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 Remarks and Amendments filed 01/30/2026.
Claims 5-6, 8, 13-14, and 16 have been 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
Potentially allowable subject matter
Claims 1-4, 7, 9-12, 15 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 as set forth in this Office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 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 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;
training a predetermined classification model using the historical performance
data of the student and the cluster labels assigned to the student as input
features; and
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.
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;” 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.
Lastly, 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.
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, different subjects studied by the student, 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 features indicative of the student's performance in the different 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).
Response to Arguments
Applicant canceled claims 5-6, 8, 13-14, and 16 and amended claims 1-4, 7, 9-12, and 15 on 01-30-2026. Applicant’s remarks also filed 01-30-2026, have been fully considered but are moot in view of the new grounds of rejection necessitated by applicant’s amendments. Note the new 101 rejections. Also note the following:
Pertaining to independent claims 1 and 9 exemplified in the limitations of method claim 1, the prior art of Martin (US 2018/0240015 A1) as shown teaches much of applicant’s claims but does not teach all of the features. 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, e.g. “Test assignments Teacher Input” [teacher teaching methods] and “DALI/PLM” where PLM refers to “Personal Learning Map (PLM) for each student” [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 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,
different subjects studied by the student, 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 [0052]-[0071] regarding a collection of data sets, called a student’s personal learning map, which includes identification of the student, subjects studied by the student, grades [scores], teachers, personal expectations, historical and current academic conditions and performance, etc…; see also [0082]-[0084] and [0092]);
Although Martin teaches the above limitations, and per [0002] teaches: “…The invention also relates to use of natural language processing, neural language processing, logistic regression analysis, clustering, machine learning including use of training data sets, and other techniques to transform aggregated data into workable data sets and to generate outputs…”, he may not delve into the minutia of clustering algorithms themselves as recited below nor their use to determine “meaningful learning paths”.
The prior art of Sheoran (US 2024/0127039 A1) teaches some of the following features as shown, but does not teach all of the features:
extracting predetermined features from the dataset, said features indicative of the student's performance in the different subjects studied by the student (Sheoran, see at least [0135], e.g. teaching use of PCA to perform feature extraction; see also at least [0163].);
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 (Sheoran, see at least [0159], teaching: “…As an example, a machine model, which may be a machine learning model, may be built using a computational framework with a library, a toolbox, etc., such as, for example, those of the MATLAB framework (MathWorks, Inc., Natick, Massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor (KNN), k-means, k-medoids, hierarchical clustering, etc…”);
determining an optimal number of meaningful clusters to be used to identify meaningful learning paths and meaningful subject groups relevant to the student (Sheoran, see citations noted supra, including also at least [0145]: “…As the trial commenced with images belonging to 6 classes, taking the value of k=6 can be an acceptable starting assumption. However, where the number of classes is not known a priori, an optimal value of k may be determined using a technique such as the elbow technique. The elbow technique may be implemented using one or more frameworks (e.g., consider the scikit-learn framework) where the KElbowVisualizer implements the elbow technique to help fine an optimal number of clusters by fitting a model with a range of values for k….”)
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 (Sheoran, see at least [0152]: “… As to an OCR feature, where text is identifiable in a document, an image, etc., an OCR feature may be implemented for character recognition where characters, words, phrase, etc., may be extracted. In such an example, such content may be associated with one or more other types of content. For example, consider metadata, a cluster label, etc….”); and
Neither Martin nor Sheoran, separately or combined, appear to teach the following idea:
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
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
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