DETAILED ACTION
NOTE: The present application is being examined under the pre-AIA first to invent provisions. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Remarks
The Amendment filed 1/23/26 has been entered. Claims 1-2, 4-9, 11-13, 15-17, 30-31, 33, and 35 are pending in the application and are under examination.
Claim Rejections - 35 USC § 101
Claims 1-2, 4-9, 11-13, 15-17, 30-31, 33, and 35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1 = YES: The claimed invention is to a system (claims 1-2, 4-9, 11, and 35), product (claims 12-13 and 15-17), or method (claims 30-31 and 33), and thus fall under one of the four statutory categories (Step 1: YES).
STEP 2A, Prong 1 = YES: The claim(s) recite(s) a series of steps which can be practically performed by one or more humans through mental process (i.e., observation, evaluation, judgement, and/or opinion)(see MPEP § 2106.04(a)(2), subsection III) and/or certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II). Moreover, the claims recite steps akin to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, which the court in Electric Power Group held to recite a mental process. Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). This includes a system and method of providing a learning system including a learning management system (i.e., human interaction, teaching) and analyzing information captured in the learning system (i.e., mental observation and evaluation), comprising the following:
store information for the learning management system, the information associated with at least one of … learning environment data, organizational data and usage data, the information comprising aggregate data based on a plurality of interactions between a plurality of users and the learning management system … (mental process: collect data including observation);
capture, automatically, usage data of a plurality of academic tools used by the plurality of users as well as academic performance data for each of the plurality of users and aggregate the usage data and academic performance data for statistical analysis (mental process: collect data including observation and evaluate collected data);
…statistically analyze the aggregate usage data to identify usage patterns based at least in part on the aggregate usage data and combine the usage patterns with the academic performance data to determine if there is at least one positive correlation between academic performance data and the aggregate usage data, and if so, to recommend at least one tool of the plurality of academic tools having the positive correlation between academic performance and the aggregate usage data to at least one of the plurality of users (mental process: evaluate or analyze collected data, and display results of evaluation or analysis; certain methods of organizing human activity: human interaction, teaching);
wherein the at least one positive correlation is above a predetermined threshold (mental evaluation);
… determines at least one negative correlation (mental evaluation);
wherein the at least one negative correlation corresponds to at least one variable that acts as a detriment for at least one of the plurality of users (mental evaluation);
wherein the at least one negative correlation corresponds to factors relating to at least one of user demographic information, user behavioral characteristics, user learning preferences, user teaching preferences, educational delivery mechanisms (mental evaluation);
…generates at least one report in the form of at least one of: a mosaic plot, a heat diagram, a correlogram, a pie chart, a tree diagram, and a chart (mental evaluation);
…generates at least one report identifying specific users that are performing at a level below a predetermined threshold (mental evaluation);
…generates at least one report identifying subject matter in an educational curriculum which was not adequately understood by learning users (mental evaluation);
…generates at least one report communicating at least one academic tool to which at least one specific learning user responds better than other academic tools of the plurality of academic tools (mental evaluation);
in claims 1-2, 4-9, 11, and 35;
storing information for the…learning system, the information associated with at least one of … learning environment data, organizational data and usage data, the information comprising aggregate data based on a plurality of interactions between a plurality of users and the…learning system … (mental process: collect data including observation);
capturing, automatically, usage data of a plurality of academic tools used by the plurality of users as well as academic performance data for each of the plurality of users and aggregate the usage data and academic performance data for statistical analysis (mental process: collect data including observation and evaluate collected data);
…statistically analyze the aggregate usage data to identify usage patterns based on the aggregate usage data and combine the usage patterns with the academic performance data to determine if there is at least one positive correlation between academic performance data and the aggregate usage data, and if so, to recommend at least one tool of the plurality of academic tools having the positive correlation between academic performance and the aggregate usage data to at least one of the plurality of users (mental process: evaluate or analyze collected data, and display results of evaluation or analysis; certain methods of organizing human activity: human interaction, teaching),
wherein the at least one positive correlation is above a predetermined threshold (mental evaluation);
… determines at least one negative correlation (mental evaluation);
wherein the at least one negative correlation corresponds to at least one variable that acts as a detriment for at least one of the plurality of users (mental evaluation);
wherein the at least one negative correlation corresponds to factors relating to at least one of user demographic information, user behavioral characteristics, user learning preferences, user teaching preferences, educational delivery mechanisms (mental evaluation);
in claims 12-13 and 15-17; and
identifying a plurality of users associated with a learning management system (mental process: collect data including observation, evaluate or analyze collected data);
providing … the learning management system to … the plurality of users associated with the learning management system (i.e., human interaction, teaching);
storing information associated with at least one of … learning environment data, organizational data, and usage data, the information comprising aggregate data based on a plurality of interactions between a plurality of users and the learning management system … (mental process: collect data including observation);
capture, automatically. usage data of a plurality of academic tools used by the plurality of users as well as academic performance data for each of the plurality of users and aggregate the usage data and academic performance data for statistical analysis (mental process: collect data including observation and evaluate collected data);
…statistically analyze the aggregate usage data to identify usage patterns based at least in part on the aggregate usage data and combine the usage patterns with the academic performance data to determine if there is at least one positive correlation between academic performance data and the aggregate usage data, and, if so, to recommend at least one tool of the plurality of academic tools having the positive correlation between academic performance and the aggregate usage data to at least one of the plurality of users (mental process: evaluate or analyze collected data, and display results of evaluation or analysis; certain methods of organizing human activity: human interaction, teaching),
wherein the at least one positive correlation is above a predetermined threshold (mental evaluation);
…determines at least one negative correlation and recommends mechanisms the tool having the negative correlation not be used by at least one of the plurality of user (mental evaluation);
wherein the usage data comprise the times of…submissions of quizzes and the responses submitted within the quizzes by the plurality of users (further defines abstract ideas identified above),
in claims 30-31 and 33.
The steps identified above are akin to organizing human activity and/or mental processes, and thus fall within an enumerated category of abstract ideas. Note that even if most humans would use a physical aid (e.g., pen and paper) to help them complete the recited steps above, the use of such physical aid does not negate the mental nature of these limitations.
Therefore, the claims recite an abstract idea (Step 2A, Prong 1: YES).
STEP 2A, Prong 2 = NO: This judicial exception is not integrated into a practical application.
To the extent the claims recite additional elements related to defining a computer environment to implement the abstract idea above (i.e., describing the abstract idea identified under Prong 1 in the context of a computer program product comprising a plurality of computing devices that communicate over a network with a learning management system; and at least one server configured to provide the learning management system over the network, communicate with the plurality of computing devices, store information identified under prong 1 as an abstract idea, output information identified under Prong 1 as an abstract idea via a user device interface; and implement at least one analytics engine, wherein the analytics engine is configured to perform the steps identified under Prong 1 as abstract ideas; describing the learning system as electronic and the environmental data as related to e-learning; defining interactions between a plurality of users and the learning management system as via the computing devices), they are recited at a high level of generality such that they do not amount to a particular machine or technical improvement thereof, nor do they represent an improvement in any other technology. Rather, the generic manner which these additional elements are claimed amount to mere instructions to implement the abstract idea in a computer environment, i.e., field of use, and thus do not integrate the judicial exception into a practical application.
It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the physical components identified above does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Therefore, the claims are directed to an abstract idea (Step 2A, Prong 2: YES).
STEP 2B = NO: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as provided under Prong 2, the additional elements are recited at a high level of generality, and for the purpose of insignificant pre and post-solution activity, e.g., data collection or data output.
Moreover, the specification of the instant application further demonstrates that the additional elements are recited for their well-understood, routine and conventional functionality, which refers to elements of the computer system in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)(e.g., see par. 0056: […] the computing device may be a mainframe computer, a server, a personal computer, a laptop, a personal data assistant, a tablet computer, a smartphone, or a cellular telephone […] The output information may be applied to one or more output devices, in known fashion; par. 0057: Each such program may be stored on a non-transitory storage media or a device […] readable by a general […] purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein (emphasis added)). Thus, the additional elements identified in Prong 2, defining the field of use as a computer-implemented environment with computer components referred to by name alone with no particular structure claimed or disclosed, amount to merely automating a manual process which the courts have held to be insufficient in showing an improvement in computer-functionality. See Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017); see also LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).
Moreover, the claimed computer components perform limitations which amount to receiving or transmitting data over a network, e.g., a plurality of computing devices that communicate over a network with a learning management system; and at least one server configured to provide the learning management system over the network, communicate with the plurality of computing devices, which the courts have held to be well-known, routine, and conventional functions of a computer, and thus do not amount to significantly more than the abstract idea. see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Lastly, the claimed computer components perform limitation which amount to storing and retrieving information in memory, e.g., storing information, which the courts have held to be well-known, routine, and conventional functions of a computer, and thus do not amount to significantly more than the abstract idea. see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Therefore, the claims are not directed to significantly more than the abstract idea (Step 2B: NO).
Therefore, claims 1-2, 4-9, 11-13, 15-17, 30-31, 33, and 35 are not directed to patent eligible subject matter.
RESPONSE TO ARGUMENTS
Applicant's arguments filed 1/23/26 have been fully considered but they are not persuasive.
Under Step 2A, Applicant argues the claims are not directed to an abstract idea. Specifically, Applicant argues the claims are “not simply directed to organizing mental processes managing human engagement nor would the human mind be properly equipped to perform the claim limitations.” Applicant asserts that the capturing step of claim 1 (i.e., “capture, automatically, usage data of a plurality of academic tools used by the plurality of users as well as academic performance data for each of the plurality of users and aggregate the usage data and academic performance data for statistical analysis”), and subsequent aggregating and analyzing steps to determine patterns and positive correlations, represent an improvement to the learning experience of the users of the electronic learning system. Applicant argues this is a technical improvement of an electronic learning system given the complexity and sheer amount of data to be combined. Applicant contends this is not practically performed in or by the human mind. Reply 9-10. Applicant further argues that adapting the academic tools is necessarily “improving technology associated with e-learning.” Applicant contends, without any evidence, that even with a small number of users, the system would create a large amount of data that needs to be aggregated, correlated, and individualized, and thus is not possible as a mental process. Applicant concludes that specialized technology of an improved/modified electronic learning system rather than a general purpose computer, are required. Examiner notes that Applicant has not traversed the analysis under Step 2A, Prong 1, which provides that the claims recite an abstract idea. With respect to Step 2A, Prong 2, the claims are directed to an abstract idea because the additional elements are recited at a high level of generality and thus amount to merely defining a field of use or generic technological environment in which to perform the abstract idea identified under Prong 1. As provided under the updated rejection above, the abstract idea includes the new limitation of “capture, automatically, usage data of a plurality of academic tools used by the plurality of users as well as academic performance data for each of the plurality of users and aggregate the usage data and academic performance data for statistical analysis” as this is a step practically performed by mental processes, including mental observation and evaluation. This limitation lacks any particular detail that would preclude performance by mental processes. To the extent defining this step as being performed “automatically” is considered an additional element that inherently requires a computer, which Examiner does not concede, it is recited in a result-based manner with no details whatsoever how the result is achieved automatically, and thus does not offer meaningful limitations beyond generally linking the abstract idea to a generic computer, i.e., field of use. Applicant’s assertions regarding “complexity” or “sheer amount of data” being outside the capability of a human performing the step by mental processes is conclusive, and not commensurate in scope with the claimed invention which does not require specific details that would preclude performance by mental processes, but for the generic computer environment claimed. Further, the capturing, aggregating, and analysis steps are recited in a result-based manner and do not include details how they are performed that are not practically performable by humans taking mental steps. Contrary to Applicant’s assertion that the claimed steps are too complex based on sheer amount of data to process, the claims define the aggregating as “aggregate the usage data and academic performance data for statistical analysis” and the analyzing as “statistically analyze the aggregate usage data to identify usage patterns based at least in part on the aggregate usage data, the usage patterns including and combine the usage patterns with the academic performance data to determine if there is at least one positive correlation between academic performance data and the aggregate usage data” which are practically capable of performance by mental evaluation, using pen and paper if needed to evaluate the data. Thus, to the extent the claims require aggregating data and analyzing the data to determine patterns and correlations in a manner that improves the efficiency of a learning management system, it is practically performable by human analog, i.e., mental evaluation. Thus, Applicant’s assertion that these steps would inherently require large amounts of data even with few users is both conclusive and would not preclude performance by human analog. To the extent the claims require “a large amount of data,” which Examiner disagrees, it merely describes the abstract idea itself and the claims lack any detail whatsoever beyond generally linking the abstract idea to a generic field of use, as provided above. Applicant’s argument that this would necessarily require a specialized computer is contradictory to the Applicant’s specification which expressly discloses the use of a general-purpose computer (see Spec, par. 0057). Moreover, under the broadest reasonable interpretation, the claimed computer environment is recited so generically that it is interpreted as including a general-purpose computer. Therefore, the claims do not represent a technical improvement to e-learning or any other technology.
Under Step 2B, Applicant further argues the claims recite additional elements that amount to significantly more than the abstract idea based on an alleged improvement to the technology of an electronic learning system and to the field of academic tool selection for users within the electronic learning system. Specifically, Applicant argues that the claims demonstrate a technology rooted solution to a technical problem in tracking, analyzing and selecting academic tools for electronic learning, which Applicant contends amounts to significantly more than mental processes or methods of organizing human behavior. Applicant argues that the claims require “various specific requirements, is automatic, and integrated into a practical application such that it does not pre-empt any alleged abstract idea.” Applicant further argues that the claims recite “features that allow for the automatic capture of and aggregation of significant amounts of data and provide the ability to analyze the data to determine if there is a positive correlation between academic tools and academic performance in the electronic learning system.” Applicant argues the system “automatically, and in near real time, provides information that would be done perhaps once for a course if event attempted by a human individually.” Applicant argues that the “information about academic tools can be updated regularly, so that academic tools are not locked in place and can be adapted on an on-going basis” which “offer an ability of an electronic learning system to quickly and efficiently adjust academic tools in a manner that is other than what is well-understood, routine, and conventional in the field.” Applicant concludes this represent “technological improvements [that] allow the electronic learning system to be able to adapt academic tools in a substantive manner that would not be available in the human mind.” Reply 10-11. At the outset, preemption is not a standalone test for patent eligibility, and thus Applicant’s argument that the claims do not preempt any alleged abstract idea is not persuasive. Further, for the same reasons provided to Applicant’s arguments regarding Step 2A, the instant claims recite an abstract (i.e., mental process and certain methods of organizing human activity), including capturing, analyzing and recommending academic tools, and are directed to an abstract idea because the additional elements are recited in the claims at a high level of generality to merely define a generic field of use. To the extent the claims recite e-learning or an electronic learning system, the claims lack sufficient detail defining these constructs in a manner that represent a technical improvement. Rather, they are referred to by name alone and/or in conjunction with generically-recited components that under the broadest reasonable interpterion include a general-purpose computer environment. Applicant’s assertions regarding the “amount” of data or “specific requirements” are conclusory at best. To the extent the disclosed invention requires an amount of data outside the capability of a human performing them mentally, it is not reflected in the claims, nor are there any other requirements in the claims that amount to more than a generic field of use to perform the identified abstract idea. Moreover, stating that a step is performed automatically or in near real-time, does not offer meaningful limitations beyond generally linking the abstract idea to any general-purpose computer performing its basic functions. Again, the claims lack any technical detail defining how this is accomplished in a manner that represent a technical improvement to the computer functionality. Updating an academic tool, which is interpreted as including information (Spec, par. 0072), is also recited with no technical detail whatsoever, and thus is interpreted as merely defining the abstract idea itself. Moreover, adapting academic tools is not claimed, and thus Applicant’s argument is not commensurate in scope with the claimed invention. Rather, the academic tools are claimed as either being “used by a plurality of users” and subsequently recommended to users. These limitations merely define the abstract idea itself because information can be read by a user and subsequently recommended by a human through interpersonal interactions, e.g., teaching. Further, Applicant’s specification expressly discloses the use of a general-purpose computer, and thus the claimed computer is interpreted under the broadest reasonable interpretation as performing well-understood, routine, and conventional activity (see Spec, par. 0057). Therefore, the claims do not represent a technical improvement.
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 James Hull whose telephone number is 571-272-0996. The examiner can normally be reached on Monday-Friday from 8:00am to 5:00pm MST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai, can be reached at telephone number 571-272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/JAMES B HULL/Primary Examiner, Art Unit 3715