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 1-25 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the limitation " the pipeline" in “wherein determining the ethical concern includes identifying changes in contextual information relating to the pipeline from one or more artifacts”. There is insufficient antecedent basis for this limitation in the claim.
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 1-25 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.
Claims 1, 14 and 15 as drafted, recite a process that, under its broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “determining an ethical concern relating to a development pipeline by:… identifying precepts from input information sources; and expressing the identified precepts as intents using a domain model; identifying a constraint that mitigates the ethical concern by mapping the intents to corresponding actions using a database of correspondences between known intents and actions and adding the constraint to the development pipeline” as drafted, is a process that, under its broadest reasonable interpretation, recite the abstract idea of mental processes. These limitations encompass a human mind carrying out these functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. The claims recites the following additional elements “a computer readable storage medium,” “a hardware processor,” “a system,” “a memory that stores a computer program,” and “iteratively generating a development pipeline”, “executing the development pipeline concurrent with the iterative generating”, and “obtaining processed input information sources using a miner”. The additional elements ““a computer readable storage medium,” “a hardware processor,” “a system,” “a memory that stores a computer program,” and “iteratively generating a development pipeline”, “executing the development pipeline concurrent with the iterative generating”, are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). The additional element “obtaining processed input information sources using a miner” does nothing more than add insignificant extra solution activity to the judicial exception, such as data gathering and outputting the results of the abstract idea to perform a task. See MPEP 2106.05(g). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “a computer readable storage medium,” “a hardware processor,” “a system,” “a memory that stores a computer program,” and “iteratively generating a development pipeline”, “executing the development pipeline concurrent with the iterative generating” are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). As to the additional element “obtaining processed input information sources using a miner” the courts have identified gathering data and displaying the output of the abstract idea is well-understood, routine, conventional activity. See MPEP 2106.05(d). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 2 and 16 further define “the development pipeline is a DevOps pipeline script and the constraint is an entry in the DevOps pipeline script that performs a check to ensure the ethical concern is not triggered” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “the development pipeline is a DevOps pipeline script and the constraint is an entry in the DevOps pipeline script that performs a check to ensure the ethical concern is not triggered” are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 3 and 17 further define “the DevOps pipeline script further includes instructions to build and deploy a project” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “the DevOps pipeline script further includes instructions to build and deploy a project” are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 4 and 18 further define the “determining” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 5 and 19 recite the addition element “wherein executing the development pipeline includes executing a new development pipeline responsive to adding the constraint for the new ethical concern” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “wherein executing the development pipeline includes executing a new development pipeline responsive to adding the constraint for the new ethical concern” are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 6 and 20 further define “the development pipeline develops and deploys a machine learning model” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “the development pipeline develops and deploys a machine learning model” are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 7 and 21 further define the “ethical concern” as part of the “determining” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 8 and 22 further define the “constraint” as part of the “identifying” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 9 further define the “determining” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 10 further define the “identifying” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 11 and 24 as drafted, recite a process that, under its broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “determining a new ethical concern relating to a bias in training data for the machine learning model that was not known at initial execution of the DevOps pipeline script by:… identifying precepts from input information sources; and expressing the identified precepts as intents using a domain model; identifying a constraint that mitigates the ethical concern by mapping the intents to corresponding actions using a database of correspondences between known intents and actions and adding the constraint as an entry in the DevOps pipeline script that performs a check to ensure the new ethical concern is not triggered” as drafted, is a process that, under its broadest reasonable interpretation, recite the abstract idea of mental processes. These limitations encompass a human mind carrying out these functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. The claims recites the following additional elements “a computer readable storage medium,” “a hardware processor,” “a system,” “a memory that stores a computer program,” and “iteratively updating a DevOps pipeline script that includes instructions to build and deploy a machine learning model”, “and executing the updated DevOps pipeline script concurrent with the iterative generating”, and “obtaining processed input information sources using a miner”. The additional elements “a computer readable storage medium,” “a hardware processor,” “a system,” “a memory that stores a computer program,” and “iteratively updating a DevOps pipeline script that includes instructions to build and deploy a machine learning model”, “and executing the updated DevOps pipeline script concurrent with the iterative generating”, are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). The additional element “obtaining processed input information sources using a miner” does nothing more than add insignificant extra solution activity to the judicial exception, such as data gathering and outputting the results of the abstract idea to perform a task. See MPEP 2106.05(g). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “a computer readable storage medium,” “a hardware processor,” “a system,” “a memory that stores a computer program,” and “iteratively updating a DevOps pipeline script that includes instructions to build and deploy a machine learning model”, “and executing the updated DevOps pipeline script concurrent with the iterative generating” are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). As to the additional element “obtaining processed input information sources using a miner” the courts have identified gathering data and displaying the output of the abstract idea is well-understood, routine, conventional activity. See MPEP 2106.05(d). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 12 and 25 further define the “constraint” as part of the “identifying” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 13 further define the “determining” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 23 as drafted, recite a process that, under its broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “identify changes in contextual information relating to the development pipeline from one or more artifacts” as drafted, is a process that, under its broadest reasonable interpretation, recite the abstract idea of mental processes. These limitations encompass a human mind carrying out these functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
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.
Claim(s) 1-6, 9-11, 13-20 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jagannath (US 20180032322 A1) in view of Zhang (US 11586849 B2) further in view of Gruber (US 20120016678 A1).
Regarding Claim 1, Jagannath (US 20180032322 A1) teaches
A computer-implemented method for development pipeline generation, comprising: iteratively generating a development pipeline, including: (Para 0010, "DevOps solutions allow enterprises to quickly design, build, test, deploy, and maintain software applications. DevOps solutions accomplish this by facilitating continuous deployment and release pipeline management, resulting in faster release lifecycles without compromising application quality."; Para 0017, "The DevOps toolchain may include a series of lifecycle stages that aid in the development, deployment, and management of an application through the application's lifecycle … Each DevOps lifecycle stage may have an associated set of users, user privileges, tasks, DevOps policies, and environments relevant for that stage.") Examiner Comments: Jagannath teaches iteratively generating a development pipeline through repeated lifecycle stages where the pipeline is updated and redeployed based on testing and deployment outcomes, directly mapping to iteratively generating a development pipeline as it automates and refines the process over multiple iterations;
adding the constraint to the development pipeline (Para 0013, "DevOps application deployment packages may be generated for DevOps applications based on DevOps application models for deploying the DevOps applications … The DevOps application deployment packages may be provided deployment tool plugins associated with the determined application deployment tools and the deployment tool plugins may execute deployment operations based on deployment properties included in the DevOps application deployment packages to deploy DevOps applications using the determined application deployment tools."; Para 0036, "Deployment tool plugins 113 may execute deployment operations based on deployment properties included DevOps application deployment packages.") Examiner Comments: Jagannath teaches adding the constraint to the development pipeline by incorporating deployment properties and plugins into the packages, as these properties act as constraints integrated into the pipeline for execution;
executing the development pipeline concurrent with the iterative generating. (Para 0040, "In some examples, steps of method 300 may be executed substantially concurrently or in a different order than shown in FIG. 3 … In some examples, some of the steps of method 300 may, at certain times, be ongoing and/or may repeat."; Para 0035, "Deployment tool plugins 113 may execute deployment operations to deploy DevOps applications in DevOps application deployment environment 130 using application deployment tools 120.") Examiner Comments: Jagannath teaches executing the development pipeline concurrent with the iterative generating by running deployment operations simultaneously with lifecycle iterations, allowing ongoing execution while updates occur.
Jagannath did not specifically teach
determining an ethical concern relating to a development pipeline by:
obtaining processed input information sources using a miner;
identifying precepts from input information sources;
and expressing the identified precepts as intents using a domain model;
identifying a constraint that mitigates the ethical concern by mapping the intents to corresponding actions using a database of correspondences between known intents and actions.
However, Zhang (US 11586849 B2) teaches
determining an ethical concern relating to a development pipeline; (Col 1:, ln 22-33, "Machine learning, the most common form of AI today, is inherently a form of statistical discrimination. The discrimination can become objectionable to one or more users when it places certain groups represented by the data at systematic advantage and other groups represented by the data at systematic disadvantage."; Col 14: ln 1-7, "The main fairness issue of the German Credit dataset is that it leads to classifiers that may disproportionately penalize young people under 26 years old (e.g., people 25 years old and younger). Among the 1,000 instances in the dataset, 190 were under the age of 26, and they were 21% less likely to get the “Good” credit rating than the rest of the population.") Examiner Comments: Zhang teaches determining an ethical concern relating to a development pipeline by evaluating models for bias metrics like discrimination against groups, as bias is an ethical concern in AI development pipelines;
identifying a constraint that mitigates the ethical concern. (Col 7: ln 34-60, "The independence criterion can require that all analyzed groups represented by the given data receive equal rate of favorable treatment by the machine learning model. This roughly corresponds to the notion of equity, wherein each data group, regardless on its context, can receive support to have an equal outcome. … The separation criterion can require that the false positive rates and the false negative rates are similar across all groups represented by the given data. This roughly corresponds to the notion of equality, which entails that every data group is supported at the same level.") Examiner Comments: Zhang teaches identifying a constraint that mitigates the ethical concern by defining fairness metrics like independence and separation criteria as rules to balance bias in model outputs, directly serving as constraints added to mitigate bias.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s teaching into Zhang’s in order to incorporate bias mitigation into DevOps pipelines ensures that software development processes account for fairness and societal impacts, reducing risks in AI-integrated systems as both references address automated development workflows and Zhang explicitly adapts to changing societal preferences for ethical AI.
Jagannath and Zhang did not specifically teach
by: obtaining processed input information sources using a miner;
identifying precepts from input information sources;
and expressing the identified precepts as intents using a domain model;
by mapping the intents to corresponding actions using a database of correspondences between known intents and actions.
However, Gruber (US 20120016678 A1) teaches
by: obtaining processed input information sources using a miner; identifying precepts from input information sources; and expressing the identified precepts as intents using a domain model; identifying a constraint that mitigates the ethical concern by mapping the intents to corresponding actions using a database of correspondences between known intents and actions. (Para [0010], “ the intelligent automated assistant systems of the present invention can perform any or all of: actively eliciting input from a user, interpreting user intent, disambiguating among competing interpretations, requesting and receiving clarifying information as needed, and performing (or initiating) actions based on the discerned intent.”; Para [0367], “Domain models 1056 component(s) include representations of the concepts, entities, relations, properties, and instances of a domain.”; Para [0088], “For example, the user may provide input to assistant 1002 such as “I need to wake tomorrow at 8 am”. Once assistant 1002 has determined the user's intent, using the techniques described herein”; Para [0110], “task flow models 1086” as mappings from intents to actions/services; Para [0381], “data from domain model component(s) 1056 may be associated with other model modeling components including ... task flow models 1086”.) Examiner Comments: Gruber teaches obtaining/processing multiple input sources (via the assistant’s input processing modules acting as a “miner”), identifying precepts/concepts/relations from those sources, expressing them as intents using a domain model, and mapping intents to actions via task flow correspondences (database-like), directly teaching the amended determination and constraint-identification steps.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s and Zhang’s teaching into Gruber’s in order to incorporate intent extraction and domain-model mapping from processed inputs into DevOps pipelines, enabling automatic ethical adaptation as Gruber’s system turns raw contextual sources into actionable steps that can be added as pipeline constraints.
Regarding Claim 2, Jagannath, Zhang and Gruber teach
The method of Claim 1.
Jagannath did not specifically teach wherein the development pipeline is a DevOps pipeline script and the constraint is an entry in the DevOps pipeline script that performs a check to ensure the ethical concern is not triggered.
However, Zhang teaches
wherein the development pipeline is a DevOps pipeline script and the constraint is an entry in the DevOps pipeline script that performs a check to ensure the ethical concern is not triggered. (Col 1: ln 61-Col 2: ln 3, "The metric component can define the fairness metric as an independence criterion of the machine learning model… The metric component can further define a second fairness metric from the plurality of fairness metrics as a separation criterion of the machine learning model."; Col 8: ln 50-61, "The metric component 112 can employ disparate impact ratio to filter out one or more machine learning model settings that do not satisfy one or more defined constraint thresholds (e.g., defined by one or more policy makers via the one or more input devices 106 and/or networks 104 ).") Examiner Comments: Zhang teaches the constraint as an entry (fairness check metric like disparate impact ratio) that performs a check to ensure the ethical concern (bias) is not triggered, combined into Jagannath's script for ethical verification.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s teaching into Zhang’s in order to incorporate bias mitigation into DevOps pipelines ensures that software development processes account for fairness and societal impacts, reducing risks in AI-integrated systems as both references address automated development workflows and Zhang explicitly adapts to changing societal preferences for ethical AI.
Regarding Claim 3, Jagannath, Zhang and Gruber teach
The method of Claim 2.
Jagannath teaches wherein the DevOps pipeline script further includes instructions to build and deploy a project. (Para 0013, "In some examples, DevOps application deployment packages may be generated for DevOps applications based on DevOps application models for deploying the DevOps applications."; Para 0017, "DevOps application manager 110 may manage the lifecycle of DevOps applications. A DevOps application may be an application that is designed to be deployed through various DevOps lifecycle stages."; Para 0023, "Using application deployment tools 120 to deploy DevOps applications may include using application deployment tools 120 to configure DevOps application deployment environment 130 for DevOps application deployment.") Examiner Comments: Jagannath explicitly teaches the DevOps pipeline script including instructions to build and deploy a project, directly mapping to this limitation.
Regarding Claim 4, Jagannath, Zhang and Gruber teach
The method of Claim 1.
Jagannath did not specifically teach
wherein determining the ethical concern includes a new ethical concern that was not known at initial execution of the development pipeline.
However, Zhang teaches
wherein determining the ethical concern includes a new ethical concern that was not known at initial execution of the development pipeline. (Col 2: ln 25-38, "An advantage of such a computer program product can be a bias mitigation scheme for machine learning models that can adapt to changes in societal preferences (e.g., as expressed by one or more policy makers)."; Col 4: ln 28-46, "Additionally, various embodiments can include eliciting user preferences regarding the one or more fairness and utility metrics to determine a threshold setting for the machine learning model.") Examiner Comments: Zhang teaches determining a new ethical concern (changes in societal preferences for bias) not known at initial execution, as the system adapts iteratively to new fairness preferences.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s teaching into Zhang’s in order to incorporate bias mitigation into DevOps pipelines ensures that software development processes account for fairness and societal impacts, reducing risks in AI-integrated systems as both references address automated development workflows and Zhang explicitly adapts to changing societal preferences for ethical AI.
Regarding Claim 5, Jagannath, Zhang and Gruber teach
The method of Claim 4.
Jagannath teaches wherein executing the development pipeline includes executing a new development pipeline responsive to adding the constraint for the new ethical concern. (Para 0035, "Deployment operation instructions may include instructions to execute various deployment operations. Examples of deployment operations include deploying a DevOps application (i.e., deploying an application in DevOps application deployment environment 130), undeploying a DevOps application (i.e., removing an application from DevOps application deployment environment 130), and redeploying a DevOps application (i.e., removing an application from DevOps application deployment environment 130 and deploying the application again in DevOps application deployment environment 130)."; Para 0017, "Each DevOps lifecycle stage may have an associated set of users, user privileges, tasks, DevOps policies, and environments relevant for that stage.") Examiner Comments: Jagannath teaches executing a new pipeline responsive to updates by redeploying after changes, mapping to executing a new pipeline after adding constraints.
Regarding Claim 6, Jagannath, Zhang and Gruber teach
The method of Claim 1.
Jagannath did not specifically teach
wherein the development pipeline develops and deploys a machine learning model.
However, Zhang teaches
wherein the development pipeline develops and deploys a machine learning model. (Col 1: ln 48-60, "The computer executable components can comprise a model component that can evaluate a machine learning model at a plurality of threshold settings to generate a sample set and can define a relationship between a fairness metric and a utility metric of the machine learning model based on the sample set."; Col 7: ln 20-33, "The model component 108 can evaluate one or more machine learning models at a plurality of threshold settings to generate a sample set and/or define a relationship between one or more fairness metrics and utility metrics of the machine learning model based on the sample set.") Examiner Comments: Zhang teaches the pipeline for developing and deploying ML models by optimizing AI models, combined with Jagannath's deployment for ML projects.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s teaching into Zhang’s in order to incorporate bias mitigation into DevOps pipelines ensures that software development processes account for fairness and societal impacts, reducing risks in AI-integrated systems as both references address automated development workflows and Zhang explicitly adapts to changing societal preferences for ethical AI.
Regarding Claim 9, Jagannath, Zhang and Gruber teach
The method of Claim 1.
Jagannath did not specifically teach wherein determining the ethical concern includes identifying changes in contextual information relating to the pipeline from one or more artifacts.
However, Zhang teaches
wherein determining the ethical concern includes identifying changes in contextual information relating to the pipeline from one or more artifacts. (Col 2: ln 25-38, "An advantage of such a computer program product can be a bias mitigation scheme for machine learning models that can adapt to changes in societal preferences (e.g., as expressed by one or more policy makers)."; Col 5: ln 1-23, "Further, one or more embodiments can include generating one or more visualizations that can depict a relationship between various fairness metrics and utility metrics to one or more policy makers.") Examiner Comments: Zhang teaches identifying changes in contextual information (societal preferences as artifacts) relating to the pipeline, as it adapts to new fairness contexts.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s teaching into Zhang’s in order to incorporate bias mitigation into DevOps pipelines ensures that software development processes account for fairness and societal impacts, reducing risks in AI-integrated systems as both references address automated development workflows and Zhang explicitly adapts to changing societal preferences for ethical AI.
Regarding Claim 10, Jagannath, Zhang and Gruber teach
The method of Claim 1.
Jagannath did not specifically teach
wherein identifying the constraint includes looking up the ethical concern in a database to select a predetermined constraint associated with the ethical concern that mitigates the ethical concern.
However, Zhang teaches
wherein identifying the constraint includes looking up the ethical concern in a database to select a predetermined constraint associated with the ethical concern that mitigates the ethical concern. (Col 8: 1-17, "Example metrics that can relate to this notion of fairness can include, but are not limited to: statistical parity difference, disparate impact ratio, a combination thereof, and/or the like."; Col 8: ln 50-61, "The metric component 112 can employ disparate impact ratio to filter out one or more machine learning model settings that do not satisfy one or more defined constraint thresholds (e.g., defined by one or more policy makers via the one or more input devices 106 and/or networks 104 ).") Examiner Comments: Zhang teaches looking up predefined fairness metrics (constraints) associated with the concern from system definitions, mapping to selecting from a database-like component.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s teaching into Zhang’s in order to incorporate bias mitigation into DevOps pipelines ensures that software development processes account for fairness and societal impacts, reducing risks in AI-integrated systems as both references address automated development workflows and Zhang explicitly adapts to changing societal preferences for ethical AI.
Regarding Claim 11, Jagannath (US 20180032322 A1) teaches
A computer-implemented method for DevOps pipeline generation, comprising:
iteratively updating a DevOps pipeline script that includes instructions to build and deploy a machine learning model, including: (Para 0013, " In some examples, DevOps application deployment packages may be generated for DevOps applications based on DevOps application models for deploying the DevOps applications "; Para 0017, "The DevOps toolchain may include a series of lifecycle stages that aid in the development, deployment, and management of an application through the application's lifecycle."; Para 0035, "redeploying a DevOps application (i.e., removing an application from DevOps application deployment environment 130 and deploying the application again in DevOps application deployment environment 130).") Examiner Comments: Jagannath teaches iteratively updating a DevOps pipeline script with build and deploy instructions, applicable to ML models in combination;
adding the constraint as an entry in the DevOps pipeline script that performs a check to ensure the new ethical concern is not triggered; (Para 0013, "The DevOps application deployment packages may be provided deployment tool plugins associated with the determined application deployment tools and the deployment tool plugins may execute deployment operations based on deployment properties included in the DevOps application deployment packages to deploy DevOps applications using the determined application deployment tools."; Para 0036, "Deployment tool plugins 113 may execute deployment operations based on deployment properties included DevOps application deployment packages.") Examiner Comments: Jagannath teaches adding the constraint as an entry in the DevOps pipeline script by incorporating properties that perform checks during deployment;
executing the updated DevOps pipeline script concurrent with the iterative generating. (Para 0040, "In some examples, steps of method 300 may be executed substantially concurrently or in a different order than shown in FIG. 3."; Para 0035, "Deployment tool plugins 113 may execute deployment operations to deploy DevOps applications in DevOps application deployment environment 130 using application deployment tools 120.") Examiner Comments: Jagannath teaches concurrent execution with iterative updates.
Jagannath did not specifically teach
determining a new ethical concern relating to a bias in training data for the machine learning model that was not known at initial execution of the DevOps pipeline script
by: obtaining processed input information sources using a miner;
identifying precepts from input information sources;
and expressing the identified precepts as intents using a domain model;
identifying a constraint that mitigates the new ethical concern by mapping the intents to corresponding actions using a database of correspondences between known intents and actions.
However, Zhang (US 11586849 B2) teaches
determining a new ethical concern relating to a bias in training data for the machine learning model that was not known at initial execution of the DevOps pipeline script; (Col 14: ln 1-6, "The main fairness issue of the German Credit dataset is that it leads to classifiers that may disproportionately penalize young people under 26 years old (e.g., people 25 years old and younger). Among the 1,000 instances in the dataset, 190 were under the age of 26, and they were 21% less likely to get the “Good” credit rating than the rest of the population."; Col 2: 24-38, "An advantage of such a computer program product can be a bias mitigation scheme for machine learning models that can adapt to changes in societal preferences (e.g., as expressed by one or more policy makers).") Examiner Comments: Zhang teaches determining a new ethical concern relating to a bias in training data that was not known at initial execution, as the system adapts to emerging fairness issues in datasets;
identifying a constraint that mitigates the new ethical concern. (Col 8: 50-61, "The metric component 112 can employ disparate impact ratio to filter out one or more machine learning model settings that do not satisfy one or more defined constraint thresholds (e.g., defined by one or more policy makers via the one or more input devices 106 and/or networks 104 )."; Col 8: 1-17, "Often, perfect independence cannot be achieved, and relaxation on the one or more criteria is allowed. Further, the degree of relaxation can vary from one domain to another and is context-dependent.") Examiner Comments: Zhang teaches identifying a constraint (disparate impact ratio thresholds) that mitigates the new ethical concern by filtering biased settings.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s teaching into Zhang’s in order to incorporate bias mitigation into DevOps pipelines ensures that software development processes account for fairness and societal impacts, reducing risks in AI-integrated systems as both references address automated development workflows and Zhang explicitly adapts to changing societal preferences for ethical AI.
Jagannath and Zhang did not specifically teach
by: obtaining processed input information sources using a miner;
identifying precepts from input information sources;
and expressing the identified precepts as intents using a domain model;
by mapping the intents to corresponding actions using a database of correspondences between known intents and actions.
However, Gruber (US 20120016678 A1) teaches
by: obtaining processed input information sources using a miner; identifying precepts from input information sources; and expressing the identified precepts as intents using a domain model; identifying a constraint that mitigates the ethical concern by mapping the intents to corresponding actions using a database of correspondences between known intents and actions. (Para [0010], “ the intelligent automated assistant systems of the present invention can perform any or all of: actively eliciting input from a user, interpreting user intent, disambiguating among competing interpretations, requesting and receiving clarifying information as needed, and performing (or initiating) actions based on the discerned intent.”; Para [0367], “Domain models 1056 component(s) include representations of the concepts, entities, relations, properties, and instances of a domain.”; Para [0088], “For example, the user may provide input to assistant 1002 such as “I need to wake tomorrow at 8 am”. Once assistant 1002 has determined the user's intent, using the techniques described herein”; Para [0110], “task flow models 1086” as mappings from intents to actions/services; Para [0381], “data from domain model component(s) 1056 may be associated with other model modeling components including ... task flow models 1086”.) Examiner Comments: Gruber teaches obtaining/processing multiple input sources (via the assistant’s input processing modules acting as a “miner”), identifying precepts/concepts/relations from those sources, expressing them as intents using a domain model, and mapping intents to actions via task flow correspondences (database-like), directly teaching the amended determination and constraint-identification steps.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath’s and Zhang’s teaching into Gruber’s in order to incorporate intent extraction and domain-model mapping from processed inputs into DevOps pipelines, enabling automatic ethical adaptation as Gruber’s system turns raw contextual sources into actionable steps that can be added as pipeline constraints.
Regarding Claim 13, Jagannath, Zhang and Gruber teach
The method of Claim 11.
Jagannath did not specifically teach
wherein determining the ethical concern includes identifying changes in contextual information relating to the DevOps pipeline script.
However, Zhang teaches
wherein determining the ethical concern includes identifying changes in contextual information relating to the DevOps pipeline script. (Col 2: ln 25-38, "An advantage of such a computer program product can be a bias mitigation scheme for machine learning models that can adapt to changes in societal preferences (e.g., as expressed by one or more policy makers)."; "Col 4: ln 47-62, Further, one or more embodiments can include generating one or more visualizations that can depict a relationship between various fairness metrics and utility metrics to one or more policy makers.") Examiner Comments: Zhang teaches identifying changes in contextual information (societal changes) relating to the pipeline.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath, Zhang and Zoldi’s teaching into Zhang’s in order to incorporate bias mitigation into DevOps pipelines ensures that software development processes account for fairness and societal impacts, reducing risks in AI-integrated systems as both references address automated development workflows and Zhang explicitly adapts to changing societal preferences for ethical AI.
Regarding Claim 14, is a computer program product claim corresponding to the method claim above (Claim 1) and, therefore, is rejected for the same reasons set forth in the rejection of claim 1.
Regarding Claim 15, is a system claim corresponding to the method claim above (Claim 1) and, therefore, is rejected for the same reasons set forth in the rejection of claim 1.
Regarding Claim 16, is a system claim corresponding to the method claim above (Claim 2) and, therefore, is rejected for the same reasons set forth in the rejection of claim 2.
Regarding Claim 17, is a system claim corresponding to the method claim above (Claim 3) and, therefore, is rejected for the same reasons set forth in the rejection of claim 3.
Regarding Claim 18, is a system claim corresponding to the method claim above (Claim 4) and, therefore, is rejected for the same reasons set forth in the rejection of claim 4.
Regarding Claim 19, is a system claim corresponding to the method claim above (Claim5) and, therefore, is rejected for the same reasons set forth in the rejection of claim 5.
Regarding Claim 20, is a system claim corresponding to the method claim above (Claim 6) and, therefore, is rejected for the same reasons set forth in the rejection of claim 6.
Regarding Claim 24, is a system claim corresponding to the method claim above (Claim 11) and, therefore, is rejected for the same reasons set forth in the rejection of claim 11.
Claim(s) 7-8, 12, 21-22, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jagannath (US 20180032322 A1) in view of Zhang (US 11586849 B2) and Gruber (US 20120016678 A1) further in view of Zoldi (US 20230085575 A1).
Regarding Claim 7, Jagannath, Zhang and Gruber teach
The method of Claim 6.
Jagannath, Zhang and Gruber did not specifically teach
wherein the ethical concern is one of bias in training data for the machine learning model.
However, Zoldi (US 20230085575 A1) teaches
wherein the ethical concern is one of bias in training data for the machine learning model. (Para 0003, "It is not uncommon for the training data to include values or trends that reflect societal bias or other types of bias. This can be due to a variety of reasons, such as the way data was collected or the source of data … self-learning models may be trained based on discriminatory or illegal biases learned by the model under the influence of data values in the training dataset as the model learns a multitude of possible relationships in the training data, unless special precaution is taken.") Examiner Comments: Zoldi teaches the ethical concern as bias in training data, directly mapping to this limitation for ML models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath, Zhang and Gruber’s teaching into Zoldi’s in order to specifically address training data biases in pipelines, as Zoldi provides a systematic way to eliminate biased features and interactions, enhancing Zhang's general mitigation with targeted training-stage constraints.
Regarding Claim 8, Jagannath, Zhang, Gruber and Zoldi teach
The method of Claim 7.
Jagannath, Zhang and Gruber did not specifically teach
wherein the constraint includes bias mitigation during one of pre-processing, training, and post-processing.
However, Zoldi teaches
wherein the constraint includes bias mitigation during one of pre-processing, training, and post-processing. (Claim 1, "training the predictive model using the first list and the second list to eliminate bias from the predictive model by removing the features and feature combinations in the combined list as model input and allowed nonlinearities expressed in the predictive model which include features in the first list or combinations of features in the second list of sets of input features."; Para 0013, "After identifying combinations of features whose interaction leads to biased latent features, the model is retrained and the process is repeated until the model has no biased latent feature left.") Examiner Comments: Zoldi teaches bias mitigation during training by removing biased features and retraining, mapping to mitigation during training.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath, Zhang and Gruber’s teaching into Zoldi’s in order to specifically address training data biases in pipelines, as Zoldi provides a systematic way to eliminate biased features and interactions, enhancing Zhang's general mitigation with targeted training-stage constraints.
Regarding Claim 12, Jagannath, Zhang and Gruber teach
The method of Claim 11.
Jagannath, Zhang and Gruber did not specifically teach wherein the constraint includes bias mitigation during one of pre-processing, training, and post-processing.
However, Zoldi (US 20230085575 A1) teaches
wherein the constraint includes bias mitigation during one of pre-processing, training, and post-processing. (Claim 1, "training the predictive model using the first list and the second list to eliminate bias from the predictive model by removing the features and feature combinations in the combined list as model input and allowed nonlinearities expressed in the predictive model which include features in the first list or combinations of features in the second list of sets of input features.") Examiner Comments: Zoldi teaches bias mitigation during training by removing biased features and combinations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath, Zhang and Gruber’s teaching into Zoldi’s in order to specifically address training data biases in pipelines, as Zoldi provides a systematic way to eliminate biased features and interactions, enhancing Zhang's general mitigation with targeted training-stage constraints.
Regarding Claim 21, is a system claim corresponding to the method claim above (Claim 7) and, therefore, is rejected for the same reasons set forth in the rejection of claim 7.
Regarding Claim 22, is a system claim corresponding to the method claim above (Claim 8) and, therefore, is rejected for the same reasons set forth in the rejection of claim 8.
Regarding Claim 25, is a system claim corresponding to the method claim above (Claim 12) and, therefore, is rejected for the same reasons set forth in the rejection of claim 12.
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jagannath (US 20180032322 A1) in view of Zhang (US 11586849 B2) and Gruber (US 20120016678 A1) further in view of Lohia (US 11636386 B2).
Regarding Claim 23, Jagannath, Zhang and Gruber teach
The system of Claim 15.
Jagannath, Zhang and Gruber did not specifically teach
wherein the computer program further causes the hardware processor to identify changes in contextual information relating to the development pipeline from one or more artifacts.
However, Lohia (US 11636386 B2) teaches
wherein the computer program further causes the hardware processor to identify changes in contextual information relating to the development pipeline from one or more artifacts. (Col 1: ln 25-47, "identifying one or more instances of bias by observing a change to one or more of the values in the mappings in response to modifying one or more class designations among the data points in the mappings;"; Claim 6, "The at least two classes of data points comprise a majority class and a minority class, distinguished in accordance with a predetermined threshold value.") Examiner Comments: Lohia teaches identifying changes in contextual information by observing value changes in mappings due to class modifications, relating to bias contexts in data artifacts.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jagannath, Zhang and Gruber’s teaching into Lohia’s in order to provide precise detection mechanisms for Zhang's as Lohia's correlation and perturbation analysis identifies indirect biases, enabling more robust ethical checks in iterative pipelines.
Response to Arguments
Applicant’s arguments with respect to claims 1-25 have been considered but are moot because the arguments do not apply to the previous cited sections of the references used in the previous office action. The current office action is now citing additional references to address the newly added claimed limitations.
Conclusion
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.
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/AMIR SOLTANZADEH/Examiner, Art Unit 2191 /WEI Y MUI/Supervisory Patent Examiner, Art Unit 2191