Prosecution Insights
Last updated: April 19, 2026
Application No. 17/627,137

MODEL TO DYNAMICALLY PREDICT PATIENT'S DISCHARGE READINESS IN GENERAL WARD

Final Rejection §101
Filed
Jan 14, 2022
Examiner
MACASIANO, MARILYN G
Art Unit
3622
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Koninklijke Philips N V
OA Round
4 (Final)
57%
Grant Probability
Moderate
5-6
OA Rounds
3y 5m
To Grant
74%
With Interview

Examiner Intelligence

Grants 57% of resolved cases
57%
Career Allow Rate
313 granted / 549 resolved
+5.0% vs TC avg
Strong +17% interview lift
Without
With
+17.3%
Interview Lift
resolved cases with interview
Typical timeline
3y 5m
Avg Prosecution
41 currently pending
Career history
590
Total Applications
across all art units

Statute-Specific Performance

§101
38.3%
-1.7% vs TC avg
§103
31.6%
-8.4% vs TC avg
§102
15.8%
-24.2% vs TC avg
§112
4.5%
-35.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 549 resolved cases

Office Action

§101
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 . Status of Claims This Office Action is in response to the communication filed on 09/03/2025. Claims 1, 6, 9, 11-13, 16 and 19 have been amended. Claims 7 and 17 have been previously cancelled. 5. Claims 1-6, 8-16 and 18-20 are currently pending and are considered below. Claim Rejections - 35 USC § 101 6. 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. 7. Claims 1-6, 8-16 and 18-20 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. Claims 1 and 11 (a method and a non-transitory machine-readable storage medium) recites calculating, transition score (TS) values of a patient based on a determination of stability of the patient using patient vital sign information comprising one or more of blood pressure, heart rate, respiration rate, temperature peripheral capillary oxygen saturation (SpO2) and age; computing a current TS lower bound value based on a set of the TS values in a TS time window; determining if a length of stay of the patient is greater than a first time window, greater than an expected length of stay, and greater than a lower evaluation window; determining if the current TS lower bound value is less than a lower threshold; and producing an indication to evaluate the patient for discharge from the general ward if determined that the length of stay of the patient is greater than the first time window, greater than the expected length of stay, and greater than the lower evaluation window and the current TS lower bound value is less than the lower threshold, wherein a machine learning technique predicts, based on data of the patient, an optimal value for at least one the first time window, the lower evaluation window, or the lower threshold for determining patient discharge readiness. These recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity. 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. This judicial exception is not integrated into a practical application because the claims at best would use a processor performing the calculating, a transition score (TS) values of a patient, computing a current TS lower bound value, determining if a length of stay of the patient, determining if the current TS lower bound value is less than a lower threshold; and producing an indication to evaluate the patient for discharge, and predicting an optimal value for at least one of the first time window as such the use of a processor and networks are recited at a high level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts to no more than mere instructions to apply the exception using a generic computer component-MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic device. 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 in practicing the abstract idea. The claims are directed to an abstract idea. Viewed as a whole the claims do not include additional elements that are efficient to amount to significantly more than the judicial exception because as discussed above, the additional elements of producing no recommendation regarding patient discharge amounts to no more than mere instructions to apply the exception using a generic computer component. For the same reason these elements are not sufficient to provide an inventive concept. For these reasons, there is no inventive concepts in the claims and thus the claims are not patent eligible. As for dependent claims 2-6, 8-10, 12-16 and 18-20, these claims recite limitations that further define the same abstract idea noted in claims 1 and 11. In addition, they recite the additional elements of determining the length of stay of the patient amounts to no more than mere instructions to apply the exception using a generic computer component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible. Response to Arguments 8. Applicant's arguments filed 09/03/2025 with respect to the rejection of claims 1-6, 8-16 and 18-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues that the “…Here, Applicant asserts that the claims as a whole are not directed to the abstract idea of certain methods of organizing human activity. 1. Mental Processes and Mathematical Concepts Although the Patent Office fails to officially assert that the claims are directed to the abstract ideas of a mental process or a mathematical concept, the Patent Office makes a fleeting reference to these categories of abstract ideas. Specifically, in the “Response to Arguments” section of the rejection, the Patent Office asserts that (emphasis added): Additionally, the step can also be a mental process, since these are identifying patients for discharge from a general ward in a hospital, can be performed by a human mind. The producing an indication to evaluate the patient for discharge and predicting an optimal value can be extra solutions activity and an additional element that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. Notably, whether “a step” is or comprises a mental process is not the analysis under 35 U.S.C. 101; rather, the analysis is whether the claim as a whole is directed to the abstract idea of a mental process. Here, the claim as a whole is directed to an improvement in a method for identifying patients for discharge from a general ward in a hospital, not to a mental process. Indeed, there is no such assertion by the Patent Office in the entirety of the current rejection. Accordingly, Applicant simply notes that the claims are not directed to the abstract idea of a mental process. The Patent Office further asserts in the “Response to Arguments” section of the rejection that (emphasis added): The other limitation of claim 1 involve a machine learning technique predicts an optimal value. The specification recites “...use of a number of possible machine- learning algorithms including as examples decision trees, random forests support vector machines, neural networks, and recurrent neural networks.” Spec. paragraph 0036. These techniques involve mathematical relationships, formulas/equations, or calculations. October 2019 Update: Subject Matter Eligibility 3-4. Thus, the Examiner correctly determined that a model trained by a machine learning involves mathematical concepts. The Rejection of claims 1-6, 8-16 and 18-20 under 35 U.S.C. 101 have been maintained by the Examiner…” Remarks pages 7-15 9. The examiner notes that the elements “…calculating a current TS…; and computing a current TS…”is part of the data gathering element of the abstract idea. Additionally, the determination steps can be performed by the human mind which falls under mental process. The elements of performing the calculating, the computing and the producing can fall under certain methods of organizing human activity specifically interaction between people since MPEP recites “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (Such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping” MPEP 2106.04(a)(2). Furthermore, the claims in Example 39 is directed to “creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non- facial images; training the neural network in a first stage using the first training set; creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and training the neural network in a second stage using the second training set”. The instant claim predicts patient discharge. The computer has not been improved rather it uses the generic computer and a high level machine learning model to execute the abstract idea. Finally the claims in claim 3Of Example 47 is directed to detecting anomalies in network traffic, detecting anomaly with network packets, then detecting a source address, then dropping the malicious network packets in real time and blocking future traffic from the source addresses therefore enhancing security by acting in real time to proactively prevent network intrusions. The instant claim predicts, based on data of the patient, an optimal value for at least one of the first time window, the lower evaluation window, and the lower threshold for determining patient discharge readiness. 10. Applicant argues that “…The claims comprise a practical application at least because the invention applies and uses the alleged abstract idea in a particular technological environment. The claims recite a combination of elements encompassing a method for identifying patients for discharge from a general ward in a hospital. The elements of the independent claims, for example, when viewed as a whole integrate the alleged abstract idea into a practical application by sufficiently limiting the use of the alleged abstract idea to the very specific system. These novel and non-obvious claims are not just a drafting effort designed to monopolize all methods or systems for identifying patients for discharge from a general ward in a hospital. Accordingly, the claims do indeed incorporate any judicial exception into a practical application…” Remarks pages 15-17 11. Examiner notes that the judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including a processor, a machine learning model and a non-transitory machine-readable storage medium which are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f)” and “The claim recites the additional element of a machine learning and a processor configured to calculating a current TS…; computing a current TS…; predicting patient discharge” is part of the data gathering element of the abstract idea; which amounts to extra- solution activity concerning mere data gathering. The specification 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).” 12. Applicant's arguments filed 09/03/2025 with respect to the rejection of claims 1-6, 8-16 and 18-20 under 35 U.S.C. 102/103(a) have been fully considered and they are persuasive. Therefore, the rejection has been withdrawn. Conclusion 13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. 14. Alden (U.S. Pub. No. 2011/0071851) discloses Alden teaches the clinical process driver 15 may provide and the graphical display of the user station 16 may include a care progression timeline 50. As shown, the care progression timeline depicts significant points in time, such as a preadmission visit or screening of the patient (designated "Pre"), the admission of the patient (designated "Pre-op" or "Admit"), the surgery (designated "S"), the current time, and/or a forecast of the discharge date based upon the care progression plan and the current status of the plurality of progression steps 42 (see at least paragraphs 0057, 0061 and 0081). 15. Lancaster et al. (U.S. Pub. No. 2007/0150307) discloses about with reference to FIG. 5, a screen 500 displaying an exemplary case manager work list is shown. The case manager work list includes the patient's name 502, the actual length of stay for the patient 504 and the predicted length of stay 506 for the patient. Exemplary patient Jane Doe has an actual length of stay of two days while her predicted length of stay calculated was three days. The actual length of stay and predicted length of stay can be displayed to a variety of users including care providers such as physicians, nurses, bed management teams, discharge planners, and case managers (see at least paragraph 0031). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Alden to modify to include the teaching of Lancaster in order for case managers to view which patients may be ready for discharge and/or determine which patients may require an extended stay for which utilization review is needed (see at least paragraph 0032). 16. Ong et al. (U.S. Pub. No. 2014/0257122) discloses the triage system 100 utilizes physiological and cardiac data measurements, compiled with medical status information, and processes such inputs within an intelligent machine-learning scoring system which compares the present input to correlated past patient diagnoses, in order to provide an insightful risk score as to the risk of ACS in the patient (see at least paragraphs 0089, 0144, 0148 and 0153). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Alden to modify to include the teaching of Ong in order to simulate the process of real-world decision making (see at least paragraph 0157). 17. Hettig et al. (U.S. Pub. No. 2020/0066415) talks about data from the physiological monitor may include one or more of the following: heart rate data, electrocardiograph (EKG) data, respiration rate data, patient temperature data, pulse oximetry data, and blood pressure data. The system of the first aspect may be configured such that the first score may be at or near a maximum value if the following criteria exist: i) the patient's temperature is greater than about 38.3° Celsius (C) (about 101° Fahrenheit (F)) or less than about 35.6° C. (about 96° F.), ii) the patient's heart rate is greater than 90 beats per minute; and iii) the patient's respiration rate is greater than 20 respirations per minute (see at least paragraphs 0011, 0021 and 0036). 18. Edelson et al. (U.S. Patent No. 11,410,777) talks about evaluating a patient's cardiac risk and/or mental status. Cardiac risk evaluation may be based (e.g., only) on the patient's respiratory rate, heart rate, diastolic blood pressure, age, and/or mental status. An aggregate score, which is indicative likelihood of the patient's cardiac risk, may be calculated based (e.g., only) on the patient's respiratory rate, heart rate, diastolic blood pressure, age, and/or mental status. If the calculated aggregate score exceeds a predetermined threshold, the patient may be identified as having a critical cardiac risk, and actions may be taken to treat the patient. The cardiac risk evaluation may be based further on the patient's mental status, where the patient's mental status may be evaluated based on a game with visual indicators (see at least the Abstract). 19. Nikolova-Simons et al. (U.S. Pub. No. 2013/0325515) discloses evaluating a patient record including patient data parameters of a patient, predicting a change in the patient record for all possible treatment options, generating a discharge recommendation based on at least one of the patient record and the predicted change in the patient record and displaying the discharge recommendation to a user (see at least paragraph 0002). 20. THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action. 21. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, llana Spar can be reached on 571)270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 12/11/2025
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Prosecution Timeline

Jan 14, 2022
Application Filed
Mar 23, 2024
Non-Final Rejection — §101
Sep 24, 2024
Response Filed
Jan 05, 2025
Final Rejection — §101
Apr 07, 2025
Request for Continued Examination
Apr 08, 2025
Response after Non-Final Action
May 30, 2025
Non-Final Rejection — §101
Aug 21, 2025
Interview Requested
Sep 03, 2025
Response Filed
Dec 11, 2025
Final Rejection — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

5-6
Expected OA Rounds
57%
Grant Probability
74%
With Interview (+17.3%)
3y 5m
Median Time to Grant
High
PTA Risk
Based on 549 resolved cases by this examiner. Grant probability derived from career allow rate.

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