Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is in reference to the communication filed on 23 DEC 2025.
Amendments to claims 1, 9, 13, 14, have been entered and considered. No claims added or removed.
Claims 1-15 are present and have been examined.
Claim Rejections - 35 USC § 112
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 1-8 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 has been amended to state: “An artificial intelligence based patient assessment system for assessing a patient, comprising: a patient assessment system.” As the claim now recites a system comprising a same named system, the scope of the claim is indefinite, as it is unclear if the intent is for the same system to be repeated, or if one system encompasses another. The newly amended portion does not appear to rely on the preamble for antecedent basis (i.e., “a patient assessment system” rather than “the patient assessment system”), but nevertheless the scope of the claim is unclear due to the repetition of these terms. Put another way, the language is similar to using a word itself in its own definition. Correction is required.
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 1-15 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-15 the independent claim(s) 1, 9, 13, 14, each recite(s) a method or system, both of which are a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-15, the independent claim(s) (claims 1, 9, 13, 14, ) is/are directed, in part, to “a patient assessment system for assessing a patient comprising at least one AI based algorithm; …process data to provide a training result for the at least one AI based algorithm to process data…; receiving the training result…; generate an assessment including a diagnosis, a recommended decision and/or a recommended action based on the training result; and provide the training result and or the assessment to the patient assessment system” as in claims 1, 13, and “…a patient assessment system… to process data to provide a result for at least one artificial intelligence based algorithm…; …generating the training result; generate an assessment including a diagnosis, a recommended decision and/or a recommended action based on the training result; and provide the training result and/or the assessment to the patient assessment system” as in claims 9, 14.
These claim elements are considered to be abstract ideas because they are directed to mathematical concepts such as a relationships, formulas, equations, and/or calculations. The training and subsequent application of an “AI based algorithm” is a mathematical concept. Further, the elements of assessing a patient, processing information to create a result, (i.e. generating an assessment) as well as providing the result of the training/assessment, recite arguably a mental process, which includes concepts performed in the human mind such as observation, evaluation, judgment, and opinion. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, formulas, equations, or calculations, then it falls within the “mathematical concept” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind such as observation, evaluation, judgement, opinion, then it falls within the “mental process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements to perform the claim steps. Claim 1, 13 recites a local data system disparate from the patient assessment system, wherein information contained within is only accessible via the local data system; computer code provided to a local data system from a patient assessment system and is adapted to process data in the system, and receiving data upon execution of the code. Claims 9, 14 have been amended similarly with regard to the local data store. Claim 9 recites a processor, and claim 13 recites a computer readable medium and a processor. Claims 9, 14 recite similar elements of sending receiving and executing code.
Examiner finds that the processors in each of the independent claims
Examiner finds that the amended elements regarding the “local data system” are found to be at best analogous to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Examiner also notes this appears to be an “application” of federated learning, a well-known technique in distributed machine learning. Similarly, the computer code and the execution thereof is also found to be merely using a computer as a tool (at best). Examiner also notes that the sending and receiving of the code/data, as well as the storage therein of data is found to be insignificant extra solution activity (see MPEP 2106.05g).
The processors in claims 1, 9, 13, as well as the computer readable medium are found to be the equivalent of adding the term “apply it” to the general realm of computing (see MPEP 2106.05(f)), and that these elements represent no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Examiner further notes no improvement is found to the functioning of the computer or any other technology or technological field (see MPEP 2106.05(a)).
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: Claim 1, 13 recites computer code provided to a local data system from a patient assessment system and is adapted to process data in the system, and receiving data upon execution of the code. Each of claims 1, 9, 13, 14 have been amended to recite a local data system disparate from the patient assessment system. Claims 9, 14 recite similar elements of sending receiving and executing code. Claims 1, 9, 13 further recite a processor, and claims 1, 13 recite a non-transitory computer readable medium. When considered individually, the identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in
[0014] The computer code may be (directly) executable, such as in the form of a native code and/or a native application. It may also generally use principles of edge computing and/or edge applications and/or smart applications. The computer code may be provided with access rights to access (confidential) data stored in the local data system (or confidential parts thereof), if executed in the local data system. The functions of the patient assessment system may be implemented in an embedded application, for example. The embedded application may be based on AI algorithms, such as an artificial neural network, for example.
[0018] The data of the local data system may include one or more patient data sets. For example, they may be stored in an electronic health file, e.g., in a hospital system. The patient data may only be locally accessed (e.g., within the local data system, e.g., disparate from the patient assessment system).
[0048]… The computer code may be adapted to, when executed, process data to provide a training result for at least one AI based algorithm (of the patient assessment system), wherein the computer code is adapted to be executed in the local data system to process data stored in the local data system, e.g., as outlined herein. The local data system may further comprise means for executing the computer code to generate the training result. Further, the local data system may comprise means for providing the training result to the patient assessment system. Hence, the data may effectively be used by the patient assessment system for training without the data leaving the local data system.
[0053] The method may comprise the step of providing computer code to a local data system, wherein the computer code is adapted to, when executed, process data to provide a training result for the AI based algorithm, wherein the computer code is adapted to be executed in the local data system to process data stored in the local data system. The method may include providing the computer code to the local data system by a patient assessment system as described herein. The method may further include the step of receiving the training result from the local data system upon execution of the computer code in the local data system.
[0059] Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, FPGA, CD/DVD or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-8, 10-12, 15:
Dependent claims 2-8, 10-12, 15 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as the updating of the algorithm, providing the results, and securing the transmission(s). While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
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) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Priyankara et al (US 20190380592 A1, hereinafter Priyankara) in view of Knighton et al (US 20210225463 A1 hereinafter Knighton).
In reference to claim 1, 13:
Priyankara teaches: An artificial intelligence (AI) based patient assessment system, and method, both for assessing a patient, comprising:
A computer readable medium (at least [fig 1 and related text] computer readable instructions) including at least one AI based algorithm (at least [fig 3 and related text] “a signal processing component/module/unit algorithm 108 for processing the set of signals,”);
a processor configured to (at least [fig 1 and related text’ processor and memory on server 100):
provide computer code to a local data system wherein the computer code is adapted to, when executed, process data to provide a training result for the at least one AI based algorithm, wherein the computer code is adapted to be executed in the local data system to process data stored in the local data system (at least [071] “Data from the set of features (also known as indices) including the identity pulse features from the identity pulse 40 can be compared to the patient database 110 to obtain predictive results on vascular health conditions of the patient 20. In addition, data from the set of features associated with the patient 20 can be updated into the patient database 110, thereby including the patient 20 into the patient population inside the patient database 110. The inclusion of new data into the patient database 20 facilitates training of and learning by the machine learning component 112.” At [089] “ Data from the set of features may be used for assessing risks of vascular health conditions of the patient 20, i.e. a diagnosis mode or process, and/or use for training the machine learning component 112, i.e. a training mode or process.” See [fig 1 and related text] “In another embodiment as shown in FIG. 1, the system 10 includes an electronic device 300 communicatively linked to or communicable with the monitoring device 200 and/or server 100. The electronic device 300 functions as an intermediary device between the server 100 and monitoring device 200, such that the monitoring device 200 is communicable with the server 100 via the electronic device 300. For example, the monitoring device 200 measures the vascular activity of the patient 20 and automatically communicates data derived from these vascular activity measurements to the server 100 via the electronic device 300.”);
receive the training result from the local data system upon execution of the computer code in the local data system (at least [fig 3 and related text] The communication from the monitoring device 200 to the server 100 occurs through the API 104 implemented in the application 102 of the server 100. Once data including the set of signals (digital signals) is communicated to and received by the application 102, the set of signals is processed by the signal processing component 108 and the set of features is determined. Data from set of features including the identity pulse features are stored in the patient database 110 for comparison by the machine learning component 112. After storing the data, the set of features are classified and the patient risk assessment of vascular health conditions is generated using the machine learning module 112, such as by calculating probability scores or values associated with such risks… The patient risk assessment for vascular health conditions is shown to the patient 20 (client side). In one embodiment, the patient risk assessment is communicated to the electronic device 300 and presented to the patient 20 on a graphical user interface (GUI) 302, e.g. web browser or mobile application executed on the electronic device 300.”) and;
generate an assessment including a diagnosis a recommended decision and/or a recommended action based on the training result (at least [fig 2 and related text] step 410 – generating a patient risk assessment of vascular health; at [089] “Data from the set of features may be used for assessing risks of vascular health conditions of the patient 20, i.e. a diagnosis mode or process, and/or use for training the machine learning component 112, i.e. a training mode or process. “).
Although Priyankara as cited teaches all the limitations above, as well as modeling at remote locations (see fig 1 and related text), Examiner notes that the reference does not teach Federated learning as claimed. Knighton however does teach:
A patient assessment system (at least [figs 4a/4b and related text, see also fig 12 and related text] clinical trial assessment system, and allergy prediction applications).
A local data system that is disparate from the patient assessment system, wherein information contained within the local data system is only accessible via the local data system (at least [041] “End users use application programs on their respective devices (also referred to as edge devices) by which end users collect data, train, compute, and evaluate data stored in devices these application programs run on. No data leaves devices where it is stored and computed. “ at [fig 3 and related text] “Each edge device can comprise a memory, configured to store end user data and a federated learner, configured to send gradients (or tensors), wherein such gradients (or tensors) are calculated based on the end user data stored in the memory… A federated learner (Flea) can be implemented as an end user side library, built for an edge device environment, to perform local model update calculations using data collected in the edge device environment. The Flea can perform post-processing after model updating, including applying perturbations (e.g., encryption and introduction of noise for privacy purposes), sharing the model update with a central update repository (i.e., an FL aggregator), optionally downloading updated models, evaluating updated models, and sharing evaluation metrics across platforms, e.g., Flea-iOS (for iPhones), Flea-Android (for Android phones), Flea-kubernetes (for node clients), etc.”); and
A processor operatively associated with a non-transitory computer readable medium including at least one AI based algorithm; the processor configured to:
Provide computer code to a local data system, wherein the computer code is adapted to, when executed, process data to provide a training result for the at least one AI based algorithm, wherein the code is adapted to be executed in the local data system to process data stored in the local data system (at least fig 10 and related text] “We refer to FIG. 10, presenting a federated loop 1015 to explain the federated learning workflow. In a federated workflow, we start with a base model 1051 that may have been trained in this conventional manner. Once this base model 1051 is trained, refinement can proceed without centrally collecting any further data. Instead, the base model 1051 is distributed to individual edge devices 151A-151N. These edge devices perform local training to generate local model updates 1057, using data (not shown) that is on those devices. The federated workflow aggregates the local updates into a new global model 1059 which will become our next base model 1051 that will be used for inference and additional rounds of training a federated loop 1015. Again, updating via the federated loop 1015 does not require centrally collecting data. Instead, we're sending the model to the data for training, not bringing data to the model for training. This is a decentralized workflow instead of a centralized workflow.”);
Receive the training result from the local data system upon execution of the computer code in the local data system (at least [041] “ Devices later federate data globally by sending “derived insights,” technically a bunch of tensors, to a computing cloud where all these derived insights are averaged. Devices then receive from the computing cloud an updated matrix which can improve local prediction of these devices. The improved local prediction again improves derived insights as updates. With federated learning, a device on the edge can send potentially de-identified updates to a model instead of having to accept the burden of sending over the entirety of its raw data in order for the model to be updated.”);
Providing the training result and or the assessment to the patient assessment system (at least [fig 4a/4b, 12 and related text] “The clinical trial objective mapper maps inputs to target mapping which can be a prediction of allergy risk such as “none”, “mild”, “moderate”, or “severe”. A trained clinical trial objective mapper 1205 can be used to predict a disease or symptoms of a disease, efficacy of a treatment, drug or therapy, health anomaly such as when patient or end user's input data is out bounds or out of range compared with participants of the clinical trial. The model prediction can be displayed using a prediction interface 1210.” “at [o46] “he target mapping maps participant-specific clinical data to an objective of a virtual clinical trial. The participant-specific clinical trial data is the input to the clinical trial objective mapper and is entered by the respective participants on their edge devices. Examples of objective of a clinical trial can include predicting a disease, predicting symptom of disease, or predicting efficacy of a treatment, etc.”)).
Priyankara and Knighton are analogous references as both disclose a means of health predictions using modeling. One of ordinary skill in the art therefore would have found it obvious to include the federated modeling as taught by Knighton, as Knighton teaches that federated learning (i.e. device side training of a model vs. centralized training) is particularly applicable to sensitive data such as anything pertaining to PII/health information: “ As a result, federated learning greatly reduces privacy concerns since the data never leaves these devices, just an encrypted, perturbed gradient of data leave. Federated learning further greatly reduces ownership concerns as end users are enabled to opt in or out to share updates created in devices. Federated learning further greatly reduces security concern, because there is no single point of failure overall to the whole system, and hackers cannot hack millions of phones one by one. We use federated learning to conduct clinical trials or studies as described in the following sections.” (see 0041). As such, in order to protect patient privacy of sensitive information, the use of federated learning as taught by Knighton would have been an obvious improvement to the modeling of Priyanka.
In reference to claim 2:
Priyankara further teaches wherein the processor is configured to generate the computer code (at least [fig 3 and related text] server 100).
In reference to claim 3:
Priyankara further teaches wherein the processor is configured to update the at least one algorithm based on the training result (at least [096] “The machine learning component 112 is continuously trained for different groups in the population of patients 20 by introducing more training data obtained from the patients 20. The machine learning component 112 can adaptively optimize its classification using the set of features derived from the training data set for each group or sub-population.”).
In reference to claim 4:
Priyankara further teaches: wherein the updated algorithm is adapted for determining a medical condition of the patient at least in part based on data associated with the patient and based on the training result (at least [071] “The inclusion of new data into the patient database 20 facilitates training of and learning by the machine learning component 112. Furthermore, the patient population may be classified based on their vascular health status. For example, some patients 20 may have vascular health conditions which can be detected by standard medical tests, while other patients 20 may have vascular health conditions in the early stages which cannot be detected by standard medical tests.” At [095] “In the training mode or process, different SVM models are trained depending on the personal parameters input by the patient 20. After the training and classification of the SVM models for a given set of features, the probability scores or values associated with such risks are calculated to determine the likelihood of the set of features belonging to the group which resulted from the SVM classification.”)
In reference to claim 5:
Priyankara further teaches wherein the processor is configured to forward the determined medical condition to the patient and/or to medical staff (at least [fig 3 and related text] “The patient risk assessment for vascular health conditions is shown to the patient 20 (client side). In one embodiment, the patient risk assessment is communicated to the electronic device 300 and presented to the patient 20 on a graphical user interface (GUI) 302, e.g. web browser or mobile application executed on the electronic device 300. In another embodiment, the monitoring device 200 includes a graphical user interface on which the patient 20 can directly view the patient risk assessment. The patient risk assessment may be presented to the patient 20 in a visual or graphic format such that they can be readily understood by the patient 20.”).
In reference to claim 6:
Priyankara further teaches: wherein the algorithm and/or the updated algorithm is and/or are configured to determine a medical condition of the patient based on data associated with the patient; generate, in case the medical condition of the patient cannot be determined based on the data associated with the patient, the computer code based on data associated with the patient, wherein the computer code is adapted for obtaining a training result required for determining the medical condition (at least [044] “After storing the data, the set of features are classified and the patient risk assessment of vascular health conditions is generated using the machine learning module 112, such as by calculating probability scores or values associated with such risks. The vascular health conditions may be related to some vascular anomaly or some cardiovascular disease and the probability scores or values may indicate risks of the patient 20 having or being diagnosed such disease conditions. When these risks are assessed for individuals who are not diagnosed with such diseases, then the probability scores or values may indicate future tendency of having such diseases, thereby giving predictive insights to the patient 20.”).
In reference to claim 7:
Priyankara further teaches: wherein the processor is configured to provide the computer code to the local data system in a secured manner and/or the means for receiving the training result from the local data system is adapted to the receive the training result in a secured manner. (at least [046] “It will be appreciated that communications between the server 100, monitoring device 200, and electronic device 300 may occur via wired connections or via wireless communication protocols. The communications may be secured by protocols such as Hypertext Transfer Protocol Secure (HTTPS) and/or implemented with network security systems such as computing firewalls, as will be readily understood by the skilled person.”
In reference to claim 8:
Priyankara further teaches: wherein the system is a cardiologic system (at least [figs 1, 2, 4, 5] system measures cardiovascular signals/analyzes them.).
In reference to claim 9, 14:
Priyankara further teaches: A local data system, and A method, both comprising:
receiving computer code from a patient assessment system, wherein the computer code is adapted to, when executed, process data to provide a training result for at least one artificial-intelligence (AI) based algorithm, wherein the computer code is adapted to be executed in the local data system to process data stored in the local data system (at least [071] “Data from the set of features (also known as indices) including the identity pulse features from the identity pulse 40 can be compared to the patient database 110 to obtain predictive results on vascular health conditions of the patient 20. In addition, data from the set of features associated with the patient 20 can be updated into the patient database 110, thereby including the patient 20 into the patient population inside the patient database 110. The inclusion of new data into the patient database 20 facilitates training of and learning by the machine learning component 112.” At [089] “ Data from the set of features may be used for assessing risks of vascular health conditions of the patient 20, i.e. a diagnosis mode or process, and/or use for training the machine learning component 112, i.e. a training mode or process.” See [fig 1 and related text] “In another embodiment as shown in FIG. 1, the system 10 includes an electronic device 300 communicatively linked to or communicable with the monitoring device 200 and/or server 100. The electronic device 300 functions as an intermediary device between the server 100 and monitoring device 200, such that the monitoring device 200 is communicable with the server 100 via the electronic device 300. For example, the monitoring device 200 measures the vascular activity of the patient 20 and automatically communicates data derived from these vascular activity measurements to the server 100 via the electronic device 300.”);
executing the computer code to generate the training result (at least [figs 2, 3, and related text] “The electronic device 300 thus captures the personal parameters and communicates them to the server 100 for training the machine learning component 112, improving the accuracy of results and classifications for different groups of patients 20. The personal parameters that are captured from the patient 20 include, but are not limited to the followings list.” At [095] “In the training mode or process, different SVM models are trained depending on the personal parameters input by the patient 20. After the training and classification of the SVM models for a given set of features, the probability scores or values associated with such risks are calculated to determine the likelihood of the set of features belonging to the group which resulted from the SVM classification.”);
generate an assessment including a diagnosis a recommended decision and/or a recommended action based on the training result (at least [fig 2 and related text] step 410 – generating a patient risk assessment of vascular health; at [089] “Data from the set of features may be used for assessing risks of vascular health conditions of the patient 20, i.e. a diagnosis mode or process, and/or use for training the machine learning component 112, i.e. a training mode or process. “)
Although Priyankara as cited teaches all the limitations above, as well as modeling at remote locations (see fig 1 and related text), Examiner notes that the reference does not teach Federated learning as claimed. Knighton however does teach:
The local data system that is disparate from the patient assessment system, wherein information contained within the local data system is only accessible via the local data system (at least [041] “End users use application programs on their respective devices (also referred to as edge devices) by which end users collect data, train, compute, and evaluate data stored in devices these application programs run on. No data leaves devices where it is stored and computed. “ at [fig 3 and related text] “Each edge device can comprise a memory, configured to store end user data and a federated learner, configured to send gradients (or tensors), wherein such gradients (or tensors) are calculated based on the end user data stored in the memory… A federated learner (Flea) can be implemented as an end user side library, built for an edge device environment, to perform local model update calculations using data collected in the edge device environment. The Flea can perform post-processing after model updating, including applying perturbations (e.g., encryption and introduction of noise for privacy purposes), sharing the model update with a central update repository (i.e., an FL aggregator), optionally downloading updated models, evaluating updated models, and sharing evaluation metrics across platforms, e.g., Flea-iOS (for iPhones), Flea-Android (for Android phones), Flea-kubernetes (for node clients), etc.”).
Providing the training result and or the assessment to the patient assessment system (at least [fig 4a/4b, 12 and related text] “The clinical trial objective mapper maps inputs to target mapping which can be a prediction of allergy risk such as “none”, “mild”, “moderate”, or “severe”. A trained clinical trial objective mapper 1205 can be used to predict a disease or symptoms of a disease, efficacy of a treatment, drug or therapy, health anomaly such as when patient or end user's input data is out bounds or out of range compared with participants of the clinical trial. The model prediction can be displayed using a prediction interface 1210.” “at [o46] “he target mapping maps participant-specific clinical data to an objective of a virtual clinical trial. The participant-specific clinical trial data is the input to the clinical trial objective mapper and is entered by the respective participants on their edge devices. Examples of objective of a clinical trial can include predicting a disease, predicting symptom of disease, or predicting efficacy of a treatment, etc.”)).
Priyankara and Knighton are analogous references as both disclose a means of health predictions using modeling. One of ordinary skill in the art therefore would have found it obvious to include the federated modeling as taught by Knighton, as Knighton teaches that federated learning (i.e. device side training of a model vs. centralized training) is particularly applicable to sensitive data such as anything pertaining to PII/health information: “ As a result, federated learning greatly reduces privacy concerns since the data never leaves these devices, just an encrypted, perturbed gradient of data leave. Federated learning further greatly reduces ownership concerns as end users are enabled to opt in or out to share updates created in devices. Federated learning further greatly reduces security concern, because there is no single point of failure overall to the whole system, and hackers cannot hack millions of phones one by one. We use federated learning to conduct clinical trials or studies as described in the following sections.” (see 0041). As such, in order to protect patient privacy of sensitive information, the use of federated learning as taught by Knighton would have been an obvious improvement to the modeling of Priyanka.
In reference to claim 10:
Priyankara further teaches: wherein the wherein the processor is configured for receiving the computer code is adapted to receive the computer code in a secured manner from the patient assessment system and/or providing the training result to the patient assessment system in a secured manner (at least [046] “It will be appreciated that communications between the server 100, monitoring device 200, and electronic device 300 may occur via wired connections or via wireless communication protocols. The communications may be secured by protocols such as Hypertext Transfer Protocol Secure (HTTPS) and/or implemented with network security systems such as computing firewalls, as will be readily understood by the skilled person.”)
In reference to claim 11:
Priyankara further teaches: wherein the local data system is at least one of a data system of: a hospital, a health insurance company, a clinic network, a doctoral association, a medical IT-service provider, a health care provider, a network for medical services, national network for health data, an authority (at least [009] “An advantage of the present disclosure is that by reconstructing and using an identity pulse for comparison, there is a more standardized approach which patients, clinics, medical facilities, etc. can adopt to assess risks of vascular health conditions.” At [0103] “The use of an identity pulse provides a more standardized approach which patients, clinics, medical facilities, etc. can adopt for making comparisons and assessing risks of vascular health conditions.”)
In reference to claim 12:
Priyankara further teaches: a system comprising a patient assessment system for assessing a patient (at least [fig 1 and related text] “The system 10 further includes a monitoring device 200 connected or connectable to a patient/subject/user 20 and communicatively linked to or communicable with the server 100… n another embodiment as shown in FIG. 1, the system 10 includes an electronic device 300 communicatively linked to or communicable with the monitoring device 200 and/or server 100. The electronic device 300 functions as an intermediary device between the server 100 and monitoring device 200, such that the monitoring device 200 is communicable with the server 100 via the electronic device 300. For example, the monitoring device 200 measures the vascular activity of the patient 20 and automatically communicates data derived from these vascular activity measurements to the server 100 via the electronic device 300. Alternatively, the monitoring device 200 may first communicate the data to the electronic device 300 for storage, and the electronic device 300 communicates the data to the server 100 at a later time.”)
In reference to claim 15:
Priyankara further teaches: A computer program comprising instructions which, when executed, cause a computer to perform the steps according to the method of claim 13 (at least [fig 1 and related text] “The system 10 includes a host server or server 100 having a processor and a memory configured to store computer-readable instructions. The processor (also referred to as a central processor unit or CPU) is in communication with memory devices including secondary storage (such as disk drives or memory cards), read only memory (ROM), and random access memory (RAM). The processor may be implemented as one or more CPU chips. The processor executes instructions, codes, computer programs, scripts which it accesses from hard disk, floppy disk, optical disk (these various disk based systems may all be considered secondary storage), flash drive, ROM, RAM, or network connectivity devices. The server 100 may include one or multiple processors. Thus, while instructions may be discussed as executed by a processor, the instructions may be executed simultaneously, serially, or otherwise executed by one or multiple processors.”
Response to Arguments
Applicant’s remarks as filed on 23 DEC 2025 have been fully considered.
Applicant’s amendments to claim 1 have negated the rejection for signals, as such the rejection is withdrawn.
Applicant’s remarks regarding the 101 rejection of claims 1-15 begin on page 7 of the response. The amended limitations have been addressed above. Examiner respectfully submits that no improvement is found to the functioning of the computing devices as amended, nor has an improvement to a technical field been found. Storage of data is not found to integrate the abstract idea into a practical application.
Applicant turns to the prior art rejection on page 8 of the remarks. Applicant’s remarks on page 9 appear focused on the newly amended limitations, to which Knighton has been cited. Knighton provides extensive disclosure regarding federated learning and it’s applicability to the healthcare environment. Applicant’s remaining remarks regarding dependent claims 2-8, 10-12, 15 are noted but found moot in view of the updated rejection above.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US20190180841, to Douglas, discloses a means of modeling disease risks using artificial intelligence.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on 571-270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687