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 .
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 1-16, 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to Judicial Exceptions without significantly more. The claims recite mathematical relationships, mathematical formulas or equations, mathematical calculation and a mental process. This judicial exception is not integrated into a practical application because the recitation of generic computer and generic computer components does not sufficient to integrate the recited judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims only recites generic computer components, which are well-understood, routine, and conventional.
Revised Patent Subject Matter Eligibility Guidance
The USPTO has published revised guidance on the application of § 101. USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) (“Guidance”). Under the Guidance, the Examiner first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Guidance, Step 2A, prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure (MPEP) § 2106.05(a)-(c), (e)-(h) (9th Ed., Rev. 08.2017, 2018)) (Guidance, Step 2A, prong 2).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do the Examiner then look to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field (see MPEP § 2106.05(d)); or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. (Guidance (Step 2B)).
Evaluate Step 2A Prong One
(a) identify the specific limitation(s) in the claim that recites an abstract idea;
(b) determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
In TABLE 1 below, the Examiner identifies in italics the specific claim limitations that recite an abstract idea.
TABLE 1
Independent Claim 1
Analysis Under Revised Guidance
(a) A method for information fusion, applied to a server in a distributed training system comprising:
(b) in response to that a communication triggering condition is met, acquiring a local parameter of each of workers in a distributed training system, wherein the communication triggering condition comprises all key nodes participating in a current round of training complete tasks of the current round of training
“acquiring a local parameter…” is an abstract idea, i.e., “a mathematical calculation” and “mental process”, for observing and receiving information/data, which can be performed in the human mind or with the aid of pen and paper.
(c) selecting, from each of the workers, N key nodes participating in the next round of training
“selecting… N key nodes …” is an abstract idea, i.e., “a mathematical calculation” or “a mental process”, to select a particular node based on a certain value/criteria, which can be performed in the human mind.
(d) fusing local parameters of the N key nodes to obtain a global parameter
“fusing local parameters of the N keys nodes to obtain a global parameter…” is an abstract idea, i.e., a “mathematical calculation”, “mathematical formula” and “a mental process”, to perform mathematical calculation based on the local parameters in the human mind or with the aid of pen and paper.
(e) sending the global parameter to each of the workers
“sending the global parameter to each of the worker…” is an abstract idea, i.e., “a mental process” or “human activity”, which can be performed by a human to communicate data to the workers.
(f) sending a training command to the key nodes to the key nodes execute tasks of the next round of training based on the global parameter.
“sending a training command to the key nodes…” is an abstract idea, i.e., “a mental process” or “human activity”, which can be performed by human to issue a command to the nodes.
In view of the above analysis, Claim 1 recites an abstract idea under the Revised Guidance because the limitations (b) – (f) each recite mathematical relationship, mathematical calculation and/or a mental process. Independent claims 12, 20 also recite an abstract idea because it includes similar limitations (b) – (f) and further recites the limitation “performing a compression operation on local parameters, which is also an abstract idea, i.e., “mathematical calculation”. Dependent claims 2-11, 13-16 and 21-23 also recite abstract idea because they include limitations (b) – (f) by virtue of their dependencies to claims 1, 12 and 20, respectively.
Dependent claims 2-11, 13-16 and 21-23 further recite additional limitations. However, these limitations are also recite abstract idea, i.e., “mathematical concept – mathematical formulas or equations, mathematical calculations” similar to the limitations of claims 1, 12 and 20 discussed above.
Evaluate Step 2A Prong Two:
Evaluate whether the claim as a whole integrated the recited Judicial exception into a Practical Application of the exception.
Having determined that the claims recite a judicial exception, the analysis under the Guidance turns now to determining whether there are “additional element that integrate the judicial exception into a practical application”. The examiner determines whether the recited judicial exception is integrated into a practical application that exception by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exceptions; and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application”.
Independent claim 1 does not recite any additional element that integrate the judicial exception into a practical application. Independent claim 20 recites limitations “a non-transitory computer readable storage medium”, “a processor” which are simply a generic computer component to store and execute computer instructions, which causes a generic computer system to perform the operations recited in limitations (b)-(h). The “non-transitory medium” “processor” recited in the claims are so generically that is represents no more than mere generic computer component to apply the judicial exception on a computer. The recitation of generic computer and generic computer components does not sufficient to integrate the recited judicial exception into a practical application. Guidance at 52 n.14 (“Performance of a claim limitation using generic computer components does not necessarily preclude the claim limitation from being in the mathematical concepts grouping.”)
Evaluate Step 2B:
Evaluate whether the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well-known. See MPEP 2106.05(g). The claim does not add any specific limitations beyond what is well-understood, routine, and conventional. Here, claims 1, 12, 20 recite “non-transitory medium”, “processor”, which are mere generic computer components that are recited at a high level of generality, and, as disclosed in the specification, is also well-understood, routine, conventional activity when expressed at this high level of generality. Mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the claims do not provide an inventive concept (significantly more than the abstract idea) and is not eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11, 20-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1, 20 recite “sending a training command to the key node to the key nodes execute tasks of the next round of training based on the global parameter”, the meaning of this limitation is unclear.
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 1-2, 11-12, 21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vargaftik et al. (US 2023/0342599 A1), hereinafter “Vargaftik”.
As per claim 1, Vargaftik teaches a method for information fusion, applied to a server in a distributed training system comprising:
“in response to that a communication triggering condition is met, acquiring a local parameter of each workers in a distributed training system, wherein the communication triggering condition comprises all key nodes participating in a current round of training complete tasks of the current round of training” at [0019]-[0022] and Figs. 1, 4, 7;
(Vargaftik teaches a distributed training system 100 comprising a set of N host computer 105 (i.e., “workers”) on which N training machine 110 (i.e., “nodes”) execute for training an ML model. Each of the ML training machines 110 computers various local training parameters and sends the local training parameters to the parameter server 115)
“selecting, from each of the workers, N key nodes participating in the next round of training” at [0022];
(Vargaftik teaches only a subset of training machines perform this forward propagation and backpropagation of errors at each iteration (e.g., a randomly selected subset for each iteration) to compute the local gradients)
“fusing local parameters of the N key nodes to obtain a global parameter” at [0021];
(Vargaftik teaches the parameter server 115 is responsible for generating a global parameter for each set of local parameters and returning this global parameter to the training machines 110)
“sending the global parameter to each of the worker sending a training command to the key nodes to the key nodes execute task of the next round of training based on the global parameter” at [0021].
(Vargaftik teaches returning this global parameter to the training machines 110. The training machines 110 use the global parameters to update the ML model so that they can execute the next training iteration)
As per claim 2, Vargaftik teaches the method for information fusion as claimed in claim 1, wherein “all the key nodes participating in the current round of training completing the tasks of the current round of training comprises: all the key nodes participating in the current round of training completing a preset number of iterative training processes” at [0019]-[0022] and Figs. 1, 4, 7.
As per claim 11, Vargaftik teaches the method for information fusion as claimed in claim 1, wherein “fusing the local parameters of the N key nodes to obtain the global parameter comprises: calculating an average value of the local parameters of the N key nodes, and determining the average value as the global parameter” at [0058].
As per claim 21, Vargaftik teaches the method of claim 1, wherein “each of worker is connected to the server in a two-way manner, and there is no direct connection between the workers at [0019]-0021] and Fig. 1.
As per claim 12, Vargaftik teaches a method for data communication, applied to a worker in a distributed training system comprising:
“in response to that a communication triggering condition is met, performing a compression operation on local parameters of each of the workers based on a preset compression algorithm, transmitting the compressed local parameters to a server” at [0019]-[0028] and Fig. 1 ;
(Vargaftik teaches a distributed training system 100 comprising a set of N host computer 105 (i.e., “workers”) on which N training machine 110 (i.e., “nodes”) execute for training an ML model. Each of the ML training machines 110 computers various local training parameters (e.g., gradients). Vargaftik teaches the smart NICs 125 compress local gradients computed by the training machine 110 and send them to the parameter server 115 via the network 120)
“acquiring a global parameter sent by the server, wherein the global parameter is obtained by using, by the server, local parameters of N key nodes” at [0021];
(Vargaftik teaches the parameter server 115 is responsible for generating a global parameter for each set of local parameters and returning this global parameter to the training machines 110)
“in response to that receiving a training command set by the server, executing corresponding training tasks based on the global parameter” at [0021].
(Vargaftik teaches returning this global parameter to the training machines 110. The training machines 110 use the global parameters to update the ML model so that they can execute the next training iteration)
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.
Claims 3-10, 13-16, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Vargaftik as applied to claims above, and in view of Zhu et al. (US 2023/0385652 A1), hereinafter “Zhu”.
As per claim 3, Vargaftik teaches the method for information fusion as claimed in claim 1 discussed above. Vargaftik does not teach wherein “selecting, from each of the workers, the N key nodes participating in the next round of training comprises: calculating an average parameter of the local parameters of the key nodes, determining a deviation of the local parameter of each of the workers from the average parameter, and selecting N workers with the minimum deviation as the key nodes participating in the next round of training” as claimed. However, Zhu teaches a federated learning network including a server and multiple clients, including steps of “selecting, from each of the workers, the N key nodes participating in the next round of training comprises: calculating an average parameter of the local parameters of the key nodes, determining a deviation of the local parameter of each of the workers from the average parameter, and selecting N workers with the minimum deviation as the key nodes participating in the next round of training” at [0038]-[0039], [0071]-[0075], [0084]. Thus, it would have been obvious to one of ordinary skill in the art to combine Zhu with Vargaftik’s teaching because “applying these techniques, the effect of data divergence is minimized and the number of rounds or iterations to achieve convergence of the data is reduced”, as suggest by Zhu at [0039].
As per claim 4, Vargaftik and Zhu teach the method for information fusion as claimed in claim 3 discussed above. Zhu also teaches: wherein “determining the deviation of the local parameter of each of the workers from the average parameter comprises: sending the average parameter to each of the workers to each of the workers calculates the deviation of own local parameter of each of the workers from the average parameter, and returning the deviation of own local parameter local parameter of each of the workers from the average parameter to the server” at [0038]-[0039], [0071]-[0075], [0084].
As per claim 5, Vargaftik teaches the method for information fusion as claimed in claim 1 discussed above. Vargaftik does not explicitly teach: “dividing a training model into a plurality of training sub-models, and assigning the training sub-models to each of the workers” as claimed. However, Zhu teaches a federated learning network including a server and multiple clients, including steps of “dividing a training model into a plurality of training sub-models, and assigning the training sub-models to each of the workers” at [0054]-[0064]. Thus, it would have been obvious to one of ordinary skill in the art to combine Zhu with Vargaftik’s teaching because “applying these techniques, the effect of data divergence is minimized and the number of rounds or iterations to achieve convergence of the data is reduced”, as suggest by Zhu at [0039].
As per claim 6, Vargaftik and Zhu teach the method for information fusion as claimed in claim 5 discussed above. Zhu also teaches: wherein “dividing the training model into the plurality of training sub-models comprises: dividing the training model into the plurality of training sub-models in a horizontal direction or a vertical direction” at [0054]-[0064].
As per claim 7, Vargaftik teaches the method for information fusion as claimed in claim 1 discussed above. Vargaftik does not explicitly teach: “assigning a plurality of training samples to each of the workers to each of the workers executes an iterative training process based on the corresponding training samples” as claimed. However, Zhu teaches a federated learning network including a server and multiple clients, including steps of “assigning a plurality of training samples to each of the workers to each of the workers executes an iterative training process based on the corresponding training samples” at [0054]-[0064]. Thus, it would have been obvious to one of ordinary skill in the art to combine Zhu with Vargaftik’s teaching because “applying these techniques, the effect of data divergence is minimized and the number of rounds or iterations to achieve convergence of the data is reduced”, as suggest by Zhu at [0039].
As per claim 8, Vargaftik and Zhu teach the method for information fusion as claimed in claim 7 discussed above. Zhu also teaches: wherein “assigning the plurality of training samples to each of the workers comprises: assigning the plurality of training samples to each of the workers based on a sampling method, or splitting the plurality of training samples according to a data dimension, and assigning split training samples to each of the workers” at [0054]-[0064].
As per claim 9, Vargaftik and Zhu teach the method for information fusion as claimed in claim 8 discussed above. Zhu also teaches: wherein “assigning the plurality of training samples to each of the workers based on the sampling method comprises: assigning the plurality of training samples to each of the workers by means of put- back random sampling and/or local scrambling sampling; or assigning the plurality of training samples to each of the workers by means of put-back random sampling and/or global scrambling sampling” at [0054]-[0064]
As per claim 10, Vargaftik and Zhu teach the method for information fusion as claimed in claim 8 discussed above. Zhu also teaches: wherein “splitting the plurality of training samples according to the data dimension, and assigning split training samples to each of the workers comprises: in response to that each training sample has a multi-dimensional attribute or feature, splitting the plurality of training samples according to different attributes, and assigning split sample subsets to the corresponding workers” at [0054]-[0064].
Claims 13-16, 22-23 recite similar limitations as in claims 3-10 above and therefore rejected by the same reasons.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
August 25, 2026
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