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
Claims 1-10 were previously pending and subject to a non-final Office Action having a notification date of July 2, 2025 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on September 23, 2025 (the “ previous Amendment”), after which a Notice of Non-Compliant Amendment was mailed on November 12, 2025. Thereafter, Applicant filed another amendment on December 14, 2025, amending claims 1-3, 5, 6, 8, and 9.
The present Final Office Action addresses pending claims 1-10 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Objections to Drawings
These objections are withdrawn in view of the Amendment.
Response to Applicant’s Arguments Regarding Objections to Claims
These objections are withdrawn in view of the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §112
While most of these rejections are withdrawn in view of the Amendment, new rejections are presented below as necessitated by the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
On page 11 of the Amendment, Applicant takes the position that the present claims are not directed to "mental processes" because they include certain limitations (e.g., hardware accelerators, CCM, etc.) that are not practically performable in the human mind with pen and paper. The Examiner disagrees that the present claims do not recite and are not directed to "mental processes."
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). MPEP 2106.05(III).
Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019). MPEP 2106.05(III)(A).
However, claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer"). MPEP 2106.05(III)(C).
In the present case, the independent claims recite a mental process because a person could practically in their mind with pen and paper perform "rule-governed…preemptive decision-making" via managing and applying one or more rule sets and enforcing compliance with privacy or regulatory constraints (e.g., ensuring that use of medical sensor data complies with HIPAA), filtering/encrypting/anonymizing data received from a data collection interface before inferencing (e.g., removing PII and replacing with anonymous identifiers), analyzing incoming sensor data based on the rule sets and generating one or more predictive risk profiles (e.g., indicating a degree to which a user is at risk for various different medical conditions), and initiating preemptive tasks based on the predictive output and subject to filtering (e.g., determining actions configured to prevent a dangerous health event). The above-noted hardware accelerators, CCM, etc. do not take the claims out of mental processes because such limitations just amount to using computers and/or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Applicant then takes the position that the present claims integrate the abstract idea(s) into a "practical application" because they solve the "technical problem" of delivering anticipatory behavior recommendations without violating privacy regulations using the "technical solution" of governance and filtering modules operator across a hardware-executed inference layer which improves real-world decision timing, latency, and privacy compliance. Notwithstanding that Applicant does not explain which of the "additional limitations" in combination with the abstract idea allegedly provide such technical solution, but utilizing governance/filtering rules to facilitate delivering anticipatory behavior recommendations without violating privacy regulations is not a technical problem/solution in the first place because it is part of the "mental processes" and/or "certain methods of organizing human activity" abstract idea(s). That is, it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP §2106.05(a)(II). Stated differently, “practical application” analysis is made with respect to the additional limitations rather than with respect solely to the abstract idea itself. MPEP 2106.04(d).
Applicant then asserts at the bottom of page 11 of the Amendment "that the ordered combination of hardware modules, machine learning inference on real-time sensor inputs, and preemptive rule-governed orchestration presents an inventive concept. This structure goes beyond generic computing or post-hoc analysis. The use of hardware accelerators and coordinated agent control for anticipatory health behavior interventions is not taught or suggested by conventional systems." While the Examiner has not asserted that the arrangement of limitations in the independent claims is conventional/routine/well-known, the Examiner has nevertheless found the additional limitations to not provide "significantly more" than the at least one abstract idea for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The 35 USC 101 rejection is maintained.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §102/103
Starting at page 12 of the Amendment, Applicant asserts "Molero discloses threshold-triggered alerts based on physiological monitoring (e.g., glucose spikes or heart rate thresholds). It lacks any anticipatory model, and no machine-learned predictive profile is used to initiate pre-threshold interventions. In contrast, Claim 1 now explicitly recites predictive risk profiles generated by inference agents, which are used before any threshold is breached." In response, the Examiner initially notes that "anticipatory" models and using predictive risk profiles "before any threshold is breached" are not recited in the present claims. Assuming such anticipatory models are referring to the inference agents/models recited in the present claims, the Examiner directs Applicant's attention to AI models 767 stored in storage 765 in Figure 7 while [0039], [0048], [0151], [0263]-[0284], [0343], [0348] disclose use of AI/ML models to generate inferences based on sensor data while [0279], [0317] discloses how the AI models analyze the sensor data to generate predictions regarding probability/risk of medical events, where the predictions for the subject collectively amount to one or more "predictive risk profiles."
Applicant then asserts "Claim 1 recites a multi-module, multi-agent framework including: a) Inference Agents with trained models; b) a Rules Controller that governs system operation; and c) a dedicated Action Agent that executes real-world outcomes. Molero discloses a monolithic, reactive processor logic, not a modular agent-based system governed by dynamic rule-based orchestration. There is no disclosure of independent, rule-constrained agents managed via controllers." In response, the Examiner initially notes that claim 1 does not recite a "modular agent-based system governed by dynamic rule-based orchestration." With respect to independent, rule-constrained agents managed via controllers, the Examiner will assume Applicant is referring to how the rules sets control the availability and output of the inference agents as recited in claim 1. In this regard, the Examiner directs Applicant's attention to [0266]-[0269] of Molero which discloses how code 769 stored in storage/memory 765 (Figure 7) can facilitate selection of one of a plurality of AI models based on an extent to which a model-associated use condition is satisfied for a particular subject (e.g., how close particular subject-specific attributes are to those used to train the AI models) which thus amounts to "rule sets" and [0343] discloses rule-based ML models such as inferring physical activities for each cluster of sensor data based on initial rules and using rules to identify certain sensor ranges and infer activity corresponding to the ranges; all of such rules would be stored in storage/memory as that is how computing systems function. Furthermore, [0164]-[0177] discusses development, selection, and implementation of rules (governance rules) corresponding to specific criteria such as defined by a medical center (enforcing compliance with regulatory constraints) and [0190] discusses use of rules to comply with data privacy rules. Because the system is computer-implemented, there is necessary a set of code/software (a "Rules Controller") that manages/applies the various rule sets and enforces compliance with privacy/regulatory constraints. Furthermore, as the above rules are used to select one or more of the AI models, then such rules "constrain and authorize" inference operations and behavioral recommendations (e.g., see [0277], [0317], [0332], [0353]) performed by the AI models).
Applicant then asserts "Claim 1 now recites execution of inference agents on dedicated hardware accelerators (GPU, TPU, ASIC). Molero does not specify any such execution framework. Its description of data processing is entirely generic and silent on parallel processing or AI-specific accelerators. This is a major distinction under §102 analysis." In response, the Examiner notes how Figure 7 of Molero illustrates how system 750 includes AI models 767 (inference agents) while [0255] discloses how the processors 760 (which would execute the AI models/inferencing agents 767 because that is how computing systems function) can include a GPU.
Applicant then asserts "Claim 1 recites a Content Control Module implemented in hardware/software, which performs filtering, encryption, and anonymization of raw data prior to inferencing. Molero contains no such disclosure. It merely processes sensor data directly and sends alerts; there is no privacy-preserving preprocessing pipeline." The Examiner disagrees because [0044], [0272] of Molero discloses performing pre-processing of the data such as filtering/encoding which would before the inferencing performed by the ML/AI models because that is what "pre-processing" means and such pre-processing/filtering/encoding would necessarily be performed by some set of code/software executed by one or more processors (collectively, a "CCM") because the system is computer-implemented.
On pages 13-14 of the Amendment, Applicant asserts "Claim 3 recites a Rules Controller that dynamically manages and applies governance rules derived from a distributed ledger (e.g., Hyperledger Fabric), with rule changes based on real-time environmental and user-specific data. Neither Molero nor Akdemir discloses or suggests a rules engine that draws from a permissioned, distributed ledger (e.g., blockchain), let alone one that dynamically modifies constraints in real time based on context. Molero uses static thresholding, and Akdemir discusses dynamic recommendation feedback but not immutable, auditable rule storage or enforcement architecture. The combination does not render the distributed governance functionality of Claim 3 obvious." In response, the Examiner notes that claim 3 does not recite an RC that dynamically manages and applies governance rules derived from a distributed ledger with rule changes based on real-time environmental and user-specific data or a rules engine that draws from a permissioned, distributed ledger (e.g., blockchain), let alone one that dynamically modifies constraints in real time based on context. Molero and Akdemir disclose the limitations of claim 3 as set forth in the rejection below.
Applicant then asserts "Claim 5 requires that the inference agents perform prediction on streaming sensor data using parallel processing within a hardware accelerator (e.g., GPU or TPU), with the ability to reconfigure model architecture based on detected drift or anomalous inputs. This limitation introduces two distinct innovations not present in either Molero or Akdemir. (1) stream-based inferencing optimized through AI-specific hardware, and (2) runtime model reconfiguration. Molero discloses basic health monitoring, and Akdemir references predictive personalization, but neither teaches or suggests the ability to detect drift and dynamically alter model structure at runtime. Therefore, Applicant respectfully asserts that Claim 5 is non-obvious." With respect, none of these limitations are recited in claim 5. The Examiner asserts that Molero and Hassanzadeh disclose the limitations of claim 5 as set forth in the rejection below.
Applicant then asserts "Claim 7 recites a Content Control Module that filters, encrypts, and anonymizes streaming sensor data in compliance with regulatory constraints prior to inferencing. Molero includes no such data preprocessing, and Akdemir discusses adaptation to user preferences, not enforceable privacy pre-processing. Filtering and anonymization of raw data at ingest time, before inferencing occurs, imposes a hardware and software constraint that significantly alters system behavior and prevents data leakage or misuse. The cited art fails to teach this proactive privacy layer." Again, none of these limitations are recited in claim 7 and the Examiner asserts that Molero, Hemophilia Nutrition, and Talbot disclose the limitations of claim 7 as set forth in the rejection below.
Finally, on page 15 of the Amendment, Applicant asserts "Claim 9 involves an Action Agent that initiates preemptive tasks through physical interfaces and actuators, conditioned not only on inference outcomes but on real-time rule enforcement by the Rules Controller. Molero's actions are reactive and threshold-based; Akdemir's system is user-in-the-loop. The combination does not yield the claimed capability of autonomous agent-triggered actions gated by machine-enforced governance logic. The modular coordination of these agents is not disclosed or suggested." Again, none of these limitations are recited in claim 9 and the Examiner asserts that Molero, Fleming, and Molero Leon '126 disclose the limitations of claim 9 as set forth in the rejection below.
The Examiner also notes how Applicant never addresses the specific paragraph citations and remarks made by Examiner in relation to the various claims and instead merely presents Applicant's high-level interpretations of Molero Leon and the other references. If Applicant responds to the present Final Office Action, Applicant is respectfully requests to address the Examiner's specific paragraph citations and remarks in relation to the actual limitations recited in the present claims.
The 35 USC 102 and 103 rejections are maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 now recites how the rule sets "constrain and authorize inference operations and behavioral recommendations." However, neither the paragraphs cited by Applicant at page 5 of the Amendment nor any other portions of the present specification appear to provide support for this limitation.
Claim 1 also now recites how inference agents are configured to "generate one or more predictive risk profiles." However, neither the paragraphs cited by Applicant at page 5 of the Amendment nor any other portions of the present specification appear to provide support for this limitation.
Claim 1 also now recites how the rule sets "[control] the availability and output of said one or more inference agents." Again, neither the paragraphs cited by Applicant at page 5 of the Amendment nor any other portions of the present specification appear to provide support for this limitation.
Claim 1 also now recites how the one or more preemptive tasks initiated by the Action Agent are "subject to filtering by said RC." Again, neither the paragraphs cited by Applicant at page 5 of the Amendment nor any other portions of the present specification appear to provide support for this limitation.
If Applicant maintains that the above limitations are supported in the present specification, Applicant is requested to indicate specific paragraph numbers providing specific support for these limitations.
Claims 2-5 are rejected based on their dependency from claim 1.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "said predictive output" in the second to last line. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-5 are rejected based on their dependency from claim 1.
Regarding claim 6, it is now unclear if the "at least one sensor" from limitation "d" is referring to the "one or more onboard or networked sensors" from limitation "c. i." For purposes of examination, the Examiner will assume they are the same.
Claim 7 is rejected based on its dependency from claim 6.
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 1-10 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 1-5 are directed to a system (i.e., a machine), claims 6 and 7 are directed to a device (i.e., a machine), and claims 8-10 are directed to a method (i.e., a process). Accordingly, claims 1-10 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system for performing rule-governed, artificial intelligence (AI)-based preemptive decision-making, comprising:
a. a processor configured to execute non-transitory computer-readable instructions stored in memory;
b. a data collection interface comprising one or more physical sensors selected from the group consisting of biometric sensors, image sensors, environmental sensors, and wearable monitoring devices, said sensors generating real-time, machine-readable data streams;
c. a memory storing one or more rule sets and one or more machine learning inference agents, said rule sets comprising executable instructions that constrain and authorize inference operations and behavioral recommendations;
d. an Inference Agent Controller (IAC) configured to activate said one or more machine learning inference agents, each said inference agent comprising one or more trained inference models, each said inference agent executing upon a hardware accelerator selected from the group consisting of graphics processing unit (CPU), tensor processing unit (TPU), or application-specific integrated circuit (ASIC), said one or more inference agents being configured to analyze incoming sensor data and generate one or more predictive risk profiles;
e. a Rules Controller (RC) configured to manage and apply said one or more rule sets stored in said memory and enforce compliance with privacy or regulatory constraints by controlling the availability and output of said one or more inference agents;
f. a Content Control Module (CCM) implemented in software and hardware, configured to filter, encrypt, or anonymize said machine-readable sensor data prior to inferencing; and
g. an Action Agent coupled to an output interface or actuator, said Action Agent configured to initiate one or more preemptive tasks through a physical system or external user interface based on said predictive output of said Inference Agents and subject to filtering by said RC.
The Examiner submits that the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a person could practically in their mind with pen and paper perform "rule-governed…preemptive decision-making" via managing and applying one or more rule sets and enforcing compliance with privacy or regulatory constraints (e.g., ensuring that use of medical sensor data complies with HIPAA), filtering/encrypting/anonymizing data received from the data collection interface before inferencing (e.g., removing PII and replacing with anonymous identifiers), analyzing incoming sensor data based on the rule sets and generating one or more predictive risk profiles (e.g., indicating a degree to which a user is at risk for various different medical conditions), and initiating preemptive tasks based on the predictive output and subject to filtering (e.g., determining actions configured to prevent a dangerous health event).
These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the foregoing underlined limitations constitute “certain methods of organizing human activity” because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). For instance, the steps of analyzing sensor data of a user and generating a predictive risk profile of the user based on the analyzed sensor data are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C). As another example, the steps of managing and applying rule sets, enforcing compliance with regulatory constraints, and initiating preemptive tasks is similar to communicating a notification to a user via a device when preset spending limits stored in a database are reached. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 USPQ2d 1636 (Fed. Cir. 2015). MPEP 2106.04(a)(2)(II)(C).
Independent claim 6 includes limitations that recite at least one abstract idea. Specifically, independent claim 6 recites:
A computing device for preemptive artificial intelligence assisted (AI-assisted) task execution, comprising:
a. a physical housing;
b. a processor within said housing;
c. a non-transitory computer-readable medium storing executable instructions that, when executed by the processor, cause the device to:
i. receive data from one or more onboard or networked sensors;
ii. filter said data based on rule-governed privacy logic;
iii. execute at least one machine learning model on an accelerator device operably connected to said processor to infer contextual insights;
iv. evaluate one or more preloaded governance rules against said inferred contextual insights;
v. trigger an output action via a connected user interface or automation actuator; and
d. at least one sensor operatively connected to said processor.
The Examiner submits that the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a person could practically in their mind with pen and paper perform "preemptive…task execution" via receiving data from one or more onboard or networked sensors (e.g., visually observing data on a screen indicative of the sensed data), filtering the data based on rule-governed privacy logic (e.g., removing PII and other sensitive information), inferring "contextual insights" of the data (e.g., determining that the data collectively indicates a user is likely to become hypoglycemic in the near future), evaluating one or more preloaded governance rules against the inferred insights (e.g., determining if a glucose level associated with the likely hypoglycemic event is below a threshold), and triggering an output action via a connected user interface or automation actuator (e.g., determining and displaying one or more actions to avoid the event, such as consuming fast-acting carbohydrates). These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the foregoing underlined limitations constitute “certain methods of organizing human activity” because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). For instance, the steps of analyzing sensor data of a user and inferring a contextual state of the user based on the analyzed sensor data are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C). As another example, the steps of evaluating one or more preloaded governance rules against the inferred state (e.g., determining if a glucose level associated with the likely hypoglycemic event is below a threshold) and triggering an output action via a connected user interface or automation actuator (e.g., determining and displaying one or more actions to avoid the event, such as consuming fast-acting carbohydrates) is similar to communicating a notification to a user via a device when preset spending limits stored in a database are reached. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 USPQ2d 1636 (Fed. Cir. 2015). MPEP 2106.04(a)(2)(II)(C).
Independent claim 8 includes limitations that recite at least one abstract idea. Specifically, independent claim 8 recites:
A method for executing a preemptive artificial intelligence (AI)-guided task, comprising:
a. receiving, by a computing system, situational input data from one or more sensors;
b. processing said data via a Content Control Module (CCM) to enforce privacy or compliance filtering;
c. activating, by an Inference Agent Controller (IAC), a machine learning inference engine to analyze said filtered input and derive a contextual prediction;
d. applying, by a Rules Controller (RC), one or more domain-specific governance rules to the contextual prediction; and
e. transmitting, by an Action Agent, an intervention signal to a user interface or external system.
The Examiner submits that the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a person could practically in their mind with pen and paper execute a preemptive task via receiving input from sensors (e.g., visually observing data on a screen indicative of the sensed data), "processing" the data to enforce privacy/compliance filtering (e.g., removing PII and replacing with anonymous identifiers), analyzing the filtered input to derive a contextual prediction (e.g., determining that the data collectively indicates a user is likely to become hypoglycemic in the near future), and applying one or more domain-specific governance rules to the contextual prediction (e.g., determining if a glucose level associated with the likely hypoglycemic event is below a threshold). These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the foregoing underlined limitations constitute “certain methods of organizing human activity” because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). For instance, the steps of analyzing sensor data of a user and deriving a contextual prediction based on the analyzed sensor data are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C). As another example, the steps of applying preloaded governance rules against the contextual prediction and triggering intervention is similar to communicating a notification to a user via a device when preset spending limits stored in a database are reached. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 USPQ2d 1636 (Fed. Cir. 2015). MPEP 2106.04(a)(2)(II)(C).
Accordingly, the claims recite at least one abstract idea.
Furthermore, dependent claims 3, 7, 9, and 10 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claim 3 calls for dynamically selecting an inference agent based on input modality and resource availability which a person could practically perform in their mind with pen and paper ("mental processes") at such high level of generality.
-Claim 7 recites how the output action is a dietary recommendation which just further defines the "mental processes" and "certain methods of organizing human activity" discussed above.
-Claim 9 calls for generating an audit log containing a confidence score, a model identifier, and an applied rule set which a person can practically perform in their mind with pen and paper ("mental processes") at such high level of generality.
-Claim 10 recites how the governance rules are dynamically updated based on user response or environmental change which a person can practically perform in their mind with pen and paper ("mental processes") at such high level of generality.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
Independent Claim 1:
A system for (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) performing rule-governed, artificial intelligence (AI)-based preemptive decision-making, comprising:
a. a processor configured to execute non-transitory computer-readable instructions stored in memory (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f));
b. a data collection interface comprising one or more physical sensors selected from the group consisting of biometric sensors, image sensors, environmental sensors, and wearable monitoring devices, said sensors generating real-time, machine-readable data streams (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f));
c. a memory storing one or more rule sets and one or more machine learning inference agents, said rule sets comprising executable instructions that constrain and authorize inference operations and behavioral recommendations (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f));
d. an Inference Agent Controller (IAC) (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) configured to activate said one or more machine learning inference agents, each said inference agent comprising one or more trained inference models (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), each said inference agent executing upon a hardware accelerator selected from the group consisting of graphics processing unit (CPU), tensor processing unit (TPU), or application-specific integrated circuit (ASIC) (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), said one or more inference agents being configured to analyze incoming sensor data and generate one or more predictive risk profiles;
e. a Rules Controller (RC) configured to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) manage and apply said one or more rule sets stored in said memory (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) and enforce compliance with privacy or regulatory constraints by controlling the availability and output of said one or more inference agents (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f));
f. a Content Control Module (CCM) implemented in software and hardware, configured to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) filter, encrypt, or anonymize said machine-readable sensor data prior to inferencing; and
g. an Action Agent coupled to an output interface or actuator, said Action Agent configured to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) initiate one or more preemptive tasks through a physical system or external user interface (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) based on said predictive output of said Inference Agents and subject to filtering by said RC (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)).
Independent Claim 6:
A computing device for (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) preemptive artificial intelligence assisted (AI-assisted) (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) task execution, comprising:
a. a physical housing (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f));
b. a processor within said housing (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f));
c. a non-transitory computer-readable medium storing executable instructions that, when executed by the processor, cause the device to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
i. receive data from one or more onboard or networked sensors;
ii. filter said data based on rule-governed privacy logic;
iii. execute at least one machine learning model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) on an accelerator device operably connected to said processor to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) infer contextual insights;
iv. evaluate one or more preloaded governance rules against said inferred contextual insights;
v. trigger an output action via a connected user interface or automation actuator;
d. at least one sensor operatively connected to said processor (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)).
Independent Claim 8:
A method for executing a preemptive artificial intelligence (AI)-guided (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) task, comprising:
a. receiving, by a computing system (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), situational input data from one or more sensors;
b. processing said data via a Content Control Module (CCM) (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) to enforce privacy or compliance filtering;
c. activating, by an Inference Agent Controller (IAC) (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), a machine learning inference engine to (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) analyze said filtered input and derive a contextual prediction;
d. applying, by a Rules Controller (RC) (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), one or more domain-specific governance rules to the contextual prediction; and
e. transmitting, by an Action Agent, an intervention signal to a user interface or external system (extra-solution activity (transmitting data) as noted below, see MPEP § 2106.05(g)).
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the system including processor executing instructions stored in memory, data collection interface including one or more physical sensors, memory storing rule sets and inferencing models, various controllers and action agent coupled to an output interface or actuator; computing device including physical housing, processor, non-transitory computer-readable medium storing instructions, and accelerator device operably connected to the processor; and computing system including various modules, controllers, and agents; the Examiner submits that these limitations amount to merely using computers and/or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of the preemptive task execution somehow being AI-based/assisted/guided via ML inferencing agents/model/engine and the rule sets "controlling the availability and output of said one or more inference agents," the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id.
Regarding the additional limitation of transmitting the intervention signal to the UI or external system, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (transmitting data) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, independent claims 1, 6, and 8 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, independent claims 1, 6, and 8 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 2 recites how the memory includes configuration data for "Virtual Experts" that each include a "modular logic container" with the rule set(s) and decision thresholds which amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)). In other words, storing configuration data in memory just amounts to using computers as tools in conjunction with performing the above-noted at least one abstract idea.
Claim 4 recites how the Action Agent transmits a recommendation to a user device which merely represents insignificant extra-solution activity (transmitting data) (see MPEP § 2106.05(g)).
Claim 5 recites how the CCM applies homomorphic encryption before processing by the IAC which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
Claim 7 recites how the sensor is a continuous glucose monitor which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, independent claims 1, 6, and 8 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the system including processor executing instructions stored in memory, data collection interface including one or more physical sensors, memory storing rule sets and inferencing models, various controllers and action agent coupled to an output interface or actuator; computing device including physical housing, processor, non-transitory computer-readable medium storing instructions, and accelerator device operably connected to the processor; and computing system including various modules, controllers, and agents; the Examiner submits that these limitations amount to merely using computers and/or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of the preemptive task execution somehow being AI-based/assisted/guided via ML inferencing agents/model/engine and the rule sets "controlling the availability and output of said one or more inference agents," the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id.
Regarding the additional limitations directed to transmitting the intervention signal to the UI or external system which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea (see MPEP § 2106.05(g)) as discussed above, the Examiner has reevaluated such limitations and determined such limitations to not be unconventional as they merely consist of receiving/transmitting data over a network. See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); See MPEP 2106.05(d)(II).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-10 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4, 6, 8, and 10 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by U.S. Patent App. Pub. No. 2023/0377747 to Molero Leon et al. ("Molero Leon"):
Regarding claim 1, Molero Leon discloses a system for performing rule-governed, artificial intelligence (AI)-based preemptive decision-making ([0044]-[0045] discusses using one or more ML models (which is a type of AI) to analyze input data to generate predictions of bleeding events and treatment recommendations (e.g., to reduce/preempt the risk of dangerous outcomes per [0122], [0324]) while [0277] discusses using rules that relate predictions to recommendations), comprising:
a. a processor configured to execute non-transitory computer-readable instructions stored in memory (processor 760 and/or processor 710 in Figure 7 which executes instructions per [0357]);
b. a data collection interface comprising one or more physical sensors selected from the group consisting of biometric sensors, image sensors, environmental sensors, and wearable monitoring devices (user device 705 can include one or more wearable physiological/movement/etc. sensors per [0204], [0234], [0318], [0338] which collectively amount to a "data collection interface"), said sensors generating real-time, machine-readable data streams ([0234] discusses how the sensor data is processed (such that the sensor data is "machine-readable") and which is received/generated continuously per the end of [0318] (such that it is a real-time data stream));
c. a memory (storage 765 and/or storage 715 in Figure 7) storing one or more rule sets ([0277] discloses rules/functions, [0161] discloses data-privacy rules, [0164]-[0177] discloses a custom rule-set; [0266]-[0269] discloses how code 769 stored in storage/memory 765 (Figure 7) can facilitate selection of one of a plurality of AI models based on an extent to which a model-associated use condition is satisfied for a particular subject (e.g., how close particular subject-specific attributes are to those used to train the AI models) which thus amounts to "rule sets"; and [0343] discloses rule-based ML models such as inferring physical activities for each cluster of sensor data based on initial rules and using rules to identify certain sensor ranges and infer activity corresponding to the ranges; all of such rules would be stored in storage/memory as that is how computing systems function) and one or more machine learning inference agents (AI models 767 stored in storage 765 in Figure 7; also, [0039], [0048], [0151], [0263]-[0284], [0343], [0348] disclose use of AI/ML models to generate inferences based on sensor data, all of which would be stored in storage/memory as that is how computing systems function), said rule sets comprising executable instructions (because the system is computer-implemented, the above rules include executable instructions) that constrain and authorize inference operations and behavioral recommendations (as the above rules are used to select one or more of the AI models, then such rules "constrain and authorize" inference operations and behavioral recommendations (e.g., see [0277], [0317], [0332], [0353]) performed by the AI models);
d. an Inference Agent Controller (IAC) configured to activate one or more machine learning inference agents ([0044], [0266]-[0269], [0350] disclose selecting via an ML classifier model or the like (which would occur via code/software/controller, an "IAC") one or more AI/ML models (which are "ML Inference Agents" because they perform inferences as noted above)), each said inference agent comprising one or more trained inference models (the AI models are trained per [0266]-[0276]), each said inference agent executing upon a hardware accelerator selected from the group consisting of graphics processing unit (GPU), tensor processing unit (TPU), or application-specific integrated circuit (ASIC) (Figure 7 illustrates how system 750 includes AI models 767 while [0255] discloses how the processors 760 (which would execute the AI models/inferencing agents 767 because that is how computing systems function) can include a GPU), said one or more inference agents being configured to analyze incoming sensor data and generate one or more predictive risk profiles ([0279], [0317] discloses how the AI models analyze the sensor data to generate predictions regarding probability/risk of medical events, where the predictions for the subject collectively amount to one or more "predictive risk profiles");
e. a Rules Controller (RC) configured to manage and apply said one or more rule sets stored in said memory and enforce compliance with privacy or regulatory constraints (as noted above, [0266]-[0269] discloses how code 769 stored in storage/memory 765 (Figure 7) can facilitate selection of one of a plurality of AI models based on an extent to which a model-associated use condition is satisfied for a particular subject (e.g., how close particular subject-specific attributes are to those used to train the AI models) which thus amounts to "rule sets"; and [0343] discloses rule-based ML models such as inferring physical activities for each cluster of sensor data based on initial rules and using rules to identify certain sensor ranges and infer activity corresponding to the ranges; all of such rules would be stored in storage/memory as that is how computing systems function; furthermore, [0164]-[0177] discusses development, selection, and implementation of rules (governance rules) corresponding to specific criteria such as defined by a medical center (enforcing compliance with regulatory constraints) and [0190] discusses use of rules to comply with data privacy rules; because the system is computer-implemented, there is necessary a set of code/software (a "Rules Controller") that manages/applies the various rule sets and enforces compliance with privacy/regulatory constraints) by controlling the availability and output of said one or more inference agents (as the rule sets serve to select one or more of the AI models (inference agents) to be applied to the data of a particular subject as noted above, then such selection "controls the availability and output" of the AI models/inference agents);
f. a Content Control Module (CCM) implemented in hardware and software, configured to filter, encrypt, or anonymize said machine-readable sensor data prior to inferencing ([0044], [0272] discloses performing pre-processing of the data such as filtering/encoding which would before the inferencing performed by the ML/AI models because that is what "pre-processing" means; furthermore, such pre-processing/filtering/encoding would be performed by some set of code/software executed by one or more processors (collectively, a "CCM") because the system is computer-implemented); and
g. an Action Agent coupled to an output interface or actuator, said Action Agent configured to initiate one or more preemptive tasks through a physical system or external user interface based on said predictive output of said Inference Agents ([0303], [0317], [0324], [0353] discuss generating and outputting on a display (output interface) of a user device an alert regarding a predicted health event and a recommendation to prevent the event (preemptive tasks), where the recommended task is output by the ML/AI models per [0044]-[0045]; furthermore, there would be some code/software ("Action Agent") that displays the recommendation (initiates the preemptive task); still further, the display through which the recommendation/preemptive task is output is a physical system/external user interface) and subject to filtering by said RC ([0277] discloses how rules can relate predictions to recommendations based on subject-specific data such that the recommendations would be "filtered" by the RC (i.e., the set of code/software that implements the rules) based on the predictions and subject-specific data; also, [0302] disclose how recommended treatments can be adjusted/selected/filtered based on predicted probabilities).
Regarding claim 2, Molero Leon discloses the system of Claim 1, further including wherein said memory includes configuration data for Virtual Experts, each comprising a modular logic container with said one or more rule sets and decision thresholds ([0166]-[0177] discusses how different medical centers can have different rule sets with decision thresholds (e.g., if inhibitor titer is greater than 5 BU, then increase dosage of treatment or change treatment) stored in a data store/registry 140 (memory), where [0252]-[0253] discloses how the system 750 of Figure 7 can include a cloud based system (e.g., the cloud network 130 of Figure 1); each collection of rule sets and decision thresholds is a "modular logic container" of a respective "Virtual Expert" (which represents each respective medical center)).
Regarding claim 4, Molero Leon discloses the system of Claim 1, further including wherein the Action Agent transmits a recommendation to a user device selected from the group consisting of a smartwatch, smartphone, or tablet ([0303], [0317], [0324], [0353] discuss generating and outputting on a display (output interface) of a user device an alert regarding a predicted health event and a recommendation to prevent the event (preemptive tasks), where the recommended task is output by the ML/AI models per [0044]-[0045]; furthermore, there would be some code/software ("Action Agent") that displays the recommendation; [0204], [0318] discloses how the user device can be a smartphone/smart watch/etc..
Regarding claim 6, Molero Leon discloses a computing device (server 135 in Figure 1 and/or central AI system 750 in Figure 7; also see [0253]) for preemptive artificial intelligence assisted (AI)-assisted task execution ([0044]-[0045] discusses using one or more ML models (which is a type of AI) to analyze input data to generate predictions of bleeding events and treatment recommendations (e.g., to reduce/preempt the risk of dangerous outcomes per [0122], [0324]), comprising:
a. a physical housing (a computing system necessarily has a housing);
b. a processor within said housing (processor 760 in Figure 7 which would be within the housing);
c. a non-transitory computer-readable medium storing executable instructions that, when executed by the processor ([0357] discloses a non-transitory computer readable storage medium containing instructions executable by the processor), cause the device to:
i. receive data from one or more onboard or networked sensors ([0272] discusses performing pre-processing on collected raw data which is from networked sensors 725/730; also see [0292]-[0293], [0305], [0318] which discloses receiving patient data from sensors);
ii. filter said data based on rule-governed privacy logic ([0190] discusses use of rules to comply with data privacy rules which would be implemented by code/software ("rule-governed privacy logic") and which would result in filtering the data (e.g., removing PII));
iii. execute at least one machine learning model on an accelerator device operably connected to said processor to infer contextual insights ([0342]-[0343] discloses using an ML model to infer/predict a physical activity (contextual state) of a user; also, [0044]-[0045] discloses using ML models to generate predictions of health states (infer contextual insights); still further, [0255] discloses how the processors 760 (which would execute the ML models because that is how computing systems function) can include a graphical processing unit such that the ML model can be executed on an "accelerator device");
iv. evaluate one or more preloaded governance rules against said inferred contextual insights ([0277] discusses using rules that relate predictions to recommendations such that the rules would be evaluated against the predicted state to determine recommendations);
v. trigger an output action via a connected user interface or automation actuator ([0303], [0317], [0324], [0353] discuss generating and outputting on a display (output interface) of a user device an alert regarding a predicted health state and a recommendation, where the recommended task is output by the ML/AI models per [0044]-[0045]); and
d. at least one sensor operatively connected to said processor ([0272] discusses performing pre-processing on collected raw data which is from networked sensors 725/730 which are operatively connected to processor 710; also see [0292]-[0293], [0305], [0318] which discloses receiving patient data from sensors which are operatively connected to processor 760)).
Regarding claim 8, Molero Leon discloses a method for executing a preemptive artificial intelligence (AI)-guided task ([0044]-[0045] discusses using one or more ML models (which is a type of AI) to analyze input data to generate predictions of bleeding events and treatment recommendations (e.g., to reduce/preempt the risk of dangerous outcomes per [0122], [0324]), comprising:
a. receiving, by a computing system, situational input data from one or more sensors ([0272] discusses performing pre-processing on collected raw data which is from networked sensors 725/730; also see [0292]-[0293], [0305], [0318] which discloses receiving patient data from sensors such as physiological data (situational input data); furthermore, Figure 7 illustrates computing system(s));
b. processing said data via a Content Control Module (CCM) to enforce privacy or compliance filtering ([0190] discusses use of rules to comply with data privacy rules which would be implemented by code/software ("CCM") and which would result in filtering the data (e.g., removing PII));
c. activating, by an Inference Agent Controller (IAC), a machine learning inference engine to analyze said filtered input and derive a contextual prediction ([0342]-[0343] discloses using an ML model/engine to infer/predict a physical activity (contextual prediction) of a user; also, [0044]-[0045] discloses using ML models to generate predictions of health states (derive contextual prediction); still further, there is some set of software/code/instructions (an "Inference Agent Controller") that executes/activates the ML model/engine to analyze the data to derive the contextual prediction);
d. applying, by a Rules Controller (RC), one or more domain-specific governance rules to the contextual prediction ([0277] discusses using rules ("domain-specific governance rules") that relate predictions to recommendations such that the rules would be applied to the contextual prediction to determine recommendations; furthermore, there is some set of software/code/instructions ("RC") that applies the rules to the contextual prediction); and
e. transmitting, by an Action Agent, an intervention signal to a user interface or external system ([0303], [0317], [0324], [0353] discuss generating and outputting on a display (user interface) of a user device an alert ("intervention signal") regarding a predicted health state and a recommendation, where there is some set of software/code/instructions ("Action Agent") that transmits the signal to the user interface).
Regarding claim 10, Molero Leon discloses the system of Claim 8, further including wherein said governance rules are dynamically updated based on user response or environmental change ([0277] discusses adjusting (updating) the rules based on subject-specific data (user response)).
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 3 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0377747 to Molero Leon et al. ("Molero Leon") in view of U.S. Patent App. Pub. No. 2024/0274292 to Akdemir et al. ("Akdemir"):
Regarding claim 3, Molero Leon discloses the system of Claim 1, further including wherein said IAC dynamically selects an inference agent based on input modality ([0266] discusses selecting the set of AI models (inference agents) based on hemophilia type/inhibitor status (input modality); [0350] discusses dynamically selecting the model (inference agent) based on sensor data (input modality))…
However, Molero Leon appears to be silent regarding the inference agent being selected based on resource availability.
Nevertheless, Akdemir teaches ([0094]-[0095]) that it was known in the machine learning art to select an ML Model based on resource availability to advantageously facilitate increased predictive performance.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the inference agent of Molero Leon to be selected based on resource availability as taught by Akdemir to advantageously facilitate increased predictive performance and because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0377747 to Molero Leon et al. ("Molero Leon") in view of U.S. Patent App. Pub. No. 2023/0025754 to Hassanzadeh et al. ("Hassanzadeh"):
Regarding claim 5, Molero Leon discloses the system of Claim 1, further including anonymizing the subject records to prevent identification of the subjects ([0193]).
However, Molero Leon appears to be silent regarding wherein said CCM applies homomorphic encryption prior to processing by the IAC.
Nevertheless, Hassanzadeh teaches ([0030]) that it was known in the machine learning and healthcare informatics art to apply homomorphic encryption to client-specific medical data before processing by a trained ML model to advantageously ensure privacy of sensitive medical data in a manner that allows the trained ML model to operate directly on the encrypted data and generate and an encrypted prediction that can later be decrypted to obtain a prediction based on the medical data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the CCM to apply homomorphic encryption before processing by the IAC in the system of Molero Leon as taught by Hassanzadeh to advantageously ensure privacy of sensitive medical data in a manner that allows the IAC to operate directly on the encrypted data and generate and an encrypted prediction that can later be decrypted to obtain a prediction based on the medical data. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0377747 to Molero Leon et al. ("Molero Leon") in view of NPL "Hemophilia Nutrition for Management" ("Hemophilia Nutrition") and U.S. Patent App. Pub. No. 2018/0272065 to Talbot et al. ("Talbot"):
Regarding claim 7, Molero Leon discloses the device of Claim 6, further including where the hemophilia diagnosis can be based on glucose levels ([0095]).
However, Molero Leon appears to be silent regarding wherein said sensor includes a continuous glucose monitor, and said output action comprises a dietary recommendation.
Nevertheless, Hemophilia Nutrition teaches that it was known in the healthcare informatics art to recommend particular diets having particular nutrients to maintain bone density/health, improve blood clotting, etc. for people with hemophilia.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the output action of Molero Leon to have been a dietary recommendation as taught by Hemophilia Nutrition to advantageously recommend actions to maintain bone density/health, improve blood clotting, etc. for people with hemophilia. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Furthermore, Talbot teaches ([0024]) that it was known in the healthcare informatics art to measure a person's glucose levels (with a continue glucose monitor per [0074]), analyze the glucose levels to determine a risk score using a model (ML per [0134]), and generate recommendation to reduce the risk score.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have measured the glucose levels of Molero Leon with a continuous glucose monitoring sensor as taught by Talbot to facilitate determination of recommended actions to reduce a risk of a health event and because continuous glucose sensors are a common type of glucose monitor. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2023/0377747 to Molero Leon et al. ("Molero Leon") in view of U.S. Patent App. Pub. No. 2023/0409647 to Fleming et al. ("Fleming") and U.S. Patent App. Pub. No. 2023/0207126 to Molero Leon et al. ("Molero Leon '126").
Regarding claim 9, Molero Leon discloses the method of claim 8, but appears to be silent regarding generating an audit log containing a confidence score, a model identifier, and the applied rule set.
Nevertheless, Fleming teaches ([0172]) that it was known in the machine learning art to display on a user interface (generate an "audit log") an indication of a selected ML model and a confidence value associated with predictions from the selected model which advantageously provides a user with an indication as to whether the selected model is sufficient for analysis or whether another model should be selected/trained.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have generated an indication (an "audit log") of selected model and confidence value associated with predictions from the selected model in the system of Molero Leon as taught by Fleming which advantageously provides a user with an indication as to whether the selected model is sufficient for analysis or whether another model should be selected/trained. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Furthermore, Molero Leon '126 discloses ([0115]) that it was known in the healthcare informatics art to display in a user interface a subset of rules utilized to determine treatment recommendations for a subject along with estimated responsiveness metrics to advantageously allow users to gauge effectiveness of selected rules and determine whether new rules should be utilized.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the displayed indication of the Molero Leon/Fleming combination to further include the applied rules as taught by Molero Leon '126 (such that the "audit log" includes the selected model, the confidence score, and the applied rule(s)) to advantageously allow users to gauge effectiveness of selected rules and determine whether new rules should be utilized. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686