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
In response to the amendment filed 6/15/2026; claims 1-3, 6-7,11-13,16,18-19,21,25-26,28-33 are pending; Claims 28 – 33 have been added; and claims 4-5,8-10,14-15,17,20,22-24 and 27 have been cancelled.
Claim Objections
Claim 11 is objected to because of the following informalities:
Claim 11 recites a repeated term, i.e., “present, by the machine learning model, the educational challenge to the group of two or more students student through a student interface”.
Appropriate correction is required.
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.
Claims 1-2, 11-12, 18-19 and 28-32 are rejected under 35 U.S.C. 103 as being unpatentable over Mallar et al. (US 2023/0055847 A1) in view of Essafi et al. (US 2017/0124894 A1) and Kanuganti et al. (US 2022/0319181 A1)
Re claims 1, 11, 19:
Mallar teaches 1. A computer-implemented method (Mallar, Abstract), comprising:
training, using profiles of a plurality of students at an educational institution (Mallar, [0056], “a learner metric”; [0071], “all the past data from the plurality of learners 12 is stored and used to train the AI model”; [0055], “Historical subject-based assessment and historical subject-based assignment data are generated using time series analysis based on post-session tests and/or assignments completed by a learner”), a machine learning model to generate educational challenges (Mallar, [0071], “a training module 227 configured to train the AI model”; [0033], “providing educational content”; [0056], “a learner metric”; [0054], “demographic data include school category, school tier, school location, grade level, learning goal, or combinations thereof. The term "learning goal" as used herein refers to a target outcome desired from the learning session … learning goals may include: studying for a particular grade (e.g., grade VIth, grade Xth, grade XIIt , and the like”; [0053]; [0065], “learner demographics”);
selecting a group of two or more students with diverse backgrounds from the plurality of students (Mallar, [0033], “the plurality of learners 12 may be located at different geographical locations while engaging in the online interactive learning session and may belong to the same or different demographics”; [0069], “find the right mix of learners from different groups to which they are currently assigned such that they complement each other's learning”; [0082]);
generating, using the machine learning model, an educational challenge based on a curriculum and a context or interest relevant to all students in the group of two or more students (Mallar, [0065], “learner group parameters and instructor group parameters include learner engagement scores, learner demographics, learner performance metrics, learner-instructor assistant rapport metrics and the like”; [0009], “The group optimizer is configured to dynamically reassign one or more learners of the plurality of learners to an optimized set of groups, based on an AI model, the plurality of learner features, the plurality of group parameters, and the learner engagement data”; [0069]);
presenting, by the machine learning model, the educational challenge to the group of two or more students through a student interface (Mallar, [0033], “the interaction session is aimed at providing educational content”; [0041], “Examples of written content include alpha-numeric text data, graphs, figures, scientific notations, gifs, and videos”);
in response to presenting the educational challenge to the group of two or more students, collecting feedback regarding the educational challenge from engagement with the educational challenge by at least one student in the group of two or more students (Mallar, fig. 7, 308 - “BASED ON AN AI MODEL, THE PLURALITY OF LEARNER FEATURES, THE PLURALITY OF GROUP PARAMETERS, AND THE LEARNER ENGAGEMENT DATA”; [0038], “The data module 210 is configured to access in-session data, post-session data, class data, and learner engagement data for the plurality of learners 12”; [0056], “The term "learner engagement data" as used herein refers to a learner metric that measures, in real-time, the engagement level of each learner of the plurality of learners attending the live learning session”), wherein collecting feedback regarding the educational challenge comprises: capturing a video of the students interacting with the educational challenge (Mallar, [0045] “The term "video data" as used herein refers to the video content recorded from the cameras of the corresponding computing devices as well as the data accessed by processing the video content such as emotion, attention levels, interest levels, and the like”; [0060], “generate the learner engagement score include whiteboard data, audio data, video data, messaging data, browsing data, or in-session assessment data”); and
fine-tuning the machine learning model based on the feedback regarding the educational challenge (Mallar, [0056], “learner engagement score of each learner of the plurality of learners is used to optimize the AI model used to assign the plurality of learners to an optimized set of groups”; [0060], “the engagement score generator 223 is configured to generate, in-real-time, from a trained AI model”; [0071], “a training module 227 configured to train the AI model based on the learner engagement data, as described herein earlier … all the past data from the plurality of learners 12 is stored and used to train the AI model. The training module 227 may be further configured to train the AI model based on or more additional suitable data, not described herein. In some embodiments, the training module 227 is configured to train the AI model at defined intervals, e.g., weekly, bi-weekly, fortnightly, monthly, etc. In some other embodiments, the training module 227 is configured to train the AI model continuously in a dynamic manner”).
11. A system, comprising:
at least one processor;
at least one memory coupled to the at least one processor that includes instructions that, when executed by the at least one processor, cause the system to:
training, using profiles of a plurality of students at an educational institution, a machine learning model to generate educational challenges;
select a group of two or more students with diverse backgrounds from the plurality of students;
generate, using the machine learning model, an educational challenge based on a curriculum goal provided by an instructor and a context or interest relevant to all students in the group of two or more students;
present, by the machine learning model, the educational challenge to the group of two or more students student through a student interface;
in response to presenting the educational challenge to the group of two or more students collect feedback regarding the educational challenge from engagement with the educational challenge by at least one student in the group of two or more students, wherein to collect feedback regarding the educational challenge, the instructions further cause the system to: capture a video of the group of two or more students; and fine-tune the machine learning model based on the feedback regarding the educational challenge (See claim 1 rejection above).
19. A computer-implemented method, comprising:
training, using profiles of a plurality of students at an educational institution, a machine learning model to generate educational challenges;
selecting a group of two or more students with diverse backgrounds from the plurality of students;
generating, using the machine learning model, an educational challenge based on a curriculum goal provided by the instructor, wherein the educational challenge addresses the curriculum goal in a context that is relatable to all students in the group of two or more students;
distributing, by the machine learning model, the educational challenge to the group of two or more students through a content delivery platform;
in response to distributing the educational challenge to the group of two or more students, collecting feedback regarding the educational challenge from engagement with the educational challenge by at least one student in the group of two or more students, wherein to collect feedback regarding the educational challenge, the instructions further cause the system to: capture a video of the group of two or more students; and
fine-tuning the machine learning model based on the feedback regarding the educational challenge (See claim 1 rejection above).
Mallar does not explicitly disclose generating, using the machine learning model, an educational challenge based on a curriculum goal provided by an instructor.
Essafi et al. (US 2017/0124894 A1) teaches systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students (Essafi, Abstract). Essafi teaches generating, using the machine learning model, an educational challenge based on a curriculum goal provided by an instructor (Essafi, fig. 11; [0100], “a teacher plans lessons for 100% of the curriculum”; [0103], “which can be a deterministic algorithm, and process 2, which can include a machine learning process”; [0089], “The education instrumentation platform (e.g., as shown in FIG. 3) can provide one or more client applications running on client devices 102 to interact with the EI system 30”). Therefore, in view of Essafi, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system and method described in Mallar, by providing the curriculum goal (lesson plan) as taught by Essafi, in order to establish a plurality of learning objectives set forth by the teacher according to education standards (Essafi, [0080]; Abstract).
Re claim 19: Mallar does not explicitly disclose receiving social media data from a social media network for a plurality of students associated with an instructor.
Essafi teaches receiving social media data from a social media network for a plurality of students associated with an instructor; saving the social media data with demographic data in a student profile for each of the plurality of students in a non-volatile data repository; (Essafi, [0069], “The data collector 304 can receive other data, such as surveys (e.g. student surveys, community surveys, parents feedback), or other manually input data (e.g., spreadsheets), social media data from social networks, or other online data (e.g., from teacher or school rating websites)”). Therefore, in view of Essafi, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Mallar, by providing the social media data as taught by Essafi, since Essafi suggests that the analysis module can analyze the collected data (e.g., the preprocessed data stored in database) to generate a plurality of models that represent one or more education processes. The generated models can include mathematical or statistical models that simulate the impact or effect of various factors or variables in the collected data on various learning outcomes … a generated model can illustrate the inter-dependencies between factors such as student behavior, student's social, cultural or economic background, parents' educational level, family structure, school size, school resources, class size, student-teacher ratio, teacher qualifications, extra-curriculum activities, and how these factors affect learning out come in math.
Mallar teaches using video data to access the emotion and attention level of the student (Mallar, [0045] “The term "video data" as used herein refers to the video content recorded from the cameras of the corresponding computing devices as well as the data accessed by processing the video content such as emotion, attention levels, interest levels, and the like).
Kanuganti et al. (US 2022/0319181 A1) teaches a system and method for managing education of students in real-time (Kanuganti, Abstract). Kanuganti futher teaches wherein collecting feedback regarding the educational challenge comprises: capturing a video of the group of two or more students interacting with the educational challenge (Kanuganti, [0045], “emotion determination module 226 detects a set of emotions associated with each of the set of students based on the received learning data by using the education management-based AI model … the emotion determination module 226 may detect the set of emotions by analyzing the one or more real-time images, the one or more real-time videos and the one or more videos of each of the set of students captured by the one or more cameras placed methodically by using the education management-based AI model”; [0046], “interaction management module 228 identifies the set of students and the one or more teachers in the one or more real-time images, the one or more real-time videos, the one or more real-time audios or any combination thereof of each of the set of students and the one or more teachers by using the education management-based AI model. Further, the interaction management module 228 identifies the one or more objects in the one or more real-time images, the one or more real-time videos or a combination thereof of the one or more objects by using the education management-based AI model”; [0049]; [0071], “the set of interaction parameters include number of questions asked by the one or more teachers, number of students who tried to answer, a set of responses of questions received from the set of students, number of students who raised hand, set of emotions associated with each of the set of students, action performed by the set of students, the set of activities performed by the one or more teachers, number of responses that are relevant, number of student names called by the one or more teachers, duration of eye contact”); and performing, using a second machine learning model, sentiment analysis on the video to determine one or more sentiments regarding the educational challenge (Kanuganti, [0048], “content generation module 230 is configured to generate one or more personalized content for each of one or more students with low engagement based on the set of interaction parameters, the determined engagement factor and a set of predefined content information by using the education management-based AI model”; [0068], “the AI-based method 800 includes generating one or more recommendations to reduce the one or more learning gaps based on the received learning data”; [0073]; [0076]). Therefore, in view of Kanuganti, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Mallar, by analyzing the images of the students as taught by Kanuganti, since engagement during the lesson lets the one or more teachers are notified about low engaged students to call to action, verified for a name call of the person student of interest, provides information about subject weakness of individual students and is also vital to understand weakness of incorporated education facility, red flag cases such as, abnormal social interactions, distress, bullying, and the like are determined based on the one the set of interaction parameters by using the education management-based AI model (Kanuganti, [0046]).
Re claims 2, 12:
Mallar does not explicitly disclose a social media data, nor disclose financial information. Essaifi teaches the missing features: 2. The computer-implemented method of claim 1, further comprising generating the profiles of the plurality of students, wherein generating the profiles of the plurality of students comprises: acquiring student information, teacher information, academic records, and financial information for the plurality of students from a school database of the educational institution; collecting social media data from a social media network for the plurality of students; and saving the student information, the teacher information, the academic records, the financial information, and the social media data with demographic data in a corresponding student profile for each student in a student profile database. 12. The system of claim 11, wherein the instructions further cause the system to: acquire student information, teacher information, academic records, and financial information for the plurality of students from a school database of the educational institution; collect social media data from a social media network for the plurality of students; save the student information, the teacher information, the academic records, the financial information, and the social media data with demographic data in a corresponding student profile for each student in a student profile database (Essafi, [0069], “The data collector 304 can receive other data, such as surveys (e.g. student surveys, community surveys, parents feedback), or other manually input data (e.g., spreadsheets), social media data from social networks, or other online data (e.g., from teacher or school rating websites)”; [0063], “The data collector 304 can receive student information data (e.g., name, ID, age, gender, parents' education level(s), parents' occupations, social/cultural/economic background information, academic performance, behavior, attendance, etc.), class or grade information data (e.g., class size(s), teacher-student ratio, extra curriculum activities, etc.), books' information data (e.g., books used in each subject), educational applications' information data (e.g., computer or mobile applications used in school), school facilities' information data, or a combination thereof from the student information system(s)”). Therefore, in view of Essafi, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Mallar, by providing the social media data/economic background as taught by Essafi, since Essafi suggests that the analysis module can analyze the collected data (e.g., the preprocessed data stored in database) to generate a plurality of models that represent one or more education processes. The generated models can include mathematical or statistical models that simulate the impact or effect of various factors or variables in the collected data on various learning outcomes … a generated model can illustrate the inter-dependencies between factors such as student behavior, student's social, cultural or economic background, parents' educational level, family structure, school size, school resources, class size, student-teacher ratio, teacher qualifications, extra-curriculum activities, and how these factors affect learning out come in math.
Re claim 18:
18. The system of claim 11, wherein the instructions further cause the processor to report the one or more sentiments associated with the interaction to the instructor (Mallar, [0056], “the learner metric may correspond to a learner engagement score generated in real-time during the live learning session”).
Re claims 28, 29, 30:
Mallar does not explicitly disclose CNN for detecting user sentiments. Kanuganti teaches 28. The computer-implemented method of claim 1 / 29. The system of claim 13, 30. The computer-implemented method of claim 19, wherein the second machine learning model comprises a convolutional neural network (CNN), and wherein performing sentiment analysis on the video to determine one or more sentiments regarding the educational challenge comprises:
detecting, using the CNN, a face of at least one student in the group of two or more students within a video frame selected from the video (Kanuganti, [0046], “the set of interaction parameters include … set of emotions associated with each of the set of students … duration of eye contact … facial direction of the set of students and one or more teacher… video and images are processed using Convolutional Neural Network (CNN) based AI Models to detect information of interest, such as the set of interaction parameters”; [0071]);
extracting, from the face of the at least one student in the group of two or more students, a plurality of features to identify one or more regions of the face that represent facial expressions (Kanuganti, [0046]; [0071]; [0076]; and
classifying, using the CNN, an emotion exhibited by the at least one student in the group of two or more students based on the plurality of features (Kanuganti, fig. 2, 226; [0030], “an emotion determination module 226”; [0045], “the set of emotions include happy, sad, anger, contempt, disgust, fear, surprise, cry, laugh, scared, confusion, excitement and the like”). Therefore, in view of Kanuganti, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/system described in Mallar, by providing the CNN for detecting user expression as taught by Kanuganti, since video and images are processed using Convolutional Neural Network (CNN) based AI Models to detect information of interest, such as the set of interaction parameters, for example, the actions performed by the set of students may be sleeping, talking and the like (Kanguganti, [0046]).
Re claims 31 – 32:
Mallar does not explicitly disclose RNN model. Kanuganti teaches 31. The computer-implemented method of claim 1 / 32. The system of claim 13, wherein the educational challenge comprises at least one of text, speech, an image, or a video, and wherein the machine learning model is a generative machine learning model comprising one or more of a generative pre-trained transformer (GPT) model, a recurrent neural network (RNN), a variational autoencoder (VAE), or a generative adversarial network (GAN) (Kanuganti, [0037]; [0046]; [0062]; [0071]). Therefore, in view of Kanuganti, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/system described in Mallar, by providing the CNN for detecting user expression as taught by Kanuganti, since video and images are processed using RNN model, since the one or more real-time audios are processed using the education management-based AI model i.e., language detection model, to detect the language of teaching, based on the language using Recurrent Neural Network (RNN) based AI Speech Mode to converts the one or more real-time audios associated with the one or more teachers into Unicode text based on the detected language by using the education management based AI model (Kanuganti, [0037]).
Claims 3, 13 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Mallar, Essafi and Kanuganti as applied to claims 1, 11 and 19 above, and further in view of Weldemariam et al. (US 2020/0045119 A1),
Re claims 3, 13 and 26:
Mallar does not explicitly disclose performing sentiment analysis on at least one of a text, an emoji, or a meme in the one or more social media posts to determine one or more sentiments regarding the educational challenge.
Weldemariam teaches system, methods and techniques are provided, which in various aspects may identify one or more (e.g., micro-level) mentorship or tutoring activities based on learning improvement plans or predicted a learning curve for a student or learner and post on one or more social media networks or apps (Weldemariam, Abstract). Weldemariam further teaches 3. The computer-implemented method of claim 1, wherein collecting feedback regarding the educational challenge from engagement with the educational challenge by the at least one student comprises: monitoring social media input through the student interface, wherein the social media input comprises one or more social media posts by the at least one student; and performing sentiment analysis on at least one of a text, an emoji, or a meme in the one or more social media posts to determine one or more sentiments regarding the educational challenge. 13. The system of claim 11, wherein the instructions further cause the system to: monitor social media input through the student interface, wherein the social media input comprises one or more social media posts by the at least one student; and perform sentiment analysis on at least one of a text, an emoji, or a meme in the one or more social media posts to determine one or more sentiments regarding the educational challenge. 26. The computer-implemented method of claim 19, wherein collecting feedback regarding the educational challenge from engagement with the educational challenge by the at least one student comprises: monitoring social media input through a student interface, wherein the social media input comprises one or more social media posts by the at least one student; and performing sentiment analysis on at least one of a text, an emoji, or a meme in the one or more social media posts to determine one or more sentiments regarding the educational challenge (Weldemariam, [0029], “Restructuring the profile may include analyzing the user historical data across multiple social media networks or apps (e.g., user posts, discussions, profile data which may include previous experience, job history, education history, previous mentorships, time available for mentoring, and other data sources)”; [0047], “video sharing website or platform including video posts (e.g., including education video), interactions with posts on such video sharing website or platform”; [0027], “generates one or more effective and optimal learning improvement strategies along engagement, performance, interaction and/or social activity metrics”). Therefore, in view of Weldemariam, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method / system described in Mallar, by providing the social media post / discussion as taught by Weldemariam, since a learning improvement strategy implemented may improve the user's (learner's) performance, engagement, interaction and/or social activities (Weldemariam, [0041]; [0052]).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mallar, Essafi and Kanuganti as applied to claims 2 and 12 above, and further in view of Bruckner et al. (US 2019/0147760 A1)
Re claims 6, 16:
Mallar does not explicitly disclose one or more image in the social media data. Bruckner teaches the miss features: 6. The computer-implemented method of claim 2, further comprising: extracting, using a third machine learning model, content from one or more images in the social media data; and adding the content to the social media data. 16. The system of claim 12, wherein the instructions further cause the system to: extract, using a third machine learning model, content from one or more images in the social media data; and add the content to the social media data (Bruckner, [0022], “The social media data 126 may include online articles shared by the user 102 on social media sites, social media posts generated by the user 102, pictures or other media shared by the user 102, or the like”). Therefore, in view of Bruckner, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/system described in Mallar, by posting picture as taught by Bruckner, since Bruckner suggests that user tends to post pictures of historical sites or locations on their social media account(s), this information can be incorporated by the machine learning model into the user's customized user profile such that content relating to historical sites or events later presented to the user can be annotated or otherwise enhanced with pictures of the historical sites or events in order to reinforce concepts and make the content more tailored to the user's interests or preferences (Bruckner, [0022]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mallar, Essafi and Kanuganti as applied to claim 1 above, and further in view of Smith et al. (US 2007/0218446 A1)
Re claim 7:
Mallar does not explicitly disclose instructor approval. Smith teaches a system and method for providing a tutoring service over a network comprises a tutoring application server on the network that is capable of serving one or more student interfaces and tutor interfaces over the network (Smith, Abstract). Smith teaches 7. The computer-implemented method of claim 1, further comprising: presenting, by the machine learning model, the educational challenge to the instructor through an instructor interface for approval before distributing the educational challenge; and updating the educational challenge based on instructor feedback (Smith, [0111], “If so, the selected content is loaded at 924 and the process returns to the start of event loop 920. If the tutor has not made a selection at 922, the process determines whether the student has requested content be loaded at 932. If so, a tutor approval subroutine 934 is run. If the tutor approves at 936, the selected content is loaded at 924 and the process returns to the event loop 920. If the tutor does not approve at 936, the process returns to the start of event loop 920”; fig. 9). Therefore, in view of Smith, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Mallar, by providing instructor approval as taught by Smith, in order to allow an instructor to review and certify before presenting the content to the student
Claims 21 are rejected under 35 U.S.C. 103 as being unpatentable over Mallar, Essafi, Weldemariam and Kanuganti as applied to claim 3 above, and further in view of Ahuja et al. (US 2017/0004720 A1)
Re claim 21:
Mallar teaches 21. The computer-implemented method of claim 3, further comprising: correlating the one or more sentiments with engagement states spanning from an initial review of the educational challenge through a submission of a response to the educational challenge (Mallar, [0064], “The group of instructor assistants 24 may facilitate individual learner interactions either during the live learning session (e.g., responding to in-session messages, reviewing in-session assessments, etc.)”; fig. 7, 308 - “THE LEARNER ENGAGEMENT DATA”; [0038], “The data module 210 is configured to access in-session data, post-session data, class data, and learner engagement data for the plurality of learners 12”; [0056], “The term "learner engagement data" as used herein refers to a learner metric that measures, in real-time, the engagement level of each learner of the plurality of learners attending the live learning session”). Mallar does not explicitly disclose outputting the one or more sentiments and the engagement states to the instructor, thereby allowing the instructor to determine if a topic or a concept needs further exploration. Ahuja teaches a system may an online education platform configured to provide an online course over a network to a plurality of computing devices (Ahuja, Abstract). Ahuja teaches 21. The method of claim 3, further comprising: correlating the one or more sentiments with engagement states spanning from an initial review of the educational challenge through a submission of a response to the educational challenge (Ahuja, Abstract, “The online education platform may include a content editor may provide an authoring tool on a computing device associated with an instructor of the online course. The authoring tool may develop or change the education content associated with the online course”; [0085], “the interface of the authoring tool 170 provides a grading metric 160 that indicates the passing threshold for the online course 104”; [0088], “the analytic results 164 may include the percentage of learners having completed the lecture 180”; “the authoring tool” allows an instructor to curriculum and curriculum outcome); and outputting the one or more sentiments and the engagement states to the instructor, thereby allowing the instructor to determine if a topic or a concept needs further exploration (Ahuja, [0039]; [0043], “the online course analyzer 110 may be configured to determine the engagement metric(s) 158 based on the learners' tracked interactions or engagements as collected by the learner tracking unit 118”; [0046], “while viewing the engagement metrics 158 and/or the grading metrics 160, the instructor may institute a change to the education content 136, and the change may be carried out by the content editor 134 which converts the education content 136 having a first format”). Therefore, in view of Ahuja, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method /system described in Mallar, by making changes to education content in response to engagement metric as taught by Ahuja, since the instructor uses his/her computing device to connect to the online education platform to view the learners' performance and engagement via the instructor dashboard and can make adjustments to the education content via an authoring tool that interacts with a content editor which makes any instructor edits compatible with a format of the online education platform to increase performance and engagement (Ahuja, [0039]).
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Mallar, Essafi and Kanuganti as applied to claim 28 above, and further in view of Wu et al. (US 2025/0005923 A1).
Re claim 33:
Mallar does not explicitly disclose surprise or intrigued by the second student. Wu et al. (US 2025/0005923 A1) teaches a team monitoring system receives data for determining user situational awareness and/or surprise for each team member (Wu, Abstract). Wu teaches 33. The computer-implemented method of claim 28, wherein the emotion exhibited by the at least one student is surprised or intrigued by input provided by a second student, and further comprising: updating the machine learning model in response to determining the at least one student is surprised or intrigued by the input provided by the second student while the at least one student is solving the educational challenge with the second student (Wu, [0022]; [0023], “the situational awareness and/or surprise of the first team member may be partially based on the first team member's response to the actions of the second team member”; [0021]; [0030], “Likewise, the system may receive data related to factors specific to the task. Such task specific data provides the additional metric of context when assessing 204, 206 user situational awareness and/or surprise. Such analysis may include processing via machine learning, neural network algorithms”; [0027]; [0032], “a neural network”). Therefore, in view of Wu, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Mallar, by assessing the mental state of the user based on the action of team as taught by Wu, in order to assess the emotional state of each member in the group so that the group can work more efficiently (Wu, Abstract, “A team metric of situational awareness and/or surprise is determined for the entire team based on individual user situational awareness and/or surprise correlated to discreet portions of a task”).
Claims 25 are rejected under 35 U.S.C. 103 as being unpatentable over Mallar, Essafi and Kanuganti as applied to claim 1 above, and further in view of Ahuja et al. (US 2017/0004720 A1) and Wu et al. (US 2025/0005923 A1).
Re claim 25:
Mallar does not explicitly disclose outputting a summary of the student’s achievement; nor disclose engagement states as surprise. The combination of Ahuja and Wu teaches 25. The computer-implemented method of claim 1, further comprising: in response to presenting the educational challenge, outputting a summary of what the student learned in relation to the educational challenge, the summary including a list of engagements of the student that have been classified by the machine learning model (Ahuja, figs. 13 – 22) as surprise (Wu, [0022]; [0023], “the situational awareness and/or surprise of the first team member may be partially based on the first team member's response to the actions of the second team member”; [0021]). Therefore, in view of Ahuja, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Mallar, by providing a summary (i.e., instructor dashboard) as taught by Ahuja, in order the instructor dashboard 154 may provide the instructor with the most relevant and interesting data regarding the online class's performance so that the instructor can make determinations on how well the learners are understanding the education content, and determine whether his/her online course could benefit from any adjustments (Ahuja, [0038]). In view of Wu, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Mallar, by assessing the surprise mental as taught by Wu, in order to assess the emotional state of each member in the group so that the group can work more efficiently (Wu, Abstract, “A team metric of situational awareness and/or surprise is determined for the entire team based on individual user situational awareness and/or surprise correlated to discreet portions of a task”).
Response to Arguments
Applicant’s arguments with respect to claims 1-3, 6-7, 11-13, 16,18-19,21,25-26,28-33 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues: paragraph 0033 of Mallar discloses that interactive sessions are "aimed at providing educational content," the reference does not teach or suggest that the educational content provided during the interactive session is generated by a machine learning model. As previously discussed, Mallar teaches that the disclosed AI model is trained to dynamically assign one or more learners to an optimized set of groups during a live learning session delivered via an online learning platform. The reference does not teach that the AI model "[generates] ... an educational challenge based on a curriculum goal provided by an instructor and a context or interest relevant to all students in the group of two or more students," as required by the independent claims. Furthermore, since Mallar does not disclose a machine learning model that is configured to generate educational content or challenges, Applicant submits that the reference additionally fails to disclose steps or technical features to train and fine-tune a machine learning model to generate educational content or challenges. Furthermore, since Mallar does not disclose a machine learning model that is configured to generate educational content or challenges, Applicant submits that the reference additionally fails to disclose steps or technical features to train and fine-tune a machine learning model to generate educational content or challenges.
The examiner submits that Mallar teaches the limitations: generating, using the machine learning model, an educational challenge based on a curriculum and a context or interest relevant to all students in the group of two or more students (Mallar, [0065], “learner group parameters and instructor group parameters include learner engagement scores, learner demographics, learner performance metrics, learner-instructor assistant rapport metrics and the like”; [0009], “The group optimizer is configured to dynamically reassign one or more learners of the plurality of learners to an optimized set of groups, based on an AI model, the plurality of learner features, the plurality of group parameters, and the learner engagement data”; [0069]).
Mallar teaches selecting a group of two or more students with diverse backgrounds from the plurality of students (Mallar, [0033], “the plurality of learners 12 may be located at different geographical locations while engaging in the online interactive learning session and may belong to the same or different demographics”; [0069], “find the right mix of learners from different groups to which they are currently assigned such that they complement each other's learning”; [0082]).
The newly cited reference: Kanuganti et al. (US 2022/0319181 A1) teaches the new limitations: wherein collecting feedback regarding the educational challenge comprises: capturing a video of the group of two or more students interacting with the educational challenge; and performing, using a second machine learning model, sentiment analysis on the video to determine one or more sentiments regarding the educational challenge.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACK YIP whose telephone number is (571)270-5048. The examiner can normally be reached Monday thru Friday; 9:00 AM - 5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, XUAN THAI can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JACK YIP/Primary Examiner, Art Unit 3715
Prosecution Insights