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 .
Application Status
Present office action is in response to preliminary amendment filed 07/08/2025. Claim 1 is cancelled. Claims 2-21 are added and currently pending in the application.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 2-21 are rejected on the ground of nonstatutory obviousness-type double patenting as being obvious over claims 1-20 of US Patent No. 11,568,753. The claims are not patentably distinct from each other as shown by representative independent claims below:
US Patent No. 11,568,753
US Application No. 18/970,748
Claim 20: A method for conducting automated skills mastery assessments, the method comprising: assessing, by processing circuitry, engagements of a plurality of learners with a plurality of electronic learning resources via an electronic-learning platform to produce a plurality of mastery assessments, wherein the plurality of electronic learning resources is configured to present content related to a plurality of skills logically arranged in a skills hierarchy comprising a plurality of skills families, each mastery assessment of the plurality of mastery assessments corresponds to a respective learner of the plurality of learners and one or more skills of a given skill family of a plurality of skill families, wherein each respective skill family comprises descendant and ancestor relationships between a set of skills of the plurality of skills belonging to the respective family, and the plurality of skills are connected to the plurality of electronic learning resources via a plurality of logical indicators of skill-resource relationships, each logical indicator of the plurality of logical indicators defining a connection between a given resource of the plurality of electronic learning resources and a given skill of the plurality of skills, wherein each respective electronic learning resource of the plurality of electronic learning resources is connected, via at least one of the logical indicators, to at least one skill of the plurality of skills, wherein each logical indicator of at least a portion of the logical indicators indicates a weight representing a relative strength of evidence of mastery in the respective skill that an interaction with the respective electronic learning resource carries, and at least a portion of the plurality of electronic learning resources are each connected, via a respective two or more logical indicators of the plurality of logical indicators, to two or more skills, wherein each logical indicator of the respective two or more logical indicators indicates a strength representing a relative strength of presentation of the respective skill within the respective electronic learning resource, and producing each mastery assessment comprises applying a set of mastery assessment parameters to at least one mastery assessment algorithm configured to calculate a skills level based on interactions of the respective learner with a set of learning resources linked by a set of logical indicators of the plurality of logical indicators to the respective one or more skills, wherein at least a portion of the mastery assessment parameters correspond to applying the strengths and/or weights of the respective set of logical indicators in calculating the respective mastery assessment; using historic interaction data derived through the engagements of the plurality of learners with the plurality of electronic learning resources and historic evaluation metrics derived through a portion of the engagements of the plurality of learners with the plurality of electronic learning resources, training, by the processing circuitry, at least one machine learning algorithm to forecast evaluation outcomes of learning resource engagements by analyzing a time series of interactions of each learner of the plurality of learners with a collection of learning resources related to a given skill family of the plurality of skill families, wherein a first machine learning algorithm of the at least one machine learning algorithm is a regression-based model, and the first machine learning algorithm is trained to forecast the evaluation outcomes based at least in part on engagement patterns and engagement timings indicative of one or more of skill fading, imparting learning, initial level of mastery, or a difficulty of acquiring mastery; applying, by the processing circuitry, the at least one machine learning algorithm to sets of historic user interactions of a set of learners with one or more electronic learning resources of the plurality of electronic learning resources to produce predicted evaluation outcomes corresponding to the historic learner interactions; comparing, by the processing circuitry, the predicted evaluation outcomes with actual evaluation outcomes for the set of learners; and based on one or more differences between the predicted evaluation outcomes and the actual evaluation outcomes, refining, by the processing circuitry, at least one parameter of the set of mastery assessment parameters.
Claim 2: A method for developing a learner's mastery of one or more skills of a skills hierarchy through a plurality of electronic learning resources of an e-learning platform, the method comprising: creating a skills hierarchy, accessible by processing circuitry, wherein two or more skills are logically connected as an ancestor skill and a descendant skill, the ancestor skill representing a genus of skills and the descendant skill representing a species of the genus, storing to non-volatile computer-readable media of the e-learning platform, a plurality of electronic learning resources, wherein each electronic learning resource of the plurality of electronic learning resources is comprised of one or more elements, each element of the one or more elements is logically connected to one or more skills of the skills hierarchy, and each element is associated with at least one mastery assessment parameter indicating the element's relevance to the development of the one or more logically connected skills, selecting, through a content recommendation engine trained by at least one machine learning algorithm of a plurality of machine learning algorithms, a first electronic learning resource, presenting, through a graphical user interface engine trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the first electronic learning resource of the plurality of electronic resources to the learner, recording to non-volatile computer-readable media, by the processing circuitry, a first interaction from the learner in response to the first electronic learning resource, evaluating, through a first skills evaluation engine of one or more skills evaluation engines trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the first interaction, to create a first assessment of the learner's mastery of one or more skills logically connected to one or more elements of the first electronic learning resource, based on the learner's mastery of the one or more logically connected skills, automatically selecting, through the content recommendation engine, a second electronic learning resource of the plurality of electronic resources, wherein one or more elements of the second electronic learning resource is logically connected to one or more skills logically connected to one or more elements of the first electronic learning resource, predicting, through an evaluation prediction engine trained by at least one machine learning algorithm of the plurality of machine learning algorithms, a predicted evaluation of the second interaction to the second electronic learning resource, presenting, through a graphical user interface engine trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the second electronic learning resource of the plurality of electronic resources to the learner, recording to non-volatile computer-readable media, by the processing circuitry, an actual second interaction from the learner in response to the second electronic learning resource, evaluating, through a second skills evaluation engine of the one or more skills evaluation engines trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the actual second interaction to create a second assessment of the learner's mastery of the one or more skills logically connected to one or more elements of both the first electronic learning resource and the second electronic learning resource, evaluating, through a third skills evaluation engine of the one or more skills evaluation engines trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the difference between the predicted second interaction and the actual second interaction, training, by the processing circuitry, at least one machine learning algorithm of the plurality of machine learning algorithms, to change at least one master assessment parameter, using the difference between the predicted second interaction and the actual second interaction training, based on the second assessment of the learner's mastery of the one or more logically connected skills, automatically selecting, through the content recommendation, a third electronic learning resource of the plurality of electronic resources.
Claim 1: A system for conducting automated skills mastery assessments, the system comprising: a data architecture stored to at least one non-volatile computer readable medium, the data architecture comprising a plurality of electronic learning resources, a plurality of skills arranged in a skills hierarchy comprising a plurality of skill families, each skill family comprising descendant and ancestor relationships between a respective set of skills of the plurality of skills belonging to the respective skill family, and a plurality of logical indicators of skill-resource relationships, each logical indicator of the plurality of logical indicators defining a connection between a given resource of the plurality of electronic learning resources and a given skill of the plurality of skills, wherein each respective electronic learning resource of the plurality of electronic learning resources is connected, via at least one of the logical indicators, to at least one skill of the plurality of skills, wherein each logical indicator of at least a portion of the logical indicators indicates a weight representing a relative strength of evidence of mastery in the respective skill that an interaction with the respective electronic learning resource carries, and at least a portion of the plurality of electronic learning resources are each connected, via a respective two or more logical indicators of the plurality of logical indicators, to two or more skills, wherein each logical indicator of the respective two or more logical indicators indicates a strength representing a relative strength of presentation of the respective skill within the respective electronic learning resource; and software logic for executing on processing circuitry and/or hardware logic configured to perform operations comprising assess engagements of a plurality of learners with the plurality of electronic learning resources via an electronic-learning platform to produce a plurality of mastery assessments, wherein each mastery assessment of the plurality of mastery assessments corresponds to a respective learner of the plurality of learners and one or more skills of a given skill family of the plurality of skill families, and producing each mastery assessment comprises applying a set of mastery assessment parameters to at least one mastery assessment algorithm configured to calculate a skills level based on interactions of the respective learner with a set of learning resources linked by a set of logical indicators of the plurality of logical indicators to the respective one or more skills, wherein at least a portion of the mastery assessment parameters correspond to applying the strengths and/or weights of the respective set of logical indicators in calculating the respective mastery assessment, using historic interaction data derived through the engagements of the plurality of learners with the plurality of electronic learning resources and historic evaluation metrics derived through a portion of the engagements of the plurality of learners with the plurality of electronic learning resources, train at least one machine learning algorithm to forecast evaluation outcomes of learning resource engagements by analyzing a time series of interactions of each learner of the plurality of learners with a collection of learning resources related to a given skill family of the plurality of skill families, wherein a first machine learning algorithm of the at least one machine learning algorithm is a regression-based model, and the first machine learning algorithm is trained to forecast the evaluation outcomes based at least in part on engagement patterns and engagement timings indicative of one or more of skill fading, imparting learning, initial level of mastery, or a difficulty of acquiring mastery, apply the at least one machine learning algorithm to sets of historic time series of user interactions of a set of learners with one or more electronic learning resources of the plurality of electronic learning resources to produce predicted evaluation outcomes corresponding to the historic learner interactions, compare the predicted evaluation outcomes with actual evaluation outcomes for the set of learners, and based on one or more differences between the predicted evaluation outcomes and the actual evaluation outcomes, refine at least one parameter of the set of mastery assessment parameters.
Claim 21: A system for developing a learner's mastery of one or more skills of a skills hierarchy through a plurality of electronic learning resources of an e-learning platform, the system comprising: at least one non-volatile computer readable medium configured to store a skills hierarchy and the plurality of electronic learning resources, accessible by processing circuitry, wherein the skills hierarchy is comprised of two or more skills logically connected, by processing circuitry, as an ancestor skill and a descendant skill, the ancestor skill representing a genus of skills and the descendant skill representing a species of the genus, and each electronic learning resource of the plurality of electronic learning resources is comprised of one or more elements, each element of the one or more elements is logically connected to one or more skills of the skills hierarchy, and each element is associated with at least one mastery assessment parameter indicating the element's relevance to the development of the one or more logically connected skills, and the processing circuitry, configured to perform operations comprising, selecting, through a content recommendation engine trained by at least one machine learning algorithm of a plurality of machine learning algorithms, a first electronic learning resource, presenting, through a graphical user interface engine trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the first electronic learning resource of the plurality of electronic resources to the learner, recording to non-volatile computer-readable media, by the processing circuitry, a first interaction from the learner in response to the first electronic learning resource, evaluating, through a first skills evaluation engine of one or more skills evaluation engines trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the first interaction, to create a first assessment of the learner's mastery of one or more skills logically connected to one or more elements of the first electronic learning resource, based on the learner's mastery of the one or more logically connected skills, automatically selecting, through the content recommendation engine, a second electronic learning resource of the plurality of electronic resources, wherein one or more elements of the second electronic learning resource is logically connected to one or more skills logically connected to one or more elements of the first electronic learning resource, predicting, through an evaluation prediction engine trained by at least one machine learning algorithm of the plurality of machine learning algorithms, a predicted evaluation of the second interaction to the second electronic learning resource, presenting, through a graphical user interface engine trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the second electronic learning resource of the plurality of electronic resources to the learner, recording to non-volatile computer-readable media, by the processing circuitry, an actual second interaction from the learner in response to the second electronic learning resource, evaluating, through a second skills evaluation engine of the one or more skills evaluation engines trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the actual second interaction to create a second assessment of the learner's mastery of the one or more skills logically connected to one or more elements of both the first electronic learning resource and the second electronic learning resource, evaluating, through a third skills evaluation engine of the one or more skills evaluation engines trained by at least one machine learning algorithm of the plurality of machine learning algorithms, the difference between the predicted second interaction and the actual second interaction, training, by the processing circuitry, at least one machine learning algorithm of the plurality of machine learning algorithms, to change at least one master assessment parameter, using the difference between the predicted second interaction and the actual second interaction training, based on the second assessment of the learner's mastery of the one or more logically connected skills, automatically selecting, through the content recommendation, a third electronic learning resource of the plurality of electronic resources.
The claims are not patentably distinct from each other because the limitations set forth in claims 2-21 of the instant application are comparable in scope to the combination of claims 1-20 of US Patent No. 11,568,753. Any claimed subject matter differences would have been obvious variations in view of known knowledge and at least the prior art cited in the Information Disclosure Statement (IDS) by Applicant filed 04/18/2025.
Allowable Subject Matter
Claims 2-21 would be allowed if the Double Patenting rejections were overcome with properly filed terminal disclaimers.
Conclusion
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
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