DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the RCE, Remarks, and Amendments filed 09/01/2026.
Claims 5-6, 8, 13-14, and 16 remain canceled.
Claims 1-4, 7, 9-12, 15 have been amended.
Claims 1-4, 7, 9-12, 15 have been examined and are pending.
(AIA ) Examiner Note
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/01/2026, has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation 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 35 U.S.C. 112 (pre-AIA ), first paragraph:
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 1-4, 7, 9-12, 15 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 claims contain subject matter which is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1 and 9 recite limitations directed towards the following:
“…processing the input data by the clustering algorithm and the multi-output machine learning algorithm to provide an output data, via the application server, to the user, and wherein the output data comprises a teacher output and a student output; … wherein the student output further includes generation of a student interest rating for each of the study topics, by the multi-output machine learning algorithm, and wherein the machine learning algorithm generates the student interest rating based on an evaluation of a body language of the student and the microphone input, and wherein the evaluation of the body language of the student includes the evaluation of at least one of the student's posture, head movements, gestures, and facial expressions during classes, and wherein the evaluation of the microphone input includes the evaluation of at least one of the student's speech and tone thereof during the classes, active talk time during the classes, and a level of participation in each of the classes.”
Similarly dependent claims 7 and 15 recite limitations directed towards the following:
“…wherein the teacher output comprises curated course content and at least one optimal teaching method for teaching the student, and wherein the curated course content is generated by the multi-output machine learning algorithm based on the subjects previously studied by the student, the study topics previously studied by the student, the teachers who taught previously studied subjects and previously studied study topics, and academic performance of the student, and wherein the optimal teaching method is identified by the multi-output machine learning algorithm,…”
These limitations recite functionality attributed to a generic “machine learning algorithm”. However, the broadly claimed functionality (i.e. the steps as claimed) are not general functions that any generic machine learning algorithm nor any general purpose computer can accomplish without special programming (e.g. a specific machine learning algorithm). Nonetheless, Applicant asserts that these broadly claimed functions/steps which deliver the results as claimed are his invention. However, the Specification does not provide sufficient written description support for the recited functionality – there is no description of any special or specific machine learning algorithm capable of performing the steps as noted, i.e.: “…generat[ing] the student interest rating based on an evaluation of a body language of the student and the microphone input, and wherein the evaluation of the body language of the student includes the evaluation of at least one of the student's posture, head movements, gestures, and facial expressions during classes, and wherein the evaluation of the microphone input includes the evaluation of at least one of the student's speech and tone thereof during the classes, active talk time during the classes, and a level of participation in each of the classes… wherein the teacher output comprises curated course content and at least one optimal teaching method for teaching the student, and wherein the curated course content is generated by the multioutput machine learning algorithm based on the subjects previously studied by the student, the study topics previously studied by the student, the teachers who taught previously studied subjects and previously studied study topics, and academic performance of the student, and wherein the processor identifies the optimal teaching method by executing the multi-output machine learning algorithm, etc…”
This deficiency further indicates that the Specification describes only "a mere wish or plan for" achieving the functionality, as now claimed, by some generic “machine learning algorithm”. See Eli Lilly, 119 F .3d at 1566 (citation omitted). As such, the broadly recited limitation "merely recite[s] a description of the problem to be solved," and leaves to future inventors to "complete an unfinished invention." See Ariad, 598 F.3d at 1353. In this case, without the Specification describing any particular algorithm to achieve the claimed function of generating a de-duplicated advertising campaign, one of ordinary skill in the art would not have reasonably concluded that the inventors invented the claimed invention or that they possessed the claimed subject matter at the time of filing of the application. See Vasudevan, 782 F.3d at 683; see also Regents, 119 F.3d at 1566; § 112 Guidance at 61.
To satisfy the written description requirement of 35 U.S.C. § 112, first paragraph, the Specification must reasonably convey to an artisan of ordinary skill that Appellant had possession of the claimed invention at the time the application was filed. Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 682 (Fed. Cir. 2015) (citing Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en banc)). Functional claim language that merely describes an intended result and fails to support the scope of the claimed invention is insufficient to show possession, even when the claim recitations are found word-for-word in the Specification. Vasudevan, 782 F.3d at 682 ("[t]he written description requirement is not met if the specification merely describes a 'desired result"') (citing Ariad, 598 F.3d at 1349); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968 (Fed. Cir. 2002) ("[t]he appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy" the written description requirement). The Specification must explain, for example, how Appellant intended to achieve the claimed function to satisfy the written description requirement. Vasudevan, 782 F.3d at 683. While "[t]here is no rigid requirement that the disclosure contain 'either examples or an actual reduction to practice,"' due to the written description requirement, the Specification must set forth "an adequate description that 'in a definite way identifies the claimed invention' in sufficient detail such that a person of ordinary skill would understand that the inventor had made the invention at the time of filing." Allergan, Inc. v. Sandoz Inc., 796 F.3d 1293, 1308 (Fed. Cir. 2015) (citing Ariad, 598 F.3d at 1352); see also Examining Computer-Implemented Functional Claim Limitations for Compliance with 35 US.C. 112, 84 F.R. 57, 61- 62 (January 7, 2019) ("112 Guidance").
Here, the Specification does not sufficiently support any particular “machine learning algorithm” which is capable of performing the recited functionality as recited in the aforementioned limitations; i.e. as recited as noted per claims 1 and 9.
The specification does not provide a single technique nor method nor algorithm regarding how a machine learning model is intended to perform the claimed generat[ing] the student interest rating, etc… Instead, the only place the Specification mentions “rating” or “interest rating” is at [0065] stating: “…The multi-output machine learning algorithm takes in the information and generates an interest rating for the student on that particular topic.…" The rest of the entire original disclosure fails to illuminate any particular machine learning algorithm capable or otherwise used to perform these functions. For example, although Specification at [0057] states the following: “…In addition, to the above, the student's body language, gestures, speaking time, tone, and expressions also give a good degree of understanding of how attentive or engaged a student is inside the classroom. The method uses a camera and a microphone either from the student's device or installed inside the classroom to evaluate a student's body language, including posture, head movements, facial expressions etc., gestures such as hand raises, head nods etc., and speaking time is measured through microphone input and filtered to include only relevant speaking time to provide an estimate of engagement. These variables will be measured at a frequency optimized for the network, system configuration, manual intervention by the institute' s administration and the privacy policies in place to help measure the student engagement and attentiveness during any given lesson….”, respectfully, this is not a description of any particular machine learning algorithm capable of performing the functionality claimed.
Under these circumstances, the Examiner has determined the Specification merely states a wish that machine learning could be used to perform the claimed steps as recited in the claims, but the Specification does not demonstrate the Applicant was in possession of such a capable machine learning nor how applicant intended to create such machine learning capable of performing the steps now claimed. The Specification does not describe the claimed invention in sufficient detail such that an ordinarily skilled artisan would understand that the inventor had made the invention at the time of filing. Thus, the Examiner rejects claims 1 and 9 under 35 U.S.C. § 112(a) for a lack of written description support.
Dependent claims 2-4, 7, 10-12, and 15 inherit the deficiencies of their parent claim and are also rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-4, 7, 9-12, 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the Subject Matter Eligibility Guidance outlined in the MPEP 2106, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106, as follows:
Per Independent claims 1 and 9:
processing the input data by the clustering algorithm and the multi-output machine learning algorithm, to provide an output data… to the user, and wherein the output data comprises a teacher output and a student output; wherein the student output includes an identification of an optimum academic track for the student,
and wherein the optimum academic track is determined by an implementation of the clustering algorithm, wherein the implementation of the clustering algorithm includes:
…applying hierarchical clustering and thereby identifying meaningful clusters of
the predetermined features, and creating a hierarchy of meaningful clusters
based on a similarity between the student's performance in each of the subjects;
determining an optimal number of meaningful clusters to be used to identify
meaningful learning paths and meaningful subject groups relevant to the student;
assigning cluster labels to the student based on the hierarchy of meaningful clusters;
training a predetermined classification model using the historical performance
data of the student and the cluster labels assigned to the student as input
features;
determining the meaningful learning paths and meaningful subject groups
relevant to the student, based on the cluster labels assigned to the student and
the historical performance data of the student’
wherein the student output further includes generation of a student interest rating for
each of the study topics, by the multi-output machine learning algorithm, and wherein
the multi-output machine learning algorithm generates the student interest rating based
on an evaluation of a body language of the student and the microphone input, and
wherein the evaluation of the body language of the student includes the evaluation of at
least one of the student's posture, head movements, gestures, and facial expressions
during classes, and wherein the evaluation of the microphone input includes the
evaluation of at least one of the student's speech and tone thereof during the classes,
active talk time during the classes, and a level of participation in each of the classes.
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106. Specifically, these limitations fall within a combination of the groups Mathematical Concepts (e.g. mathematical relationships; mathematical formulas or equations; mathematical calculations), Mental Processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion), and Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
That is, the step of: processing the input data by the clustering algorithm and the multi-output machine learning algorithm, to provide an output data… to the user, and wherein the output data comprises a teacher output and a student output; wherein the student output includes an identification of an optimum academic track for the student, as drafted, is business decision to process generic information [input data] by generic algorithms with a hope/wish of providing generic output recommendations for students and teachers [output data comprising a teacher output and a student output], e.g. a supposedly “optimum” academic track related to generic student performance evaluation and thus falling into Certain Methods of Organizing Human Activity.
At this high-level of generality, there is no technical solution and the problem to be solved is not technical in nature but instead is a business decision to output information deemed as important, by the business, to teachers and students regarding evaluation of performance of a student. The desired output is completely subjective and entirely dependent upon whichever method is desired by the business to train the generic algorithms.
Regarding the step of “the implementation of the clustering algorithm includes… extracting predetermined features from the dataset, said features indicative of the student's performance in the different subjects studied by the student; applying hierarchical clustering and thereby identifying meaningful clusters of the predetermined features, and creating a hierarchy of meaningful clusters based on a similarity between the student's performance in the different subjects; determining an optimal number of meaningful clusters to be used to identify meaningful learning paths and meaningful subject groups relevant to the student; assigning cluster labels to the student based on the hierarchy of meaningful clusters; … determining the meaningful learning paths and meaningful subject groups relevant to the student, based on the cluster labels assigned to the student and the historical performance data of the student; etc…” these steps are nothing more than Mathematical Concepts and Mental Processess. Note that the courts have found the use of a physical aid (e.g., pencil and paper, or a slide rule, even a generic computer) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." Similarly here, extracting features from a dataset may be manual and/or a mental exercise (e.g. identifying by inspection which features, as predetermined in one’s mind are important, are present within the dataset). Similarly, the hierarchical clustering is a mathematical principle which may be performed manually at this high-level of generality as currently claimed. The same finding is made regarding the determination of a supposed “optimal number of meaningful clusters…”, the “assigning cluster labels…”, and “determining the meaningful learning paths” steps as recited.
Additionally, the “training a predetermined classification model using the historical performance data of the student and the cluster labels assigned to the student as input features” is akin to “teaching, and following rules or instructions” which falls squarely in the realm of Certain Methods of Organizing Human Activity. Note, there is no particular training method being claimed. Instead, this is the abstract idea of teaching a model to follow particular desired instructions.
Lastly, the statement regarding: “…the output further includes generation of a student interest rating for each of the study topics…[the] machine learning algorithm generates the student interest rating based on an evaluation of a body language of the student and the microphone input, and wherein the evaluation of the body language of the student includes the evaluation of at least one of the student's posture, head movements, gestures, and facial expressions during classes, and wherein the evaluation of the microphone input includes the evaluation of at least one of the student's speech and tone thereof during the classes, active talk time during the classes, and a level of participation in each of the classes.” Appears to be a wish that a machine learning algorithm could generate such a student interest rating from these audio and visual variables. However, there is no particular machine learning model contemplated in the specification for performing these functions or by which the input variables are definitely related to the desired output; this finding only lends to the conclusion that applicant’s feature is an abstraction and a wish for something to exist to accomplish these desired results from the provided input. Regardless of the level of detail provided by applicant in describing the input variables and desired outputs, Examiner finds that a complete lack of disclosure of the inner workings of the supposed machine learning algorithm points to a lack of anything significantly more than an abstract idea.
Furthermore, the mere nominal recitation of a generic computer components (per independent claim 9), such as “the system comprising: a processor; a memory communicably coupled to the processor, the memory storing computer executable instructions, which when executed by the processor, cause said processor to: [execute the claimed method]” does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts and link them to a field of use (i.e. in this case a business decision regarding generic student performance evaluation) or serve as insignificant extra-solution activity (e.g. mere data-gathering, transmittal, and/or storge of information). The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea.
These additional limitations are as follows: “receiving, by an application server, an input data comprising student history, current study data, microphone input, camera input, teaching methods, and learning path parameters from a user; and wherein the user is one of a teacher and a student; providing, by the application server, the input data to a plurality of algorithms; and wherein the plurality of algorithms include a clustering algorithm and a multioutput machine learning algorithm; …by the application server… creating a dataset comprising a plurality of attributes, including a student ID, subjects available to the student for study, study topics, student performance scores, student learning path labels, professions preferred by the student, historical performance data of the student, and attendance records of the student; extracting predetermined features from the dataset, said predetermined features indicative of the student's performance in each of the subjects studied by the student”
However, these elements do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a technique nor technical solution for “receiving” data regardless of the description of the data. Furthermore, the “receiving” cannot imply nor connote some inventive technique of acquisition as the receiving is explicitly noted as being provided from “a user” – i.e. human intervention is necessary to effect the provision of this data such that the system properly receives it. Furthermore, the explanation that the intended “user” who interacts to provide data is a “teacher” and/or “student” further supports the finding that this step of “receiving” data is insignificant pre-solution activity. Additionally, the step of “providing, by the application server, the input data” to generic algorithms is likewise insignificant pre-solution activity. Furthermore, the description of the algorithms as belonging to known class of algorithms (i.e. a clustering algorithm and a multioutput machine learning algorithm) is not significantly more than the abstract idea but instead merely provides context that the applicant abstract idea makes use of known algorithms which applicant did not invent. Therefore, these features merely serve to generally “apply” the aforementioned concepts using generic computer components, or link them to a field of use (i.e. in this case a business decision regarding generic student performance evaluation), or are insignificant pre-solution or insignificant extra-solution activity in regards to the already identified abstract idea and they do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components or “link” them to a field of use, or as insignificant pre-solution or insignificant extra-solution activity in regards to the already identified abstract idea. For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (i.e. a server) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claims 2 and 10 recite the following: “wherein the student history includes previous subjects and topics covered by the student; and wherein the current study data includes current subjects and the topics being studied by the student, and wherein the microphone input includes audio input from a microphone capturing the student's speech and/or tone thereof, active talk time and class participation, and wherein the camera input includes visual input from a camera capturing the student's facial expressions and body language; and wherein the teaching methods include information on instructional strategies implemented by the teacher, and approaches and techniques followed by the teacher for teaching the student, and wherein the learning path parameters include user-defined parameters for customizing a learning path of the student based on the different subjects studied by the student, interests of the student and career goals of the student.” However, these features are nothing more than descriptions of data and these descriptions are themselves at a high-level of generality. The descriptions of data do not provide any further insight into the already identified abstract idea and they are not significantly more than the already identified abstract idea.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and therefore the claims are not found to be patent eligible.
Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 103 (AIA )
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claims 1, 2, 9, 10 are rejected under 35 U.S.C. 103 as obvious over Martin (US 2018/0240015 A1; hereinafter, “Marin”) in view of Srinivas (US 2012/0288841 A1; hereinafter, “Srinivas”) in view of Aslan (US 2023/0360551 A1; hereinafter, “Aslan”).
Claims 1, 9: (Currently Amended)
Pertaining to independent claims 1 and 9 exemplified in the limitations of method claim 1, Martin is found to teach the following:
A method for adaptive student performance evaluation, the method comprising:
receiving, by an application server, an input data comprising student history,
current study data (Martin, see at least Fig. 1 and also [0010]-[0014] in view of [0052]-[0059] and [0092]-[0093] regarding “student information repository 130” which receives as noted per [0052] student “registration” and “student information” such as “grades” [student history] and other information such as student “interests” and as noted per [0059] current and historical student information gathered and stored in a student’s PLM, and per [0092]: “…Datasets include academic performance history, subject-based and non-subject based communication content understanding, and social and interpersonal behavioral analysis…” and as noted per [0014]: “…For example, the DALI PC/PM function(s) may identify poor study [current study data] or other personal habits and ways to positively adjust demeanor…”; see also [0103]-[0104]), microphone input, camera input (Martin, see citations noted supra, including at least [0103]-[0104] e.g.: “…These inputs 1402 and 1404 are collected through various machine "senses" such as the chat-logs, microphone, webcam (speech-to-text and facial recognition), and any specific data fields used by a student and mined through machine-learning Natural Language Analysis and Processing NPL (e.g., parsing)….”), teaching methods, and learning path parameters from a user, and wherein the user is one of a teacher and a student (Martin, again see citations noted supra, at least Fig. 2, and [0010]-[0014]: e.g. “Test assignments Teacher Input” [teacher teaching methods] and “DALI/PLM” where PLM refers to “Personal Learning Map (PLM) for each student” where DALI provides: “…academic major advice and other academic pathway advice.” [meaningful learning paths] based on student’s PLM [learning path parameters]; see also at least [0049] teaching: “…For instance, instead of students the processes could be used to monitor teacher-related data and to provide recommendations to teachers for ways to improve performance. Likewise, the invention may be used in manufacturing, commercial, professional and other work environments to monitor employee activities and present recommendations for improvement of the individual and the process…”; and note [0103]-[0104], teaching: “…In accordance with the DALI integration with KAS/PLM invention, sensory inputs include, but are not limited to: academic achievement/performance (gathered or provided: current and historical); internal academic-related communications factors (shared by student and teacher, current and historical)…”; i.e. users of the system include both students and teachers and each may use the system to receive evaluation and recommendations for improvement in their own role);
providing, by the application server, the input data to a plurality of algorithms; and
wherein the plurality of algorithms include a clustering algorithm and a multioutput
machine learning algorithm (104) (Martin, see citations noted supra, including at least Figs. 1, 8 and associated disclosure e.g. [0002] regarding: “…The invention also relates to use of …clustering [clustering algorithms], machine learning including use of training data sets, and other techniques to transform aggregated data into workable data sets and to generate outputs [multioutput from machine learning algorithm]….”; and per at least [0069] in view of [0083]-[0088], Martin teaches, e.g.: “The neural language model used by DALI is able to recognize several words in a category… a category of words within a particular data set may be similar in structure, but they can still be encoded separately from each other… Words that may appear with
similar features, and thereby treated with similar meaning, are then considered neighbor [clustered] words, and can then be semantically mapped accordingly… FIG. 8 illustrates a simplified example of the neural language distributed representation PLM 802 (words that may appear with similar features, and thereby treated [clustered] with similar meaning, are then considered neighbor words and can be semantically mapped), as input into a multi-hidden layer DNN 700 [multioutput machine learning algorithm], and as output as a recommendation model 804. The system learns by way of learning feedback into PLM 802 as a signal received when the user selects user interface response element 806. In this manner the DALI system is configured as a DNLN model with PLM and Suggestion/Recommendation response loop...”); and
processing the input data by the clustering algorithm and the multi-output machine learning algorithm to provide an output data, by the application server, to the user, and wherein the output data comprises a teacher output (Martin, see citations noted supra in view of at least [0049]: “…For instance,… processes could be used to monitor teacher-related data and to provide recommendations [teacher output] to teachers for ways to improve performance…”) a student output, and wherein the student output includes an identification of an optimum academic track for the student, and wherein the optimum academic track is determined by an implementation of the clustering algorithm (Martin, see citations noted supra, including again at least [0084]-[0088], e.g.: “…DALI elevates the educational experience for students by making active and dynamic academic course corrective suggestions [student output includes optimum academic track for the student] and recommendations as an intelligent virtual academic advisor within and external to the classroom… DALI intervenes during the academic experience to provide personal counseling [more student output] about specific external (non-subject) issues and events that may be negatively affecting academic performance…”)
and wherein the implementation of the clustering algorithm includes:
creating a dataset comprising a plurality of attributes, including a student ID,
subjects available to the student for study, study topics, student performance scores, student learning path labels, professions preferred by the student, historical performance data of the student, and attendance records of the student (Martin, see citations noted supra, including also at least Fig. 11 and [0052]-[0074] regarding a collection of data sets pertaining to each student, which is used to generate a student’s personal learning map. Martin teaches the following various student information is collected and displayed: student information collected during student registration such as “…demographic…, gender…, personal information…, and/or other information about the student” [student ID], “registration of students to courses” and “…you may want to consider taking Game 489: Advanced Game Animation (i .e., proposed higher level course) next quarter" [subjects available to the student for study], “Registration system 104 may be configured to display course listings, requirements, and/or other course-related information” [study topics], “academic records (e.g. grades, etc…)” [student performance scores], “academic major” [learning path label], “students' chosen professional pathway” [professions preferred by the student], “academic performance history” [historical performance data of the student], “historical and current academic conditions” and “a student's performance, e.g., attending class” [attendance records of the student], etc…; see also [0082]-[0084] and [0092]; );
extracting predetermined features from the dataset, said predetermined features indicative of the student's performance in each of the subjects studied by the student (Martin, see citations noted supra, e.g. again per at least [0052] “academic records (e.g. grades, etc…)” in view of at least [0073], teaching knowledge and display of information based on such student “grades” [a predetermined feature indicative of student’s performance in the subject studied by the student], e.g.: “…DALI 150 presents to an individual student user a Suggestion 502 "Based on your (i.e., individual student receiving message) current excellent grades in Game 310: Game Art & Animation (i.e., online course) [i.e. a subject studied by the student], you may want to consider taking Game 489: Advanced Game Animation (i .e., proposed higher level course) next quarter." The underlining represents an embedded link to enable the student to access information related to the course for further consideration and potentially registration. The determination to present the proposed course may be based on student performance in present course "Game 310" as well as student's major [another feature], stated interest [another feature], professional pathway identified, and other attributes [other features], criteria and captured data…”);
[…]
training a predetermined classification model by using the historical performance data of the student and the cluster labels assigned to the student as input features (Martin, see citations noted supra, including also at least Figs. 5-8 and [0056]-[0069], teaching e.g.: “…The DALI system uses a Personal Learning Map as a collection of data sets [labeled input features] associated with individual users, such as students. The DALI [a predetermined classification model] "learns" about [therefore is trained on] each student's evolving internal (academic) and external conditions, short-term and long-term academic/professional, and personal expectations over time and such data is stored as historical and current data sets as discussed in detail below. These data sets… include academic performance (gathered: current and historical [historical performance data]) [cluster labels for performance data], external nonacademic-related extenuating circumstantial factors (shared by student captured: current and historical), and behavioral (social) analysis (shared by student captured: current and historical) [more cluster labels]. These four data sets can be assigned variables as can be seen below as an example of a single student's Personal Learning Map Variables and Sub-Variables:…”; see also at least [0073]-[0088], e.g. Martin discloses: “…an algorithmic methodology for the (supervised) [which requires labeled data] massive weight learning (training) of DALI.”; see also at least [0091]-[0092], and [0108] e.g.: …Mass Student informational data from both Semantic and Episodic sensory inputs 1402, 1404 pass through the Working Memory Module 1500, as data must be classified (i.e. clustered), e.g., as NLP parsing with relevant tags [assigned labels] and indexed…”. Note also per Fig. 5 Martin discloses DALI’s output/suggestions from his trained model appear to be predicated upon labeled user data, e.g. “per #506, DALI has working knowledge of the student’s “social tendencies” [e.g. a cluster label assigned to a student] which it has correlated to the student’s “drop in academic achievement the past quarter” [performance data]. In view of these teachings, Examiner finds there is motivation to train Martin’s DALI DNN, e.g. as depicted per at least Fig. 18, by using both his disclosed historical performance data of his student in addition to cluster labels assigned to a student at least because he teaches his data must in part be labeled, i.e. as part of his supervised learning, and because his output is correlated with both a student’s historical performance data and a label regarding the student ‘s social behavior where the term social denotes a classification of the user into some labeled cluster of users, e.g. a student’s “social tendencies” [a cluster label assigned to a student]. Therefore these variables which are correlated are implied to both be one of his as input features to his DNN of Fig. 8 such that the DNN learns such correlation and because MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.)
determining the meaningful learning paths and meaningful subject groups relevant to the student based on the cluster labels assigned to the student and the historical performance data of the student (Martin, see citations noted supra, e.g. again per at least Fig. 8 and [0073]-[0088]: “…The determination to present the proposed course [meaningful learning paths and meaningful subject groups] may be based on student performance [historical performance data] in present course "Game 310" [a label for student performance] as well as student's major [e.g. a cluster label], stated interest [e.g. another cluster label], professional pathway identified [e.g. another cluster label], and other attributes [e.g. more labels, etc..], criteria and captured data…”; The only difference between the claim limitation and the teachings of Martin is that Martin, although he teaches labels assigned to students, e.g. as noted supra such as “social tendencies” [a label assigned to a student], he may not explicitly reference this particular label as one of his “student's major, stated interest, professional pathway identified, and other attributes, criteria…” [e.g. cluster labels]. However, Examiner finds that Martin’s student’s “social tendencies” [a cluster label assigned to a student], is merely one type of “other attribute”. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention use as Martin’s “social tendencies” [a cluster label assigned to a student], as a basis by which to make Martin’s explicitly stated determination to present the proposed course [meaningful learning paths and meaningful subject groups] in addition to his explicit prediction of this determination based upon student performance [historical performance data] because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649. ); and
Although Martin teaches the above limitations, and per Fig. 25, and associated disclosure, e.g. [0002] and [0010]-[0014], Martin teaches: “…The invention also relates to use of …clustering [clustering algorithms], machine learning including use of training data sets, and other techniques to transform aggregated data into workable data sets and to generate outputs [multioutput from machine learning algorithm]….”; and he teaches a machine learning-based Deep Academic Learning Intelligence (DALI) used for student academic advising, professional mentoring, and personal counseling and His DALI creates personalized learning maps (PLMs) for each student which comprises all available information about a student and based on these variables, Martin teaches his DALI performs in part the following: “…active and dynamic academic course corrective suggestions and recommendations and delivers same to the individual student”, all of which implies he performs some type of clustering and analysis of student behaviors to identify similar student behaviors which may lead to improved performance where such analysis forms the basis for Martin’s stated “academic course corrective suggestions and recommendations” which he teaches is delivered to students, he may not explicitly delve into the minutia of his mentioned clustering as part of his DALI, nor specific use of clustering for the purpose of determining his “…academic major advice and other academic pathway advice.” [meaningful learning paths]. However, regarding such features as recited below, Martin in view of Srinivas teaches the following:
applying hierarchical clustering and thereby identifying meaningful clusters of the predetermined features, and creating a hierarchy of meaningful clusters based on a similarity between the student's performance in each of the subjects (Srinivas, see at least Figs. 2-7 and [0027]-[0037] regarding techniques of clustering students, including hierarchical clustering, based in part on student performance and student assessment data; e.g.: “…The determination of the number of clusters may be performed using a hierarchical clustering algorithm… a similarity between two students may be represented by a 3-tuple: (N.sub.c, N.sub.w, N.sub.a), where N.sub.c represents the number of questions that both students answered correctly, N.sub.w represents the number of questions that both students answered incorrectly, and N.sub.s represents the number of questions that both students skipped. A symmetric similarity matrix may be generated based on the similarity tuples between each pair of students… [0034] The plurality of students may be organized 125 into a number of clusters based on the similarity values between each pair of students. In an embodiment, the plurality of students may be organized 125 into a number of clusters based on the distance or similarity values using a hierarchical clustering algorithm… More generally, two clusters each containing assessment data pertaining to one or more students may be combined in each iteration by determining the two clusters that have the smallest distance between them… etc.” );
determining an optimal number of meaningful clusters to be used to identify meaningful learning paths and meaningful subject groups relevant to the student (Srinivas, see citations noted supra, in view of at least Figs. 3-4 and [0038]-[0042) teaching the total number of clusters [i.e. the optimal number of meaningful clusters] is determined through a discontinuity or greatest change in total error detection algorithm, e.g.: “…the number of clusters in which to organize 125 the plurality of students may be automatically determined 110 based on the assessment data for the plurality of students. This determination 110 may be performed based on the total error associated with the clusters at each iteration of a hierarchical clustering algorithm… the iteration in which the biggest jump in total error occurred may be automatically determined…”
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assigning cluster labels to the student based on the hierarchy of meaningful
clusters (Srinivas, see citations noted supra, e.g. [0034]: “…The plurality of students may be organized 125 into a number of clusters based on the similarity values between each pair of students. In an embodiment, the plurality of students may be organized 125 into a number of clusters based on the distance or similarity values using a hierarchical clustering algorithm…”; see also at least [0043] e.g. individual clusters are identified and each student is uniquely identified as belonging to one of the clusters.);
Therefore, the Examiner understands that the limitations in question are merely applying known techniques of Srinivas (directed towards techniques of clustering students via hierarchal clustering into specific [labeled] clusters) which are applicable to a known base device/method of Martin (already directed towards training neural networks such as a Deep neural network DNN models on labeled student data as well as student performance data to make academic suggestions and recommendations) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Srinivas to the device/method of Martin in order to perform the limitation because Srinivas’ techniques of clustering students are pertinent to Martin’s training of deep neural networks on student data especially in view of Marint’s disclosure at [0002] regarding his invention relates to network-based systems and methods for monitoring user behaviors and performances and aggregating behavior and performance related data into workable data sets for processing and generating recommendations. The invention also relates to use of natural language processing, neural language processing, logistic regression analysis, clustering, and machine learning etc… and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Although Maring/Srinivas teach the above limitations and Martin as shown supra teaches applicant’s feature regarding receiving input data comprising student history, current study data, microphone input, camera input, etc… and teaches, e.g. per [0052], obtaining student “interests”, they may not explicitly teach the nuance below regarding determination of student interest from analysis of body language and microphone input, etc… However, regarding these features, Martin/Srinivas in view of Aslan teaches the following:
wherein the student output further includes generation of a student interest rating for
each of the study topics, by the multi-output machine learning algorithm, and wherein
the multi-output machine learning algorithm generates the student interest rating based
on an evaluation of a body language of the student and the microphone input, and
wherein the evaluation of the body language of the student includes the evaluation of at
least one of the student's posture, head movements, gestures, and facial expressions
during classes, and wherein the evaluation of the microphone input includes the
evaluation of at least one of the student's speech and tone thereof during the classes,
active talk time during the classes, and a level of participation in each of the classes (Aslan, see at least [0032]-[0043] and Table 1 teaching determination of student interest via analysis of student “body language” such as “body posture, head pose, etc…” as well as “verbal participation” [microphone input includes the evaluation of at least one of the student's… level of participation])
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Aslan (directed towards determination of student interest via analysis of student “body language” such as “body posture, head pose, etc…” as well as “verbal participation” [microphone input includes the evaluation of at least one of the student's… level of participation]) which is applicable to a known base device/method of Martin/Srinivas (already directed towards determining student interests and collection of student information via camera and microphone) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Aslan to the device/method of Martin/Srinivas in order to perform the limitation in question because Aslan is pertinent to the objectives of Martin, e.g. determination of student interests and are analogous art in the same field of endeavor (at least G06Q30/02) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 2, 10: (Currently Amended)
Martin/Srinivas/Aslan teach the limitations upon which these claims depend. Furthermore, the combination Martin/Srinivas/Aslan teach the following:
The method according to claim 1, wherein the student history includes previous subjects and previous study topics covered by the student, and wherein the current study data includes current subjects and current study topics being studied by the student (Martin, see citations noted supra, e.g. Fig. 11 and [0052]-[0074] e.g.:
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) and wherein the camera input includes visual input from a camera capturing the student's facial expressions and the body language of the student (Aslan, see citations noted supra, e.g. Aslan, see at least [0032]-[0043] and Table 1 teaching: student “body language” such as “facial expression”, “body posture, head pose, etc…”), and wherein the teaching methods include information on instructional strategies implemented by the teacher, and approaches and techniques followed by the teacher for teaching the student (Martin, see citations noted supra, e.g. again per at least [0010]-[0014]: e.g. “Test assignments [information on instructional strategies, etc…] Teacher Input” [teacher teaching methods]), and wherein the learning path parameters include user-defined parameters for customizing a learning path of the student, based on the subjects studied by the student, interests of the student, and career goals of the student (Martin, see citations noted supra, including again at least [0010]-[0014] regarding student’s PLM [learning path parameters] which, as noted per [0020], are fed as input parameters to DALI’s DNN along and then which may be updated and fine-tuned based on student feedback [user defined parameters] for customizing a learning path of the student. The feedback is in regards to and based on subjects studied by the student, the student’s interests, and decided professional pathway [career goal] as already previously noted.)
Potentially allowable subject matter
Claims 3, 4, 7, 11, 12, 15 appear to not be obvious in view of the presently discovered prior art and therefore if rewritten or amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101 and 112 as set forth in this Office action, these features may be allowable. However, Examiner has reviewed Applicant’s entire original disclosure and does not have recommendations regarding subject matter which if included would overcome the rejections currently made of record.
Response to Arguments
Applicant amended claims 1-4, 7, 9, 10, 11, 12, 15, on 09/01/2026. Claims 5-6, 8, 13-14, and 16 are canceled. Applicant’s remarks also filed 09/01/2026, have been fully considered but are moot in view of the new grounds of rejection necessitated by applicant’s amendments. Note the new 35 USC 101, 112, and 103 rejections with Martin in view of Srinivas and Aslan.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. 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, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Sittner/
Primary Examiner, Art Unit 3621
