DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 11/20/2025 for application number 17/772,883.
Claims 1-14 and 16 are 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 .
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) 1-11, 14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Epler et al. (US 2003/0187615 A1) in view of Chen et al. (US 10,990,850 B1) and Nadeau et al. (US 2018/0316502 A1).
In reference to claim 1, Epler teaches a computer-implemented method (para. 0001) for maintaining privacy during attribution of a condition, the method comprising: training, by a remote system, a machine-learned model to infer, based on signals received by a user device in a geographic region, whether a person associated with the user device has the condition (central server, which is a remote system, trains an ML model to determine different diseases, para. 0051-58, from signals received from ER devices in different locations, para. 0030-39); wherein the signals comprise … text-based signals associated with user devices (signals include text in EMR records, para. 0030-39) … collecting, by the remote system and from the group of user devices, aggregated statistics (central server aggregates data from ER devices, para. 0030-39) … determining, by the remote system, based on the aggregated statistics, an incidence rate of the condition in a particular subregion of the geographic region (incidence rate map, is determined for different spatial areas, para. 0050); and outputting, by the remote system, based on the incidence rate of the condition in the particular subregion, an incidence map of the geographic region indicating a different incidence rate between multiple subregions of the geographic region (map is displayed and shows areas with different incidence rates, like high, medium, and low, para. 0050).
However, Epler does not explicitly teach deploying, by the remote system, copies of the machine-learned model for local execution at each of a group of user devices; collecting, by the remote system and from the group of user devices, aggregated statistics of inferences made by the copies of the machine-learned model.
Chen teaches deploying, by the remote system, copies of the machine-learned model for local execution at each of a group of user devices (ML model is deployed to edge devices from provider for local prediction, col. 4, lines 4-28); collecting, by the remote system and from the group of user devices, aggregated statistics of inferences made by the copies of the machine-learned model (inferences can be sent back to provider, col. 4, line 29 – col. 5, line 55).
It would have been obvious to one of ordinary skill in art, having the teachings of Epler and Chen before the earliest effective filing date, to modify the machine learning as disclosed by Epler to include the local execution as taught by Chen.
One of ordinary skill in the art would have been motivated to modify the machine learning of Epler to include the local execution of Chen because it can help make for faster inferences at the device where the data was collected (Chen, col. 1, lines 5-15).
However, Epler and Chen do not explicitly teach wherein the signals comprise daily routine signals … associated with user devices, wherein the daily routine signals are based on location and mobility of user devices; and for each individual machine-learned model of the copies of the machine-learned model, instructing a respective user device of the group of user devices to perform federated learning on a respective copy of the machine-learned model on the respective user device of the group of user devices.
Nadeau teaches wherein the signals comprise daily routine signals … associated with user devices, wherein the daily routine signals are based on location and mobility of user devices (model can be configured to learn user’s habitual activity based on device’s location and movements, para. 0020); and for each individual machine-learned model of the copies of the machine-learned model, instructing a respective user device of the group of user devices to perform federated learning on a respective copy of the machine-learned model on the respective user device of the group of user devices (each user device performs federated learning on local models, para. 0021).
It would have been obvious to one of ordinary skill in art, having the teachings of Epler, Chen, Nadeau before the earliest effective filing date, to modify the machine learning as disclosed by Epler to include the federated learning of location as taught by Nadeau.
One of ordinary skill in the art would have been motivated to modify the machine learning of Epler to include the federated learning of location of Nadeau because it can help learn local changes while maintaining privacy (Nadeau, para. 0021).
In reference to claim 2, Epler teaches the method of claim 1, wherein the condition comprises an infectious disease comprising malaria, chikungunya, zika, influenza, ebola, or dengue (influenza, para. 0032-33).
In reference to claim 3, Epler does not explicitly teach the method of claim 1, wherein training the machine-learned model comprises running the machine-learned model in inference mode based on ground truth data for the geographic region and the signals received by the user device in the geographic region.
Chen teaches the method of claim 1, wherein training the machine-learned model comprises running the machine-learned model in inference mode based on ground truth data for the geographic region and the signals received by the user device in the geographic region (training includes running the ML model and ground truth data, col. 6, line 62 – col. 7, line 35).
It would have been obvious to one of ordinary skill in art, having the teachings of Epler and Chen before the earliest effective filing date, to modify the training as disclosed by Epler to include the training as taught by Chen.
One of ordinary skill in the art would have been motivated to modify the training of Epler to include the training of Chen because it can help adapt and provide more accurate models over time (Chen, col. 1, line 66 – col. 2, line 22).
In reference to claim 4, Epler teaches the method of claim 1, … training the machine-learned model to infer whether the person associated with the user device has the condition comprises: creating training data including examples of the signals received by the user device when the person associated with the user device has the condition and examples of the signals received by the user device when the person associated with the user device does not have the condition … (ML model to trained with data showing user has or does not have condition, para. 0051-58).
Epler does not explicitly teach wherein the copies of the machine-learned model are old copies of the machine-learned model … retraining, based on the emulated training data, the machine-learned model; and generating new copies of the machine-learned model.
Chen teaches wherein the copies of the machine-learned model are old copies of the machine-learned model … retraining, based on the emulated training data, the machine-learned model; and generating new copies of the machine-learned model (model is retrained on new training data and new copies are deployed, col. 9, line 7 – col. 10, line 23).
It would have been obvious to one of ordinary skill in art, having the teachings of Epler and Chen before the earliest effective filing date, to modify the training as disclosed by Epler to include the retraining as taught by Chen.
One of ordinary skill in the art would have been motivated to modify the training of Epler to include the retraining of Chen because it can help adapt and provide more accurate models over time (Chen, col. 1, line 66 – col. 2, line 22).
In reference to claim 5, Chen teaches the method of claim 4, the method further comprising: deploying, by the remote system and to replace the old copies of the machine-learned model, the new copies of the machine-learned model for local execution at each of the group of user devices (retrained models are deployed, col. 9, line 7 – col. 10, line 23).
In reference to claim 6, Epler teaches the method of claim 1, wherein collecting the aggregated statistics of inferences comprises: … inferring, based on the first portion of the aggregated statistics, a first subregion from the multiple subregions of the geographic region; attributing, based at least in part on the first portion of the aggregated statistics, a first rate of incidence of the condition to the first subregion; and generating the incidence map of the geographic region by indicating the first rate of incidence of the condition throughout the first subregion (incident rate for a first area is determined, para. 0050).
However, Epler does not explicitly teach receiving a first portion of the aggregated statistics of inferences from a first user device of the group of user devices.
Chen teaches receiving a first portion of the aggregated statistics of inferences from a first user device of the group of user devices (inferences made at edge devices are sent back to provider, col. 4, line 29 – col. 5, line 55).
It would have been obvious to one of ordinary skill in art, having the teachings of Epler and Chen before the earliest effective filing date, to modify the machine learning as disclosed by Epler to include the local execution as taught by Chen.
One of ordinary skill in the art would have been motivated to modify the machine learning of Epler to include the local execution of Chen because it can help make for faster inferences at the device where the data was collected (Chen, col. 1, lines 5-15).
In reference to claim 7, Epler teaches the method of claim 6, further comprising: receiving a second portion of the aggregated statistics from a second user device of the group of user devices, wherein: the first subregion from the multiple subregions of the geographic region is further inferred based on the second portion of the aggregated statistics; and the first rate of incidence of the condition is further attributed to the first subregion based on the second portion of the aggregated statistics (a plurality of ER devices send in data, para. 0030-39, that is used to determine subregion incidence rate, para. 0050).
In reference to claim 8, Epler teaches the method of claim 6, further comprising: modeling, based on the aggregated statistics, a second rate of incidence of the condition for a second subregion from the multiple subregions; and generating the incidence map of the geographic region by further indicating the second rate of incidence of the condition throughout the second subregion (incidence rate can be determined for second region, para. 0050).
In reference to claim 9, Epler teaches the method of claim 8, wherein the group of user devices are located outside the second subregion at the time the inferences were made by the copies of the machine-learned model (actual locations of epidemiological cases are displayed in map, para. 0049-50, so it would be obvious that the ER devices, which are the user devices, could be at a different location, which is to say a person would get sick outside of the ER).
In reference to claim 10, Epler teaches the method of claim 1, wherein the aggregated statistics of inferences made by the copies of the machine-learned model indicate presence or absence of the condition (ML model predicts a health-related event, including a condition like the flu, etc., para. 0054-57).
In reference to claim 11, Chen teaches the method of claim 1, wherein the remote system comprises multiple remote systems (see at least remote servers 620 and 640 in fig. 6).
In reference to claim 14, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 16, Epler teaches a computer-implemented method (para. 0001) for maintaining privacy during attribution of a condition, the method comprising: … a machine-learned model trained to infer, based on signals received by the user device while in a particular subregion of a geographic region, whether a person associated with the user device has the condition; inputting, to the machine-learned model, one or more of the signals received by the user device at two or more different intervals of time (data entered into EMR is extracted in real time for use as the signals as it is entered, para. 0041, so the inputs would be over time); responsive to inputting the one or more of the signals, determining a series of inferences made by the machine-learned model indicating in each inference whether the person associated with the user device has the condition (central server, which is a remote system, trains an ML model to determine different diseases, para. 0051-58, from signals received from ER devices in different locations and can predict if user has a disease, para. 0030-39); generating, by the user device, based on the series of inferences made by the machine-learned model, aggregated statistics as to whether people in the particular subregion have the condition (incidence rate map, is determined for different spatial areas, para. 0050); and outputting, to the remote system, an indication of the aggregated statistics to a remote system that generates an incidence map of the geographic region indicating a different incidence rate between multiple subregions of the geographic region including the particular subregion (map is displayed and shows areas with different incidence rates, like high, medium, and low, para. 0050).
However, Epler does not explicitly teach receiving, by a user device and from a remote system, a copy of a machine-learned model
Chen teaches receiving, by a user device and from a remote system, a copy of a machine-learned model (ML model is deployed to edge devices from provider for local prediction, col. 4, lines 4-28).
It would have been obvious to one of ordinary skill in art, having the teachings of Epler and Chen before the earliest effective filing date, to modify the machine learning as disclosed by Epler to include the local execution as taught by Chen.
One of ordinary skill in the art would have been motivated to modify the machine learning of Epler to include the local execution of Chen because it can help make for faster inferences at the device where the data was collected (Chen, col. 1, lines 5-15).
Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Epler et al. (US 2003/0187615 A1) in view of Chen et al. (US 10,990,850 B1) and Nadeau et al. (US 2018/0316502 A1) as applied to claim 1, and in further view of Nag (US 2016/0267238 A1).
In reference to claim 12, Epler, Chen, and Nadeau do not explicitly teach the method of claim 1, wherein collecting the aggregated statistics of the inferences made by the copies of the machine-learned model is responsive to obtaining an indication of consent from a user of each user device from the group of user devices to collect the aggregated statistics.
Nag teaches the method of claim 1, wherein collecting the aggregated statistics of the inferences made by the copies of the machine-learned model is responsive to obtaining an indication of consent from a user of each user device from the group of user devices to collect the aggregated statistics (para. 0041).
It would have been obvious to one of ordinary skill in art, having the teachings of Epler, Chen, Nadeau, and Neg before the earliest effective filing date, to modify the data collection as disclosed by Epler to include the permission as taught by Nag.
One of ordinary skill in the art would have been motivated to modify the data collection of Epler to include the permission of Nag because it can helps more easily collect health data for research while balancing privacy (Nag, para. 0005).
In reference to claim 13, Epler teaches the method of claim 1, wherein outputting the incidence map of the geographic region comprises outputting the incidence map (para. 0050).
However, Epler, Chen, and Nadeau do not explicitly teach outputting … to an application executing at a remote subscriber device.
Nag teaches outputting … to an application executing at a remote subscriber device (third party researcher can view data on app, para. 0038).
It would have been obvious to one of ordinary skill in art, having the teachings of Epler, Chen, Nadeau, and Neg before the earliest effective filing date, to modify the map as disclosed by Epler to include the remote device as taught by Nag.
One of ordinary skill in the art would have been motivated to modify the map of Epler to include the remote device of Nag because it is unclear in Epler where the map is displayed, and the researcher device of Nag would provide a way of researchers to access the map.
Response to Arguments
Applicant’s arguments with respect to 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see new reference Nadeau above.
With respect to the 101 rejection, the Examiner notes that he finds to be patent eligible at step 2B for amounting to significantly more than the abstract idea itself. Specifically, the amended independent claims now require (1) training a ML model to infer if a user has a condition based on daily routine signals comprising location and text signals, (2) deploying copies of the ML model to user devices, (3) performing federated learning on each copy of the ML model at the user device. When these additional limitations are considered in ordered combination with the other additional limitations and the abstract idea, the claim amounts to significantly more than the abstract idea itself because the claim now reflects the disclosed technical improvement (see paragraphs 3-9 of the specification as filed).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144