DETAILED ACTION
This action is in response to communication filed on 05 January 2026. Claims 1, 5-9, 11, 13-15 and 18-20 are amended. Claims 4 and 17 are canceled. Claims 21-22 are added. Claims 1-3, 5-16 and 18-22 are pending in the application and have been considered below.
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 Arguments
Applicant argues that [“Applicant respectfully submits that the system processor 110 and other components of the electronic learning system 30 correspond to a system back-end. For example, as shown below in FIG. 1 of Bilic, the electronic learning system 30 clearly performs back-end processing, in contrast to front-end devices or processing resources such as computing devices 20” (Page 11 )].
Examiner respectfully disagrees. BILIC discloses “For example, the electronic learning system 30 can include one or more processing components, such as computing servers 32. Each computing server 32 can include one or more processor. The processors provided at the computing servers 32 can be referred to as “system processors” while processors provided at computing devices 20 can be referred to as “device processors”. The computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer)” (fig. 1, par. 0081) (emphasis added). Therefore, BILIC envisages using devices 20 (such as laptops or PCs) as servers.
Applicant further argues that [“Accordingly, the combination of Subramanyan, Kane, and Bilic cannot be relied upon to disclose at least: "responsive to the learner's inputs and using the one or more front-end processing resources, compute a progress score and a performance score for the learner's progress towards the first terminal objective."” (Page 12 )].
Examiner respectfully disagrees. BILIC discloses “Referring now to FIG. 2, which is a block diagram 100 of some components that may be implemented in the electronic learning system 30 according to some embodiments. In the example of FIG. 2, the various illustrated components are provided at one of the computing servers 32” (fig. 2, par. 0092) (emphasis added). Furthermore, as stated above, BILIC envisages using devices 20 as servers 32 “The computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer)” (fig. 1, par. 0081) (emphasis added). Therefore, BILIC teaches that devices 20 could also be used as servers (i.e. front-end device can run the learning system modules; see also claim 1 rejection below).
Thus, the combination of SUBRAMANYAN, KANE and BILIC adequately discloses applicant's claimed limitation. Examiner respectfully reminds Applicants that during examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004).
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.
Claims 1-3, 5-8, 15-16 and 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over SUBRAMANYAN et al. (US20020178181A1) in view of KANE et al. (US20210141616A1) and further view of BILIC et al. (US20160358494A1).
As to claim 1, SUBRAMANYAN teaches:
a digital learning system comprising: one or more front-end processing resources operative to execute machine-readable instructions (see fig. 1, par. 0028, wherein FIG. 1 depicts an internet-based method and system for creating and developing content for eLearning; see also par. 0081, wherein the computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer); as taught by SUBRAMANYAN)
defining terminal objectives and enabling objectives for a digital learning course (see fig. 1, par. 0043, wherein The subject matter expert may categorize or characterize the raw content uploaded to the storyboard server by designating the material as, for example, write-ups, manuals, audio, video, or graphics. The subject matter expert may enter or characterize the main or terminal objective of the material and the more specific or enabling objectives, and information on the user environment and audience demographics; as taught by SUBRAMANYAN);
create the digital learning course comprising the defined terminal objectives and the defined enabling objectives (see fig. 2, par. 0048, wherein as templates, learning objects, and frames are modified, a history of these items is stored in the storyboard server. Users can revert back to previous versions. When the users and developers sign off on the content, the content is ready to go 24; see also fig. 3, par. 0050, wherein content developers create the relevant media for the storyboard pages, incorporate it into the network or webpages of the storyboard and receive feedback from other users and developers, such as the instructional designer and subject matter experts 38, and collaborate further with the users and developers until the final content 39 is achieved; as taught by SUBRAMANYAN).
SUBRAMANYAN does not expressly teach to: receive, via an author-side graphical user interface (GUI), low code inputs, receive, via the author-side GUI, low code inputs, defining weighted associations between a first terminal objective and a first subset of enabling objectives that guide learners toward the first terminal objective; based on the received low code inputs, provide, via a learner-side GUI, the created digital learning course; receive, via the learner-side GUI, inputs from a learner as the learner engages with the created digital learning course; and responsive to the learner's inputs and using the one or more front-end processing resources, compute a progress score and a performance score for the learner's progress towards the first terminal objective.
In similar field of endeavor, KANE teaches to:
receive, via an author-side graphical user interface (GUI), low code inputs, receive, via the author-side GUI, low code inputs (see figs. 2-4, par. 0019, wherein the disclosed LCNC framework provides a distributed software development environment that enables the creation of software (e.g., applications) through graphical user interfaces and configurations instead of traditional hand-coded programming. A low code (LC) model enables developers of varied experience levels to create applications using a visual user interface in combination with model-driven logic as taught by KANE)
based on the received low code inputs (see fig. 12, par. 0123, wherein Process 1200 details some embodiments of the LCNC framework that enables the creation of software (e.g., applications) through graphical user interfaces and configurations instead of traditional hand-coded programming. The LCNC engine 400 enables developers of varied experience levels to create applications using a visual user interface in combination with model-driven logic (as provided by the UI/IOs discussed below); as taught by KANE),
provide, via a learner-side GUI, the created digital learning course (see figs. 1-4, par. 0075, wherein at least one of the software modules 238 can be configured within the system to output data to at least one user 231 via at least one graphical user interface rendered on at least one digital display; as taught by KANE).
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 SUBRAMANYAN apparatus to include the teachings of KANE receive, via an author-side graphical user interface (GUI), low code inputs, receive, via the author-side GUI, low code inputs, based on the received low code inputs, and provide, via a learner-side GUI, the created digital learning course. Such a person would have been motivated to make this combination as LCNC framework is beneficial for reducing the amount of traditional hand coding and enabling accelerated delivery of business applications. The LCNC framework also lowers the initial cost of setup, training, deployment and maintenance of applications and services (see par. 0019, KANE).
SUBRAMANYAN and KANE do not expressly teach defining weighted associations between a first terminal objective and a first subset of enabling objectives that guide learners toward the first terminal objective; receive, via the learner-side GUI, inputs from a learner as the learner engages with the created digital learning course; and responsive to the learner's inputs and using the one or more front-end processing resources, compute a progress score and a performance score for the learner's progress towards the first terminal objective.
In similar field of endeavor, BILIC teaches:
defining weighted associations between a first terminal objective and a first subset of enabling objectives that guide learners toward the first terminal objective (see par. 0006, wherein for each learning objective of the set of learning objectives, selecting, from a plurality of resources accessible to the electronic learning system, one or more resources assigned a relevance score at least satisfying a relevance threshold for that learning objective, the relevance score representing an estimated degree of correlation between that learning objective and a content of the respective resource, and the relevance threshold indicating a minimum relevance score required for a resource to be selected for a learning objective; see also fig. 4, par. 00017; see also par. 0180, wherein the learning path component 114 can generate the combined score by applying a first weight to the relevance score and a second weight to the system learn value. The combined score may be a sum of the weighted relevance score and the weighted system learn value. The first weight and the second weight may, in some embodiments, each be numerical values that, together, sum to one; see also par. 0181, wherein described with reference to FIGS. 8A to 11, the electronic learning system 30 can generate the initial learning path 512 based on estimated correlations between the received learning objectives 320, 330; as taught by BILIC)
receive, via the learner-side GUI, inputs from a learner as the learner engages with the created digital learning course (see par. 0095, wherein The system processor 110 may also, based on user response inputs received via the interface component 116, initiate the evaluation component 118 to determine a competence level of the user in respect of at least one learning objective; see also figs. 7-8B, pars. 0155-0156, wherein at 750, the system processor 110 monitors a feedback usage indicator for each evaluation resource; The feedback usage indicator can generally represent an amount of user interaction with that evaluation resource; as taught by BILIC);
and responsive to the learner's inputs and using the one or more front-end processing resources, compute a progress score and a performance score for the learner's progress towards the first terminal objective (see par. 0081, wherein the computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer); see also figs. 8A-8B, par. 0157, wherein with each use of the evaluation resource in the learning path 512, the corresponding feedback usage indicator increases in value and the electronic learning system 30 can also collect usage data related to those interactions by the users with the evaluation resources; see par. 0160, wherein the electronic learning system 30 can assign a system learn value to each resource selected at 720 in response to each time a user completes a corresponding evaluation resource; as taught by BILIC).
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 SUBRAMANYAN and KANE apparatus to include the teachings of BILIC for defining weighted associations between a first terminal objective and a first subset of enabling objectives that guide learners toward the first terminal objective; receive, via the learner-side GUI, inputs from a learner as the learner engages with the created digital learning course; and responsive to the learner's inputs and using the one or more front-end processing resources, compute a progress score and a performance score for the learner's progress towards the first terminal objective. Such a person would have been motivated to make this combination as it is beneficial for the author and the user to know the correlation between objectives and achievements, and providing a way to measure the success of the training course afterwards. It is also more flexible to be able to run the whole system on a laptop or a PC for testing or debugging purposes (see also BILIC, pars. 0002-0004).
As to claim 2, SUBRAMANYAN, KANE and BILIC teach the limitations of claim 1. KANE further teaches:
wherein the low code inputs comprise at least one of drag-and-drop-related inputs, pull-down-menu-related inputs, and point- and-click-related inputs (see figs. 1-4, par. 0011, wherein the LCNC framework can comprise a development platform that can be a visually integrated development environment that allows citizen developers to drag-and-drop application components, connect them together, and create an application; as taught by KANE).
As to claim 3, SUBRAMANYAN, KANE and BILIC teach the limitations of claim 1. SUBRAMANYAN further teaches:
the terminal objectives comprise desired outcomes for learners engaging with the digital learning course; and the enabling objectives comprise learning experiences that guide learners towards the terminal objectives (see par. 0044, wherein creating content for eLearning wherein the storyboarding process comprises collaboratively deciding the objectives of the eLearning program, defining the audience for the same and, based on the objectives and audience, to collect information that goes into making the program, structuring it, breaking it in modules of the appropriate sizes, and defining the look and feel of the program; as taught by SUBRAMANYAN).
As to claim 5, SUBRAMANYAN, KANE and BILIC teach the limitations of claim 1. BILIC further teaches:
wherein computing the progress score and the performance score for the first terminal objective comprises: computing, for enabling objectives of the first subset of enabling objectives, a progress score and a performance score for the learner's progress towards the enabling objective
(see par. 0016, wherein generate the learning path based on (i) the relevance score assigned to each resource of the one or more resources and (ii) a system learn value assigned to each resource selected for the subset of learning objectives associated with that evaluation type resource; see also par. 0157, wherein with each use of the evaluation resource in the learning path 512, the corresponding feedback usage indicator increases in value and the electronic learning system 30 can also collect usage data related to those interactions by the users with the evaluation resources; see par. 0160, wherein the electronic learning system 30 can assign a system learn value to each resource selected at 720 in response to each time a user completes a corresponding evaluation resource; as taught by BILIC); based on the weighted associations between the first terminal objective and the first subset of enabling objectives, computing the progress score for the learner's progress towards the first terminal objective as a weighted sum of the computed progress scores for the learner's progress towards the first subset of enabling objectives; and based on the weighted associations between the first terminal objective and the first subset of enabling objectives, computing the performance score for the learner's progress towards the first terminal objective as a weighted sum of the computed performance scores for the learner's progress towards the first subset of enabling objectives (see par. 0006, wherein one or more resources assigned a relevance score at least satisfying a relevance threshold for that learning objective, the relevance score representing an estimated degree of correlation between that learning objective and a content of the respective resource; see also par. 0008, wherein generating the learning path based on the relevance score assigned to each resource and the system learn value assigned to each resource includes: applying a first weight to the relevance score and a second weight to the system learn value; and generating a combined score based on the weighted relevance score and the weighted system learn value; as taught by BILIC).
As to claim 6, SUBRAMANYAN, KANE and BILIC teach the limitations of claim 1. BILIC further teaches:
wherein the one or more front-end processing resources are further operative to execute machine-readable instructions to: transmit, to a back-end learning management system, records related to the computed progress score and the computed performance score for the learner's progress towards the first terminal objective (see fig. 2, par. 0155, wherein at 750, the system processor 110 monitors a feedback usage indicator for each evaluation resource ... The feedback usage indicator can be stored in the learning path database 146; see also par. 0087, wherein the data storage components 34 can store various data associated with the operation of the electronic learning system 30. For example, course data 35, such as data related to a course's framework, educational content, and/or records of assessments, may be stored at the data storage components 34. The data storage components 34 may also store user data, which includes information associated with the users 12, 14. The user data may include a user profile for each user 12, 14, for example …The data storage components 34 may also store data associated with the learning path, such as learning objectives and learning path data associated with the learning path; see also par. 0081, wherein the computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer); as taught by BILIC).
As to claim 7, SUBRAMANYAN, KANE and BILIC teach the limitations of claim 1. BILIC further teaches:
wherein the one or more front-end processing resources are further operative to execute machine-readable instructions to: responsive to at least one of the computed progress score and the computed performance score for the learner's progress towards the first terminal objective, modify content of the digital learning course provided to the learner via the learner-side GUI (see par. 0058, wherein learning paths can be generated based on an estimated degree of correlation between the learning objectives and the one or more resources. In some embodiments, the electronic learning systems described herein can update the learning path based on usage data associated with certain types of resources, such as evaluation resources. An evaluation resource is a resource that involves some degree of interaction between the user and the electronic learning system in order to evaluate a proficiency of the user with one or more learning objectives. When a usage amount of the evaluation resource at least satisfies a predefined threshold, the systems described herein can determine that the collected usage data is sufficient and can then proceed to update the learning path based, at least partially, on that collected usage data; see also par. 0081, wherein the computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer); as taught by BILIC).
As to claim 8, SUBRAMANYAN, KANE and BILIC teach the limitations of claim 1. BILIC further teaches wherein:
the one or more front-end processing resources are further operative to execute machine-readable instructions to receive, via the author-side GUI, defining content pages that arrange digital learning content for the enabling objectives (see figs. 3-17, par. 0101, wherein reference is now made to FIG. 3, which is a screenshot 200 of an example user interface 210 for receiving example learning objectives 220, 230 by the electronic learning system 30. The user interface 210 is provided via a browser application 202 in this example; see also par. 0081, wherein the computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer); as taught by BILIC);
and creating the digital learning course comprises implementing the content pages as web pages or segments of web pages provided via the learner-side GUI (see figs. 3-17, par. 0111, wherein FIG. 5 is a screenshot of an example user interface 400 showing an example learning path generated based on the example learning objectives 220, 230 received via the user interface 210 in FIG. 3; see also par. 0074, wherein the connection request initiated from the computing devices 20 a, 20 b may be initiated from a web browser and directed at the browser-based communications application on the electronic learning system 30; as taught by BILIC).
KANE further teaches:
low code inputs (see figs. 2-4, par. 0019, wherein the disclosed LCNC framework provides a distributed software development environment that enables the creation of software (e.g., applications) through graphical user interfaces and configurations instead of traditional hand-coded programming. A low code (LC) model enables developers of varied experience levels to create applications using a visual user interface in combination with model-driven logic as taught by KANE),
based on the received low code inputs (see fig. 12, par. 0123, wherein Process 1200 details some embodiments of the LCNC framework that enables the creation of software (e.g., applications) through graphical user interfaces and configurations instead of traditional hand-coded programming. The LCNC engine 400 enables developers of varied experience levels to create applications using a visual user interface in combination with model-driven logic (as provided by the UI/IOs discussed below); as taught by KANE).
As to claim 22, SUBRAMANYAN, KANE and BILIC teach the limitations of claim 1. BILIC further teaches:
the digital learning system of claim 1, further comprising a client device that includes the one or more front-end processing resources to compute the progress score and the performance score (see par. 0081, wherein the computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer); see also figs. 8A-8B, par. 0157, wherein with each use of the evaluation resource in the learning path 512, the corresponding feedback usage indicator increases in value and the electronic learning system 30 can also collect usage data related to those interactions by the users with the evaluation resources; see par. 0160, wherein the electronic learning system 30 can assign a system learn value to each resource selected at 720 in response to each time a user completes a corresponding evaluation resource; as taught by BILIC).
Claim 15 amounts to the method performed by the system of claim 1. Accordingly, claim 15 is rejected for substantially the same reasons as presented above for claim 1 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 16 amounts to the method performed by the system of claim 3. Accordingly, claim 16 is rejected for substantially the same reasons as presented above for claim 3 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 18 amounts to the method performed by the system of claim 5. Accordingly, claim 18 is rejected for substantially the same reasons as presented above for claim 5 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 19 amounts to the method performed by the system of claim 7. Accordingly, claim 19 is rejected for substantially the same reasons as presented above for claim 7 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 20 amounts to the method performed by the system of claim 6. Accordingly, claim 20 is rejected for substantially the same reasons as presented above for claim 6 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 21 amounts to the method performed by the system of claim 22. Accordingly, claim 21 is rejected for substantially the same reasons as presented above for claim 22 and based on the references’ disclosure of the necessary supporting hardware and software.
Claims 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over BILIC et al. (US20160358494A1) in view of KANE et al. (US20210141616A1) and further view of SUBRAMANYAN et al. (US20020178181A1).
As to claim 9, BILIC teaches
a digital learning system (see fig. 1, par. 0001, wherein the described embodiments relate to methods and systems associated with providing a learning path for an electronic learning system; as taught by BILIC)
comprising: an author-side graphical user interface (GUI) comprising: a terminal objective field (see par. 0028, wherein FIG. 4 is a screenshot of an example user interface for receiving example learning objectives by the electronic learning system; as taught by BILIC),
an enabling objective field (see par. 0030, wherein FIG. 6 is a screenshot of an example user interface showing an example learning path generated based on the example learning objectives received via the user interface in FIG. 4; as taught by BILIC),
and a content page field arranging digital learning content for the enabling objectives (see fig. 12, par. 0209, wherein one or more learning objectives 320, 330 can be assigned a mandatory status. The mandatory status can indicate that the actions in the learning path 512 associated with that learning objective are required for the user, and cannot be removed from the learning path 512 despite the electronic learning system 30 determining that the user has reached the mastery level in respect of that learning objective. That is, the mandatory status can override the effects of the mastery status 1240, 1242; see also par. 0210, wherein For example, in FIG. 12, the subject specific learning objective 1230 a, which corresponds to the subject specific learning objective 330 a, is assigned the mandatory status 1250; as taught by BILIC);
one or more front-end processing resources operative to execute machine-readable instructions to: via the author-side GUI create the digital learning course having the defined terminal objectives, the defined enabling objectives, and the arranged digital learning content (see par. 0038, wherein FIG. 13 is a screenshot of an example user interface showing an example learning path generated based on the learning objectives in FIG. 12; see also par. 0081, wherein the computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer); as taught by BILIC);
and provide the created digital learning course to learners via a learner-side GUI; and the learner-side GUI configured to display the created digital learning course and receive inputs from learners as the learners engage with the created digital learning course (see figs 12-13, par. 0214, wherein An example learning path 1312 will now be described with reference to FIG. 13, which is a screenshot 1300 of an example user interface 1310 showing the learning path 1312 generated based on the learning objectives 1220, 1230 in FIG. 12; see also par. 0215, wherein the learning path 1312 includes a first series of actions 1330 associated with the second group 520 b of the learning path 512, a second series of actions 1340 associated with the third group 520 c of the learning path 512 and a third series of actions 1350 associated with a new group 1320 (“Promotion of Products and Services”); as taught by BILIC);
wherein the one or more front-end processing resources (see par. 0081, wherein the computing servers 32 may be a computing device 20 (e.g. a laptop or personal computer); as taught by BILIC),
responsive to a learner's inputs received via the learner-side GUI (see par. 0095, wherein The system processor 110 may also, based on user response inputs received via the interface component 116, initiate the evaluation component 118 to determine a competence level of the user in respect of at least one learning objective; see also figs. 7-8B, pars. 0155-0156, wherein at 750, the system processor 110 monitors a feedback usage indicator for each evaluation resource; The feedback usage indicator can generally represent an amount of user interaction with that evaluation resource; as taught by BILIC),
compute a progress score and a performance score for the learner's progress towards the defined terminal objectives (see figs. 8A-8B, par. 0157, wherein with each use of the evaluation resource in the learning path 512, the corresponding feedback usage indicator increases in value and the electronic learning system 30 can also collect usage data related to those interactions by the users with the evaluation resources; see par. 0160, wherein the electronic learning system 30 can assign a system learn value to each resource selected at 720 in response to each time a user completes a corresponding evaluation resource; as taught by BILIC).
BILIC does not expressly teach configured to receive low code inputs defining terminal objectives for a digital learning course, configured to receive low code inputs defining enabling objectives for the digital learning course, configured to receive low code inputs, based on low code inputs received.
In similar field of endeavor, KANE teaches:
configured to receive low code inputs, configured to receive low code inputs, configured to receive low code inputs (see figs. 2-4, par. 0019, wherein the disclosed LCNC framework provides a distributed software development environment that enables the creation of software (e.g., applications) through graphical user interfaces and configurations instead of traditional hand-coded programming. A low code (LC) model enables developers of varied experience levels to create applications using a visual user interface in combination with model-driven logic as taught by KANE),
based on low code inputs received (see fig. 12, par. 0123, wherein Process 1200 details some embodiments of the LCNC framework that enables the creation of software (e.g., applications) through graphical user interfaces and configurations instead of traditional hand-coded programming. The LCNC engine 400 enables developers of varied experience levels to create applications using a visual user interface in combination with model-driven logic (as provided by the UI/IOs discussed below); as taught by KANE).
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 BILIC apparatus to include the teachings of KANE configured to receive low code inputs, configured to receive low code inputs, configured to receive low code inputs, based on low code inputs received. Such a person would have been motivated to make this combination as LCNC framework is beneficial for reducing the amount of traditional hand coding and enabling accelerated delivery of business applications. The LCNC framework also lowers the initial cost of setup, training, deployment and maintenance of applications and services (see par. 0019, KANE).
BILIC and KANE do not expressly teach defining terminal objectives for a digital learning course, defining enabling objectives for the digital learning course.
In similar field of endeavor, SUBRAMANYAN teaches defining terminal objectives for a digital learning course, defining enabling objectives for the digital learning course (see fig. 1, par. 0043, wherein The subject matter expert may categorize or characterize the raw content uploaded to the storyboard server by designating the material as, for example, write-ups, manuals, audio, video, or graphics. The subject matter expert may enter or characterize the main or terminal objective of the material and the more specific or enabling objectives, and information on the user environment and audience demographics; as taught by SUBRAMANYAN).
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 BILIC and KANE apparatus to include the teachings of SUBRAMANYAN defining terminal objectives for a digital learning course, defining enabling objectives for the digital learning course. Such a person would have been motivated to make this combination as what is desired, therefore, is a method and system collecting, creating, and developing content for eLearning, which will simplify and improve the process of storyboarding by enabling team members to communicate and develop storyboards more efficiently and definitively (see par. 0007, SUBRAMANYAN).
Claim 10 amounts to the system that is analogous to a system with a combination of limitations from the system of claim 1 and claim 3. Accordingly, claim 10 is rejected for substantially the same reasons as presented above for combination of limitations from 1 and claim 3, and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 11 amounts to the system that is analogous to the system of claim 4. Accordingly, claim 11 is rejected for substantially the same reasons as presented above for claim 4 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 12 amounts to the system that is analogous to the system of claim 5. Accordingly, claim 12 is rejected for substantially the same reasons as presented above for claim 5 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 13 amounts to the system that is analogous to the system of claim 6. Accordingly, claim 13 is rejected for substantially the same reasons as presented above for claim 6 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 14 amounts to the system that is analogous to the system of claim 7. Accordingly, claim 14 is rejected for substantially the same reasons as presented above for claim 7 and based on the references’ disclosure of the necessary supporting hardware and software.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Publication Number
Filing Date
Title
US20140147824A1
2012-11-28
Independent e-learning standard engines
US20180096127A1
2017-09-21
Associating multiple e-learning identities with a single user
US9799227B2
2014-08-11
Team management for a learning management system
US20160180248A1
2015-08-20
Context based learning
US20220366896A1
2022-05-10
Intelligent training and education bot
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KOOROSH NEHCHIRI whose telephone number is (408)918-7643. The examiner can normally be reached M-F, 11-7 PST.
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, William L. Bashore can be reached at 571-272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KOOROSH NEHCHIRI/Examiner, Art Unit 2174
/WILLIAM L BASHORE/ Supervisory Patent Examiner, Art Unit 2174
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