DETAILED ACTION
This office action is based on the claim set filed on 10/06/2025.
Claims 1, 5, 12, 19, 22, 24, 26, 28, and 30, have been amended.
Claims 3-4, 6-11, 14-18, 21, 25, 29, and 32, have been canceled.
Claims 1-2, 5, 12-13, 19-20, 22-24, 26-28, 30-31, and 33, are currently pending and have been examined.
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 .
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 1-2, 5, 12-13, 19-20, 22-24, 26-28, 30-31, and 33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-2, 5, 26-28, and 33 are drawn to a system, Claim 12-13 and 30-31 are drawn to a method, and Claim 19-20 and 22-24 are drawn to an art of manufacture, which are within the four statutory categories (i.e., a machine and a process). Claims 1-2, 5, 12-13, 19-20, 22-24, 26-28, 30-31, and 33 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the claimed invention represents an abstract idea of a series of steps that recite a process for applying clinical events to determine a patient length of stay and predict expected time until discharge. This abstract idea could have been performed mentally but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for steps citing a process directed to collecting, grouping, classifying, patient care journey to predicate an outcome which both the instant claims and the abstract idea are defined as Mental Process.
Independent claim 1, and similarly claims 12, 19 steps, recites the step of:
“a processor; and a memory that stores executable instructions, that when executed by the processor, facilitate performance of operations, comprising:
generating training data from a training data pool, the training data pool comprising longitudinal journey data for patients that were discharged from an inpatient medical facility over a past period of time, wherein the longitudinal journey data tracks their care from time of admittance to the inpatient medical facility to time of discharge from the inpatient medical facility, and wherein the generating comprises:
clustering patients represented in the training data pool as a function of their length of stay (LOS) at the inpatient medical facility;
for each day of the past period of time:
identifying a first subset of the patients that were admitted at the inpatient medical facility on the day based on their LOS;
randomly selecting a second subset of the patients from the first subset as a function of a defined percentage of the first subset;
generating training samples for respective patients of the second subset, comprising, for each patient of the second subset, generating a training sample comprising features included in the longitudinal journey data for the patient tracked up to the day;
aggregating training data samples generated for all days of the period of time
training a LOS forecasting model using the training data samples and a supervised machine learning process to predict an expected duration of time until discharge of the patients from the inpatient medical facility, resulting in generation of a trained version of the LOS model using less training time relative to another trained version of the LOS model trained using the supervised machine learning process and an entirety of the training data pool
applying the trained version of the LOS forecasting model to new data samples for new patients at the inpatient medical facility, the new data samples respectively comprising the longitudinal patient journey data for the new patients;
generating output data indicating expected durations of time until discharge of the new patients from the inpatient medical facility as a result of the applying;
rendering the output data via a graphical user interface
receiving feedback information via the graphical user interface identifying patient needs and barriers associated with placing the new patients at respective discharge destinations of the new patients
retraining the LOS forecasting model based on the output data and the feedback information to generate an updated version of the LOS forecasting model with improved accuracy
applying the updated version of the LOS forecasting model to the new data samples to predict updated output data for corresponding ones of the new patients, the updated output data comprising updated expected durations of time until discharge of the corresponding ones of the new patients”
These limitations, as drafted, given the broadest reasonable interpretation, cover performance of the limitations in the mind that constitute Mental Processes, but for the recitation of generic computer components. This abstract idea could have been performed by a human mind with the aid of pencil and paper but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for steps citing a process directed to predicting length of stay through collecting patients care journey such as patient events from admission to discharge and cluster the patient based on length of stay (LOS) to generate a training data to apply on a LOS forecasting model to predict an expected duration of time until discharge, which are steps that could be performed mentally that are similar to the steps of observing, evaluating, judgment and opinion which are citing a process for which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment (i.e., as implemented by one or more hardware processors configured to execute specific instructions stored in the memory). In particular, the claims recite the additional elements such as “system, processor, memory, non-transitory computer-readable storage medium, supervised machine learning” that is/are disclosed at a high - level of generality and includes known hardware components that implements the identified abstract idea, (see, Applicant, para 23). This recitation of additional elements, “e.g., processor, components, supervised machine learning, etc.” to perform the noted steps, (e.g., training/configuring and reconfiguring/retraining/updating a model using the training/configuration data samples and a supervised machine learning process), that is/are merely implemented as a tool such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea because the steps reciting additional elements that are mere data implemented using a general purpose computing components being used in ordinary capacity to perform the steps such that causing the computer system to perform the instructions, see MPEP 2106.05(f) and mere data gathering and outputting process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). For example, the supervised machine learning is recited in the claims in a high level of generality and is described in the specification in an arbitrary form without disclosing how a specific algorithm using available data for allowing the model to learn patterns and relationships within the data and implement it to perform the claimed function, see (Applicant, [0065]). Moreover, the training process in the specification is described as configuration and reconfiguration/updating a model based on a user, facility, department, institute, etc., feedback, see at least (Applicant, [0109], [0139]). Accordingly, looking at the claims as a whole, individually and in combination, these additional elements provide no integration of the abstract ideas into a practical application because they appear to merely automate a manual process, such that no meaningful limits on practicing the abstract idea are introduced and the computing elements are merely utilized as tools to perform the abstract ideas. The claim as a whole is therefore directed to an abstract idea.
Under step 2B, The claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because, as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f), and mere data gathering that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). 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 and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2, 5, 13, 20, 22-24, 26-28, 30-31 and 33, include all of the limitations of claim(s) 1, 12, and 19, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as:
As for claims 5, 22, 24, 26, 28, 33, the claim(s) recites limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 2, 13, 20, 23, 27, 30-31, the claim(s) recites limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept. This judicial exception is not integrated into a practical application. In particular, the claim(s) recite additional elements such as “supervised machine learning, system” recited in the claim(s) at a high level to perform the claims steps (e.g., train[ing], apply[ing] by the system). These additional elements have been interpreted to be computing components with a general - purpose processor, that it amounts to no more than mere instructions to perform the steps of the claim(s), such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f), that is merely uses the computer as a tool to perform the abstract idea, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). Thus, the judicial exceptions recited in claims is/are 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. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Response to Amendment
Applicant's arguments filed 10/06/2025 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 11-17.
On page 13 of the remarks, the Applicant reciting Example 37, claim 2, and argues “Likewise, the subject claims recites "training a LOS forecasting model using the training data samples and a supervised machine learning process ...,” This training process cannot be practically applied or performed as a mental process in the mind, as it requires using a supervised machine learning process, which is a process that requires performance by a machine ... Therefore, the claims are not directed to a judicial exception and are eligible under 2A, Prong 1”, Examiner respectfully disagree. As explained in the Examiner response to argument mailed on 07/07/2025 and as per the above rejection, the claim(s), under BRI, recite a process for creating patients care data over the care time frame and forecasting LOS and time expected for discharge based on the process of grouping patients LOS and identify patients based on admission and the percentage of patients admitted each day and using the data to predict LOS which are reciting an abstract idea which recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions, see MPEP § 2106.04(a)(2)(III) and Electric Power Group v. Alstom., S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
Moreover, the claims are not similar to Example 37, claim 2, where the claim recite a process for monitoring usage for each icon and allocating the amount of usage for each icon in the computing memory and based on recognizing these amounts, the processor automatically arrange/re-arrange icons on a display according to frequency of use which integrate the claim(s) into a practical application providing an improved user interface for electronic devices. Unlike the instant claims of the present application reciting, under BRI, a judicial exception for a process that enable a user to perform collection of patients data during a period of care and estimate discharge time frame therefore recite an abstract idea but for the use of computing components recited at high level of generality and as a tool to configure a model. “Claims can recite a mental process even if they are claimed as being performed on a computer”, see MPEP 2106.04(a)(2)(III)(C). Therefore, the instant claim is not analogues to Example 37, claim 2.
On page 14 of the remarks, the Applicant argues “The August 4, 2025 USPTO Memorandum from Deputy Commissioner Charles Kim (hereinafter "Memo") reminds examiners that the "mental process" grouping is not without limits and should not be expanded to encompass claim limitations that cannot practically be performed in the human mind. The Memo further emphasizes that limitations involving artificial intelligence and machine learning which inherently require computer implementation fall outside the scope of mental processes. As amended, claim 1 recites concrete steps that are not performable in the human mind, including: clustering patient ..., generating training subsets ..., applying a supervised machine learning process to generate a trained LOS forecasting model, rendering output data via a graphical user interface, receiving user feedback identifying patient needs and discharge barriers, and retraining the forecasting model based on that feedback to produce updated discharge predictions. These steps necessarily require computer processing, data storage, and iterative machine learning operations. No human could perform these operations mentally, as they involve processing vast longitudinal patient datasets, running a supervised training pipeline, and dynamically retraining forecasting models”, Examiner respectfully disagree. First, the “memo” as referred to, discussed the “2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence” where the guidance section (II) “Overview of the USPTO's Patent Subject Matter Eligibility Guidance” recites “The USPTO's subject matter eligibility guidance is found in MPEP sections 2103-2106.07(c) and is used to analyze claims across all technologies, including Al inventions, which are generally considered to be computer-implemented inventions”, where the claims are analyzed under Step 2A, Prong One to determine if a judicial exception is recited which the current claims (YES), then the claims should be evaluated Step 2A. Prong Two analysis an evaluation of the judicial considerations identified in MPEP 2106.04(d), subsection I; 2106.04(d)(1 ); 2106.04(d) (2); and 2106.0S(a)-(c) and (e)-(h), such as whether the additional element(s) is(are) insignificant extra-solution activity. Second, while a human with the aid of pen and paper can perform the function of collecting and clustering patient data, generating training subsets from the collected patient data, and create/generate a LOS forecasting model and present (render) it and collected/receive a feedback to update the generated forecasting or predication model while the current claims recite, under BRI, additional elements, i.e., “processor, memory, non-transitory computer-readable storage medium, supervised machine learning”, to perform the steps of training, applying, and rendering the data using a computing components such as configuring a computing component to perform functions such as "forecasting or estimating", “rendering data on a display” through leveraging computing technology in a well understood manner of providing an input and receiving output that amounts to no more than mere instructions using a generic computer component and no more than adding the words "apply it" and is not configured in a manner other than what any off-the-shelf, commercially available processor is capable of being programmed to do which is a tool applying the steps of the claim.
On page 14-15 of the remarks, the Applicant argues “the claims recite a
computer-implemented LOS forecasting system that applies learned models to clinical data,
renders forecasts in a GUI, and incorporates human feedback to retrain models. Such limitations
are more akin to Example 39 of the USPTO's subject matter eligibility examples, which explains that "training a neural network in a first stage using a first training set" does not recite a judicial exception even though it involves machine learning”, Examiner respectfully disagree. Example 39 describes a process of training a neural network using a modified first training set to output a second training set and using the second training set to train the neural network and classify an input based upon a previous training process. Example 39 discloses expanded training set is developed by applying transformation functions on an acquired set of facial images and these transformations can include affine transformations to detect faces in distorted images while limiting the number of false positives. The system is retrained with an updated or second training set containing the false positives produced after face detection has been performed on a set of non-facial images. In contrast, the claim as amended provides no description of how a machine learning model is trained to provide an improved neural network, supervised machine learning in the preset claims, but only disclosing the combination of collecting and clustering patient data, generating training subsets from the collected patient data, and create/generate a LOS forecasting model and present (render) it and collected/receive feedback to update the generated forecasting or predication model.
While it is true that the claims recite a applying the data to a machine learning for carrying out the outcome function, the machine learning is merely invoked as a tool to implement the steps of the claim. As explained above that applying a computer implemented process using machine learning process to provide an output which is a mere mental process and involve mathematical calculation that can be performed by human actor as in Electric Power Group v. Alstom and in Benson. see MPEP 2106.04(a)(2)(III)(c). In fact, the “memo” discussed the AI example in the “July 2024 Subject Matter Eligibility Examples” where Example 47, claim 2, recite using an artificial neural network (ANN) performing the steps of receiving, discretizing to generate training data, using the training data to train the ANN, analyzing, and outputting which are similar steps recited in current application instant claim(s) as such Example 47, claim 2 was found in ineligible.
On page 15-16 of the remarks, the Applicant argues “Furthermore, even if the claims could be considered to be directed to a judicial exception, they recited additional elements which in combination, integrate the claim as a whole into a practical application... The claimed invention solves this problem by introducing a feedback-integrated discharge planning system wherein the LOS forecast is displayed in a graphical interface.”, Examiner respectfully disagree. As described above, the claim does not describe a particular improvement of computer’s functionality or a technical field, rather using additional elements, “e.g., supervised machine learning”, to perform the steps abstract idea such as collecting and grouping data through leveraging computing technology in a well understood manner however improving upon an abstract idea does not make the abstract idea any less abstract. In light of the Alice decision and the guidance provided in the 2019 PEG, the features listed in the claims, are not considered an improvement to another technology or technical field, or an improvement to the functioning of the computer itself rather describes an improvement to patient LOS forecasting and discharge planning which is solving a health facility an administrative problem, using computers, for determining patient LOS and discharge. The alleged benefits that Applicants tout such as are due to predicting patient discharge, using computers, rather than any improvement to another technology or technical field, or an improvement to the functioning of the computer itself. In addition, by relying on computing devices to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 "use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept).
On page 16-17 of the remarks, the Applicant argues “Such an approach constitutes significantly more than the application of an abstract mathematical model to patient data, as it represents an improvement to the functioning of a computer-based forecasting system itself. Similar to the patent-eligible technologies in McRO v. Bandai and CardioNet v. InfoBionic, which provided concrete improvements in how computers generated and processed information in specific domains, the claimed invention improves the technical field of healthcare IT by enabling a structured, feedback-integrated loop that adaptively generates LOS predictions in a distributed hospital computing environment”, Examiner respectfully disagree. Examiner finds that there is no evidence in Applicant's as-filed disclosure that Applicant's claimed invention is performing functions that previously only humans could perform as in McRO. The Examiner notes that in McRO the as-filed disclosure explicitly described that computers could not previously be programmed to perform the particular type of animation described and that only human animators were previously capable of such animation. Because the claimed invention solved this particular problem, the court found that the claimed invention was an improvement to computer technology. There is no such problem described in Applicant's disclosure.
In addition, the claim(s) in CardioNet focuses on specific means and/or methods that improve cardiac monitoring such as detecting an atrial fibrillation in light of cardiac beat to beat variability that avoids false positives and false negatives and identify sustained episodes that increased clinical significance. In contrast, the instant invention limitations disclose collecting patient care data and analyzing the clinical LOS to determine and estimate discharge using model(s) using a supervised machine learning recited as generic computing component(s). Thus, the instant claim does not provide improvement to a computing system or a technical field.
On page 17 of the remarks, the Applicant argues “Finally, even if the claims were found to recite an abstract idea (which Applicant does not concede), the claims as a whole recite significantly more under Step 2B”, Examiner respectfully disagree. As mentioned above, predicting patient discharge, using computers, do not indicate any improvement to another technology or technical field, or an improvement to the functioning of the computer itself and while relying on computing devices to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 "use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept). As discussed in the rejection above, the components of the instant system, when taken alone, each execute in a manner conventionally expected of these components. At best, Applicant has claimed features that may improve an abstract idea. However, an improved abstract idea is still abstract, (SAP America v. Investpic *2-3 ("'We may assume that the techniques claimed are "groundbreaking, innovative, or even brilliant," but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89-90 (2012).
Therefore, the Examiner has addressed the Applicant argument(s) and found this argument is not found to be persuasive. Hence, Examiner remains the 101 rejections of claims which have been updated to address Applicant's amendments.
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 extension fee 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 date of this final action.
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687