DETAILED ACTION
This is a first Office Action (“Action”) on the merits to the application filed Nov. 17, 2023. Claims 1-8 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 Objections
Claims 1-8 are objected to because of the following informalities:
Claims 1-3, the first line in each claim should be changed to “A variable optimization system that is constituted by n (n is” for grammatical reasons.
Claims 4-8 are also objected to for depending from claim 1.
Appropriate correction is required.
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.
Claims 1-8 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Throughout the claims there appear phrases, such as, “[Math. 72]” or “[Math. 73]”, for example. It is not entirely clear in what way these phrases should affect the interpretation of the claim. As a result, they should be removed.
Also throughout the claims, some phrases are in parenthesis, such as in claim 1, “(where, μ, η and ρ are predetermined vectors, βi|j is a weight in the i-th node corresponding to the j-th node, ui|j is a temporary variable in the i-th node corresponding to the j-th node, and sign (Ai|j) is a sign of an identity matrix Ai|j)”, as one example. It is unclear why these phrases are in parenthesis and, as a result, how they should be interpreted. If these limitations are needed for claim interpretation, they should not be in parenthesis.
Claim 1 recites variable “R”, however, it is not clear how this variable is used in any of the recited equations or in the other limitations recited in the claims. As a result, the presence of “R” in the claims creates confusion since it is not immediately clear how this variable should limit the claim.
Claims 2 and 3 each recite variables “R” and “K”, however, it is not clear how these variables are used in any of the recited equations or in the other limitations recited in the claims. As a result, their presence in the claims creates confusion since it is not immediately clear how they should limit the claims.
Claim 2 recites the limitations “Ψ is a distribution for each type of learning data accumulated in the n nodes, and a mini-batches xi,MB is a mini-batch generated from the learning data set xi in accordance with the distribution Ψ.” These variables are not used in any of the equations or steps recited in claim 2. Thus, it is unclear how these variables relate to the rest of the limitations recited in claim 2. Claim 4, which depends from claim 1, recites similar limitations but neither claims 1 or 4 further use these variables. Thus, it is also unclear how these variables relate to the rest of the limitations recited in claim 4.
The remaining dependent claims, if not mentioned above, are also rejected as indefinite at least for their respective dependencies from a rejected base claim.
Lastly, the claims are not written in a traditional format usually seen in U.S. claim drafting, which is acceptable. However, while an attempt has been made to point out language that results in indefiniteness, all claims should be reviewed in light of the comments above for similar issues, claim language, and claim formatting that may lead to indefiniteness but has not been expressly identified.
For the reasons above, claims 1-8 are indefinite and rejected under 35 U.S.C.§ 112(b).
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
To determine whether claimed subject matter is patent eligible section 2106 of the MPEP requires specific evaluation of the limitations recited. In step 1, a determination is made as to whether a claim is directed to a statutory category (i.e., a process, machine, manufacture, and composition of matter). If so, then a determination is made as to whether the claim is directed to patent ineligible subject matter, such as an abstract idea, using a three part test: First, in step 2A, prong 1 the abstract idea is identified; Second, in step 2A, prong 2 the abstract idea is determined to be integrated into a practical application or not; and Third, in step 2B the additional claim limitations are evaluated individually and as a whole to determine if they amount to an inventive concept (i.e., determining whether the limitations are significantly more than the abstract idea itself).
While the claims fall within at least one of the four statutory categories of patent eligible subject matter (i.e., step 1 is satisfied because all claims are directed to an apparatus or article of manufacture, that is, the “system”), they are nonetheless patent ineligible for being directed to an abstract idea without reciting significantly more.
Initially, the following explanation is based not only on the guidance in the MPEP, but also the “2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence,” published on July 17, 2024 (89 FR 58128) (AI-SME Update). Moreover, the Office has provided examples of patent ineligible subject matter that are relevant to the claimed subject matter of this application. In particular, example 48,1 is relevant to the discussion below of claims 1-8 and should be reviewed for further guidance and support of the determination that claims 1-8 are directed to patent ineligible subject matter.
Step 2A, Prong 1: Independent Claims 1, 2, and 3
Claims 1, 2, and 3 are directed to an abstract idea, specifically the recitation of mathematical concepts and mental processes. See MPEP 2106.04(a)(2), subsections I and III. Claims 1, 2, and 3 each recite numerous mathematical equations that make up operations executed by respective processors of each claimed system. Each of claims 1, 2, and 3 additionally recite various other operations performed by the claimed processors, including but not limited to, “updating” and “receiving” values, where each of the “updating” and “receiving” are defined as mathematical equations. There are no other limitations recited in claims 1, 2, and 3 that show a use for or application of any of the resulting values of the mathematical equations. Any additional limitations are merely defining the various variables recited in each mathematical equation or attempt to generally link the abstract idea as executed on a generically recited computer. As a result, most of claims 1, 2, and 3 are directed to mathematical concepts that could be carried out in the human or at least on paper. Thus, prong one of step 2A is satisfied and the analysis continues below.
Step 2A, Prong 2: Independent Claims 1, 2, and 3
The invention describes several known issues when using current techniques to train machine learning models using data across several nodes, where the data may statistically heterogeneous from one another and communication between nodes be asynchronous and/or sparse. See Spec. as filed, ¶31. Updating of model variables may have too much variance. See Spec. as filed, ¶32. As such, training the model “cannot be stably advanced and the global model cannot be learned.” See id. The described invention provides a solution so that “when there is a statistical deviation in a learning data set distributed and accumulated in a plurality of nodes, or communication between nodes is asynchronous and sparse, a variable of model can be stably optimized so as to conform to the learning data set.” See Spec. as filed, ¶74.
While the described invention may result in an optimized system, claims 1, 2, and 3 do not recite or integrate any limitations that would tie the abstract idea noted above to this practical application. Moreover, there are no limitations that show even an improvement to the training of any machine learning model, etc. The claims only recite limitations that attempt to cover the mathematics underlying the claimed invention and not claim any practical application of the mathematical relationships described in the specification. Since there are no limitations to show integration into a practical application, prong 2 of step 2A is satisfied and the analysis continues below.
Step 2B: Independent Claims 1, 2, and 3
The only other limitations that claims 1, 2, and 3 each additionally recite “the variable optimization system comprising a processor configured to execute operations.” This additional limitation does not amount to significantly more when taken individually or when combined with the other limitations making up the abstract idea.
A “processor” that executes operations is nothing more than reciting a general computer or hardware to carry out the abstract idea, which is not significantly more. See MPEP § 2106.05(b). Moreover, even when considering all limitations together, the claims merely recite instructions to implement an abstract idea or other exception on a computer, and/or insignificant extra-solution activity, which does not provide an inventive concept. As a result, these additional limitations are recited in such a way that shows nothing more than insignificant extra solution activity or mere instructions to apply the exception (i.e., abstract idea) using a generic computer. See MPEP §§ 2106.05(b), and 2106.05(d), subsection II.
Based on the analysis in steps 2A and 2B as explained above, claims 1, 2, and 3 recite an abstract idea without significantly more and are directed to patent ineligible subject matter.
Dependent Claims 4-8
Claims 4-6 merely recite additional mathematical equations or are defining variables of mathematical equations. As such, these claims too are directed to an abstract idea without significantly more and are directed to patent ineligible subject matter for the same reasons as presented above for claims 1, 2, and 3.
Claim 7 recites, “the learning data set accumulated in each node in the n nodes indicates statistical deviation of more than a predetermined threshold from another learning data set accumulated in another node in the n nodes.” This is nothing more than further defining the “learning data set,” which is considered a form of defining variables of the mathematical equations, or defining the results of the mathematical equations. This is, thus, also directed to an abstract idea without significantly more.
Claim 8 recites, “communications among the n nodes are asynchronous and sparse based on a predetermined time.” This is insignificant as the limitation is merely defining a time frame over which the data is evaluated. While minorly narrower than claim 1, from which claim 8 depends, this provides no meaningful limit on the abstract idea and does not show an integration into a practical application.
For the reasons above, all of the limitations recited in claims 4-8 are mathematical concepts and/or can be performed in the human mind, and/or do not add meaningful limits on the abstract idea itself and do not make the claims patent eligible.
As a result, none of dependent claims 4-8 recite significantly more than the abstract idea recited in independent claims 1, 2, and 3 from which they respectively depend.
For the reasons explained above, none of claims 1-8 are directed to patent eligible subject matter under section 101 and are thus rejected.
Conclusion
Claims 1-8 are rejected under 35 U.S.C. §§ 101 and 112(b).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Japanese patent JP 7522076 is directed to optimizing variables of a model using machine learning. U.S. patent application publication no. 2021/0158226 is directed to using a cost function to train a machine learning model.
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/JOSHUA KADING/ Primary Patent Examiner, Art Unit 3993
1 https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf.
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