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 .
DETAILED ACTION
This action is in reference to the communication filed on 5 DEC 2025.
Applicant has elected Group 1, claims 1-4, 7, 8, 10, 16-26, 29.
Claims 1-4, 7, 8, 10, 16-26, 29 are present and have been examined.
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, 7, 8, 10, 16-26, 29 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-4, 7, 8, 10, 16-26, 29 the independent claim(s) 1, 29 recite(s) a system and an apparatus, each of which is a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-4, 7, 8, 10, 16-26, 29 , the independent claim(s) (claims 1, 29) is/are directed, in part, to:
A system for generating an interactive dashboard
transmitting a patient ID to a management platform;
receiving, from the management platform, at least one
employing a machine learning model to generate an acuity score based on the patient data, the acuity score representing probability and level of a type of host response by the patient;
employing a machine learning model to generate one or more prognostic values based on the patient data, the prognostic value representing a probability of an adverse event;
determining a ranking of the at least one parameter according to an influence score associated with the at least one parameter; and
generating
an acuity indicator displaying the acuity score and an associated risk category;
at least one prognostic indicator displaying the at least one prognostic value and one or more risk categories associated with the at least one prognostic value; and
a list displaying the parameters according to the ranking.
These claim elements are considered to be abstract ideas because they are directed to a mental process, i.e. concepts performed in the human mind including observation, evaluation, judgement, and opinion. Receiving patient information, generating a acuity score representing probability and level of host response, generating a prognostic value, determining a ranking, are all examples of concepts such as observation, evaluation, and judgement. Similarly, generating a view of including these elements and these rankings is also a process of observation and evaluation.
The claims further recite examples of mathematical concepts, such as mathematical relationships, formulas, equations, or calculations. Employing machine learning models to generate acuity scores and prognostic values, as well as ranking these values based on an influence score, and displaying parameters based on a ranking are all examples of mathematical concepts as identified above.
If a claim limitation, under its broadest reasonable interpretation, concepts performed in the human mind and/or mathematical relationships/formulas/equations/calculations, then it falls within the “mental processes” and/or “mathematical concepts” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements: Claim 1 recites a “dashboard graphical user interface” for displaying indicators, as well as “one or more processors and one or more memory devices,” as well as the use of “electronic record” and a “client device” upon which the GUI is displayed. Claim 29 recites similar elements. Both claims generally recite the sending and receiving of information using a processor/computer. The “one or more processors and one or more memory devices,” as well as the “client device” in claims 1, 29 are recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Similarly, the term electronic records appears descriptive rather than functional, i.e. “apply it” rather than a meaningful limitation. Examiner notes that both displaying information and the sending/receiving of information between devices/platforms are generally found to be examples of adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g). Examiner finds no improvement to the functioning of the processors, memories, client devices, displays, data transmission, or any other technology or technical field in the above identified elements as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e).
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: Claim 1 recites a “dashboard graphical user interface” for displaying indicators, as well as “one or more processors and one or more memory devices,” as well as the use of “electronic record” and a “client device” upon which the GUI is displayed. Claim 29 recites similar elements. Both claims generally recite the sending and receiving of information using a processor/computer.
When considered individually, the identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0054] Servers 130 may include any device having an appropriate processor, memory, and communications capability for hosting the collection of images and a data pipeline engine. The data pipeline engine may be accessible by various client devices 110 over network 150. Client devices 110 can be, for example, desktop computers, mobile computers, tablet computers (e.g., including e-book readers), mobile devices (e.g., a smartphone or PDA), or any other devices having appropriate processor, memory, and communications capabilities for accessing the data pipeline engine on one of servers 130. In accordance with various embodiments, client devices 110 may be used by healthcare professionals such as physicians, nurses or paramedics, accessing the data pipeline engine on one of servers 130 in a real-time emergency situation (e.g., in a hospital, clinic, ambulance, or any other public or residential environment). In some embodiments, one or more users of client devices 110 (e.g., nurses, paramedics, physicians, and other healthcare professionals) may provide clinical data to the data pipeline engine in one or more server 130, via network 150.
[0056] Client device 110 and server 130 may include a memory 220-1 and 220-2 (hereinafter, collectively referred to as “memories 220”), and a processor 212-1 and 212-2 (hereinafter, collectively referred to as “processors 212”), respectively. Memories 220 may store instructions which, when executed by processors 212, cause either one of client device 110 or server 130 to perform one or more steps in methods as disclosed herein. Accordingly, processors 212 may be configured to execute instructions, such as instructions physically coded into processors 212, instructions received from software in memories 220, or a combination of both.
[0060] Client device 110 may access data pipeline engine 240 through an application 222 or a web browser installed in client device 110. Processor 212-1 may control the execution of application 222 in client device 110. In accordance with various embodiments, application 222 may include a user interface displayed for the user in an output device 216 of client device 110 (e.g., a graphical user interface, GUI). A user of client device 110 may use an input device 214 to enter input data as metrology information or to submit a query to data pipeline engine 240 via the user interface of application 222. In accordance with some embodiments, an input data may be sent to client devices in with an associated ranking of importance to enable validations and/or user review. Input device 214 may include a stylus, a mouse, a keyboard, a touch screen, a microphone, or any combination thereof. Output device 216 may also include a display, a headset, a speaker, an alarm or a siren, or any combination thereof.
[0141] Computer system 2300 (e.g., client device 110 and server 130) may include a bus 2308 or other communication mechanism for communicating information, and a processor 2302 (e.g., processors 212) coupled with bus 2308 for processing information. By way of example, the computer system 2300 may be implemented with one or more processors 2302. processor 2302 may be a general-purpose microprocessor, a microcontroller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable entity that can perform calculations or other manipulations of information.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-4, 7, 8, 10, 16-26:
Dependent claims 2-4, 7, 8, 10, 16-22, 24-26 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as the types of data or outcomes provided to the models, descriptive elements about the models themselves, additional means of calculating parameters/values, and additional elements for display on the GUI While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Dependent claim 23 does not recite any additional abstract ideas, however the claim does nominally recite the use of an API. Examiner finds that this is insufficient to amount to a finding of a practical application as it is at best adding the word “apply it” or using the API as a tool to execute the sending and receiving of information. No improvement to the function of the API nor any other technical area is found. As such this element is insufficient to amount to significantly more.
Non-Obvious Subject Matter
Claims 1-4, 7, 8, 10, 16-26, 29 are believed to be free from the prior art.
The closest prior art of record is believed to be:
US2022/0044809, to Bihorac et al, which discloses calculating an acuity score on a per patient basis, as well as a mortality prediction, and specifically teaches a machine learning algorithm to calculate either of these values per patient. The reference teaches existing baseline models to train one or more algorithms, as well as calculating these values on an interval and displaying the results to practitioners in a real time dashboard.
“DeepSofa: A continuous acuity score for critically ill patients using clinically interpretable deep learning” teaches similar methods.
US 20200043612, to McNair discloses the calculation of a risk score for a given patient, which may be combined with other predictive scores, and/or calibrated based on evolving factors or the clinicians evaluations. This calculation uses EHR of the patient, and can also include risk predictions for co-occurring conditions. This information may be displayed in a plurality of ways on a user interface for a clinician’s benefit.
US 20230207125, to Tgavalekos et al discloses a diagnosis specific acuity score for a patient, on a primary and secondary diagnosis for a given patient (i.e. more than one acuity score). The model itself is trained on prior outcomes of a diagnosis and as compared to a patient cohort, and includes a probability of a deterioration from the one or more diagnosis and or interventions, as determined by the respective acuity scores.
US 20210052217, to Zhao, teaches generating multiple acuity scores for a patient, using cohort baselines and a plurality of trained models, wherein the multiple acuity scores are calculated on a plurality of known scoring mechanisms such as Glasgow Coma Scale, Mortality Probability Model, and SOFA among others. These multiple acuity scores are calculated in real time based on the patient’s status, and periods of lower acuity scores (i.e. lower risk) are collected to reflect a baseline and to provide future insights if the patient’s score increases, as well as to inform the cohort model/training.
US 20200194124, to Kramer, discloses continually calculating and updating an acuity score for a patient, and updating a dashboard with the updated score. Kramer also teaches the use of a “trigger” in the sense of an averse event to the patient such as a change in a vital sign or other issue such as infection, and changing the display based on the presence and/or number of “triggers” noted.
“Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement” by Hernandez-Suarez et al teaches an application to prediction of mortality after a specific procedure, using an acuity parameter in the AI prediction of the patient’s outcome to the standard procedure as well as potential complications.
Examiner finds that the references generally teach calculating an acuity score for a patient based on the patient’s general health situation, related cohorts, and the use of a predictive model to do so and update the value in real time, wherein an acuity score represents the general condition of a patient and the likely outcome or prognosis based on one or more conditions or interfering factors. The references also generally teach a value representative of a mortality based on an acuity score. However, the references when taken separately or in combination do not teach the interleaving of the acuity score model and the prognostic value model as claimed, i.e. the result of one of the models used in the subsequent model, nor specifically calculating a separate prognostic value which predicts not only an outcome of an adverse event, but the actual probability of the event as combined with the acuity score as calculated and ranked in relation to one another as specifically required by the claims.
The Examiner hereby asserts that the totality of the evidence neither anticipates nor renders obvious the particular combination of elements as claimed. That is, the Examiner emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for combining or otherwise modifying the available prior art to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art because any combination of the evidence at hand to reach the combination of features as claimed would require a substantial reconstruction of Applicant' s claimed invention relying on improper hindsight bias.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at 571-270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687