DETAILED ACTION
Remarks
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/5/26 has been entered. Claim(s) 1-2, 4-9, 11-13, 15-17, 30-31, 33, and 35 is/are pending and 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 and determine whether at least one tool of the plurality of academic tools is statistically ineffective 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 and determine whether at least one tool of the plurality of academic tools is statistically ineffective 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 and determine whether at least one tool of the plurality of academic tools is statistically ineffective 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
35 USC § 101 – Rejections
Applicant's arguments filed 6/5/26 have been fully considered but they are not persuasive.
Applicant argues “[t]he presently claimed combination of features is not simply directed to mental processes or organizing human activity nor would the human mind be properly equipped to perform the claim limitations.” In particular, Applicant argues that steps (iv) and (v) of claim 1 (and similarly in claim 12 and 30) cannot be practically performed in or by the human mind. However, the capture step can be performed by mental process because it can be practically performed by a person observing a group of users, while the implement step can be performed by mental process because it can be practically performed by mental evaluation, e.g., aggregate/collect data and analyze said data. Further, the use of an analytics engine as recited in the implement step is recited at a high level of generality with no technical detail whatsoever defining the analytics engine with any particular detail or how it functions to perform the recited task. To the extent that allowing administrators or instructors to analyze patterns involving the relationship between grades and academic tool access is considered an improvement in the art, which Examiner does not concede, at best it describes an improvement to the abstract idea itself based on the lack of technical details in the claims. This is further evidenced by the lack of any technical detail defining said analytics engine in the written description, which only refers to it be name alone. Applicant further argues that “an improvement to an electronic learning system, by adapting the academic tools, is necessarily improving technology associated with e- learning.” This is also not persuasive, as neither the claims recite limitations to adapting academic tools. At best, the claims merely recite recommending tools which is practically capable of being performed by a human teacher to a human student by speaking, and thus an interaction between individuals, which constitutes an abstract idea. Applicant further argues that “it would be clear that even a small number of users, accessing a plurality of computing devices and utilizing a plurality of tools over a plurality of interactions with the system creates a large amount of data that then needs to be aggregated, correlated and individualized is not possible as a mental process,” which is not supported by the claims nor any evidence of record. Therefore, this statement by Applicant is a conclusory statement, and thus not persuasive. Further, the claims do not require any “specialized technology of an improved/modified electronic learning system”. Applicant’s statement that “a human may not be able to perceive the trends as to which tools are more or less effective and which should be discontinued as they are not achieving the desired outcomes” is also a conclusory statement unsupported by evidence of record. Further, Applicant argues that “this goes well beyond the abstract idea of organizing human activity in that it is an evaluation of an academic tool, not fundamental economic principles or practices; commercial or
legal interactions; or managing personal behavior or relationships or interactions between people.” However, these are a non-exhaustive list of examples of abstract ideas, and thus do not limit abstract ideas to only these concepts.
Applicant argues further argues that “the claim, as a whole, provides an improvement to the technology of an electronic learning system and to the field of academic tool selection for users within the electronic learning system.” Applicant argues that “the presently claimed combination of features demonstrate a technology rooted solution to a technical problem in tracking, analyzing and selecting academic tools for electronic learning, and thus amounts to significantly more than mental processes or methods of organizing human behaviour.” In particular, Applicant argues that “[t]he presently claimed features provide an improved system for capturing, aggregating, correlating, and analyzing the plurality of interactions of a plurality of users with a plurality of academic tools to allow the identification and recommendation of academic tools in an e-learning environment” However, as provided in the rejection above, the identified steps are interpreted under Step 2A, Prong 1 as abstract ideas because they are practically capable of performance by mental observation, evaluation, and analysis. In other words, but for the generic recitation of a computer environment drafted at a high level of generality to perform these steps, the claims do not preclude performance by a human mentally. Nor do the claims recite any technical detail that amounts to a technical solution to a technical problem, and the specification is also silent as to any technical improvement. Applicant contends that “the presently claimed combination recites 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 or if academic tools are not performing as they should.” However, the claims do not recite any particular technical detail defining how the capturing and aggregating steps are performed. Rather, the claim merely states that usage data is “captured, automatically” with no further detail, and thus does not represent a technical improvement. The same applies to the claimed aggregating step, which is recited in a manner that does not preclude the performance by a human through mental evaluation. For example, claim 1 merely states that “(iii) store information for the learning management system, the information associated with at least one of e-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 via the computing devices”. Thus, the task of storing aggregated data in this manner does not require technical detail that would preclude a human from mentally observing, recording, and storing the recorded observations. Applicant’s argument related to the amount of data is not supported by the claims or evidence of record, and thus amounts to a conclusory statement. Applicant further 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 Applicant contends is “other than what is well-understood, routine, and conventional in the field.” First, the claims do not require adapting academic tools. Second, to the extent the claims recite equivalent language, which Examiner does not concede, it lacks any technical detail whatsoever and thus does not represent a technical improvement. Further, a human could practically perform the task of making ongoing recommendations to another human on which academic tools to use, and thus the claims recite an abstract idea. Based on the lack of technical detail in the claims, the claims are also directed to the abstract idea without significantly more than the abstract idea.
Therefore, Applicant’s arguments are not persuasive, and the rejection is maintained.
Conclusion
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.
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/JAMES B HULL/Primary Examiner, Art Unit 3715
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