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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1, 3-9, 11 and 13-19 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 4 and 8-10 of copending Application No. 18/425,715 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because they largely overlap, see below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
18/649,072 Instant Application
18/425,715 co-pending application
Claims 1 and 11. A computer-implemented method for skill prediction, comprising:
aggregating locally trained parameters from a plurality of client systems to generate updated global parameters;
clustering parameterized vectors from the client systems into prototype clusters;
determining a centroid of each prototype cluster;
matching the parameterized vectors from the client systems to centroids of the prototype clusters to identify sets of updated local prototype vectors; and
distributing the updated global parameters and the updated local prototype vectors to the plurality of client systems.
Claims 3 and 13. The method of claim 1, wherein determining the centroid of each prototype cluster includes identifying a mean of vectors in the prototype cluster.
Claims 4 and 14. The method of claim 1, wherein the locally trained parameters include parameters of a convolution layer and an imitation learning layer of a skill prediction model.
Claims 5 and 15. The method of claim 4, wherein the skill prediction model is implemented as a machine learning model.
Claims 6 and 16. The method of claim 4, wherein the skill prediction model accepts a patient's state as input and outputs a treatment recommendation.
Claims 7 and 17. The method of claim 6, wherein the treatment recommendation includes an interpretable identification of local prototype vectors to explain the treatment recommendation for decision making purposes by medical personnel.
Claims 8 and 18. The method of claim 6, further comprising automatically implementing the treatment recommendation at a client system to treat the patient.
Claims 9 and 19. The method of claim 1, wherein the parameterized vectors include skill prototypes relating to expert demonstrations in an imitation learning system.
1. A computer-implemented method for training a healthcare treatment machine learning model, comprising:
aggregating local weights from a plurality of clients to update a set of global weights for an imitation-based skill learning model;
clustering a set of local prototype vectors from the plurality of clients to generate a plurality of clusters;
selecting representative vectors for the plurality of clusters as a set of global prototypes;
determining client-specific prototype vectors for the plurality of clients based on the representative vectors; and
distributing the updated set of global weights and the client-specific prototype vectors to the plurality of clients.
4. The method of claim 1, wherein selecting the representative vectors includes determining respective centroids of the plurality of clusters.
2. The method of claim 1, wherein the set of global weights includes weights of a convolution layer and weights of an imitation learning layer. 5. The method of claim 1, further comprising learning the local weights and the local prototype vectors at the plurality of clients based on initial global weights and initial prototypes.
1. A computer-implemented method for training a healthcare treatment machine learning model, comprising:
9. The method of claim 8, further comprising:
measuring a patient's state information;
selecting a treatment action based on a skill predicted by the imitation-based skill learning model, based on the measured state information; and
notifying a medical professional of the treatment action to assist the medical professional in decision-making for patient management.
8. The method of claim 1, wherein the local prototype vectors correspond to treatment actions that can be performed in a medical context. 9. …
notifying a medical professional of the treatment action to assist the medical professional in decision-making for patient management.
10. The method of claim 9, wherein the treatment action includes an instruction to a treatment system to automatically administer a treatment to a patient.
9. The method of claim 8, further comprising:
measuring a patient's state information;
selecting a treatment action based on a skill predicted by the imitation-based skill learning model, based on the measured state information…
Claims 2, 10, 12 and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/425,715 in view of US20220237508A1 (‘508).
18/649,072 Instant Application
18/425,715 co-pending application
Claims 2 and 12. The method of claim 1, wherein aggregating the locally trained parameters includes averaging the locally trained parameters.
Claims 10 and 20. The method of claim 1, further iteratively receiving updated locally trained parameters and distributing the updated global parameters and the updated local prototype vectors.
‘7151. …aggregating local weights from a plurality of clients to update a set of global weights for an imitation-based skill learning model;…
‘715 doesn’t teach averaging.
However, US20220237508A1
Teaches that “A common approach for implementing FL is to average the learned parameters from each client node to arrive at a set of aggregated learned parameter values.” Para 4.
‘508, ‘715 and the claims are all directed to federated learning. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to average local parameters because doing so provides a known and predictable mechanism for combining multiple clients’ locally trained parameters into a global set of parameters.
‘715 claim 1. … clustering a set of local prototype vectors from the plurality of clients to generate a plurality of clusters;
selecting representative vectors for the plurality of clusters as a set of global prototypes;
determining client-specific prototype vectors for the plurality of clients based on the representative vectors; and
distributing the updated set of global weights and the client-specific prototype vectors to the plurality of clients.
‘715 doesn’t teach that the update is iterative.
However, US20220237508A1
Teaches that “ training may take place in multiple rounds, with client nodes sending local learned parameter information to the server and receiving adjusted values for the learnable parameters of centralized global model from the server in each round…”
‘715, ‘508 and the claims are all directed to federated learning. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to iterate the “training process until the global model 126 converges on a set of global learned parameter values representing an optimized solution to the task…” ‘508 para 65.
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 1-7, 9-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mathematical concepts without significantly more. The claim(s) recite(s) the mathematical concept of:
1. A computer-implemented method for skill prediction, comprising:
aggregating locally trained parameters from a plurality of client systems to generate updated global parameters;
clustering parameterized vectors from the client systems into prototype clusters;
determining a centroid of each prototype cluster;
matching the parameterized vectors from the client systems to centroids of the prototype clusters to identify sets of updated local prototype vectors; and
2. The method of claim 1, wherein aggregating the locally trained parameters includes averaging the locally trained parameters.
3. The method of claim 1, wherein determining the centroid of each prototype cluster includes identifying a mean of vectors in the prototype cluster.
4. The method of claim 1, wherein the locally trained parameters include parameters of a convolution layer and an imitation learning layer of a skill prediction model.
5. The method of claim 4, wherein the skill prediction model is implemented as a machine learning model.
6. The method of claim 4, wherein the skill prediction model accepts a patient's state as input and …
7. The method of claim 6, wherein the treatment recommendation includes an interpretable identification of local prototype vectors to …
9. The method of claim 1, wherein the parameterized vectors include skill prototypes relating to expert demonstrations in an imitation learning system.
11. A system for skill prediction, comprising:
…
aggregate locally trained parameters from a plurality of client systems to generate updated global parameters;
cluster parameterized vectors from the client systems into prototype clusters;
determine a centroid of each prototype cluster;
match the parameterized vectors from the client systems to centroids of the prototype cluster to identify sets of updated local prototype vectors; and
…
12. The system of claim 11, wherein the computer program further causes the hardware processor to average the locally trained parameters.
13. The system of claim 11, wherein the computer program further causes the hardware processor to identify a mean of vectors in the prototype cluster.
14. The system of claim 11, wherein the locally trained parameters include parameters of a convolution layer and an imitation learning layer of a skill prediction model.
15. The system of claim 14, wherein the skill prediction model is implemented as a machine learning model.
16. The system of claim 14, wherein the skill prediction model accepts a patient's state as input …
17. The system of claim 16, wherein the treatment recommendation includes an interpretable identification of local prototype vectors to …
19. The system of claim 11, wherein the parameterized vectors include skill prototypes relating to expert demonstrations in an imitation learning system.
This judicial exception is not integrated into a practical application because the following additional element merely link the abstract idea to the technical field of medicine:
6… outputs a treatment recommendation.
7… explain the treatment recommendation for decision making purposes by medical personnel.
16… and outputs a treatment recommendation.
17… explain the treatment recommendation for decision making purposes by medical personnel.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the following additional elements are either generic computer parts or insignificant extra-solution activity:
1. … distributing the updated global parameters and the updated local prototype vectors to the plurality of client systems.
10. The method of claim 1, further iteratively receiving updated locally trained parameters and distributing the updated global parameters and the updated local prototype vectors.
11… a hardware processor; and
a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to:… a hardware processor; and
a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to:
20. The system of claim 11, wherein the computer program further causes the hardware processor to iteratively receive updated locally trained parameters and distributing the updated global parameters and the updated local prototype vectors.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-5, 9-11, 13-15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over FedProto: Federated Prototype Learning across Heterogeneous Clients by Tan et al, PROTOTYPE-BASED CLUSTEREDFEDERATEDLEARNINGFOR SEMANTIC SEGMENTATION OF AERIAL IMAGES by Zhang et al and US20210374612A1 to Yu et al (Yu2).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over FedProto: Federated Prototype Learning across Heterogeneous Clients by Tan et al, PROTOTYPE-BASED CLUSTEREDFEDERATEDLEARNINGFOR SEMANTIC SEGMENTATION OF AERIAL IMAGES by Zhang et al, US20210374612A1 to Yu et al (Yu2) and US20220058507A1 to El-Khamy et al.
Claims 6-8 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over FedProto: Federated Prototype Learning across Heterogeneous Clients by Tan et al, PROTOTYPE-BASED CLUSTEREDFEDERATEDLEARNINGFOR SEMANTIC SEGMENTATION OF AERIAL IMAGES by Zhang et al, US20210374612A1 to Yu et al (Yu2) and US20210065009A1 to Yu et al (Yu9).
Tan teaches claims 1 and 11. A computer-implemented method for skill prediction, comprising: (Tan fig. 1)
aggregating locally trained parameters from a plurality of client systems to generate updated global parameters; (Tan fig. 1 “First, the clients update their local prototype sets by minimizing the loss of classification error LS and the distance between global prototypes and local prototypes LR. Then, the clients send their prototypes to the central server. The central server generates global prototypes and returns them to all clients to regularize the training of local models.” Global prototypes are the generated updated global parameters.)
(Tan fig. 1 “the clients send their prototypes to the central server. The central server generates global prototypes and returns them to all clients to regularize the training of local models.” The regularization sent back to local models is the updated local prototype vectors.)
distributing the updated global parameters and the updated local prototype vectors to the plurality of client systems. (Tan fig. 1 “the clients send their prototypes to the central server. The central server generates global prototypes and returns them to all clients to regularize the training of local models.”)
Tan doesn’t teach clustering to generate the global prototype.
However, Zhang teaches clustering parameterized vectors from the client systems into prototype clusters; (Zhang p. 2227-2228 “multi-model FL framework named FL with prototype based hierarchical clustering (FedPHC), tailored for the se mantic segmentation of remote sensing images. It clusters different clients into various groups using prototypical representations of clients’ datasets to reduce the domain discrepancy within each group and train multiple representative global models.”)
Tan, Zhang and the claims are all updating client models with global parameters. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to cluster prototypes in order “to reduce the domain discrepancy within each group and train multiple representative global models.” Zhang p. 2227-2228
Tan and Zhang don’t teach a centroid.
Yu2 teaches determining a centroid of each prototype cluster; (Yu2 para 25 “There are many approaches to find prototypes in the data. Any clustering algorithm that returns actual data points as cluster centers would qualify for selecting prototypes.”)
matching the parameterized vectors from the client systems to centroids of the prototype clusters to identify sets of updated local prototype vectors; and (Yu2 para 31 “To learn the option prototype go, the exemplary methods map each segment into an option embedding… and cluster to them to find K central nodes as option prototypes…” Central nodes are centroids.)
Tan, Zhang, Yu2 and the claims are all selecting prototypes using clusters. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to identify a prototype based on a centroid match in a cluster because “presumed natural prototypes …[are] central tendencies of the categories.” Yu2.
Tan teaches claims 2 and 12. The method of claim 1, wherein aggregating the locally trained parameters includes (Tan fig. 1 “First, the clients update their local prototype sets by minimizing the loss of classification error LS and the distance between global prototypes and local prototypes LR. Then, the clients send their prototypes to the central server. The central server generates global prototypes and returns them to all clients to regularize the training of local models.” Global prototypes are the generated updated global parameters.)
Tan doesn’t teach averaging.
However, El-Khamy teaches averaging the locally trained parameters. (El-Khamy para 8 “The server averages the client models to generate an averaged model. The server trains the averaged model using the synthetic dataset to generate an updated global model.”)
Tan, El-Khamy and the claims all update a global model with local models/parameters. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to average the local models to “achieve a more fair model across different client devices.” El-Khamy para 27.
Yu2 teaches claims 3 and 13. The method of claim 1, wherein determining the centroid of each prototype cluster includes identifying a mean of vectors in the prototype cluster. (Yu2 para 31 “To learn the option prototype go, the exemplary methods map each segment into an option embedding… and cluster to them to find K central nodes as option prototypes…” Central nodes are centroids. Yu2 para 38 shows that the K nodes are vectors, “K option prototype embedding …, k={1, 2, 3, . . . , K} vectors as learnable parameters.”)
Tan teaches claims 4 and 14. The method of claim 1, wherein the locally trained parameters include parameters of a convolution layer and (Tan p. 6 “We consider a multi-layer CNN which consists of 2 convolutional layers then 2 fully connected layers…”)
Tan doesn’t use the word imitation learning.
However, Yu2 teaches an imitation learning layer. (Yu2 para 46 “an imitation learning objective function is conducted to learn the segment embeddings and option prototype embeddings to mimic expert's policy πE…. (reproduced below) can be any imitation learning method, e.g., a behavior cloning loss or an adversarial imitation learning objective.”)
Tan teaches claims 5 and 15. The method of claim 4, wherein the skill prediction model is implemented as a machine learning model. (Tan p. 6 “We consider a multi-layer CNN which consists of 2 convolutional layers then 2 fully connected layers…”)
Tan teaches claims 6 and 16. The method of claim 4, wherein the skill prediction model accepts a (Tan p. 3 “Prototype-based model inference In the inference stage of the learning task, we can simply predict the label ˆy…”)
Tan doesn’t teach patients.
However, Yu9 teaches skill prediction model accepts a patient's state as input and outputs a treatment recommendation. (Yu9 fig. 1 and para 29 “The DTR system 108 includes a set of rules for how treatment should progress, based on updates to the patient's monitored information. As just one example of such a rule, if a patient's blood pressure were to drop below a threshold, the DTR system 108 may indicate an appropriate medical response and adjustment to treatment.”)
Tan, Yu9 and the claims are all prediction models. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to apply Tan’s machine learning to patient treatment because “there is a need to automate the process of medical decision-making…” Yu9 para 3.
Yu9 teaches claims 7 and 17. The method of claim 6, wherein the treatment recommendation includes an interpretable identification of local prototype vectors to explain the treatment recommendation for decision making purposes by medical personnel. (Yu9 para 16 “the DTR can take input information, such as a patient's medical history, laboratory results, and demographic information, and output recommended treatments to improve the effectiveness of the treatment program.”)
Yu9 teaches claims 8 and 18. The method of claim 6, further comprising automatically implementing the treatment recommendation at a client system to treat the patient. (Yu9 apply treatment based on DTR 210)
Yu2 teaches claims 9 and 19. The method of claim 1, wherein the parameterized vectors include skill prototypes relating to expert demonstrations in an imitation learning system. (Yu2 para 46 “an imitation learning objective function is conducted to learn the segment embeddings and option prototype embeddings to mimic expert's policy…”)
Tan teaches claims 10 and 20. The method of claim 1, further iteratively receiving updated locally trained parameters and distributing the updated global parameters and the updated local prototype vectors. (Tan p. 5 “The expected de crease per round is given in Theorem 1. We denote e ∈ {1/2,1,2,...,E} as the local iteration, and t as the global communication round.”)
Conclusion
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/AUSTIN HICKS/ Primary Examiner, Art Unit 2142
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