Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/25/2025 has been entered. Claims 1, 4, 14, 17 and 19 are amended. Claims 2 and 15 are cancelled. Claims 1, 3-14 and 16-21 are currently pending in the application.
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, 3-14 and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
In regard to independent claim 1:
Step 1: Statutory Category?
Independent Claim 1 recites “A computer implemented method comprising:”. Independent Claim 1 falls within the “process” category of 35 U.S.C. § 101.
Step 2A – Prong 1: Judicial Exception Recited?
The Independent Claim 1/Revised 2019 Guidance Table below identifies in italics the specific claim limitations found to recite an abstract idea and in bold the additional (non-abstract) claim limitations that are generic computer components.
Independent Claim 1
Revised 2019 Guidance
A computer implemented method comprising:
A process (method) is a statutory
subject matter class. See 35 U.S.C.
§ 101 (“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.”).
[L1] obtaining, by one or more processors, a predefined learning objective identifying a topic for a class session including multiple students;
The “one or more processors” is an additional non-abstract limitation.
“[O]btaning a predefined learning objective identifying a topic for a class session including multiple students” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
Alternatively, “obtaining a learning objective for a class session” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that a person/educator could obtain information by reading and/or hearing the information. See January 2019 Memorandum, 84 Fed. Reg. at 52. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372–72 (Fed. Cir. 2011) (obtaining transaction data can be performed by a person reading records of transactions from a database…).
[L2] monitoring, by the one or more processors, student interactions with computing devices during the class session to collect student interaction data, the student interactions including content displayed on a student computing device;
The “one or more processors” “computing devices” and “student computing device” are additional non-abstract limitations.
“[M]onitoring student interactions during the class session to collect student interaction data …” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could watch and/or listen to what students are going.
[L3a] analyzing, by the one or more processors, the collected student interaction data including content viewed by the student using a machine learning model trained to identify relevancy of the content displayed on the student computing device to the topic to identify patterns indicative of engagement or disengagement with the learning objective,
The “one or more processors”, “machine learning model” and “student computing device” are additional non-abstract limitations.
“Analyzing … the collected student interaction data including content viewed by the student… to identify relevancy of the content… ” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that humans (person/educator) have long analyzed information, mentally and/or using pen and paper.
[L3b] wherein the machine learning model applies a topic analysis algorithm to the content accessed by the students to determine a relevance score relative to the predefined learning objective topic,
The “machine learning model” is an additional non-abstract limitation.
“a topic analysis algorithm to the content accessed by the students to determine a relevance score relative to the predefined learning objective topic” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could visually and/or mentally determine a relevance score relative to the predefined learning objective topic.
[L3c] wherein the machine learning model is trained using a supervised learning algorithm, and the training data comprises labeled examples of student interactions that have been annotated as 'engaged' or 'disengaged' based on their correlation with the predefined learning objective topic;
The “machine learning model” is an additional non-abstract limitation.
“[T]he machine learning model is trained using a supervised learning algorithm, and the training data comprises labeled examples of student interactions that have been annotated as 'engaged' or 'disengaged' based on their correlation with the predefined learning objective topic” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
[L4] determining, by the one or more processors, an engagement status for each student based on the relevance score and a predetermined engagement threshold;
The “one or more processors” is an additional non-abstract limitation.
“Determining… an engagement status for each student based on the relevance score and a predetermined engagement threshold” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could determine an engagement status for each student mentally and/or using pen and paper.
[L5] generating, by the one or more processors, real-time feedback for an educator based on the engagement status or each student, wherein the feedback includes actionable recommendations for interventions to enhance engagement.
The “one or more processors” is an additional non-abstract limitation.
“Generating … real-time feedback for an educator …” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., data presentation. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
Alternatively, “generating … real-time feedback for an educator …” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could generate feedback verbally and/or in writing.
[L6] in response to the engagement status indicating disengagement relative to the engagement threshold for a student, automatically controlling the student's computing device to alter content displayed on the device.
The “student's computing device” is an additional non-abstract limitation.
“[A]utomatically controlling the student's computing device to alter content displayed on the device” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., data presentation. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
It is apparent that, other than reciting the “one or more processors”, “student computing device” and “machine learning model” additional non-abstract limitations noted in the Independent Claim 1/Revised 2019 Guidance Table above, nothing in the claim precludes the steps from practically being performed by a human as a certain method of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), in the mind, and/or using pen and paper. The mere nominal recitation of the “one or more processors”, “student computing device” and “machine learning model” and automation of a manual process does not take the claim out of the certain method of organizing human activity and mental processes groupings. Accordingly, the claim recites an abstract idea under Step 2A: Prong 1.
Step 2A – Prong 2: Integrated into a Practical Application?
The body of the claim, as noted in the Independent Claim 1/Revised 2019 Guidance Table above, recites the additional limitations of the “one or more processors”, “student computing device” and “machine learning model”. The originally filed Specification provides supporting exemplary descriptions of generic computer components: at least pages ¶ 79: the machine learning model is trained using a supervised learning algorithm … ; ¶ 80: The supervised learning algorithm may include one or more of a Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting Machines, or Neural Networks … ; ¶ 81: The topic analysis algorithm may utilize Natural Language Processing (NLP) techniques to extract features from text, including one or more of the following: named entity recognition, pair-of-speech tagging, sentiment analysis, or topic modeling; ¶ 82: The topic modeling may be pe1fonned using Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF)… ; ¶ 83: The machine learning model may include an image recognition component that utilizes Convolutional Neural Networks (CNNs)… ; ¶ 84: the machine learning model applies sequence analysis algorithms to assess patterns in student activity over time, including one or more of the following: Hidden Markov Models (HMMs); Recurrent Neural Networks (RNNs), or Long Short-Term Memory networks (LSTMs)… ; ¶ 85: the machine learning model utilizes anomaly detection techniques lo identify deviations from typical engagement patterns, employing algorithm as such as One--Class SVM or Isolation Forest; ¶ 86: The machine learning model may incorporate clustering techniques to group students based on similarity in engagement patterns, using algorithms such as K-means clustering or hierarchical clustering; ¶ 116: … The software may be executed on a digital signal processor, ASIC, microprocessor, or other type of processor operating on a computer system, such as a personal computer, server or other computer system, turning such computer system into a specifically programmed machine … The lack of details about the “one or more processors”, “student computing device” and “machine learning model” indicates that each of the above-mentioned additional elements is a generic computer component, performing generic (a) function(s). See Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.”). The claim does not recite (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP § 2106.05(a)); (ii) a “particular machine” to apply or use the judicial exception (see MPEP § 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (see MPEP § 2106.05(c)); or (iv) any other meaningful limitation (see MPEP § 2106.05(e)). See 84 Fed. Reg. at 55. The claimed invention merely implements the abstract idea using instructions executed on generic computer components, as shown in bold above, and as supported in the above noted pertinent portions of the Specification. The instant claim merely uses a programmed computer as a tool to perform an abstract idea. See MPEP § 2106.05(f). The additional limitations noted above, [L1] obtaining data (i.e., data gathering), [L3c] “the machine learning model is trained using a supervised learning algorithm” (i.e., data gathering), [L5] generate real-time feedback (i.e., data presentation) and [L6] “automatically controlling the student's computing device to alter content displayed on the device” (i.e., data presentation) reflect the type of extra-solution activity (i.e., activities in addition to the judicial exception) the courts have determined insufficient to transform judicially excepted subject matter into a patent-eligible application when they are claimed in a merely generic manner. See MPEP § 2106.05(g); see, e.g., CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011) (“We have held that mere ‘[data-gathering] step[s] cannot make an otherwise nonstatutory claim statutory.”’ (alterations in original) (quoting In re Grams, 888 F.2d 835, 840 (Fed. Cir. 1989))); see also Elec. Power, 830 F.3d at 1354 (“[W]e have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis.”). The instant claim as a whole merely uses computer instructions to implement the abstract idea on a computer or, alternatively, merely uses a computer as a tool to perform the abstract idea. No additional limitations are recited in the body of the claim. The claim limitations amount to merely indicating a field of use or technological environment (a computer) in which to apply a judicial exception and, as such, cannot integrate the judicial exception into a practical application. See MPEP § 2106.05(h). Hence, as per MPEP §§ 2106.05(a)–(c), (e)–(h), the additional elements in claim 1, namely the “one or more processors”, “student computing device” and “machine learning model” do not, either individually or in combination, integrate the abstract idea into a practical application. Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong 2: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amounts to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above (¶¶ 79-86, 116) describes the “one or more processors” “computing devices” and “machine learning model” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional element(s) “in a manner that indicates that the additional element(s) is/are sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional). The generic description of the “one or more processors” “computing devices” and “machine learning model” indicates the steps performed by the additional elements are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. In particular, each of the recited [L1] obtaining data (i.e., data gathering), [L3c] “the machine learning model is trained using a supervised learning algorithm” (i.e., data gathering), [L5] generate real-time feedback (i.e., data presentation) and [L6] “automatically controlling the student's computing device to alter content displayed on the device” (i.e., data presentation) is nothing more than well-understood, routine, and conventional activity because these limitations are not distinguished from generic, conventional data gathering and data presentation with a computer. Considered as an ordered combination, the computer components of representative independent claim 1 add nothing that is not already present when the steps are considered separately. The sequence of obtaining, monitoring, analyzing, determin[ing], training, determining, generating and controlling is equally generic and conventional. Hence, the additional element(s) are generic, well-known, and conventional computing elements. The use of the additional element(s) either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to independent Claim 14:
Independent claim 14 is a machine-readable storage device, which falls within the “machine” category of 35 U.S.C. § 101. The 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, the operations comprising steps similar to those of representative independent Claim 1. As a result, independent claim 14 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 19:
Independent claim 19 is “a device comprising:”, which falls within the “machine” category of 35 U.S.C. § 101. The device comprising: a processor; and a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising steps similar to those of representative independent Claim 1. As a result, independent claim 4 is rejected similarly to representative independent Claim 1.
In regard to the dependent claims:
Dependent claims 3-13, 16-18 and 20-21 include all the limitations of respective independent claims 1, 14 and 19 from which they depend and as such recite the same abstract idea(s) noted above for claims 1, 14 and 19. None of the additional claim activities is used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 3-13, 16-18 and 20-21 integrates the judicial exception into a practical application. While dependent claims 3-13, 16-18 and 20-21 may have a narrower scope than the representative claim, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 3-13, 16-18 and 20-21 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-6, 8-14 and 16-21 are rejected under 35 U.S.C. 103 as being obvious over Wellman et al. (US 20140205990 A1) (Wellman) in view of Bar et al. (US 20180308180 A1) (Bar) and OLIVIERI et al. (US 20200294408 A1) (OLIVIERI).
Re claims 1, 14 and 19:
[Claim 1] Wellman teaches or at least suggests a computer implemented method comprising: obtaining, by one or more processors, a predefined learning objective identifying a topic for a class session including multiple students; monitoring, by the one or more processors, student interactions with computing devices during the class session to collect student interaction data, the student interactions including content displayed on a student computing device; analyzing, by the one or more processors, the collected student interaction data including content viewed by the student using a machine learning model trained to identify relevancy of the content displayed on the student computing device to the topic to identify patterns indicative of engagement or disengagement with the learning objective (at least ¶ 1: electronic learning systems… using machine learning to identify patterns in student engagement relative to electronic learning systems; ¶ 5: … associating a student with at least one identified archetypal learning pattern using machine learning; ¶ 7: a set of student; ¶ 9: a plurality of archetypal learning patterns comprises one or more course archetypal learning patterns associated with a single instructional course of an electronic learning system; ¶ 10: a machine learning ensemble comprising a plurality of learned functions from multiple classes. A plurality of learned functions of a machine learning ensemble, in one embodiment, are selected from a larger plurality of generated learned functions; ¶ 11: Received data … is collected using a browser extension installed in internet browsers for each of a plurality of students; ¶ 12: an activity monitor module is configured to receive monitored electronic learning interactions of one or more students. A machine learning module… is configured to compare, using machine learning, monitored electronic learning interactions to a plurality of archetypal learning patterns; FIG. 5 and associated text; ¶ 18: a plurality of archetypal learning patterns includes a course archetypal learning pattern associated with a single electronic course; ¶ 72: The accuracy and/or effectiveness of machine learning predictions, recommendations …; ¶ 84: the result module 206 may send an evaluation of electronic learning material to an electronic learning publisher 106 from the machine learning module 204 (e.g., how effective the material is at engaging students or the like)), wherein the machine learning model applies a topic analysis algorithm to the content accessed by the students to determine a relevance score relative to the predefined learning objective topic; determining, by the one or more processors, an engagement status for each student (at least ¶ 1: using machine learning to identify patterns in student engagement relative to electronic learning systems; ¶ 2: determine whether students using electronic learning materials are engaged by the material; ¶ 16: Monitored electronic learning interactions… include text selected by one or more students from electronic learning material; ¶ 58: FIG. 1A depicts one embodiment of a system 100 for determining student engagement. The system 100, in the depicted embodiment, includes a student engagement module 102 configured to determine student engagement and/or dis-engagement using machine learning; ¶ 62: an archetypal learning pattern for a student 104 comprises a representation of habits and/or interactions of a plurality of students 104 having a similar level or tier of learning outcomes (e.g., scores, grades, or the like); ¶ 71: the data associated with electronic learning interactions that the activity monitor module 202 monitors or otherwise receives may include one or more indirect or supplemental indicators of a student 104's engagement, or conversely dis-engagement, with electronic learning material; ¶ 74: an archetypal learning pattern for students may comprise a representation of habits and/or interactions of a plurality of students having a similar level or tier of learning outcomes (e.g., scores, grades, or the like), such as previously monitored students 104 … an archetypal learning or teaching pattern for electronic learning material (e.g., an electronic lesson, an electronic assignment, an electronic test or quiz) may comprise a representation of electronic learning material or components thereof, grouped by effectiveness at teaching and/or engaging students 104, or the like); ¶ 80: the machine learning module 204 may evaluate an electronic lesson, assignment, quiz, test, or other electronic learning material based on a level of student engagement with regard to the material; ¶ 84: the result module 206 may send an evaluation of electronic learning material to an electronic learning publisher 106 from the machine learning module 204 (e.g., how effective the material is at engaging students or the like)). The above at least suggests determining … an engagement status for each student based on the relevance score and a predetermined engagement threshold. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Wellman as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Wellman further teaches or at least suggests wherein the machine learning model is trained using a supervised learning algorithm, and the training data comprises labeled examples of student interactions that have been annotated as 'engaged' or 'disengaged' based on their correlation with the predefined learning objective topic (at least ¶ 92: process workload data using a machine learning ensemble 222 to obtain a result, such as a prediction, a classification, a confidence metric, an answer, a recognized pattern, a recommendation, an evaluation, or the like; ¶ 92: a learned function may accept instances of one or more features as input, and provide a result, such as a classification, a confidence metric, an inferred function, a regression function, an answer, a recognized pattern, a recommendation, an evaluation, or the like; ¶ 94: certain learned functions may receive the output or result of one or more other learned functions as input, such as a Bayes classifier, a Boltzmann machine, or the like; ¶ 103: Providing a classification… using a machine learning ensemble 222; ¶ 119: … the machine learning compiler module 302 includes an extender module 308… the extender module 308 may extend a learned function or combined learned function by adding a probabilistic model layer, such as a Bayesian belief network layer, a Bayes classifier layer, a Boltzman layer, or the like; ¶ 120; ¶¶ 133, 134: the machine learning ensemble 222 provides a classification and a confidence metric for each instance of workload data input into the machine learning ensemble 222, or the like)). It is known that data labeling and annotations are essential processes in the field of machine learning and artificial intelligence (AI). They involve annotating or tagging data to provide labels or metadata that help train machine learning models to recognize patterns, make predictions, or perform specific tasks. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Wellman as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”). In the event the above interpretation is view as not being reasonable, as previously noted, the concept and advantages of machine learning training using labelled items were old and well known to one of ordinary skill in the art before the effective filing date of the invention, as evident in Bar (at least ¶ 21: (a) Analyzing accumulated raters feedbacks to determine the preferences of specific raters and/or specific rater groups/segments; (b) Generating content targeting data for Raters, based on their ‘preference history’ as expressed in their accumulated feedbacks, and utilizing/offering generated targeting data; (c) Identifying rating characteristics within multiple ratings of depictions of specific objects/attributes; and revealing trends and patterns of general interest associated with the specific objects/attributes; (d) Calculating and providing perceived rater feedbacks based on the statistical analysis of stored information from previous ratings (without receiving further human ratings); (e) Applying an Internet bot for inviting potentially relevant Raters based on system-determined rater groups for which it seeks additional raters; and/or (f) Building and applying a neural network model—trained with sets of object/attribute depictions and their respective actual human raters' ratings—to later generate perceived rater feedbacks for new object/attribute depictions (without receiving further human ratings); ¶ 81: an accumulated raters feedbacks analyzer to determine the preferences of specific raters and/or specific rater groups/segments by referencing the accumulated raters feedbacks database shown; a raters content-targeting data generator for receiving analyzed raters feedback data, generating system raters associated content targeting data and storing it in the shown raters preferences database; an object/attribute trends identifier for receiving analyzed raters feedback data, identifying general object/attribute related trends, and storing identified trends in the shown objects/attributes interest patterns database accessible by advertisers and content-providers; a perceived raters feedback generator, including a statistical analyzer and a neural network model, for providing perceived rater feedbacks based on the statistical analysis of stored information from previous ratings in the shown raters preferences database and/or the neural model after it was trained with accumulated actual human ratings; and an additional rater retrieving bot, communicatively connected to an internet gateway, for identifying and inviting potentially relevant raters based on notifications form the raters feedback evaluation logic rater groups for which it seeks additional raters; ¶ 125: provided rater evaluations, in accordance with some embodiments, may for example, take the form of: designations, ratings, labels, and/or tags—to the user's auto uploaded face depiction and/or to certain attributes thereof. Automatically provided rater evaluations may include designations in regard to raters' preferred, liked, disliked and/or commented-to user-depictions; and/or may relate/compare to prior automatically provided rater evaluations of the same user; ¶ 132: the neural network model may be trained by supervised learning, wherein training data object/attribute depictions are fed to the model along with respective ‘correct’ rater feedback—made by actual human raters). Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have applied Bar’s machine learning features to modify Wellman as claimed because this would amount to no more than applying a known technique to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Wellman in view of Bar teaches or at least suggests generating, by the one or more processors, real-time feedback for an educator based on the engagement status or each student, wherein the feedback includes actionable recommendations for interventions to enhance engagement (at least Wellman: ¶ 13: A machine learning module, in a further embodiment, is configured to determine a recommended learning action using machine learning. A result module… is configured to send an alert to at least one student. In one embodiment, an alert recommends a learning action for a student to take. An alert… comprises a real-time notification presented to at least one student during electronic learning interactions of the at least one student; ¶ 33: the result module 206 may present an alert to a student 104 as a real-time notification during electronic learning interactions of the student with electronic learning material. The machine learning module 204 … may process data from the activity monitor module 202 substantially in real-time to associate a student and/or electronic learning interactions with one or more archetypal learning patterns, to determine recommended learning actions, or the like. The result module 206 may present a pop-up, sidebar, dropdown, or other real-time notification and/or recommendation to a student 104 during a lesson, assignment, test, or other presentation of electronic learning material … a real-time alert or recommended action may allow a student 104 to adjust interaction with an electronic learning system substantially immediately, without waiting for offline analysis, a next lesson, or the like; ¶ 62: The student engagement module 102 may use machine learning to recommend or prescribe a learning action to a student 104, to an authority at a learning institution 108, or the like; ¶ 65: The student engagement module 102… may compare monitored or collected student interaction data to the archetypal learning patterns to determine a student engagement… recommend a learning action… using machine learning).
Wellman in view of Bar appear to be silent on but OLIVIERI teaches or at east suggests in response to the engagement status indicating disengagement relative to the engagement threshold for a student, automatically controlling the student's computing device to alter content displayed on the device (at least ¶ 10: alert the user in real-time when the user's attentiveness drops. The system may also make corrective action suggestions to the user or to a staff member; ¶ 13: In response to the one or more drops in the attentiveness level, system 102 performs one or more corrective actions in response to the drops in the attentiveness level and based on the inattentiveness information … if system 102 is in an active mode, system 102 may perform a corrective action by sending the user alerts to inform the user of any drops in the attentiveness level. In various embodiments, system 102 delivers such alerts to the user immediately, in real-time so that the user may refocus and pay attention during the learning session; ¶ 29: perform a corrective action by generating one or more suggested actions to the user. The system may then provide the one or more suggested actions to the user. For example, the system may recommend to the user that the user should review the material again for a particular lesson, where the user's attentiveness level dropped below a predetermined threshold; ¶ 34: inform the user of any particular patterns on inattentiveness and possible recommendations for improvement). Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have used the real-time inattentiveness corrective action features of OLIVIERI and to have modified Wellman in view of Bar as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
In regard to independent Claim 14:
Independent claim 14 is 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, the operations comprising steps similar to those of representative independent Claim 1. As a result, independent claim 14 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 19:
Independent claim 19 is a device comprising: a processor; and a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising steps similar to those of representative independent Claim 1. As a result, independent claim 4 is rejected similarly to representative independent Claim 1.
Re claims 3 and 16:
[Claims 3 and 16] Wellman in view of Bar and OLIVIERI teaches or at least suggests wherein the supervised learning algorithm includes one or more of the following: Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting Machines, or Neural Networks (at least Wellman: ¶ 95: The function generator module 301 may generate learned functions from multiple different machine learning classes, models, or algorithms. For example, the function generator module 301 may generate decision trees; decision forests; … support vector machines …).
Re claims 4 and 17:
[Claims 4 and 17] Wellman in view of Bar and OLIVIERI teaches or at least suggests wherein altering content displayed on the device includes one or more of: (i) automatically surfacing the learning objective as a reminder to the student, (ii) automatically presenting a list of instructor-provided or system-determined resources on the student device, and (iii) automatically navigating the student device to a suggested resource (at least OLIVIERI: ¶ 29: perform a corrective action by generating one or more suggested actions to the user. The system may then provide the one or more suggested actions to the user. For example, the system may recommend to the user that the user should review the material again for a particular lesson, where the user's attentiveness level dropped below a predetermined threshold; ¶ 30: the system may also recommend and offer the user a meditation exercise as a way to help the user to disconnect from his or her current thoughts that may distract from the learning session; ¶ 34: enable the user to select to review an entire course or to review the particular portions of the learning session where the user was inattentive).
Wellman in view of Bar and OLIVIERI appears to be silent on wherein the topic analysis algorithm employs Natural Language Processing (NLP) techniques to extract features from text, including one or more of the following: named entity recognition, part-of-speech tagging, sentiment analysis, or topic modeling. However, the Examiner previously took official notice that the concept and advantages of using Natural Language Processing (NLP) techniques to extract features from text, including one or more of the following: named entity recognition, part-of-speech tagging, sentiment analysis, or topic modeling were old and well-known at the time before the effective filing date of the invention. Since Applicant did not traverse this officially noticed facts by specifically pointing out supposed errors, the officially noticed facts taken in the Final Rejection dated 10/07/2025 are now considered admitted prior art. See MPEP § 2144.03(C) and particularly Chevenard, 139 F.2d at 713, 60 USPQ at 241 ("[I]n the absence of any demand by appellant for the examiner to produce authority for his statement, we will not consider this contention."). Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Wellman in view of Bar and OLIVIERI as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claims 5-6, 18 and 20:
[Claims 5-6, 18 and 20] Wellman in view of Bar and OLIVIERI teaches or at least suggests wherein monitoring student interactions comprises periodically receiving representations of content displayed on a student computing device (at least OLIVIERI: ¶ 31: generate a report that includes the aggregated inattentiveness information. The system may generate a report at a predetermined time (e.g., after a learning session is completed, etc.) or periodically (e.g., daily, weekly, monthly, etc.)). Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have used the real-time inattentiveness corrective action features of OLIVIERI and to have modified Wellman in view of Bar as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
In the event the above interpretation is viewed as not being reasonable, the Examiner previous took official notice that the concept and advantages of a computer/controller/processor continuously or periodically or dynamically updating data were old and well-known to one of ordinary skill in the art before the effective filing date of the invention. Since Applicant did not traverse this officially noticed facts by specifically pointing out supposed errors, the officially noticed facts taken in the Final Rejection dated 10/07/2025 are now considered admitted prior art. See MPEP § 2144.03(C) and particularly Chevenard, 139 F.2d at 713, 60 USPQ at 241 ("[I]n the absence of any demand by appellant for the examiner to produce authority for his statement, we will not consider this contention."). Hence, it would have been obvious to one skilled in the art at the time before the effective filing date of the invention to have modified the teachings of Wellman in view of Bar and OLIVIERI as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Wellman in view of Bar and OLIVIERI teaches or at least suggests calculating a length of time that a site is being displayed on the student computing device by comparing the received representations (at least Wellman: ¶ 6: Associating each student with at least one identified archetypal learning pattern, in certain embodiments, comprises correlating a time series path for a particular student with the associated at least one identified archetypal learning pattern; ¶ 79: … associate a particular student with an archetypal learning pattern… the machine learning module 204 correlating a time series path for the particular student with the an archetypal learning pattern (e.g., a nearest archetypal learning pattern, a most similar archetypal learning pattern, or the like)).
Re claim 8:
[Claim 8] Wellman in view of Bar and OLIVIERI teaches or at least suggests wherein the machine learning model applies sequence analysis algorithms to assess patterns in student activity over time (at least Wellman: ¶ 95: The function generator module 301 may generate learned functions from multiple different machine learning classes, models, or algorithms. For example, the function generator module 301 may generate decision trees; decision forests; kernel classifiers and regression machines with a plurality of reproducing kernels; non-kernel regression and classification machines such as logistic, CART, multi-layer neural nets with various topologies; Bayesian-type classifiers such as Naive Bayes and Boltzmann machines; logistic regression; multinomial logistic regression; probit regression; AR; MA; ARMA; ARCH; GARCH; VAR; survival or duration analysis; MARS; radial basis functions; support vector machines; k-nearest neighbors; geospatial predictive modeling; and/or other classes of learned function). However, Wellman in view of Bar and OLIVIERI appears to be silent on the machine learning model including one or more of the following: Hidden Markov Models (HMMs), Recurrent Neural Networks (RNNs), or Long Short-Term Memory networks (LSTMs). This feature would have been an obvious matter of choice at the time before the effective filing date of the invention at least in view of Wellman’s teaching of generating “learned functions from multiple different machine learning classes, models, or algorithms”.
Re claim 9:
[Claim 9] Wellman in view of Bar and OLIVIERI teaches or at least suggests wherein the machine learning model utilizes anomaly detection techniques to identify deviations from typical engagement patterns (at least Wellman: ¶ 145: The learned functions 502… include various collections of selected learned functions 502 from different classes including a collection of decision trees 502a, configured to receive or process… a collection of support vector machines ("SVMs") 502b…).
Additionally, and/or alternatively, this feature would have been an obvious matter of choice at the time before the effective filing date of the invention at least in view of Wellman’s teaching of generating “learned functions… from different classes”.
Re claim 10:
[Claim 10] Wellman in view of Bar and OLIVIERI teaches or at least suggests techniques to group students based on similarity in engagement patterns (at least Wellman: ¶ 74: An archetypal learning or teaching pattern for an authority such as a teacher or professor may comprise a representation of habits, interactions, and/or teaching methods grouped by effectiveness at teaching and/or engaging students 104, or the like. Similarly, an archetypal learning or teaching pattern for electronic learning material (e.g., an electronic lesson, an electronic assignment, an electronic test or quiz) may comprise a representation of electronic learning material or components thereof, grouped by effectiveness at teaching and/or engaging students 104, or the like). In the event the above interpretation is viewed as not being reasonable, the Examiner previously took official notice that the concept and advantages of clustering similar users together using machine learning techniques such as K-means clustering methods were old and well-known to one of ordinary skill in the art before the effective filing date of the invention. Since Applicant did not traverse this officially noticed facts by specifically pointing out supposed errors, the officially noticed facts taken in the Final Rejection dated 10/07/2025 are now considered admitted prior art. See MPEP § 2144.03(C) and particularly Chevenard, 139 F.2d at 713, 60 USPQ at 241 ("[I]n the absence of any demand by appellant for the examiner to produce authority for his statement, we will not consider this contention."). Hence, it would have been obvious to one skilled in the art at the time before the effective filing date of the invention to have modified the teachings of Wellman as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claim 11:
[Claim 11] Wellman in view of Bar and OLIVIERI teaches or at least suggests wherein the machine learning model is configured to update its parameters based on reinforcement learning, with the one or more processors providing feedback to the model based on an effectiveness of previous engagement status determinations and interventions (at least Wellman: ¶ 74: an archetypal learning pattern for students may comprise a representation of habits and/or interactions of a plurality of students having a similar level or tier of learning outcomes (e.g., scores, grades, or the like), such as previously monitored students , previous academic periods (e.g., semester, trimester, quarter), different courses, different electronic learning publishers 106, different learning institutions 108, or the like. An archetypal learning or teaching pattern for electronic learning material (e.g., an electronic lesson, an electronic assignment, an electronic test or quiz) may comprise a representation of electronic learning material or components thereof, grouped by effectiveness at teaching and/or engaging students 104, or the like); ¶ 76: … form or generate a machine learning ensemble for a plurality of different archetypal learning patterns using training data and/or test data such as previously monitored students 104, previous academic periods (e.g., semester, trimester, quarter), different courses, different electronic learning publishers 106, different learning institutions 108, or the like). However, Wellman appears to be silent on wherein the machine learning model is configured to update dynamically. Nonetheless, the Examiner previously took official notice that the concept and advantages of a computer/controller/processor continuously or periodically or dynamically updating data were old and well-known to one of ordinary skill in the art before the effective filing date of the invention. Since the Applicant did not traverse the officially noticed facts by specifically pointing out supposed errors, the officially noticed facts taken in the Final Rejection dated 10/07/2025 are now considered admitted prior art. See MPEP § 2144.03(C) and particularly Chevenard, 139 F.2d at 713, 60 USPQ at 241 ("[I]n the absence of any demand by appellant for the examiner to produce authority for his statement, we will not consider this contention."). Hence, it would have been obvious to one skilled in the art at the time before the effective filing date of the invention to have modified the teachings of Wellman as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claim 12:
[Claim 12] Wellman in view of Bar and OLIVIERI teaches or at least suggests wherein the machine learning model uses a feature extraction process to identify key variables from the collected data, including but not limited to duration of engagement with specific content accessed (at least Wellman: ¶ 16: monitored electronic learning interactions include an amount of time one or more students remain on a presented electronic learning page). Wellman appears to be silent on wherein the machine learning model uses a feature extraction process to identify key variables from the collected data, including but not limited to a frequency of resource access, duration of engagement with specific content, and diversity of resources accessed. The Examiner previously took official notice that the concept and advantages of logging information including but not limited to frequency, duration and diversity were old and well-known at the time before the effective filing date of the invention. Since the Applicant did not traverse the officially noticed facts by specifically pointing out supposed errors, the officially noticed facts taken in the Final Rejection dated 10/07/2025 are now considered admitted prior art. See MPEP § 2144.03(C) and particularly Chevenard, 139 F.2d at 713, 60 USPQ at 241 ("[I]n the absence of any demand by appellant for the examiner to produce authority for his statement, we will not consider this contention."). Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Wellman as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claim 13:
[Claim 13] Wellman in view of Bar and OLIVIERI appears to be silent on wherein the machine learning model is further trained using transfer learning techniques, leveraging pre-trained models on related tasks to enhance its ability to analyze educational content. By definition, transfer learning is a machine learning technique where a model trained on one task is repurposed as the foundation for a second task. This approach is beneficial when the second task is related to the first or when data for the second task is limited. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Wellman as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claim 21:
[Claim 21] Wellman in view of Bar and OLIVIERI teaches or at least suggests generating a graph or chart based on relevancy scores to identify lower performing students (at least Wellman: ¶ 74: an archetypal learning pattern for students may comprise a representation of habits and/or interactions of a plurality of students having a similar level or tier of learning outcomes (e.g., scores, grades, or the like), such as previously monitored students 104 … an archetypal learning or teaching pattern for electronic learning material (e.g., an electronic lesson, an electronic assignment, an electronic test or quiz) may comprise a representation of electronic learning material or components thereof, grouped by effectiveness at teaching and/or engaging students 104, or the like)).
Additionally, and/or alternatively, this feature amounts to no more than applying a known technique of generating a graph or chart based on given information to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claim 7 is rejected under 35 U.S.C. 103 as being obvious over Wellman in view of Bar and OLIVIERI, as applied to claim 1 above, and further in view of Lackman et al. (US 20180211333 A1) (Lackman).
Re claim 7:
[Claim 7] Wellman in view of Bar and OLIVIERI teaches or at least suggests monitoring user interactions with presented page, lesson, or other learning material of an electronic learning system (throughout Wellman reference). Wellman additionally discloses multi-layer neural nets with various topologies (¶ 90). However, Wellman in view of Bar and OLIVIERI appears to be silent on wherein the machine learning model includes an image recognition component that utilizes Convolutional Neural Networks (CNNs) to analyze visual content accessed by the students and determine its relevance to the predefined learning objective. Nonetheless, the concept and advantages of extracting features from various content items by performing image analysis of image in the content items using machine learning to recognize similar content items (e.g., similar images) were old and well-known at the time before the effective filing date of the invention, as evident in Lackman (¶ 5: The system then trains a machine learning model to recognize similar content items (e.g., similar images; ¶ 61: Different machine learning techniques-such as … neural networks) and produce audience demographic criteria based on the image analysis, the profiles of the users who interacted with the content items, and details about the interactions). Additionally, it is common knowledge that Neural networks come in various types, including Convolutional Neural Networks (CNNs) used for image processing. Hence, it would have been obvious to one skilled in the art at the time before the effective filing date of the invention to have used Lackman’s feature of recognizing similar images in viewed content, along with the common knowledge of using Convolutional Neural Networks (CNNs) for image processing to modify Wellman in view of Bar and OLIVIERI as claimed because this would amount to no more than applying known techniques to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Response to Arguments
The Rejection of Claims Under § 101
Applicant essentially argues that “[T]he amended claim recites a specific technological action that effects a change in the student computing device causing it to alter content displayed on the device” and that “[T]he changing of the state of another device integrates the alleged abstract idea into a practical application with a concrete device-control step”. It appears that Applicant’s remark references the added limitation “in response to the engagement status indicating disengagement relative to the engagement threshold for a student, automatically controlling the student's computing device to alter content displayed on the device”. Applicant’s arguments have been fully considered but they are not persuasive.
As noted in the rejections above, the added limitation is no more than generic conventional data presentation. It is common for a teacher/lecturer/presenter to alter the information being presented/delivered to an audience when the teacher/lecturer/presenter notices a lack of engagement from the audience. As additionally shown in the rejections above using claim 1 as representative, the claimed generic computer components are readily available computing elements used as a basic tool to automatically display content. Representative claim 1 merely recites the earlier noted abstract ideas themselves alongside insignificant extra-solution processes and conventional hardware. See OIP Techs., 788 F.3d at 1362–63. “[M]erely requir[ing] generic computer implementation” “does not move into [§] 101 eligibility territory.” see Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1368 (Fed. Cir. 2019) (“collecting, analyzing, manipulating, and displaying data” are abstract ideas); see also Ex parte Zachary Cutler, Appeal 2019-002737 (PTAB 2020) (holding ineligible a claim to a method for establishing and maintaining neural and attentional engagement of a subject through the use of stimuli provided by a computing device coupled to a display and to an input device).
In light of the foregoing, the Examiner maintains that each of Applicant’s pending claims 1, 3-14 and 16-21 considered as a whole, is directed to a patent-ineligible abstract idea that is not integrated into a practical application, and does not include an inventive concept.
The Rejection of Claims Under § 103
Applicant’s arguments have been fully considered but are moot in view of new
grounds of rejections necessitated by Applicant’s amendment.
Conclusion
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.
The prior art made of record and not relied upon is listed in the attached PTO
Form 892 and is considered pertinent to applicant's disclosure.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715