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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/2/2026 has been entered.
Response to Arguments
Applicant's arguments filed with respect to rejections under 35 US 101 have been fully considered but they are not persuasive. Applicant challenges the position that the claims recite abstract ideas. In response to Applicant’s comments regarding certain methods of organizing human activity and also considering the lengthy amendments to the claims, Examiner has updated the claim analysis under 35 USC 101. More specifically, the predicting…; receiving…; receiving…; predicting…; identifying…; receiving…; predicting…: predicting…: and predicting… limitations are mental processes. Each of the listed limitations involve observations and evaluations. Making a prediction is an evaluation one can practically perform in the mind or with pen and paper. Identifying is an observation one can practically perform in the mind or with pen and paper. Similarity, receiving is an observation that one can practically perform in the mind or with pen and paper. The claim recites two predicting limitations, wherein the predicting is by a simulator and computer-implemented machine learning model, which amounts to using a computer as a tool to perform the abstract predicting step. Performing a constrained optimization is a mathematical operation wherein an objective function is optimized with respect to variables in the presence of constraints on those variables. As such, the limitation is considered mathematical concepts. Modifying a schedule is a mental process that can practically be performed in the mind or with pen and paper. Implementing the schedule modification electronically amounts to using a computer as a tool to perform the abstract idea. In at least claim 1, the simulator and reference to an electronic schedule do not integrate the abstract idea into a practical application. This analysis extends to independent claim 15. The claimed system comprises at least one database; a processor; a user interface; and at least one computer-readable memory encoded with instructions, that when executed, cause the processor to perform the abstract limitations identified above. This amounts to using a computer as a tool to perform an abstract idea and instructions to implement an abstract idea on a computer. There is no integration into a practical application.
Examiner appreciates Applicant’s comments directed to the categorization of the abstract idea as certain methods of organizing human activity. In response Examiner has removed the assertion, but notes the claims comprise a series of instructions of how to generate recommended shift re-assignments and modify a nurse schedule. Following rules or instructions falls within managing personal behavior or relationships or interactions between people and are therefore recognized as certain methods of organizing human activity. MPEP 2016.04(a) II. C.
Applicant asserts the claim provides improvements to patient care quality and are technical improvements within the field of medical care. Examiner disagrees. The specification [0014] states, “Advantageously, this allows the systems and methods disclosed herein to be used in a wide variety of healthcare settings to understand likely working condition preferences and create nursing worker schedules according to those preferences, thereby both improving worker experience and reducing turnover-associated costs to healthcare providers”. This purported advantage is to an abstract idea and not to any technology or technical field. As stated by Applicant, the improvement can be provided by one or more additional elements present in the claim and/or by the additional element in combination with the judicial exception. In this case, the additional elements including a simulator, computer-implemented machine learning model and electronic schedule of claim 1 and the system of claim 15, taken alone or in combination with the abstract idea fail to integrate the abstract idea into a practical application since they amount to using a computer as a tool to implement an abstract idea.
On pages 16-17, Applicant asserts the complex data manipulations cannot be performed by a human. To that, Examiner notes that the complexity of the abstract ideas, the evaluations and observations and other mathematical concepts, does not weigh in favor of eligibility. Using a computer as a tool to perform the abstract steps does not integrate the abstract idea.
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.
Claim(s) 1, 7, 15, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1, 7, 15, and 19 is/are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test.
As per Prong One of Step 2A of the §101 eligibility analysis set forth in MPEP 2106, the Examiner notes that the claims recite mental processes and certain methods of organizing human activity.
More specifically, independent claims 1 and 15 recite:
predicting a plurality of first shift-specific pluralities of resignation likelihoods, each first shift-specific plurality including resignation likelihoods for one nurse of a plurality of nurses for a shift to which the nurse is currently scheduled, by, for each nurse of the plurality of nurses: [mental process – evaluation - making a prediction]
receiving a set of attributes for the nurse, the set of attributes including one or more attributes that describe the nurse; [mental process – observation]
receiving a plurality of current-shift variables for the shift to which the nurse is currently scheduled, each current-shift variable of the plurality of current-shift variables describing a characteristic of a work condition of a first the shift to which the nurse is currently scheduled shift in a nursing workplace, such that the plurality of current-shift variables describe a plurality of work conditions of the shift to which the nurse is currently scheduled; [mental process – observation/evaluation]
predicting one first shift-specific plurality of resignation likelihoods for the first plurality of work conditions by simulating
identifying a set of re-schedulable nurses from the plurality of nurses based on the plurality of first shift-specific pluralities of resignation likelihoods, each nurse of the set of re-schedule nurses having a corresponding first shift- specific plurality of resignation likelihoods that includes a threshold number of resignation likelihoods that exceeds a threshold resignation likelihood; [mental process – observation/evaluation]
receiving a plurality of shift-specific pluralities of alternative shift variables for a plurality of available shifts, each shift-specific plurality of alternative shift variables having shift variables describing characteristics of work conditions of one available shift of the plurality of available shifts; [mental process – observation/evaluation]
predicting a plurality of nurse-specific pluralities of second shift-specific pluralities of resignation likelihoods, each nurse-specific plurality of second shift- specific pluralities of resignation likelihoods including resignation likelihoods predicted for one nurse of the set of re-schedulable nurses based on the plurality of available shifts, wherein predicting the plurality of pluralities of second pluralities of resignation likelihoods comprises, for each nurse of the set of re-schedulable nurses: [mental process – observation/evaluation]
predicting one nurse-specific plurality of second shift-specific pluralities of resignation likelihoods based on the plurality of shift-specific pluralities of alternative shift variables by, for each shift-specific plurality of alternative shift variables of the plurality of shift- specific pluralities of alternative shift variables: [mental process – observation/evaluation]
predicting one second shift-specific plurality of resignation likelihoods of the nurse-specific plurality of second shift- specific pluralities of resignation likelihoods by simulating,
performing a constrained optimization, using the threshold quantity of resignation likelihoods and the threshold resignation likelihood as constraints, to generate a plurality of recommended shift re-assignments for the set of re- schedulable nurses that is predicted, according to resignation likelihoods of the plurality of nurse-specific pluralities of second shift-specific pluralities of resignation likelihoods, to reduce a quantity of nurses assigned to available shifts predicted to have second shift-specific pluralities of resignation likelihoods including more than the threshold quantity of resignation likelihoods that exceed the threshold resignation likelihood; [mathematical concepts] and
plurality of recommended shift re-assignments. [mental process – observation/evaluation]
The claims recite data analysis steps to predict a resignation likelihood based on nursing work conditions. The claims include observations and evaluations that can be practically performed in the mind or with pen and paper. Further, the claims comprise a series of instructions of how to generate recommended shift re-assignments and modify a nurse schedule. Following rules or instructions falls within managing personal behavior or relationships or interactions between people and are therefore recognized as certain methods of organizing human activity. MPEP 2016.04(a) II. C.
The nominal recitation of a simulator, computer implemented machine learning and electronic schedule in claims 1 and the system of claim 15 does not necessarily preclude the claim from reciting an abstract idea as evidenced by the analysis at Prong 2 of Step 2A.
Regarding Prong Two of Step 2A, a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. The simulator, computer implemented machine learning and electronic schedule of claim 1 and claim 15, and the system with database, processor, user interface and computer readable memory also in claim 15 amount to using a computer as a tool to perform the abstract idea. There is no improvement to the technology or technological field and therefore is no integration into a practical application.
The dependent claims add additional details to the abstract idea and some recite additional elements that do not integrate the abstract idea into a practical application.
Claims 7 and 19 recite the observed attributes which is mental process [evaluation] and certain methods of organizing human activity. There is no integration of the abstract idea into a practical application.
The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually and in combination, the system and software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense.
Lastly and in accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, and when considered individually or combination, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fama et al, US 10115065 – Systems and Methods for Automatic Scheduling of a Workforce - Users will be able to create their time bank plan using optimization algorithms based on all employee factors, from work hours, new hires, attrition, as well as forecasted incoming volume. It provides extensive tools to modify or reuse the time bank plan. It incorporates the time bank constraints into an advanced scheduling system that takes into consideration not only the time bank plan, but also any manual adjustments made during the time bank period.
Araki et al, US 2025/0103673 - COMBINATORIAL OPTIMIZATION PROBLEM SOLUTION DEVICE AND COMBINATORIAL OPTIMIZATION PROBLEM SOLUTION METHOD - quantum computer solves combinatorial optimization problems using the energy function in the Ising model as input. Examples of combinatorial optimization problems include the traveling salesman problem, the knapsack problem, the graph partitioning problem, and the nurse shift scheduling problem
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JOHNNA LOFTIS
Primary Examiner
Art Unit 3625
/JOHNNA R LOFTIS/Primary Examiner, Art Unit 3625
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