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 July 1, 2026 has been entered. Claims 1-20 remain pending in the application. Claims 1-3, 6, 8-9, 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 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, wherein the first training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”) comprises training performance data associated with a first group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the first group of users assigned to a first instructor of the automated training system;
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 a second training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”), the second training performance data set comprising training performance data associated with a second group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the second group of users assigned to one or more second instructors of the automated training system, 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 second training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”);
determining a distribution of performance values based on the first training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”);
determining a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
determining a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
generating a training modification recommendation for the automated training system based at least on ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information ... and modifying content of the training course based on the performance comparison”) the concordance correlation coefficient, the skewness metric, and the kurtosis metric, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information”);
and communicating the training modification recommendation to the automated training system.
Fucke discloses
simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”),
the first group of users assigned to a first instructor of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”)
the second group of users assigned to one or more second instructors of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”)
determining a distribution of performance values based on ([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.)
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, simulate flight conditions, the first group of users assigned to a first instructor of the automated training system, the second group of users assigned to one or more second instructors of the automated training system, determining a distribution of performance values based on, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, and instructors 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, and instructors 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 comprising ([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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 comprising, as disclosed by Fleming, to provide a trainer performance evaluation and standardized trainer performance levels 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 a trainer performance evaluation and standardized trainer performance levels 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 ([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.);
the concordance correlation coefficient ([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.)
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 concordance correlation coefficient and communicating the training modification recommendation to the automated training system, as disclosed by Hardee, to provide physical features, analysis algorithms, training regimens and 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 physical features, analysis algorithms, training regimens and exercises for mechanisms that are provided to implement a personalized training recommendation system.
Kil discloses
determining a skewness metric based on the distribution of performance values, the 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”);
determining a kurtosis metric based on the distribution of performance values, the 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”);
the skewness metric ([0086], “Meta features describe the … distributions, such as, … skewness”)
and the kurtosis metric ([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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, determining a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; determining a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; the skewness metric, and the kurtosis metric, as disclosed by Kil, to provide skewness 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 skewness and kurtosis for a student data-to-insight-to-action-to-learning analytics system.
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 instructor is associated with first geographic region and the one or more second instructors are associated with a second geographic region different from the first geographic region.
Fucke discloses
wherein the first instructor is associated with ([0036], “During … training … instructors … may retrieve stored user data”)
and the one or more second instructors are associated with ([0036], “During … training … 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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, wherein the first instructor is associated with and the one or more second instructors are associated with, 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.
Hardee discloses
first geographic region ([0017], “The cognitive system further ingests … data regarding a geographical area”)
a second geographic region different from the first geographic region ([0017], “The cognitive system further ingests … data regarding a geographical area”)
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 concordance correlation coefficient and communicating the training modification recommendation to the automated training system, first geographic region, a second geographic region different from the first geographic region, as disclosed by Hardee, to provide physical features, analysis algorithms, training regimens, exercises, cognitive systems, and geographical areas 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 physical features, analysis algorithms, training regimens, exercises, cognitive systems, and geographical areas for mechanisms that are provided to implement a personalized training recommendation system.
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 concordance correlation coefficient 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 physical features, analysis algorithms, training regimens, exercises, cognitive systems, and locations 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 physical features, analysis algorithms, training regimens, exercises, cognitive systems, and locations for mechanisms that are provided to implement a personalized training recommendation system.
In regards to claim 9, Kratzer does not disclose wherein the training data comparison set further comprises a control performance data set, the control performance data set comprising data associated with a performance standard for automated flight training.
Fleming discloses
wherein the training data comparison set further comprises a control performance data set, the control performance data set comprising data associated with a performance standard for ([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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 comprising, wherein the training data comparison set further comprises a control performance data set, the control performance data set comprising data associated with a performance standard for, as disclosed by Fleming, to provide a trainer performance evaluation and standardized trainer performance levels 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 a trainer performance evaluation and standardized trainer performance levels for methods, apparatuses, and data processor program products capable of enabling data management associated with an athleticism development program.
Fucke discloses
automated flight training ([0049], “The method … enables automatic capture of behaviors and performance of a training subject … in a flight simulator”).
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, simulate flight conditions, the first group of users assigned to a first instructor of the automated training system, the second group of users assigned to one or more second instructors of the automated training system, determining a distribution of performance values based on, automated flight training, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, instructors, and a training subject 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, and instructors for a flight crew training method.
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”).
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 concordance correlation coefficient 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 physical features, analysis algorithms, training regimens, 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 physical features, analysis algorithms, training regimens, 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, determining a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; determining a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; the skewness metric, and the kurtosis metric, wherein the first training metric is the skewness metric based on the distribution of performance values, as disclosed by Kil, to provide skewness 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 skewness and kurtosis 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, determining a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; determining a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; the skewness metric, and the kurtosis metric, wherein the second training metric is a kurtosis metric based on the distribution of performance values, as disclosed by Kil, to provide skewness 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 skewness 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, determining a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; determining a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; the skewness metric, and the kurtosis metric, 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 skewness, kurtosis, 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 skewness, kurtosis, and shape statistics for a student data-to-insight-to-action-to-learning analytics system.
In regards to claim 16, Kratzer discloses the following limitations with the exception of the underlined limitation.
wherein receiving the first training performance data set comprises: receiving a plurality of performance data requirements from a plurality of implementations of ([0022], “the subject disclosure include receiving … performance data for … training”) the automated training system;
processing the plurality of performance data requirements to generate ([0022], “the subject disclosure include receiving … performance data for … training”) a single data mapping for the plurality of performance data requirements ([0153], “elements … can employ … software that provides … a one-for-one mapping”);
and generating the first training performance data set using ([0022], “the subject disclosure include receiving … performance data for … training”) the single data mapping ([0153], “elements … can employ … software that provides … a one-for-one mapping”).
Fucke discloses
the automated training system ([0049], “The method … enables automatic capture of behaviors and performance of a training subject … in a flight simulator”).
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, wherein receiving the first training performance data set comprises: receiving a plurality of performance data requirements from a plurality of implementations of; processing the plurality of performance data requirements to generate a single data mapping for the plurality of performance data requirements; and generating the first training performance data set using the single data mapping, as disclosed by Kratzer, simulate flight conditions, the first group of users assigned to a first instructor of the automated training system, the second group of users assigned to one or more second instructors of the automated training system, determining a distribution of performance values based on, the automated training system, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, and instructors 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, and instructors for a flight crew training method.
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, wherein the first training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”) comprises training performance data associated with a first group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the first group of users assigned to a first instructor of the automated training system;
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 a second training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”), the second training performance data set comprising training performance data associated with a second group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the second group of users assigned to one or more second instructors of the automated training system, 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 second training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”);
determine a distribution of performance values based on the first training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”);
determine a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
determine a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
generate a training modification recommendation for the automated training system based at least on ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information ... and modifying content of the training course based on the performance comparison”) the concordance correlation coefficient, the skewness metric, and the kurtosis metric, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information”);
and communicate the training modification recommendation to the automated training system.
Fucke discloses
simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”),
the first group of users assigned to a first instructor of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”)
the second group of users assigned to one or more second instructors of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”)
determine a distribution of performance values based on ([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.)
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generate a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, simulate flight conditions, the first group of users assigned to a first instructor of the automated training system, the second group of users assigned to one or more second instructors of the automated training system, determine a distribution of performance values based on, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, and instructors 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, and instructors 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 comprising ([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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generate a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 comprising, as disclosed by Fleming, to provide a trainer performance evaluation and standardized trainer performance levels 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 a trainer performance evaluation and standardized trainer performance levels 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 ([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.);
the concordance correlation coefficient ([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.)
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generate a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 concordance correlation coefficient and communicate the training modification recommendation to the automated training system, as disclosed by Hardee, to provide physical features, analysis algorithms, training regimens and 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 physical features, analysis algorithms, training regimens and exercises for mechanisms that are provided to implement a personalized training recommendation system.
Kil discloses
determining a skewness metric based on the distribution of performance values, the 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”);
determining a kurtosis metric based on the distribution of performance values, the 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”);
the skewness metric ([0086], “Meta features describe the … distributions, such as, … skewness”)
and the kurtosis metric ([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 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generate a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, determine a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; determine a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; the skewness metric, and the kurtosis metric, as disclosed by Kil, to provide skewness 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 skewness and kurtosis 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generate a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 concordance correlation coefficient 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 physical features, analysis algorithms, training regimens, 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 physical features, analysis algorithms, training regimens, exercises, and training course information 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, wherein the first training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”) comprises training performance data associated with a first group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the first group of users assigned to a first instructor of the automated training system;
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 a second training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”), the second training performance data set comprising training performance data associated with a second group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the second group of users assigned to one or more second instructors of the automated training system, 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 second training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”);
determining a distribution of performance values based on the first training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”);
determining a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
determining a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance;
generating a training modification recommendation for the automated training system based at least on ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information ... and modifying content of the training course based on the performance comparison”) the concordance correlation coefficient, the skewness metric, and the kurtosis metric, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information”);
and communicating the training modification recommendation to the automated training system.
Fucke discloses
simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”),
the first group of users assigned to a first instructor of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”)
the second group of users assigned to one or more second instructors of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”)
determining a distribution of performance values based on ([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.)
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, simulate flight conditions, the first group of users assigned to a first instructor of the automated training system, the second group of users assigned to one or more second instructors of the automated training system, determining a distribution of performance values based on, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, and instructors 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, and instructors 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 comprising ([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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 comprising, as disclosed by Fleming, to provide a trainer performance evaluation and standardized trainer performance levels 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 a trainer performance evaluation and standardized trainer performance levels 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 ([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.);
the concordance correlation coefficient ([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.)
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 concordance correlation coefficient and communicating the training modification recommendation to the automated training system, as disclosed by Hardee, to provide physical features, analysis algorithms, training regimens and 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 physical features, analysis algorithms, training regimens and exercises for mechanisms that are provided to implement a personalized training recommendation system.
Kil discloses
determining a skewness metric based on the distribution of performance values, the 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”);
determining a kurtosis metric based on the distribution of performance values, the 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”);
the skewness metric ([0086], “Meta features describe the … distributions, such as, … skewness”)
and the kurtosis metric ([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 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, determining a skewness metric based on the distribution of performance values, the skewness metric measuring statistical asymmetry of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; determining a kurtosis metric based on the distribution of performance values, the kurtosis metric measuring statistical peakedness of the distribution of performance values relative to normal distribution characteristics expected for optimal flight training performance; the skewness metric, and the kurtosis metric, as disclosed by Kil, to provide skewness 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 skewness and kurtosis 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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 concordance correlation coefficient 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 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 to implement a personalized training recommendation system.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Kratzer in view of Fucke, Fleming, Hardee, Kil, and US 20210241645 A1 (“Onur”)
In regards to claim 2, Kratzer does not disclose wherein the first instructor is associated with a first airline and the one or more second instructors are associated with a second airline different from the first airline.
Fucke discloses
wherein the first instructor is associated with ([0036], “During … training … instructors … may retrieve stored user data”)
and the one or more second instructors are associated with ([0036], “During … training … 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, simulate flight conditions, the first group of users assigned to a first instructor of the automated training system, the second group of users assigned to one or more second instructors of the automated training system, determining a distribution of performance values based on, wherein the first instructor is associated with, and the one or more second instructors are associated with, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, and instructors 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, and instructors for a flight crew training method.
Onur discloses
a first airline ([0089], “an operator may be an airline”)
a second airline different from the first airline ([0089], “an operator may be an airline”)
Kratzer and Onur 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, a first airline and a second airline different from the first airline, as disclosed by Onur, to provide an airline for quantitative pilot evaluation during training. One skilled in the art would understand and recognize the value of the addition of an airline for quantitative pilot evaluation during training.
In regards to claim 3, Kratzer does not disclose wherein the first instructor and each of the one or more second instructors are associated with a same airline and the first instructor is different from each of the one or more second instructors.
Fucke discloses
wherein the first instructor and each of the one or more second instructors are associated with ([0036], “During … training … instructors … may retrieve stored user data”)
and the first instructor is different from each of the one or more second instructors ([0036], “During … training … 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, simulate flight conditions, the first group of users assigned to a first instructor of the automated training system, the second group of users assigned to one or more second instructors of the automated training system, determining a distribution of performance values based on, wherein the first instructor and each of the one or more second instructors are associated with and the first instructor is different from each of the one or more second instructors, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, and instructors 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, and instructors for a flight crew training method.
Onur discloses
a same airline ([0089], “an operator may be an airline”)
Kratzer and Onur 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, a same airline, as disclosed by Onur, to provide an airline for quantitative pilot evaluation during training. One skilled in the art would understand and recognize the value of the addition of an airline for quantitative pilot evaluation during training.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kratzer in view of Fucke, Fleming, Hardee, Kil, Onur, 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, 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.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kratzer in view of Fucke, Fleming, Hardee, Kil, and Thiruvengada.
In regards to claim 8, Kratzer does not disclose wherein the first instructor is associated with first curriculum competency and the one or more second instructors are associated with a second curriculum competency different from the first curriculum competency.
Fucke discloses
wherein the first instructor is associated with ([0036], “During … training … instructors … may retrieve stored user data”)
and the one or more second instructors are associated with ([0036], “During … training … 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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, simulate flight conditions, the first group of users assigned to a first instructor of the automated training system, the second group of users assigned to one or more second instructors of the automated training system, determining a distribution of performance values based on, wherein the first instructor is associated with, and the one or more second instructors are associated with, as disclosed by Fucke, to provide a flight simulator, flight crew performance elements, and instructors 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, and instructors for a flight crew training method.
Thiruvengada discloses
first curriculum competency ([0011], “The … automated training system … includes … a curriculum manager module … and a curriculum adjustment module”)
a second curriculum competency different from the first curriculum competency ([0011], “The … automated training system … includes … a curriculum manager module … and a curriculum adjustment module”)
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, wherein the first training performance data set comprises training performance data associated with a first group of users of the automated training system, a second training performance data set, the second training performance data set comprising training performance data associated with a second group of users of the automated training system, the first training performance data set and the second training performance data set, the first training performance data set, generating a training modification recommendation for the automated training system based at least on, wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor, as disclosed by Kratzer, first curriculum competency, a second curriculum competency different from the first curriculum competency, as disclosed by Thiruvengada, to provide a curriculum manager module and a curriculum adjustment module 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 a curriculum manager module and a curriculum adjustment module for an automated training system and method based on performance evaluation.
Response to Arguments
Applicant's arguments filed July 1, 2026 have been fully considered but after Examiner’s further search, the prior art disclosed the amended subject matter. Claims 1-20 are pending in this application. With respect to claim 1, Applicant submits that “The cited portions of Kratzer, Fleming, Kil, Hardee, and Fucke, individually or in combination, fail to disclose the specific combination of amended claim 1” (See SUBMISSION ACCOMPANYING REQUEST FOR CONTINUED EXAMINATION PURSUANT TO 37 C.F.R. § 1.114, REMARKS, Claims 1-3 and 5-16, page 10, paragraph 3.), “The combination of these references does not arrive at the claimed combination of limitations.” (See SUBMISSION ACCOMPANYING REQUEST FOR CONTINUED EXAMINATION PURSUANT TO 37 C.F.R. § 1.114, REMARKS, Claims 1-3 and 5-16, page 16, paragraph 1.), and “The cited references, individually and in combination, fail to teach or suggest a system in which training performance data associated with a first instructor’s students is analyzed, through a skewness metric, a kurtosis metric, and a concordance correlation coefficient, against training performance data associated with one or more second instructors’ students for the purpose of generating a training modification recommendation comprising an indication of instructor performance.” (See SUBMISSION ACCOMPANYING REQUEST FOR CONTINUED EXAMINATION PURSUANT TO 37 C.F.R. § 1.114, REMARKS, Claims 1-3 and 5-16, page 16, paragraph 2.) 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”), wherein the first training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”) comprises training performance data associated with a first group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), a second training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”), the second training performance data set comprising training performance data associated with a second group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the first training performance data set and the second training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”); the first training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”); generating a training modification recommendation for the automated training system based at least on ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information ... and modifying content of the training course based on the performance comparison”), wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information”), Fucke discloses simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”), the first group of users assigned to a first instructor of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”) the second group of users assigned to one or more second instructors of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”) determining a distribution of performance values based on ([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.), 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 comprising ([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 ([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.); the concordance correlation coefficient ([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.) 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 determining a skewness metric based on the distribution of performance values, the 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”); determining a kurtosis metric based on the distribution of performance values, the 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”); the skewness metric ([0086], “Meta features describe the … distributions, such as, … skewness”) and the kurtosis metric ([0086], “Meta features describe the … distributions … and shape statistics, such as … kurtosis”).
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 submits that “the cited portions of Kratzer, Fucke, Fleming, Hardee, and Kil, individually or in combination, fail to disclose each and every element of claim 17” (See SUBMISSION ACCOMPANYING REQUEST FOR CONTINUED EXAMINATION PURSUANT TO 37 C.F.R. § 1.114, REMARKS, Claims 17 and 18, page 18, 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”), wherein the first training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”) comprises training performance data associated with a first group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), a second training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”), the second training performance data set comprising training performance data associated with a second group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the first training performance data set and the second training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”); the first training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”); generate a training modification recommendation for the automated training system based at least on ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information ... and modifying content of the training course based on the performance comparison”), wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information”), Fucke discloses simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”), the first group of users assigned to a first instructor of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”) the second group of users assigned to one or more second instructors of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”) determining a distribution of performance values based on ([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.), 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 comprising ([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 ([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.); the concordance correlation coefficient ([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.) 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 determine a skewness metric based on the distribution of performance values, the 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”); determine a kurtosis metric based on the distribution of performance values, the 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”); the skewness metric ([0086], “Meta features describe the … distributions, such as, … skewness”) and the kurtosis metric ([0086], “Meta features describe the … distributions … and shape statistics, such as … kurtosis”).
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 submits that “the cited portions of Kratzer, Fleming, Kil, Hardee, and Fucke 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”), wherein the first training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”) comprises training performance data associated with a first group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), a second training performance data set ([0010], “FIG. 2F depicts … data for measuring effects of a training course and for detecting a performance change”), the second training performance data set comprising training performance data associated with a second group of users of the automated training system ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”), the first training performance data set and the second training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”); the first training performance data set ([0020], “Automated systems can collect data about performance of … employees … to … identify appropriate training and … evaluate the effectiveness of the training”); generating a training modification recommendation for the automated training system based at least on ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information ... and modifying content of the training course based on the performance comparison”), wherein the training modification recommendation comprises an indication of instructor performance associated with the first instructor ([0021], “aspects include producing ... recommended courses ... based on the training recommendation ... comparing the performance information ... with the metric information”), Fucke discloses simulate flight conditions ([0008], “a method for training flight crew in a flight simulator is disclosed”), the first group of users assigned to a first instructor of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”) the second group of users assigned to one or more second instructors of the automated training system ([0036], “During … training … instructors … may retrieve stored user data”) determining a distribution of performance values based on ([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.), 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 comprising ([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 ([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.); the concordance correlation coefficient ([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.) 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 determining a skewness metric based on the distribution of performance values, the 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”); determining a kurtosis metric based on the distribution of performance values, the 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”); the skewness metric ([0086], “Meta features describe the … distributions, such as, … skewness”) and the kurtosis metric ([0086], “Meta features describe the … distributions … and shape statistics, such as … kurtosis”).
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.
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/LISA H ANTOINE/
Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
Prosecution Insights