DETAILED ACTION
Status of the Claims
The following is a Final Office Action in response to amendments and remarks filed 20 February 2026.
Claims 1 and 11 have been amended.
Claims 1-20 are pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
Applicant’s remarks with respect to the prior art references have been fully considered but are moot on grounds of new rejections as necessitated by amendments. The Examiner refers Applicant to the updated rejections and objections below.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by the Applicants in regards to distinctly and specifically pointing out the supposed errors in the Examiner's prior office action (37 CFR 1.111). The Examiner asserts that the Applicants only argue that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities: The claims, as amended, recite an “interaction tribute” which appears to be the typographical error of “interaction attribute.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims recite the newly amended limitation “generate a user activity data vector and a cue datum vector based on the user activity data, wherein the user activity data comprises the converted handwritten text, wherein each vector comprises at least a numerical element corresponding to a measured interaction tribute” however there is no discussion, throughout the entirety of the specification and drawings, how a vector is able to be generated from handwriting. Paragraph [0023] states “With continued reference to FIG. 1, in some embodiments, user activity data 120 may include at least one cue datum 124. As used in this disclosure, a "cue datum" refers to a specific piece of information or signal that triggers a particular action or response within the system. In some cases, cue datum 124 may be derived from the user's interactions, preferences, or behaviors within the digital environment. For instance, and without limitation, cue datum 124 may be generated, by processor 104, when a user spends an extended period on a particular task, indicating potential difficulty or heightened interest. Alternatively, cue datum 124 may include one or more specific semantic unit, for example, without limitation, a keyword or phrase used by the user in a search, a command, a response, and/or the like, signaling a specific intent or request.” Paragraph [0054] states “...datum may include a subset of user activity data vector u (i.e., sub-vector) or a differentiation of user activity data vector u. For instance, and without limitation, processor 104 may generate a cue datum vector w based on a plurality of user activity data vectors arranged in a chronological order, wherein the cue datum vector w may capture a "rate of change" or a "sudden change" in user activities. In some cases, cue datum vector w may be represented as w = [~[, ~d, ~c], wherein ~f, ~d and ~c are the changes in frequency of logins, duration of interactions, and count of items clicked, respectively, over a specific time interval. In a non-limiting example, if user 116 suddenly increases login frequency but reduces interaction duration, cue datum vector w may capture these changes as significant change, potentially indicating a shift in user behavior or a response toa a specific event or feature in digital environment." The only mention of handwriting is in Paragraph [0046]-[0047] which states “As a non-limiting example, and still referring to FIG. 1, user data 112 may include a handwritten note, wherein the handwritten note may be presented, by user 116, to a camera, and processor 104 may be configured to employ optical character recognition (OCR) techniques to automatically convert the handwritten text (i.e., images) into digital format for further processing as described below. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine learning processes. Still referring to FIG. 1, in some cases OCR may be an "offline" process, which analyses a static document or image frame. In some cases, handwriting movement analysis can be used as input to handwrite recognition. For example, instead of merely using shapes of glyphs and words, this technique may capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information can make handwriting recognition more accurate. In some cases, this technology may be referred to as "online" character recognition, dynamic character recognition, real-time character recognition, and intelligent character recognition” which is clearly discussing how handwriting is able to be captured/recognized, however there is no further discussion as to how handwriting is then used to generate vectors whatsoever. While digital user activity is discussed as being used to generate vectors (i.e. clicks, logins, online interactions, search prompts), there is no mention or discussion as to how handwriting is the user activity which is used to generate the vectors. As such, the Examiner asserts this as evidence that the newly amended claims are new matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4-7, 10-12, 14-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Davier et al. (US PG Pub. 2019/0130511) further in view of Everest (US Patent No. 11,893,464) and George et al. (US PG Pub. 2019/0335006).
As per claims 1 and 11, Davier discloses an apparatus and method for assisted learning, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to (server, computer processor, computer readable medium, Davier ¶36):
receive user data pertaining to a user, wherein the user data comprises user activity data (assessment and behavior inputs, for learning analytics process, Davier ¶34 and ¶45-¶46; see also ¶31);
determine an interaction indicator by evaluating the user activity data including the converted handwritten text using a behavioral analysis module, wherein determining the interaction indictor comprises (assessment and behavior inputs, for learning analytics process, Davier ¶34 and ¶45-¶46; at-risk behavior, ¶29):
using the behavioral analysis module, the user activity data using a scoring function, wherein the scoring function includes a frequency, duration, and significance of an interaction of a user, wherein an overall scoring of the user activity data as a function of the interaction of the user is weighted as a function of a weighting coefficient, wherein the weighting coefficient is adjusted as a function of a real-time iterative feedback loop to refine the weighting coefficient (score, Davier ¶40-¶41 and ¶45; In some examples, the learner attribute profile 1032 (rf. FIG. 1C) may also include assessment questions or types of assessment questions correlated to the particular skill, and CDM or BKT outputs. The learner attribute profile may further include weightings for each entry corresponding to test preparation data 154, socioemotional data 156, and/or behavioral data 158. For example, the learner attribute profile may include the probabilities generated by the CDM and/or BKT processes for each skill and assessment question, and corresponding weighting parameters indicating that the learner was in a happy mood when taking the assessment and when practicing the corresponding skill prior to taking the assessment, that the learner is an extravert and had studied the skill at regular intervals, and that the learner sought tutoring or extra assistance with the skill using particular system resources. The diagnostic model 172 may then determine that the learner did not adequately perform on the subset of assessment questions relating to that skill, and take into account the learner's test preparation practices, behavior, and socioemotional state. The feedback model 174 may then correlate this data identified in the learner attribute profile with available system resources and historical data about effective use of those resources, as correlated with learner attribute profiles that are similar to the learner's, and generate a feedback set that includes recommendations for system resources that the learner may use to improve, improvements to study habits, and other insight for the learner as may be appropriate. The assessment evaluation of the learner's performance and corresponding feedback may then be presented to the learner through learner interface 140, ¶48; probability the learner has mastered the one or more skills, ¶56)
averaging the scoring of the user activity data to derive a composite score, wherein the composite score represents an overall interaction level of a user, wherein the user activity data is classified into one or more user categories based on the composite score (probability the learner has mastered the one or more skills, Davier ¶56)
outputting, using the behavioral analysis module, the interaction indicator as a function of the composite score (output, Davier ¶51 and ¶53);
identifying a user [profile for the learner] based on user activity data (the learner-specific behavioral parameters include a type of learning resource accessed by the learner, a time spent by the learner on a task, a participation level of the learner with an interactive interface, or a persistence ratio of a number of times retaking an assessment compared with the probability that one or more skills from the category will be mastered. Example of obtaining the learner-specific behavioral parameters may include receiving behavioral indications from a learner input device. For example, the learner input device may include a mouse, a microphone, a keyboard, or a touchscreen. Determining if any of the learner-specific behavioral parameters correlate to at-risk behavior may include obtaining historical behavioral data from a historical assessment database, Davier ¶26; Learning analytics process 150 may also be applied to socioemotional data 156. For example, socioemotional data 156 may include a psychological profile for the learner. The psychological profile may be acquired using a questionnaire or other type of interactive answer and response system to deliver and score a psychological evaluation as known in the art. In some examples, the psychological evaluation may be a Myers-Briggs Type Indicator (MBTI), or similar, personality assessment. Socioemotional data 156 may also include data about a learner's current mood, e.g., happy, sad, angry, frustrated, or otherwise. In some embodiments, the mood data may be acquired by the way of self-assessment instruments, or may be signaled by the inferences made on the basis of learner analytics process 150 by the Learning Analytics Server 130 or via externally acquired indicators. For example, the learner may select a particular emoji within the learner interface or a social media application that indicates the learner's current mood. Socioemotional data 156 may also include empirical data regarding the learner's interaction with third-party applications. For example, the learner's propensity to use online forums, chat rooms, social media tools, blogs, articles, e-books, or other applications to interact with other learners, teachers, tutors, or experts in a community, or to access and interact information and tools to assist with learning. Socioemotional data 156 may also include historical benchmark data stored in data store 120 regarding socioemotional data from the same learner, or different learners, as correlated with learner performance metrics, such as performance on assessments, Learner analytics process 150 may also be applied to behavioral data 158. For example, behavioral data 158 may include empirical data captured from a learner's historical practice and test taking activities. For example, behavioral data 158 may include a learner's study habits, including the learner's propensity to take practice assessments or quizzes, use simulators, or interact with system learning tools without interruption, or at regular intervals. Behavioral data 158 may be captured using data from learner interface 140 that monitors when the learner is using the system and the specific tools with which the learner is interacting. Behavioral data 158 may also be acquired from sensor 112 or from a self-assessment of the learner, ¶45-¶46); and
validating the user [profile for the learner] against a pre-defined set of behavioral [profiles] (As the learner interacts with the system by taking assessments, practicing, studying, or performing other learning activities, the learning analytics server 130 may generate feedback and insight to help the learner improve his or her level of mastery for one or more skills. In some embodiments, the learner may select or be assigned a learning goal or set of goals (e.g., master addition within the next five days). Learning analytics server 130 may then generate feedback based on the learner's current level of mastery of addition, rate of learning, and related behavioral and socioemotional characteristics, and empirical data received from data store 120. Learning analytics server may then determine that learners with similar behaviors (e.g., study habits), socioemotional characteristics (e.g., psychological profile and tendencies), and assessment performance on assessment questions relating to addition would benefit by implementing a particular study regiment, e.g., by practicing certain questions, receiving tutoring, studying more or less often, getting more sleep, and so on. This feedback may be presented to the learner via learner interface 140, and the learner's particular goals may be updated, Davier ¶42);
selectively initiate a user event based on the validated user [profile for the learner] (For example, the learner attribute profile may include the probabilities generated by the CDM and/or BKT processes for each skill and assessment question, and corresponding weighting parameters indicating that the learner was in a happy mood when taking the assessment and when practicing the corresponding skill prior to taking the assessment, that the learner is an extravert and had studied the skill at regular intervals, and that the learner sought tutoring or extra assistance with the skill using particular system resources. The diagnostic model 172 may then determine that the learner did not adequately perform on the subset of assessment questions relating to that skill, and take into account the learner's test preparation practices, behavior, and socioemotional state. The feedback model 174 may then correlate this data identified in the learner attribute profile with available system resources and historical data about effective use of those resources, as correlated with learner attribute profiles that are similar to the learner's, and generate a feedback set that includes recommendations for system resources that the learner may use to improve, improvements to study habits, and other insight for the learner as may be appropriate. The assessment evaluation of the learner's performance and corresponding feedback may then be presented to the learner through learner interface 140, Davier ¶48; see also ¶53 providing a list of resources and recommendations); and
iteratively listen for a user response to the user event, wherein the user response alerts a subsequent interaction indicator upon a re-evaluation of the user activity data using the behavioral analysis module (FIG. 7 is a diagram illustrating an example of a BKT process. For example, responses may be tracked over time for each question selected within the common category to determine a correct response rate. For a particular skill, a learner's response sequence 1 to n may be used to predict the response to question n+1. The prediction may be based on a sequence of items that are dichotomously scored, each item corresponding to a skill. The BKT process may track the learner's knowledge over time based on the learner's performance. The learner may learn on each question, for example, with the assistance of learning resources and feedback. Thus, the correct response rate may change over time, Davier ¶64); and
trigger a re-assessment of the user (The assessment evaluation of the learner's performance and corresponding feedback may then be presented to the learner through learner interface 140, Davier ¶48; FIG. 3 is a flow chart illustrating an example BKT process. Referring to FIG. 3, a BKT process 300 may include presenting a subset of assessment questions from a common category (e.g., from a subset of assessment questions relating to one or more skills) at step 305 and determining a category-specific learning rate and updated learner attribute profile by tracing the accuracy of the responses to those assessment questions over time at step 310. BKT process may be implemented to as a two-state learning model to determine whether a particular skill is either learned or unlearned. The learner attribute profile may then be updated with the output from the BKT process for each skill. The update to the skill masteries may be determined by tracing indications as to whether a learner's response to one or more assessment questions is correct over a given time period, ¶58; assigned a learning goal for next 5 days, ¶42) (Examiner interprets the ability to track assessment question answers historically and over time, updating the skill masteries based upon some assigned learning goal as the ability to have some sort of trigger for a re-assessment at the end of the learning goal);
re-categorize the user based on the user response and a corresponding subsequent interaction indicator (The multidimensional learning data may include assessment data 152, which may include data relating to an assessment examination that includes a set of assessment questions. Assessment data 152 may also include characteristics associated with each assessment question, including question difficulty, and a corresponding skill classification. Assessment data 152 may also include a learner's responses to the assessment questions, e.g., as received from a learner interface 140, as well as historical responses to the same assessment questions from the same learner or other learners, as received from data store 120. Analysis of the assessment data may be performed according to methods disclosed herein. Learning analytics process 150 may also be applied to test preparation data 154. For example, test preparation data 154 may include data indicating the tests preparation activities performed by a learner with respect to one or more skills. In some examples, test preparation data 154 includes a time spent studying one or more skills, data from practice questions, quizzes, and or examinations, performance on skill simulators, time spent and performance on related educational games or simulators, together with other types of test preparation data as known in the art, Davier ¶43-¶44).
While Davier does disclose the ability to scan response sheets and tests and OCR forms with a scanner (Davier ¶49 and ¶54) Davier does not expressly disclose comprising handwritten text; convert the handwritten text into digital format by an optical character recognition (OCR) process, wherein converting the handwritten text into the digital format comprises converting images of the handwritten text in into the digital format and further comprises: pre-processing image components of the images, wherein pre-processing the image components comprises: de-skewing at least one of the image components by applying a transform to the at least one of the image components; using binarization to convert at least a portion of one of the images from color or greyscale to a binary image format; and using normalization to normalize an aspect ratio of at least one of the image components; implementing an OCR algorithm comprising a matrix matching process, wherein implementing the OCR algorithm comprises: comparing pixels of at least one of the pre-processed images to pixels of a stored glyph on a pixel-by-pixel basis; and ascertaining a similar font and scale therebetween based on the comparison; and post-processing an output of the matrix matching process to increase OCR accuracy by constraining the output to a lexicon containing a set of words whose occurrence is permitted generate a user activity data vector and a cue datum vector based on the user activity data, wherein the user activity data comprises the converted handwritten text, wherein each vector comprises at least a numerical element corresponding to a measured interaction tribute.
However, Everest teaches:
comprising handwritten text; convert the handwritten text into digital format by an optical character recognition (OCR) process, wherein converting the handwritten text into the digital format comprises converting images of the handwritten text in into the digital format and further comprises: pre-processing image components of the images, wherein pre-processing the image components comprises: de-skewing at least one of the image components by applying a transform to the at least one of the image components; using binarization to convert at least a portion of one of the images from color or greyscale to a binary image format; and using normalization to normalize an aspect ratio of at least one of the image components; implementing an OCR algorithm comprising a matrix matching process, wherein implementing the OCR algorithm comprises: comparing pixels of at least one of the pre-processed images to pixels of a stored glyph on a pixel-by-pixel basis; and ascertaining a similar font and scale therebetween based on the comparison; and post-processing an output of the matrix matching process to increase OCR accuracy by constraining the output to a lexicon containing a set of words whose occurrence is permitted (OCR process, matrix matching, de-skew, software such as Cuneiform and Tesseract, decompose glyphs, algorithms, Everest Col. 17 line 16-Col. 19 line 22);
generate a user activity data vector and a cue datum vector based on the user activity data, wherein the user activity data comprises the converted handwritten text, wherein each vector comprises at least a numerical element corresponding to a measured interaction tribute (n some cases, extracted feature can be compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR. In some embodiments, machine-learning process like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) can be used to compare image features with stored glyph features and choose a nearest match. OCR may employ any machine-learning process described in this disclosure, for example machine-learning processes described with reference to FIGS. 2-5. Exemplary non-limiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multi-language, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract is free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States, Everest Col. 18 lines 38-54 and Col. 21 lines 24-58);
Both the Everest and Davier references are analogous in that both are directed towards/concerned with user profile tracking and document management. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Everest’s ability to OCR handwritten documents (including the use of off-the-shelf software as mentioned in both the instant specification and in Everest) in Davier’s system to improve the system and method with reasonable expectation that this would result in an e-learning management system that is able to improve the user experience and more easily store documents electronically in order to track profile information such as educational accomplishments or assessments.
The motivation being that currently there are over 17 million online education learners in the United States, however, online education systems lack personalization and interaction with learners often finding it difficult to focus. An effective online education system remains an elusive goal (Everest Col. 1 lines 14-18).
The combination of Everest and Davier do not expressly disclose generating the behavioral analysis module, wherein the behavioral analysis module comprises an input layer of nodes, at least one intermediate layers, and an output layer of nodes, wherein a connection between nodes is created, wherein the connection between nodes in adjacent layers is adjusted to produce desired values at the output nodes.
However, George teaches generating the behavioral analysis module, wherein the behavioral analysis module comprises an input layer of nodes, at least one intermediate layers, and an output layer of nodes, wherein a connection between nodes is created, wherein the connection between nodes in adjacent layers is adjusted to produce desired values at the output nodes (the selection model includes a neural network generated using, for example, machine learning techniques, George ¶31; FIG. 3 illustrates an example of a multi-layer neural network 300 that can be used for user behavior classification according to certain embodiments. Multi-layer neural network 300 includes an input layer 310, a hidden (or intermediate) layer 320, and an output layer 330. In many implementations, multi-layer neural network 300 includes two or more hidden layers and can be referred to as a deep neural network. A neural network with a single hidden layer is generally sufficient to model any continuous function. However, such a network may need an exponentially larger number of nodes when compared to a neural network with multiple hidden layers. It has been shown that a deeper neural network can be trained to perform much better than a comparatively shallow network, ¶46-¶47; convolutional neural network, ¶24, ¶36, and ¶60).
The George, Everest, and Davier references are analogous in that both are directed towards/concerned with tracking and improving user experiences within an interactive learning system. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use George’s method of interactively tailoring user content based upon user behavior in Everest and Davier’s system to improve the system and method with reasonable expectation that this would result in an e-learning management system that is able to improve the user experience and tailor the content for the user.
The motivation being that customization techniques present limitations with respect to structured interactive computing environments. For instance, current techniques are often limited to simple metrics (e.g., progress rate or error rate) that fail to account for user status and characteristics such as user capabilities, needs, personality, preferences, engagement level, current progress, etc. Therefore, the interactive computing systems cannot tailor the interactive content during a session to users with a suitable level of particularity, such as customizing interactive educational content in a manner that enhances user interaction and comprehension (George ¶3).
While Davier, Everest, and George discloses a system for dynamic learning diagnostics and feedback wherein users or learners are able to be profiled based upon behavioral and socioemotional data, the combination of Davier, Everest, and George do not expressly disclose the “user archetype.”
However, the Examiner asserts that the user archetype is simply a label for how a user or learner is classified or identified based upon their learning style and/or personality and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., profiling user behavior with a label such as an archetype, personality type, learning style etc.) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the profiled data to include a user archetype since the specific type of label/classification that identifies a user or learner’s personality type, learning style etc. does not functionally alter or relate to the steps of the method and merely labeling the information differently from that in the prior art does not patentably distinguish the claimed invention.
Furthermore, one of ordinary skill, before the effective filing date of the claimed invention, would have found it obvious to repeat the processes in claims 1 and 11 for a re-assessment or additional assessments because duplication is obvious, MPEP 2144.04.VI.B. The duplication of parts (or steps) has no patentable significance unless a new and unexpected result is produced. Examiner finds no evidence that performing the processes in claims 1 and 11 for re-assessment or additional assessments would produce new and unexpected results as compared to performing the processes in claims 1 and 11 for only a first assessment.
As per claims 2 and 12, Davier, Everest, and George disclose as shown above with respect to claims 1 and 11. Davier further discloses wherein the user activity data comprises at least one cue datum (Learning analytics process 150 may also be applied to socioemotional data 156. For example, socioemotional data 156 may include a psychological profile for the learner. The psychological profile may be acquired using a questionnaire or other type of interactive answer and response system to deliver and score a psychological evaluation as known in the art. In some examples, the psychological evaluation may be a Myers-Briggs Type Indicator (MBTI), or similar, personality assessment. Socioemotional data 156 may also include data about a learner's current mood, e.g., happy, sad, angry, frustrated, or otherwise. In some embodiments, the mood data may be acquired by the way of self-assessment instruments, or may be signaled by the inferences made on the basis of learner analytics process 150 by the Learning Analytics Server 130 or via externally acquired indicators. For example, the learner may select a particular emoji within the learner interface or a social media application that indicates the learner's current mood. Socioemotional data 156 may also include empirical data regarding the learner's interaction with third-party applications. For example, the learner's propensity to use online forums, chat rooms, social media tools, blogs, articles, e-books, or other applications to interact with other learners, teachers, tutors, or experts in a community, or to access and interact information and tools to assist with learning. Socioemotional data 156 may also include historical benchmark data stored in data store 120 regarding socioemotional data from the same learner, or different learners, as correlated with learner performance metrics, such as performance on assessments, Learner analytics process 150 may also be applied to behavioral data 158. For example, behavioral data 158 may include empirical data captured from a learner's historical practice and test taking activities. For example, behavioral data 158 may include a learner's study habits, including the learner's propensity to take practice assessments or quizzes, use simulators, or interact with system learning tools without interruption, or at regular intervals. Behavioral data 158 may be captured using data from learner interface 140 that monitors when the learner is using the system and the specific tools with which the learner is interacting. Behavioral data 158 may also be acquired from sensor 112 or from a self-assessment of the learner, Davier ¶45-¶46).
As per claims 4 and 14, Davier, Everest, and George disclose as shown above with respect to claims 1 and 11. Davier further discloses wherein identifying the user archetype comprises: training a user archetype classifier using user archetype training data, wherein the user archetype training data comprises a plurality of user activity data as input correlated to a plurality of user archetypes as output; and classifying the user activity data into the user archetype using the trained user archetype classifier (Learning analytics process 150 may also be applied to socioemotional data 156. For example, socioemotional data 156 may include a psychological profile for the learner. The psychological profile may be acquired using a questionnaire or other type of interactive answer and response system to deliver and score a psychological evaluation as known in the art. In some examples, the psychological evaluation may be a Myers-Briggs Type Indicator (MBTI), or similar, personality assessment. Socioemotional data 156 may also include data about a learner's current mood, e.g., happy, sad, angry, frustrated, or otherwise. In some embodiments, the mood data may be acquired by the way of self-assessment instruments, or may be signaled by the inferences made on the basis of learner analytics process 150 by the Learning Analytics Server 130 or via externally acquired indicators. For example, the learner may select a particular emoji within the learner interface or a social media application that indicates the learner's current mood. Socioemotional data 156 may also include empirical data regarding the learner's interaction with third-party applications. For example, the learner's propensity to use online forums, chat rooms, social media tools, blogs, articles, e-books, or other applications to interact with other learners, teachers, tutors, or experts in a community, or to access and interact information and tools to assist with learning. Socioemotional data 156 may also include historical benchmark data stored in data store 120 regarding socioemotional data from the same learner, or different learners, as correlated with learner performance metrics, such as performance on assessments, Learner analytics process 150 may also be applied to behavioral data 158. For example, behavioral data 158 may include empirical data captured from a learner's historical practice and test taking activities. For example, behavioral data 158 may include a learner's study habits, including the learner's propensity to take practice assessments or quizzes, use simulators, or interact with system learning tools without interruption, or at regular intervals. Behavioral data 158 may be captured using data from learner interface 140 that monitors when the learner is using the system and the specific tools with which the learner is interacting. Behavioral data 158 may also be acquired from sensor 112 or from a self-assessment of the learner, Davier ¶45-¶46).
As per claims 5 and 15, Davier, Everest, and George disclose as shown above with respect to claims 1 and 11. Davier further discloses wherein validating the user archetype comprises: calculating a similarity metric between the user activity data and each behavioral archetype within the pre-defined set of behavioral archetypes; and comparing the similarity metric against a pre-determined threshold (the learner-specific behavioral parameters include a type of learning resource accessed by the learner, a time spent by the learner on a task, a participation level of the learner with an interactive interface, or a persistence ratio of a number of times retaking an assessment compared with the probability that one or more skills from the category will be mastered. Example of obtaining the learner-specific behavioral parameters may include receiving behavioral indications from a learner input device. For example, the learner input device may include a mouse, a microphone, a keyboard, or a touchscreen. Determining if any of the learner-specific behavioral parameters correlate to at-risk behavior may include obtaining historical behavioral data from a historical assessment database, Davier ¶26; Learning analytics process 150 may also be applied to socioemotional data 156. For example, socioemotional data 156 may include a psychological profile for the learner. The psychological profile may be acquired using a questionnaire or other type of interactive answer and response system to deliver and score a psychological evaluation as known in the art. In some examples, the psychological evaluation may be a Myers-Briggs Type Indicator (MBTI), or similar, personality assessment. Socioemotional data 156 may also include data about a learner's current mood, e.g., happy, sad, angry, frustrated, or otherwise. In some embodiments, the mood data may be acquired by the way of self-assessment instruments, or may be signaled by the inferences made on the basis of learner analytics process 150 by the Learning Analytics Server 130 or via externally acquired indicators. For example, the learner may select a particular emoji within the learner interface or a social media application that indicates the learner's current mood. Socioemotional data 156 may also include empirical data regarding the learner's interaction with third-party applications. For example, the learner's propensity to use online forums, chat rooms, social media tools, blogs, articles, e-books, or other applications to interact with other learners, teachers, tutors, or experts in a community, or to access and interact information and tools to assist with learning. Socioemotional data 156 may also include historical benchmark data stored in data store 120 regarding socioemotional data from the same learner, or different learners, as correlated with learner performance metrics, such as performance on assessments, Learner analytics process 150 may also be applied to behavioral data 158. For example, behavioral data 158 may include empirical data captured from a learner's historical practice and test taking activities. For example, behavioral data 158 may include a learner's study habits, including the learner's propensity to take practice assessments or quizzes, use simulators, or interact with system learning tools without interruption, or at regular intervals. Behavioral data 158 may be captured using data from learner interface 140 that monitors when the learner is using the system and the specific tools with which the learner is interacting. Behavioral data 158 may also be acquired from sensor 112 or from a self-assessment of the learner, ¶45-¶46; determine if any of the learner-specific behavioral parameters correlate to at-risk behavior. The system may also correlate the set of learner-specific behavioral parameters to the learning rate and the learner attribute profile. In some embodiments, the system may present a set of behavioral improvement recommendations on the learner interface, ¶29; see also ¶38) (Examiner notes the ability to compare learner behavior data to historic at-risk behavior as the ability to calculate and compare a similarity metric against a threshold).
As per claims 6 and 16, Davier, Everest, and George disclose as shown above with respect to claims 1 and 11. Davier further discloses wherein the user event comprises at least one user prompt (For example, the learner attribute profile may include the probabilities generated by the CDM and/or BKT processes for each skill and assessment question, and corresponding weighting parameters indicating that the learner was in a happy mood when taking the assessment and when practicing the corresponding skill prior to taking the assessment, that the learner is an extravert and had studied the skill at regular intervals, and that the learner sought tutoring or extra assistance with the skill using particular system resources. The diagnostic model 172 may then determine that the learner did not adequately perform on the subset of assessment questions relating to that skill, and take into account the learner's test preparation practices, behavior, and socioemotional state. The feedback model 174 may then correlate this data identified in the learner attribute profile with available system resources and historical data about effective use of those resources, as correlated with learner attribute profiles that are similar to the learner's, and generate a feedback set that includes recommendations for system resources that the learner may use to improve, improvements to study habits, and other insight for the learner as may be appropriate. The assessment evaluation of the learner's performance and corresponding feedback may then be presented to the learner through learner interface 140, Davier ¶48; see also ¶53 providing a list of resources and recommendations).
As per claims 7 and 17, Davier, Everest, and George disclose as shown above with respect to claims 1 and 11. Davier further discloses wherein selectively initiating the user event comprises: generating a plurality of user events using an event generation model trained using user event training data, wherein the user event training data comprises a plurality of behavioral archetypes as input correlated to a plurality of user events as output; and selecting at least one user event from the plurality of user events based on the validated user archetype (For example, the learner attribute profile may include the probabilities generated by the CDM and/or BKT processes for each skill and assessment question, and corresponding weighting parameters indicating that the learner was in a happy mood when taking the assessment and when practicing the corresponding skill prior to taking the assessment, that the learner is an extravert and had studied the skill at regular intervals, and that the learner sought tutoring or extra assistance with the skill using particular system resources. The diagnostic model 172 may then determine that the learner did not adequately perform on the subset of assessment questions relating to that skill, and take into account the learner's test preparation practices, behavior, and socioemotional state. The feedback model 174 may then correlate this data identified in the learner attribute profile with available system resources and historical data about effective use of those resources, as correlated with learner attribute profiles that are similar to the learner's, and generate a feedback set that includes recommendations for system resources that the learner may use to improve, improvements to study habits, and other insight for the learner as may be appropriate. The assessment evaluation of the learner's performance and corresponding feedback may then be presented to the learner through learner interface 140, Davier ¶48; see also ¶53 providing a list of resources and recommendations).
As per claims 10 and 20, Davier, Everest, and George disclose as shown above with respect to claims 1 and 11. Davier further discloses wherein iteratively listen for the user response to the user event comprises: receiving a subsequent user activity data pertaining to the user; and scoring the subsequent user activity data as a function of the user activity data using the behavioral analysis module (The BKT process may track the learner's knowledge over time based on the learner's performance. The learner may learn on each question, for example, with the assistance of learning resources and feedback. Thus, the correct response rate may change over time, Davier ¶64).
Claim(s) 3, 8-9, 13, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Davier et al. (US PG Pub. 2019/0130511), Everest (US Patent No. 11,893,464), George et al. (US PG Pub. 2019/0335006) and further in view of Venkatasubramanyam (US PG Pub. 2021/0264804).
As per claims 3 and 13, Davier, Everest, and George disclose as shown above with respect to claims 1 and 11. The combination of Davier, Everest, and George do not expressly disclose wherein receiving the user data comprises receiving the user data using a chatbot.
However, Venkatasubramanyam teaches wherein receiving the user data comprises receiving the user data using a chatbot (chatbot, provide feedback, Venkatasubramanyam, ¶23, ¶30, and ¶69).
The Venkatasubramanyam, George, Everest, Li, and the Davier references are analogous in that both are directed towards/concerned with tracking and improving users’ learning. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Venkatasubramanyam’s ability to provide feedback via a chatbot in George, Everest, and Davier’s system to improve the system and method with reasonable expectation that this would result in a learning management system that is able to provide real-time interactive feedback.
The motivation being that there is a long felt but unresolved need for a computer-implemented method and a system that provides adaptive and personalized e-learning based on the continually, artificially learned, and unique characteristics of a knowledge seeker (Venkatasubramanyam ¶4).
As per claims 8 and 18, Davier, Everest, and George disclose as shown above with respect to claims 1 and 11. The combination of Davier, Everest, and George do not expressly disclose wherein selectively initiating the user event further comprises: generating a decision tree as a function of the plurality of user events, wherein the decision tree comprises: a plurality of nodes comprising at least a root node and at least a terminal node connected to the at least a root node, wherein: the at least a root node contains a first user event of the plurality of user events; and the at least a terminal node contains a second user event of the plurality of user events, wherein the first user event is a pre-requisite of the second user event.
However, Venkatasubramanyam teaches wherein selectively initiating the user event comprises: generating a plurality of user events using an event generation model trained using user event training data, wherein the user event training data comprises a plurality of behavioral archetypes as input correlated to a plurality of user events as output; and selecting at least one user event from the plurality of user events based on the validated user archetype; wherein selectively initiating the user event further comprises: generating a decision tree as a function of the plurality of user events, wherein the decision tree comprises: a plurality of nodes comprising at least a root node and at least a terminal node connected to the at least a root node, wherein: the at least a root node contains a first user event of the plurality of user events; and the at least a terminal node contains a second user event of the plurality of use events, wherein the first user event is a pre-requisite of the second user event (The SLKRS builds the experiences to engage the knowledge seeker in a concept through multiple senses as opposed to just spewing out text. The SLKRS links together all asset types in a huge graph, based on their proximity to weighted tags and search terms that a knowledge seeker might use to search for information in the SLKRS. The SLKRS generates decision trees to be able to parse the assimilated data to build the experiences. A decision tree, analogous to flowcharts, begins at a root node and culminates in a leaf node with a decision. The SLKRS utilizes the decision trees to arrive at decisions in building experiences for the knowledge seekers, Venkatasubramanyam ¶26).
The Venkatasubramanyam, George, Everest, and the Davier references are analogous in that both are directed towards/concerned with tracking and improving users’ learning. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Venkatasubramanyam’s ability use decision trees in order to create an experience for knowledge seekers in George, Li, Everest, and Davier’s system to improve the system and method with reasonable expectation that this would result in a learning management system that is able to provide a tailored learning experience.
The motivation being that there is a long felt but unresolved need for a computer-implemented method and a system that provides adaptive and personalized e-learning based on the continually, artificially learned, and unique characteristics of a knowledge seeker (Venkatasubramanyam ¶4).
As per claims 9 and 19, Davier, Everest, George, and Venkatasubramanyam disclose as shown above with respect to claims 8 and 18. Venkatasubramanyam further teaches wherein selectively initiating the user event further comprises: traversing the generated decision tree as a function of the interaction indicator; and selectively initiating the user event based on the decision tree traversal (The SLKRS builds the experiences to engage the knowledge seeker in a concept through multiple senses as opposed to just spewing out text. The SLKRS links together all asset types in a huge graph, based on their proximity to weighted tags and search terms that a knowledge seeker might use to search for information in the SLKRS. The SLKRS generates decision trees to be able to parse the assimilated data to build the experiences. A decision tree, analogous to flowcharts, begins at a root node and culminates in a leaf node with a decision. The SLKRS utilizes the decision trees to arrive at decisions in building experiences for the knowledge seekers, Venkatasubramanyam ¶26).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 ANDREW B WHITAKER whose telephone number is (571)270-7563. The examiner can normally be reached on M-F, 8am-5pm, EST.
If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
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