DETAILED ACTION
Introduction
This office action is in response to Applicant’s submission filed on August 1, 2024.
Claims 1-20 are pending in the application. As such, claims 1-20 have been examined.
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 .
Drawings
The drawings were received on August 1, 2024. These drawings have been accepted and considered by the Examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
An information handling system comprising:
a hardware processor executing computer-readable program code instructions of an intent dependency determination software application to receive one or more capability intent action policies describing controls for execution of capability intent actions to be implemented at the information handling system;
the hardware processor executing computer-readable program code instructions to identify one or more capability dependencies by semantic or lexical similarity matching one or more capabilities of a plurality of artificial intelligence (AI) productivity tool-enablable software applications or an AI productivity tool module executable at the information handling system with a first capability intent action policy,
where the capabilities are responsive to a user-query input received at the AI productivity tool module with at least one capability intent action; and
the hardware processor executing computer-readable program code instructions of a policy control managing subagent to transmit the first capability intent action policy to the one or more AI productivity tool-enablable software applications or the AI productivity tool module for application of the first capability intent action policy to adjust execution of the at least one intent action pursuant to the first capability intent action policy at the information handling system.
The claim limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind. For example,
“a hardware processor executing computer-readable program code instructions of an intent dependency determination software application to receive one or more capability intent action policies describing controls for execution of capability intent actions to be implemented at the information handling system” in the context claim encompasses a person obtaining security control policies,
“the hardware processor executing computer-readable program code instructions to identify one or more capability dependencies by semantic or lexical similarity matching one or more capabilities of a plurality of artificial intelligence (AI) productivity tool-enablable software applications or an AI productivity tool module executable at the information handling system with a first capability intent action policy” in the context claim encompasses a person determining which capabilities need to be monitored for security,
“where the capabilities are responsive to a user-query input received at the AI productivity tool module with at least one capability intent action” in the context claim encompasses a person monitoring the use of any capability,
“the hardware processor executing computer-readable program code instructions of a policy control managing subagent to transmit the first capability intent action policy to the one or more AI productivity tool-enablable software applications or the AI productivity tool module for application of the first capability intent action policy to adjust execution of the at least one intent action pursuant to the first capability intent action policy at the information handling system” in the context of this claim encompasses a person either allowing the capability to be performed, or not.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
an information handling system
a hardware processor
a computer-readable program code instructions
a plurality of artificial intelligence (AI) productivity tool-enablable software applications
an AI productivity tool module
a policy control managing subagent
an intent dependency determination software application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
An information handling system comprising:
a hardware processor executing computer-readable program code instructions of an intent dependency determination software application to receive one or more capability intent action policies describing controls to be implemented for execution of capability intent actions responsive to a user-query input received at an artificial intelligence (AI) productivity tool module executing at the information handling system;
the hardware processor executing computer-readable program code instructions of the intent dependency determination software application to identify capability dependencies of each of a plurality of AI productivity tool-enablable software applications executable at the information handling system that are affected by the one or more capability intent action policies;
the hardware processor to map each of the capability intent action policies with an affected capability associated with each of the AI productivity tool-enablable software applications in an AI productivity tool policy database memory at the information handling system; and
the hardware processor executing computer-readable program code instructions of a policy control managing subagent to transmit the capability intent action policies to the AI productivity tool-enablable software applications for application of the capability intent action policies to adjust execution of a plurality of capability intent actions by capabilities of the AI productivity tool-enablable software applications pursuant to a corresponding capability intent action policy at the information handling system.
The claim limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind. For example,
“a hardware processor executing computer-readable program code instructions of an intent dependency determination software application to receive one or more capability intent action policies describing controls to be implemented for execution of capability intent actions responsive to a user-query input received at an artificial intelligence (AI) productivity tool module executing at the information handling system” in the context claim encompasses a person obtaining security control policies,
“the hardware processor executing computer-readable program code instructions of the intent dependency determination software application to identify capability dependencies of each of a plurality of AI productivity tool-enablable software applications executable at the information handling system that are affected by the one or more capability intent action policies” in the context claim encompasses a person determining which capabilities need to be monitored for security,
“the hardware processor to map each of the capability intent action policies with an affected capability associated with each of the AI productivity tool-enablable software applications in an AI productivity tool policy database memory at the information handling system” in the context claim encompasses a person creating a manual database on paper regarding the use of any capability and the associated security policy,
“the hardware processor executing computer-readable program code instructions of a policy control managing subagent to transmit the capability intent action policies to the AI productivity tool-enablable software applications for application of the capability intent action policies to adjust execution of a plurality of capability intent actions by capabilities of the AI productivity tool-enablable software applications pursuant to a corresponding capability intent action policy at the information handling system” in the context of this claim encompasses a person either allowing the capability to be performed, or not.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
an information handling system
a hardware processor
a computer-readable program code instructions
a plurality of artificial intelligence (AI) productivity tool-enablable software applications
an AI productivity tool module
a policy control managing subagent
an intent dependency determination software application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
The dependent claims do not add limitations that would either integrate the recited abstract idea into a practical application or could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
the first capability intent action policy including a hardware processor intent action policy that describes allowed and disallowed processors that may be used by the AI productivity tool-enablable software applications and AI productivity tool modules executing one or more machine learning (ML) model algorithms to provide responsive capability intent actions to the user-query input.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the first capability intent action policy including a hardware processor intent action policy that describes allowed and disallowed processors that may be used by the AI productivity tool-enablable software applications and AI productivity tool modules executing one or more machine learning (ML) model algorithms to provide responsive capability intent actions to the user-query input” in the context of this claim encompasses a person determining whether to allow the action, or not.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
hardware processors
AI productivity tool-enablable software applications
AI productivity tool modules
one or more machine learning (ML) model algorithms
a natural language processing model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
the capability intent action policies including a capability-limiting intent action policy that describes capabilities available from the AI productivity tool-enablable software applications or the AI productivity tool module that are to be allowed or disallowed to be implemented.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the capability intent action policies including a capability-limiting intent action policy that describes capabilities available from the AI productivity tool-enablable software applications or the AI productivity tool module that are to be allowed or disallowed to be implemented” in the context of this claim encompasses a person determining whether to allow the action, or not.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
AI productivity tool-enablable software applications
the AI productivity tool module.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 4 and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
the hardware processor to receive the user-query input and execute a semantic similarity comparison to correlate the user query input to a capability intent value for the at least one capability intent action; and
the hardware processor to execute computer readable code instructions of the policy control managing subagent to determine if the first capability intent action policy is associated with the at least one capability intent action from a database storing the mappings of the first capability intent action policy to the at least one capability intent action before applying adjustment to the execution of the at least one capability intent action.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the hardware processor to receive the user-query input and execute a semantic similarity comparison to correlate the user query input to a capability intent value for the at least one capability intent action” in the context of this claim encompasses a person identifying that an action matches a policy,
“the hardware processor to execute computer readable code instructions of the policy control managing subagent to determine if the first capability intent action policy is associated with the at least one capability intent action from a database storing the mappings of the first capability intent action policy to the at least one capability intent action before applying adjustment to the execution of the at least one capability intent action” in the context of this claim encompasses a person identifying that an action needs to be controlled, or not.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the hardware processor
the policy control managing subagent
a database.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 5 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
the hardware processor to receive the user-query input and execute a semantic similarity comparison to correlate the user query input to a capability intent value for a second capability intent action;
the hardware processor to execute computer readable code instructions of the policy control managing subagent to determine if any capability intent action policies are associated with the second capability intent action from a database storing the mappings of the capability intent action policies to the capabilities; and
executing the second capability intent action when no capability intent action policies are associated with the second capability intent action.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the hardware processor to receive the user-query input and execute a semantic similarity comparison to correlate the user query input to a capability intent value for a second capability intent action” in the context of this claim encompasses a person identifying that an action matches a policy,
“the hardware processor to execute computer readable code instructions of the policy control managing subagent to determine if any capability intent action policies are associated with the second capability intent action from a database storing the mappings of the capability intent action policies to the capabilities” in the context of this claim encompasses a person identifying that an action needs to be controlled, or not,
“executing the second capability intent action when no capability intent action policies are associated with the second capability intent action” in the context of this claim encompasses a person going ahead and not preventing the action.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a hardware processor
computer readable code instructions
policy control managing subagent
a database.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 6 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
an AI productivity tool policy database that maintains the capability intent action policies and mappings of the capability intent action policies and capability dependencies relative to their respective AI productivity tool-enablable software applications and AI productivity tool modules at the information handling system.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“an AI productivity tool policy database that maintains the capability intent action policies and mappings of the capability intent action policies and capability dependencies relative to their respective AI productivity tool-enablable software applications and AI productivity tool modules at the information handling system” in the context of this claim encompasses a person keeping his manual paper records of the policies and related action current.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
an AI productivity tool policy database
AI productivity tool-enablable software applications
AI productivity tool modules
information handling system.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 7 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
the hardware processor executing computer-readable program code instructions of an intent identification software application to invoke one or more machine learning (ML) model algorithms to perform the semantic similarity comparison or a lexical similarity comparison for the received capability intent action policies with one or more capabilities of the AI productivity tool-enablable software applications or functions of the AI productivity tool module and identify the capability dependencies of the capability intent action policies to a corresponding capability of the AI productivity tool-enablable software applications or function of the AI productivity tool module.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the hardware processor executing computer-readable program code instructions of an intent identification software application to invoke one or more machine learning (ML) model algorithms to perform the semantic similarity comparison or a lexical similarity comparison for the received capability intent action policies with one or more capabilities of the AI productivity tool-enablable software applications or functions of the AI productivity tool module and identify the capability dependencies of the capability intent action policies to a corresponding capability of the AI productivity tool-enablable software applications or function of the AI productivity tool module” in the context of this claim encompasses a person invoking performing a comparison to identify actions to be controlled.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the hardware processor
computer-readable program code instructions
an intent identification software application
one or more machine learning (ML) model algorithms
one or more capabilities of the AI productivity tool-enablable software applications
the AI productivity tool module.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 8 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
the hardware processor executing computer-readable program code instructions of the intent identification software application to invoke a machine learning (ML) model to generate vectorized policy intent value for each of the received intent action policies, generate vectorized capability intent values of each of the capabilities associated with each of the AI productivity tool-enablable software applications, and match the vectorized policy intent values and vectorized capability intent values based on a semantic similarity comparison score to map each of the capability intent action policies with one or more capabilities associated with the AI productivity tool-enablable software applications to identify the one or more capability dependencies.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the hardware processor executing computer-readable program code instructions of the intent identification software application to invoke a machine learning (ML) model to generate vectorized policy intent value for each of the received intent action policies, generate vectorized capability intent values of each of the capabilities associated with each of the AI productivity tool-enablable software applications, and match the vectorized policy intent values and vectorized capability intent values based on a semantic similarity comparison score to map each of the capability intent action policies with one or more capabilities associated with the AI productivity tool-enablable software applications to identify the one or more capability dependencies” in the context of this claim encompasses a person invoking generating a comparison to identify actions to be controlled.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the hardware processor
computer-readable program code instructions
intent identification software application
a machine learning (ML) model
the AI productivity tool-enablable software applications.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
the first capability intent action policy including a provider-limiting intent action policy that describes if and when the information handling system may transmit and receive data over a wired or wireless connection to a remote server to support executing the computer-readable program code instructions of the AI productivity tool-enablable software applications or AI productivity tool module.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the first capability intent action policy including a provider-limiting intent action policy that describes if and when the information handling system may transmit and receive data over a wired or wireless connection to a remote server to support executing the computer-readable program code instructions of the AI productivity tool-enablable software applications or AI productivity tool module” in the context of this claim encompasses a person either allowing the capability of transmit and receive to be performed, or not.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the information handling system
a remote server
the computer-readable program code instructions
the AI productivity tool-enablable software applications
AI productivity tool module.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
executing computer readable program code instructions, via the hardware processor, to identify capability dependencies of capabilities of AI productivity tool-enablable software applications or functions of the AI productivity tool module executable at the information handling system that are affected by the capability intent action policies by executing a semantic similarity comparison or a lexical similarity comparison between a first capability intent action policy and a plurality of the capabilities to identify a first policy subset of the capabilities to be correlated to the first capability intent action policy; and
executing computer readable program code instructions of a policy control managing subagent to transmit the first capability intent action policy to the AI productivity tool-enablable software applications or the AI productivity tool module for application of the first capability intent action policy to adjust execution of a plurality of capability intent actions for the first policy subset of the capabilities according to the first capability intent action policy at the information handling system.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“executing computer readable program code instructions, via the hardware processor, to identify capability dependencies of capabilities of AI productivity tool-enablable software applications or functions of the AI productivity tool module executable at the information handling system that are affected by the capability intent action policies by executing a semantic similarity comparison or a lexical similarity comparison between a first capability intent action policy and a plurality of the capabilities to identify a first policy subset of the capabilities to be correlated to the first capability intent action policy” in the context of this claim encompasses a person identifying that an action matches a policy in a subset of policies,
“executing computer readable program code instructions of a policy control managing subagent to transmit the first capability intent action policy to the AI productivity tool-enablable software applications or the AI productivity tool module for application of the first capability intent action policy to adjust execution of a plurality of capability intent actions for the first policy subset of the capabilities according to the first capability intent action policy at the information handling system” in the context of this claim encompasses a person identifying that an action needs to be controlled, and executing control over the action.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
computer readable program code instructions
the hardware processor
AI productivity tool-enablable software applications
functions of the AI productivity tool module
a policy control managing subagent
the information handling system.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
the hardware processor to receive the user-query input and execute a semantic similarity comparison to correlate the user query input to a capability intent value for a capability intent action by execution of a machine learning (ML) model algorithm by generating vectorized policy intent values for each of the received capability intent action policies, by generating vectorized capability intent values of each of the capabilities associated with each of the AI productivity tool-enablable software applications, and matching the vectorized intent action policy values and vectorized capability intent values via a similarity comparison score between each of the vectorized policy intent values and the vectorized capability intent values; and
the hardware processor to execute computer readable code instructions of the policy control managing subagent to determine if any capability intent action policies are associated with the capability intent action from AI productivity tool policy database memory storing mappings of the capability intent action policies to the capabilities to determine that a first capability intent action policy applies to the capability intent action and limiting the execution of the capability intent action pursuant to the first capability intent action policy.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the hardware processor to receive the user-query input and execute a semantic similarity comparison to correlate the user query input to a capability intent value for a capability intent action by execution of a machine learning (ML) model algorithm by generating vectorized policy intent values for each of the received capability intent action policies, by generating vectorized capability intent values of each of the capabilities associated with each of the AI productivity tool-enablable software applications, and matching the vectorized intent action policy values and vectorized capability intent values via a similarity comparison score between each of the vectorized policy intent values and the vectorized capability intent values” in the context of this claim encompasses a person deciding if an action needs to be controlled by matching the action to a policy,
“the hardware processor to execute computer readable code instructions of the policy control managing subagent to determine if any capability intent action policies are associated with the capability intent action from AI productivity tool policy database memory storing mappings of the capability intent action policies to the capabilities to determine that a first capability intent action policy applies to the capability intent action and limiting the execution of the capability intent action pursuant to the first capability intent action policy” in the context of this claim encompasses a person executing control over the action.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
hardware processor
a machine learning (ML) model algorithm
the AI productivity tool-enablable software applications
computer readable code instructions
policy control managing subagent
AI productivity tool policy database memory.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
the hardware processor to receive the user-query input and execute a semantic similarity comparison to correlate the user query input to a capability intent value for a capability intent action;
the hardware processor to execute computer readable code instructions of the policy control managing subagent to determine if any capability intent action policies are associated with the capability intent action from the AI productivity tool policy database memory storing the mappings of the capability intent action policies to the capabilities; and
executing the capability intent action when no capability intent action policies are associated with the capability intent action.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the hardware processor to receive the user-query input and execute a semantic similarity comparison to correlate the user query input to a capability intent value for a capability intent action” in the context of this claim encompasses a person determining if an action matches a policy,
“the hardware processor to execute computer readable code instructions of the policy control managing subagent to determine if any capability intent action policies are associated with the capability intent action from the AI productivity tool policy database memory storing the mappings of the capability intent action policies to the capabilities” in the context of this claim encompasses a person determining if an action matches a policy in a database,
“executing the capability intent action when no capability intent action policies are associated with the capability intent action” in the context of this claim encompasses a person allowing the action.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
hardware processor
computer readable code instructions
the policy control managing subagent
the AI productivity tool policy database.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites:
the capability intent action policies including a provider-limiting intent action policy that describes if and when the information handling system may transmit and receive data over a wired or wireless connection to a remote server supporting executing the computer-readable program code instructions of the AI productivity tool-enablable software applications.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“the capability intent action policies including a provider-limiting intent action policy that describes if and when the information handling system may transmit and receive data over a wired or wireless connection to a remote server supporting executing the computer-readable program code instructions of the AI productivity tool-enablable software applications” in the context of this claim encompasses a person either allowing the capability of transmit and receive to be performed, or not.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
the information handling system
a remote server
the computer-readable program code instructions
AI productivity tool-enablable software applications.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over An (US Patent Pub. No. 20110154442 A1), in view of Cai et al. (US Patent Pub. No. 20170214677 A1), hereinafter Cai.
Regarding claim 1, An teaches an information handling system (An in [0010] teaches a terminal security control server for installing a security control software module in the personal communication terminal)
comprising:
a hardware processor executing computer-readable program code instructions of an intent dependency determination software application to receive one or more capability intent action policies describing controls for execution of capability intent actions to be implemented at the information handling system (An in [0002] teaches security control for personal communication terminals, such as mobile phones, and more particularly, to a security control system and method for personal communication terminals, which can prevent information leakage using personal communication terminals by establishing a security control policy for personal communication terminals, carried by workers or visitors in security areas requiring security falling of organizations such as companies, laboratories, universities, institutions and the like and limiting the use of components and computing resources of personal communication terminals within security areas based on the security control policy, and in [0010, Fig. 6] teaches a terminal registration agent for registering information on a personal communication terminal of a worker or visitor present within a security area into a terminal security control server, a security control policy for the personal communication terminal, and information of components to be provided; a zone notification node for providing the information of the personal communication terminal that has entered a control zone covered by the zone notification node in the security area when the personal communication terminal moves to the control zone; and the terminal security control server for installing a security control software module in the personal communication terminal, configuring computing resources and components permitted within the control zone based on a security control policy and zone information, and providing the configured computing resources and components to the personal communication terminal);
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the hardware processor executing computer-readable program code instructions to identify one or more capability dependencies [by semantic or lexical similarity matching one or more capabilities of a plurality of artificial intelligence (AI) productivity tool-enablable software applications or an AI productivity tool module executable at the information handling system] with a first capability intent action policy (An in [0010] teaches the terminal security control server for installing a security control software module in the personal communication terminal, configuring computing resources and components permitted within the control zone based on a security control policy and zone information),
where the capabilities are responsive to a user-query input received at the AI productivity tool module with at least one capability intent action (An in [0061] teaches when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101);
and
the hardware processor executing computer-readable program code instructions of a policy control managing subagent to transmit the first capability intent action policy to the one or more AI productivity tool-enablable software applications or the AI productivity tool module for application of the first capability intent action policy to adjust execution of the at least one intent action pursuant to the first capability intent action policy at the information handling system (An in [0061] teaches when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101).
An teaches the hardware processor executing computer-readable program code instructions to identify one or more capability dependencies, and the first capability intent action policy.
An does not teach, however Cai teaches
[the hardware processor executing computer-readable program code instructions to identify one or more capability dependencies] by semantic or lexical similarity matching one or more capabilities of a plurality of artificial intelligence (AI) productivity tool-enablable software applications or an AI productivity tool module executable at the information handling system with [a first capability intent action policy] (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
Regarding claim 9, An teaches a method of implementing capability intent action policies in an information handling system (An in [0010] teaches a terminal security control server for installing a security control software module in the personal communication terminal, and in [0011] teaches this may be a method)
comprising:
executing computer-readable program code instructions, via a hardware processor, of an intent dependency determination software application to receive a first capability intent action policy of a plurality of capability intent action policies describing controls for execution of capability intent actions to be implemented at the information handling system (An in [0002] teaches security control for personal communication terminals, such as mobile phones, and more particularly, to a security control system and method for personal communication terminals, which can prevent information leakage using personal communication terminals by establishing a security control policy for personal communication terminals, carried by workers or visitors in security areas requiring security falling of organizations such as companies, laboratories, universities, institutions and the like and limiting the use of components and computing resources of personal communication terminals within security areas based on the security control policy, and in [0010, Fig. 6] teaches a terminal registration agent for registering information on a personal communication terminal of a worker or visitor present within a security area into a terminal security control server, a security control policy for the personal communication terminal, and information of components to be provided; a zone notification node for providing the information of the personal communication terminal that has entered a control zone covered by the zone notification node in the security area when the personal communication terminal moves to the control zone; and the terminal security control server for installing a security control software module in the personal communication terminal, configuring computing resources and components permitted within the control zone based on a security control policy and zone information, and providing the configured computing resources and components to the personal communication terminal)
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in response to user query inputs received by an artificial intelligence (AI) productivity tool module (An in [0061] teaches when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101);
executing computer readable program code instructions, via the hardware processor, to identify capability dependencies [of capabilities of AI productivity tool-enablable software applications or functions of the AI productivity tool module executable at the information handling system that are affected by the capability intent action policies] to identify a first capability having a first capability intent action that is correlated to the first capability intent action policy (An in [0010] teaches the terminal security control server for installing a security control software module in the personal communication terminal, configuring computing resources and components permitted within the control zone based on a security control policy and zone information);
and
executing computer readable program code instructions of a policy control managing subagent to transmit the first capability intent action policy to the AI productivity tool-enablable software application or the AI productivity tool module for application of the first capability intent action policy to adjust execution of the first capability intent action according to the first capability intent action policy at the information handling system (An in [0061] teaches when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101).
An teaches the hardware processor executing computer-readable program code instructions to identify one or more capability dependencies, and the first capability intent action policy.
An does not teach, however Cai teaches
[executing computer readable program code instructions, via the hardware processor, to identify capability dependencies of capabilities of AI productivity tool-enablable software applications or functions of the AI productivity tool module executable at the information handling system] that are affected by the [capability intent action policies] to identify a first capability having a first capability intent action that is correlated to the [first capability intent action policy] (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Raleigh et al. (US Patent Pub. No. 20230125134 A1), hereinafter Raleigh, in view of Gomez et al. (US Patent Pub. No. 20230046392 A1), hereinafter Gomez.
Regarding claim 2, An, as modified above, teaches the information handling system of claim 1.
An, as modified above, teaches the first capability intent action policy, the AI productivity tool-enablable software applications, AI productivity tool modules, the capability intent actions, and the user-query input.
An, as modified above, does not teach, however Raleigh teaches
further comprising:
[the first capability intent action policy] including a hardware processor intent action policy that describes allowed and disallowed processors [that may be used by the AI productivity tool-enablable software applications and AI productivity tool modules executing one or more machine learning (ML) model algorithms to provide responsive capability intent actions to the user-query input] (Raleigh in [0187] teaches using secure execution environment protects secure service processor elements 1604 and the data path between secure service processor elements 1604 and the device I/O ports. In some embodiments, secure service processor elements 1604 include the portions of the service processor that are desired to be protected from malware or unauthorized user tampering or configuration changes, including but not limited to the secure service processor elements responsible for policy enforcement, I/O port communication activity monitoring and reporting, I/O port communication control or traffic control, application activity monitoring, application control, application access control or traffic control, network destination monitoring and reporting, network destination access control or traffic control, and device environment monitoring and integrity verification. Network stack 136 is also shown in FIG. 16 in the secure execution environment, but in general not all of the network stack functions need to be implemented in the secure execution environment, provided that the data path below the monitoring point in secure service processor elements 1604 and I/O modems 250 is secured (e.g., unauthorized data path access is not available or allowed). In the embodiment shown in FIG. 16, secure service processor elements 1604 interact with network stack 136 to implement the various I/O port activity monitoring and control functions described herein. Non-secure service processor elements 1602 are also included but not limited to user interface elements).
Raleigh is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Raleigh to allow for using a combination of secure and non-secure processors. Motivation to do so would allow for using a series of secure messages that directly or implicitly instruct the programs running in a secure software or firmware instruction execution environment to allow unrestricted or less restricted network access for a period of time that is either predetermined or is specified in a message from the network device processor to the program running in a secure software or firmware instruction execution environment (Raleigh [0056]).
An, as modified above, does not teach, however Gomez teaches
[the first capability intent action policy including a hardware processor intent action policy that describes allowed and disallowed processors that may be used by the AI productivity tool-enablable software applications and AI productivity tool modules] executing one or more machine learning (ML) model algorithms [to provide responsive capability intent actions to the user-query input] (Gomez in [0044] teaches a policy repository 400 may be accessed to retrieve one or more security policies associated with communications in a thread or stream. The policies may include, for example, a policy associated with an employer of the recipient of the communications, but also could include policies relating to communications generally or to other categories. This may be performed using a mix of machine learning (using machine-learned sub-model 402) and rules (using rules execution component 404). The machine-learned sub-model 402 may be trained using a machine learning algorithm to detect that a communication requests information that is subject to a security policy, such as a bitcoin wallet address or a wire transfer, or otherwise asks for money or a password. The machine-learned sub-model 402 may also be trained to identify actual information subject to a security policy, sent from the potential target, such as a password).
Gomez is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Gomez to allow for using a machine learning algorithm. Motivation to do so would allow for a combination of machine learning and rule-based techniques to be used to automatically detect social engineering attacks in a computer system (Gomez [Abstract]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Pasdar (US Patent Pub. No. 20190372938 A1).
Regarding claim 3, An, as modified above, teaches the information handling system of claim 1.
An, as modified above, teaches the first capability intent action policy, the AI productivity tool-enablable software applications, and the AI productivity tool module.
An, as modified above, does not teach, however Pasdar teaches
further comprising:
[the capability intent action policies including] a capability-limiting intent action policy that describes capabilities available [from the AI productivity tool-enablable software applications or the AI productivity tool module] that are to be allowed or disallowed to be implemented (Pasdar in [0096] teaches using a first policy rule with a successful trigger match then applies its associated action, that is used to perform at least one act on the object of the trigger match. In each policy category of connectivity, networking or security an action may be performed such as to allow or disallow a connection and specify associated connection attributes and limits (connectivity), assign a layer 2 and layer 3 attribute to a netspace and its associated interfaces (network), or allow, deny or redirect (to forward the communication to another policy rule or set of policy rules) the communication (security)).
Pasdar is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Pasdar to allow for using an action which may be performed such as to allow or disallow a connection and specify associated connection attributes and limits. Motivation to do so would allow for using a threat management module, to determine if the allowed communication is malicious, and a content control module to assess if the communication content adheres to content standards defined by the operator (Pasdar [0097]).
Claims 4-6, 12-14, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Karapantelakis et al. (US Patent Pub. No. 20240155435 A1), hereinafter Karapantelakis.
Regarding claims 4 and 12, An, as modified above, teaches the information handling system and method of claims 1 and 9.
An, as modified above, teaches the hardware processor, the first capability intent action policy, the AI productivity tool-enablable software applications, AI productivity tool modules, the policy control managing subagent, the capability intent actions, and the user-query input.
An, as modified above, does not teach, however Cai teaches
further comprising:
[the hardware processor to receive the user-query input and] execute a semantic similarity comparison to correlate the user query input to a capability intent [value] for the at least one capability intent action (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
An, as modified above, does not teach, however Karapantelakis teaches
a capability intent value (Karapantelakis in [0088] teaches converting intents into feature vectors)
[the hardware processor to execute computer readable code instructions of the policy control managing subagent to] determine if the first capability intent action policy is associated with the at least one capability intent action from a database storing the mappings [of the first capability intent action policy to the at least one capability intent action before applying adjustment to the execution of the at least one capability intent action] (Karapantelakis in [0107] teaches mapping these policies to the requested intents (QoS vectors) is a task that can be done using an ML model, owing to the fact that in the long term, many QoS intents will be correlated, for example resolution improvement for streaming of one application will be useful for another application in the future).
Karapantelakis is considered to be analogous to the claimed invention because it is in the same field of mapping policies to intents. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Karapantelakis to allow for mapping policies to intents. Motivation to do so would allow for being offered more flexible and transparent fulfillment of QoS requests, particularly when capacity in the communication network may be insufficient to immediately fulfill a QoS request, and wireless device owners requesting QoS for their devices are provided with information that allows them to better manage their devices and services (Karapantelakis [0115]).
Regarding claims 5 and 13, An, as modified above, teaches the information handling system and method of claims 1 and 9.
An, as modified above, teaches the hardware processor, the first capability intent action policy, the AI productivity tool-enablable software applications, AI productivity tool modules, the policy control managing subagent, the capability intent actions, and the user-query input.
An further teaches
a second capability intent action (An in [0002] teaches security control for personal communication terminals, such as mobile phones, and more particularly, to a security control system and method for personal communication terminals, which can prevent information leakage using personal communication terminals by establishing a security control policy for personal communication terminals, carried by workers or visitors in security areas requiring security falling of organizations such as companies, laboratories, universities, institutions and the like and limiting the use of components and computing resources of personal communication terminals within security areas based on the security control policy [this can repeated for each capability intent action])
executing the second capability intent action when no capability intent action policies are associated with the second capability intent action (An in [0002] teaches security control for personal communication terminals, such as mobile phones, and more particularly, to a security control system and method for personal communication terminals, which can prevent information leakage using personal communication terminals by establishing a security control policy for personal communication terminals, carried by workers or visitors in security areas requiring security falling of organizations such as companies, laboratories, universities, institutions and the like and limiting the use of components and computing resources of personal communication terminals within security areas based on the security control policy, and in [0010, Fig. 6] teaches a terminal registration agent for registering information on a personal communication terminal of a worker or visitor present within a security area into a terminal security control server, a security control policy for the personal communication terminal, and information of components to be provided; a zone notification node for providing the information of the personal communication terminal that has entered a control zone covered by the zone notification node in the security area when the personal communication terminal moves to the control zone; and the terminal security control server for installing a security control software module in the personal communication terminal, configuring computing resources and components permitted within the control zone based on a security control policy and zone information, and providing the configured computing resources and components to the personal communication terminal, and teaches in [0059] FIG. 6 depicts a schematic diagram of the concept of security control of a personal communication terminal of a visitor entering the security area 100 of the organization in accordance with the present invention. The security area of the present invention includes, for example, four zones: a main entrance A1; a walking path B1; a first floor waiting room C1; and a second-floor meeting room D1. Each of the zones can be managed by one or more zone notification nodes 104 and 104-1, and teaches in [0060] First, a visitor terminal 101 before entering a security area 100 has no component and is not under security control, as shown in FIG. 6. , and teaches in [0061] However, when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101).
An, as modified above, does not teach, however Cai teaches
further comprising:
[the hardware processor to receive the user-query input and execute] a semantic similarity comparison to correlate the user query input to a capability intent [value] for a [second] capability intent action (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
An, as modified above, does not teach, however Karapantelakis teaches
a capability intent value (Karapantelakis in [0088] teaches converting intents into feature vectors)
[the hardware processor to execute computer readable code instructions of the policy control managing subagent to] determine if the [first capability intent action policy] is associated with the at least one [capability intent action] from a database storing the mappings [of the capability intent action policy to the capabilities] (Karapantelakis in [0107] teaches mapping these policies to the requested intents (QoS vectors) is a task that can be done using an ML model, owing to the fact that in the long term, many QoS intents will be correlated, for example resolution improvement for streaming of one application will be useful for another application in the future).
Karapantelakis is considered to be analogous to the claimed invention because it is in the same field of mapping policies to intents. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Karapantelakis to allow for mapping policies to intents. Motivation to do so would allow for being offered more flexible and transparent fulfillment of QoS requests, particularly when capacity in the communication network may be insufficient to immediately fulfill a QoS request, and wireless device owners requesting QoS for their devices are provided with information that allows them to better manage their devices and services (Karapantelakis [0115]).
Regarding claims 6 and 14, An, as modified above, teaches the information handling system and method of claims 1 and 9.
An, as modified above, teaches the first capability intent action policy, an AI productivity tool, AI productivity tool-enablable software applications, AI productivity tool modules, the information handling system, and capability dependencies.
An, as modified above, does not teach, however Karapantelakis teaches
further comprising:
[an AI productivity tool policy] database that maintains the [capability intent action policies] and mappings of the [capability intent action policies] and [capability dependencies] relative to their respective AI productivity tool-enablable software applications and AI productivity tool modules at the information handling system (Karapantelakis in [0107] teaches mapping these policies to the requested intents (QoS vectors) is a task that can be done using an ML model, owing to the fact that in the long term, many QoS intents will be correlated, for example resolution improvement for streaming of one application will be useful for another application in the future).
Karapantelakis is considered to be analogous to the claimed invention because it is in the same field of mapping policies to intents. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Karapantelakis to allow for mapping policies to intents. Motivation to do so would allow for being offered more flexible and transparent fulfillment of QoS requests, particularly when capacity in the communication network may be insufficient to immediately fulfill a QoS request, and wireless device owners requesting QoS for their devices are provided with information that allows them to better manage their devices and services (Karapantelakis [0115]).
Regarding claim 17, An teaches an information handling system (An in [0010] teaches a terminal security control server for installing a security control software module in the personal communication terminal)
comprising:
a hardware processor executing computer-readable program code instructions of an intent dependency determination software application to receive one or more capability intent action policies describing controls to be implemented for execution of capability intent actions (An in [0002] teaches security control for personal communication terminals, such as mobile phones, and more particularly, to a security control system and method for personal communication terminals, which can prevent information leakage using personal communication terminals by establishing a security control policy for personal communication terminals, carried by workers or visitors in security areas requiring security falling of organizations such as companies, laboratories, universities, institutions and the like and limiting the use of components and computing resources of personal communication terminals within security areas based on the security control policy, and in [0010, Fig. 6] teaches a terminal registration agent for registering information on a personal communication terminal of a worker or visitor present within a security area into a terminal security control server, a security control policy for the personal communication terminal, and information of components to be provided; a zone notification node for providing the information of the personal communication terminal that has entered a control zone covered by the zone notification node in the security area when the personal communication terminal moves to the control zone; and the terminal security control server for installing a security control software module in the personal communication terminal, configuring computing resources and components permitted within the control zone based on a security control policy and zone information, and providing the configured computing resources and components to the personal communication terminal)
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responsive to a user-query input received at an artificial intelligence (AI) productivity tool module executing at the information handling system (An in [0061] teaches when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101);
the hardware processor executing computer-readable program code instructions of the intent dependency determination software application to identify capability dependencies of each of a plurality of AI productivity tool-enablable software applications executable at the information handling system [that are affected by the one or more capability intent action policies] (An in [0010] teaches the terminal security control server for installing a security control software module in the personal communication terminal, configuring computing resources and components permitted within the control zone based on a security control policy and zone information);
and
the hardware processor executing computer-readable program code instructions of a policy control managing subagent to transmit the capability intent action policies to the AI productivity tool-enablable software applications for application of the capability intent action policies to adjust execution of a plurality of capability intent actions by capabilities of the AI productivity tool-enablable software applications pursuant to a corresponding capability intent action policy at the information handling system (An in [0061] teaches when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101).
An teaches the hardware processor executing computer-readable program code instructions to identify one or more capability dependencies, and the first capability intent action policy.
An does not teach, however Cai teaches
[the hardware processor executing computer-readable program code instructions of the intent dependency determination software application to identify capability dependencies of each of a plurality of AI productivity tool-enablable software applications executable at the information handling system] that are affected by the one or more capability intent action policies (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
An, as modified above, does not teach, however Karapantelakis teaches
[the hardware processor] to map each of the [capability intent action policies] with an affected capability associated with [each of the AI productivity tool-enablable software applications in an AI productivity tool policy database memory at the information handling system] (Karapantelakis in [0107] teaches mapping these policies to the requested intents (QoS vectors) is a task that can be done using an ML model, owing to the fact that in the long term, many QoS intents will be correlated, for example resolution improvement for streaming of one application will be useful for another application in the future).
Karapantelakis is considered to be analogous to the claimed invention because it is in the same field of mapping policies to intents. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Karapantelakis to allow for mapping policies to intents. Motivation to do so would allow for being offered more flexible and transparent fulfillment of QoS requests, particularly when capacity in the communication network may be insufficient to immediately fulfill a QoS request, and wireless device owners requesting QoS for their devices are provided with information that allows them to better manage their devices and services (Karapantelakis [0115]).
Regarding claim 19, An, as modified above, teaches the information handling system of claim 17.
An, as modified above, teaches the hardware processor, the first capability intent action policy, the computer readable code instructions, the AI productivity tool-enablable software applications, AI productivity tool modules, the policy control managing subagent, the capability intent actions, and the user-query input.
An further teaches
further comprising:
executing the capability intent action when no capability intent action policies are associated with the capability intent action (An in [0002] teaches security control for personal communication terminals, such as mobile phones, and more particularly, to a security control system and method for personal communication terminals, which can prevent information leakage using personal communication terminals by establishing a security control policy for personal communication terminals, carried by workers or visitors in security areas requiring security falling of organizations such as companies, laboratories, universities, institutions and the like and limiting the use of components and computing resources of personal communication terminals within security areas based on the security control policy, and in [0010, Fig. 6] teaches a terminal registration agent for registering information on a personal communication terminal of a worker or visitor present within a security area into a terminal security control server, a security control policy for the personal communication terminal, and information of components to be provided; a zone notification node for providing the information of the personal communication terminal that has entered a control zone covered by the zone notification node in the security area when the personal communication terminal moves to the control zone; and the terminal security control server for installing a security control software module in the personal communication terminal, configuring computing resources and components permitted within the control zone based on a security control policy and zone information, and providing the configured computing resources and components to the personal communication terminal, and teaches in [0059] FIG. 6 depicts a schematic diagram of the concept of security control of a personal communication terminal of a visitor entering the security area 100 of the organization in accordance with the present invention. The security area of the present invention includes, for example, four zones: a main entrance A1; a walking path B1; a first floor waiting room C1; and a second-floor meeting room D1. Each of the zones can be managed by one or more zone notification nodes 104 and 104-1, and teaches in [0060] First, a visitor terminal 101 before entering a security area 100 has no component and is not under security control, as shown in FIG. 6. , and teaches in [0061] However, when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101).
An, as modified above, does not teach, however Cai teaches
[the hardware processor to receive the user-query input and] execute a semantic similarity comparison to correlate the [user query input] to a [capability intent value for a capability intent action] (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like),
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
An, as modified above, does not teach, however Karapantelakis teaches
[the hardware processor to execute computer readable code instructions of the policy control managing subagent to] determine if any [capability intent action policies] are associated with the [capability intent action] from the AI productivity tool policy database memory storing the mappings of the [capability intent action policies to the capabilities] (Karapantelakis in [0107] teaches mapping these policies to the requested intents (QoS vectors) is a task that can be done using an ML model, owing to the fact that in the long term, many QoS intents will be correlated, for example resolution improvement for streaming of one application will be useful for another application in the future).
Karapantelakis is considered to be analogous to the claimed invention because it is in the same field of mapping policies to intents. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Karapantelakis to allow for mapping policies to intents. Motivation to do so would allow for being offered more flexible and transparent fulfillment of QoS requests, particularly when capacity in the communication network may be insufficient to immediately fulfill a QoS request, and wireless device owners requesting QoS for their devices are provided with information that allows them to better manage their devices and services (Karapantelakis [0115]).
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Gomez, in view of Redkar et al. (US Patent Pub. No. 20180276553 A1), hereinafter Redkar.
Regarding claims 7 and 15, An, as modified above, teaches the information handling system and method of claims 1 and 9.
An, as modified above, teaches the hardware processor executing computer-readable program code instructions, the first capability intent action policy, an AI productivity tool, AI productivity tool-enablable software applications, AI productivity tool modules, the information handling system, and capability dependencies.
An, as modified above, does not teach, however Cai teaches
further comprising:
[the hardware processor executing computer-readable program code instructions of an intent identification software application to invoke one or more machine learning (ML) model algorithms] to perform the semantic similarity comparison or a lexical similarity comparison [for the received capability intent action policies with one or more capabilities of the AI productivity tool-enablable software applications or functions of the AI productivity tool module and identify the capability dependencies of the capability intent action policies to a corresponding capability of the AI productivity tool-enablable software applications or function of the AI productivity tool module] (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
An, as modified above, does not teach, however Gomez teaches
[invoke a] machine learning (ML) model algorithms (Gomez in [0044] teaches a policy repository 400 may be accessed to retrieve one or more security policies associated with communications in a thread or stream. The policies may include, for example, a policy associated with an employer of the recipient of the communications, but also could include policies relating to communications generally or to other categories. This may be performed using a mix of machine learning (using machine-learned sub-model 402) and rules (using rules execution component 404). The machine-learned sub-model 402 may be trained using a machine learning algorithm to detect that a communication requests information that is subject to a security policy, such as a bitcoin wallet address or a wire transfer, or otherwise asks for money or a password. The machine-learned sub-model 402 may also be trained to identify actual information subject to a security policy, sent from the potential target, such as a password).
Gomez is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Gomez to allow for using a machine learning algorithm. Motivation to do so would allow for a combination of machine learning and rule-based techniques to be used to automatically detect social engineering attacks in a computer system (Gomez [Abstract]).
An, as modified above, does not teach, however Redkar teaches
[the hardware processor executing computer-readable program code instructions of] an intent identification software application to invoke one or more [machine learning (ML) model algorithms to perform the semantic similarity comparison or a lexical similarity comparison for the received capability intent action policies with one or more capabilities of the AI productivity tool-enablable software applications or functions of the AI productivity tool module and identify the capability dependencies of the capability intent action policies to a corresponding capability of the AI productivity tool-enablable software applications or function of the AI productivity tool module] (Redkar in [0033] teaches identifying an intent to invoke a model).
Redkar is considered to be analogous to the claimed invention because it is in the same field of identifying an intent to invoke a model. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Redkar to allow for identifying an intent to invoke a model. Motivation to do so would allow for providing visualizations which may be interactive and allow the user to dive deeper into the results, explore additional related information, or submit subsequent queries related to the results (Redkar [0030]).
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Gomez, in view of Karapantelakis, in view of Redkar.
Regarding claims 8 and 16, An, as modified above, teaches the information handling system and method of claims 1 and 9.
An, as modified above, teaches the hardware processor executing computer-readable program code instructions, the intent identification software application, the first capability intent action policy, an AI productivity tool, AI productivity tool-enablable software applications, AI productivity tool modules, the information handling system, and capability dependencies.
An, as modified above, does not teach, however Cai teaches
further comprising:
[the hardware processor executing computer-readable program code instructions of the intent identification software application to invoke a machine learning (ML) model to generate vectorized policy intent value for each of the received intent action policies, generate vectorized capability intent values of each of the capabilities associated with each of the AI productivity tool-enablable software applications, and] match the [vectorized] policy intent values and [vectorized] capability intent [values] based on a semantic similarity comparison score [to map each of the capability intent action policies with one or more capabilities associated with the AI productivity tool-enablable software applications to identify the one or more capability dependencies] (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
An, as modified above, does not teach, however Gomez teaches
[invoke a] machine learning (ML) model (Gomez in [0044] teaches a policy repository 400 may be accessed to retrieve one or more security policies associated with communications in a thread or stream. The policies may include, for example, a policy associated with an employer of the recipient of the communications, but also could include policies relating to communications generally or to other categories. This may be performed using a mix of machine learning (using machine-learned sub-model 402) and rules (using rules execution component 404). The machine-learned sub-model 402 may be trained using a machine learning algorithm to detect that a communication requests information that is subject to a security policy, such as a bitcoin wallet address or a wire transfer, or otherwise asks for money or a password. The machine-learned sub-model 402 may also be trained to identify actual information subject to a security policy, sent from the potential target, such as a password).
Gomez is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Gomez to allow for using a machine learning algorithm. Motivation to do so would allow for a combination of machine learning and rule-based techniques to be used to automatically detect social engineering attacks in a computer system (Gomez [Abstract]).
An, as modified above, does not teach, however Karapantelakis teaches
[the hardware processor executing computer-readable program code instructions of the intent identification software application to invoke a machine learning (ML) model] to generate vectorized policy intent value for each of the received intent action policies, generate vectorized capability intent values [of each of the capabilities associated with each of the AI productivity tool-enablable software applications, and match the vectorized policy intent values and vectorized capability intent values based on a semantic similarity comparison score to map each of the capability intent action policies with one or more capabilities associated with the AI productivity tool-enablable software applications to identify the one or more capability dependencies] (Karapantelakis in [0107] teaches mapping these policies to the requested intents (QoS vectors) is a task that can be done using an ML model, owing to the fact that in the long term, many QoS intents will be correlated, for example resolution improvement for streaming of one application will be useful for another application in the future [this can be repeated for each policy intent and each capability intent]).
Karapantelakis is considered to be analogous to the claimed invention because it is in the same field of mapping policies to intents. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Karapantelakis to allow for mapping policies to intents. Motivation to do so would allow for being offered more flexible and transparent fulfillment of QoS requests, particularly when capacity in the communication network may be insufficient to immediately fulfill a QoS request, and wireless device owners requesting QoS for their devices are provided with information that allows them to better manage their devices and services (Karapantelakis [0115]).
An, as modified above, does not teach, however Redkar teaches
[the hardware processor executing computer-readable program code instructions of the intent identification software application to] invoke a [machine learning (ML) model to generate vectorized policy intent value for each of the received intent action policies, generate vectorized capability intent values of each of the capabilities associated with each of the AI productivity tool-enablable software applications, and match the vectorized policy intent values and vectorized capability intent values based on a semantic similarity comparison score to map each of the capability intent action policies with one or more capabilities associated with the AI productivity tool-enablable software applications to identify the one or more capability dependencies] (Redkar in [0033] teaches identifying an intent to invoke a model).
Redkar is considered to be analogous to the claimed invention because it is in the same field of identifying an intent to invoke a model. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Redkar to allow for identifying an intent to invoke a model. Motivation to do so would allow for providing visualizations which may be interactive and allow the user to dive deeper into the results, explore additional related information, or submit subsequent queries related to the results (Redkar [0030]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Redkar.
Regarding claim 10, An, as modified above, teaches the method of claim 9.
An, as modified above, teaches the capability intent action policy, the provider-limiting intent action policy, and executing the computer-readable program code instructions of the AI productivity tool-enablable software applications or AI productivity tool module.
An, as modified above, does not teach, however Redkar teaches
further comprising:
[the first capability intent action policy including a provider-limiting intent action policy] that describes if and when the information handling system may transmit and receive data over a wired or wireless connection to a remote server [to support executing the computer-readable program code instructions of the AI productivity tool-enablable software applications or AI productivity tool module] (Redkar in [0033] teaches one constraint may be a data sovereignty constraint that specifies that data cannot be transmitted out of a particular jurisdiction or into a specific set of jurisdictions, in such a scenario, those models that require data to be transmitted to another jurisdiction may be filtered out).
Redkar is considered to be analogous to the claimed invention because it is in the same field of identifying an intent to invoke a model. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Redkar to allow for filtering out data which cannot be transmitted out of a particular jurisdiction. Motivation to do so would allow for providing visualizations which may be interactive and allow the user to dive deeper into the results, explore additional related information, or submit subsequent queries related to the results (Redkar [0030]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Bollineni (US Patent Pub. No. 20230188570 A1).
Regarding claim 11, An, as modified above, teaches the method of claim 9.
An, as modified above, teaches the computer readable program code instructions, the hardware processor, the capability dependencies, the AI productivity tool-enablable software applications, the AI productivity tool module, the information handling system, the capability intent action policies, thecomputer readable program code instructions, the policy control managing subagent,
An further teaches
further comprising:
executing computer readable program code instructions of a policy control managing subagent to transmit the first capability intent action policy to the AI productivity tool-enablable software applications or the AI productivity tool module for application of the first capability intent action policy to adjust execution of a plurality of capability intent actions for the [first policy subset] of the capabilities according to the first capability intent action policy at the information handling system (An in [0061] teaches when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101).
An, as modified above, does not teach, however Cai teaches
[executing computer readable program code instructions, via the hardware processor, to identify capability dependencies of capabilities of AI productivity tool-enablable software applications or functions of the AI productivity tool module executable at the information handling system that] are affected by the [capability intent action policies] by executing a semantic similarity comparison or a lexical similarity comparison between a first [capability intent action policy] and a plurality of the [capabilities] to identify a [first policy subset] of the [capabilities] to be correlated to the first [capability intent action policy] (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
An, as modified above, does not teach, however Bollineni teaches
a first policy subset [of the capabilities to be correlated to the first capability intent action policy] (Bollineni in [0025] teaches using a first subset of security policies (e.g., that includes one or more security policies) and may determine, based on the source zone group and the destination zone group, a second subset of security policies (e.g., that includes one or more security policies)).
Bollineni is considered to be analogous to the claimed invention because it is in the same field of policies and rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Bollineni to allow for using a first subset of security policies. Motivation to do so would allow for network device which provides security (e.g., by identifying and applying a security policy based on the zones or zone groups) without association with a routing domain (e.g., that is based on routing instances) (Bollineni [0012]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Karapantelakis, in view of Gomez.
Regarding claim 18, An, as modified above, teaches the information handling system of claim 17.
An, as modified above, teaches the hardware processor, the first capability intent action policy, the computer readable code instructions, the AI productivity tool-enablable software applications, AI productivity tool modules, the policy control managing subagent, the capability intent actions, and the user-query input.
An further teaches
further comprising:
to determine that a first capability intent action policy applies to the capability intent action and limiting the execution of the capability intent action pursuant to the first capability intent action policy (An in [0061] teaches when the visitor terminal 101 enters the security area 100 and is registered in the security control server 105, the terminal security control server 105 remotely installs a security control software module in the visitor terminal 101, and forcibly controls the use of computing resources of the visitor terminal 101 through the security control software module. At this time, the terminal security control server 105 can, for example, define and apply three rules as shown in FIG. 6 as a security control policy to control the visitor terminal 101).
An, as modified above, does not teach, however Cai teaches
[the hardware processor to receive the user-query input and] execute a semantic similarity comparison to correlate the [user query input] to a [capability intent value] for a [capability intent action] (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like),
matching the [vectorized intent action policy value]s and [vectorized capability intent values] via a similarity comparison score between each of the [vectorized policy intent values] and the vectorized capability intent values] (Cai in [0026] teaches a plurality of policy rules grouped together based on reasons, such as, semantic similarity, domain similarity, or the like).
Cai is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Cai to allow for grouping policy rules together based on semantic similarity. Motivation to do so would allow for a program to be arranged to implement one or more network management/application control policies that are associated with the selected DNS server, for example, the program may include instructions for applying policies, such as, blacklists, whitelists, pattern matching, conditions, or the like, that may be used to determine the validity of the provided message (Cai [0157]).
An, as modified above, does not teach, however Karapantelakis teaches
a capability intent value (Karapantelakis in [0088] teaches converting intents into feature vectors)
[the hardware processor to execute computer readable code instructions of the policy control managing subagent] to determine if any capability intent action policies are associated with the capability intent action from AI productivity tool policy database memory storing mappings of the capability intent action policies to the capabilities (Karapantelakis in [0107] teaches mapping these policies to the requested intents (QoS vectors) is a task that can be done using an ML model, owing to the fact that in the long term, many QoS intents will be correlated, for example resolution improvement for streaming of one application will be useful for another application in the future),
[by execution of a machine learning (ML) model algorithm] by generating vectorized policy intent values for each of the received [capability intent action policies], by generating vectorized [capability intent] values of each of the capabilities associated with each of [the AI productivity tool-enablable software applications], and [matching] the vectorized intent action policy values and vectorized [capability intent] values [via a similarity comparison score between each] of the vectorized policy intent values and the vectorized [capability intent] values (Karapantelakis in [0107] teaches mapping these policies to the requested intents (QoS vectors) is a task that can be done using an ML model, owing to the fact that in the long term, many QoS intents will be correlated, for example resolution improvement for streaming of one application will be useful for another application in the future [this can be repeated for each policy intent and each capability intent]).
Karapantelakis is considered to be analogous to the claimed invention because it is in the same field of mapping policies to intents. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Karapantelakis to allow for mapping policies to intents. Motivation to do so would allow for being offered more flexible and transparent fulfillment of QoS requests, particularly when capacity in the communication network may be insufficient to immediately fulfill a QoS request, and wireless device owners requesting QoS for their devices are provided with information that allows them to better manage their devices and services (Karapantelakis [0115]).
An, as modified above, does not teach, however Gomez teaches
by execution of a machine learning (ML) model algorithm [by generating vectorized policy intent values for each of the received capability intent action policies, by generating vectorized capability intent values of each of the capabilities associated with each of the AI productivity tool-enablable software applications, and matching the vectorized intent action policy values and vectorized capability intent values via a similarity comparison score between each of the vectorized policy intent values and the vectorized capability intent values] (Gomez in [0044] teaches a policy repository 400 may be accessed to retrieve one or more security policies associated with communications in a thread or stream. The policies may include, for example, a policy associated with an employer of the recipient of the communications, but also could include policies relating to communications generally or to other categories. This may be performed using a mix of machine learning (using machine-learned sub-model 402) and rules (using rules execution component 404). The machine-learned sub-model 402 may be trained using a machine learning algorithm to detect that a communication requests information that is subject to a security policy, such as a bitcoin wallet address or a wire transfer, or otherwise asks for money or a password. The machine-learned sub-model 402 may also be trained to identify actual information subject to a security policy, sent from the potential target, such as a password).
Gomez is considered to be analogous to the claimed invention because it is in the same field of policy rules. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Gomez to allow for using a machine learning algorithm. Motivation to do so would allow for a combination of machine learning and rule-based techniques to be used to automatically detect social engineering attacks in a computer system (Gomez [Abstract]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over An, in view of Cai, in view of Karapantelakis, in view of Redkar.
Regarding claim 20, An, as modified above, teaches the system of claim 17.
An, as modified above, teaches the capability intent action policy, the provider-limiting intent action policy, and executing the computer-readable program code instructions of the AI productivity tool-enablable software applications or AI productivity tool module.
An, as modified above, does not teach, however Redkar teaches
further comprising:
[the first capability intent action policy including a provider-limiting intent action policy] that describes if and when the information handling system may transmit and receive data over a wired or wireless connection to a remote server [to support executing the computer-readable program code instructions of the AI productivity tool-enablable software applications or AI productivity tool module] (Redkar in [0033] teaches one constraint may be a data sovereignty constraint that specifies that data cannot be transmitted out of a particular jurisdiction or into a specific set of jurisdictions, in such a scenario, those models that require data to be transmitted to another jurisdiction may be filtered out).
Redkar is considered to be analogous to the claimed invention because it is in the same field of identifying an intent to invoke a model. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified An, as modified above, further in view of Redkar to allow for filtering out data which cannot be transmitted out of a particular jurisdiction. Motivation to do so would allow for providing visualizations which may be interactive and allow the user to dive deeper into the results, explore additional related information, or submit subsequent queries related to the results (Redkar [0030]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL J. MUELLER whose telephone number is (571)272-1875. The examiner can normally be reached M-F 9:00am-5:00pm (Eastern).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel C. Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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PAUL MUELLER
Examiner
Art Unit 2657
/PAUL J. MUELLER/Examiner, Art Unit 2657
Prosecution Insights