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 in response to application 18/647540 filled on 04/26/024.
Claims 1-20 are currently pending and have been examined.
Detailed Action
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 1-20 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.
Step 1:
Claims 1-20 are drawn to a method and system, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One:
Independent claims 1 and 18 recite creating a first plurality of employee combinations; and simulating for each employee combination a first predicted outcome score. The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity by identifying and reporting events preceding a pattern in a set of user data. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea (Step 2A Prong One: YES).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including a simulator and a computer machine learning model, which are additional elements that are recited at a high level of generality (e.g., the model generates predictions no more than a statement that said model is “configured” to generate predicted outcome scores s) such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed (e.g., the model language is incidental to what it is “configured” to perform). Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The claims recite the additional element of receiving a first plurality of patient health profiles; receiving a first plurality of employee profiles; selecting a first preferred employee combination and scheduling healthcare employees of the first preferred employee combination, which are considered limitations directed to insignificant extra-solution activity that do not amount to an inventive concept because the limitations do not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, the claimed receiving limitations are incidental to the performance of the recited abstract idea of identifying and reporting events preceding a pattern in a set of user data and the scheduling step is an extra solution activity. See: MPEP 2106.05(g)
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B:
The claims do 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, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See: MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, and
Paragraph 18, where “Memory 104 is configured to store information and, in some examples, can be described as a computer-readable storage medium. Memory 104, in some examples, is described as computer-readable storage media. In some examples, a computer-readable storage medium can include a non-transitory medium. The term “non-transitory” can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). In some examples, memory 104 is a temporary memory. As used herein, a temporary memory refers to a memory having a primary purpose that is not long-term storage. Memory 104, in some examples, is described as volatile memory. As used herein, a volatile memory refers to a memory that that the memory does not maintain stored contents when power to the memory 104 is turned off. Examples of volatile memories can include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories.”
Paragraph 4, wherein “The instructions, when executed, cause the processor to receive a first plurality of patient health profiles from the patient database, receive a first plurality of employee profiles from the employee database, create a first plurality of employee combinations, simulate, a first predicted outcome score using a simulator for each employee combination, select a first preferred employee combination of the plurality of predicted employee combinations based on the simulated first predicted outcome scores, and modify the electronic employee scheduling system to schedule healthcare employees of the first preferred employee combination during the first time period. Each patient health profile of the first plurality of patient profiles corresponds to one patient of the first plurality of patients”.
Paragraph 33, where “the programs of patient scheduling module 110 can be configured to analyze a patient health profile for a patient to determine one or more periods of time which are associated with improved patient outcomes. Patient scheduling module 110 can, for example, including one or more computer-implemented machine learning models configured to analyze patient health profiles and output scores or values describing patient outcome. The programs of patient scheduling module 120 can be run iteratively to recommend patient appointment times for as many time periods, shifts, and/or appointment windows as is desirable for a given healthcare facility 180A–N and/or for hospital system 182.”
Paragraph 34, where “As referred to herein, a “patient outcome score” is a score that describes the positivity of the expected outcome of a particular visit to a healthcare facility. As described previously, the patient outcome score can be influenced by patient experience in the healthcare facility and, accordingly patient experience can be used to build and/or train models to predict expected patient outcomes. In some examples, employee scheduling module 120 can include one or more trained computer-implemented machine learning models trained to output patient outcome scores for combinations of one or more patients and one more healthcare employees. The program(s) of employee scheduling module 120 can also be used to select a preferred employee combination for a given combination of patients according to the patient outcome scores.”
The claims recite the additional element of receiving a first plurality of patient health profiles; receiving a first plurality of employee profiles; selecting a first preferred employee, and scheduling healthcare employees, which amounts to extra-solution activity concerning mere data gathering and displaying. The specification (e.g., as excerpted above) does not provide any indication that the additional elements are anything other than well‐understood, routine, and conventional functions when claimed in a merely generic manner (as they are here). See: MPEP 2106.05(g).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2-17 and 19-20 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. The claims further define how the employee data is received and analyzed. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein. The claim
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wayne et al. (US 12541734 B2) teaches systems and method for bootstrap scheduling by matching employees using a neural network. Brown et al. (US 2020/0411169 A1) teaches “The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. The system can further evaluate the information using various machine learning models and/or optimization models/algorithms to determine how to schedule performance of the tasks with respect to time and location and how to assign resources (e.g., workers and optionally non-human resources) to the tasks in a manner that results in performing the tasks in the most efficient and effective manner, using the right resources at the right time for the right patient in the right place.” Cinnor et al. (US 2020/0151634 A1) teaches “ the depicted demand/supply matching process data flow 100 supports probabilistic demand/supply matching determined as a function of an adaptive algorithm optimizing scheduling efficiency based on matching service consumers, service providers, and time slots. In the illustrated example, interactions between consumers, providers, and time slots are modeled to determine a score predicting an outcome characteristic related to each modeled interaction. In an illustrative example, a consumer score may be determined as a function of the consumer's historical no-show probability, the consumer's physical condition, and the consumer's distance from the practice facility where service may be rendered in a given time slot. In some embodiments, a consumer, provider, or time slot score may be determined as a function of a probabilistic estimate of no-shows, delays, or cancellations; practice resource utilization/allocation preferences; demand forecasting; and, other related factors”. Hosny et al. (US 2022/0375583 A1) teaches “A method for a patient to identify and select a health care provider to service the patient's particular need and subsequently schedule an appointment with the health care provider is described. Data regarding the patient and data regarding the provider are used to determine matching physicians to the patient's needs. The patient has the ability to select among matching physicians, rank ordered based on a combination of patient and other criteria, and the patient is offered an average rating for each physician, where the rating is based on reliable reviews of at least other patients.” The arts fail to teach “creating a first plurality of employee combinations, each employee combination of the first plurality of employee combinations including at least two healthcare employees of the first plurality of healthcare employees; simulating, for each employee combination, a first predicted outcome score using a simulator, a first computer-implemented machine learning model, the first plurality of patient health profiles, and employee profiles of the first plurality of employee profiles corresponding to the healthcare employees of the employee combination, the first predicted outcome score describing a health outcome for the first plurality of patients, wherein the first computer-implemented machine learning model is configured to generate predicted outcome scores based on patient health profiles and employee profiles”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAROUN P KANAAN whose telephone number is (571)270-1497. The examiner can normally be reached Monday-Friday 8:00-5:00.
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MAROUN P. KANAAN
Primary Examiner
Art Unit 3687
/MAROUN P KANAAN/Primary Examiner, Art Unit 3687