DETAILED ACTION
This office action is in response to communication filed on 6 July 2026.
Claims 1, 3 – 5, and 7 – 20 are presented for examination.
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 .
Response to Amendment
In the response filed 6 July 2026, Applicant amended claims 1, 7, 8, and 20. Claims 2 and 6 are canceled.
Amendments to claims 1, 7, 8, and 20 are insufficient to overcome the 35 USC § 101 rejection. Therefore, the 35 USC § 101 rejection of claims 1, 3 – 5, and 7 – 20 are maintained.
Amendments to claims 1, 7, 8, and 20 are sufficient to overcome the 35 USC § 112 rejection. Therefore, the 35 USC § 112 rejection of claims 1 – 20 are withdrawn.
Response to Arguments
Applicant's arguments filed 6 July 2026 have been fully considered but they are not persuasive.
In the remarks regarding the 35 USC 101 rejection, Applicant argues that claims are not directed to abstract ideas without significantly more. Examiner respectfully disagrees. Rolling dependent claims into independent claims reciting training using a neural network does not change the entirety of the claimset as reciting abstract ideas without significantly more. Training using a neural network does not describe how the training occurs. Mathematical weighting is not a computational operation, but merely a mathematical concept using automation as an “apply it.” There is no requirement or intertwining of the neural network to perform the training. The training could be merely changing the association between inputs and outputs, which is further abstract functionality. Applicant recites training a neural network as involving “adjusting internal weights across interconnected nodes through iterative processing of training data” which absolutely can be performed by the human mind, albeit slower. This is why the “apply it” is pertinent here. Technology such as a neural network is not claimed in a way that it is integral or necessary for the functionality to occur. Iterative calculations to adjust mathematical weighting is not a technical concept nor does it require a specific technical system, contrary to Applicant’s argument. Applicant’s reference to the USPTO Subject Matter Eligibility Example 47, Claim 3 is not comparable to these instant claims. Making detections and changes to network packets is wholly different from Applicant’s claim to changing mathematical weighting, which is an abstract function.
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, 3 – 5, and 7 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of abstract ideas without significantly more. The independent claims recite generating work attributes model for an agent, receiving shift data describing evaluation shifts worked by the agent, determining values for shift parameters associated with each of the evaluation shifts, determining an adherence score of the agent for each of the evaluation shifts in relation to the adherence metric based on the shift data, the adherence score indicating how closely the agent’s actual activities during the corresponding evaluation shift conform to expected work behaviors, creating a training dataset that includes training samples for respective ones of the evaluation shifts, wherein, each training sample includes the determined values of the shift parameters paired with the adherence score achieved in relation to the adherence metric for one of the evaluation shifts, and training the work attributes model for the agent using the training dataset, determining a key shift parameter and the determined value for the parameter which is a shift parameter determined by the model that statistically correlates with the agent achieving a better score relation to the adherence metric, transmitting the determined value for the key shift parameter to an agent scheduler, generating a work schedule for the agent covering future shifts by mathematically weighting variables associated with the determined value of the key shift parameter so that a likelihood of the agent receiving future shifts that have the determined value for the key shift parameter is increased. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance section 2106 of the MPEP (hereinafter, MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method and the system are directed to an eligible categories of subject matter. Step 1 is satisfied.
With respect to Step 2A prong 1 of MPEP 2106, it is next noted that the claims recite an abstract idea by reciting concepts of business relations such as shift scheduling and worker performance, which falls into the “certain methods of organizing human activity” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106. The claimed invention also recites an abstract idea that falls within the mental processes grouping, as independent claims describe receiving data and monitoring performance of an agent, which are observations. Additionally, the mathematical concepts grouping is recited, as claims describe generating a mathematical model and training that model. The limitations reciting the abstract idea in independent claims 1 and 20 are generating work attributes model for an agent, receiving shift data describing evaluation shifts worked by the agent, determining values for shift parameters associated with each of the evaluation shifts, determining an adherence score of the agent for each of the evaluation shifts in relation to the adherence metric based on the shift data, the adherence score indicating how closely the agent’s actual activities during the corresponding evaluation shift conform to expected work behaviors, creating a training dataset that includes training samples for respective ones of the evaluation shifts, wherein, each training sample includes the determined values of the shift parameters paired with the adherence score achieved in relation to the adherence metric for one of the evaluation shifts, and training the work attributes model for the agent using the training dataset, determining a key shift parameter and the determined value for the parameter which is a shift parameter determined by the model that statistically correlates with the agent achieving a better score relation to the adherence metric, transmitting the determined value for the key shift parameter to an agent scheduler, generating a work schedule for the agent covering future shifts by mathematically weighting variables associated with the determined value of the key shift parameter so that a likelihood of the agent receiving future shifts that have the determined value for the key shift parameter is increased.
With respect to Step 2A Prong Two of the MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to computer implementation, automated modeling process, automated agent scheduling application, machine learning model having a neural network, autoencoder machine learning model, automated messaging, a processor, and a memory, to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they are directed to the use of generic computing elements to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the MPEP 2106) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: computer implementation, automated modeling process, automated agent scheduling application, machine learning model having a neural network, autoencoder machine learning model, automated messaging, a processor, and a memory. These elements have been considered, but merely serve to tie the invention to a particular operating environment, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. This does not amount to significantly more than the abstract idea, and it is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of concepts of further defining parameter criteria, further defining the adherence metric, and generating work schedules and shifts, by way of examples, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Allowable Subject Matter
Claims 1, 3 – 5, and 7 – 20 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101, set forth in this Office action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA GURSKI whose telephone number is (571)270-5961. The examiner can normally be reached Monday to Thursday 7am to 5pm EST.
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/AMANDA GURSKI/Primary Examiner, Art Unit 3625
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