DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a Final action in response to the communications filed on 04/13/2026.
Applicant has amended claims 1, 3, 8, and 15;
Claims 1, 3 – 8, 10 – 15, and 17 – 23 are pending in this application.
Response to Remarks
Examiner’s Response to Claim Rejections:
Response to Claim Rejections under 35 U.S.C. § 101;
Response to Claim Rejections under 35 U.S.C. § 103;
Response to Claim Rejections under 35 U.S.C. § 112.
Examiner’s Response to Claim Rejections under 35 U.S.C. § 101.
Applicant argues the amended claims are no longer directed to an abstract idea or mental process, but instead recite a specific, concrete technical implementation of a machine learning-driven training compliance system.
Examiner respectfully disagrees. Applicant’s claim 1 recites abstract ideas of certain methods of organizing human activity and mathematical concepts. Claim 1 recites receiving behavior data associated with a user wherein the behavior data includes at least a time of day, a session duration, and a notification to which the user previously responded to initiate a training unit associated with a previously completed training unit by the user; computing a responsiveness score based on the user's historical responses to prior notifications, and selecting as the reminder the channel whose responsiveness score exceeds a predetermined threshold a training duration, and a reminder time for the user; generating a reminder based on the user's predicted behavior, wherein the reminder comprises an uncompleted sub-unit; generating a completion time within a threshold of the training duration for the user; transmitting the reminder to the user at the reminder time; and analyzing the user's activity after the reminder was transmitted to determine whether the user completed the sub-unit. These limitations involve certain activity between a person and a computer, for example between a user and training unit; and these limitations also recite mathematical concepts where they manipulate data using mathematical functions, and the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic of the user’s behavior. Claims 8 and 15 are substantially similar and recite the same subject matter as claim 1.
The judicial exceptions are not integrated into a practical application. The additional elements of a notification type to which the user previously responded to initiate a training unit, a personalized reminder, processing the behavior data using vector instructions resident in a processor set, the processor set including processing circuitry configured for parallel execution, iteratively updating a personalized predictive behavior model associated with the user using a multi-layer neural network having multiple hidden layers trained with the processed behavior data, to refine predictions of a behavior of the user over time, generating, using the personalized predictive behavior model, updating the personalized predictive behavior model to improve future predictions, a memory, one or more processors, a training compliance management system, one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors, and a computer readable storage medium; and these additional elements are generic computer components that are gathering user behavior data, analyzing the data, predicting training for a user, transmitting a notification, updating the prediction model. These additional elements are not significantly more than the judicial exceptions; and the claims as whole are mere instructions to apply an exception using generic computer components to resolve the business problem of managing employee training. There is no improvement to the computer nor is there an improvement to a technological area. For these reasons rejection under 35 U.S.C. § 101 remains for all pending claims.
Examiner’s Response to Claim Rejections under 35 U.S.C. § 103.
Applicant argues that Korenblit in view of Soffer and Wright fail to disclose or render obvious each and every element recited in claims 1, 8, and 15.
Examiner respectfully agrees. Examiner’s cited art, Korenblit, Shmuel et al. (U.S. Publication No. 2007/0195944) hereinafter “Korenblit” in view of Soffer, Ronen Aharon (U.S. Publication 2017/0372267) hereinafter “Soffer” in view of Wright, David et al. (U.S. Publication No. 2023/0351435) hereinafter “Wright” fail to teach “wherein the data includes behavior data associated with the user and progress or completion of the uncompleted sub-unit of the training unit” and “modifying the personalized predictive behavior model associated with the user using the updated behavior data.” Accordingly, Examiner has removed rejection under 35 U.S.C. § 103.
Examiner’s Response to Claim Rejections under 35 U.S.C. § 112(a).
Examiner has removed rejection under 35 U.S.C. § 112(a), as Applicant’s amendments are sufficient to overcome rejection pursuant to the statute.
Claim Rejections: 35 U.S.C. § 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, 3 – 8, 10 – 15, and 17 – 23, are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 8, and 15:
receiving behavior data associated with a user wherein the behavior data includes at least a time of day, a session duration, and a notification to which the user previously responded;
computing for each method of notification of a plurality of methods of notification, a responsiveness score based on the user's historical responses to prior notifications, and selecting as a reminder the method of notification whose responsiveness score exceeds a predetermined threshold;
generating, a training duration, a reminder, and the reminder time for the user;
generating a completion time within a threshold of the training duration for the user;
transmitting the reminder to the user at the reminder time;
The limitations of claim 1, under its broadest reasonable interpretation recites certain methods of organizing human activity. The claim particularly recites the activity of managing interactions where there are management of interactions between a human and a computer. For example, we have receiving behavior data associated with a user wherein the behavior data includes at least a time of day, a session duration, and a notification to which the user previously responded to initiate a training unit associated with a previously completed training unit by the user; computing a responsiveness score based on the user's historical responses to prior notifications, and selecting as the reminder the channel whose responsiveness score exceeds a predetermined threshold a training duration, and a reminder time for the user; generating a reminder based on the user's predicted behavior, wherein the reminder comprises an uncompleted sub-unit; generating a completion time within a threshold of the training duration for the user; transmitting the reminder to the user at the reminder time; where these limitations all involve certain activity between a person and a computer. Claims 8 and 15 are substantially similar and recite the same subject matter as claim 1. Accordingly, claims 1, 8, and 15 recite certain methods of organizing human activity.
The limitations of claim 1, under its broadest reasonable interpretation recite mathematical concepts. For example, claim 1 recites iteratively updating a personalized predictive behavior model associated with the user using a multi-layer neural network having multiple hidden layers trained with the processed behavior data, to refine predictions of a behavior of the user over time; computing, for each candidate notification channel, a responsiveness score based on the user's historical responses to prior notifications, and selecting as the reminder type the channel whose responsiveness score exceeds a predetermined threshold; generating, using the personalized predictive behavior model, a training duration, a reminder type, and a reminder time for the user; dynamically evaluating sub-units of a training unit that each have a completion time within a threshold of the training duration for the user, wherein the sub-units are generated based on a predicted availability of the user; generating a reminder based on the user's predicted behavior wherein the reminder comprises an uncompleted sub-unit of the sub-units of the training unit all involve mathematical relationships where the claim limitations constitute mathematical concepts to manipulate the data using mathematical functions, and the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic. Accordingly, claim 1 recites mathematical concepts.
The dependent claims encompass the same abstract ideas as well. For instance, claims 3, 10, and 17 are directed towards observing a set of users with a common characteristic, and evaluating a semi-specialized predictive behavior model using personalized predictive behavior models corresponding to each user of the set of users; claims 4, 11, and 18 are directed towards observing an indication of a new user associated with the common characteristic, and evaluating a training reminder for the new user using the semi-specialized predictive behavior model; claims 5, 12, and 19 are directed towards observing the reminder type is a text message, a social media message, an email, or a phone call; claims 6, 13, and 20 are directed towards in response to identifying a different uncompleted sub-unit of the training unit associated with the user, evaluating a different reminder comprising the different uncompleted sub-unit of the training unit; claims 7 and 14 are directed towards in response to determining that there are no uncompleted sub-units associated with the training unit, evaluating a notification indicating completion of the training unit; claim 21 is directed towards evaluating sub-units of the training unit includes dividing the training unit into sub- units that each have a duration that is based on the predicted availability of the user; claim 22 is directed towards evaluating adjusting the threshold completion time of the sub-units based on the user's historical session durations and predicted attention span; and claim 23 is directed towards observing the personalized predictive behavior model is further updated based on external data sources, including calendar availability, weather conditions, or other contextual factors affecting the user's availability. Accordingly, the dependent claims encompass the same abstract ideas.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of a training compliance management system from a user device associated with the user, a method of notification to which the user previously responded to initiate a training unit, personalized reminder, reminder type, processing the behavior data using a processor set including at least one computer processor, personalized reminder, the processor set including at least one computer processor, a link to the uncompleted sub-unit, iteratively updating a personalized predictive behavior model associated with the user using a multi-layer neural network having multiple hidden layers trained with the processed behavior data, to refine predictions of a behavior of the user over time, and generating, using the personalized predictive behavior model, receiving, from the user device, data associated with the uncompleted sub-unit transmitted in the personalized reminder, wherein the data includes behavior data associated with the user and progress or completion of the uncompleted sub-unit of the training unit, updating the behavior data to include the data received from the user device, and modifying the personalized predictive behavior model associated with the user using the updated behavior data. In addition to reciting the additional elements of claim 1, claim 8 recites the additional elements of a system, a memory, one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors, a training compliance management system, and a computer readable storage medium; in addition to reciting the additional elements of claim 1, claim 15 recites the additional elements of a computer readable storage medium and a processor. However, the additional elements of a training compliance management system from a user device associated with the user, a method of notification to which the user previously responded to initiate a training unit, personalized reminder, reminder type, processing the behavior data using a processor set including at least one computer processor, personalized reminder, to include the data received from the user device, the processor set including at least one computer processor, iteratively updating a personalized predictive behavior model associated with the user using a multi-layer neural network having multiple hidden layers trained with the processed behavior data, to refine predictions of a behavior of the user over time, a link to the uncompleted sub-unit, generating, using the personalized predictive behavior model, receiving, from the user device, data associated with the uncompleted sub-unit transmitted in the personalized reminder, wherein the data includes behavior data associated with the user and progress or completion of the uncompleted sub-unit of the training unit, updating the behavior data to include the data received from the user device, modifying the personalized predictive behavior model associated with the user using the updated behavior data, a system, a memory, one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors, a training compliance management system, a computer readable storage medium, a computer readable storage medium and a processor are considered generic computer components as per Applicant’s Specifications shown below:
“[0026] Client computer 101 may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database 130. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment 100, detailed discussion is focused on a single computer, specifically computer 101, to keep the presentation as simple as possible. Computer 101 may be located in a cloud, even though it is not shown in a cloud in Figure 1. On the other hand, computer 101 is not required to be in a cloud except to any extent as may be affirmatively indicated.”
and thus is not practically integrated nor significantly more.
The claims do not include additional elements that are sufficient to amount significantly more than the judicial exceptions. Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g. , processor). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). the additional elements do not impose meaningful limits on practicing the idea. Thus, the claims are directed to an abstract idea. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 3 – 7, and 10 – 14, and 17 – 23 when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1, 3 – 8, 10 – 15, and 17 – 23 are not patent eligible under 35 U.S.C. § 101.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday. Examiner can be reached via Fax at 571-483-7338. 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 Beth Boswell can be reached at (571) 272-6737.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
06/26/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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