specific Inputs 106), generate a plurality of sets of software codes (One or More Outputs 114/Source Code 102) for the specific task associated with the ML model (“…In a variety of embodiments, the inputs may include application specific inputs 106, external inputs 108, revisions 110, and production inputs 112. Based on the inputs, the AI module 104 can generate one or more outputs 114. The outputs 114 can include at least the source code 102. The source code 102 can embody some or all of the inputs. For example, the source code 102 can embody or implement the rules or constraints of the software application being developed. How the AI module 104 generates the outputs 114 will be discussed further below…” Col. 3 Ln. 27-49, Col. 6 Ln. 1-46), wherein each set of software codes is specific to a different one of the currently available hardware resources ((Application Specific Inputs 106/(memory allocation/hardware components).
Kaitha is silent with reference to a regression test engine stored in the memory, executable by at least one of the one or more computing processor devices and configured to: perform regression testing on each of the plurality of sets of software codes to determine which one of the plurality of sets of software codes is a most optimal set of software codes for completing the specific task, wherein the most optimal is defined by at least one of(i) a rate at which the specific task is completed, and (ii) an accuracy of completing the specific task; and
wherein each ML model is further configured to learn, over time, by receiving results of the regression testing including which one of the plurality of sets of software codes is the most optimal.
Khanet teaches a regression test engine (regression model) stored in the memory, executable by at least one of the one or more computing processor devices and configured to:
perform regression testing on each of the plurality of sets of software codes (plurality of candidate software modules) to determine which one of the plurality of sets of software codes (first software module) is a most optimal (a fitness function) set of software codes for completing the specific task (identify, using a first profile of a first target execution environment, a first software module implementing a first prediction model)/ identify the first software module for deployment to the first target execution environment, the processing device may evaluate, for a plurality of candidate software modules, a fitness function reflecting a degree of compatibility of each candidate software module with the first target execution environment), wherein the most optimal is defined by at least one of (i) a rate at which the specific task is completed, and (ii) an accuracy of completing the specific task (a software module to be deployed within a target execution environment may be characterized by a model size, which may be represented by a function of the operational requirements (e.g., the desired accuracy and/or response time)) (“…Referring to FIG. 8, at block 810, a processing device implementing the method may identify, using a first profile of a first target execution environment, a first software module implementing a first prediction model. In one embodiment, the first target execution environment may be provided by an IoT device. In various embodiments, the model may include a prediction model, a classification model, and/or a regression model. The profile of the first target execution environment may specify the amount of memory available to the target execution environment and/or a parameter of a processing unit of the target execution environment…In one embodiment, a software module to be deployed within a target execution environment may be characterized by a model size, which may be represented by a function of the operational requirements (e.g., the desired accuracy and/or response time) and one or more parameters of the target execution environment profile (e.g., the processing unit type, the processor clock frequency, the amount of memory available to the execution environment, and/or other characteristics of the execution environment)…In one embodiment, in order to identify the first software module for deployment to the first target execution environment, the processing device may evaluate, for a plurality of candidate software modules, a fitness function reflecting a degree of compatibility of each candidate software module with the first target execution environment. In one embodiment, the fitness function may be represented by a weighted sum of terms, such that each term represents a difference of a value of a parameter of a model-specific optimal execution environment and a value of the parameter of the target execution environment. Upon evaluating the fitness function for the candidate software modules, the processing device may select the candidate software module which yields the optimal (e.g., minimal or maximal) value of the fitness function, as described in more detail herein above…(59) At block 820, the processing device implementing the method may cause the identified first software module to be deployed to the first target execution environment. In one embodiment, the processing device may make the software module available for download and transmit to the target execution environment a message specifying a uniform resource identifier (URI) of the location within a cloud-based container storage from which the identified software module may be downloaded. In one embodiment, the software module may be encrypted and cryptographically signed by the processing device in order to be securely transmitted to the target execution environment. In one embodiment, the processing device may push the software module to one or more execution environments responsive to detecting a certain triggering event, such as the software module having been updated in the code repository, as described in more detail herein above…At block 830, the processing device implementing the method may identify a second software module implementing a second prediction model to be invoked by the first prediction model, for implementing a multi-layer deployment. In one embodiment, the prediction accuracy of the second prediction model may exceed the prediction accuracy of the first prediction model. In one embodiment, the second target execution environment may be provided by a cloud-based container managed by a cloud management service, as described in more detail herein above…At block 840, the processing device implementing the method may cause the identified second software module to be deployed to the second target execution environment. The second target execution environment may be provided by a cloud-based infrastructure stack, as described in more detail herein above. Responsive to completing operations of block 840, the method may terminate…” Col. 10 Ln. 55-67, Col. 11 Ln. 1-51) ; and
wherein each ML model is further configured to learn, over time, by receiving results of the regression testing including which one of the plurality of sets of software codes is the most optimal (“…Machine learning-based methods discover pattern in historical data and construct predictive models using these patterns. The models may predict values of binary attributes (binary classification), categorical attributes (multi-class classification), or numeric attributes (regression). In one embodiment, the output of a classification function may reflect the degree of association of an image being analyzed with a certain category of images (e.g., reflecting the probability of the image being associated with the certain category). Upon evaluating a chosen classification function (also referred to as “prediction” function) for each category of a certain set of categories, the image may be associated with the category corresponding to the optimal (e.g., minimal or maximal) value of the classification function…” Col. 2 Ln. 1-9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Kaitha with the teaching of Khanet because the teaching of Khanet would improve the system of Kaitha by providing a type of supervised learning technique used to predict continuous, and numerical values.
As to claims 10 and 16, see the rejection of claim 1 above, expect for a non-transitory computer-readable medium.
Kaitha teaches a non-transitory computer-readable medium (Non-Transitory Computer Readable Medium).
Claims 2, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 11,416,224 B1 issued to Kaitha in view of U.S. Pat. No. 10,803,392 B1 issued to Khanet et al. as applied to claims 1, 10 and 16 above, and further in view U.S. Pub. No. 2018/0349114 A1 to Brown et al.
As to claim 2, Kaitha as modified by Khanet teaches the system of Claim 1, however it is silent with reference to wherein each ML model is further configured to: receive the first inputs that define the plurality of currently available hardware resources for executing software code, wherein the plurality of currently available hardware resources include processing units comprising at least one of Central Processing Units (CPUs) and Graphics Processing Units (GPUs.
Brown teaches wherein each ML model is further configured to:
receive the first inputs that define the plurality of currently available hardware resources for executing software code, wherein the plurality of currently available hardware resources include processing units comprising at least one of Central Processing Units (CPUs) and Graphics Processing Units (GPUs) , and in response to receiving the first inputs, generate at least one set of software codes for the specific task associated with the ML model (The set of set of hardware and/or processing requirements 616 may include information indicating that the ML model 610 requires the use of a GPU and/or CPU and/or a cloud computing service for particular operations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Kaitha and Khanet with the teaching of Brown because the teaching of Brown would improve the system of Kaitha and Khanet by providing a CPU and/or GPU for processing computing instructions to specification/requirement.
As to claims 11 and 17, see the rejection of claim 2 above.
Claims 3, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 11,416,224 B1 issued to Kaitha in view of U.S. Pat. No. 10,803,392 B1 issued to Khanet et al.
as applied to claims 1, 10 and 16 above, and further in view U.S. Pub. No. 2015/0106384 A1 to GO et al.
As to claim 3, Kaitha as modified by Khanet teaches the system of Claim 1, however it is silent with reference to wherein each ML model is further configured to: receive second inputs that define at least one Software Development Kit (SDK) version associated with the specific task, and in response to receiving the first and second inputs, generate plurality of sets of software codes for the specific task associated with the ML model, wherein each set of software codes is specific different one of the currently available processing units and one of the at least one SDK versions.
GO teaches wherein each ML model is further configured to: receive second inputs that define at least one Software Development Kit (SDK) version associated with the specific task (a version of a target software development kit of an application), and in response to receiving the first and second inputs, generate plurality of sets of software codes for the specific task associated with the ML model, wherein each set of software codes is specific different one of the currently available processing units (one or more hardware requirements of an application) and one of the at least one SDK versions (a version of a target software development kit of an application) (“…As shown at 101, evaluating an application for tablet compatibility may include evaluating, for example, objective requirements associated with the tablet compatibility of the application based on at least one tablet compatibility criterion. Tablet compatibility criteria, as mentioned above, may include any characteristics, qualities, factors and/or features that may help to determine whether an application is compatible with a tablet. For example, tablet compatibility criterion may be a measurement of whitespace in a screenshot taken of an application, a version of a target software development kit of an application, a resolution of a screenshot taken of an application, a maximum software development kit version of an application, one or more hardware requirements of an application, a supported screen size of an application, a high resolution resource folder in an application, and the like. Tablet compatibility criteria may include any combination of tablet compatibility criterion…As an example of an implementation of the disclosed subject matter, a tablet application classifier may evaluate applications (such as a weather application, a traffic application, and a photo editing application) for tablet compatibility and quality. The tablet application classifier may analyze each application based on tablet compatibility criteria such as the presence of tablet-specific screenshots, the SDK version to which the application is targeted, the maximum SDK version that the application can support, telephony requirements by the application, the target screen sizes of the application, the pixel density of the application's graphics, and the percent of whitespace in the tablet-specific screenshots. Based on these criteria, the tablet application classifier may assign a tablet compatibility score to each application…” paragraphs 0015/0032).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Kaitha Khanet with the teaching of GO because the teaching of GO would improve the system of Kaitha and Khanet Liu by providing a comprehensive package of tools, libraries, documentation, code samples, and APIs that helps developers build applications for a specific platform (like Android, iOS, Windows) or integrate complex features (like payments, ads, maps) quickly, essentially providing all necessary "building blocks" to create software without starting from scratch, thereby accelerating development and ensuring functionality.
As to claims 12 and 18, see the rejection of claim 3 above.
Claims 4, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 11,416,224 B1 issued to Kaitha in view of U.S. Pat. No. 10,803,392 B1 issued to Khanet et al. as applied to claims 1, 10 and 16 above, and further in view U.S. Pub. No. 2023/0359458 A1 to Evans et al.
As to claim 4, Kaitha as modified by Khanet teaches the system of Claim 1, however it is silent with reference to wherein each ML model is further configured to: receive second inputs that define user-specified constraints for determining the most optimal set of software codes, wherein the user-specified constraints define at least one of (i) required time constraints for completing the specific task, (ii) required accuracy constraints for completing the specific task, and (iii) resource acquisition constraints for acquiring at least one of the currently available hardware resources.
Evans teaches wherein each ML model is further configured to: receive second inputs that define user-specified constraints for determining the most optimal set of software codes (user), wherein the user-specified constraints define at least one of (i) required time constraints for completing the specific task, (ii) required accuracy constraints for completing the specific task, and (iii) resource acquisition constraints for acquiring at least one of the currently available hardware resources (“…A set of attributes may be determined for an identified ML model, including, but not limited to, one or more model parameters, an indication as to a training data source, a type inference, and/or a set of hyperparameters. As another example, a layer hyperparameter, an activation function, and/or other architectural features for the ML model may be programmatically defined (e.g., as may be specified via one or more lambda functions and/or code attributes in a source file). In some instances, it may be determined whether the ML model should be trained locally or using a remote training service and/or whether the ML model should be trained using a central processing unit (CPU) and/or graphics processing unit (GPU). Such attributes may be automatically determined based on the content of software code, based on a set of preferences (e.g., as may be pre-defined defaults by an IDE and/or specified by a user), or received as user input (e.g., in response to one or more prompts or via a user interface of an IDE), among other examples…The ML model may be trained according to a set of constraints, which may be determined from software code, according to a set of defaults, and/or according to one or more user preferences. Example constraints include, but are not limited to, a model latency, memory performance, task accuracy, an amount of CPU, TPU, and/or GPU hours, a set of hardware with which to perform training (e.g., a generation, model, or type of GPU), and/or a maximum training cost. In examples where it is determined that training is unable to satisfy the set of constraints, one or more candidate models (e.g., that satisfy a subset of constraints and/or are closest to satisfying a constraint) may be presented for selection by a user or, as another example, a candidate model may be automatically selected…” paragraphs 0019/0022/0024).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Kaitha and Khanet with the teaching of Evans because the teaching of Evans would improve the system of Kaitha and Khanet by providing an Integrated Development Environment (IDE) that allows user or client manage and control computing resource allocation.
As to claims 13 and 19, see the rejection of claim 4 above.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 11,416,224 B1 issued to Kaitha in view of U.S. Pat. No. 10,803,392 B1 issued to Khanet et al. as applied to claim 1 above, and further in view U.S. Pub. No. 2020/0313987 A1 to Hintermeister.
As to claim 6, Kaitha teaches the system of Claim 1, wherein each ML model is further configured to: receive first inputs that define the plurality of currently available hardware resources for executing software code (memory allocation/hardware components) (“…The application specific inputs 106 refer to inputs representing variables, rules, constraints, or other requirements of the software application to be developed. For example, the application specific inputs 106 can include application requirements 122 and dependencies 124. The application requirements 122 refer to application specific features and can include the features and functions that the software application must implement or perform. For example and without limitation, the application requirements 122 can include the descriptions of the specific interfaces the software application must have; the memory allocation and usage requirements for the software application; the classes, methods, or functions the software application must implement or use; safety and security requirements of the software application; what programming languages the software should be implemented in; etc. The dependencies 124 refer to inputs representing what components, systems, or other software or hardware the software application depends on, or must interface with, to perform its functions. For example and without limitation, the dependencies 124 can include descriptions or parameters indicating the hardware components the software application must interface with; what other software applications or application programming interfaces (APIs) the software application must interface with; what external systems the software application must interface with; etc…” Col. 3 Ln. 50-67, Col. 4 Ln. 1-8).
Hintermeister teaches wherein the currently available hardware resources are further defined as chosen from the group consisting of (i) commercially available hardware resources (“…Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses…” paragraph 0084), and (ii) inventoried hardware resources (“…Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter)…” paragraph 0067).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Kaitha and Khanet with the teaching of Hintermeister because the teaching of Hintermeister would improve the system of Kaitha and Khanet by providing a metering and pricing services to track as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources (Hintermeister paragraph 0084).
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 11,416,224 B1 issued to Kaitha in view of U.S. Pat. No. 10,803,392 B1 issued to Khanet et al. as applied to claims 1 and 10 above, and further in view of U.S. Pub. No. 2012/0131181 A1 to Birkler et al.
As to claim 9, Kaitha as modified by Khanet teaches the system of Claim 1, wherein each ML model is further configured to:
receive second inputs that define one or more newly available hardware resources for executing software code, and in response to receiving the first and second inputs, generate at least one additional set of software codes for the specific task associated with the ML model, wherein the at least one additional set of software codes is specific to or at least one of the one or more newly available hardware resources and determine whether the at least one additional set of software codes is more optimal than the most optimal set of software codes.
Birkler teaches wherein each ML model is further configured to:
receive second inputs that define one or more newly available hardware resources for executing software code (Self-Adapting Application Connector Management Component 100), and in response to receiving the first and second inputs, generate at least one additional set of software codes for the specific task associated with the ML model, wherein the at least one additional set of software codes is specific to or at least one of the one or more newly available hardware resources and determine whether the at least one additional set of software codes is more optimal than the most optimal set of software codes (compute a self-adapting configuration change based on the monitoring, wherein the self-adapting configuration change includes changing the configuration of the at least one application 40 and/or the at least one adapter 50 according to the computed configuration change) (“…Referring to FIG. 3, a method for managing a workload in a heterogeneous environment 1, which includes at least one application 40, an interoperability layer 5 for inter-application communication, and at least one adapter 50 for connecting the at least one application 40 to the interoperability layer 5, is illustrated. The method uses at least one self-adapting application connector management component 100 to monitor performance and/or workload characteristics of the at least one application 40 and/or the at least one adapter 50, and compute a self-adapting configuration change based on the monitoring, wherein the self-adapting configuration change includes changing the configuration of the at least one application 40 and/or the at least one adapter 50 according to the computed configuration change. The self-adapting application connector management component 100 reads at least one service level agreement (SLA), and monitors compliance with the at least one service level agreement (SLA)…The configuration of the at least one application 40 and the at least one adapter 50 is changed dynamically to adapt to changes to or non-compliance with resource consumption, workload, and service level agreements (SLA). This is accomplished, at least partly, by monitoring performance and workload characteristics of the at least one application 40 and the at least one adapter 50. The performance and workload characteristics may include service level agreement (SLA) characteristics, available hardware and/or configuration of the at least one application 40 and/or the at least on adapter 50, and/or resources consumed by the at least one application 40 and/or the at least one adapter 50…This monitor service 22 captures SAP workload characteristics and monitors CPU, memory, disk, and network resources by leveraging OS capabilities. After the initial deployment is complete and the performance monitoring is started, self-adapting inner loops make sure that the configuration is continuously adapting to changing workloads…At regular intervals as indicated by the service level agreement (SLA), the performance and throughput is measured and compared against the performance and throughput required by the service level agreement (SLA). For the application connector 50 (WebSphere adapter for SAP) this comparison occurs at step S40A and may result in several outcomes. If the measurements are within the acceptable range and no scheduled tasks in the packaged application 40 (SAP Netweaver ECC) require configuration changes, the monitoring continues at step S30A without making any changes to the configuration. If, on the other hand, the measurements are outside of the acceptable range and/or there are scheduled tasks in the packaged application 40 (SAP Netweaver ECC) requiring configuration changes, self-adapting configuration processing is started at step S50A…To determine the required changes to the configuration, a prediction algorithm may consider, for example, service level agreement (SLA) requirements, estimated impact on performance/throughout by scheduled tasks if applicable, performance history (Patterns determined by JCA connection usage based on performance monitoring infrastructure (PMI) service monitoring can indicate, for example, how much additional resources might be feasible to allocate if more resources are required), the available average free hardware resources where the application connector instances 50 (WebSphere Adapter for SAP) and the packaged application 40 (SAP Netweaver ECC) is running (here, measurements of the CPU, memory, disk, and network resource consumption may be used), the available free hardware (based on the configuration maintained by the enterprise application connection manager 100…” paragraphs 0035/0036/0043-0045).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Kaitha and Khanet with the teaching of Birkler because the teaching of Birkler would improve the system of Kaitha and Khanet by providing a dynamic or self re-configuration of resources for executing workload.
As to claim 15, see the rejection of claim 9 above.
Response to Arguments
Applicant’s arguments with respect to claims 1-4, 6, 9-13, and 15-19 have been considered but are moot because the new ground of rejection relies on additional reference not applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure..
U.S. Pat. No. 12,079,113 B1 issued to Mohan et al. and directed to resource management for software tests.
U.S. Pat. No. 11,074,164 B2 issued to Yarabolu et al. and directed to real-time regression test generator.
U.S. Pub. No. 2020/0159525 A1 to Bhalla et al. and directed to a system and method for automation of task identification and control in a software lifecycle.
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/CHARLES E ANYA/Primary Examiner, Art Unit 2194
