DETAILED ACTION
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 .
The action is in response to claims dated 6/14/2023.
Claims pending in the case: 1-8
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim(s) 1 in the relevant part reads: “evaluate, at the processor, the plurality of minimally connected networks using a random sample from each respective class of a plurality of classes within a local dataset”.
It is unclear what this evaluation process is doing, i.e. what about the network is being evaluated. Network evaluation may be for any of the network parameters such as size, speed, stability, performance, feature, architecture and so on. It is not clear what is being evaluated.
Further since class of data may be based on data type, information type, data location, data usage among others, it is also unclear what is being referred to as “class” i.e. what group of data in the dataset may be considered to be of the same class.
As such, a person of reasonable skill in the art would not be apprised of the metes and bounds of the invention.
For the purpose of examination, “class” is interpreted as a group of data.
Claim(s) 1 further reads: “facilitate, at the processor, a validation exchange between one or more minimally connected networks …”. It is unclear what is being referred to as “a validation exchange”, i.e. what is being validated and what information is exchanged in not clear. As such, a person of reasonable skill in the art would not be apprised of the metes and bounds of the invention.
For the purpose of examination, a reasonable interpretation was not possible.
Claim(s) 1 further reads: “assess, based on the validation exchange, a suitability of one or more minimally connected networks …”. It is unclear what is to be assessed to determine suitability. It is also unclear what criteria may be considered as suitable. As such, a person of reasonable skill in the art would not be apprised of the metes and bounds of the invention.
For the purpose of examination, a reasonable interpretation was not possible.
All claims dependent on this/these claim(s) are also rejected under 35 U.S.C. 112(b) due to the virtue of their respective direct and indirect dependencies.
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.
Claim(s) 1-3, 6, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20220060390) in view of Gaier (Weight Agnostic Neural Networks).
Regarding Claim 1, Li teaches, A system, comprising: a processor in communication with a memory, the memory including instructions (Li: [17]: processor executing instructions), which, when executed, cause the processor to:
initialize, at the processor, a plurality of … connected networks (Gaier: Fig. 8, [139]: coordinator with connected networks; [129, 154]: initialize instances);
evaluate, at the processor, the plurality of … connected networks using a random sample from each respective class of a plurality of classes within a local dataset associated with a first client A (Li: [139-142]: local models using local data sets);
facilitate, at the processor, a validation exchange between one or more minimally connected networks of the plurality of minimally connected networks of the first client A and one or more minimally connected networks of the plurality of … connected networks of a second client B (Li: Fig. 8, [139-142]: facilitate exchanges may be between the coordinator and devices and between one device to another);
assess, based on the validation exchange, a suitability of one or more … connected networks of the plurality of minimally connected networks of the first client A with respect to the second client B (Li: [141-142]: assess model parameters from clients and transfer to clients);and
select one or more … connected networks of the plurality of minimally connected networks of the first client A to share with the second client B (Li: [8, 142]: Transfer of a selected model parameters from one client to another via the coordinator);
However Gaier does not specifically teach,
minimally connected networks;
Gaier teaches, minimally connected networks (Gaier: Pg. 2 last para: networks may be “minimal architectures that can represent solutions to various tasks”);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li and Gaier because the combination would enable using client devices with weight agnostic neural networks in federated learning. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would enable using a model appropriate for different tasks in specific situation. The combination enables using a neural network architecture that has been introduced in the art with good performance with reduced training effort (see Gaier Pg. 1 section 1).
Please also refer to the 112b rejections above.
Regarding claim 2, Li and Gaier teach the invention as claimed in claim 1 above and, wherein the memory further includes instructions, which, when executed, cause the processor to:
(1) apply, at the processor, a weight-agnostic network search methodology for current generation g of a plurality of generations G to a first plurality of minimally connected networks of the plurality of minimally connected networks using a random sample from each respective class of a plurality of classes within a local dataset associated with the first client A (Li: [139-142]: develop local models using local data sets) (Gaier: Pg. 4 Fig. 2, section “Topology Search” : agnostic search to generate next generation architecture);
(2) facilitate, at the processor, a first validation exchange of the current generation g between a first percentage of the first plurality of minimally connected networks of the first client A and a first percentage of a second plurality of minimally connected networks of the plurality of minimally connected networks of a second client B (Li: Fig. 8, [139-142]: facilitate exchanges may be between the coordinator and devices and between one device to another);
(3) estimate, based on the first validation exchange, how one or more remaining networks of the first plurality of minimally connected networks would perform on a local dataset associated with the second client B using a trained estimator that incorporates a reward per class of the plurality of classes within the local dataset associated with the first client A as features for a regression model of the trained estimator (Gaier: Pg. 5, section “Performance and Complexity” : evaluate performance using averaging of cumulative reward over rollouts (class));
(4) apply, at the processor, a per-class weighted averaging of rewards of the first plurality of minimally connected networks (Gaier: Pg. 5, section “Performance and Complexity” : evaluate performance using averaging of cumulative reward over rollouts (class));
(5) facilitate, at the processor, a second validation exchange of the current generation g between a second percentage of the first plurality of minimally connected networks of the first client A and a second percentage of the second plurality of minimally connected networks of the second client B (Li: Fig. 8, [139-142]: exchanges of updates between the coordinator and devices and between one device to another made regularly); and
(6) select a set of best-performing networks of the one or more minimally connected networks based on the second validation exchange (Li: [80, 97]: selected subset) (Gaier: Pg. 11, section A.5: best networks identified at each run).
Regarding claim 3, Li and Gaier teach the invention as claimed in claim 2 above and, wherein the memory further includes instructions, which, when executed, cause the processor to:
evolve, at the processor, the first plurality of minimally connected networks of the current generation g of the plurality of generations G (Gaier: Pg. 4 Fig. 2, section “Topology Search” : agnostic search to generate next generation architecture);
evaluate the first plurality of minimally connected networks based on the random samples from each respective class of the local dataset of the first client A (Gaier: Pg. 5, section “Performance and Complexity” : evaluate performance using averaging of cumulative reward over rollouts); and
average the evaluations of the first plurality of minimally connected networks over a first quantity of classes of the plurality of classes within the local dataset associated with the first client A (Gaier: Pg. 5, section “Performance and Complexity” : evaluate performance using averaging of cumulative reward over rollouts (class)).
Regarding claim 6, Li and Gaier teach the invention as claimed in claim 2 above and, wherein the memory further includes instructions, which, when executed, cause the processor to: iteratively repeat steps (1)-(6) at each generation g of the plurality of generations G (Gaier: Pg. 3, section 3 [4]: algorithm repeats over successive generations).
Regarding claim 8, Li and Gaier teach the invention as claimed in claim 1 above and, wherein the memory further includes instructions, which, when executed, cause the processor to:
assign, at the processor, a category to a minimally connected network of the plurality of minimally connected networks associated with the first client A or the second client B based on one or more characteristics of the minimally connected network (Gaier: Pg. 2-3, section 4 [4]: group based on a task); and
select, at the processor, one or more minimally connected networks of the plurality of minimally connected networks from one or more categories to send to the second client B or the first client A (Li: [80, 97]: selected subset; [8, 142]: Transfer of a selected model parameters from one client to another via the coordinator) (Gaier: Pg. 11, section A.5: best networks identified at each run).
Claim Rejections using prior art
For claims 4-5, 7 a prior art rejection has not been presented as a reasonable interpretation was not possible for all the limitations as claimed due to a lack of clarity of the independent claim on which they depend. The applicant is requested to address the 112b rejections to facilitate a complete search on the limitations of these claims and help further prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in the attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
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