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 .
Claims 1-21 are rejected. Claims 1-21 are pending.
Amendment
The amendment dated 12 January 2026 amends claims 1,16 and 21.
Priority
This application claims a priority date corresponding to the filing date of 01 June 2021.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: The Statutory Categories
Claims 1,16 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method for scalable modular architecture for clinician defined analytics, a non-transitory computer-readable medium storing executable instructions for clinician defined analytics, and a scalable modular architecture system for clinician defined analytics. All are within a statutory class for subject matter eligibility purposes.
Step 2A Prong One: The Abstract Idea
The limitations of (claim 1 being representative) a method for providing a scalable modular architecture for clinician defined analytics, comprising: measuring […] one or more clinical measurements of a patient, […]; aggregating […] the one or more clinical measurements; extracting […] clinical measurement vectors from the aggregated clinical measurements for one or more decision system; selecting […] one or more clinical measurements necessary for triggering one or more decision systems from a pool of incoming data; and performing computations […] to define an analytics engine per analytics specifications and operations […] by learning optimal decisions based on the outcomes of prior definitions and operations as drafted, is a process that, under the broadest reasonable interpretation covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting (claim 1) one or more sensors, devices, and a command center, (claim 16) a computer, a command center, sensor(s) and one or more devices and (claim 21) a scalable modular architecture system for clinician defined analytics and a command center the claimed invention amounts to managing personal behavior or interaction between people (i.e., a person following a series of rules or steps). For example, but for the various general-purpose computer elements, the claims encompass a person aggregating the one or more clinical measurements, extracting clinical measurement vectors from the aggregated clinical measurements for one or more decision system, selecting appropriate clinical measurements relevant to triggering one or more decision systems from a pool of incoming data; and performing computations to define an analytics engine per analytics specifications and operations in the manner identified in the abstract idea supra. The Examiner notes that “certain methods of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of (claim 1,16 and 21) the command center that implements the identified abstract idea. These additional elements are not exclusively described by the applicant and are recited at a high level of generality (i.e., a generic general-purpose computer or components thereof) such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, 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. The claims are directed to an abstract idea.
The claims further recite the additional elements of the sensors and one or more devices and/or one or more relay devices. Each of these additional elements are recited at a high level of generality (i.e., as a general means of transmitting data) and their action amounts to the mere collection of data and transmission of data, which is a form of extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Independent claim 1 and 16 also recite the following additional elements: “wherein the command center is organized in layers of hierarchy including organization, floors, theatres, and patients, and works in concert with modules including the one or more sensors of one or more devices, one or more relay devices and one or more electronic health records, wherein each module is categorized into the following levels of hierarchy as: engine, system, layer, process and tasks, and wherein each level of hierarchy will be a self-contained functionality in a microservices architecture with processing done in one or more distributed compute nodes with multiple redundancies…” and “…adding and releasing computing hardware and software for the patient or population of patients in modular fashion to ensure scalability.” Independent claim 21 also recites the following additional elements: “wherein the command center works in concert with modules including one or more sensors of one or more devices, one or more relay device and one or more electronic health records, wherein each module is categorized into the following levels of hierarchy as: engine, system, layer, process and tasks, wherein each level of hierarchy may be a self-contained functionality in a microservices architectures with processing done in one or more distributed compute nodes with multiple redundancies…” and “ adds and releases computing hardware and software for the patient or population of patients in modular fashion to ensure scalability.” MPEP 2106.05(f) indicates that a consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. These identified additional elements are no more than mere recitation of the words “apply it” (or an equivalent) and/or are instructions to implement an abstract idea or other exception on a computer and therefore cannot provide a practical application. Accordingly, even in combination, this additional element does 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: Significantly More
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 elements of using a general-purpose computer (or components thereof) to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the sensors and one or more devices. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. The prior art of record indicates that collecting of measurements of data via sensors is well-understood, routine, and conventional in the field. See US 20220184406 A1 (hereafter Lycke), which teaches at Figure 1 Item 180 “an outside monitoring device” placed on the patient’s body (one or more sensors for capturing sensor data on the patient’s body), and in the Background at para. [0002] teaches that the ICD is surgically implanted in the chest and monitors the person’s ECG. See US 11869671 B1 (hereafter McNair), McNair which teaches at Col. 10 line 12 that patient-related data will be derived from a non-surgical wearable monitoring device that gathers sample data. Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Independent claim 1 and 16 also recite the following additional elements: “wherein the command center is organized in layers of hierarchy such as organization, floors, theatres, and patients, and works in concert with modules including the one or more sensors of one or more devices, one or more relay devices and one or more electronic health records, wherein each module is categorized into the following levels of hierarchy as: engine, system, layer, process and tasks, and wherein each level of hierarchy will be a self-contained functionality in a microservices architecture with processing done in one or more distributed compute nodes with multiple redundancies…” and “…adding and releasing computing hardware and software for the patient or population of patients in modular fashion to ensure scalability.” Independent claim 21 also recites the following additional elements: “wherein the command center works in concert with modules including one or more sensors of one or more devices, one or more relay device and one or more electronic health records, wherein each module is categorized into the following levels of hierarchy as: engine, system, layer, process and tasks, wherein each level of hierarchy may be a self-contained functionality in a microservices architectures with processing done in one or more distributed compute nodes with multiple redundancies…” and “ adds and releases computing hardware and software for the patient or population of patients in modular fashion to ensure scalability.” MPEP 2106.05(f) indicates that a consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. The identified additional element is no more than mere recitation of the words “apply it” (or an equivalent) and/or are instructions to implement an abstract idea or other exception on a computer and therefore cannot provide significantly more. Accordingly, even in combination, this additional element does 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.
Dependent Claim Abstractions and Additional Elements
Claims 2-15 and claims 17-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 2 merely describes a clinical decision support system that is triggered via a defined analytics engine. Claim 3 merely describes the defined analytics engine further. Claim 4 merely describes the evaluation tasks and verification of syntax. Claim 5 merely describes the optimization process. Claim 6 merely describes the computation process for the decision support system. Claim 7 merely describes defining and setting, by a clinician, the analytics specifications and operations. Claim 8 merely describes defining and setting the analytics specifications and operation. Claim 9 merely describes the decision layer. Claim 10 merely describes the Boolean process. Claim 11 merely describes a decision structure whose result may trigger a multiple alert. Claim 12 merely describes the decision structure process and an alert. Claim 13 merely describes the mixed process. Claim 14 merely describes assisting a clinician in selecting definitions of the analytics specifications and operations by learning. Claim 15 merely describes integrating clinical measurements into an electronic health record (EHR). Claim 17 merely describes defining and setting, by a clinician, the analytics specifications and operations. Claim 18 merely describes the decision layer.
Claims 8 and 17 include the additional element of a clinician portal which is interpreted to be part of the command center (general purpose computer; see Spec. Para. 0026). The clinician portal was interpreted as were the other computers and computer components above.
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1,2,7,14,16-18,20,21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0257095 A1 (hereafter Zacharia) in view of US 2009/0248438 A1 (hereafter Tyler) further in view of Behr (Hierarchical function distribution – a design principle for advanced multicomputer architectures) in view of US 2015/0019241 A1 (hereafter Bennett).
Regarding Claim 1
Zacharia teaches:
A method for providing a scalable modular architecture for clinician defined analytics, comprising: measuring, by one or more sensors of one or more devices, one or more clinical measurements of a patient, wherein the one or more devices are placed on one or more locations of the patient’s body, or implanted inside the patient’s body; [Zacharia teaches at para. [0042] data pool 30 includes multiple sources for contributing data. Zacharia teaches at para. [0042] one source of information for the data pool is from one or more medical treatment machine 32, such as a continuous renal replacement therapy (“CRRT”) machine, an intermittent hemodialysis (“IHD”) machine, an infusion pump, a ventilator, diagnostic monitors, patient sensors, hospital bed settings, blood pressure systems, hemodynamic monitors, or patient weight scales, for example. The blood pressure systems are interpreted to be the one or more devices are placed on one or more locations of the patient’s body. Zacharia teaches at para. [0048] that systems 10a and 10b are configured to collect data from data sources 32 to 42 in real time or near real time for each patient and data pool 30. Collectively, this interpreted as measuring, by one more sensors of one or more devices, one or more clinical measurements of a patient, wherein the one or more devices are placed on one or more locations of the patient’s body.]
aggregating, by a command center, the one or more clinical measurements; [Zacharia teaches at para. [0042] data pool 30 includes multiple sources for contributing data. Zacharia teaches at para. [0042] one source of information for the data pool is from one or more medical treatment machine 32, such as a continuous renal replacement therapy (“CRRT”) machine, an intermittent hemodialysis (“IHD”) machine, an infusion pump, a ventilator, diagnostic monitors, patient sensors, hospital bed settings, blood pressure systems, hemodynamic monitors, or patient weight scales, for example. Zacharia teaches at para. [0041] that system 10a and 10b enable the integration of a number of automated, manual, or device-based data streams in a critical care environment to continuously monitor and trigger appropriate rule or learning modules in an appropriate context. Zacharia teaches at para. [0048] that systems 10a and 10b are configured to collect data from data sources 32 to 42 in real time or near real time for each patient and data pool 30. Collectively, this teaches aggregating, by a command center, the one or more clinical measurements.]
[…]
selecting, by the command center, one or more clinical measurements necessary for triggering one or more decision systems from a pool of incoming data; [Zacharia teaches at para. [0041] that system 10a and 10b enable the integration of a number of automated, manual, or device-based data streams in a critical care environment to continuously monitor and trigger appropriate rule or learning modules in an appropriate context. The appropriate rule or learning modules are interpreted as one or more decision systems. Zacharia teaches at para. [0050] in an embodiment, rule engine 50 for each hospital 100a to 100n of system 10 a includes a plurality of rule modules 52a to 52. Zacharia teaches at para. [0050] each rule module 52a to 52n is dedicated to making a risk assessment for a different adverse health condition for a patient. Zacharia teaches at para. [0050] for example, one rule module 52a will be dedicated to determining the risk of AKI (first adverse health condition), while a second rule module 52b will be dedicated to determining a risk of nephrotoxic regimen (second, different adverse health condition). Collectively, this teaches selecting, by the command center, one or more clinical measurements necessary for triggering one or more decision systems from a pool of incoming data.]
wherein the command center is organized in layers of hierarchy including organization, floors, theatres, and patients, and works in concert with modules including one or more sensors of one or more devices, one or more relay devices and one or more electronic health records, [Zacharia teaches at claim 9 the medical treatment system of claim 4, wherein the plurality of medical treatment machines are located at multiple hospitals, and the plurality of sources of data external to the medical treatment machines includes data from electronic medical records of the multiple hospitals. Zacharia teaches at para. [0042] one source of information for the data pool is from one or more medical treatment machine 32, such as a continuous renal replacement therapy (“CRRT”) machine, an intermittent hemodialysis (“IHD”) machine, an infusion pump, a ventilator, diagnostic monitors, patient sensors, hospital bed settings, blood pressure systems, hemodynamic monitors, or patient weight scales, for example. The hemodynamic monitors are interpreted as one or more relay devices. Zacharia teaches at para. [0042] that machine 32 will be present in the hospital room or will be a home-based machine. Collectively, this teaches wherein the command center is organized in layers of hierarchy including organization, floors, theatres, and patients and works in concert with modules including one or more sensors of one or more devices, one or more relay devices and one or more electronic health records.]
[…]
Zacharia may not explicitly teach:
[…]
performing computations, by the command center, to define an analytics engine per analytics specifications and operations by learning optimal decisions based on the outcomes of prior definitions and operations,
extracting, by the command center, clinical measurement vectors from the aggregated clinical measurements for one or more decision system;
and adding and releasing computing hardware and software […] in modular fashion to ensure scalability,
wherein each module is categorized into the following levels of hierarchy as: engine, system, layer, process and tasks, and wherein each level of hierarchy may be a self-contained functionality in a microservices architecture with processing done in one or more distributed compute nodes with multiple redundancies.
Tyler teaches:
[…]
extracting, by the command center, clinical measurement vectors from the aggregated clinical measurements for one or more decision system; [Tyler teaches at Claim 7 similarity metrics are determined based on a distance between two context vectors, each context vector characterizing one or more attributes of a procedure.]
[…].
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the medical machine learning system and method of Zacharia to the method of characterizing relationships among procedure using similarity metrics of Tyler with the motivation of reducing waste through mitigating healthcare fraud (Tyler para. [0002]).
Zacharia/Tyler may not explicitly teach:
[…]
performing computations, by the command center, to define an analytics engine per analytics specifications and operations by learning optimal decisions based on the outcomes of prior definitions and operations,
and adding and releasing computing hardware and software for the patient or population of patients in modular fashion to ensure scalability,
wherein each module is categorized into the following levels of hierarchy as: engine, system, layer, process and tasks, and wherein each level of hierarchy may be a self-contained functionality in a microservices architecture with processing done in one or more distributed compute nodes with multiple redundancies.
Behr teaches:
[…]
and adding and releasing computing hardware and software for the patient or population of patients in modular fashion to ensure scalability, [Behr teaches beneath Figure 4 on page 322 that hierarchical function distribution offers the advantage in that it lends itself toward a “natural” modularization of system software and, thus, decomposition of system software complexity. Behr teaches at pg. 318 Modular Extensibility that it is possible to augment performance and functionality of a computer without major changes in the system software, just by adding more modules to the system. This is adding and releasing computing hardware and software in a modular fashion to ensure scalability.]
wherein each module is categorized into the following levels of hierarchy as: engine, system, layer, process and tasks and wherein each level of hierarchy may be a self-contained functionality in a microservices architecture with processing done in one or more distributed compute nodes with multiple redundancies. [Behr teaches at pg. 318 a novel architectural design method called hierarchical function distribution. Behr teaches at Figure 4 an Example of a hierarchical function distribution scheme. The “Program execution machine” and “operating system machine” were interpreted as the “engines.” The “Program execution machine” and “operating system machine” were interpreted as the running the process and tasks. The entirety of Figure 4 is interpreted as the “system.” The operating system level, programming language level and application oriented language level is interpreted as “layers.” Behr teaches at pg. 323 to demonstrate the validity of the principle of hierarchical function distribution, the architectural design of the UPPER system shall be described. Behr teaches next sentence, that UPPER is a distributed multicomputer system that will consist of up to 16 processing nodes. This teaches a self-contained functionality in a microservices architecture with processing done in one or more distributed computer nodes with multiple redundancies. Collectively, Behr teaches wherein each module is categorized into the following levels of hierarchy as: engine, system, layer, process and tasks, and wherein each level of hierarchy may be a self-contained functionality in a microservices architecture with processing done in one or more distributed compute nodes with multiple redundancies.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the medical machine learning system and method of Zacharia to the method of characterizing relationships among procedure using similarity metrics of Tyler to the hierarchical function distribution for advanced multicomputer architecture of Behr with the motivation of integrating a new design principle called hierarchical function distribution, which was introduced to cope with the task of designing innovative multicomputer systems with complex functionality (Behr at the Abstract).
Zacharia/Tyler/Behr may not explicitly teach:
performing computations, by the command center, to define an analytics engine per analytics specifications and operations by learning optimal decisions based on the outcomes of prior definitions and operations,
Bennett teaches:
performing computations, by the command center, to define an analytics engine per analytics specifications and operations by learning optimal decisions based on the outcomes of prior definitions and operations, [Bennett teaches at the Abstract the present invention involves a system and method of providing decision support for assisting medical treatment decision-making. Bennett teaches at the Abstract a patient agent software module processes information about a particular patient. The software module of Bennett is the command center. Bennett teaches at the Abstract a doctor agent software module processes information about a health status of a particular patient, beliefs relating to patient treatments, and the actual effects of treatment decisions. Bennett teaches at the abstract, by filtering information over time from the patient agent into the doctor agent, a plurality of decision outcome nodes are created and formed into a patient-specific outcome tree with the plurality of decision-outcome nodes. Bennett teaches at the Abstract an optimal treatment is determined by evaluating the plurality of decision-outcome nodes with a cost per unit change function to output the optimal treatment. Bennett teaches at the Abstract when additional information is available from at least one of the patient agent and the doctor agent, filtering, creating and determining steps are repeated thus allowing for the system to “reason over time”, continuously updating and learning as new information is received. Collectively, Bennett teaches performing computations, by the command center, to define an analytics engine per analytics specifications and operations by learning optimal decisions based on the outcomes of prior definitions and operations.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the medical machine learning system and method of Zacharia to the method of characterizing relationships among procedure using similarity metrics of Tyler to the hierarchical function distribution for advanced multicomputer architecture of Behr to the clinical decision making artificial intelligence object oriented system and method of Bennett with the motivation of addressing event he answer to the basic healthcare question of “What’s wrong with this person,” which often remains elusive in the modern era-let alone clear answers on the most effective treatment for an individual or how we achieve lower costs and greater efficiency (Bennett at para. [0005]).
Regarding Claim 16 and 21
Due to their similarity to Claim 1, Claim(s) 16 and claim 21 are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
Regarding Claim 2
Zacharia/Tyler/Behr/Bennett teach the method of claim 1. Zacharia/Tyler/Behr/Bennett further teach:
wherein one or more decision support systems are triggered using the defined analytics engine. [Zacharia teaches at para. [0049] a server computer, interpreted to have non-transitory computer readable storage media and at least one processor. Zacharia teaches at para. [0041] a system which can diagnose, predict, and prevent a number of renal related conditions in a critical care environment using real time patient data. The system taught by Zacharia is interpreted as the decision support systems. Zacharia teaches at para. [0041] systems 10a and 10b enable the integration of a number of automated, manual, or device-based data streams in a critical care environment to continuously monitor and trigger appropriate rule or learning modules in an appropriate context. The system triggering the appropriate rule or learning modules in an appropriate context is interpreted as the defined analytics engine. Collectively, Zacharia teaches wherein one or more decision support systems are triggered using the defined analytics engine.]
Regarding Claim 20
Due to its similarity to Claim 2, Claim(s) 20 is similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Regarding Claim 7
Zacharia/Tyler/Behr/Bennett teach the method of claim 1. Zacharia/Tyler/Behr/Bennet may not explicitly teach:
further comprising, defining and setting, by a clinician, the analytics specifications and operations. [Zacharia teaches at para. [0053] system 10a will also be configured to enable the clinician or user to revise the non-learning algorithm if the clinician or user feels confident that a change is needed, e.g., to remove or add an input and/or change a weight associated with the input.]
Regarding Claim 14
Zacharia/Tyler/Behr/Bennett teach the method of claim 1. Zacharia/Tyler/Behr/Bennett further teach:
further comprising assisting a clinician in selecting definitions of the analytics specifications and operations by learning optimal decisions based on outcomes of prior definitions and operations. [This claim amounts to machine learning techniques applied to a clinical decision support system with training. Zacharia teaches at para. [0041] a system which can diagnose, predict, and prevent a number of renal related conditions in a critical care environment using real time patient data which contains an interface for clinicians or other caregivers to access the system. Zacharia teaches at para. [0068] using machine learning and AI to power the learning modules of a clinical decision support system. Zacharia teaches at para. [0071] training the machine learning models to determine the appropriate prediction or diagnostic elements by viewing the dataset against targeted results]
Regarding Claim 17
Zacharia/Tyler/Behr/Bennett teach the non-transitory computer readable medium of claim 16. Zacharia/Tyler/Behr/Bennett further teach:
further comprising: defining and setting, by a clinician, the analytics specifications and operations, wherein the defining and setting the analytics specifications and operation includes: setting, by a clinician portal, processes of a decision layer for defining explicit explanatory mathematical structure of a decision support system; [Zacharia teaches at para. [0049] a server computer, interpreted to have non-transitory computer readable storage media and at least one processor. Zacharia teaches at para. [0041] a system which can diagnose, predict, and prevent a number of renal related conditions in a critical care environment using real time patient data which contains an interface for clinicians or other caregivers to access the system. The interface of Zacharia is interpreted to be a physician portal. Zacharia teaches at para. [0041] continuously monitoring and triggering appropriate rule or learning modules. Zacharia teaches at para. [0065] machine learning and artificial intelligence that includes algorithms. The machine learning structure of Zacharia is interpreted to be processes of a decision layer for defining explicit explanatory mathematical structure of a decision support system.]
defining operands required for the decision support system in the definition layer, wherein the operand includes sensor inputs including at least one of vital sign measurements or lab results. [The Specification at para. [0027] teaches that the operand may be sensor inputs such as vital sign measurements, lab results such as blood parameter measurements and other measurements. Zacharia teaches at para. [0051] rules modules that quantify results or risk assessments for adverse health conditions. In the course of relaying an embodiment at para. [0051], Zacharia teaches selecting the appropriate laboratory tests and clinical measurements necessary for that health condition. Zacharia recites at para. [0051] serum creatine, which is interpreted to be at least one vital sign measurements or lab results. The rules modules are interpreted as definition layers. The teaching of Zacharia is interpreted to be defining operands required for the decision support system (which Zacharia describes as outputting a health prediction or assessment).]
Regarding Claim 18
Zacharia/Tyler/Behr/Bennett teach the non-transitory computer readable medium of claim 17. Zacharia/Tyler/Behr/Bennett further teach:
wherein the decision layer includes at least one of a Boolean process, scoring process, dynamic process, or mixed process. [Zacharia teaches at para. [0074] a support tool that employs binary flags that trigger other binary flags. The binary flags are interpreted to be a Boolean processes. Additionally, at para. [0075] Zacharia teaches that the training tool of the learning engine will employ an artificial neural network, a Bayesian network, a decision tree, a support vector machine and the training model. The decision layer of Zacharia is interpreted to be a mixed process.]
Claim(s) 3-6,8-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0257095 A1 (hereafter Zacharia) in view of US 2009/0248438 A1 (hereafter Tyler) in view of Behr (Hierarchical function distribution – a design principle for advanced multicomputer architectures) in view of US 2015/0019241 A1 (hereafter Bennett) in view of US 2018/0121622 A1 (hereafter Armstrong).
Regarding Claim 3
Zacharia/Tyler/Behr/Bennett teach the method of claim 2. Zacharia/Tyler/Behr/Bennett may not explicitly teach:
wherein the defined analytics engine further performs evaluation tasks, optimization process, and computation process of the decision support system based on the definitions of the clinician.
Armstrong teaches:
wherein the defined analytics engine further performs evaluation tasks, optimization process, and computation process of the decision support system based on the definitions of the clinician. [Armstrong at para. [0026] teaches an analytic server, interpreted to be an analytics engine. Armstrong at para. [0025] teaches that the analytic server comprises a clinical decision support engine. Armstrong teaches at para. [0033] that the analytic server will dynamically format and optimize data for evaluation. Armstrong at Figure 2 teaches generating and determining a decision tree. Armstrong at para. [0020] teaches stateless decision support system involves a knowledge set database, which is interpreted to be containing the definitions of the clinician. Collectively, the teachings of Armstrong are interpreted to be the defined analytics engine further performs evaluation tasks, optimization process, and computation process of the decision support system based on the definitions of the clinician.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the medical machine learning system and method of Zacharia to the method of characterizing relationships among procedure using similarity metrics of Tyler to the hierarchical function distribution for advanced multicomputer architecture of Behr to the clinical decision making artificial intelligence object oriented system and method of Bennett to the stateless decision support fee of Armstrong with the motivation of replacing traditional stateful decision support engines which are no longer suitable (Armstrong at para. [0004]).
Regarding Claim 4
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 3. Zacharia/Tyler/Behr/Bennett/Armstrong further teach:
wherein the evaluation tasks include evaluation and compilation of the definitions into a mathematical form used for computation, [Zacharia teaches at para. [0073] post-processing tool 70, in an embodiment, averages, normalizes, and/or aggregates learning engine 60 output to optimize for accuracy, e.g., against a target clinical condition. The term aggregates is interpreted to mean evaluation and compilation of the definitions into a mathematical form used for computation.]
and verification of syntax involving automated compilation rules or a manual input table of operand values run through a mathematical form to ensure correct output. [The Specification at para. [0027] teaches that the operand may be a sensor input such as vital sign measurements, lab results such as blood parameter measurements, and other measurements. Zacharia teaches at para. [0072] input data preparation, involving machine learning and artificial intelligence, that includes a preprocessing tool that normalizes, standardizes, pre-treats, and/or removes outliers of quantitative or qualitative data to ensure model performance and accuracy. Zacharia teaches at para. [0073] post-processing tool 70, in an embodiment, averages, normalizes, and/or aggregates learning engine 60 output to optimize for accuracy, e.g., against a target clinical condition. Zacharia teaches that the preprocessing tool 68 removes unnecessary words such as “it,” “the” and the like from the natural language data to reduce a processing burden. Removing unnecessary words to reduce processing burden is interpreted as verification of syntax via compilation rules. Zacharia teaches at para. [0072] artificial intelligence or machine learning that is interpreted to be automated. Collectively, Zacharia teaches the verification of syntax involving automated compilation rules.]
Regarding Claim 5
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 4. Zacharia/Tyler/Behr/Bennett/Armstrong further teach:
wherein the optimization process includes minimization of states or declarative statements by reducing unreachable states or statements. [Zacharia teaches at para. [0089] that based on the states detected above, engines 50 and 60 will output to clinicians, or other users of the logic engine, additional logic layers to provide appropriate contextual information to the user to build corresponding treatment bundle recommendations for high probability state or predictions. This teaches wherein the optimization process includes minimization of states (i.e. disease states) by reducing unreachable states.]
Regarding Claim 6
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 5. Zacharia/Tyler/Behr/Bennett/Armstrong further teach:
wherein the computation process uses data vectors for the operands and the optimized mathematical structure for computing the elements of the decision support system. [The Specification at para. [0027] teaches that the operand may be a sensor input such as vital sign measurements, lab results such as blood parameter measurements, and other measurements. Thus, this limitation becomes wherein the process uses data vectors for lab measurements and the optimized mathematical structure for computing the elements of the decision support system. Zacharia teaches at para. [0068] a computation process involved with a renal clinical decision support system that uses data vectors, interpreted to be clinically relevant lab data (i.e. a patient’s blood measurements).]
Regarding Claim 8
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 7. Zacharia/Tyler/Behr/Bennett/Armstrong further teach:
wherein the defining and setting the analytics specifications and operation includes: setting, by a clinician portal, processes of a decision layer for defining explicit explanatory mathematical structure of a decision support system; [Zacharia teaches at para. [0052] that when the clinician or user selects the “see risk evaluation” button, a screen or partial screen appears showing the regression model or other type of non-learning algorithm used to determine the risk evaluation. Zacharia teaches at para. [0053] system 10a will also be configured to enable the clinician or user to revise the non-learning algorithm if the clinician or user feels confident that a change is needed, e.g., to remove or add an input and/or change a weight associated with the input. Collectively, Zacharia teaches wherein the defining and setting the analytics specifications and operation includes: setting, by a clinician portal, processes of a decision layer for defining explicit explanatory mathematical structure of a decision support system.]
defining operands required for the decision support system in the definition layer, wherein the operand includes sensor inputs including at least one of vital sign measurements or lab results. [The Specification at para. [0027] teaches that the operand may be sensor inputs such as vital sign measurements, lab results such as blood parameter measurements and other measurements. Zacharia teaches at para. [0051] taking AKI as an example adverse health condition, the rule module 52a will include an algorithm utilizing serum creatine, cardiac risk scoring, patient age, patient gender, and blood pressure over time, which outputs a result for the patient. Zacharia teaches at para. [0051] for that adverse health condition, the rule module 52a compares the result to a clinical setpoint, and outputs a patient status, which will indicate that the patient is not at risk for the particular health condition or trigger a flag, notification, alert, and/or alarm if the patient is at risk for, or experiencing, the particular health condition. Zacharia teaches at para. [0051] to provide some variability within a rule module 52a to 52n for different patients, it is contemplated for the clinical setpoint of the rule module to be varied based upon patient characteristics, such as physiological characteristics, including but not limited to age, sex, weight, body mass index, characteristics blood pressure, and the like. The blood pressure is interpreted to be a vital sign measurement.]
Regarding Claim 9
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 8. Zacharia/Tyler/Behr/Bennett/Armstrong further teach
wherein the decision layer includes at least one of a Boolean process, scoring process, dynamic process, or mixed process. [Zacharia teaches at para. [0074] a support tool that employs binary flags that trigger other binary flags. The binary flags are interpreted to be a Boolean processes. Additionally, at para. [0075] Zacharia teaches that the training tool of the learning engine will employ an artificial neural network, a Bayesian network, a decision tree, a support vector machine and the training model. The decision layer of Zacharia is interpreted to be a mixed process.]
Regarding Claim 10
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 9. Zacharia/Tyler/Behr/Bennett/Armstrong further teach:
wherein the Boolean process defines a decision structure whose result may trigger a single alert. [Zacharia teaches at para. [0074] that support tool 72 will employ usage criteria, e.g., via triggering a binary flag, a quantified result (which in itself will trigger another binary flag), and/or direction indicator. The binary flag is interpreted to be an alert. Also, Zacharia teaches at para. [0076] that the system 10b then causes an alert to be communicated to the patient or caregiver, so that the patient is then tested for adverse patient condition A.]
Regarding Claim 11
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 9. Zacharia/Tyler/Behr/Bennett/Armstrong further teach:
wherein the scoring process that defines a decision structure whose result generates scores that may trigger a multiple alert. [Zacharia teaches at para. [0074] that support tool 72 will employ usage criteria, e.g., via triggering a binary flag, a quantified result (which in itself will trigger another binary flag), and/or direction indicator. Also, Zacharia teaches at para. [0076] that the system 10b then causes an alert to be communicated to the patient or caregiver, so that the patient is then tested for adverse patient condition A. Zacharia teaches at claim 9 the medical treatment system of claim 4, wherein the plurality of medical treatment machines are located at multiple hospitals, and the plurality of sources of data external to the medical treatment machines includes data from electronic medical records of the multiple hospitals. Zacharia teaches at para. [0007] the system and method use the data streams to continuously monitor and trigger alerts indicative of an onset of renal related conditions such as AKI. Zacharia teaches at para. [0007 the system and method enable the development of diagnostic and predictive rules or learning engines that drive downstream flags, quantified risk scores, or risk clustering for target patients. Zacharia at para. [0007] teaches that the system and method further provide for appropriate alert, intervention, or communication techniques via multiple deliver channels to ensure full care team awareness of the identified target issues. Collectively, the teachings of Zacharia are interpreted to be wherein the scoring process that defines a decision structure whose result generates scores that may trigger a multiple alert.]
Regarding Claim 12
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 9. Zacharia/Tyler/Behr/Bennett/Armstrong further teach:
wherein the dynamic process defines a decision structure where the process transitions from one state to a next state until the process reaches a final state that may trigger a single alert. [Zacharia teaches at para. [0074] that support tool 72 will employ usage criteria, e.g., via triggering a binary flag, a quantified result (which in itself will trigger another binary flag), and/or direction indicator. This teaches the dynamic process defines a decision structure where the process transitions from one state to a next state until the process reaches a final state. Also, Zacharia teaches at para. [0076] that the system 10b then causes an alert to be communicated to the patient or caregiver, so that the patient is then tested for adverse patient condition A. Collectively, this teaches wherein the dynamic process defines a decision structure where the process transitions from one state to a next state until the process reaches a final state that may trigger a single alert.]
Regarding Claim 13
Zacharia/Tyler/Behr/Bennett/Armstrong teach the method of claim 9. Zacharia/Tyler/Behr/Bennet/Armstrong further teach:
wherein the mixed process is a dynamic process with each state being a Boolean process or a scoring process. [Zacharia teaches using a single or dynamic phase machine learning or appropriate AI Techniques enables the learning module system to develop a set of learning modules that predict or detect results pertaining to adverse health conditions in real world settings. This teaches wherein the mixed process is a dynamic process. Zacharia teaches at para. [0007] the system and method enable the development of diagnostic and predictive rules or learning engines that drive downstream flags, quantified risk scores, or risk clustering for target patients. Zacharia teaches at para. [0051] taking AKI as an example adverse health condition, the rule module 52a will include an algorithm utilizing serum creatinine, cardiac risk scoring, patient age, patient gender, and blood pressure over time, which outputs a result for a patient. Collectively, Zacharia teaches wherein the mixed process is a dynamic process with each state being a scoring process. ]
Claim(s) 15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0257095 A1 (hereafter Zacharia) in view of US 2009/0248438 A1 (hereafter Tyler) further in view of Behr (Hierarchical function distribution – a design principle for advanced multicomputer architectures) in view of US 2015/0019241 A1 (hereafter Bennett) in view of US 2001/0012913 A1 (hereafter Iliff).
Regarding Claim 15
Zacharia/Tyler/Behr/Bennett teach the method of claim 1. Zacharia/Tyler/Behr/Bennett may not explicitly teach:
further comprising integrating the one or more clinical measurements into an electronic health record (EHR).
Iliff teaches:
further comprising integrating the one or more clinical measurements into an electronic health record (EHR). [Iliff teaches at Figure 600 Item 630 examining subjective-objective health measurements recently added to the patient medical record. The subjective-objective health measurements recently added to the patient record are interpreted as integrating the one or more clinical measurements into an electronic health record (EHR).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the medical machine learning system and method of Zacharia to the method of characterizing relationships among procedure using similarity metrics of Tyler to the hierarchical function distribution for advanced multicomputer architecture of Behr to the hierarchical function distribution for advanced multicomputer architecture of Behr to the clinical decision making artificial intelligence object oriented system and method of Bennett to the disease management system and method including correlation assessment of Iliff with the motivation of allowing a patient to access an automated process for managing a specific health problem called a disease.
Regarding Claim 19
Zacharia/Tyler/Behr/Bennett teach the non-transitory computer readable medium of claim 18. Zacharia/Tyler/Behr/Bennett further teach:
wherein the Boolean process defines a decision structure whose result may trigger a single alert, [Zacharia teaches at para. [0074] that support tool 72 will employ usage criteria, e.g., via triggering a binary flag, a quantified result (which in itself will trigger another binary flag), and/or direction indicator. The binary flag is interpreted to be an alert. Also, Zacharia teaches at para. [0076] that the system 10b then causes an alert to be communicated to the patient or caregiver, so that the patient is then tested for adverse patient condition A.]
wherein the scoring process that defines a decision structure whose result generates scores that may trigger a multiple alert, [Zacharia teaches at para. [0074] that support tool 72 will employ usage criteria, e.g., via triggering a binary flag, a quantified result (which in itself will trigger another binary flag), and/or direction indicator. Also, Zacharia teaches at para. [0076] that the system 10b then causes an alert to be communicated to the patient or caregiver, so that the patient is then tested for adverse patient condition A. Zacharia teaches at claim 9 the medical treatment system of claim 4, wherein the plurality of medical treatment machines are located at multiple hospitals, and the plurality of sources of data external to the medical treatment machines includes data from electronic medical records of the multiple hospitals. Zacharia teaches at para. [0007] the system and method use the data streams to continuously monitor and trigger alerts indicative of an onset of renal related conditions such as AKI. Zacharia teaches at para. [0007] the system and method enable the development of diagnostic and predictive rules or learning engines that drive downstream flags, quantified risk scores, or risk clustering for target patients. Zacharia at para. [0007] teaches that the system and method further provide for appropriate alert, intervention, or communication techniques via multiple deliver channels to ensure full care team awareness of the identified target issues. Collectively, the teachings of Zacharia are interpreted to be wherein the scoring process that defines a decision structure whose result generates scores that may trigger a multiple alert.]
wherein the dynamic process defines a decision structure where the process transitions from one state to a next state until the process reaches a final state that may trigger a single alert, [Zacharia teaches at para. [0074] that support tool 72 will employ usage criteria, e.g., via triggering a binary flag, a quantified result (which in itself will trigger another binary flag), and/or direction indicator. This teaches the dynamic process defines a decision structure where the process transitions from one state to a next state until the process reaches a final state. Also, Zacharia teaches at para. [0076] that the system 10b then causes an alert to be communicated to the patient or caregiver, so that the patient is then tested for adverse patient condition A. Collectively, this teaches wherein the dynamic process defines a decision structure where the process transitions from one state to a next state until the process reaches a final state that may trigger a single alert.]
Zacharia/Tyler/Behr/Bennett may not explicitly teach:
and wherein the mixed process is a dynamic process with each state being a Boolean process.
Iliff teaches:
and wherein the mixed process is a dynamic process with each state being a Boolean process. [Iliff teaches at para. [0100], [0101], [0102], [0103] the medical information gathered during an assisted consultation is written to the patient’s medical record if the following three conditions are met: (a) the assistant’s ASST_PERM flag is True, (b) the ASST_EXP timestamp has not been reached and (c) the assistant has a relationship pointer to the patient record. Iliff teaches at para. [0104] if any of these conditions are not met, then any new medical information gathered on this patient will be saved to the Pending file 252 (Fig. 3) for off-line verification by the system administrator. Each of the three conditions is a binary (yes/no, 0/1, etc.) decision and so the teachings of Iliff are interpreted to be and wherein the mixed process is a dynamic process with each state being a Boolean process.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the medical machine learning system and method of Zacharia to the method of characterizing relationships among procedure using similarity metrics of Tyler to the hierarchical function distribution for advanced multicomputer architecture of Behr to the clinical decision making artificial intelligence object oriented system and method of Bennett to the disease management system and method including correlation assessment of Iliff with the motivation of allowing a patient to access an automated process for managing a specific health problem called a disease.
Response to Arguments
Claim Objections
The Examiner thanks the Applicant for the explanation. The objection concerning the “compute node” is withdrawn.
Claim Interpretation
The Examiner agrees. Structure is recited in the claim for command center such that interpretation under 35 U.S.C. 112(f) is not necessary.
35 U.S.C. 101 Argument Responses
Applicant argues that when the elements of Claim 1 are combined, an inventive concept may be found in the non-conventional and non-generic arrangement of the additional elements (applicant recites the entire claim with emphasis on a newly amended the following): “…define an analytics engine per analytics specification and operations by learning optimal decisions based on the outcomes of prior definitions and operations…” and “…adding and releasing computing hardware and software for the patient or population of patient in modular fashion to ensure scalability.”
The Examiner notes that Ex Parte Desjardins distinguished a computer implemented invention that was not directed to a judicial exception but was directed to a technical solution to a technical problem, in part, addressing the issue of catastrophic forgetting that occurred in continual learning systems: an improvement in the functioning of the computer or an improvement to other technology or technical field. For example, the Specification associated with this referenced application at para. [0021] indicates by training the model on a new task by adjusting values of parameters of the model to optimize an objective function that depends in part on how important the parameters are to previously learned task(s), the model can effectively learn new tasks in succession whilst protecting knowledge about previous tasks. In Ex Parte Desjardins, the independent claim focuses on this specific training mechanism resulting in at least one of the improvements outlined in the specification of application 16/319,040. In short, the features resulting in this supposed same technical solution to a technical problem were apparently integrated into the claims. In the instant case the cited amendment is part of the abstract idea. The amendment functions as a whole with the other recited limitations to describe a complex abstract idea. It is not apparent to one of ordinary skill in the art how the amended instant limitations function as a whole with the remaining limitations to demonstrate a integration of the judicial exception into a practical application of the exception. For example, the iterative training mechanism resulted in the supposed improvement to training is not in the current claims: by learning optimal decision based on the outcomes of prior definitions and operations…does not describe how optimal decisions are made based on outcomes of prior definitions and operations, only that they are made. As recited, “learning optimal decisions based on the outcomes of prior definitions and operations…” is an abstract idea. Here, there is not an improvement to the computer via technical solution to a technical problems, as was at the core of Ex Parte Desjardins: the claimed subject matter in that case provided technical improvement over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training. In fact, with the amendment Applicant has attempted to claim additional areas of judicial exception. For example, devoid of the additional element “by the command center,” the limitation performing computations […] to define an analytics engine per analytics specifications and operations by learning optimal decisions based on the outcomes of prior definitions and operations” is categorizable as a mental process. Pointing to another judicial exception in order to nuance an abstract idea is not integrating a judicial exception into a practical application of the exception nor is it providing significantly more.
MPEP 210605(f) Mere Instructions to Apply An Exception indicates that another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. In summary, the recitation of “…adding and releasing computing hardware and software for the patient or population of patient in modular fashion to ensure scalability” invokes “Apply It” because it is a generalized instruction to implement the abstract idea/judicial exception on a computer.
When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following:
Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
The claim invokes computers or other machinery merely as a tool to perform and existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
The particularity or generality of the application of the judicial exception.
A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more.
Finally, step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? Examiners should answer this question by first identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). The way in which the additional elements use or interact with the exception may integrate it into a practical application or provide significantly more. Here, the combination of additional elements does not result in any relationship to the abstract idea by virtue of being considered collectively, and the additional elements are instructions to apply the judicial exception on a computer environment.
Applicant argues that the December 2025 memo “advance notice of change to the MPEP in light of Ex Parte Desjardins” (the memo) citing Ex Parte Desjardins specifically states, “because “software can make non-abstract improvement to computer technology, just as hardware improvement can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract.” ID. at 1336. (Desjardins, page 8)” and that “Examiners are expected to consider existing precedent like Enfish, as discussed in MPEP 2106, in addition to these updates when assessing eligibility under 35 U.S.C. 101 particularly when evaluating claims related to machine learning or artificial intelligence.” Specifically, the memo states, “In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore, the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO). Applicant respectfully submits that similar to Ex Parte Desjardins, claims in the present application recite, “define an analytics engine per analytics specifications and operations by learning optimal decisions based on the outcomes of prior definitions and operations” and hence provide “An improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to effectively learn new tasks; Ex Parte Desjardins, Appeal No 2024-000567, Application No. 16/319,040 (PTAB September 26, 2025, Appeals Review Panel Decision).
Of course the Examiner defers to the PTAB decisions. The Examiner has answered this question in the relevant response above.
Applicant argues that when the elements of Claim 1 are combined, not only an inventive concept may be found in the non-conventional and non-generic arrangement of the additional elements but also provides improvement in the technical field and hence the claims as a whole are integrated the alleged judicial exception instead into a practical application because a solution is implement to:… (emphasis added sections repeated here): “define an analytics engine per analytics specifications and operations by learning optimal decisions based on the outcomes of prior definitions and operations” and “…adding and releasing computing hardware and software for the patient or population of patients in modular fashion to ensure scalability.” Applicant submits that the elements of Claim 1, whether taken individually or in combination are clearly not directed to “an abstract idea” as alleged in the Office Action.
This argument has been addressed by the relevant response above.
35 U.S.C. 103 Argument Responses
Applicant argues that the claims do not recite the as amended features. In particular Applicant argues that the features of the present invention as now recited in the claims are not taught or suggested by Zacharia, whether taken individually or in combination with Behr, Tyler, Armstrong and Illif. The prior art, Applicant argues, does not teach: “…by learning optimal decisions based on the outcomes of prior definitions and operations” as recited by the amended Claim 1.
Here, “…by learning optimal decisions based on the outcomes of prior definitions and operations” as recited by the amended Claim 1 is taught by Bennett. Please see the updated rejection.
Applicant argues that Zacharia, Behr, Tyler, Armstrong and Illif individually or in combination, fails to teach or even suggest the amended claims.
The Examiner will respond where Applicant claims that specific features are not taught. See answer above.
Conclusion
Nakamura teaches an automated information collection and evaluation of clinical data that makes diagnosis as would a clinical decision support system, and was thus considered relevant. Kuo (A Rule-Based Clinical Decision Model to Support Interpretation of Multiple Data in Health Examinations) teaches a rule based clinical decision model to support interpretation of multiple data in health examinations that is relevant to the clinical decision related limitations herein. Hassan Shafique (US 20210118136 A1) teaches a predictive engine and the application of machine learning to oncology. Zhou (In depth mining of clinical data: the construction of clinical prediction model with R) teaches a clinical prediction model that is tangentially relevant to the subject matter herein. Nanji teaches an intraoperative real-time clinical decision support with user interfaces and alerts. None were used in this office action.
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 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.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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