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 .
Response to Amendment
The amendment filed March 2, 2026 has been entered. Claims 1-20 remain pending in the application. Claims 1, 8-10, 16-17, and 19 have been amended.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3 and 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220292999 A1 (“Kratzer”) in view of US 20160358498 A1 (“Fucke”), US 20140108394 A1 (“Fleming”), US 20180307801 A1 (“Hardee”), and US 20170256172 A1 (“Kil”).
In regards to claim 1, Kratzer discloses the following limitations with the exception of the underlined limitations.
a method comprising: receiving a first training performance data set from an automated training system configured to ([0022], “the subject disclosure include receiving … performance data for … training”) simulate flight conditions;
analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set comprising performance threshold data for flight simulation exercises, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set;
determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
determining a second metric based on the distribution of performance values;
generating a training modification recommendation for the automated training system based at least on the correlation, the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications, automated curriculum adjustments to improve training outcomes, and an alert indicating that flight training performance fails to satisfy a performance threshold ([0053], “Upon a detection of inventory levels for a particular component exceeding a threshold value, the real time training system … may provide to an inventory manager and employees particular training”);
and communicating the training modification recommendation to the automated training system to automatically implement modified flight simulation parameters.
Fucke discloses
simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”),
comprising performance threshold data for flight simulation exercises ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance”);
determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance” Examiner notes that evaluating all elements of flight crew performance may include determining performance value distribution and determining performance asymmetry relative to benchmark calculations.);
the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes ([0014], “stored student data might be used to update previously stored flight performance standards … the method may adapt the current training session by modifying some of its parameters”),
to automatically implement modified flight simulation parameters ([0010], The training session … may be automatically generated by a processor of the flight simulator, when a set of training parameters are provided).
Kratzer and Fucke are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, simulate flight conditions, comprising performance threshold data for flight simulation exercises; determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks; the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes, to automatically implement modified flight simulation parameters, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, stored student data, a training session, and a processor for a flight crew training method. One skilled in the art would understand and recognize the value of the addition of a flight simulator, flight crew performance elements, stored student data, a training session, and a processor for a flight crew training method.
Fleming discloses
analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set ([0054], “the method includes … trainer performance evaluation and correlating the … attained score for … trainer performance … to … standardized trainer performance level”)
Kratzer and Fleming are considered analogous to the claimed invention because they are in the same field of training and development programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set, as disclosed by Fleming, to provide a method that includes trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program. One skilled in the art would understand and recognize the value of the addition of trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program.
Hardee discloses
wherein analyzing the first training performance data set to determine the correlation ([0015], “The … embodiments utilize a … computing system to analyze the physical features and other characteristics … and correlates those physical features and characteristics with similar characteristics”) comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as averages, medians, … and the like” Examiner notes that a concordance correlation coefficient uses statistical values (means and variances) of the two variables being compared.);
determining a second metric based on the distribution of performance values ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as … distributions”);
generating a training modification recommendation for the automated training system based at least on the correlation ([0140], “Based on the correlation, areas of … insufficient performance are identified … and a training regimen and/or exercises … are generated … to the user”);
and communicating the training modification recommendation to the automated training system ([0140], “training regimen and/or exercises … are … output to the user”)
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determining a second metric based on the distribution of performance values, generating a training modification recommendation for the automated training system based at least on the correlation and communicating the training modification recommendation to the automated training system, as disclosed by Hardee, to provide a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system.
Kil discloses
wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance ([0086], “Meta features describe the … distributions, such as, … skewness”);
and an alert indicating ([0064], “delivered … interventions are in the form of … messages” Examiner notes that an alert is a type of message that conveys important or time-sensitive information, often requiring immediate action from the user.);
Kratzer and Kil are considered analogous to the claimed invention because they are in similar fields of training and student analytics programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, and an alert indicating, as disclosed by Kil, to provide distributions such as skewness and messages for a student data-to-insight-to-action-to-learning analytics system. One skilled in the art would understand and recognize the value of the addition of distributions such as skewness and messages for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 2, Kratzer discloses
wherein the training data comparison set comprises a second training performance data set ([0088], “course effectiveness data may be based on comparisons of learner data before taking the course and after taking the course”).
In regards to claim 3, Kratzer does not disclose wherein the first training performance data set comprises training data associated with a first group of users of the automated training system; and the second training performance data set comprises training data associated with a second group of users of the automated training system.
Fleming discloses
wherein the first training performance data set comprises training data associated with a first group of users of the automated training system ([0054], “Trainer performance data is … capable of assessing a plurality of trainer performance evaluation components” Examiner notes that the first group of users can be trainers.);
and the second training performance data set comprises training data associated with a second group of users of the automated training system ([0019], “various aspects include … subscriber performance data for a plurality of … subscribers” Examiner notes that the second group of users can be subscribers.).
Kratzer and Fleming are considered analogous to the claimed invention because they are in the same field of training and development programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set, as disclosed by Fleming, to provide a method that includes trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program. One skilled in the art would understand and recognize the value of the addition of trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program.
In regards to claim 5, Kratzer does not disclose wherein the first group of users is associated with a first geographical area and the second group of users is associated with a second geographical area.
Hardee discloses
wherein the first group of users is associated with a first geographical area ([0017], “The cognitive system further ingests … data regarding a geographical area designated by the user.”)
and the second group of users is associated with a second geographical area ([0017], “The cognitive system further ingests … data regarding a geographical area designated by the user.”).
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determining a second metric based on the distribution of performance values, generating a training modification recommendation for the automated training system based at least on the correlation and communicating the training modification recommendation to the automated training system, wherein the first group of users is associated with a first geographical area and the second group of users is associated with a second geographical area, as disclosed by Hardee, to provide a computing system, analysis algorithms, insufficient performance correlations and training exercises, and geographical area data for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, and insufficient performance correlations and training exercises, and geographical area data for mechanisms that are provided to implement a personalized training recommendation system.
In regards to claim 6, Kratzer does not disclose wherein the first group of users is associated with a first instructor and the second group of users is associated with a second instructor.
Fucke discloses
wherein the first group of users is associated with a first instructor ([0036], “During the training session … instructors … may retrieve stored user data”) and the second group of users is associated with a second instructor ([0036], “During the training session … instructors … may retrieve stored user data”).
Kratzer and Fucke are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, simulate flight conditions, comprising performance threshold data for flight simulation exercises; determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks; the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes, to automatically implement modified flight simulation parameters, wherein the first group of users is associated with a first instructor and the second group of users is associated with a second instructor, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, stored student data, a training session, a processor, and stored user data for a flight crew training method. One skilled in the art would understand and recognize the value of the addition of a flight simulator, flight crew performance elements, stored student data, a training session, a processor, and stored user data for a flight crew training method.
In regards to claim 7, Kratzer does not disclose wherein the first group of users is associated with a first training location and the second group of users is associated with a second training location.
Hardee discloses
wherein the first group of users is associated with a first training location ([0017], “The cognitive system further ingests … data regarding a … user's … location.”)
and the second group of users is associated with a second training location ([0017], “The cognitive system further ingests … data regarding a … user's … location.”).
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determining a second metric based on the distribution of performance values, generating a training modification recommendation for the automated training system based at least on the correlation and communicating the training modification recommendation to the automated training system, wherein the first group of users is associated with a first training location and the second group of users is associated with a second training location, as disclosed by Hardee, to provide a computing system, analysis algorithms, insufficient performance correlations and training exercises, and location data for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, insufficient performance correlations and training exercises, and location data for mechanisms that are provided to implement a personalized training recommendation system.
In regards to claim 8, Kratzer does not disclose wherein the second metric comprises a kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance.
Kil discloses
wherein the second metric comprises a kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance ([0086], “Meta features describe the … distributions … and shape statistics, such as … kurtosis”).
Kratzer and Kil are considered analogous to the claimed invention because they are in similar fields of training and student analytics programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, and an alert indicating, wherein the second metric comprises a kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, as disclosed by Kil, to provide distributions such as skewness, messages, and kurtosis for a student data-to-insight-to-action-to-learning analytics system. One skilled in the art would understand and recognize the value of the addition of distributions such as skewness, messages, and kurtosis for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 9, Kratzer discloses the following limitation with the exception of the underlined limitation.
wherein generating the training modification recommendation is based on identifying outlier performance patterns indicated by the kurtosis metric exceeding a predetermined threshold ([0053], “Upon a detection of inventory levels for a particular component exceeding a threshold value, the real time training system … may provide to an inventory manager and employees particular training”).
Kil discloses
wherein generating the training modification recommendation is based on identifying outlier performance patterns indicated by the kurtosis metric exceeding ([0086], “model builder … builds … models for each student-success metric and intervention program … models are selected … by extracting meta features on … distributions … Meta features describe the … distributions, such as, but not limited to, modes, degree of overlap, … and shape statistics, such as mean, standard deviation, skewness, and kurtosis”).
Kratzer and Kil are considered analogous to the claimed invention because they are in similar fields of training and student analytics programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, and an alert indicating, wherein the second metric comprises a kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, wherein generating the training modification recommendation is based on identifying outlier performance patterns indicated by the kurtosis metric exceeding, as disclosed by Kil, to provide distributions such as skewness, messages, and kurtosis for a student data-to-insight-to-action-to-learning analytics system. One skilled in the art would understand and recognize the value of the addition of distributions such as skewness, messages, and kurtosis for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 10, Kratzer discloses
wherein the training modification recommendation comprises a recommendation to update training material, or an indication of a corrective action associated with one or more users of the automated training system ([0023], “aspects of the subject disclosure include receiving key performance indicator (KPI) performance data … and recommending a training course for the employee, wherein the recommending is based on the KPI performance data” Examiner notes that the key performance indicator can be used for an alert.).
In regards to claim 11, Kratzer discloses
wherein the training modification recommendation comprises a training performance report ([0084], “performance reports are generated for the real time training system”).
In regards to claim 12, Kratzer discloses the following limitations with the exception of the underlined limitation.
wherein the training performance report comprises a graphical representation ([0088], “feedback information may be in graphical format, such as bar charts and pie charts”) based at least on the correlation, the method further comprising ([0048], “analytics include … whether there are correlations to … post-training performance”):
analyzing the first training performance data set to determine one or more values of a first training metric based on the first training performance data set ([0107], “percentile … values provide an indication of whether an employee's performance has improved … in response to the training the employee has received”);
analyzing the first training performance data set to determine one or more values of a second training metric based on the first training performance data set ([0110], “goal values … may be established … attainment percentile data … may be obtained … attainment percentile data … may be used to identify underperformers, or employees whose performance does not meet the goal”);
and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric.
Hardee discloses
and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric ( [0091], “training course information may be output in a graphical manner on the client computing device” Examiner notes that the graphical manner may include the first training metric on the first axis and the second training metric on the second axis.).
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, wherein the training performance report comprises a graphical representation based at least on the correlation, the method further comprising: analyzing the first training performance data set to determine one or more values of a first training metric based on the first training performance data set;
analyzing the first training performance data set to determine one or more values of a second training metric based on the first training performance data set, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determining a second metric based on the distribution of performance values, generating a training modification recommendation for the automated training system based at least on the correlation and communicating the training modification recommendation to the automated training system, and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric, wherein the first training metric is the skewness metric based on the distribution of performance values, as disclosed by Hardee, to provide a computing system, analysis algorithms, insufficient performance correlations and training exercises, and training course information for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, insufficient performance correlations and training exercises, and training course information for mechanisms that are provided to implement a personalized training recommendation system.
In regards to claim 13, Kratzer does not disclose wherein the first training metric is the skewness metric based on the distribution of performance values.
Kil discloses
wherein the first training metric is the skewness metric based on the distribution of performance values ([0086], “Meta features describe the … distributions, such as, … skewness”).
Kratzer and Kil are considered analogous to the claimed invention because they are in similar fields of training and student analytics programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, and an alert indicating, wherein the first training metric is the skewness metric based on the distribution of performance values, as disclosed by Kil, to provide distributions such as skewness and messages for a student data-to-insight-to-action-to-learning analytics system. One skilled in the art would understand and recognize the value of the addition of distributions such as skewness and messages for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 14, Kratzer does not disclose wherein the second training metric is a kurtosis metric based on the distribution of performance values.
Kil discloses
wherein the second training metric is a kurtosis metric based on the distribution of performance values ([0086], “Meta features describe the … distributions, such as, … kurtosis”).
Kratzer and Kil are considered analogous to the claimed invention because they are in similar fields of training and student analytics programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, and an alert indicating, wherein the first training metric is the skewness metric based on the distribution of performance values, wherein the second training metric is a kurtosis metric based on the distribution of performance values, as disclosed by Kil, to provide distributions such as skewness, messages, and kurtosis for a student data-to-insight-to-action-to-learning analytics system. One skilled in the art would understand and recognize the value of the addition of distributions such as skewness, messages, and kurtosis for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 15, Kratzer does not disclose wherein the first training metric or the second training metric is a mathematical moment metric based on the distribution of performance values.
Kil discloses
wherein the first training metric or the second training metric is a mathematical moment metric based on the distribution of performance values ([0086], “Meta features describe … shape statistics” Examiner notes that a mathematical moment is a measure that describes the shape of a distribution of a set of points.).
Kratzer and Kil are considered analogous to the claimed invention because they are in similar fields of training and student analytics programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, and an alert indicating, wherein the first training metric or the second training metric is a mathematical moment metric based on the distribution of performance values, as disclosed by Kil, to provide distributions such as skewness, messages, and shape statistics for a student data-to-insight-to-action-to-learning analytics system. One skilled in the art would understand and recognize the value of the addition of distributions such as skewness, messages, and shape statistics for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 16, Kratzer does not disclose wherein the concordance correlation coefficient measures both precision and accuracy of agreement between the first training performance data set and the training data comparison set.
Hardee discloses
wherein the concordance correlation coefficient measures both precision and accuracy of agreement ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as averages, medians, … and the like” Examiner notes that a concordance correlation coefficient uses statistical values (means and variances) of the two variables being compared.).
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determining a second metric based on the distribution of performance values, generating a training modification recommendation for the automated training system based at least on the correlation and communicating the training modification recommendation to the automated training system, wherein the concordance correlation coefficient measures both precision and accuracy of agreement, as disclosed by Hardee, to provide a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system.
Fleming discloses
between the first training performance data set and the training data comparison set ([0054], “the method includes … trainer performance evaluation and correlating the … attained score for … trainer performance … to … standardized trainer performance level”).
Kratzer and Fleming are considered analogous to the claimed invention because they are in the same field of training and development programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set, between the first training performance data set and the training data comparison set, as disclosed by Fleming, to provide a method that includes trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program. One skilled in the art would understand and recognize the value of the addition of trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program.
In regards to claim 17, Kratzer discloses the following limitations with the exception of the underlined limitations.
A system comprising: a memory configured to store instructions ([0069], “The workflow … may be implemented in any suitable data processing system … including … memory”);
and one or more processors configured to ([0069], “The workflow … may be implemented in any suitable data processing system … including a processor”):
receive a first training performance data set from an automated training system configured to ([0022], “the subject disclosure include receiving … performance data for … training”) simulate flight conditions;
analyze the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set comprising performance threshold data for flight simulation exercises, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set;
determine a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
determine a second metric based on the distribution of performance values;
generate a training modification recommendation for the automated training system based at least on the correlation, the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications, automated curriculum adjustments to improve training outcomes, and an alert indicating that flight training performance fails to satisfy a performance threshold ([0053], “Upon a detection of inventory levels for a particular component exceeding a threshold value, the real time training system … may provide to an inventory manager and employees particular training”);
and communicate the training modification recommendation to the automated training system to automatically implement modified flight simulation parameters.
Fucke discloses
simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”),
comprising performance threshold data for flight simulation exercises ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance”);
determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance” Examiner notes that evaluating all elements of flight crew performance may include determining performance value distribution and determining performance asymmetry relative to benchmark calculations.);
the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes ([0014], “stored student data might be used to update previously stored flight performance standards … the method may adapt the current training session by modifying some of its parameters”),
to automatically implement modified flight simulation parameters ([0010], The training session … may be automatically generated by a processor of the flight simulator, when a set of training parameters are provided).
Kratzer and Fucke are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system comprising: a memory configured to store instructions; and one or more processors configured to: receive a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, simulate flight conditions, comprising performance threshold data for flight simulation exercises; determine a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks; the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes, to automatically implement modified flight simulation parameters, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, stored student data, a training session, and a processor for a flight crew training method. One skilled in the art would understand and recognize the value of the addition of a flight simulator, flight crew performance elements, stored student data, a training session, and a processor for a flight crew training method.
Fleming discloses
analyze the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set ([0054], “the method includes … trainer performance evaluation and correlating the … attained score for … trainer performance … to … standardized trainer performance level”)
Kratzer and Fleming are considered analogous to the claimed invention because they are in the same field of training and development programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system comprising: a memory configured to store instructions; and one or more processors configured to: receive a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, analyze the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set, as disclosed by Fleming, to provide a method that includes trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program. One skilled in the art would understand and recognize the value of the addition of trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program.
Hardee discloses
wherein analyzing the first training performance data set to determine the correlation ([0015], “The … embodiments utilize a … computing system to analyze the physical features and other characteristics … and correlates those physical features and characteristics with similar characteristics”) comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as averages, medians, … and the like” Examiner notes that a concordance correlation coefficient uses statistical values (means and variances) of the two variables being compared.);
determine a second metric based on the distribution of performance values ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as … distributions”);
generate a training modification recommendation for the automated training system based at least on the correlation ([0140], “Based on the correlation, areas of … insufficient performance are identified … and a training regimen and/or exercises … are generated … to the user”);
and communicate the training modification recommendation to the automated training system ([0140], “training regimen and/or exercises … are … output to the user”)
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system comprising: a memory configured to store instructions; and one or more processors configured to: receive a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determine a second metric based on the distribution of performance values, generate a training modification recommendation for the automated training system based at least on the correlation and communicate the training modification recommendation to the automated training system, as disclosed by Hardee, to provide a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system.
Kil discloses
wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance ([0086], “Meta features describe the … distributions, such as, … skewness”);
and an alert indicating ([0064], “delivered … interventions are in the form of … messages” Examiner notes that an alert is a type of message that conveys important or time-sensitive information, often requiring immediate action from the user.);
Kratzer and Kil are considered analogous to the claimed invention because they are in similar fields of training and student analytics programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system comprising: a memory configured to store instructions; and one or more processors configured to: receive a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, and an alert indicating, as disclosed by Kil, to provide distributions such as skewness and messages for a student data-to-insight-to-action-to-learning analytics system. One skilled in the art would understand and recognize the value of the addition of distributions such as skewness and messages for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 18, Kratzer discloses the following limitations with the exception of the underlined limitation.
wherein the training modification recommendation comprises a graphical representation ([0088], “feedback information may be in graphical format, such as bar charts and pie charts”) based at least on the correlation ([0048], “analytics include … whether there are correlations to … post-training performance”),
the one or more processors further configured to ([0069], “The workflow … may be implemented in any suitable data processing system … including a processor”):
analyze the first training performance data set to determine one or more values of a first training metric based on the first training performance data set ([0107], “percentile … values provide an indication of whether an employee's performance has improved … in response to the training the employee has received”);
analyze the first training performance data set to determine one or more values of a second training metric based on the first training performance data set ([0110], “goal values … may be established … attainment percentile data … may be obtained … attainment percentile data … may be used to identify underperformers, or employees whose performance does not meet the goal”).
and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric.
Hardee discloses
and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric ([0091], “training course information may be output in a graphical manner on the client computing device” Examiner notes that the graphical manner may include a first metric on a first axis information and a second metric on a second axis information.).
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system comprising: a memory configured to store instructions; and one or more processors configured to: receive a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, wherein the training modification recommendation comprises a graphical representation based at least on the correlation, the one or more processors further configured to: analyze the first training performance data set to determine one or more values of a first training metric based on the first training performance data set;
analyze the first training performance data set to determine one or more values of a second training metric based on the first training performance data set, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determine a second metric based on the distribution of performance values, generate a training modification recommendation for the automated training system based at least on the correlation and communicate the training modification recommendation to the automated training system, and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric, as disclosed by Hardee, to provide a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system.
In regards to claim 19, Kratzer discloses the following limitations with the exception of the underlined limitations.
A non-transient, computer-readable medium storing instructions executable by ([0166], “computer-readable storage media provide … storage of data, data structures, computer-executable instructions”) one or more processors to perform operations comprising: [0069], “The workflow … may be implemented in any suitable data processing system … including a processor”): receiving a first training performance data set from an automated training system configured to ([0022], “the subject disclosure include receiving … performance data for … training”) simulate flight conditions;
analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set comprising performance threshold data for flight simulation exercises, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set;
determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
determining a second metric based on the distribution of performance values;
generating a training modification recommendation for the automated training system based at least on the correlation, the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications, automated curriculum adjustments to improve training outcomes, and an alert indicating that flight training performance fails to satisfy a performance threshold ([0053], “Upon a detection of inventory levels for a particular component exceeding a threshold value, the real time training system … may provide to an inventory manager and employees particular training”);
and communicating the training modification recommendation to the automated training system to automatically implement modified flight simulation parameters.
Fucke discloses
simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”),
comprising performance threshold data for flight simulation exercises ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance”);
determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance” Examiner notes that evaluating all elements of flight crew performance may include determining performance value distribution and determining performance asymmetry relative to benchmark calculations.);
the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes ([0014], “stored student data might be used to update previously stored flight performance standards … the method may adapt the current training session by modifying some of its parameters”),
to automatically implement modified flight simulation parameters ([0010], The training session … may be automatically generated by a processor of the flight simulator, when a set of training parameters are provided).
Kratzer and Fucke are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a non-transient, computer-readable medium storing instructions executable by one or more processors to perform operations comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, simulate flight conditions, comprising performance threshold data for flight simulation exercises; determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks; the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes, to automatically implement modified flight simulation parameters, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, stored student data, a training session, and a processor for a flight crew training method. One skilled in the art would understand and recognize the value of the addition of a flight simulator, flight crew performance elements, stored student data, a training session, and a processor for a flight crew training method.
Fleming discloses
analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set ([0054], “the method includes … trainer performance evaluation and correlating the … attained score for … trainer performance … to … standardized trainer performance level”)
Kratzer and Fleming are considered analogous to the claimed invention because they are in the same field of training and development programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a non-transient, computer-readable medium storing instructions executable by one or more processors to perform operations comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set, as disclosed by Fleming, to provide a method that includes trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program. One skilled in the art would understand and recognize the value of the addition of trainer performance evaluation and score correlation for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program.
Hardee discloses
wherein analyzing the first training performance data set to determine the correlation ([0015], “The … embodiments utilize a … computing system to analyze the physical features and other characteristics … and correlates those physical features and characteristics with similar characteristics”) comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as averages, medians, … and the like” Examiner notes that a concordance correlation coefficient uses statistical values (means and variances) of the two variables being compared.);
determining a second metric based on the distribution of performance values ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as … distributions”);
generating a training modification recommendation for the automated training system based at least on the correlation ([0140], “Based on the correlation, areas of … insufficient performance are identified … and a training regimen and/or exercises … are generated … to the user”);
and communicating the training modification recommendation to the automated training system ([0140], “training regimen and/or exercises … are … output to the user”)
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a non-transient, computer-readable medium storing instructions executable by one or more processors to perform operations comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determining a second metric based on the distribution of performance values, generating a training modification recommendation for the automated training system based at least on the correlation and communicating the training modification recommendation to the automated training system, as disclosed by Hardee, to provide a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, and insufficient performance correlations and training exercises for mechanisms that are provided to implement a personalized training recommendation system.
Kil discloses
wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance ([0086], “Meta features describe the … distributions, such as, … skewness”);
and an alert indicating ([0064], “delivered … interventions are in the form of … messages” Examiner notes that an alert is a type of message that conveys important or time-sensitive information, often requiring immediate action from the user.);
Kratzer and Kil are considered analogous to the claimed invention because they are in similar fields of training and student analytics programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a non-transient, computer-readable medium storing instructions executable by one or more processors to perform operations comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance, and an alert indicating, as disclosed by Kil, to provide distributions such as skewness and messages for a student data-to-insight-to-action-to-learning analytics system. One skilled in the art would understand and recognize the value of the addition of distributions such as skewness and messages for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 20, Kratzer discloses the following limitations with the exception of the underlined limitation.
wherein the training modification recommendation comprises a graphical representation ([0088], “feedback information may be in graphical format, such as bar charts and pie charts”) based at least on the correlation, the operations further comprising ([0048], “analytics include … whether there are correlations to … post-training performance”):
analyzing the first training performance data set to determine one or more values of a first training metric based on the first training performance data set ([0107], “percentile … values provide an indication of whether an employee's performance has improved … in response to the training the employee has received”);
analyzing the first training performance data set to determine one or more values of a second training metric based on the first training performance data set ([0110], “goal values … may be established … attainment percentile data … may be obtained … attainment percentile data … may be used to identify underperformers, or employees whose performance does not meet the goal”);
and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric.
Hardee discloses
and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric ( [0091], “training course information may be output in a graphical manner on the client computing device” Examiner notes that the graphical manner may include the first training metric on the first axis and the second training metric on the second axis.).
Kratzer and Hardee are considered analogous to the claimed invention because they are in the same field of training programs. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a non-transient, computer-readable medium storing instructions executable by one or more processors to perform operations comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, wherein the training modification recommendation comprises a graphical representation based at least on the correlation, the operations further comprising: analyzing the first training performance data set to determine one or more values of a first training metric based on the first training performance data set; analyzing the first training performance data set to determine one or more values of a second training metric based on the first training performance data set, as disclosed by Kratzer, wherein analyzing the first training performance data set to determine the correlation comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set; determining a second metric based on the distribution of performance values, generating a training modification recommendation for the automated training system based at least on the correlation and communicating the training modification recommendation to the automated training system, and wherein a first axis of the graphical representation is associated with the first training metric and a second axis of the graphical representation is associated with the second training metric, as disclosed by Hardee, to provide a computing system, analysis algorithms, insufficient performance correlations and training exercises, and training course information for mechanisms that are provided to implement a personalized training recommendation system. One skilled in the art would understand and recognize the value of the addition of a computing system, analysis algorithms, insufficient performance correlations and training exercises, and training course information for mechanisms that are provided to implement a personalized training recommendation system.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kratzer in view of Fucke, Fleming, Hardee, and Kil, and US 20110111385 A1 (“Thiruvengada”).
In regards to claim 4, Kratzer does not disclose wherein the first group of users is associated with training in a first training curriculum, and the second group of users is associated with the first group of users training in a second training curriculum.
Thiruvengada discloses
wherein the first group of users is associated with training in a first training curriculum ([0022], “FIG. 5 illustrates a functional block diagram of a training system that provides … dynamic curriculum adjustment” Examiner notes that a dynamic curriculum is a flexible and adaptable curriculum which can accommodate the needs of the first group of users.),
and the second group of users is associated with the first group of users training in a second training curriculum ([0022], “FIG. 5 illustrates a functional block diagram of a training system that provides … dynamic curriculum adjustment” Examiner notes that a dynamic curriculum is a flexible and adaptable curriculum which can accommodate the needs of the second group of users.).
Kratzer and Thiruvengada are considered analogous to the claimed invention because they are in the same field of training programs and systems. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: receiving a first training performance data set from an automated training system configured to, that flight training performance fails to satisfy a performance threshold, as disclosed by Kratzer, wherein the first group of users is associated with training in a first training curriculum, and the second group of users is associated with the first group of users training in a second training curriculum, as disclosed by Thiruvengada, to provide dynamic curriculum adjustment for an automated training system and method based on performance evaluation. One skilled in the art would understand and recognize the value of the addition of dynamic curriculum adjustment for an automated training system and method based on performance evaluation.
Response to Arguments
Applicant's arguments filed March 2, 2026 have been fully considered but after Examiner’s further search, the prior art disclosed the amended subject matter. Applicant amended claims 1, 8-10, 16-17, and 19. Claims 1-20 are pending in this application.
With respect to amended claim 1, Applicant argues “the cited portions of Kratzer, Fleming, Kil, Hardee, Fucke, and Thiuvengada fail to disclose each and every element of claim 1” (See RESPONSE TO NON-FINAL OFFICE ACTION, REMARKS, Claims 1-16, page 9, paragraph 1.) Examiner acknowledges Applicant’s remarks. Regarding claim 1, Kratzer discloses a method comprising: receiving a first training performance data set from an automated training system configured to ([0022], “the subject disclosure include receiving … performance data for … training”) and that flight training performance fails to satisfy a performance threshold ([0053], “Upon a detection of inventory levels for a particular component exceeding a threshold value, the real time training system … may provide to an inventory manager and employees particular training”), Fucke discloses simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”), comprising performance threshold data for flight simulation exercises ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance”); determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance” Examiner notes that evaluating all elements of flight crew performance may include determining performance value distribution and determining performance asymmetry relative to benchmark calculations.); the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes ([0014], “stored student data might be used to update previously stored flight performance standards … the method may adapt the current training session by modifying some of its parameters”), to automatically implement modified flight simulation parameters ([0010], The training session … may be automatically generated by a processor of the flight simulator, when a set of training parameters are provided), Fleming discloses analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set ([0054], “the method includes … trainer performance evaluation and correlating the … attained score for … trainer performance … to … standardized trainer performance level”), Hardee discloses wherein analyzing the first training performance data set to determine the correlation ([0015], “The … embodiments utilize a … computing system to analyze the physical features and other characteristics … and correlates those physical features and characteristics with similar characteristics”) comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as averages, medians, … and the like” Examiner notes that a concordance correlation coefficient uses statistical values (means and variances) of the two variables being compared.); determining a second metric based on the distribution of performance values ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as … distributions”); generating a training modification recommendation for the automated training system based at least on the correlation ([0140], “Based on the correlation, areas of … insufficient performance are identified … and a training regimen and/or exercises … are generated … to the user”); and communicating the training modification recommendation to the automated training system ([0140], “training regimen and/or exercises … are … output to the user”), and Kil discloses wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance ([0086], “Meta features describe the … distributions, such as, … skewness”); and an alert indicating ([0064], “delivered … interventions are in the form of … messages” Examiner notes that an alert is a type of message that conveys important or time-sensitive information, often requiring immediate action from the user.).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during
examination. Under BRI, the words of a claim must be given their plain meaning unless
such meaning is inconsistent with the specification, and it is improper to import claim
limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejection of amended claim 1, as obvious over Kratzer in view of Fucke, Fleming, Hardee, and Kil is maintained. Consequently, the rejections of dependent claims 2-16 are maintained.
With respect to amended claim 17, Applicant argues “the cited portions of Kratzer, Fleming, Kil, Hardee, Fucke, and Thiuvengada fail to disclose each and every element of claim 17” (See RESPONSE TO NON-FINAL OFFICE ACTION, REMARKS, Claims 17 and 18, page 10, paragraph 1.) Examiner acknowledges Applicant’s remarks. Regarding claim 17, Kratzer discloses a system comprising: a memory configured to store instructions; and one or more processors configured to: receive a first training performance data set from an automated training system configured to ([0022], “the subject disclosure include receiving … performance data for … training”) and that flight training performance fails to satisfy a performance threshold ([0053], “Upon a detection of inventory levels for a particular component exceeding a threshold value, the real time training system … may provide to an inventory manager and employees particular training”), Fucke discloses simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”), comprising performance threshold data for flight simulation exercises ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance”); determine a distribution of performance values based on the first training performance data set and calculate a first metric indicating asymmetry in performance relative to desired training benchmarks ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance” Examiner notes that evaluating all elements of flight crew performance may include determining performance value distribution and determining performance asymmetry relative to benchmark calculations.); the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes ([0014], “stored student data might be used to update previously stored flight performance standards … the method may adapt the current training session by modifying some of its parameters”), to automatically implement modified flight simulation parameters ([0010], The training session … may be automatically generated by a processor of the flight simulator, when a set of training parameters are provided), Fleming discloses analyze the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set ([0054], “the method includes … trainer performance evaluation and correlating the … attained score for … trainer performance … to … standardized trainer performance level”), Hardee discloses wherein analyzing the first training performance data set to determine the correlation ([0015], “The … embodiments utilize a … computing system to analyze the physical features and other characteristics … and correlates those physical features and characteristics with similar characteristics”) comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as averages, medians, … and the like” Examiner notes that a concordance correlation coefficient uses statistical values (means and variances) of the two variables being compared.); determine a second metric based on the distribution of performance values ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as … distributions”); generate a training modification recommendation for the automated training system based at least on the correlation ([0140], “Based on the correlation, areas of … insufficient performance are identified … and a training regimen and/or exercises … are generated … to the user”); and communicate the training modification recommendation to the automated training system ([0140], “training regimen and/or exercises … are … output to the user”), and Kil discloses wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance ([0086], “Meta features describe the … distributions, such as, … skewness”); and an alert indicating ([0064], “delivered … interventions are in the form of … messages” Examiner notes that an alert is a type of message that conveys important or time-sensitive information, often requiring immediate action from the user.).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during
examination. Under BRI, the words of a claim must be given their plain meaning unless
such meaning is inconsistent with the specification, and it is improper to import claim
limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejection of amended claim 17, as obvious over Kratzer in view of Fucke, Fleming, Hardee, and Kil is maintained. Consequently, the rejection of dependent claim 18 is maintained.
With respect to amended claim 19, Applicant argues “the cited portions of Kratzer, Fleming, Kil, Hardee, Fucke, and Thiuvengada fail to disclose each and every element of claim 19” (See RESPONSE TO NON-FINAL OFFICE ACTION, REMARKS, Claims 19 and 20, page 11, paragraph 1.) Examiner acknowledges Applicant’s remarks. Regarding claim 19, Kratzer discloses a non-transient, computer-readable medium storing instructions executable by one or more processors to perform operations comprising: receiving a first training performance data set from an automated training system configured to ([0022], “the subject disclosure include receiving … performance data for … training”) and that flight training performance fails to satisfy a performance threshold ([0053], “Upon a detection of inventory levels for a particular component exceeding a threshold value, the real time training system … may provide to an inventory manager and employees particular training”), Fucke discloses simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”), comprising performance threshold data for flight simulation exercises ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance”); determining a distribution of performance values based on the first training performance data set and calculating a first metric indicating asymmetry in performance relative to desired training benchmarks ([0008], “the present disclosure provides a … method for … evaluating flight crew performance by summarizing all elements of flight crew performance” Examiner notes that evaluating all elements of flight crew performance may include determining performance value distribution and determining performance asymmetry relative to benchmark calculations.); the first metric, and the second metric, wherein the training modification recommendation comprises flight simulation exercise modifications and automated curriculum adjustments to improve training outcomes ([0014], “stored student data might be used to update previously stored flight performance standards … the method may adapt the current training session by modifying some of its parameters”), to automatically implement modified flight simulation parameters ([0010], The training session … may be automatically generated by a processor of the flight simulator, when a set of training parameters are provided), Fleming discloses analyzing the first training performance data set to determine a correlation between the first training performance data set and a training data comparison set ([0054], “the method includes … trainer performance evaluation and correlating the … attained score for … trainer performance … to … standardized trainer performance level”), Hardee discloses wherein analyzing the first training performance data set to determine the correlation ([0015], “The … embodiments utilize a … computing system to analyze the physical features and other characteristics … and correlates those physical features and characteristics with similar characteristics”) comprises determining a concordance correlation coefficient associated with the first training performance data set and the training data comparison set ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as averages, medians, … and the like” Examiner notes that a concordance correlation coefficient uses statistical values (means and variances) of the two variables being compared.); determining a second metric based on the distribution of performance values ([0123], “information may be analyzed using analysis algorithms … to correlate and calculate values for various characteristics, which may include statistical values for such characteristics, such as … distributions”); generating a training modification recommendation for the automated training system based at least on the correlation ([0140], “Based on the correlation, areas of … insufficient performance are identified … and a training regimen and/or exercises … are generated … to the user”); and communicating the training modification recommendation to the automated training system ([0140], “training regimen and/or exercises … are … output to the user”), and Kil discloses wherein the first metric comprises a skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance ([0086], “Meta features describe the … distributions, such as, … skewness”); and an alert indicating ([0064], “delivered … interventions are in the form of … messages” Examiner notes that an alert is a type of message that conveys important or time-sensitive information, often requiring immediate action from the user.).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during
examination. Under BRI, the words of a claim must be given their plain meaning unless
such meaning is inconsistent with the specification, and it is improper to import claim
limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejection of amended claim 19, as obvious over Kratzer in view of Fucke, Fleming, Hardee, and Kil is maintained. Consequently, the rejection of dependent claim 20 is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lisa Antoine whose telephone number is (571)272-4252. The examiner can normally be reached Monday - Thursday 8:30 am - 6:30 pm ET.
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LISA H ANTOINE
Examiner
Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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