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 .
Response to Amendment
Amendment received on 10/24/2025 is acknowledged and entered. Claims 2-3, 7-8, 13, 18-19 and 33-34 have been canceled. Claims 1, 4, 9-10, 14-15, 20-21 and 24 have been amended. New claims 35-36 have been added. Claims 1, 4-6, 9-12, 20-32 and 35-36 are currently pending in the application.
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, 4-6, 9-12, 20-32 and 35-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
In determining whether a claim falls within an excluded category, the Examiner is guided by the Court’s two-part framework, described in Mayo and Alice. Id. at 217-18 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 75-77 (2012)); Bilski v. Kappos, 561 U.S. 593, 611 (2010); 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019); the October 2019 Update of the 2019 Revised Guidance (Oct. 17, 2019); 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (July 17, 2024), and the USPTO’s Paten Subject Matter Eligibility Memorandum of August 4, 2025.
Step 1
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability (i.e., laws of nature, natural phenomena, and abstract ideas). Alice Corp. v. CLS Bank Int'l, 573 U. S. ____ (2014).
The broadest reasonable interpretation of independent claims 1 and 9 encompasses a computer system (e.g., hardware such as a processor and memory) that implements the recited functions. If assuming that the system comprises a device or set of devices, then the system is directed to a machine, which is a statutory category of invention.
Claim 24 is directed to a statutory category, because a series of steps for determining a coverage plan satisfies the requirements of a process (a series of acts).
(Step 1: Yes).
Next, the claim is analyzed to determine whether it is directed to a judicial exception.
Step 2A – Prong 1
Claim 24 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more of determining a coverage plan for a healthcare facility. The claim recites:
24. A computer implemented method for determining a coverage plan for a healthcare facility, comprising
applying, using an electronic device, a forecasting model to patient volume data received from a data storage unit and then generating in response patient volume forecast data, wherein the forecasting model includes a plurality of hyperparameters and the patient volume data covers a patient volume over a first selected period of time,
tuning, using the electronic device, one or more of the plurality of hyperparameters by applying thereto a tuning technique, wherein the tuning unit generates in response tuned patient volume forecast data,
generating, using the electronic device and with a simulation engine, simulation data in response to the tuned patient volume forecast data and acuity level data, wherein the simulation data includes the patient volume over a second period of time, wherein the second period of time is shorter than the first period of time, wherein the simulation data includes a visual representation of a staffing coverage requirement of one or more physical locations of the healthcare facility, and
generating, using the electronic device, a coverage plan in response to and based on the simulation data, coverage data received from a data storage unit, and coverage rules data from a coverage rules unit,
generating, based on the coverage plan, one or more coverage recommendations based on coverage needs and costs per shift for the healthcare facility, and
generating one or more user interfaces suitable for displaying the coverage plan on a display device.
The limitations of receiving data; applying a forecasting model; tuning parameters; generating simulation data; generating a coverage plan based on the simulated and received data; generating coverage recommendations, and displaying the plan, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, which may be practically performed in the human mind using observation, evaluation, judgment, and opinion (MPEP 2106.04(a)(2), subsection III), and/or certain methods of organizing human activity but for the recitation of generic computer components. (Note: Examiner’s language (e.g. “receiving data”; “applying a forecasting model”; etc.) is an abbreviated reference to the detailed claim steps and is not an oversimplification of the claim language; the Examiner employing such shortcuts (that refer to more specific steps) when attempting to explain the rejection). That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind, and/or performed as organized human activity. Aside from the general technological environment (addressed below), it covers purely mental concepts and/or certain methods of organizing human activity processes, and the mere nominal recitation of a generic network appliance (e.g. an interface for inputting or outputting data, or generic network-based storage devices and displays) does not take the claim limitation out of the mental processes and/or certain methods of organizing human activity grouping.
Specifically, the utilizing statistical tools to process data and to output the estimated values - said functions could be performed by a human using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas (e.g., mental comparison regarding a sample or test subject to a control or target data in Ambry, Myriad CAFC, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in In re Grams, 888 F.2d 835 (Fed. Cir. 1989) (Grams)). In Grams, the recited functions require obtaining data or patient information (from sensors), and analyze that data to ascertain the existence and identity of an abnormality or estimated responses, and possible causes thereof. While said functions are performed by a computer, they are in essence a mathematical algorithm, in that they represent "[a] procedure for solving a given type of mathematical problem." Gottschalk v. Benson, 409 U.S. 63, 65, 93 S.Ct. 253, 254, 34 L.Ed.2d 273 (1972). Moreover, the Federal Circuit has held, “without additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). Here, the claimed subject matter is directed to the abstract idea of manipulating existing information (e.g., “received data”) to generate additional information (e.g., “forecasted data”). See id. Further, “analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, [are] essentially mental processes within the abstract-idea category.” Elec. Power, 830 F.3d at 1354; see also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1146 (Fed. Cir. 2016). “[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.” Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012).
As per the use of artificial intelligence and/or machine learning techniques (AI/ML), said recitation does not make the claim patent eligible, because said tools are utilized merely for data gathering and comparing, and are not utilized in express manipulation and control of functional aspects and/or hardware components/equipment of real-world processes and systems using output of AI models (e.g., manufacturing processes and equipment, medical treatments, communications processes and systems, logistics systems and hardware, interactive smart phone apps, etc.). It is similar to other abstract ideas held to be non-statutory by the courts. See, also, Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025), wherein the court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Additionally, applying machine learning to determining a coverage plan for a healthcare facility, an activity predating computers, did not transform the abstract idea into a patent-eligible invention.
See, also, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363 (Fed. Cir. 2015)—tailoring sales information presented to a user based on, e.g., user data and time data; TLI Communications LLC v. AV Automotive LLC 823 F.3d 607, 118 U.S.P.Q.2d 1744 (Fed. Cir. 2016) - recording, transmitting and administering digital images; and Intellectual Ventures I LLC v. Erie Indemnity Co. 850 F.3d 1315, 121 U.S.P.Q.2d 1928 (Fed Cir. 2017) - mobile interface for accessing remotely stored documents, and retrieving data from a database using an index of XML tags and metafiles.
As per receiving, storing and outputting data limitations, it has been held that “As many cases make clear, even if a process of collecting and analyzing information is ‘limited to particular content’ or a particular ‘source,’ that limitation does not make the collection and analysis other than abstract.” SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018) (citation omitted); see also In re Jobin, 811 F. App’x 633, 637 (Fed. Cir. 2020) (claims to collecting, organizing, grouping, and storing data using techniques such as conducting a survey or crowdsourcing recited a method of organizing human activity, which is a hallmark of abstract ideas).
All these cases describe the significant aspects of the claimed invention, albeit at another level of abstraction. See Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016) ("An abstract idea can generally be described at different levels of abstraction. As the Board has done, the claimed abstract idea could be described as generating menus on a computer, or generating a second menu from a first menu and sending the second menu to another location. It could be described in other ways, including, as indicated in the specification, taking orders from restaurant customers on a computer.").
Therefore, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” and/or “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A – Prong 1: Yes).
Step 2A – Prong 2
In Prong Two, the Examiner determines whether claim 24, as a whole, recites additional elements that integrate the judicial exception into a practical application of the exception, i.e., whether the additional elements apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is no more than a drafting effort designed to monopolize the judicial exception. See Guidance, 84 Fed. Reg. at 54-55. If the additional elements do not integrate the judicial exception into a practical application, then the claim is directed to the judicial exception. See id., 84 Fed. Reg. at 54. “An additional element [that] reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field” is indicative of integrating a judicial exception into a practical application. See Guidance, 84 Fed. Reg. at 55.
The Examiner determined that this judicial exception is not integrated into a practical application, because there are no meaningful limitations that transform the exception into a patent eligible application. In particular, the claim recites additional elements – using a processor to perform the steps of receiving data; applying a forecasting model; tuning parameters; generating simulation data; generating a coverage plan based on the simulated and received data; generating coverage recommendations, and displaying the plan. However, the processor in each step is recited (or implied) at a high level of generality, i.e., as a generic processor performing a generic computer functions of processing data, including receiving, storing, comparing, and outputting data. This generic processor limitation is nor more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). The processor that performs the recited steps merely automates these steps which can be done mentally or manually. Thus, while the additional elements have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." The claim only manipulates abstract data elements into another form, and does not set forth improvements to another technological field or the functioning of the computer itself and, instead, uses computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above.
Further, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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. None of the additional elements "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
The recited steps do not control or improve operation of a machine (MPEP 2106.05(a)), do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and do not apply the judicial exception with, or by use a particular machine (MPEP 2106.05(b)), but, instead, require receiving, comparing, storing and outputting data.
Regarding the use of AI/ML techniques, said steps are nothing more than an attempt to recycle preexisting AI/ML technologies to apply for a particular computing application. There are no improvements in said AI/ML techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process using the outcome of said AI/ML operations. Thus, the use of a trained machine learning models does not integrate the abstract idea of limitation into a practical application, because, under its broadest reasonable interpretation when read in light of the specification, the “forecasting”, “simulating” and “generating” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Similar to Recentive Analytics, claim 24 recites conventional machine learning models without specific improvements to the technology itself. The court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Claim 24 does not articulate "how" a technological improvement is achieved.
As per receiving, storing and outputting data limitations, these recitations amount to mere data gathering and/or outputting, is insignificant post-solution or extra-solution component and represents nominal recitation of technology. Insignificant "post-solution” or “extra-solution" activity means activity that is not central to the purpose of the method invented by the applicant. However, “(c) Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the execution of the claimed method steps. Use of a machine or apparatus that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would weigh against eligibility”. See Bilski, 138 S. Ct. at 3230 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, ___ (1978)). Thus, claim drafting strategies that attempt to circumvent the basic exceptions to § 101 using, for example, highly stylized language, hollow field-of-use limitations, or the recitation of token post-solution activity should not be credited. See Bilski, 130 S. Ct. at 3230.
Thus, the claim as a whole, outputs only data structure, - everything remains in the form of a code stored in the computer memory. 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. Therefore, the claim is directed to an abstract idea. (Step 2A – Prong 2: No).
Step 2B
If a claim has been determined to be directed to a judicial exception under revised Step 2A, examiners should then evaluate the additional elements individually and in combination under Step 2B to determine whether the provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
The Examiner determined that 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, the additional element of using a processor to perform the recited steps amount to no more than mere instructions to apply the exception using a generic computer component. The claim is now re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
The method would require a processor and memory in order to perform basic computer functions of receiving information, storing the information in a database, retrieving information from the database, comparing data, and outputting said information. These components are not explicitly recited and therefore must be construed at the highest level of generality. Based on the Specification, the invention utilizes conventional communication networks, and generic processors, which can be found in mobile devices or desktop computers, conventional memory and display devices, and the functions performed by said generic computer elements are basic functions of a computer - performing a mathematical operation, receiving, storing, comparing and outputting data - have recognized by the courts as routine and conventional activity. Specifically, regarding the recited functions, MPEP 2106.05(d)(II) defines said functions as routine and conventional, or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, 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));
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”); collecting and comparing known information in Classen 659 F.3d 1057, 100 U.S.P.Q.2d 1492 (Fed. Cir. 2011)
iii. Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
iv. Storing and retrieving information in memory, 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;
v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and
vi. A web browser’s back and forward button functionality, Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015).
Below are examples of other types of activity that the courts have found to be well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Recording a customer’s order, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016);
iii. Restricting public access to media by requiring a consumer to view an advertisement, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014);
iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
v. Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; and
vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
Regarding training a forecasting model, and obtaining data via repetitive operation of said model, said steps are nothing more than an attempt to recycle preexisting AI/ML technologies to apply for healthcare-related applications. There are no improvements in said AI/ML techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process using the outcome of said AI/ML operations. Claim 24 neither specifies a specific technical purpose for which the method is used, nor the claim defines a specific technical implementation of the method, nor the claimed method is particularly adapted for that implementation in that its design is motivated by technical considerations of the internal functioning of the computer. Said AI/ML algorithms and computations are done inside of a computer, and do not have a real-world impact and are not tied to the functionality of the computer. Further, there is no evidence that the invention lies in the training phase or execution phase or both; the Specification [0035] describes that said tuning step can employ “any suitable tuning technique, such as for example a machine learning R model-based optimization (mlrMBO) technique, also referred to as Bayesian optimization. … The tuning technique can also employ a probabilistic model, often a Gaussian Process (GP) model, to model the objective function. … The tuning technique can also select an acquisition function that guides the selection of the next hyperparameter set to evaluate by balancing exploration (e.g., sampling in regions with high uncertainty) and exploitation (e.g., sampling where the objective function is expected to be optimal). Common acquisition functions include probability of improvement (PI), expected improvement (EI), and upper confidence bound (UCB).” Therefore, said AI/ML recitation represents merely conventionally applying an existing techniques to an existing data from publicly accessible databases, with the result being not technological, but purely entrepreneurial.
Thus, the background of the current application does not provide any indication that the processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Also, the claim does not involve a non-conventional and non-generic arrangement of known, conventional pieces, as asserted, by receiving information from an external source of data. The receiving of data from an external source over a network, such as via the Internet, can fairly be characterized as insignificant extra-solution activity that does not receive patentable weight. See Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff’d sub nom Bilski v. Kappos, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity). Similar to Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014): “And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis.” Here, the claims are clearly focused on the combination of those abstract-idea processes. The advance they purport to make is a process of gathering and analyzing information of a specified content, then displaying the results, and not any particular asserted inventive technology for performing those functions. They are therefore directed to an abstract idea. As such, the additional elements, considered individually and in combination with the other claim elements, do not make the claim as a whole significantly more than the abstract idea itself.
Accordingly, a conclusion that the recited steps are well-understood, routine, conventional activity is supported under Berkheimer Option 2. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Similar to Electric Power Group v Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016) (Power Group), claim’ invocation of computers, networks, and displays does not transform the claimed subject matter into patent-eligible applications. Claim 24 does not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of known, conventional pieces,” but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices. Nothing in the claim, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information. Analogous to Power Group, claim 24 does not even require a new source or type of information, or new techniques for analyzing it. As a result, the claim does not require an arguably inventive set of components or methods, such as measurement devices or techniques that would generate new data. The claim does not invoke any assertedly inventive programming. Merely requiring the selection and manipulation of information - to provide a “humanly comprehensible” amount of information useful for users - by itself does not transform the otherwise-abstract processes of information collection and analysis into patent eligible subject matter. Merely obtaining and selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas. Therefore, the recited steps represent implementing the abstract idea on a generic computer, or “reciting a commonplace business method aimed at processing business information despite being applied on a general purpose computer” Versata, p. 53; Ultramerical, pp. 11-12.
Furthermore, the recited functions do not improve the functioning of computers itself, including of the processor(s) or the network elements. There are no physical improvements in the claim, like a faster processor or more efficient memory, and there is no operational improvement, like mathematical computation that improve the functioning of the computer. Applicant did not invent a new type of computer; Applicant like everyone else programs their computer to perform functions. The Supreme Court in Alice indicated that an abstract claim might be statutory if it improved another technology or the computer processing itself. Using a (programmed) computer to implement a common business practice does neither. The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, storing, comparing and transmitting data—see the Specification as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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. “However, it is not apparent how appellant’s programmed digital computer can produce any synergistic result. Instead, the computer will simply do the job it is instructed to do. Where is there any surprising or unexpected result? The unlikelihood of any such result is merely one more reason why patents should not be granted in situations where the only novelty is in the programming of general purpose digital computers”. See Sakraida v. Ag. Pro, Inc., 425 U.S. 273 [ 96 S.Ct. 1532, 47 L.Ed.2d 784], 189 USPQ 449 (1976) and A P Tea Co. V. Supermarket Corp., 340 U.S. 147 [ 71 S.Ct. 127, 95 L.Ed. 162], 87 USPQ 303 (1950).
Moreover, there is no transformation recited in the claim as understood in view of 35 USC 101. The steps of receiving data; applying a forecasting model; tuning parameters; generating simulation data; generating a coverage plan based on the simulated and received data; generating coverage recommendations, and displaying the plan merely represent abstract ideas which cannot meet the transformation test because they are not physical objects or substances. Bilski, 545 F.3d at 963. Said steps are nothing more than mere manipulation or reorganization of data, which does not satisfy the transformation prong. It is further noted that the underlying idea of the recited steps could be performed via pen and paper or in a person's mind. Moreover, “We agree with the district court that the claimed process manipulates data to organize it in a logical way such that additional fraud tests may be performed. The mere manipulation or reorganization of data, however, does not satisfy the transformation prong.” and “Abele made clear that the basic character of a process claim drawn to an abstract idea is not changed by claiming only its performance by computers, or by claiming the process embodied in program instructions on a computer readable medium. Thus, merely claiming a software implementation of a purely mental process that could otherwise be performed without the use of a computer does not satisfy the machine prong of the machine-or-transformation test”. CyberSource, 659 F.3d 1057, 100 U.S.P.Q.2d 1492 (Fed. Cir. 2011)
Therefore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because, when considered separately and in combination, the claim elements do not add significantly more to the exception. Considered separately and as an ordered combination, the claim elements do not provide an improvement to another technology or technical field; do not provide an improvement to the functioning of the computer itself; do not apply the judicial exception by use of a particular machine; do not effect a transformation or reduce a particular article to a different state or thing; and do not add a specific limitation other than what is well-understood, routine and conventional in the operation of a generic computer. None of the hardware recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Id., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). As per “A computer implemented method” recitations, these limitations do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment, that is, implementation via computers." Id., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Limiting the claims to the particular technological environment is, without more, insufficient to transform the claim into patent-eligible applications of the abstract idea at their core.
Accordingly, claim 24 is not directed to significantly more than the exception itself, and is not eligible subject matter under § 101. (Step 2B: No).
Further, although the Examiner takes the steps recited in the independent claim as exemplary, the Examiner points out that limitations recited in dependent claims 25-32 further narrow the abstract idea but do not make the claims any less abstract. Dependent claims 25-32 and 35-36 each merely add further details of the abstract steps recited in claim 24 without including an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. These claims "add nothing of practical significance to the underlying idea," and thus do not transform the claimed abstract idea into patentable subject matter. Ultramercial, 772 F.3d at 716. Therefore, dependent claims 25-32 and 35-36 are also directed to non-statutory subject matter.
Because Applicant’s apparatus claims 1, 4-6, 9-12, 14-17, 20-23 add nothing of substance to the underlying abstract idea, they too are patent ineligi-ble under §101
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 9-10, 14-17, 20-32 and 35-35 are rejected under 35 U.S.C. 103 as being unpatentable over Bowie et al. (US 2018/0261319 A1) (IDS of 02/12/2024) in view of Gravenor et al. (WO 2018/112185 A1) (IDS of 02/12/2024).
Claims 9 and 24. Bowie et al. (Bowie) teaches a computer implemented coverage determination system for determining a coverage plan for a healthcare facility, comprising a processor, a non-transitory memory Figs. 2 and 9; [0059] having instructions configuring the processor to:
train a forecasting model with historical patient volume data as an input and correlating the historical patient volume data with the healthcare facility, wherein the trained forecasting model generates patient volume forecast data in response to the patient volume data, wherein the forecasting model includes a plurality of hyperparameters Figs. 2, 5 and 8; [0017]; [0024]; [0044]-[0046]; [0049]-[0050], and the patient volume data covers a patient volume over a first selected period of time, Fig. 3; [0045]
tuning one or more of the plurality of hyperparameters by applying thereto a tuning technique, wherein the tuning unit generates in response tuned patient volume forecast data, [0058]
generating with a simulation engine simulation data in response to the tuned patient volume forecast data and acuity level data, wherein the simulation data includes the patient volume over a second period of time [0018]; [0026]; [0044], wherein the simulation data includes a visual representation of a staffing coverage requirement of one or more physical locations of the healthcare facility, Figs. 4-7
generating a coverage plan in response to and based on the simulation data, coverage data received from a data storage unit, and coverage rules data from a coverage rules unit. [0047]; [0058];
generating, based on the coverage plan, one or more coverage recommendations based on coverage needs and costs per shift for the healthcare facility, [0030]; [0047] -[0049]; [0054]; [0058]; and
generating one or more user interfaces suitable for displaying the coverage plan on a display device. Figs. 4-7
While Bowie does not explicitly teach: wherein the second period of time is shorter than the first period of time, Bowie discloses “Secondary independent variables may include (but are not limited to) day of week, month of year, seasons and seasonal factors such as holidays and cultural events, staff vacations, nurse sick calls, demographic trends (e.g., regional population and age trends), and seasonal disease intensity (e.g., influenza outbreak level).” [0017], thereby at least suggesting the recited limitation.
Further, Gravenor et al. (Gravenor) discloses the recited limitations at [0034]. (forecasting for a week and for next 24 hours).
Gravenor also teaches:
training a forecasting model with historical patient volume data as an input and correlating the historical patient volume data with the healthcare facility, wherein the trained forecasting model generates patient volume forecast data in response to the patient volume data, wherein the forecasting model includes a plurality of hyperparameters and the patient volume data covers a patient volume over a first selected period of time [0020]-[0027]
tuning one or more of the plurality of hyperparameters by applying thereto a tuning technique, wherein the tuning unit generates in response tuned patient volume forecast data [0028];
generating with a simulation engine simulation data in response to the tuned patient volume forecast data and acuity level data, wherein the simulation data includes the patient volume over a second period of time, wherein the second period of time is shorter than the first period of time [0030]
generating a coverage plan in response to and based on the simulation data, coverage data received from a data storage unit, and coverage rules data from a coverage rules unit. [0033]
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Bowie to include the recited limitations, as suggested in Bowie and disclosed in Gravenor, for the benefit of adjusting the forecasting based on the one or more patterns and/or abnormalities, as specifically stated in Gravenor.
Claim 10. The computer implemented system of claim 9, wherein the learning forecasting model comprises Facebook Prophet or ARIMA. Bowie; [0019]. Same rationale as applied to claim 9.
Claim 14. The computer implemented system of claim 9, wherein the first period of time is 60 days and the second period of time is 30 minutes. Same rationale as applied to claim 9.
Claims 15 and 28. The computer implemented system of claim 12, wherein the coverage data comprises one or more of default schedule data, contract staffing requirement data, relative value unit capacity data, service provider type data, cost by provider type data, additional acuity level data, predefined staffing requirements based on the acuity level, contract staffing rules data, and patient treatment related data. Same rationale as applied to claims 9 and 24.
Claims 16 and 29. The computer implemented system of claim 15, wherein the coverage rules data comprises one or more of a length of a coverage shift, one or more time period limitations on the shift, and a predetermined number of shifts per healthcare facility. Same rationale as applied to claims 1, 9 and 24.
Claims 17 and 30. The computer implemented system of claim 16, wherein the coverage plan matches staff coverage needs in one or more selected time increments for a selected period of time with the healthcare facility. Same rationale as applied to claims 1, 9 and 24.
Claim 20. The computer implemented system of claim 17, wherein the user interface generator generates a first user interface having a first window configured for displaying a selected calendar view in a calendar format, wherein each day of the calendar view can display therein selected healthcare related data, including a number of staff scheduled to work each day as well as the expected patient volume for each day. Bowie discloses an interface for receiving or outputting data. Thus, a particular layout of information displayed on the GUI appears to be an obvious matter of design choice to address business specifics. Thus, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Bowie to include the recited limitations, to address specifics of the business.
Claim 21. The computer implemented system of claim 20, wherein the first window further comprises an optimization soft button that when actuated optimizes the healthcare related data displayed in the calendar view. Same rationale as applied to claim 20.
Claim 22. The computer implemented system of claim 21, wherein when a selected day in the calendar view is selected by a user, the user interface generator generates a second interface having a second window having a plurality of vertically stacked pane elements for displaying various types of information, wherein the plurality of vertically stacked pane elements includes an uppermost pane element that displays information associated with the coverage plan generated by the coverage determination unit, a second intermediate pane element for displaying selected staff coverage information associated with the coverage plan and RVU information, and a third lowermost pane element for displaying staffing information associated with the coverage plan. Same rationale as applied to claim 20.
Claim 23. The computer implemented system of claim 22, wherein the user interface generator generates a third user interface having a third window for displaying a forecast accuracy dashboard associating an accuracy value associated the patient volume or the staff coverage of the coverage plan for one or more days. Same rationale as applied to claim 20.
Claim 25. The computer implemented system of claim 24, wherein the simulation engine simulates the coverage plan based on the tuned patient volume forecast data, the acuity level data, and lay- out data of the healthcare facility. Bowie; [0018]; [0026]; [0044]; [0058]
Claim 26. The computer implemented method of claim 25, further comprising simulating, with the simulation engine, the coverage plan based on the tuned patient volume forecast data, the acuity level data, and lay-out data of the healthcare facility. Same rationale as applied to claim 24.
Claim 27. The computer implemented system of claim 26, wherein the simulation data includes a visual representation of a coverage requirement of one or more portions of the healthcare facility. Presenting simulations results on a GUI. Same rationale as applied to claim 24.
Claim 31. The computer implemented system of claim 30, wherein the coverage plan further includes one or more coverage recommendations based on coverage needs and costs per shift for the healthcare facility. Same rationale as applied to claim 24.
Claim 32. The computer implemented system of claim 31, further comprising a user interface generator for generating in response to the coverage plan one or more user interfaces for displaying the coverage plan. Same rationale as applied to claim 24.
Claim 35. The computer-implemented method of claim 24, wherein the coverage plan has a patient volume forecast accuracy associated therewith, further comprising triggering the electronic device to automatically reset one or more of the plurality of hyperparameters of the forecasting model when an actual patient volume differs from the patient volume forecast accuracy. Bowie discloses that the forecast model resulted with more accurate scheduling predictions [0050]; [0052]; [0057], and that said modeling techniques react to volume metrics, e.g. patient volumes, [0027], thereby suggesting the recited limitations. Same rationale as applied to claim 24.
Claim 36. The computer-implemented method of claim 24, further comprising automatically importing the coverage plan into a scheduling system of the healthcare facility. Bowie; [0042]
Claims 1, 4-6, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Bowie in view of Gravenor and further in view of Thomas et al. (US 2021/0295987 A1) (IDS of 02/12/2024).
Claim 1. Bowie discloses a computer implemented coverage determination system for determining a coverage plan for a healthcare facility, comprising a processor, a non-transitory memory Figs. 2 and 9; [0059] having instructions configuring the processor to:
train a forecasting model with historical patient volume data correlated with the healthcare facility to form a trained forecasting model, wherein the trained forecasting model generates patient volume forecast data in response to input patient volume data, wherein the forecasting model includes a plurality of hyperparameters Figs. 2, 5 and 8; [0017]; [0024]; [0044]-[0046]; [0049]-[0050], and the input patient volume data covers a patient volume over a first selected period of time, Fig. 3; [0045]
tune one or more of the plurality of hyperparameters of the trained forecasting model by applying thereto a tuning technique, wherein the tuning unit generates in response tuned patient volume forecast data, [0058]
generate, with a simulation engine, simulation data in response to the tuned patient volume forecast data and acuity level data, wherein the simulation data includes the patient volume over a second period of time [0018]; [0026]; [0044], wherein the simulation data includes a visual representation of a staffing coverage requirement of one or more physical locations of the healthcare facility, Figs. 4-7
automatically generate a coverage plan in response to and based on the simulation data indicative of the coverage needs for the healthcare facility, coverage data received from a data storage unit, and coverage rules data from a coverage rules unit, wherein the simulation engine simulates the coverage plan based on the tuned patient volume forecast data, the acuity level data, and a lay-out of the healthcare facility, [0047]; [0058]
automatically generate based on the coverage plan one or more coverage recommendations based on coverage needs and costs per shift for the healthcare facility, [0030]; [0047]-[0049]; [0054]; [0058]; and
generate a user interface suitable for displaying the coverage plan on a display device. Figs. 4-7
While Bowie does not explicitly teach: wherein the second period of time is shorter than the first period of time,