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 .
Response to Amendment
The amendment filed June 15, 2026 has been entered. Claims 1-2, 4, 7-8, 10-12, 14, and 16-26 remain pending in the application. Claims 1-2, 4, 8, 11-12, 14, 16, and 19 are noted as amended, claims 3, 5-6, 9, 13, and 15 are noted as newly cancelled, and claims 21-26 are noted as newly added.
Claim Objections
Claims 1, 11, and 19 objected to because of the following informalities:
In claim 1, line 10, “and the predefined learning objective to the predefined learning objective” is not grammatically correct.
In claim 11, line 12, “and the predefined learning objective to the predefined learning objective” is not grammatically correct.
In claim 19, line 13, “and the predefined learning objective to the predefined learning objective” is not grammatically correct.
Appropriate correction is required.
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 1-2, 4, 7-8, 10-12, 14, and 16-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 11, and 19 recite a process, a computer program product/machine-readable storage device including the process, and a computer system for performing the process, the process including the steps of monitoring student interactions with student computing devices during a class session to collect student interaction data including content selected by each student and displayed on the student computing devices; obtaining an engagement score representative of a student being on track by interacting with the student selected content relevant to a predefined learning objective, wherein the engagement score is a function of a weighted relevance score derived by extracting text from the content displayed on the student computing device and determine semantic similarity between the extracted text and the predefined learning objective to the predefined learning objective to determine the relevance score of the content displayed on the student computing device to the predefined learning objective and a weighted time value of student interaction with the content displayed on the student computing device; selecting a communication, the communication selected to direct the student to interactions to increase the engagement score; monitoring student interactions following the communication to determine a post communication engagement score; and modifying an effectiveness score of the communication based on a change between the post communication engagement score and the engagement score. The recited steps, under their broadest reasonable interpretation, are monitoring student interactions with computing devices, obtaining an engagement score based on the student interactions by extracting text, determining semantic similarity, and using a weighted content relevance score and a weighted time value of student interaction, selecting a communication to increase student engagement, and modifying an effectiveness score of the communication based on the change in engagement score. The recited steps, as drafted, are a process that is a method of applying an abstract idea, specifically mental processes (evaluation (obtaining/determining an engagement score; determining semantic similarity; determining the relevance score; modifying an effectiveness score based on the engagement score change), judgement (selecting a communication to increase student engagement), observation (monitoring student interactions with computing devices during a class and following the communication to determine a post engagement score; extracting text)) and/or certain methods of organizing human activity in the form of teaching (monitoring student interactions; obtaining an engagement score; selecting a communication to increase engagement). If claim limitations, under their broadest reasonable interpretation, include a mental process and/or certain methods of organizing human activity, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claims 1, 11, and 19 recite abstract ideas.
This judicial exception is not integrated into a practical application because the claims do not recite additional elements that are significantly more than the judicial exception or meaningfully limit the practice of the judicial exception. The additional elements are the steps being performed by one or more processors; a machine-readable storage device having instructions for execution by a processor of a machine to cause the processor to perform operations to perform a method [claim 11]; a processor; a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations [claim 19]; the weighted relevance score being derived by a machine learning language model; performing natural language process; and the monitoring being performed via the machine learning language model. The additional elements are instructions for applying the judicial exceptions with a generic computing device as, under their broadest reasonable interpretation, the additional elements of processors, machine-readable storage device/memory device having instructions stored thereon, a machine learning language model, and performing natural language processing are generic computer components for performing the above method, per MPEP 2106.05(f). Under their broadest reasonable interpretation, the additional elements are generic components of a computing device used to apply the abstract idea. Further, paragraphs 0095 of the specification states the computing device can be a computer or “other computing device including the same or similar elements”. With regard to the machine learning language model and natural language processing, due to the high-level of generality of the recitation of machine learning and the performed steps (monitoring student interactions and determining a weighted relevance score by extracting text and determining semantic similarity between the extracted text and the learning objective) reciting judicial exceptions under their broadest reasonable interpretation, the limitation is interpreted as mere computer code/instructions for performing the computer functions and falls under the instructions for applying an abstract idea. As such, these additional elements are interpreted as merely instructions to apply the judicial exception. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements of a processor, memory/storage device, machine learning language model, and natural language processing used to perform the process are generic computing components/device and instructions used to apply the judicial exception and therefore fall under the “apply it” limitation of the judicial exception and do not amount to significantly more per MPEP 2106.05(f). Further, the limitations, taken in combination, add nothing that is not already present when looking at the elements taken individually. As such, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, under their broadest reasonable interpretation, the additional elements do not meaningfully limit the practice of the abstract idea and do not amount to significantly more than the judicial exceptions. Therefore, claims 1, 11, and 19 are not directed to eligible subject matter as they are abstract ideas without significantly more.
Claims 2, 4, 7-8, 10-12, 14, 16-18, and 20-26 are dependent from claims 1 and 11 and include all the limitations of the independent claims. Therefore, the dependent claims recite the same abstract idea. The limitations of the dependent claims fail to amount to significantly more than the judicial exception. For example:
The limitations of claims 2, 7-8, 12, 14-17, 21-22, and 24-26 recite clarifications of the engagement score, score threshold, the types of communications, the communications including effectiveness scores, and the effectiveness score changes based on the engagement scores. Such clarifications, under their broadest reasonable interpretation, are merely defining/selecting a type of data to be manipulated which, per MPEP 2106.05(g), is insignificant extra-solution activity. Claims 2, 12, and 21 further includes the MLLM being trained using supervised learning and applying topic modeling. The limitations are generic machine learning instructions as training MLM is well known in the art and well-understood, routine, and conventional per Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025). Therefore, the limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amount to significantly more than the judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 4, 13, and 23 recite providing an instructor interface to enable selection and the communication comprising text describing the learning objective or recommendation. The limitation is further instructions for applying the judicial exceptions with a generic computing device as the interface is recited at a high level of generality and amounts to the computing device/interface acting as an intermediary for performing the abstract idea of selecting the communication, see MPEP 2106.05(f) and the type of communication is merely defining the type of communication data. Therefore, the limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amount to significantly more than the judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 10, 18, and 20 recite further abstract ideas including tracking a time since a beginning of the class (observation mental process) and sending the communication based on the time meeting or exceeding a threshold (judgement mental process and CMOHA). As the limitations are further abstract ideas, the limitations cannot meaningfully limit or amount to significantly more than the abstract ideas of the independent claims. The additional elements of the dependent claims are further insignificant extra-solution activities including defining the changes to the effectiveness score based on the changes in engagement score. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claims is also applicable on these claims.
Accordingly, claims 2, 4, 7-8, 10-12, 14, 16-18, and 20-26 recite abstract ideas without significantly more and are not drawn to eligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
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.
Claim(s) 1, 4, 11, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha et al. (US PGPub 20230127335), hereinafter referred to as Sha, in view of Spaulding (US PGPub 20160035230), further in view of Erickson et al. (US PGPub 20190213899), hereinafter referred to as Erickson, and further in view of Vleugels et al. (US PGPub 20240274025), hereinafter referred to as Vleugels.
With regard to claims 1, 11, and 19 Sha teaches a computer implemented method [claim 1] (Paragraph 0012; “methods”), a machine-readable storage device having instructions for execution by a processor of a machine to cause the processor to perform operations to perform the method [claim 11] (Paragraphs 0088, 0091), and a device (Paragraph 0012, 0098) comprising: a processor (Paragraph 0098; “one or more hardware processors”); and a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations [claim 19] (Paragraphs 0091-0092, 0098), comprising:
monitoring, by one or more processors, student interactions with student computing devices during a class session to collect student interaction data (Paragraphs 0040, 0048, 0058-0060 teach the system receives inputs such as sensor data and participation in class activities to determine a real-time status of a student/user) including content displayed on the student computing devices (Paragraphs 0017, 0038, 0061 teach the system includes a content management system which can present content to the student via the interface and wherein the content can be inputted to the machine learning model as an input/monitored aspect);
obtaining, at the one or more processors, an engagement score representative of a student being on track by interacting with content relevant to a predefined learning objective (Paragraphs 0021, 0030, 0038, 0054 teach the system can use the data to determine student interest and/or engagement levels including with regard to the current curriculum (learning objective));
selecting, via the one or more processors, a communication (Paragraphs 0022, 0029, 0064 teach the system and/or teacher can adapt to the student based on the measurements including outputting communications including pop-ups based on the measurements), the communication selected to direct the student to interactions to increase the engagement score (Paragraphs 0022, 0047, 0062, 0064-0065 teach various types of adaption that a teacher can use to improve student performance and enjoyment in order to improve interest/engagement levels);
monitoring student interactions following the communication to determine a post communication engagement score via the machine learning language model (Paragraphs 0021, 0025, 0031, 0037, 0066 teach the system adaptively and continuously measures the student’s engagement/interest and determines learning effectiveness such that once an adaption/change/communication is made the “post” adaption engagement is determined wherein the determination and monitoring can be performed by machine learning); and
modifying an effectiveness score of the communication based on a change between the post communication engagement score and the engagement score (Paragraphs 0021, 0025, 0037, 0054, 0063 teach the system determines a learning effectiveness of the learning experience for each student and the measurement and determination is adaptive and continuous such that the learning effectiveness would include the adaptions and changes in user engagement level in real-time).
Sha may not explicitly teach wherein the engagement score is a function of a weighted relevance score derived by a machine learning language model to determine the relevance score of the content displayed on the student computing device to the predefined learning objective and a weighted time value of student interaction with the content displayed on the student computing device. However, Spaulding teaches a system and method for determining an engagement index reflecting a user’s level of engagement with a digital resource based on the actions of the user including amount of time spent with the resource/content wherein the engagement index/score is based on independent weighted factors which can include the time a student spends interacting with the content and the context of the user interaction including a specific course (learning objective) and the subject matter of the content (Abstract; Paragraphs 0008, 0075-0076, 0083, 0119, 0128).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha to incorporate the teachings of Spaulding by incorporating the teachings of calculating an engagement index/score based on weighted factors including an amount of time of a user’s interaction and the context of the content including the subject matter and course of Spaulding as part of the engagement level of Sha, as both references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha by coding the system to include monitoring the amount of time a user interacts with content or an activity and calculating the engagement level based on weighted factors including the time and the context/relevance of the content based on the subject matter and course. Upon such modification, the method and system of Sha would include wherein the engagement score is a function of a weighted relevance score derived by a machine learning language model to determine the relevance score of the content displayed on the student computing device to the predefined learning objective and a weighted time value of student interaction with the content displayed on the student computing device. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Spaulding with Sha’s system and method in order to monitor user engagement and provide educators with a better way of assessing student engagement by weighting different factors based on user needs and goals (Spaulding 0003, 0076, 0083).
Sha in view of Spaulding may not explicitly teach the content being selected by each student. However, Erickson teaches a method and system for adaptive learning including determining student engagement and interaction data with regards to content items including content sought or selected by the user/student including determining if a student is seeking unrelated material (Paragraphs 0032, 0036, 0039, 0064).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding to incorporate the teachings of Erickson by incorporating the teachings of student’s selecting and seeking out content as the content/educational material of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding by coding the system to include allowing users to seek and/or select content/materials and measuring learner/student engagement with the selected content such as determining that a user is not engaged when interacting with off-topic or unrelated content. Examiner notes that the determination of content or an activity being unrelated or off-topic would also be considered determining the relevance of the content to the topic/course. Upon such modification, the method and system of Sha in view of Spaulding would include the content being selected by each student. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Erickson with Sha in view of Spaulding’s system and method in order to monitor user engagement and focus across content and during student activities.
Sha in view of Spaulding and Erickson may not explicitly teach a weighted relevance score derived by a machine learning language model extracting text from the content displayed on the student computing device to the predefined learning objective and performing natural language processing to determine semantic similarity between the extracted text and the predefined learning objective. However, Vleugels teaches a method and system for automated generation and adaptation of an educational content database using AI that is natural language based in order to improve student engagement and understanding wherein the model processes extracted content to create vector representations/embeddings that capture the semantic significance of the educational content and compare them with the educational standards and objectives to determine a semantic similarity score representative of the relevance of the matched content to ensure content relevance and alignment with objectives and needs of the user (Paragraphs 0043-0044, 0081, 0086, 0096, 0098, 0106, 0117).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding and Erickson to incorporate the teachings of Vleugels by applying the teachings of analyzing the content using semantic analysis to determine a semantic similarity and thereby relevance of the content to an educational objective of Vleugels to the content management system of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding and Erickson by coding the model to include using natural language techniques to generate embeddings of the content including displayed content/extracted text and determining a semantic similarity between the content and the educational objectives as part of determining the weighted relevance score of Sha in view of Spaulding. Upon such modification, the method and system of Sha in view of Spaulding and Erickson would include a weighted relevance score derived by a machine learning language model extracting text from the content displayed on the student computing device to the predefined learning objective and performing natural language processing to determine semantic similarity between the extracted text and the predefined learning objective. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Vleugels’s with Sha in view of Spaulding and Erickson’s system and method in order to ensure generated/presented content is aligned with the educational objectives and needs of the student and improve student engagement and understanding (Vleugels Paragraphs 0081, 0117).
With regard to claim 4, Sha further teaches further comprising providing an instructor interface to enable selection of the communication from a list of multiple different communications (Paragraphs 0022, 0054, 0055, 0086 teach the system includes a teacher dashboard which can include actions/adaptions the teacher should take with regards to the students including a change of teaching style or a change of curriculum) wherein at least one of the multiple communications comprises text describing the learning objective (Paragraphs 0022, 0029, 0064 teach the system and/or teacher can adapt/take action to the student based on the measurements including outputting communications including emails and pop-ups which would include text. The text may be in relation to the curriculum. While the text may not explicitly describe the learning objective, per MPEP 2111.05, the specific text is interpreted as nonfunctional printed matter which thereby carries no patentable weight).
With regard to claim 14, Sha further teaches wherein the communication comprises text describing the learning objective or a resource recommendation (Paragraphs 0022, 0029, 0061, 0064, 0065 teach the system and/or teacher can adapt/take action to the student based on the measurements including outputting communications including emails and pop-ups which would include text or switching to a video presentation or to a slide presentation or another approach. The text may be in relation to the curriculum. While the text may not explicitly describe the learning objective, per MPEP 2111.05, the specific text is interpreted as nonfunctional printed matter which thereby carries no patentable weight).
Claim(s) 2, 12, and 21-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha in view of Spaulding, Erickson, and Vleugels, as applied to claims 1 and 11 above, and further in view of Garcia i Tormo et al. (US PGPub 20230285711), hereinafter referred to as Garcia and further in view of Rushkin et al. (US PGPub 20220327946).
With regard to claims 2 and 12, Sha in view of Spaulding and Erickson may not explicitly teach the machine learning language model applying topic modeling to identify underlying topics within the content and comparing the identified topics to the predefined learning objective. However, Vleugels further teaches the content can be analyzed to determine attributes including a topic of the content wherein the attributes including the topic are used to process the matched content and including ensuring relevance to the educational objectives (Paragraphs 0065, 0069, 0086, 0096, 0098, 0103, 0110, 0112, 0114).
As discussed above, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding and Erickson to incorporate the teachings of Vleugels by applying the teachings of analyzing the content using semantic analysis to determine a semantic similarity and thereby relevance of the content to an educational objective including determining attributes of the content including topics of Vleugels to the content management system of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding and Erickson by coding the model to include using natural language techniques to generate embeddings of the content including determining attributes of the content including topics wherein the attributes are used to determine the semantic similarity and relevance of the content to the educational standards and objectives. Upon such modification, the method and system of Sha in view of Spaulding and Erickson would include the machine learning language model applying topic modeling to identify underlying topics within the content and comparing the identified topics to the predefined learning objective. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Vleugels’s with Sha in view of Spaulding and Erickson’s system and method in order to ensure generated/presented content is aligned with the educational objectives and needs of the student and improve student engagement and understanding (Vleugels Paragraphs 0081, 0117).
Sha in view of Spaulding, Erickson, and Vleugels may not explicitly teach wherein the engagement score is below a predetermined engagement score threshold. However, Garcia teaches a method and system for optimizing efficacy of interventions wherein an intervention/motivator is presented when the user’s engagement is below a threshold (Paragraphs 0047, 0119).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding, Erickson, and Vleugels to incorporate the teachings of Garcia by including the engagement threshold of Garcia as the trigger for an adaption/communication of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, and Vleugels by coding the system to include an engagement threshold for triggering an adaption/communication. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, and Vleugels would include wherein the engagement score is below a predetermined engagement score threshold. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Garcia with Sha in view of Spaulding, Erickson, and Vleugels’s system and method in order to monitor user engagement and improve user engagement effectively.
Sha in view of Spaulding, Erickson, and Vleugels may not explicitly teach wherein the machine learning language model is trained using supervised learning on a dataset comprising labeled examples of student interactions annotated as engaged or disengaged based on relevance to learning objectives. However, Rushkin teaches a system and method for conducting automated skills assessments including assessing learner engagements with learning resources wherein the system trains machine learning algorithms using historic interaction data reflective of previous learner engagements with the learning resources including if the learner was engaged or not engaged or less successful with engaging the content (Abstract; Paragraphs 0060, 0064, 0068, 0072).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding, Erickson, Vleugels, and Garcia to incorporate the teachings of Rushkin by applying the technique training the machine learning model using training data reflective of historic user engagement as taught by Ruskin to the machine learning model of Rushkin, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, Vleugels, and Garcia by coding the system to include using historic interaction and engagement data to train the machine learning model. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, Vleugels, and Garcia would include wherein the machine learning language model is trained using supervised learning on a dataset comprising labeled examples of student interactions annotated as engaged or disengaged based on relevance to learning objectives. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Rushkin with Sha in view of Spaulding, Erickson, Vleugels, and Garcia’s system and method in order to improve model performance and as training a machine learning model using training data is well known in the art as discussed in Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC.
With regard to claim 22, Sha in view of Spaulding, Erickson, and Vleugels may not explicitly teach wherein the engagement score threshold is settable by an instructor. However, Garcia further teaches the engagement threshold may be set by the user or by other persons (Paragraphs 0046, 0048, 0051).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding, Erickson, and Vleugels to incorporate the teachings of Garcia by including the engagement threshold of Garcia and allowing a user to set the threshold as the trigger for an adaption/communication of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, and Vleugels by coding the system to include an engagement threshold for triggering an adaption/communication wherein the teacher of Sha could set the engagement threshold for triggering an adaption/communication. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, and Vleugels would include wherein the engagement score threshold is settable by an instructor. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Garcia with Sha in view of Spaulding, Erickson, and Vleugels’s system and method in order to monitor user engagement and improve user engagement effectively by allowing a teacher to determine a desirable/predefined threshold.
With regard to claim 23, Sha further teaches further comprising providing an instructor interface to enable selection of the communication from a list of multiple different communications (Paragraphs 0022, 0054, 0055, 0086 teach the system includes a teacher dashboard which can include actions/adaptions the teacher should take with regards to the students including a change of teaching style or a change of curriculum).
With regard to claim 24, Sha further teaches wherein at least one of the multiple communications comprises text describing the learning objective (Paragraphs 0022, 0029, 0064 teach the system and/or teacher can adapt/take action to the student based on the measurements including outputting communications including emails and pop-ups which would include text. The text may be in relation to the curriculum. While the text may not explicitly describe the learning objective, per MPEP 2111.05, the specific text is interpreted as nonfunctional printed matter which thereby carries no patentable weight).
With regard to claim 25, Sha further teaches wherein at least one of the multiple communications comprises resource recommendation (Paragraphs 0022, 0061, 0065 teach the actions recommended can include switching to a video presentation or to a slide presentation or another approach).
Claim(s) 7-9 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha in view of Spaulding, Erickson, and Vleugels, as applied to claims 4 and 13 above, and further in view of Kil et al. (US PGPub 20170256172), hereinafter referred to as Kil.
With regard to claims 7 and 16, Sha further teaches the scores including learning engagement and effectiveness levels may be aggregates of multiple students (Paragraph 0054) but Sha in view of Spaulding, Erickson, and Vleugels may not explicitly teach wherein each of the multiple communications include an effectiveness score that is updated based on a change between the post communication engagement scores and the engagement scores in response to the communication being sent to multiple students. However, Kil teaches a system and method for student data analytics including evaluating the impact of applied interventions wherein the difference between pre and post intervention metrics including student engagement for a plurality of students is used to determine/update real-time efficacy (effectiveness score) of the interventions (Paragraphs 0044, 0066, 0072, 0074, 0101, 0170).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding, Erickson, and Vleugels to incorporate the teachings of Kil by including the steps of evaluating interventions based on the difference between pre and post intervention metrics of Kil to the engagement and effectiveness levels of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, and Vleugels by coding the system to include taking the continuous and adaptive measurements of Sha and comparing pre action/adaption and post action/adaption engagement levels to determine a difference between the metrics and determine an efficacy of the adaption/action. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, and Vleugels would include wherein each of the multiple communications include an effectiveness score that is updated based on a change between the post communication engagement scores and the engagement scores in response to the communication being sent to multiple students. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Kil with Sha in view of Spaulding, Erickson, and Vleugels’s system and method in order to evaluate the actions/adaptions and provide users/students the most effective interventions to improve student engagement and performance.
With regard to claims 8 and 17, Sha in view of Spaulding, Erickson, and Vleugels may not explicitly teach wherein the effectiveness score is decreased if the post communication engagement score minus the engagement score is less than an effectiveness engagement score threshold and the effectiveness score is increased if the post communication engagement score minus the engagement score is greater than an effectiveness engagement score threshold. However, Kil further teaches evaluating the impact of applied interventions wherein the difference between pre and post intervention metrics including student engagement for a plurality of students is used to determine/update real-time efficacy (effectiveness score) of the interventions including a better performing intervention having a higher utility/efficacy score and a worse performing intervention thereby having a lower utility/efficacy score (Paragraphs 0044, 0063, 0066, 0072, 0074, 0101, 0170).
As discussed above, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding, Erickson, and Vleugels to incorporate the teachings of Kil by including the steps of evaluating interventions based on the difference between pre and post intervention metrics of Kil to the engagement and effectiveness levels of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, and Vleugels by coding the system to include taking the continuous and adaptive measurements of Sha and comparing pre action/adaption and post action/adaption engagement levels to determine a difference between the metrics and determine an efficacy of the adaption/action wherein when the intervention performs better, the intervention is given a higher/increased efficacy/utility score and vice versa. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, and Vleugels would include wherein the effectiveness score is decreased if the post communication engagement score minus the engagement score is less than an effectiveness engagement score threshold and the effectiveness score is increased if the post communication engagement score minus the engagement score is greater than an effectiveness engagement score threshold. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Kil with Sha in view of Spaulding, Erickson, and Vleugels’s system and method in order to evaluate the actions/adaptions and provide users/students the most effective interventions to improve student engagement and performance.
Claim(s) 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha in view of Spaulding, Erickson, and Vleugels, as applied to claims 1 and 11 above, and further in view of Shimomura et al. (US PGPub 20220375358), hereinafter referred to as Shimomura.
With regard to claims 10 and 18, Sha in view of Spaulding, Erickson, and Vleugels may not explicitly teach wherein the operations further comprise: tracking a time since a beginning of the class; and wherein the communication is sent to the student in response to the time meeting or exceeding a nudge time threshold. However, Shimomura teaches a system and method for class management including tracking the time elapsed since the start of a class and delivering a corresponding teaching material object or other communication to a student when a set time has elapsed (Paragraphs 0031, 0058, 0069).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding, Erickson, and Vleugels to incorporate the teachings of Shimomura by applying the steps of tracking an elapsed class time and sending communication after an elapsed time of Shimomura to the actions/adaptions of Sha, as the references and the claimed invention are directed to learning management systems that include delivering educational content to students. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, and Vleugels by coding the system to include tracking an elapsed class time and performing the action/adaption after a class time exceeds a threshold and/or transmitting the output communication/pop-up after the elapsed time threshold. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, and Vleugels would include wherein the operations further comprise: tracking a time since a beginning of the class; and wherein the communication is sent to the student in response to the time meeting or exceeding a nudge time threshold. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Shimomura with Sha in view of Spaulding, Erickson, and Vleugels’s system and method in order to improve user/student engagement and provide teachers further control over the delivery of educational content and adaptions.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha in view of Spaulding, Erickson, and Vleugels, as applied to claim 19 above, and further in view of Kil and Shimomura.
With regard to claim 20, Sha in view of Spaulding, Erickson, and Vleugels may not explicitly teach wherein the effectiveness score is decreased if the post communication engagement score minus the engagement score is less than an effectiveness engagement score threshold and the effectiveness score is increased if the post communication engagement score minus the engagement score is greater than an effectiveness engagement score threshold. However, Kil further teaches evaluating the impact of applied interventions wherein the difference between pre and post intervention metrics including student engagement for a plurality of students is used to determine/update real-time efficacy (effectiveness score) of the interventions including a better performing intervention having a higher utility/efficacy score and a worse performing intervention thereby having a lower utility/efficacy score (Paragraphs 0044, 0063, 0066, 0072, 0074, 0101, 0170).
As discussed above, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding, Erickson, and Vleugels to incorporate the teachings of Kil by including the steps of evaluating interventions based on the difference between pre and post intervention metrics of Kil to the engagement and effectiveness levels of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, and Vleugels by coding the system to include taking the continuous and adaptive measurements of Sha and comparing pre action/adaption and post action/adaption engagement levels to determine a difference between the metrics and determine an efficacy of the adaption/action wherein when the intervention performs better, the intervention is given a higher/increased efficacy/utility score and vice versa. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, and Vleugels would include wherein the effectiveness score is decreased if the post communication engagement score minus the engagement score is less than an effectiveness engagement score threshold and the effectiveness score is increased if the post communication engagement score minus the engagement score is greater than an effectiveness engagement score threshold. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Kil with Sha in view of Spaulding, Erickson, and Vleugels’s system and method in order to evaluate the actions/adaptions and provide users/students the most effective interventions to improve student engagement and performance.
Sha in view of Spaulding, Erickson, Vleugels, and Kil may not explicitly teach wherein the operations further comprise: tracking a time since a beginning of the class; and wherein the communication is sent to the student in response to the time meeting or exceeding a nudge time threshold. However, Shimomura teaches a system and method for class management including tracking the time elapsed since the start of a class and delivering a corresponding teaching material object or other communication to a student when a set time has elapsed (Paragraphs 0031, 0058, 0069).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding, Erickson, Vleugels, and Kil to incorporate the teachings of Shimomura by applying the steps of tracking an elapsed class time and sending communication after an elapsed time of Shimomura to the actions/adaptions of Sha, as both references and the claimed invention are directed to learning management systems that include delivering educational content to students. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, Vleugels, and Kil by coding the system to include tracking an elapsed class time and performing the action/adaption after a class time exceeds a threshold and/or transmitting the output communication/pop-up after the elapsed time threshold. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, Vleugels, and Kil would include wherein the operations further comprise: tracking a time since a beginning of the class; and wherein the communication is sent to the student in response to the time meeting or exceeding a nudge time threshold. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Shimomura with Sha in view of Spaulding, Erickson, Vleugels, and Kil’s system and method in order to improve user/student engagement and provide teachers further control over the delivery of educational content and adaptions.
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha in view of Spaulding, Erickson, Vleugels, Garcia, and Rushkin, as applied to claim 23 above, and further in view of Kil.
With regard to claims 7 and 16, Sha further teaches the scores including learning engagement and effectiveness levels may be aggregates of multiple students (Paragraph 0054) but Sha in view of Spaulding, Erickson, Vleugels, Garcia, and Rushkin may not explicitly teach wherein each of the multiple communications include an effectiveness score that is updated based on a change between the post communication engagement scores and the engagement scores in response to the communication being sent to multiple students. However, Kil teaches a system and method for student data analytics including evaluating the impact of applied interventions wherein the difference between pre and post intervention metrics including student engagement for a plurality of students is used to determine/update real-time efficacy (effectiveness score) of the interventions (Paragraphs 0044, 0066, 0072, 0074, 0101, 0170).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sha in view of Spaulding and Erickson to incorporate the teachings of Kil by including the steps of evaluating interventions based on the difference between pre and post intervention metrics of Kil to the engagement and effectiveness levels of Sha, as the references and the claimed invention are directed to learning management systems that include assessing user engagement with an activity/learning content. One of ordinary skill in the art would modify Sha in view of Spaulding, Erickson, Vleugels, Garcia, and Rushkin by coding the system to include taking the continuous and adaptive measurements of Sha and comparing pre action/adaption and post action/adaption engagement levels to determine a difference between the metrics and determine an efficacy of the adaption/action. Upon such modification, the method and system of Sha in view of Spaulding, Erickson, Vleugels, Garcia, and Rushkin would include wherein each of the multiple communications include an effectiveness score that is updated based on a change between the post communication engagement scores and the engagement scores in response to the communication being sent to multiple students. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Kil with Sha in view of Spaulding, Erickson, Vleugels, Garcia, and Rushkin’s system and method in order to evaluate the actions/adaptions and provide users/students the most effective interventions to improve student engagement and performance.
Response to Arguments
Applicant's arguments, see Remarks, pages 8-11, filed June 15, 2026, with respect to the rejection(s) of claim(s) 1-2, 4, 7-8, 10-12, 14, and 16-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant’s argument are as follows: A) the claims recite specific technical operations by reciting text extraction and natural language processing, B) the claims are not directed to mental processes because a human cannot mentally extract text or compute semantic similarity, C) citing Ex parte Desjardins and the recitation of machine learning that improve educational technology as a technical improvement, and D) the claims integrate the abstract ideas into a practical application by reciting a technical improvement to engagement monitoring systems and training the machine learning model. Applicant’s arguments predominantly focus on the text extraction and natural language processing. With regard to argument B, a human mind can “extract text” and “determine semantic similarity” under the broadest reasonable interpretation. The recitation of machine learning models and natural language processing are algorithms/instructions for performing the processes on a generic computing device. This is partially evidenced by Applicant’s specification as the specification and claims do not recite specific steps for performing these steps or recite steps that could not be performed mentally under a broadest reasonable interpretation. Further, the specification, paragraphs 0039-0041, merely states the natural language processing, semantic analysis, and topic modeling are performed by algorithms and listing the algorithms without specifying further steps. This shows that the algorithms are merely being used to apply the judicial exceptions rather than improving on machine learning models or technology.
Addressing applicants arguments A, C, and D, as mentioned above, the recited steps under their broadest reasonable interpretation can be performed mentally and the recitations of machine learning and natural language processing are recited at a high level of generality. This is further supported by the specification merely listing algorithms for performing the steps and analysis without teaching or reciting improvements or further details on how the steps are performed. With regard to the training of the machine learning model, as discussed above, the courts have held in Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC that training a machine learning model, even with a specific dataset related to the process, is insignificant and well-understood, routine, and conventional. Applicant continues to assert that the real-time analysis of multiple students’ displayed content and engagement monitoring is a technical improvement but, in addition to being a conclusory statement lacking support, the claimed improvement is experienced by the user by improved efficiency rather than improving the computing technology. Ex parte Desjardins does not state that the inclusion of AI/ML automatically makes a claim a technological improvement as Ex parte Desjardins reiterates the claims must provide evidence of a technological improvement such as an improvement to the performance of an AI/ML model. The instant application does not claim or provide evidence of an improvement to AI/ML models but merely application of known techniques and algorithms to performed abstract ideas. Further, efficiency improvements are inherent to applying judicial exceptions with computing technology and, per MPEP 2106.05(f), does not amount to a technical application or significantly more. As previously discussed, per applicant’s argument and specification, the claimed invention is intended to provide automation to teachers by monitoring multiple students in real-time and autonomously determining student engagement levels and providing actionable insights to educators. This shows the claimed invention is an improvement to the human experience/process rather than a technological field by automating a manual process as the improvements rely on the inherent efficiency of computing technology which per 2106.05(f) does not integrate a judicial exception into a practical application or provide an inventive concept. Finally, Applicant notes throughout the arguments the specific models/algorithms including LDA, NMF, or TF-IDF, Examiner notes that these arguments are not commensurate with the claims as the specific algorithms have not been claimed. Even if the specific algorithms were claimed, the algorithms and specification do not recite specific steps or technical improvement beyond what is known in the art as the algorithms are being applied to known applications and performing abstract ideas without significantly more. Therefore, the claims stand rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed June 15, 2026, with respect to the rejection(s) of claim(s) 1-2, 4, 7-8, 10-12, 14, and 16-20 under 35 U.S.C. 103 have been fully considered and are persuasive by virtue of applicant’s amendments to the claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. 103 in view of the newly cited combination of prior art.
Conclusion
Accordingly, claims 1-2, 4, 7-8, 10-12, 14, and 16-26 are rejected.
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.
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/CORRELL T FRENCH/Examiner, Art Unit 3715
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