DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim(s) 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claims 1-12 are drawn to a method, claims 13-18 are drawn to a system and claims 19-20 are drawn to a computer-readable storage medium, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1, 13 and 19 are nonverbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
Regarding claim 1:
Claim 1 recites: A method comprising:
performing two or more data gathering iterations comprising:
distributing experimental units to a plurality of agents having different agent configurations, the experimental units being distributed according to a sampling strategy;
populating an event log with events representing reactions of an environment to actions taken by individual agents in response to individual experimental units; and
based at least on the events in the event log, adjusting the sampling strategy for use in a subsequent data gathering iteration;
based at least on the events in the event log, predicting performance of the plurality of agents with respect to one or more evaluation metrics; and
based at least on predicted performance of the plurality of agents with respect to the one or more evaluation metrics, identifying a selected agent configuration
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 1 is directed to an abstract idea, specifically, as a mental process—concepts that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as an abstract idea of organizing human activity or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2)(II)(C).
Independent claim 1 recites in part:
populating an event log with events representing reactions of an environment to actions taken by individual agents in response to individual experimental units
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as an abstract idea of organizing human activity or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2)(II)(C). For example, one can with pen and paper fill out an event log with notes about how the environment reacts to actions taken.
and based at least on the events in the event log, adjusting the sampling strategy for use in a subsequent data gathering iteration
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as an abstract idea of organizing human activity or relationships or interactions between people (including social activities, teaching, and following rules or instructions). For example, one can analyze the event log , figure out how well many agents will do based on different ways to measure their performance.
based at least on the events in the event log, predicting performance of the plurality of agents with respect to one or more evaluation metrics
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, using the information from the event log, one can estimate how well multiple agents will perform based on certain measures.
based at least on predicted performance of the plurality of agents with respect to the one or more evaluation metrics, identifying a selected agent configuration
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as an abstract idea of organizing human activity or relationships or interactions between people (including social activities, teaching, and following rules or instructions). For example, one can look how well different options do on certain test, picking the best setup for the agents.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 1 recites in part:
“A method comprising:
performing two or more data gathering iterations comprising” as drafted, amounts to adding insignificant extra-solution activity (e.g., pre-solution activity is a step of gathering data) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“distributing experimental units to a plurality of agents having different agent configurations, the experimental units being distributed according to a sampling strategy” as drafted, amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 1 recites in part:
“A method comprising:
performing two or more data gathering iterations comprising” as drafted, amounts to adding insignificant extra-solution activity (e.g., pre-solution activity is a step of gathering data) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“distributing experimental units to a plurality of agents having different agent configurations, the experimental units being distributed according to a sampling strategy” as drafted, amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 13:
Claims 13 recites: A system comprising:
a processor; and
a storage resource storing instructions which, when executed by the processor, cause the system to:
perform two or more data gathering iterations comprising:
distributing experimental units to a plurality of agents having different agent configurations, the experimental units being distributed according to a sampling strategy;
populating an event log with events representing reactions of an environment to actions taken by individual agents in response to individual experimental units; and
based at least on the events in the event log, adjusting the sampling strategy for use in a subsequent data gathering iteration,
wherein the event log provides a basis for subsequent evaluation of the plurality of agents with respect to one or more evaluation metrics
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 13 is directed to an abstract idea, specifically, as a mental process—concepts that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as an abstract idea of organizing human activity or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2)(II)(C).
populating an event log with events representing reactions of an environment to actions taken by individual agents in response to individual experimental units
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as an abstract idea of organizing human activity or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2)(II)(C). For example, one can with pen and paper fill out an event log with notes about how the environment reacts to actions taken.
based at least on the events in the event log, adjusting the sampling strategy for use in a subsequent data gathering iteration
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as an abstract idea of organizing human activity or relationships or interactions between people (including social activities, teaching, and following rules or instructions). For example, one can analyze the event log, figure out how well many agents will do based on different ways to measure their performance.
wherein the event log provides a basis for subsequent evaluation of the plurality of agents with respect to one or more evaluation metrics
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can analyze using data from the event log and evaluate multiple agents against defined performance standards.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 13 recites in part:
“A system comprising:
a processor; and
a storage resource storing instructions which, when executed by the processor, cause the system to” as drafted, amounts to adding a generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. 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.
“perform two or more data gathering iterations comprising” as drafted, amounts to adding insignificant extra-solution activity (e.g., pre-solution activity is a step of gathering data) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“distributing experimental units to a plurality of agents having different agent configurations, the experimental units being distributed according to a sampling strategy” as drafted, amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
“A system comprising:
a processor; and
a storage resource storing instructions which, when executed by the processor, cause the system to” as drafted, amounts to adding a generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. 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.
“perform two or more data gathering iterations comprising” as drafted, amounts to adding insignificant extra-solution activity (e.g., pre-solution activity is a step of gathering data) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“distributing experimental units to a plurality of agents having different agent configurations, the experimental units being distributed according to a sampling strategy” as drafted, amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 19:
Claim 19 recites: A computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform acts comprising:
obtaining an event log of events representing reactions of an environment to actions taken by a plurality of agents in response to individual experimental units;
predicting performance of individual agents with respect to one or more evaluation metrics based at least on respective events in the event log reflecting respective actions taken by other agents;
based at least on predicted performance of the individual agents with respect to the one or more evaluation metrics, identifying a selected agent configuration;
and deploying a selected agent having the selected agent configuration
predicting performance of individual agents with respect to one or more evaluation metrics based at least on respective events in the event log reflecting respective actions taken by other agents
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, using the information from the event log, one can estimate how well multiple agents will perform based on certain measures.
based at least on predicted performance of the individual agents with respect to the one or more evaluation metrics, identifying a selected agent configuration
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as an abstract idea of organizing human activity or relationships or interactions between people (including social activities, teaching, and following rules or instructions). For example, one can look how well different options do on certain test, picking the best setup for the agents.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 19 recites in part:
“A computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform acts comprising” as drafted, amounts to adding a generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. 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.
“obtaining an event log of events representing reactions of an environment to actions taken by a plurality of agents in response to individual experimental units” as drafted, amounts to adding insignificant extra-solution activity (e.g., pre-solution activity is a step of gathering data) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“deploying a selected agent having the selected agent configuration” as drafted, amounts to adding insignificant extra-solution activity (e.g., post-solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
“A computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform acts comprising” as drafted, amounts to adding a generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. 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.
“obtaining an event log of events representing reactions of an environment to actions taken by a plurality of agents in response to individual experimental units” as drafted, amounts to adding insignificant extra-solution activity (e.g., pre-solution activity is a step of gathering data) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“deploying a selected agent having the selected agent configuration” as drafted, amounts to adding insignificant extra-solution activity (e.g., post-solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-12 are dependent on claim 1, claims 14- 18 which are dependent on claim 13 and claim 20 is dependent on claim 19, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Claim 2 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 3 incorporates the rejection of claim 2 and does not integrate the judicial exception into a practical application.
Claim 4 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 5 incorporates the rejection of claim 4 and does not integrate the judicial exception into a practical application.
Claim 6 incorporates the rejection of claim 4 and does not integrate the judicial exception into a practical application.
Claim 7 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 8 incorporates the rejection of claim 7 and does not integrate the judicial exception into a practical application.
Claim 9 incorporates the rejection of claim 7 and does not integrate the judicial exception into a practical application.
Claim 10 incorporates the rejection of claim 7 and does not integrate the judicial exception into a practical application.
Claim 11 incorporates the rejection of claim 10 and does not integrate the judicial exception into a practical application.
Claim 12 incorporates the rejection of independent claim 1 and does not integrate the judicial exception into a practical application.
Claim 14 incorporates the rejection of independent claim 13 and does not integrate the judicial exception into a practical application.
Claim 15 incorporates the rejection of independent claim 13 and does not integrate the judicial exception into a practical application.
Claim 16 incorporates the rejection of independent claim 13 and does not integrate the judicial exception into a practical application.
Claim 17 incorporates the rejection of independent claim 13 and does not integrate the judicial exception into a practical application.
Claim 18 incorporates the rejection of independent claim 13 and does not integrate the judicial exception into a practical application.
Claim 20 incorporates the rejection of independent claim 13 and does not integrate the judicial exception into a practical application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C 103 as being unpatentable over Minter. (US Pub No.: 20220245557 A1), hereinafter referred to as Minter in view of Laskawiec. (US Pub No.: 20210263749 A1), hereinafter referred to as Laskawiec.
With respect to claim 1, Minter disclose:
Distributing experimental units to a plurality of agents having different agent configurations, the experimental units being distributed according to a sampling strategy (In paragraph [0118], Minter discloses a process that looks for unusual data points or groups in a distribution corresponding to the agent population. At step 902, the system creates a profile of the agent group based on different characteristics like age or experience. At step 904, the system finds these unusual data points or groups using special methods and suggests actions for them based on specific details about their performance or behavior.)
Populating an event log with events representing reactions of an environment to actions taken by individual agents in response to individual experimental units (In paragraph [0120] Minter discloses generating predictions of metrics for one or more agents using ML logic and prior performance. The system may identify or recommend actions, including metrics details.)
Based at least on the events in the event log, predicting performance of the plurality of agents with respect to one or more evaluation metrics (In Fig. 10 and paragraph [0120], Minter discloses the system (e.g. computing system 110 or recommendations component 160) may generate predictions of metrics for one or more agents using ML logic and prior performance.)
Based at least on predicted performance of the plurality of agents with respect to the one or more evaluation metrics, identifying a selected agent configuration (In Fig. 10 and paragraph [0120], Minter discloses the system may identify or recommend actions, including metric details and behaviors to coach. At 1014, the system may provide the predicted values for metrics for agents to the machine learning logic to prioritize metrics according to goals or thresholds. )
With respect to claim 1, Minter do not explicitly disclose:
A method comprising: performing two or more data gathering iterations comprising
Based at least on the events in the event log, adjusting the sampling strategy for use in a subsequent data gathering iteration
However, Laskawiec is known to disclose:
A method comprising: performing two or more data gathering iterations comprising (In paragraph [0011], Laskawiec discloses an initial configuration and initial execution parameters to be collected and a second configuration and second execution metrics to be collected. This process may or may not be repeated multiple times.)
Based at least on the events in the event log, adjusting the sampling strategy for use in a subsequent data gathering iteration (In paragraph [0015], Laskawiec discloses that operator service 106 can analyze the setup programs running in the computing environment and find new setups for these programs. The configuration change 130 may include a change to one or more configuration settings 134. In particular, the configuration 110B includes configuration settings 112, which may specify one or more parameters of the configurations.)
Minter and Laskawiec are analogous pieces of art because both references concern the distribution of vectors for metrics that are associated with the action. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Minter, with generating predictions of metrics for one or more agents using ML logic and prior performance as taught by Minter, with multiple configurations that adjust the operation of the applications based on one or more factors as taught by Laskawiec. The motivation for doing so would have been to identify one or more result effective variables for which actions may be delivered that correlate with agent metrics (See [0074] of Minter.)
Regarding claim 2, Minter in view of Laskawiec disclose the elements of claim 1. In addition, Laskawiec disclose:
The method of claim 1, further comprising: deploying a selected agent having the selected agent configuration (In paragraph [0037], Laskawiec discloses executing the first selected configuration for execution of the application.)
Regarding claim 3, Minter in view of Laskawiec disclose the elements of claim 2. In addition, Laskawiec disclose:
The method of claim 2, wherein the selected agent configuration is selected automatically or based on user input identifying the selected agent configuration from a graphical representation of the predicted performance of the plurality of agents (In paragraph [0120], Minter discloses that the system may automatically generate an adjusted threshold customized for the agent (e.g. smart goal) based on predicted values for agent and agent metrics.)
Regarding claim 4, Minter in view of Laskawiec disclose the elements of claim 1. In addition, Minter disclose:
The method of claim 1, further comprising: determining importance weights of the individual agents based at least on corresponding probabilities that individual agents give to the actions relative to probabilities of the actions taken by other agents that are stored in the event log (In paragraph [0123], Minter discloses that the system may determine the best action to deliver based on all metrics and weighted on a per-agent basis based on combined overall variance to optimal metrics. One or more of the processes 1310–1330 of process 1300 may be computer instructions that, when executed, cause one or more processors to perform the functions of the respective process.)
Adjusting the sampling strategy based at least on the importance weights (In paragraph [0124], Minter discloses the system may establish distribution of population and calculate variances for metrics between each agent and population based on machine learning logic. At 1440, the system may determine metrics or result effective variables to optimize for best balanced outcomes.)
Regarding claim 5, Minter in view of Laskawiec disclose the elements of claim 4. In addition, Minter disclose:
The method of claim 4, further comprising: calculating respective sampling probabilities for the individual agents based at least on the importance weights (In paragraph [0124], Minter discloses that the system may establish a distribution of the population and calculate variances for metrics between each agent and population based on machine learning logic.)
Regarding claim 6, Minter in view of Laskawiec disclose the elements of claim 4. In addition, Minter disclose:
The method of claim 4, wherein adjusting the sampling strategy comprises: removing at least one agent from subsequent data gathering iterations based at least on the importance weights (In paragraph [0103], Minter discloses the action effectiveness component 413 may also add or remove one or more result-effective variables from the machine learning logic of the recommendations component 160 based on their effectiveness.)
Regarding claim 7, Minter in view of Laskawiec disclose the elements of claim 1. In addition, Minter disclose:
The method of claim 1, wherein the sampling strategy is adjusted at each data gathering iteration based at least on the predicted performance of the plurality of agents with respect to the one or more evaluation metrics (In paragraph [0120], Minter discloses that the system may provide the predicted values for metrics for agents to the machine learning logic to prioritize metrics according to goals or thresholds. At 1016, the system may automatically generate an adjusted threshold customized for the agent (e.g. smart goal) based on predicted values for agent and agent metrics.)
Regarding claim 8, Minter in view of Laskawiec disclose the elements of claim 7. In addition, Minter disclose:
The method of claim 7, wherein adjusting the sampling strategy comprises: determining respective confidence intervals of the one or more evaluation metrics for each of the plurality of agents (In paragraph [0090], Minter discloses that the variance analysis component 364 may also determine a confidence interval around other metrics or predictions of the recommendations component.)
and calculating sampling probabilities of individual agents based at least upper bounds of the confidence intervals (In paragraph [0124], Minter the system may establish a distribution of population and calculate variances for metrics between each agent and population based on machine learning logic. At 1440, the system may determine metrics or result effective variables to optimize for best balanced outcomes.)
Regarding claim 9, Minter in view of Laskawiec disclose the elements of claim 7. In addition, Minter disclose:
The method of claim 7, wherein adjusting the sampling strategy comprises: removing at least one agent from further sampling based at least on the predicted performance (In paragraph [0103], Minter discloses that the action effectiveness component 413 may also add or remove one or more result-effective variables from the machine learning logic of the recommendations component 160 based on their effectiveness. The action effectiveness component 413 may also provide updates 412 to the persona model database 152 based on the effectiveness of actions.)
Regarding claim 10, Minter in view of Laskawiec disclose the elements of claim 7. In addition, Minter disclose:
The method of claim 7, further comprising: populating a data structure with predicted aggregate values and corresponding confidence intervals for the one or more evaluation metrics (In Fig. 10 and paragraph [0120], Minter discloses the system (e.g. computing system 110 or recommendations component 160) may generate predictions of metrics for one or more agents using ML logic and prior performance.)
outputting a graphical representation of the data structure (In paragraph [0051], Minter discloses that the data provided via the file upload 116 or hardware inputs 118 may be structured and/or unstructured data. The file upload 116 and/or the hardware inputs may be associated with a graphical user interface (GUI) that overlays the connection protocols or utilizes the input of the connections.)
and identifying one or more agent configurations to sample in a subsequent data gathering iteration based at least on user input directed to the graphical representation of the data structure (In paragraph [0109], Minter discloses the process for updating and improving machine learning accuracy with iterations through separate data sets. )
Regarding claim 11, Minter in view of Laskawiec disclose the elements of claim 10. In addition, Minter disclose:
The method of claim 10, further comprising: receiving user input specifying two or more evaluation metrics (In paragraph [0124], Minter discloses that the system (e.g. computing system 110) may provide a GUI for a user to select outcomes to be optimized (e.g. sales increase) or an outcome is selected by the system. )
Regarding claim 12, Minter in view of Laskawiec disclose the elements of claim 1. In addition, Minter disclose:
The method of claim 1, further comprising: using the events in the event log, predicting performance of at least one other agent with respect to the one or more evaluation metrics, wherein the at least one other agent was not sampled when populating the event log (In Fig. 10 and paragraph [0120], Minter discloses the system may identify or recommend actions, including metric details and behaviors to coach. At 1014, the system may provide the predicted values for metrics for agents to the machine learning logic to prioritize metrics according to goals or thresholds. )
With respect to claim 13, Minter disclose:
A system comprising: a processor; and a storage resource storing instructions which, when executed by the processor, cause the system to (In paragraph [0031], Minter disclose one or more non-transitory computer-readable storage media storing instructions executable by the one or more processors to perform operations.)
Distributing experimental units to a plurality of agents having different agent configurations, the experimental units being distributed according to a sampling strategy (In paragraph [0118], Minter discloses a process that looks for unusual data points or groups in a distribution corresponding to the agent population. At step 902, the system creates a profile of the agent group based on different characteristics like age or experience. At step 904, the system finds these unusual data points or groups using special methods and suggests actions for them based on specific details about their performance or behavior.)
Populating an event log with events representing reactions of an environment to actions taken by individual agents in response to individual experimental units (In paragraph [0120] Minter discloses generating predictions of metrics for one or more agents using ML logic and prior performance. The system may identify or recommend actions, including metrics details.)
Wherein the event log provides a basis for subsequent evaluation of the plurality of agents with respect to one or more evaluation metrics (In Fig. 10 and paragraph [0120], Minter discloses the system (e.g. computing system 110 or recommendations component 160) may generate predictions of metrics for one or more agents using ML logic and prior performance.)
With respect to claim 13, Minter do not explicitly disclose:
perform two or more data gathering iterations comprising
based at least on the events in the event log, adjusting the sampling strategy for use in a subsequent data gathering iteration
However, Laskawiec is known to disclose:
Perform two or more data gathering iterations comprising (In paragraph [0011], Laskawiec discloses an initial configuration and initial execution parameters to be collected and a second configuration and second execution metrics to be collected. This process may or may not be repeated multiple times.)
Based at least on the events in the event log, adjusting the sampling strategy for use in a subsequent data gathering iteration (In paragraph [0015], Laskawiec discloses that operator service 106 can analyze the setup programs running in the computing environment and find new setups for these programs. The configuration change 130 may include a change to one or more configuration settings 134. In particular, the configuration 110B includes configuration settings 112, which may specify one or more parameters of the configurations.)
Minter and Laskawiec are analogous pieces of art because both references concern the distribution of vectors for metrics that are associated with the action. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Minter, with generating predictions of metrics for one or more agents using ML logic and prior performance as taught by Minter, with multiple configurations that adjust the operation of the applications based on one or more factors as taught by Laskawiec. The motivation for doing so would have been to identify one or more result effective variables for which actions may be delivered that correlate with agent metrics (See [0074] of Minter.)
Regarding claim 14, Minter in view of Laskawiec disclose the elements of claim 13. In addition, Minter disclose:
The system of claim 13, wherein the individual agents include machine learning agents having different hyperparameters or different feature definitions (In paragraph [0023], Laskawiec discloses that the model parameters 168 may include weights (e.g., priorities) for different features and combinations of features (e.g., configuration settings and execution metrics) and updating the model 126 may include updating one or more of the features analyzed, and the weights assigned to different features and/or combinations of features)
Regarding claim 15, Minter in view of Laskawiec disclose the elements of claim 13. In addition, Minter disclose:
The system of claim 13, wherein the individual agents include at least two different reinforcement learning agents having different reward functions, at least two different supervised learning agents having different loss functions, and at least two different rule-based agents having different rules (In paragraph [0116], Minter discloses that the quartile manager matrix 830 may evaluate agent performance by isolating movement of agents within fixed distributions of overall performance for recommended actions and may evaluate agents against the persona models using one or more rules or correlations. The attrition decision matrix 840 may evaluate agent behavior, metrics, and characteristics to identify and mitigate risks of attrition and may select one or more regression models or classification models to generate recommendations.)
Regarding claim 16, Minter in view of Laskawiec disclose the elements of claim 13. In addition, Minter disclose:
The system of claim 13, wherein the sampling strategy is based at least on respective importance weights of the individual agents (In paragraph [0124], Minter discloses that the system may establish a distribution of the population and calculate variances for metrics between each agent and population based on machine learning logic.)
Regarding claim 17, Minter in view of Laskawiec disclose the elements of claim 13. In addition, Minter disclose:
The system of claim 13, wherein the sampling strategy is adjusted based at least on predicted performance of the plurality of agents with respect to the one or more evaluation metrics (In Fig. 10 and paragraph [0120], Minter discloses that the system may identify or recommend actions, including metrics details and behaviors to coach. At 1014, the system may provide the predicted values for metrics for agents to the machine learning logic to prioritize metrics according to goals or thresholds. )
Regarding claim 18, Minter in view of Laskawiec disclose the elements of claim 13. In addition, Minter disclose:
The system of claim 13, wherein adjusting the sampling strategy comprises assigning respective probabilities to individual agents and randomly assigning the experimental units to the individual agents based on the respective probabilities (In paragraph [0123], Minter discloses that the system may determine the best action to deliver based on all metrics and weighted on a per-agent basis based on combined overall variance to optimal metrics. One or more of the processes 1310-1330 of process 1300 may be computer instructions that, when executed, cause one or more processors to perform the functions of the respective process.)
With respect to claim 19, Minter disclose:
A computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform acts comprising (In paragraph [0031], Minter disclose one or more non-transitory computer-readable storage media storing instructions executable by the one or more processors to perform operations.)
Obtaining an event log of events representing reactions of an environment to actions taken by a plurality of agents in response to individual experimental units (In paragraph [0120] Minter discloses generating predictions of metrics for one or more agents using ML logic and prior performance. The system may identify or recommend actions, including metrics details.)
Predicting performance of individual agents with respect to one or more evaluation metrics based at least on respective events in the event log reflecting respective actions taken by other agents (In Fig. 10 and paragraph [0120], Minter discloses the system (e.g. computing system 110 or recommendations component 160) may generate predictions of metrics for one or more agents using ML logic and prior performance.)
Based at least on predicted performance of the individual agents with respect to the one or more evaluation metrics, identifying a selected agent configuration (In Fig. 10 and paragraph [0120], Minter discloses the system may identify or recommend actions, including metric details and behaviors to coach. At 1014, the system may provide the predicted values for metrics for agents to the machine learning logic to prioritize metrics according to goals or thresholds. )
With respect to claim 19, Minter do not explicitly disclose:
deploying a selected agent having the selected agent configuration
However, Laskawiec is known to disclose:
Deploying a selected agent having the selected agent configuration (In paragraph [0037], Laskawiec discloses executing the first selected configuration for execution of the application.)
Minter and Laskawiec are analogous pieces of art because both references concern the distribution of vectors for metrics that are associated with the action. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Minter, with generating predictions of metrics for one or more agents using ML logic and prior performance as taught by Minter, with multiple configurations that adjust the operation of the applications based on one or more factors as taught by Laskawiec. The motivation for doing so would have been to identify one or more result effective variables for which actions may be delivered that correlate with agent metrics (See [0074] of Minter.)
Regarding claim 20, Minter in view of Laskawiec disclose the elements of claim 19. In addition, Minter disclose:
The computer-readable storage medium of claim 19, wherein the events of the event log are previously sampled using an adaptive sampling strategy that adjusts sampling probabilities of respective agents based on collected events (In paragraph [0015], Laskawiec discloses that operator service 106 can analyze the setup programs running in the computing environment and find new setups for these programs. The configuration change 130 may include a change to one or more configuration settings 134. In particular, the configuration 110B includes configuration settings 112, which may specify one or more parameters of the configurations.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
Examin