DETAILED ACTION
Acknowledgement
This non-final office action is in response to claims filed on 04/03/2025.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/02/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 2, 4, and 6 are objected to because of the following informalities:
Claims 2, 4, and 6 include the limitation of “…use the normalized values and generate for the flight at least one of;”. The semi-colon “;” should be a colon “:”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 limitation of “providing via a network in signal communication with the training server the collected data to a training server” lacks proper antecedent basis for “the training server” because “a training server” was not previously recited or introduced in the claim. This claim limitation also refers to “the training server” and “a training server”, while the following claim limitations refer to “the training server”. Are these two different training servers or the same? As written, it is hard to tell. Therefore, claim 1 is considered indefinite and is rejected under 35 U.S.C. 112(b). Dependent claims 2-9 are also rejected under 35 U.S.C. 112(b). For examination purposes, “the training server” and “a training server” is interpreted as the same.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention, “Pilot and Instructor Expertise”, is directed to an abstract idea, specifically Mental Processes, without significantly more. The claims as a whole do not include additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the abstract idea because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1-9 are directed to a statutory category, namely a process.
Step 2A (1): Claims 1-9 are directed to an abstract idea of Mental Processes, based on the following claim limitations: “A method of training a flight instructor via actual flights with students, the method comprising: collecting during a specific flight with a specific student, one of a 1st, 2nd, 3rd, 4th and Nth student's raw performance metrics data during a specific flight with a 1st instructor; collecting the aircraft's flight metrics data during the same flight; collecting the aircraft systems functional data during the same flight; … generates normalized values for each raw student performance metric to reflect one or more of the student experience, time of day, weather, and aircraft systems functional data; and,… use the normalized values and generate for the flight at least one of a numerical value for the specific one of a 1st, 2nd, 3rd, 4th and Nth student with 1st instructor performance; a numerical value for the 1st instructor overall performance with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; a competency report for the 1st instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student (claim 1); … use the normalized values and generate for the flight at least one of ; a numerical value for the specific one of a 1st, 2nd, 3rd, 4th and Nth student with 2nd instructor performance; a numerical value for the 2nd instructor overall performance with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; a competency report for the 2nd instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; and, generate an instructor effectiveness rank (IER) value of 1st instructor and 2nd instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student (claim 2); … wherein the IER value for the 1st and the 2nd instructors with the specific one of a 1st, 2nd, 3rd, 4th and Nth student is updated after each flight (claim 3); … use the normalized values and generate for the flight at least one of; a numerical value for the specific one of a 1st, 2nd, 3rd, 4th and Nth student with 3rd instructor performance; a numerical value for the 3rd instructor overall performance with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; a competency report for the 3rd instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student. generate an instructor effectiveness rank (IER) value of 1st, 2nd and 3rd instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student (claim 4); wherein the IER value for the 1st, 2nd and 3rd instructors with the specific one of a 1st, 2nd, 3rd, 4th and Nth student is updated after each flight (claim 5); use the normalized values and generate for the flight at least one of; a numerical value for the specific one of a 1st, 2nd, 3rd, 4th and Nth student with 4th instructor performance; a numerical value for the 4th instructor overall performance with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; a competency report for the 4th instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student, generate an instructor effectiveness rank (IER) value of 1st, 2nd, 3rd and 4th instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student (claim 6); wherein the IER value for the 1st, 2nd, 3rd and 4th instructors with the specific one of a 1st, 2nd, 3rd, 4th and Nth student is updated after each flight (claim 7); using the normalized data of a 1st student from a plurality of instructors and rank each instructor on competency of instructing 1st student on each of the measured competency metrics (claim 8); and using the normalized data of a 1st student from a plurality of instructors and rank each instructor on overall performance of instructing 1st student on the flight (claim 9).”. These claims describe a process of analyzing flight instructor, student, and environmental data to evaluate instructor performance and generate a competency report and ranking. These steps can practically be performed in the human mind with pen and paper via observation, evaluation, and judgment. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. The courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. Therefore, claims 1-9 are directed to an abstract idea and are not patent eligible.
Step 2A (2): The claims as a whole do not integrate this abstract idea into a practical application. In particular, claims 1, 2, and 4 recite additional elements of “providing via a network in signal communication with the training server the collected data to a training server; providing weather data of the specific flight date including at least a Meteorological Aerodrome report (METAR) via a network in signal communication with the training server; the training server generates; the training server is configured ”. The Examiner evaluated the claims in light of the Applicant’s specification and determined that the additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as a computing components that are used to perform the abstract process identified in Step 2A(1). Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)) Therefore, claims 1-9 as a whole do not include individual or a combination of additional elements that integrate the abstract idea into a practical application and thus are not patent eligible.
Step 2B: The claims as a whole do not include additional elements that are sufficient to amount to significantly more than the abstract idea. Claims 1, 2, and 4 recite additional elements of “providing via a network in signal communication with the training server the collected data to a training server; providing weather data of the specific flight date including at least a Meteorological Aerodrome report (METAR) via a network in signal communication with the training server; the training server generates; the training server is configured”. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-9 as a whole do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the abstract idea and thus are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4, and 6 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tennyson et al. (US 2020/0051459 A1).
As per claim 1, Tennyson teaches a method of training a flight instructor via actual flights with students, the method comprising (Tennyson e.g. Embodiments of the present disclosure provide systems and methods for an adaptive flight training tool [0060]. Adaptive flight training tools according to the present disclosure may be implemented as a flight training tool system 100 as illustrated in FIG. 1 [0065].):
Tennyson teaches collecting during a specific flight with a specific student, one of a 1st, 2nd, 3rd, 4th and Nth student's raw performance metrics data during a specific flight with a 1st instructor; collecting the aircraft's flight metrics data during the same flight; collecting the aircraft systems functional data during the same flight; (Tennyson e.g. System 100 may include hardware devices configured to store, retrieve, receive, and/or gather information which may be accessed by one or more other components of system 100. For instance, system 100 may include (or be configured to communicate with) hardware devices that include or store aircraft data, flight school data, flight instructor data, flight student data, flight training data (e.g., flight training plan data, flight training statistics data, etc.), weather data, airport data, etc. System 100 may access this information and perform one or more processes to provide a flight training tool [0068].)
Tennyson teaches providing via a network in signal communication with the training server the collected data to a training server; providing weather data of the specific flight date including at least a Meteorological Aerodrome report (METAR) via a network in signal communication with the training server; (Tennyson e.g. Flight training tool 102 may retrieve data from a number of resources, including database 108, scheduling tool 110, database 114, airport directory 116, weather source 118, and flight restrictions 120. For example, database 108 may contain factors used to choose activities for a lesson, such as cognitive load, rank, and the student's home airport characteristics. Scheduling tool 110 may provide flight training tool 102 with information on schedules of students, instructors, and aircraft availability [0070]. Flight training tool 102 may also access additional resources such as an airport directory 116, a weather resource 118 that provides, for instance, METARs, and flight restrictions resource 120 that provides temporary flight restrictions (TFRs) [0070].)
Tennyson teaches the training server generates normalized values for each raw student performance metric to reflect one or more of the student experience, time of day, weather, and aircraft systems functional data; and, the training server is configured to use the normalized values and generate for the flight at least one of a numerical value for the specific one of a 1st, 2nd, 3rd, 4th and Nth student with 1st instructor performance; a numerical value for the 1st instructor overall performance with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; a competency report for the 1st instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student. (Tennyson e.g. Another aspect of the present disclosure is directed to a computer-implemented flight training method. The method includes executing, via a processor, instructions stored in non-transitory computer readable medium to perform operations [0008]. The operations include...recording a score for activities in the subset, the score reflecting a student's performance of the activity during the lesson; storing the scores in the database; updating, in the database, the state of an activity in the subset based on the score recorded for the activity; and updating, in the database, the cognitive load of activities stored in the database based on at least one of: a time since the activity was last included in a lesson, or the score recorded for the activity [0008]. System 100 may implement an adaptive lesson planning process that reacts to the performance of an individual student and chooses activities in the most appropriate order. After a first lesson, flight training tool 102 may adjust the contents of each subsequent lesson based on certain inputs, such as the instructor's grading of student performance on each component of the lesson. This may provide students with a high-quality learning experience that is transparent, ensures continued student progress, and delivers insight into student and instructor performance throughout the learning process [0077]. Each activity also has parameters, including a cognitive load, age, and rank. Cognitive load reflects (e.g., is indicative of) the estimated cognitive effort for each activity, and may be represented numerically from 1 to 25 with 25 being greatest. The cognitive load of each activity may be reduced over time as the student's mastery increases. Each activity may be assigned an initial cognitive load, which changes over time according to predetermined rules as calculated by flight training tool 102 [0080]. After the lesson is created, the student and the CFI perform the lesson at step 204. At step 206, flight training tool 102 may be used to grade the lesson. For example, the CFI may record notes and scores about each of the activities performed in the lesson, including how proficient the student was and if the activity was skipped [0081].)
As per claim 2, Tennyson teaches the method of training a flight instructor via actual flights with students of claim 1, Tennyson teaches the method comprising a 2nd instructor different from 1st instructor and, the training server is configured to use the normalized values and generate for the flight at least one of; a numerical value for the specific one of a 1st, 2nd, 3rd, 4th and Nth student with 2nd instructor performance; a numerical value for the 2nd instructor overall performance with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; a competency report for the 2nd instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; and, generate an instructor effectiveness rank (IER) value of 1st instructor and 2nd instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student. (Tennyson e.g. System 100 may implement an adaptive lesson planning process that reacts to the performance of an individual student and chooses activities in the most appropriate order. After a first lesson, flight training tool 102 may adjust the contents of each subsequent lesson based on certain inputs, such as the instructor's grading of student performance on each component of the lesson. This may provide students with a high-quality learning experience that is transparent, ensures continued student progress, and delivers insight into student and instructor performance throughout the learning process [0077]. After the lesson is created, the student and the CFI perform the lesson at step 204. At step 206, flight training tool 102 may be used to grade the lesson. For example, the CFI may record notes and scores about each of the activities performed in the lesson, including how proficient the student was and if the activity was skipped [0081]. Flight training tool 102 may also provide basic reports displaying lesson history, activity grading history, progress charts, logbook counts, students, instructors, etc. [0104]. The flight training tool CFI portal may also include a student list interface 2902 as shown in FIG. 29, consistent with disclosed embodiments. Student list interface 2902 may display a list 2904 of all the students at the school. Students may have a label or tag for alerts or milestones, and may be filtered by instructor [0159]. The flight training tool school portal may also provide a CFI list interface as shown in FIG. 35. CFI list interface 3502 may show all the school's instructors in a list view, including statistics for instructors. CFI list interface 3502 may display name, instruction hours this month, flight hours this month, students, completion rate as determined by number of total students and number of students who have achieved their goals for a given period, and status [0170].)
As per claim 4, Tennyson teaches The method of training a flight instructor via actual flights with students of claim 2 the method comprising a 3rd instructor different from 1st and 2nd instructor and, the training server is configured to use the normalized values and generate for the flight at least one of; a numerical value for the specific one of a 1st, 2nd, 3rd, 4th and Nth student with 3rd instructor performance; a numerical value for the 3rd instructor overall performance with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; a competency report for the 3rd instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; generate an instructor effectiveness rank (IER) value of 1st, 2nd and 3rd instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student. (Tennyson e.g. System 100 may implement an adaptive lesson planning process that reacts to the performance of an individual student and chooses activities in the most appropriate order. After a first lesson, flight training tool 102 may adjust the contents of each subsequent lesson based on certain inputs, such as the instructor's grading of student performance on each component of the lesson. This may provide students with a high-quality learning experience that is transparent, ensures continued student progress, and delivers insight into student and instructor performance throughout the learning process [0077]. After the lesson is created, the student and the CFI perform the lesson at step 204. At step 206, flight training tool 102 may be used to grade the lesson. For example, the CFI may record notes and scores about each of the activities performed in the lesson, including how proficient the student was and if the activity was skipped [0081]. Flight training tool 102 may also provide basic reports displaying lesson history, activity grading history, progress charts, logbook counts, students, instructors, etc. [0104]. The flight training tool CFI portal may also include a student list interface 2902 as shown in FIG. 29, consistent with disclosed embodiments. Student list interface 2902 may display a list 2904 of all the students at the school. Students may have a label or tag for alerts or milestones, and may be filtered by instructor [0159]. The flight training tool school portal may also provide a CFI list interface as shown in FIG. 35. CFI list interface 3502 may show all the school's instructors in a list view, including statistics for instructors. CFI list interface 3502 may display name, instruction hours this month, flight hours this month, students, completion rate as determined by number of total students and number of students who have achieved their goals for a given period, and status [0170].)
As per claim 6, Tennyson teaches the method of training a flight instructor via actual flights with students of claim 4 the method comprising a 4th instructor different from 1st , 2nd and 3rd instructor and, the training server is configured to use the normalized values and generate for the flight at least one of ; a numerical value for the specific one of a 1st, 2nd, 3rd, 4th and Nth student with 4th instructor performance; a numerical value for the 4th instructor overall performance with the specific one of a 1st, 2nd, 3rd, 4th and Nth student; a competency report for the 4th instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student. generate an instructor effectiveness rank (IER) value of 1st, 2nd, 3rd and 4th instructor with the specific one of a 1st, 2nd, 3rd, 4th and Nth student. (Tennyson e.g. System 100 may implement an adaptive lesson planning process that reacts to the performance of an individual student and chooses activities in the most appropriate order. After a first lesson, flight training tool 102 may adjust the contents of each subsequent lesson based on certain inputs, such as the instructor's grading of student performance on each component of the lesson. This may provide students with a high-quality learning experience that is transparent, ensures continued student progress, and delivers insight into student and instructor performance throughout the learning process [0077]. After the lesson is created, the student and the CFI perform the lesson at step 204. At step 206, flight training tool 102 may be used to grade the lesson. For example, the CFI may record notes and scores about each of the activities performed in the lesson, including how proficient the student was and if the activity was skipped [0081]. Flight training tool 102 may also provide basic reports displaying lesson history, activity grading history, progress charts, logbook counts, students, instructors, etc. [0104]. The flight training tool CFI portal may also include a student list interface 2902 as shown in FIG. 29, consistent with disclosed embodiments. Student list interface 2902 may display a list 2904 of all the students at the school. Students may have a label or tag for alerts or milestones, and may be filtered by instructor [0159]. The flight training tool school portal may also provide a CFI list interface as shown in FIG. 35. CFI list interface 3502 may show all the school's instructors in a list view, including statistics for instructors. CFI list interface 3502 may display name, instruction hours this month, flight hours this month, students, completion rate as determined by number of total students and number of students who have achieved their goals for a given period, and status [0170].)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3, 5, and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Tennyson et al. (US 2020/0051459 A1) in view of Mathew et al. (US 2025/0086561 A1).
As per claim 3, Tennyson teaches the method of training a flight instructor via actual flights with students of claim 2, Tennyson does not explicitly teach, however, Mathew teaches wherein the IER value for the 1st and the 2nd instructors with the specific one of a 1st, 2nd, 3rd, 4th and Nth student is updated after each flight. (Mathew e.g. Apparatuses, method, systems, and program products are disclosed for assessing instructor ratings on a defined rating scale for skewness (Abstract). Skewness, a statistical metric that measures the symmetry of data, can be used to provide valuable insights into instructor rating data. A skewness value of zero indicates a perfectly symmetric distribution, while a negative skewness value suggests a left-skewed distribution and a positive skewness value suggests a right-skewed distribution. That is, skewness serves as a measure to determine whether the distribution of instructors' ratings is distorted or asymmetrical. Skewness analysis can help identify potential biases in the distribution of instructor ratings, such as leniency or strictness when rating students [0002]. Referring to FIG. 3, another embodiment of an apparatus 300 for assessing instructor ratings is shown [0061]. The data receiving module 202 may be configured to receive a raw instructor rating data set corresponding to a plurality of individual instructors. For example, the data receiving module 202 may receive a raw instructor rating data set corresponding to a first individual instructor 304, a second individual instructor 306, a third individual instructor 308, and a fourth individual instructor 310. Any number of individual instructors may be included in the apparatus 300 [0062]. The raw instructor rating data set of each one of the plurality of individual instructors (304, 306, 308, 310) includes a plurality of ratings corresponding to at least one evaluation period. In some examples, the raw instructor rating data set for each one of the plurality of individual instructors may correspond to more than one evaluation period. That is, more than one instructor rating per student may be included in the raw instructor rating data set and can be consolidated or analyzed separately [0062]. The skewness determination module 204 will separately determine a rating skewness of the plurality of ratings for each one of the plurality of individual instructor (304, 306, 308, 310) for each evaluation period, if more than one evaluation period is included in the raw instructor rating data set [0063]. The comparison module 206 will compare the rating skewness for each one of the plurality of individual instructors (304, 306, 308, 310) to at least one comparative rating skewness and generate a skewness comparison report for each one of the individual instructors (304, 306, 308, 310). In some examples, the at least one comparative rating skewness is a baseline skewness, such that each rating skewness is compared to the same baseline skewness [0064]. For example, the first individual instructor 304 may be compared to the second individual instructor 306 and/or the third individual instructor 308 and/or the fourth individual instructor 310. In some cases, when the rating skewness of an individual instructor is compared to others of the plurality of individual instructors (304, 306, 308, 310), each one of the skewness comparison reports may include the same information [0064]. Referring to FIG. 4, another embodiment of an apparatus 400 for assessing instructor ratings is shown. The apparatus 400 includes a data management apparatus 104. Furthermore, the data management apparatus 104 may include instances of a skewness tracking module 402 and a training adjustment module 404 [0067]. The skewness tracking module 402 is configured to track changes in the rating skewness of the plurality of ratings from each one of the plurality of evaluation periods. That is, the skewness tracking module 402 analyzes changes in the distribution of ratings over time. Changes in the rating skewness can indicate potential variations in instructional effectiveness or changes in the quality of evaluations over time. The skewness tracking module 402 enables the identification of emerging trends, areas of improvement, or noteworthy developments within the instructor rating data. The capacity to detect temporal shifts contributes to a comprehensive and up-to-date understanding of instructor performance, empowering data-driven decision-making and informed adjustments to training programs or instructional approaches [0069].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Tennyson’s Adaptive Flight Training Tool to include a comparison metric of instructors as taught by Mathew in order to assist in unveiling underlying instructor tendencies, such as leniency or strictness, and inform of additional training needs of the individual instructors (Mathew e.g. [0037]).
As per claim 5, Tennyson teaches the method of training a flight instructor via actual flights with students of claim 4, Tennyson does not explicitly teach, however, Mathew teaches wherein the IER value for the 1st, 2nd and 3rd instructors with the specific one of a 1st, 2nd, 3rd, 4th and Nth student is updated after each flight (Mathew e.g. Apparatuses, method, systems, and program products are disclosed for assessing instructor ratings on a defined rating scale for skewness (Abstract). Skewness, a statistical metric that measures the symmetry of data, can be used to provide valuable insights into instructor rating data. A skewness value of zero indicates a perfectly symmetric distribution, while a negative skewness value suggests a left-skewed distribution and a positive skewness value suggests a right-skewed distribution. That is, skewness serves as a measure to determine whether the distribution of instructors' ratings is distorted or asymmetrical. Skewness analysis can help identify potential biases in the distribution of instructor ratings, such as leniency or strictness when rating students [0002]. Referring to FIG. 3, another embodiment of an apparatus 300 for assessing instructor ratings is shown [0061]. The data receiving module 202 may be configured to receive a raw instructor rating data set corresponding to a plurality of individual instructors. For example, the data receiving module 202 may receive a raw instructor rating data set corresponding to a first individual instructor 304, a second individual instructor 306, a third individual instructor 308, and a fourth individual instructor 310. Any number of individual instructors may be included in the apparatus 300 [0062]. The raw instructor rating data set of each one of the plurality of individual instructors (304, 306, 308, 310) includes a plurality of ratings corresponding to at least one evaluation period. In some examples, the raw instructor rating data set for each one of the plurality of individual instructors may correspond to more than one evaluation period. That is, more than one instructor rating per student may be included in the raw instructor rating data set and can be consolidated or analyzed separately [0062]. The skewness determination module 204 will separately determine a rating skewness of the plurality of ratings for each one of the plurality of individual instructor (304, 306, 308, 310) for each evaluation period, if more than one evaluation period is included in the raw instructor rating data set [0063]. The comparison module 206 will compare the rating skewness for each one of the plurality of individual instructors (304, 306, 308, 310) to at least one comparative rating skewness and generate a skewness comparison report for each one of the individual instructors (304, 306, 308, 310). In some examples, the at least one comparative rating skewness is a baseline skewness, such that each rating skewness is compared to the same baseline skewness [0064]. For example, the first individual instructor 304 may be compared to the second individual instructor 306 and/or the third individual instructor 308 and/or the fourth individual instructor 310. In some cases, when the rating skewness of an individual instructor is compared to others of the plurality of individual instructors (304, 306, 308, 310), each one of the skewness comparison reports may include the same information [0064]. Referring to FIG. 4, another embodiment of an apparatus 400 for assessing instructor ratings is shown. The apparatus 400 includes a data management apparatus 104. Furthermore, the data management apparatus 104 may include instances of a skewness tracking module 402 and a training adjustment module 404 [0067]. The skewness tracking module 402 is configured to track changes in the rating skewness of the plurality of ratings from each one of the plurality of evaluation periods. That is, the skewness tracking module 402 analyzes changes in the distribution of ratings over time. Changes in the rating skewness can indicate potential variations in instructional effectiveness or changes in the quality of evaluations over time. The skewness tracking module 402 enables the identification of emerging trends, areas of improvement, or noteworthy developments within the instructor rating data. The capacity to detect temporal shifts contributes to a comprehensive and up-to-date understanding of instructor performance, empowering data-driven decision-making and informed adjustments to training programs or instructional approaches [0069].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Tennyson’s Adaptive Flight Training Tool to include a comparison metric of instructors as taught by Mathew in order to assist in unveiling underlying instructor tendencies, such as leniency or strictness, and inform of additional training needs of the individual instructors (Mathew e.g. [0037]).
As per claim 7, Tennyson teaches the method of training a flight instructor via actual flights with students of claim 4, Tennyson does not explicitly teach, however, Mathew teaches wherein the IER value for the 1st, 2nd, 3rd and 4th instructors with the specific one of a 1st, 2nd, 3rd, 4th and Nth student is updated after each flight. (Mathew e.g. Apparatuses, method, systems, and program products are disclosed for assessing instructor ratings on a defined rating scale for skewness (Abstract). Skewness, a statistical metric that measures the symmetry of data, can be used to provide valuable insights into instructor rating data. A skewness value of zero indicates a perfectly symmetric distribution, while a negative skewness value suggests a left-skewed distribution and a positive skewness value suggests a right-skewed distribution. That is, skewness serves as a measure to determine whether the distribution of instructors' ratings is distorted or asymmetrical. Skewness analysis can help identify potential biases in the distribution of instructor ratings, such as leniency or strictness when rating students [0002]. Referring to FIG. 3, another embodiment of an apparatus 300 for assessing instructor ratings is shown [0061]. The data receiving module 202 may be configured to receive a raw instructor rating data set corresponding to a plurality of individual instructors. For example, the data receiving module 202 may receive a raw instructor rating data set corresponding to a first individual instructor 304, a second individual instructor 306, a third individual instructor 308, and a fourth individual instructor 310. Any number of individual instructors may be included in the apparatus 300 [0062]. The raw instructor rating data set of each one of the plurality of individual instructors (304, 306, 308, 310) includes a plurality of ratings corresponding to at least one evaluation period. In some examples, the raw instructor rating data set for each one of the plurality of individual instructors may correspond to more than one evaluation period. That is, more than one instructor rating per student may be included in the raw instructor rating data set and can be consolidated or analyzed separately [0062]. The skewness determination module 204 will separately determine a rating skewness of the plurality of ratings for each one of the plurality of individual instructor (304, 306, 308, 310) for each evaluation period, if more than one evaluation period is included in the raw instructor rating data set [0063]. The comparison module 206 will compare the rating skewness for each one of the plurality of individual instructors (304, 306, 308, 310) to at least one comparative rating skewness and generate a skewness comparison report for each one of the individual instructors (304, 306, 308, 310). In some examples, the at least one comparative rating skewness is a baseline skewness, such that each rating skewness is compared to the same baseline skewness [0064]. For example, the first individual instructor 304 may be compared to the second individual instructor 306 and/or the third individual instructor 308 and/or the fourth individual instructor 310. In some cases, when the rating skewness of an individual instructor is compared to others of the plurality of individual instructors (304, 306, 308, 310), each one of the skewness comparison reports may include the same information [0064]. Referring to FIG. 4, another embodiment of an apparatus 400 for assessing instructor ratings is shown. The apparatus 400 includes a data management apparatus 104. Furthermore, the data management apparatus 104 may include instances of a skewness tracking module 402 and a training adjustment module 404 [0067]. The skewness tracking module 402 is configured to track changes in the rating skewness of the plurality of ratings from each one of the plurality of evaluation periods. That is, the skewness tracking module 402 analyzes changes in the distribution of ratings over time. Changes in the rating skewness can indicate potential variations in instructional effectiveness or changes in the quality of evaluations over time. The skewness tracking module 402 enables the identification of emerging trends, areas of improvement, or noteworthy developments within the instructor rating data. The capacity to detect temporal shifts contributes to a comprehensive and up-to-date understanding of instructor performance, empowering data-driven decision-making and informed adjustments to training programs or instructional approaches [0069].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Tennyson’s Adaptive Flight Training Tool to include a comparison metric of instructors as taught by Mathew in order to assist in unveiling underlying instructor tendencies, such as leniency or strictness, and inform of additional training needs of the individual instructors (Mathew e.g. [0037]).
As per claim 8, Tennyson teaches the method of training a flight instructor via actual flights with students of claim 6, the method further comprising using the normalized data of a 1st student from a plurality of instructors and rank each instructor on competency of instructing 1st student on each of the measured competency metrics (Mathew e.g. Referring to FIG. 3, another embodiment of an apparatus 300 for assessing instructor ratings is shown [0061]. The data receiving module 202 may be configured to receive a raw instructor rating data set corresponding to a plurality of individual instructors. For example, the data receiving module 202 may receive a raw instructor rating data set corresponding to a first individual instructor 304, a second individual instructor 306, a third individual instructor 308, and a fourth individual instructor 310. Any number of individual instructors may be included in the apparatus 300 [0062]. The raw instructor rating data set of each one of the plurality of individual instructors (304, 306, 308, 310) includes a plurality of ratings corresponding to at least one evaluation period. In some examples, the raw instructor rating data set for each one of the plurality of individual instructors may correspond to more than one evaluation period. That is, more than one instructor rating per student may be included in the raw instructor rating data set and can be consolidated or analyzed separately [0062]. The skewness determination module 204 will separately determine a rating skewness of the plurality of ratings for each one of the plurality of individual instructor (304, 306, 308, 310) for each evaluation period, if more than one evaluation period is included in the raw instructor rating data set [0063]. The data availability module 212 is configured to make at least the rating skewness, the skewness comparison report, and the required instructor training accessible to one or more end users, end devices, other systems, and/or the like [0059]. In some examples, the end user is a manager who oversees the individual instructor. The end user can utilize the data made available through the data availability module 212 to analyze the performance and training needs of the individual instructor under their supervision [0059]. By examining the rating skewness, the skewness comparison report, and the required instructor training, the end user gains valuable insights into areas where improvements or adjustments in training methods may be required. This empowers the end user to make informed decisions regarding the professional development and performance management of their instructors. By reviewing the competency rating comparison of pilots and instructors, the head of training can identify areas where training methods and practices need improvement to enhance overall aviation safety [0059].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Tennyson’s Adaptive Flight Training Tool to include a comparison metric of instructors as taught by Mathew in order to assist in unveiling underlying instructor tendencies, such as leniency or strictness, and inform of additional training needs of the individual instructors (Mathew e.g. [0037]).
As per claim 9, Tennyson teaches the method of training a flight instructor via actual flights with students of claim 6, Tennyson does not explicitly teach, however, Mathew teaches the method further comprising using the normalized data of a 1st student from a plurality of instructors and rank each instructor on overall performance of instructing 1st student on the flight (See claim 8 response.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure include FOR: MI, Zu-ping (CN-119479426-A) “Aviation Training Method Based On Virtual Reality” and NPL: T. Malich, V. Socha, R. Matyáš, L. Hanáková, S. Kušmírek and V. Kráčmar, "Software Solution for Visualization and Evaluation of Flight Data in Terms of Competency-Based Training," 2020 AIA A/IEEE 39th Digital Avionics Systems Conference (DASC), San Antonio, TX, USA, 2020, pp. 1-7.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ayanna Minor whose telephone number is (571)272-3605. The examiner can normally be reached M-F 9am-5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
Prosecution Insights