DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/02/2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The limitation of claims 21-22 include the limitation of “wherein the explainability and pedagogical intervention module generates Al-learned correlations that predict individual outcomes for students by clustering students based on student attributes and performance results, and wherein the explanations for the training task recommendations provide information on the impact of the training task recommendations.” In a computer implemented method, the specification must disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing (see MPEP 2161.01) In this particular case, the specification fails to meet the requirement of providing the necessary computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function. The specification, paragraph 37-38, only provide a description of the results of the prediction module without providing the methodology for the performing the claimed limitation. see also LizardTech, Inc. v. Earth Res. Mapping, Inc., 424 F.3d 1336, 1343-46, 76 USPQ2d 1724, 1730-33 (Fed. Cir. 2005); Regents of the Univ. of Cal. v. Eli Lilly & Co., 119 F.3d 1559, 1568, 43 USPQ2d 1398, 1405-06 (Fed. Cir. 1997)(“The description requirement of the patent statute requires a description of an invention, not an indication of a result that one might achieve if one made that invention.”).
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.
Claims 1-2, 4, 6-7. 9-12, 14, 16-17 and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Sawyers US 20220139252, in view of Green et al US 20110076654 , in view of Heinzinger EP 3229220 and further in view of Moss US 20190012619
Claim 1, 11 and 20: The Sawyer reference provides a teaching of a computerized system for training a student to operate an actual machine, the system comprising:
an adaptive learning artificial intelligence (ALAI) module that adapts training of a student based on student performance data (see paragraph 250), simulation performance results for the student operating a simulated machine in a simulation system that simulates operation of an actual machine (see paragraph 90, 93 student/user operating a vehicle simulator) and electronic learning content results from an electronic learning module that delivers electronic learning content to a student computing device used by the student (see paragraph 247 ), wherein the ALAI module comprises:
an Al-driven learner profile module that processes the student performance data to generate a learner profile of the student (see paragraph 194 student’s input and behavior are processed into the a user’s learning profile/persona);
an Al-driven training task recommendation module that processes the student performance data and the learner profile to generate training task recommendations for the student (see paragraph 173-175 recommendation module); and
an explainability and pedagogical intervention module that processes the student performance data, the learner profile, and the training task recommendations (see paragraph 148-151, 233-234 and 243-245).
to determine and display on an instructor computing device explanations for the training task recommendations (see paragraph 190-191 providing recommendation to the instructor) and wherein the explainability and pedagogical intervention module is in data communication with the instructor communication device (see FIG 1A item 26/28 and 4 and paragraph 190-191 showing the instructor interaction with the explainability and pedagogical intervention module).
While the Sawyer reference provides a teaching of and enables the instructor to communicate the ALAI module to perform manual overrides (see paragraph 106 “more specifically, the instructor 4 can start, stop, or alter operation of the training device 6 and a simulation being executed by the training device during a training session”); it is silent on the teaching of the manual override of the training task recommendation.
However, the Green reference provides a teaching of the manual override of the training task recommendation (see paragraph 36 “… recommendation may be modified by his instructor using the override option”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Sawyer reference with the feature of manual override of the training task recommendation, as taught by the Green reference, in order to provide the ensure that the training recommendation is appropriate for the student’s skill level (see paragraph 47).
While the Sawyer reference provide a teaching of an instructor-graded performance results, the Saywer reference is silent on the teaching of the student performance data being based on instructor-graded performance results of the student based on the student operating the actual machine. However, the Heinzinger reference provides a teaching of teaching of the student performance data being based on instructor-graded performance results of the student based on the student operating the actual machine (see page 6 last paragraph where the data is gathered from actual flight data and page 5 line 38-43 gathering data on both the simulator and actual flight data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Sawyer reference with the feature of the student performance data being based on instructor-graded performance results of the student based on the student operating the actual machine, as taught by the Heinzinger reference,, in order to provide a holistic training environment that can assess both the training type.
The Sawyer reference is silent on the teaching of the instructor to communicate the ALAI module to implement new policies and change rules; and to make interventions to prescribe training task or theoretical learning to the students; wherein the interventions are used by the ALAI module to adjust further training task recommendations.
However, the Moss reference provides a teaching of the instructor to communicate the ALAI module to implement new policies and change rules (see paragraph 62 analyzing training history to recognize trend and modifying both the training policy and the content of the training policy); and to make interventions to prescribe training task or theoretical learning to the students (see paragraph 62 adding new content to a policy and adding a new policy) ; wherein the interventions are used by the ALAI module to adjust further training task recommendations (see paragraph 60 where trends are identified and used to further update the training policy)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Sawyer reference with the feature of the instructor to communicate the ALAI module to implement new policies and change rules and to make interventions to prescribe training task or theoretical learning to the students; wherein the interventions are used by the ALAI module to adjust further training task recommendations, as taught by the Moss reference in order to ensure that the system can be updated with future training needs.
Claim 2, 12: The Sawyer reference provides a teaching of wherein the ALAI module comprises an Al student performance assessment module (see paragraph 118-119 student’s action and performance are assessed and a score is given)
Claim 4, 14: The Sawyer reference provides a teaching of wherein the ALAI module comprises a remedial training module (see paragraph 179 remediation module )
Claims 6, 16: The Sawyer reference provide a teaching of a virtual instructor module comprising a coaching Al module and a performance assessment module that coach (see paragraph 167 recommendation module coaches the user on additional training material) and assess the student when operating the simulated machine in the simulation system (see paragraph 194 assess the user’s performance during the simulation).
Claims 7, 17: The Sawyer reference provides a teaching of a learning experience platform (LXP) for receiving and processing the student performance data (see paragraph 149)
Claims 9, 19: The Sawyer reference provide a teaching wherein the explainability and pedagogical intervention module enables the instructor to intervene via the instructor computing device to modify the Al-generated recommendations (see paragraph 190 an instructor selecting a task from a list of recommended tasks).
Claim 10: The Sawyer reference provides a teaching of wherein the simulation performance results are obtained from user input received via a tangible instrument of a simulation system (see paragraph 57 actuators from a simulator).
Claims 21 and 22: The Sawyer reference provides a reaching of wherein the explainability and pedagogical intervention module generates Al-learned correlations that predict individual outcomes for students by clustering students based on student attributes and performance results (see paragraph 176 predict based on the user’s past performance and similar activities ), and wherein the explanations for the training task recommendations provide information on the impact of the training task recommendations (see paragraph 193 and 198 providing report based on the prediction module on the result of the recommendation)
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sawyers US 20220139252, in view of Green et al US 20110076654 , in view of Heinzinger EP 3229220, in view of Moss US 20190012619 and further in view of Tang US 10607084
Claims 3, 13: The Sawyer reference is silent on the teaching of wherein the ALAI module comprises a learning workflow optimization module.
However, the Tang reference provides a teaching of wherein the ALAI module comprises a learning workflow optimization module (see col. 14:50-60).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Sawyer reference with the feature of wherein the ALAI module comprises a learning workflow optimization module, as taught by the Tang reference, in order to
Claims 5, 8, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sawyers US 20220139252, in view of Green et al US 20110076654 , in view of Heinzinger EP 3229220, in view of Moss US 20190012619 further in in view of Forough US 20220130272
Claims 5, 15: The Sawyer reference is silent on the teaching of wherein the ALAI module comprises an individualized micro learning path module.
However, the Forough reference provides a teaching of wherein the ALAI module comprises an individualized micro learning path module (see paragraph 116). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Forough reference with the feature of wherein the ALAI module comprises an individualized micro learning path module, as taught by the Forough reference, in order to provide an individualized learning experience for the student that can accommodate a user’s specific need.
Claim 8, 18: The Sawyer reference provides a teaching of wherein the LXP comprises: a learning record store (LRS) module (see paragraph 136 database that stores the record of the user’s learning career).
However, the Sawyer reference is silent on the teaching of a learning management system (LMS); and a learning content management system (LCMS). However, the Forough reference provides a teaching of a learning management system (LMS) (see paragraph 96); and a learning content management system (LCMS) (see paragraph 97).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Sawyer reference with the feature of a learning management system (LMS); and a learning content management system (LCMS), as taught by the Forough reference in order to provide an efficient scheduling of learning material that conform to the user’s need.
Response to Arguments
With respect to applicant’s argument on the rejection of claim 21-22 under 35 U.S.C 112(a), the applicant argued that paragraph 26 and 35 of the specification provides the necessary details and technique to generate the AI-learned correlation. The applicant states:
[0026] "In one embodiment, the explainability and pedagogical intervention module 174 uses a SHAP (Shapley Additive Explanations) technique to generate Al-learned correlations that predict individual outcomes for students by clustering students based on student attributes and performance results. For example, performance results may include the number of attempts, results, performance gap, peer results, time to pass the exam, flight performance, or other such factors. In other embodiments, algorithms other than SHAP may be used, such as: LIME (Local Interpretable Model-Agnostic Explanations); and Multivariate Gaussian Distribution Approach."
[0035] "Student or pilot segmentation into clusters utilizes one or more data-driven AI clustering algorithms to create student profiles, identify the pattern of each profile in terms of learning performance and behavior, and then provide actionable recommendations on a cohort or cluster level. In one specific embodiment, the clustering algorithm may involve using T-distributed Stochastic Neighbor Embedding (tSNE) for dimension reduction and K-means for the clustering to generate the learner profiles."
The applicant argued that these technique, tSNE for dimension reduction, K-means for clustering, SHAP, LIME or Multivariate Gaussian Distribution technique would be understood to one of ordinary skilled in the art. Thus, would fulfill the applicant’s written description requirement for the limitation of “wherein the explainability and pedagogical intervention module generates Al-learned correlations that predict individual outcomes for students by clustering students based on student attributes and performance results, and wherein the explanations for the training task recommendations provide information on the impact of the training task recommendations”. The examiner respectfully disagrees. In order to satisfy the written description requirement, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. While the examiner can agree with that one of ordinary skilled in the art should be with the familiar with the generic technique mentioned in the specification (tSNE, K-means, SHAP, LIME or Multivariate Gaussian Distribution); it is unclear what steps should one of ordinary skilled art should take in order to transform the more generic technique to arrive to the limitation of “generates Al-learned correlations that predict individual outcomes for students by clustering students based on student attributes and performance results, and wherein the explanations for the training task recommendations provide information on the impact of the training task recommendations”. The MPEP clearly states that the it is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015). In this particular case, the examiner argued that is not enough for the possibility of one of ordinary skilled can transform the generic technique of tSNE, K-means, SHAP, LIME or Multivariate Gaussian Distribution; the specification itself must explain how the inventor intends to achieve the claimed function. Accordingly, the rejection on claims 21-22 are still maintained.
Applicant’s arguments with respect to claims 1-8, 10-18, 20-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J UTAMA whose telephone number is (571)272-1676. The examiner can normally be reached 9:00 - 17:30 Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at (571)270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT J UTAMA/Primary Examiner, Art Unit 3715