DETAILED ACTION
This action is in response to the claims filed 03/06/2024 for Application number 18/689,859. Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/10/2024, 10/13/2025, and 03/29/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
generating predicted quantitative values informative of the performance of the one or more clinical trial sites [by applying a trained federated learning model to the plurality of clinical operation data], can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “by applying a trained federated learning model to the plurality of clinical operation data”. Thus, this element in the claim is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f).
Additionally, the claim recites the additional elements – ““wherein the trained federated learning model is trained using a federated network” and “wherein the federated network renders inaccessible a first dataset of a first party to a second party, and further renders inaccessible a second dataset of the second party to the first party.”
These elements that are recited are only generally linked to the judicial exception or confine the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: obtaining input values of a plurality of clinical operation data associated with the one or more clinical trial sites. This limitation is a mere data gathering step and thus is an insignificant extra-solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a trained federated learning model to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Furthermore, the limitation of obtaining input values of a plurality of clinical operation data associated with the one or more clinical trial sites is well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components, generally linking the judicial exception to a field of use or technological environment and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the plurality of clinical operation data comprise at least one of historical clinical trial performance, site characteristic(s), and site location(s), wherein the plurality of clinical operation data are associated with the same disease indication as that planned for the prospective clinical trial at the one or more clinical trial sites. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the input values of the plurality of clinical operation data comprise at least one of NCT Number, site location, number of subjects consented, number of subjects enrolled in a trial, number of subject that completed a trial, Site Open Date (Or first patient in date), Study-Country last patient in data, and derived data associated with the one or more clinical trial sites. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites wherein the predicted quantitative values informative of performance of the one or more clinical trial sites comprise at least one of site enrollment, site default likelihood, and site enrollment rate. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5,
Step 1 Analysis: Claim 5 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 5 recites, in part, The limitations of:
performing, by a first party, data standardization on a first dataset can be considered to be an evaluation in the human mind
generating an improved federated learning model for predicting performance of one or more clinical sites can be considered to be an evaluation in the human mind
evaluating, by the first party, the improved federated learning model, can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “wherein the improved federated learning model is trained at least in part by the first party using the first dataset and is trained at least in part by the second party using a second dataset”. Thus, this element in the claim is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f).
Additionally, the claim recites the additional elements – “wherein the first dataset is accessible to the first party while inaccessible to the second party, and wherein the second dataset is accessible to the second party while inaccessible to the first party.”
These elements that are recited are only generally linked to the judicial exception or confine the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: setting up at least a portion of a federated network comprising computer spaces or secure user interfaces for the first party and a second party. This limitation is an insignificant extra-solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a trained federated learning model to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Furthermore, the limitation of setting up at least a portion of a federated network comprising computer spaces or secure user interfaces for the first party and a second party is well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components, generally linking the judicial exception to a field of use or technological environment and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 5 is further incorporated, and further, the claim recites: further comprising locally preprocessing by the first party, the first dataset, wherein the first dataset is preprocessed by applying a compiled code to split the first dataset into training, validation, and a holdout test set for evaluating the improved federated learning model. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 5 is further incorporated, and further, the claim recites: wherein a source code from the compiled code are accessible to the first party while inaccessible to the second party. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 5 is further incorporated, and further, the claim recites: wherein generating the improved federated learning model for predicting performance of one or more clinical sites comprises:
sending, by the first party, parameters locally trained on the first dataset through the federated network;
receiving, by the first party, parameters trained on the second dataset within the federated network; and
These limitations are insignificant extra-solution activities and thus the judicial exception is not integrated into a practical application.
The limitation of: updating, by the first party, the parameters of the locally trained federated learning model with the received parameters recites additional mental steps in addition to the judicial exception identified in the rejection of claim 5.
The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. These limitations are just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 5 is further incorporated, and further, the claim recites: wherein generating the improved federated learning model for predicting performance of one or more clinical sites comprises: for each training epoch of a plurality of training epochs:
sending, by the first party, parameters locally trained on the at least a portion of the first dataset through the federated network;
receiving, by the first party, parameters from the second party that has trained the locally trained federated learning model using at least a portion of the second dataset; These limitations are insignificant extra-solution activities and thus the judicial exception is not integrated into a practical application.
The limitation of: updating, by the first party, the parameters of the locally trained federated learning model with the received parameters recites additional mental steps in addition to the judicial exception identified in the rejection of claim 5.
The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. These limitations are just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 5 is further incorporated, and further, the claim recites wherein generating the improved federated learning model for predicting performance of one or more clinical sites comprises: for each training epoch:
receiving parameters from the second party that has individually trained the federated learning model using the second dataset; These limitations are insignificant extra-solution activities and thus the judicial exception is not integrated into a practical application.
The limitation of: and averaging the parameters of the locally trained model with the received parameters recites additional mental steps in addition to the judicial exception identified in the rejection of claim 5.
The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. These limitations are just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 11, the rejection of claim 10 is further incorporated, and further, the claim recites: wherein the federated network renders accessible the parameters of the locally trained federated learning model and pointers of the first dataset to the second party, and renders inaccessible the first dataset to the second party. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Claim 12 recites features similar to claim 5 and is rejected for at least the same reasons therein.
Regarding claim 13, the rejection of claim 12 is further incorporated, and further, the claim recites wherein performing data standardization comprises: receiving, by the second party, a compiled code from a first party; This limitation is an insignificant extra-solution activities and thus the judicial exception is not integrated into a practical application.
The limitation of: aligning on input data and input features of the second dataset using the compiled code, wherein the compiled code masks proprietary engineered features and a source code amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Claim 14 recites features similar to claim 6 and is rejected for at least the same reasons therein.
Regarding claim 15, the rejection of claim 12 is further incorporated, and further, the claim recites: wherein the federated network renders accessible the parameters of the locally trained federated learning model to the first party, and renders inaccessible the second dataset to the first party. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 16, the rejection of claim 12 is further incorporated, and further, the claim recites: wherein setting up at least a portion of the federated network comprises: initiating a secure connection; and sending model parameters and data pointers through the secure connection, wherein the secure connection is initiated by sharing a connection string. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application
The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Claim 17 recites features similar to claim 11 and is rejected for at least the same reasons therein.
Regarding claim 18, the rejection of claim 12 is further incorporated, and further, the claim recites: wherein an architecture of the federated learning model is accessible to the first party while inaccessible to the second party. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 19, the rejection of claim 12 is further incorporated, and further, the claim recites: wherein the federated learning model is further trained using a third dataset in a federated network, This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f). and
The limitation of: wherein the federated network further renders inaccessible the third dataset to the first party and renders inaccessible the third dataset to the second party. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 20, the rejection of claim 12 is further incorporated, and further, the claim recites: wherein the federated network comprises:
a first walled computer space accessible to a first party, wherein the first walled computer space is inaccessible to the second party;
asecond walled computer space accessible to a second, wherein the second walled computer space is inaccessible to the first party; and
a third walled computer space comprising a compiled code for processing the second dataset. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
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)(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 5-9, 12, 14-15, and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Callcut et al. ("WO 2020198542 A1", hereinafter "Callcut").
Regarding claim 5, Callcut teaches A method for developing a federated learning model for improving prediction informative of performance of one or more clinical trial sites for a prospective clinical trial (“The insights created by AI and machine learning (“ML”) hold the promise of learning the associations (i.e., correlations) within complex data necessary to enhance clinical decision making, enable treatment precision, and create digital therapeutics.” [¶0003]), comprising:
performing, by a first party, data standardization on a first dataset (“Data may be imported, optionally stored, transformed and otherwise harmonized (“data standardization”) and then computed upon by software (e.g., the algorithms/models l lOa-n) from the AI system 100.” [¶0051]);
setting up at least a portion of a federated network comprising computer spaces or secure user interfaces for the first party and a second party (“FIG. 1 shows an AI ecosystem 100 that allows for secure, federated computing on private data sets 105a-n … The AI development platform 110 includes AI system 115 and secure capsule computing services 120a-n.” [¶0049]);
generating an improved federated learning model for predicting performance of one or more clinical sites, wherein the improved federated learning model is trained at least in part by the first party using the first dataset and is trained at least in part by the second party using a second dataset (“In some embodiments, it is desired to develop a robust, superior algorithm/model that has learned from multiple disjoint private data sets 245, 250 (e.g., clinical and health data) collected by data hosts 255 from sources 260 (e.g., patients).” [¶0054]); and
evaluating, by the first party, the improved federated learning model (“running the one or more sets of testing data through the fully federated algorithm/model, and computing performance of the fully federated model based on the running of the one or more sets of testing data.” [¶0057]), wherein the first dataset is accessible to the first party while inaccessible to the second party, and wherein the second dataset is accessible to the second party while inaccessible to the first party. (See ¶0082, “For example, a first data set of the data assets may be identified as being available on a first database of a data host (a second entity) and a second data set of the data sets may be identified as being available on a second database of the data host. In this instance, the retrieval of the data assets may include physically izing the first data set and the second data set in provisioned storage in a manner that maintains data privacy…The provisioned storage may be newly provisioned in step 505 or may be existing storage in which new access permissions for the platform are defined. In other instances, the data assets may not be physically moved to provisioned storage, but a collection of logical addresses may be recorded in the provisioned storage or a pre-existing storage (physical data stores or cloud-based storage that are part of the data host’ s pre-existing infrastructure) accessible to the platform)
Regarding claim 6, Callcut teaches The method of claim 5, further comprising locally preprocessing by the first party, the first dataset, wherein the first dataset is preprocessed by applying a compiled code to split the first dataset into training, validation, and a holdout test set for evaluating the improved federated learning model (“The training/validation/test data splits include criteria for splitting data assets into training sets, validation, and/or test sets. The training data set is the set of data used to fit or train the model.” [¶0064; compiled code is implicit in order to run the training algorithm/model).
Regarding claim 7, Callcut teaches The method of claim 6, wherein a source code from the compiled code are accessible to the first party while inaccessible to the second party. (See ¶0076-¶0077, “The secure deployment capsules allow for a model to be run from within the infrastructure of the data host in an isolated manner while maintaining the privacy of the data assets and the model (e.g., the algorithms).”)
Regarding claim 8, Callcut teaches The method of claim 5, wherein generating the improved federated learning model for predicting performance of one or more clinical sites comprises:
sending, by the first party, parameters locally trained on the first dataset through the federated network (“At block 910, results including the parameters and/or training gradients for each trained instance of the model are integrated into a fully federated model. The integrating comprises aggregating the results such as the parameters and/or training gradients to obtain aggregated parameters and/or training gradients and updating the learned model parameters of the fully federate model with the aggregated parameters and/or training gradients.” [¶0103]);
receiving, by the first party, parameters trained on the second dataset within the federated network (“When the termination criteria has not been reached, then the process proceeds back to block 905 where an updated model (e.g., the fully federated model) is distributed to each data host for additional training on training data sets. This iterative process repeats until the algorithm termination criteria has been satisfied. A progress status of the algorithm training process may be reported to the algorithm developer during this step in accordance with reporting constraints.” [¶0103]); and
updating, by the first party, the parameters of the locally trained federated learning model with the received parameters. (“The update training workflow updates the learned model parameters with the aggregated parameters and/or training gradients, and outputs an updated instance of the model that captures the patterns in the training and testing data assets using the updated learned model parameters.” [¶0105])
Regarding claim 9, Callcut teaches The method of claim 5, wherein generating the improved federated learning model for predicting performance of one or more clinical sites comprises: for each training epoch of a plurality of training epochs (See ¶0103-¶0105, iterative algorithm training process implies training epoch):
sending, by the first party, parameters locally trained on the at least a portion of the first dataset through the federated network (“At block 910, results including the parameters and/or training gradients for each trained instance of the model are integrated into a fully federated model. The integrating comprises aggregating the results such as the parameters and/or training gradients to obtain aggregated parameters and/or training gradients and updating the learned model parameters of the fully federate model with the aggregated parameters and/or training gradients.” [¶0103]);
receiving, by the first party, parameters from the second party that has trained the locally trained federated learning model using at least a portion of the second dataset (See ¶0107 “FIG. 10 shows a process 1000 for validating one or more models using the platform (e.g., the platform and systems described with respect to FIGS. 1 and 2). At block 1005, an initial model is obtained. In some instances, the model obtained is a pre-existing trained model obtained from an algorithm developer”); and
updating, by the first party, the parameters of the locally trained federated learning model with the received parameters. (“The update training workflow updates the learned model parameters with the aggregated parameters and/or training gradients, and outputs an updated instance of the model that captures the patterns in the training and testing data assets using the updated learned model parameters.” [¶0105])
Regarding claim 12, it is substantially similar to claim 5 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 14, it is substantially similar to claim 6 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 15, Callcut teaches The method of claim 12, wherein the federated network renders accessible the parameters of the locally trained federated learning model to the first party, and renders inaccessible the second dataset to the first party. (“Data from the training set is passed to the secure capsule computing framework by the data host via the API and the responses of the one or more models (e.g., training gradients) are passed back to platform, where the responses are aggregated into a training report. Other than this interaction, communication from inside the secure capsule computing framework to outside the secure capsule computing framework is not allowed preventing a rogue algorithm from communicating private data outside the secure capsule computing framework.” [¶0102, See also ¶0103-¶0105])
Regarding claim 18, Callcut teaches The method of claim 12, wherein an architecture of the federated learning model is accessible to the first party while inaccessible to the second party. (“The secure deployment capsules allow for a model to be run from within the infrastructure of the data host in an isolated manner while maintaining the privacy of the data assets and the model (e.g., the algorithms). Furthermore, the data host that is running the model in the one or more secure deployment capsules may not be able to inspect, copy or modify the algorithm developer’s proprietary software.” [¶0076])
Regarding claim 19, Callcut teaches The method of claim 12, wherein the federated learning model is further trained using a third dataset in a federated network, and wherein the federated network further renders inaccessible the third dataset to the first party and renders inaccessible the third dataset to the second party. (“FIG. 1 shows an AI ecosystem 100 that allows for secure, federated computing on private data sets 105a-n (‘n’ represents any natural number), including augmentation of data from data sets 105a-n, algorithm/model HOa-n deployment, algorithm/model HOa-n validation, algorithm/model HOa-n optimization (training/testing), and federated training of algorithms/models HOa-n on multiple data sets 105a-n.” [¶0049; multiple datasets would include a third dataset])
Regarding claim 20, Callcut teaches The method of claim 12, wherein the federated network comprises:
a first walled computer space accessible to a first party, wherein the first walled computer space is inaccessible to the second party (“The secure capsule computing services 120a-n are encapsulated or otherwise portable software components that can be computed upon in a secure capsule computing service environment entirely within the computing domain of the data host.” [¶0049]);
a second walled computer space accessible to a second, wherein the second walled computer space is inaccessible to the first party (“Secure encapsulated computing services provide a portable environment to support such a computing model. Furthermore, in many cases, the stakeholder that is running the secure encapsulated computing services will want protection against the possibility that the software being hosted is malicious and could harm the host’s infrastructure or compromise data privacy.” [¶0049-¶0051]); and
a third walled computer space comprising a compiled code for processing the second dataset (See ¶0049-¶0051 “The secure capsule computing services 120a-n are encapsulated or otherwise portable software components that can be computed upon in a secure capsule computing service environment entirely within the computing domain of the data host.” Implies a third walled computer space, compiled code is inherent given multiple datasets and an associated algorithm for training using those datasets).
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.
Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over Beltre et al. ("US 20190311788 A1", hereinafter "Beltre") and further in view of Callcut.
Regarding claim 1, Beltre teaches A method for predicting performance of one or more clinical trial sites for a prospective clinical trial (Abstract), comprising:
obtaining input values of a plurality of clinical operation data associated with the one or more clinical trial sites (“Referring to FIG. 1 again, the cognitive computing system shown in FIG. 1 includes a score and rank module 80 that is used to analyze individual investigator, provider and/or research site clinical performance data, calculate the performance score, as described below, and rank them on the relevance to the given clinical research…” [¶0045]); and
generating predicted quantitative values informative of the performance of the one or more clinical trial sites by applying a trained [federated learning] model to the plurality of clinical operation data (“The performance score is configured to be an objective, highly accurate numerical evaluation based on the individual investigator, referred to as the investigator performance score, and/or site clinical research performance history, referred to as the site performance score. The performance score is expected to be available across the clinical research industry as a performance score very similar to a credit rating score and will provide investigators with an accompanying performance report to drive behavior change.” [¶0045]),
However Beltre fails to explicitly teach by applying a trained federated learning model to the plurality of clinical operation data
wherein the trained federated learning model is trained using a federated network,
and
wherein the federated network renders inaccessible a first dataset of a first party to a second party, and further renders inaccessible a second dataset of the second party to the first party.
Callcut teaches by applying a trained federated learning model to the plurality of clinical operation data (“The invention is particularly effective for developing artificial intelligence algorithms for regulated medical applications using privacy protected, harmonized clinical and health data.” [¶0002, See ¶0007 “Federated model”])
wherein the trained federated learning model is trained using a federated network (¶0007),
and
wherein the federated network renders inaccessible a first dataset of a first party to a second party, and further renders inaccessible a second dataset of the second party to the first party. (See ¶0082, “For example, a first data set of the data assets may be identified as being available on a first database of a data host (a second entity) and a second data set of the data sets may be identified as being available on a second database of the data host. In this instance, the retrieval of the data assets may include physically izing the first data set and the second data set in provisioned storage in a manner that maintains data privacy…The provisioned storage may be newly provisioned in step 505 or may be existing storage in which new access permissions for the platform are defined. In other instances, the data assets may not be physically moved to provisioned storage, but a collection of logical addresses may be recorded in the provisioned storage or a pre-existing storage (physical data stores or cloud-based storage that are part of the data host’ s pre-existing infrastructure) accessible to the platform)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Beltre’s model by implementing the FL framework for clinical trials as taught by Callcut. One would have been motivated to make this modification as the secure capsule computing framework serves the algorithm to the data assets within the data storage structure in a secure manner that preserves privacy of the data assets and the algorithm. [Abstract, Callcut]
Regarding claim 2, Beltre/Callcut teaches The method of claim 1, Beltre teaches wherein the plurality of clinical operation data comprise at least one of historical clinical trial performance, site characteristic(s), and site location(s), wherein the plurality of clinical operation data are associated with the same disease indication as that planned for the prospective clinical trial at the one or more clinical trial sites. (“Furthermore, natural language processing and machine learning models are used on top of the performance score to rank and match entities using additional structured and unstructured data. If for example, the disease area is breast cancer, then all known investigators are retrieved from the data aggregator with investigators who have previously performed breast cancer trials along with their performance score, then ranked by the score and rank module 80.” [¶0053; note under BRI, the claim recites: “at least one of” thus the corresponding mapping would teach “historical clinical trial performance”])
Regarding claim 3, Beltre/Callcut teaches The method of claim 1, Beltre teaches wherein the input values of the plurality of clinical operation data comprise at least one of NCT Number, site location, number of subjects consented, number of subjects enrolled in a trial, number of subject that completed a trial, Site Open Date (Or first patient in date), Study-Country last patient in data, and derived data associated with the one or more clinical trial sites. (“The structured data sources could be related to past clinical research study performance (e.g., compliance, speed, start-up, enrollment, violations, audit outcomes, etc.) and/or to an investigator, provider, and research site characteristics and patient population.” [¶0031; note: under BRI, the claim recites “at least one of” thus the examiner is only required to map to one of the corresponding elements])
Regarding claim 4, Beltre/Callcut teaches The method of claim 1, Beltre teaches wherein the predicted quantitative values informative of performance of the one or more clinical trial sites comprise at least one of site enrollment, site default likelihood, and site enrollment rate. (“The structured data sources could be related to past clinical research study performance (e.g., compliance, speed, start-up, enrollment, violations, audit outcomes, etc.) and/or to an investigator, provider, and research site characteristics and patient population.” [¶0031; note: under BRI, the claim recites “at least one of” thus the examiner is only required to map to one of the corresponding elements])
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Callcut in view of De Brouwer et al. ("US 20200293887 A1", hereinafter "De Brouwer").
Regarding claim 10, Callcut teaches The methodofclaim5, wherein generating the improved federated learning model for predicting performance of one or more clinical sites comprises: for each training epoch See ¶0103-¶0105, iterative algorithm training process implies training epoch):
receiving parameters from the second party that has individually trained the federated learning model using the second dataset (See ¶0103-¶0105); and
However Callcut fails to explicitly teach averaging the parameters of the locally trained model with the received parameters.
De Brouwer teaches averaging the parameters of the locally trained model with the received parameters. (“Aggregation can be performed using a federated average algorithm, applying the averaging formula 1577 in FIG. 15. This is a weighted average of the updates to the model, weighted according to the number of samples used by an edge device to produce its update.” [¶0062])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Callcut’s teachings by implementing the Fedavg algorithm of De Brouwer. One would have been motivated to make this modification in order to produce an updated model following local updates. [¶0062, De Brouwer]
Regarding claim 11, Callcut/De Brouwer teaches The method of claim 10, Callcut further teaches wherein the federated network renders accessible the parameters of the locally trained federated learning model and pointers of the first dataset to the second party, and renders inaccessible the first dataset to the second party. (See ¶0082, and further see ¶102 “The results such as the parameters and/or training gradients may be encrypted prior to transmission to ensure data asset and model privacy.”)
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Callcut in view of Kim et al. ("US 20200184070 A1", hereinafter "Kim").
Regarding claim 13, Callcut teaches The method of claim 12, wherein performing data standardization comprises:
receiving, by the second party, a compiled code from a first party (See ¶0099-¶0100, “When the data assets are transformed successfully, then the process proceeds to the optimization and/or validation process for using the harmonized data assets in the models. Advantageously, these steps to facilitate the multi- step harmonization and annotation of data for use in an algorithm are capable of being implemented without exposing the underlying data to the algorithm developer.”); and
aligning on input data and input features of the second dataset using the compiled code (“At block 610, harmonizing transformers are created for transformation of the data assets based on the present format of data in the transformer prototype set. The harmonizing transformers may be created in accordance with (i) training constraints, (ii) data preparation constraints, and (iii) validation constraints defined in block 310 of FIG. 3.” [¶0095]),
However fails to explicitly teach wherein the compiled code masks proprietary engineered features and a source code.
Kim teaches wherein the compiled code masks proprietary engineered features and a source code. (“including compiling the source code of the ML program based on the ML program annotation, inserting binary code based on the program analysis, including inserting run-time code into a confidential part of the ML program and a non-confidential part of the ML program, and generating an ML model by executing the ML program with the inserted binary code to protect the confidentiality of the ML model and the ML program from attack.” [¶0004])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Callcut’s teachings in order to mask a source code as taught by Kim. One would have been motivated to make this modification in order to protect the confidentially of the ML model. [¶0004, Kim]
Claims 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Callcut in view of Ziller et al. ("PySyft: A Library for Easy Federated Learning", hereinafter "Ziller").
Regarding claim 16, Callcut teaches The method of claim 12, Callcut teaches wherein setting up at least a portion of the federated network comprises:
initiating a secure connection (“The secure deployment capsules allow for a model to be run from within the infrastructure of the data host in an isolated manner while maintaining the privacy of the data assets and the model (e.g., the algorithms).” [¶0076]); and sending model parameters and data pointers through the secure connection (“For example, once a model is deployed to a data host as a part of a secure capsule computing service, the platform may deploy or output data resources, model parameters, or shared data for training (e.g., parent-teacher training paradigm) to the model via the algorithm APIs.” [¶0076]),
However fails to explicitly teach wherein the secure connection is initiated by sharing a connection string.
Ziller teaches wherein the secure connection is initiated by sharing a connection string. (“This server returns a unique URL which is passed to the Data Scientist who, in their Jupyter notebook, can initiate a connection to the data owner’s Duet server. This connection enables the data scientist to execute operations at the tensor level on the data owner’s data without the data leaving the data owner’s machine. This will allow hospitals and medical researchers, financial institutions and fraud investigators, satellite imagery companies and governments, and many other “duet” pairs of data owners and data scientists to understand insights, build models, and predict outcomes on valuable data that remains private.” [pg. 127, top para])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Callcut’s teachings by implementing PySyft into a Federated Learning system as taught by Ziller. One would have been motivated to make this modification in order to enable secure and private machine learning. [Abstract, Ziller]
Regarding claim 17, Callcut/Ziller teaches The method of claim 16, Callcut teaches wherein the secure connection renders accessible the parameters of the locally trained federated learning model and the pointers of the first dataset to the second party, and renders inaccessible the first dataset to the second party. (See ¶0082, and further see ¶102 “The results such as the parameters and/or training gradients may be encrypted prior to transmission to ensure data asset and model privacy.”)
Conclusion
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/MICHAEL H HOANG/ PRIMARY EXAMINER, Art Unit 2122
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