DETAILED ACTION
This action is responsive to the application filed on 09/27/2024. Claims 1-20 are pending and have been examined. This action is Non-final.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119(a)-(d) based on Indian Patent Application No. 202311065524, filed on 09/29/2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 U.S.C. 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.
Regarding claim 1,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
Step 2A Prong 1:
“detect…a selection to indicate a change to a first parameter associated with a first entity of the plurality of entities” -- The limitation of detecting a selection to indicate a change to a parameter of an entity is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
“identify…a second plurality of metrics associated with the first parameter, wherein the machine learning model identifies the second plurality of metrics responsive to a detection of a plurality of correlations between the first parameter and the second plurality of metrics” -- The limitation of identifying metrics associated with the changed parameter based on detected correlations between the parameter and the metrics is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
“generate…a prediction to indicate a plurality of changes to the first values responsive to implementation of the change” -- The limitation of generating a prediction of changes to metric values that would result from implementing the change is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
Step 2A Prong 2 and Step 2B:
“A system comprising one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to…via a display device…using the machine learning model” -- The limitation recites generic computer components at a high level of generality, used as tools, devices, and/or ML models to perform the abstract idea, which amounts to mere instructions to apply the judicial exception using a generic computer, and thus it does not integrate to a practical application, nor provides significantly more than the judicial exception (see MPEP 2106.05(f)).
“receive, from a cloud system, training data to indicate correlations between a plurality of parameters of a plurality of entities and a plurality of metrics of the plurality of entities” -- The limitation of receiving training data from a cloud system is mere data gathering, which is insignificant extra-solution activity (see MPEP 2106.05(g)). Furthermore, under Step 2B, receiving data over a network is well-understood, routine, and conventional activity (see MPEP 2106.05(d)(II)).
“train, using the training data, a machine learning model to identify the correlations between the plurality of parameters and the plurality of metrics” -- The limitation of training a machine learning model using training data is recited at a high level of generality, without any particular model architecture, training algorithm, or technical constraint on the training, and amounts to mere instructions to apply the judicial exception using a generic machine learning model (see MPEP 2106.05(f)). The limitation further generally links the use of the judicial exception to a particular technological environment or field of use, namely employment and compensation data (see MPEP 2106.05(h)).
“transmit, to the cloud system, a first Application Programming Interface (API) call to receive first values associated with the second plurality of metrics, wherein the first values pertain to the first entity” -- The limitation of transmitting an API call to receive values is mere data gathering, which is insignificant extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, receiving or transmitting data over a network is well-understood, routine, and conventional activity, for which cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
“generate, responsive to generation of the prediction, a user interface to identify the plurality of changes; and display, via the display device, the user interface” -- The limitation of generating and displaying a user interface identifying the predicted changes is mere outputting of the result of the abstract idea, which is insignificant extra-solution activity (see MPEP 2106.05(g)). Furthermore, under Step 2B, presenting data is well-understood, routine, and conventional activity (see MPEP 2106.05(d)(II)).
Therefore, claim 1 is non-patent eligible. Claims 10 and 19 are analogous to claim 1, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 2,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
Step 2A Prong 1:
“The system of claim 1, wherein the instructions further cause the one or more processors to: generate the training data by monitoring changes to values associated with the plurality of parameters to determine changes to values associated with the plurality of metrics” -- The limitation of generating data by monitoring changes to parameter values to determine changes to metric values is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
“segment the training data into a plurality of portions based on a plurality of characteristics of the plurality of entities.” -- The limitation of segmenting data into portions based on characteristics of entities is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Therefore, claim 2 is non-patent eligible. Claims 11 and 20 are analogous to claim 2, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 3,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
Step 2A Prong 1:
“The system of claim 2, wherein the detection of the plurality of correlations between the first parameter and the second plurality of metrics occurs responsive to the machine learning model identifying characteristics of the first entity.” -- The limitation of detecting correlations responsive to identifying characteristics of an entity is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Therefore, claim 3 is non-patent eligible. Claim 12 is analogous to claim 3, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 4,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
Step 2A Prong 1:
“The system of claim 1, wherein the prediction to indicate the plurality of changes to the first values responsive to implementation of the change is based on the plurality of correlations between the first parameter and the second plurality of metrics.” -- The limitation further specifies the basis of the prediction identified as a mental process in claim 1, and generating a prediction based on correlations is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Therefore, claim 4 is non-patent eligible. Claim 13 is analogous to claim 4, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 5,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
“The system of claim 1, wherein the training data is absent information to identify the plurality of entities.” -- The limitation merely specifies the content of the training data, which generally links the use of the judicial exception to a particular technological environment or field of use, and thus it does not integrate to a practical application, nor does it provide significantly more than the judicial exception (see MPEP 2106.05(h)).
Therefore, claim 5 is non-patent eligible. Claim 14 is analogous to claim 5, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 6,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
Step 2A Prong 1:
“detect implementation of the change” -- The limitation of detecting implementation of the change is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
“compare, responsive to receipt of the second values, the first values to the second values” -- The limitation of comparing first values to second values is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
“determine, responsive to comparison of the first values and the second values, a plurality of differences” -- The limitation of determining differences between values is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
“identify, based on the plurality of differences and the plurality of changes, a plurality of results with respect to the prediction” -- The limitation of identifying results with respect to the prediction is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
Step 2A Prong 2 and Step 2B:
“transmit, to the cloud system, a second API call to receive second values associated with the second plurality of metrics” -- The limitation of transmitting an API call to receive values is mere data gathering, which is insignificant extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, receiving or transmitting data over a network is well-understood, routine, and conventional activity (see MPEP 2106.05(d)(II)).
“The system of claim 1, wherein the instructions further cause the one or more processors to:…update, based on the plurality of results with respect to the prediction, the machine learning model to adjust an efficacy of the machine learning model.” -- The limitation of updating the machine learning model is recited at a high level of generality, only in terms of its result (“to adjust an efficacy”), without any particular technique for updating the model, and amounts to mere instructions to apply the judicial exception using a generic machine learning model (see MPEP 2106.05(f)). Iteratively training a machine learning model on new data is incident to the use of machine learning and does not integrate the judicial exception into a practical application or provide significantly more. Furthermore, for more information please see Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025). Unlike Ex-parte Desjardins (Appeals Review Panel 2025) (precedential), the claim does not recite a specific manner of training that improves how the machine learning model itself learns or operates; any improvement is to the accuracy of the predictions of the abstract idea.
Therefore, claim 6 is non-patent eligible. Claim 15 is analogous to claim 6, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 7,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
There are no elements to be evaluated under Step 2A Prong 1.
Step 2A Prong 2 and Step 2B:
(a) “The system of claim 1, wherein the user interface to identify the plurality of changes includes: graphical representations to identify the plurality of correlations between the first parameter and the second plurality of metrics; graphical representations to identify a trend with respect to the first parameter; and graphical representations to identify a trend with respect to a second plurality of entities.” -- The limitation merely specifies the content of the displayed output of the abstract idea in generic graphical form, which is insignificant extra-solution activity that cannot be integrated to a practical application (see MPEP 2106.05(g)). Furthermore, under Step 2B, presenting data is well-understood, routine, and conventional activity, and thus it cannot provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Therefore, claim 7 is non-patent eligible. Claim 16 is analogous to claim 7, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 8,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
Step 2A Prong 1:
“detect, responsive to prompting the user, the selection of the first parameter associated with the first entity” -- The limitation of detecting a selection of a parameter is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
Step 2A Prong 2 and Step 2B:
“The system of claim 1, wherein the instructions further cause the one or more processors to: prompt, via the display device, a user to select parameters pertaining to the first entity” -- The limitation of prompting a user via a display device to select parameters is mere data gathering, which is insignificant extra-solution activity (see MPEP 2106.05(g)), as well as performed using a generic computer component, and thus it does not integrate to a practical application (see MPEP 2106.05(f)). Further, under Step 2B, receiving or transmitting data over a network is well-understood, routine, and conventional activity, and thus the limitation does not provide significantly more than the judicial exception (see MPEP 2106.05(d)(II)).
Therefore, claim 8 is non-patent eligible. Claim 17 is analogous to claim 8, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 9,
Step 1: The claim is directed to a system, which falls under the category of machine. The claim satisfies step 1.
Step 2A Prong 1:
“The system of claim 8, wherein the instructions further cause the one or more processors to prompt the user to select the parameters based on a persona of the user.” -- The limitation of determining which parameters to prompt a user to select based on a persona of the user is a process that can be performed in the human mind using evaluation, observation, and judgment, and therefore is directed to a mental process.
There are no elements to be evaluated under Step 2A Prong 2 and Step 2B.
Therefore, claim 9 is non-patent eligible. Claim 18 is analogous to claim 9, aside from claim type and minute differences, and thus the same rejection applies as above.
Claim Rejections - 35 U.S.C. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 5, 8-10, 13, 14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20110307413 A1 by Dutta et al. (referred herein as Dutta) in view of US 20090281845 A1 by Fukuda et al. (referred herein as Fukuda) further in view of US 11727328 B2 by Petrosso et al. (referred herein as Petrosso).
Regarding claim 1, Dutta teaches:
A system comprising one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to: ([Dutta, [0032]] “memory 14 stores software modules that provide functionality when executed by processor 22”, wherein the examiner interprets the software modules that provide functionality when executed by processor 22 to be the same as the instructions that, when executed by one or more processors, cause the one or more processors to perform operations because they are both stored program code executed by a processor.)
training data to indicate correlations between a plurality of parameters of a plurality of entities and a plurality of metrics of the plurality of entities; ([Dutta, [0036]] “Database system 30 may store attributes related to employees including their background, responsibilities, performance and compensation.” AND [Dutta, [0025]] “The data mining model analyzes the attributes as they relate to all workers and identifies a pattern between the attributes and the future performance of the workers or their likelihood of attrition.”, wherein the examiner interprets the workers to be the same as the plurality of entities, the attributes including compensation to be the same as the plurality of parameters, and the performance and likelihood of attrition of the workers to be the same as the plurality of metrics because they are both characteristics of multiple subjects and outcome measures of those subjects from which a relationship is identified.)
train, using the training data, a machine learning model to identify the correlations between the plurality of parameters and the plurality of metrics; ([Dutta, [0025]] “The data mining model analyzes the attributes as they relate to all workers and identifies a pattern between the attributes and the future performance of the workers or their likelihood of attrition.” AND [Dutta, [0050]] “GLM is a parametric modeling technique. Parametric models make assumptions about the distribution of the data. When the assumptions are met, parametric models can be more efficient than non-parametric models.”, wherein the examiner interprets the data mining model that analyzes the attributes of all workers to be the same as the machine learning model trained using the training data, and the pattern between the attributes and the future performance or likelihood of attrition to be the same as the correlations because they are both learned relationships between input attributes and outcome measures derived from historical data.)
detect, via a display device, a selection to indicate a change to a first parameter associated with a first entity of the plurality of entities; ([Dutta, [0066]] “For, example a manager can change a value in the what-if column of table view 1020 of predictive analytic dashboard user interface 1000 in order to view how that change will effect attrition and performance” AND [Dutta, [0056]] “The proposed personnel action can be, for example, a salary increase/decrease or a promotion/demotion.” AND [Dutta, [0031]] “Processor 22 is further coupled via bus 12 to a display 24, such as a Liquid Crystal Display (LCD), for displaying information to a user, such as configuration information.”, wherein the examiner interprets the manager changing a value, such as a salary increase/decrease, in the what-if column of the predictive analytic dashboard user interface to be the same as a selection to indicate a change to a first parameter associated with a first entity because they are both user inputs on a displayed interface specifying a proposed change to an attribute of a particular subject.)
first values associated with the second plurality of metrics, wherein the first values pertain to the first entity; ([Dutta, [0065]] “The chart of predictive analytic dashboard user interface 1000 can display the old prediction calculated by system 10, and also display the new prediction based on the proposed action.”, wherein the examiner interprets the old prediction calculated by system 10 for the worker to be the same as the first values pertaining to the first entity because they are both current values of outcome measures for that particular subject.)
generate, using the machine learning model, a prediction to indicate a plurality of changes to the first values responsive to implementation of the change; ([Dutta, [0065]] “The chart of predictive analytic dashboard user interface 1000 can display the old prediction calculated by system 10, and also display the new prediction based on the proposed action.” AND [Dutta, [0006]] “applying a data mining tool to the attributes and the proposed personnel action to identify an impact of the proposed personnel action”, wherein the examiner interprets the new prediction of attrition and performance based on the proposed action to be the same as a prediction to indicate a plurality of changes to the first values responsive to implementation of the change because they are both model outputs estimating how multiple outcome values would change if the proposed change were made.)
generate, responsive to generation of the prediction, a user interface to identify the plurality of changes; and ([Dutta, [0056]] “The predicted impact is provided to the user via a graphical user interface, such as a table or graph.” AND [Dutta, [0065]] “The chart of predictive analytic dashboard user interface 1000 can display the old prediction calculated by system 10, and also display the new prediction based on the proposed action.”, wherein the examiner interprets the graphical user interface providing the predicted impact to be the same as a user interface to identify the plurality of changes because they are both interfaces presenting the predicted changes to the user.)
display, via the display device, the user interface. ([Dutta, [0031]] “Processor 22 is further coupled via bus 12 to a display 24, such as a Liquid Crystal Display (LCD)” AND [Dutta, [0056]] “The predicted impact is provided to the user via a graphical user interface, such as a table or graph.”, wherein the examiner interprets display 24 to be the same as the display device because they are both hardware that presents the user interface to the user.)
Dutta does not teach identify, using the machine learning model, a second plurality of metrics associated with the first parameter, wherein the machine learning model identifies the second plurality of metrics responsive to a detection of a plurality of correlations between the first parameter and the second plurality of metrics;…receive, from a cloud system…transmit, to the cloud system, a first Application Programming Interface (API) call to receive.
Fukuda teaches identify, using the machine learning model, a second plurality of metrics associated with the first parameter, wherein the machine learning model identifies the second plurality of metrics responsive to a detection of a plurality of correlations between the first parameter and the second plurality of metrics; ([Fukuda, [0024]] “Mining KPI and their correlations may be performing using learning algorithms to identify KPIs (or variables) that are most significantly correlated with other variables.” AND [Fukuda, [0049]] “getInfluenceOut finds KPIs which the focal KPI influences” AND [Fukuda, [0024]] “sensitivity of impact from one KPI on another (pair-wise) is computed”, wherein the examiner interprets the learning algorithms to be the same as the machine learning model, the KPIs which the focal KPI influences to be the same as the second plurality of metrics associated with the first parameter, and identifying the KPIs most significantly correlated with other variables by computing pair-wise sensitivity to be the same as a detection of a plurality of correlations because they are both determinations of which metrics are statistically related to a given variable.)
Dutta and Fukuda do not teach receive, from a cloud system…transmit, to the cloud system, a first Application Programming Interface (API) call to receive.
Petrosso teaches:
receive, from a cloud system, ([Petrosso, page 15, col. 18, lines 14-16] “In some embodiments, data is requested or received via an application programming interface (API) that provides access to one or more data sources 203.” AND [Petrosso, page 11, col. 10, lines 56-59] “In some cases, the computing environment 201 can correspond to an elastic computing resource where the allotted capacity of processing, network, storage, or other computing-related resources may vary over time.” AND [Petrosso, page 11 col. 10, lines 60-63] “In various embodiments, the data source 203 generally refers to internal or external systems, pages, databases, or other platforms from which various data is received or collected.”, wherein the examiner interprets the elastic computing resource where the allotted capacity of processing, network, storage, or other computing-related resources may vary over time to be the same as the cloud system because they are both network-accessible pooled computing resources whose capacity is provisioned as needed)
transmit, to the cloud system, a first Application Programming Interface (API) call to receive ([Petrosso, page 10, col. 7, lines 60-64] “In one or more embodiments, the system may collect data by a plurality of methods including, but not limited to, initiating requests at data sources (e.g., via an application programming interface (API)),” AND [Petrosso, page 15, col. 18, lines 14-16] “In some embodiments, data is requested or received via an application programming interface (API) that provides access to one or more data sources 203.”, wherein the examiner interprets initiating requests at data sources via an application programming interface to be the same as transmitting an API call to receive because they are both requests sent through a programmatic interface to obtain data from a remote source.)
Dutta, Fukuda, Petrosso, and the instant application are analogous art because they are all directed to analyzing organizational data using learned correlations between variables to predict outcome metrics that inform decisions.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “finds KPIs which the focal KPI influences” disclosed by Fukuda. One would be motivated to do so to quickly identify the metrics affected by a change so that decision making can be focused, as suggested by Fukuda ([Fukuda, [0058]] “enables the decision makers to quickly spot patterns/problems that are originated from or aggregated to the selective KPIs.”).
It would have also been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “data can be requested or received via an application programming interface (API) that provides access to one or more data sources” disclosed by Petrosso. One would be motivated to do so to flexibly access data from internal or external systems using computing resources whose capacity can vary with demand, as suggested by Petrosso ([Petrosso, page 11 col. 10, lines 57-59] “the allotted capacity of processing, network, storage, or other computing-related resources may vary over time.”). Claims 10 and 19 are analogous to claim 1, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 4, Dutta, Fukuda, and Petrosso teaches The system of claim 1 (see rejection of claim 1).
Dutta further teaches:
wherein the prediction to indicate the plurality of changes to the first values responsive to implementation of the change is based on the plurality of correlations between the first parameter and the second plurality of metrics. ([Dutta, [Abstract]] “applying a data mining tool to the attributes and the proposed personnel action to identify an impact of the proposed personnel action” AND [Dutta, [0026]] “can identify the top reasons that contribute positively or negatively in deriving the prediction value”, wherein the examiner interprets applying the data mining tool to the attributes and the proposed personnel action to identify an impact, using the top reasons that contribute positively or negatively in deriving the prediction value, to be the same as a prediction based on the plurality of correlations because they are both predictions derived from the learned relationships between the changed attribute and the outcome measures.) Claim 13 is analogous to claim 4, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 5, Dutta, Fukuda, and Petrosso teaches The system of claim 1 (see rejection of claim 1).
Dutta and Fukuda do not teach wherein the training data is absent information to identify the plurality of entities.
Petrosso teaches wherein the training data is absent information to identify the plurality of entities. ([Petrosso, page 10 col. 7, lines 30-33] “In still further embodiments, the present system may execute one or more anonymization processes on personal data in adherence to the government regulation.”, wherein the examiner interprets executing anonymization processes on personal data to be the same as the training data being absent information to identify the plurality of entities because they are both removal of identifying information from data used by the machine learning processes.)
Dutta, Fukuda, Petrosso, and the instant application are analogous art because they are all directed to analyzing organizational data using learned correlations between variables to predict outcome metrics that inform decisions.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “anonymization processes on personal data” disclosed by Petrosso. One would be motivated to do so to effectively process personal data in compliance with applicable regulations, as suggested by Petrosso ([Petrosso, page 10, col. 7, line 33] “in adherence to the government regulation.”). Claim 14 is analogous to claim 5, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 8, Dutta, Fukuda, and Petrosso teaches The system of claim 1 (see rejection of claim 1).
Dutta further teaches:
wherein the instructions further cause the one or more processors to: prompt, via the display device, a user to select parameters pertaining to the first entity; and ([Dutta, [0066]] “the what-if column of table view 1020 lists any attributes involved in the prediction that a manager or user may want to change.” AND [Dutta, [0031]] “Processor 22 is further coupled via bus 12 to a display 24, such as a Liquid Crystal Display (LCD)”, wherein the examiner interprets the what-if column listing any attributes involved in the prediction that a manager or user may want to change to be the same as prompting a user to select parameters pertaining to the first entity because they are both presentations to the user of the changeable attributes of a particular subject.)
detect, responsive to prompting the user, the selection of the first parameter associated with the first entity. ([Dutta, [0067]] “For example, a manager can change a value in the what-if column of table view 1020 of predictive analytic dashboard user interface 1000 in order to view how that change will effect attrition and performance.”, wherein the examiner interprets the manager changing a value in the what-if column to be the same as detecting the selection of the first parameter responsive to prompting the user because they are both receipt of the user’s choice of an attribute to change from the presented attributes). Claim 17 is analogous to claim 8, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 9, Dutta, Fukuda, and Petrosso teaches The system of claim 8 (see rejection of claim 8).
Dutta and Fukuda do not teach wherein the instructions further cause the one or more processors to prompt the user to select the parameters based on a persona of the user.
Petrosso teaches wherein the instructions further cause the one or more processors to prompt the user to select the parameters based on a persona of the user. ([Petrosso, page 14, col. 16, lines 46-49] “According to some embodiments, functionality of the candidate application is determined based on a particular user account or other user data 215 with which the computing device 205 is associated.” AND [Petrosso, page 10, col. 8, lines 4-5] “user account with which a recruiting agency is associated”, wherein the examiner interprets the user account, such as a user account with which a recruiting agency is associated, to be the same as the persona of the user because they are both user roles that determine the functionality and content presented to the user.)
Dutta, Fukuda, Petrosso, and the instant application are analogous art because they are all directed to analyzing organizational data using learned correlations between variables to predict outcome metrics that inform decisions.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “functionality of the candidate application is determined based on a particular user account” disclosed by Petrosso. One would be motivated to do so to effectively provide each type of user with functionality appropriate to that user, as suggested by Petrosso ([Petrosso, page 14, col. 16, lines 46-49] “According to some embodiments, functionality of the candidate application is determined based on a particular user account or other user data 215 with which the computing device 205 is associated.” ) Claim 18 is analogous to claim 9, aside from claim type and minute differences, and thus the same rejection applies as above.
Claims 2, 3, 11-12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Fukuda in view of Petrosso further in view of US 20190205811 A1 by Carpenter et al. (referred herein as Carpenter).
Regarding claim 2, Dutta, Fukuda, and Petrosso teaches The system of claim 1 (see rejection of claim 1).
Dutta, Fukuda, and Petrosso do not teach wherein the instructions further cause the one or more processors to: generate the training data by monitoring changes to values associated with the plurality of parameters to determine changes to values associated with the plurality of metrics; and, or that segment the training data into a plurality of portions based on a plurality of characteristics of the plurality of entities.
Carpenter teaches wherein the instructions further cause the one or more processors to: generate the training data by monitoring changes to values associated with the plurality of parameters to determine changes to values associated with the plurality of metrics; and ([Carpenter, [0027]] “In one embodiment, the calculation of the turnover rate is based on monitoring the list of employees ( or a subset of the employees) for changes. Such monitoring can have as little as a 24-hour turnaround in processing updates.” AND [Carpenter, [0024]] “can tie well-being program data to measurable people and business results and analyze the impact of a high-energy, high wellbeing workforce in a company.”, wherein the examiner interprets monitoring the list of employees for changes and analyzing the correlation between well-being program data of the workforce to be the same as generating the training data by monitoring changes to values associated with the parameters to determine changes to values associated with the metrics because they are both collection of observed changes in outcome data relative to program data for building a prediction model.)
segment the training data into a plurality of portions based on a plurality of characteristics of the plurality of entities. ([Carpenter, [0024]] “In one or more embodiments of the present invention, employers can review and manipulate interactive charts to understand population turnover trends in, for example, departments, locations, countries, and other categories, by slicing the data by various demographic tags.” AND [Carpenter, [0039]] “In another analysis using sample Limeade® WellBeing Assessment data of more than 500,000 employees, who came from U.S. based employers ranging in size from 1,000-20,000 employees in the healthcare, retail and technology sectors”, wherein the examiner interprets slicing the data by various demographic tags, such as departments, locations, and countries, to be the same as segmenting the training data into a plurality of portions based on a plurality of characteristics because they are both division of the data into subsets according to attributes of the groups the data describes)
Dutta, Fukuda, Petrosso, Carpenter, and the instant application are analogous art because they are all directed to analyzing organizational data using learned correlations between variables to predict outcome metrics that inform decisions.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “analyze the correlation between well-being program data and changes in employee employment status data” disclosed by Carpenter. One would be motivated to do so to effectively identify where employee turnover risk is concentrated, as suggested by Carpenter ([Carpenter, [0023]] “Embodiments of the invention can identify low- and high-risk areas for employee turnover within an organization.”). Claims 11 and 20 are analogous to claim 2, aside from claim type and minute differences, and thus the same rejection applies as above.
Regarding claim 3, Dutta, Fukuda, Petrosso, and Carpenter teaches The system of claim 2 (see rejection of claim 2).
Dutta, Fukuda, and Carpenter do not teach wherein the detection of the plurality of correlations between the first parameter and the second plurality of metrics occurs responsive to the machine learning model identifying characteristics of the first entity.
Petrosso teaches wherein the detection of the plurality of correlations between the first parameter and the second plurality of metrics occurs responsive to the machine learning model identifying characteristics of the first entity. ([Petrosso, page 14, col. 15, lines 35-37] “In various embodiments, the model data 223 may include work culture categories that can be provided as an input to machine learning processes” AND [Petrosso, page 14, col. 15, lines 57-60] “In an alternate example, a work culture category for a ‘Country X’ may cause one or more machine learning models to exclude input data related to job tenure, promotions, and employer reviews.”, wherein the examiner interprets the machine learning models excluding input data according to a work culture category for a country to be the same as the detection of the plurality of correlations occurring responsive to the machine learning model identifying characteristics of the first entity because they are both conditioning of the relationships evaluated by the model on an identified characteristic of the subject.)
Dutta, Fukuda, Petrosso, Carpenter, and the instant application are analogous art because they are all directed to analyzing organizational data using learned correlations between variables to predict outcome metrics that inform decisions.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “work culture categories that can be provided as an input to machine learning processes” disclosed by Petrosso. One would be motivated to do so to effectively limit the model to the input data relevant to the characteristics of the subject, as suggested by Petrosso ([Petrosso, page 14, col. 15, lines 57-60] “In an alternate example, a work culture category for a ‘Country X’ may cause one or more machine learning models to exclude input data related to job tenure, promotions, and employer reviews.”) Claim 12 is analogous to claim 3, aside from claim type and minute differences, and thus the same rejection applies as above.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta, Fukuda, Petrosso, and further in view of WO 2017117150 A1 by McCallum et al. (referred herein as McCallum).
Regarding claim 6, Dutta, Fukuda, and Petrosso teaches The system of claim 1 (see rejection of claim 1).
Petrosso teaches:
wherein the instructions further cause the one or more processors to: detect implementation of the change; ([Petrosso, page 10, col. 8, lines 5-7] “In various embodiments, the system may continuously and/or automatically monitor data sources for changes in candidate data and other information.”, wherein the examiner interprets monitoring data sources for changes in candidate data and other information to be the same as detecting implementation of the change because they are both observation that a change has occurred in the underlying data)
transmit, to the cloud system, a second API call to receive second values associated with the second plurality of metrics; ([Petrosso, page 10, col. 7, lines 62-64] “initiating requests at data sources (e.g., via an application programming interface (API))” AND [Petrosso, page 11, col. 10, lines 56-59] “the computing environment 201 can correspond to an elastic computing resource where the allotted capacity of processing, network, storage, or other computing-related resources may vary over time.”, wherein the examiner interprets initiating requests at data sources via an application programming interface within the elastic computing resource to be the same as transmitting a second API call to the cloud system to receive second values because they are both subsequent programmatic requests for updated data from a remote source.)
Dutta, Fukuda, and Petrosso do not teach compare, responsive to receipt of the second values, the first values to the second values;, determine, responsive to comparison of the first values and the second values, a plurality of differences;, identify, based on the plurality of differences and the plurality of changes, a plurality of results with respect to the prediction; and, or that update, based on the plurality of results with respect to the prediction, the machine learning model to adjust an efficacy of the machine learning model.
McCallum teaches:
compare, responsive to receipt of the second values, the first values to the second values; ([McCallum, [0089]] “the machine learning system establishes a baseline by analyzing historical data of the employer from two to three years back” AND [McCallum, [0092]] “An artificial intelligence tool compares predicted results to outcomes utilizing predictive analytics.”, wherein the examiner interprets the baseline established from historical data of the employer and the outcomes compared by the artificial intelligence tool to be the same as the first values and the second values because they are both metric values before and after the evaluated period that are compared.)
determine, responsive to comparison of the first values and the second values, a plurality of differences; ([McCallum, [0092]] “An artificial intelligence tool compares predicted results to outcomes utilizing predictive analytics.”, wherein the examiner interprets comparing predicted results to outcomes to be the same as determining a plurality of differences because they are both determination of the deviation between the compared values.)
identify, based on the plurality of differences and the plurality of changes, a plurality of results with respect to the prediction; and ([McCallum, [0092]] “An artificial intelligence tool compares predicted results to outcomes utilizing predictive analytics.” AND [McCallum, [0111]] “The predictive analytics applies rules through a matching, refinement and redefining of rules based on the artificial intelligence”, wherein the examiner interprets the comparison of predicted results to outcomes used for refinement of rules to be the same as identifying a plurality of results with respect to the prediction because they are both assessments of how the actual outcomes matched the prediction.)
update, based on the plurality of results with respect to the prediction, the machine learning model to adjust an efficacy of the machine learning model. ([McCallum, [0111]] “the predictive analytics applies rules through a matching, refinement and redefining of rules based on the artificial intelligence” AND [McCallum, [0088]] “a high value provider may lose that designation as quality scores are adjusted based on input to the machine learning system.”, wherein the examiner interprets the refinement and redefining of rules and the adjustment of quality scores based on input to the machine learning system to be the same as updating the machine learning model to adjust an efficacy because they are both modification of the model based on observed results to improve its outputs.)
Dutta, Fukuda, Petrosso, McCallum, and the instant application are analogous art because they are all directed to analyzing organizational data using learned correlations between variables to predict outcome metrics that inform decisions.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “continuously and/or automatically monitor data sources for changes” disclosed by Petrosso. One would be motivated to do so to efficiently generate updated predictions when the underlying data changes, as suggested by Petrosso ([Petrosso, page 10, col. 8, lines 18-21] “re-training one or more machine learning models to account for the changed data, and re-executing one or more machine learning processes to generate updated predictions”).
It would have also been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “An artificial intelligence tool compares predicted results to outcomes” disclosed by McCallum. One would be motivated to do so to effectively refine the model based on how well its predictions matched actual outcomes, as suggested by McCallum ([McCallum, [0111]] “the predictive analytics applies rules through a matching, refinement and redefining of rules based on the artificial intelligence”). Claim 15 is analogous to claim 6, aside from claim type and minute differences, and thus the same rejection applies as above.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta, Fukuda, Petrosso, Carpenter, and further in view of the NPL reference “The What-If Tool: Interactive Probing of Machine Learning Models” by Wexler et al. (referred herein as Wexler).
Regarding claim 7, Dutta, Fukuda, and Petrosso teaches The system of claim 1 (see rejection of claim 1).
Dutta further teaches:
wherein the user interface to identify the plurality of changes includes: graphical representations to identify the plurality of correlations between the first parameter and the second plurality of metrics; ([Dutta, [0062]] “table 810 that illustrates contributing factors for the predicted attrition or performance, the current value of that factor, and the level of contribution of that factor” AND [Dutta, [0056]] “The predicted impact is provided to the user via a graphical user interface, such as a table or graph.”, wherein the examiner interprets the table illustrating the contributing factors and the level of contribution of each factor to the predicted attrition or performance to be the same as graphical representations to identify the plurality of correlations because they are both displayed depictions of how strongly each attribute relates to the outcome measures.)
Dutta, Fukuda, and Petrosso do not teach graphical representations to identify a trend with respect to the first parameter; and graphical representations to identify a trend with respect to a second plurality of entities.
Wexler teaches graphical representations to identify a trend with respect to the first parameter; and ([Wexler, page 6, sec. 4.2.3] “partial dependence plots, which show how model predictions change as the value of a specific feature is adjusted for a given data point” AND [Wexler, page 5, sec. 4.2.1] “To conduct iterative what-if analyses, the user can edit, add, or delete individual feature values or entire features within an instance in the editor module and see the effect those changes have on the model prediction for that datapoint”, wherein the examiner interprets the partial dependence plots showing how model predictions change as the value of a specific feature is adjusted to be the same as graphical representations to identify a trend with respect to the first parameter because they are both plots of how the predicted outcome varies across values of a single input.)
Dutta, Fukuda, Petrosso, and Wexler do not teach graphical representations to identify a trend with respect to a second plurality of entities.
Carpenter teaches graphical representations to identify a trend with respect to a second plurality of entities. ([Carpenter, [0024]] “In one or more embodiments of the present invention, employers can review and manipulate interactive charts to understand population turnover trends in, for example, departments, locations, countries, and other categories, by slicing the data by various demographic tags.”, wherein the examiner interprets the interactive charts showing population turnover trends in departments, locations, and countries to be the same as graphical representations to identify a trend with respect to a second plurality of entities because they are both charts of outcome trends across multiple groups)
Dutta, Fukuda, Petrosso, Carpenter, Wexler, and the instant application are analogous art because they are all directed to analyzing organizational data using learned correlations between variables to predict outcome metrics that inform decisions.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “interactive charts to understand population turnover trends” disclosed by Carpenter. One would be motivated to do so to effectively compare outcome trends across groups so that practices from better-performing groups can be applied, as suggested by Carpenter ([Carpenter, [0023]] “By identifying these groups, human resource leaders can share best practices from low turnover groups”).
It would have also been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system for predicting the impact of a proposed personnel action on worker attrition and performance disclosed by Dutta to include the “partial dependence plots” disclosed by Wexler. One would be motivated to do so to efficiently show the effect of an input across its full range of values, as suggested by Wexler ([Wexler, page 6, sec. 4.2.3] “Often practitioners have questions about the effect of a feature across an entire range of values.”). Claim 16 is analogous to claim 7, aside from claim type and minute differences, and thus the same rejection applies as above.
Conclusion
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/DEVAN KAPOOR/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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