DETAILED ACTION
This office action is in response to the application filed on 6/11/2023. Claim(s) 1-10 is/are pending and are examined.
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 .
Priority/Benefit
Applicant’s priority claim is hereby acknowledged of 371 of PCT/EP2021/085951 12/15/2021 and GERMANY 10 2020 215 945.9 12/15/2020, which papers have been placed of record in the file.
Information Disclosure Statement PTO-1449
The Information Disclosure Statement(s) submitted by applicant on 6/11/2023 has/have been considered. The submission is in compliance with the provisions of 37 CFR § 1.97. Form PTO-1449 signed and attached hereto.
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.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The analysis is guided by the Supreme Court's two-step framework, described in Mayo and Alice (Alice Corp. Pty Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014) and Mayo Collaborative Servs. V. Prometheus Labs., Inc., 132 S. Ct. 1289, 1296-97 (2012)).Step 1: Is/Are the claim(s) directed to a process, machine, manufacture, or composition of matter?Answer: Yes and no. Claims 1-6 and 9 are directed to a method and a non-transitory medium. Claims 7-8 and 10 are directed to software.Step 2A Prong 1: Is/Are the claim(s) directed to a law of nature, a natural phenomenon, or an abstract idea, i.e., judicially recognized exceptions (both individually and as an ordered combination)?
Answer: Yes, the claim(s) are directed to the mental process of “training the artificial neural network as a function of a first loss function; and training the artificial neural network as a function of a second loss function, wherein the first loss function is calculated as a function of an output of the artificial neural network, and wherein the second loss function is configured in such a way that the output of the artificial neural network is essentially normalized” beyond the scope of § 101. Neural network examples are frequently worked out in textbooks and literature. Dependent claims 2-6 expand on the identified abstract idea. A similar analysis applies to independent claim 7.
Step 2A Prong 2: Is/Are the claim(s) implemented into a practical application?
Answer: No, the limitations of the claim as drafted, is a process that, under its broadest reasonable interpretation, covers implementation of the mathematical concepts which can be performed by the mind using pencil and paper but for the recitation of generic computer components in only claim 9 (i.e., “non-transitory computer readable medium”). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.Step 2B: Does/Do the claim(s) recite additional elements that when analyzed individually and in ordered combinations amount to significantly more than the judicial exception(s)?
Answer: No, the claim(s) (both individually and as an ordered combinations) does/do not transform the nature of the claim(s) into a patent-eligible application of the abstract idea (i.e., significantly more than the abstract idea implemented using generic computer components). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements to perform the processing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, claims are not patent eligible.
Also, in a separate rejection, Claim(s) 7, 8 and 10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims recite no structure whatsoever. Claim 8 is openly directed to software. Such language points to software per se when there is no language in the claim or specification by which the claim elements can be made functional and statutory. Therefore, a person of ordinary skill in the art would interpret the limitations to mean merely computer executable functions, rendering the claims comprising merely executable functions, which is non-statutory. As such, claim(s) 7, 8, and 10 is/are drawn to non-statutory subject matter. See MPEP § 2106.01.
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.
Claim(s) 1-10 is/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 applicant regards as the invention.
Regarding claim(s) 1 and 7, the term "essentially” is a relative term which renders the claim(s) indefinite. The term "essentially" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Dependent claim(s) 2-6 and 8-10 is/are rejected for the reasons presented above with respect to rejected claim(s) 1 in view of their dependence thereon.
Examiner’s Note – Claim Scope
Claim 7 recites “for classification of sensor data” is an intended use statement with no patentable weight.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1, 3, 7-8, 10, is/are rejected under AIA 35 U.S.C. 102(a)(1) as being anticipated by Ma (“Normalized Loss Functions for Dep Learning with Noisy Labels”, 2020, PMLR, 119, Pg. 1-13), already on the IDS.
Regarding claims 1, 7-8, 10, Ma teaches:
“A method for training an artificial neural network, as comprising: training the artificial neural network as a function of a first loss function (Ma, Pg. 4 Col. 2 Ln. 22 – Pg. 5 Col. 1 Ln. 38 and Pg. 7 Col. 1 Ln. 15 – Col 2 Ln. 34 teaches training the deep learning neural network using the active loss function); and training the artificial neural network as a function of a second loss function (Ma, Pg. 4 Col. 2 Ln. 22 – Pg. 5 Col. 1 Ln. 38 and Pg. 7 Col. 1 Ln. 15 – Col 2 Ln. 34 teaches training the deep learning neural network using the active pass loss (APL) framework), wherein the first loss function is calculated as a function of an output of the artificial neural network (Ma, Pg. 4 Col. 2 Ln. 22 – Pg. 5 Col. 1 Ln. 38 and Pg. 7 Col. 1 Ln. 15 – Col 2 Ln. 34 teaches training the deep learning neural network using the active loss function based on the results of the network), and wherein the second loss function is configured in such a way that the output of the artificial neural network is essentially normalized (Ma, Pg. 4 Col. 2 Ln. 22 – Pg. 5 Col. 1 Ln. 38 and Pg. 7 Col. 1 Ln. 15 – Col 2 Ln. 34 teaches training the deep learning neural network using the active loss function to minimize losses. Ma, Abstract, Pg. 7 Col. 1, Ln. 11-32 teaches that the losses are normalized to improve results)”.
Regarding claim 3, Ma teaches:
“The method according to claim 1 (Ma teaches the limitations of the parent claim as discussed above) further comprising: calculating the second loss function by adding up the output of the artificial neural network along at least one dimension (Ma, Pg. 4 Col. 2, Ln. 22-40 teaches that the loss is the summation of the losses for the whole set)”
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 of this title, 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) 2, 5-6 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of O’Connor (US 2022/0121927 A1).
Regarding claim 2, Ma teaches:
“The method according to claim 1 (Ma teaches the limitations of the parent claim as discussed above)”.
Ma does not, but in related art, O’Connor teaches: “another artificial neural network is configured to approximate a softmax function, and the other artificial neural network is applied to the output of the artificial neural network to calculate the second loss function (O’Connor, ¶ 66-70 teaches a group of neural networks connected together which includes a softmax function as an output layer of a neural network)”.
Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Ma and O’Connor, to modify the neural network training system of Ma to include the softmax function as taught in O’Connor. The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results.
Regarding claim 5, Ma teaches:
“The method according to any one claim 1(Ma teaches the limitations of the parent claim as discussed above)”.
Ma does not, but in related art, O’Connor teaches: “another artificial neural network is configured to approximate a softmax function, and the first loss function is calculated by applying the other artificial neural network to the output of the artificial neural network (O’Connor, ¶ 66-70 teaches a group of neural networks connected together which includes a softmax function as an output layer of a neural network)”.
Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Ma and O’Connor, to modify the neural network training system of Ma to include the softmax function as taught in O’Connor. The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results.
Regarding claim 6, Man and O’Connor teaches:
“The method according to claim 2 (Ma and O’Connor teaches the limitations of the parent claim as discussed above), wherein: the softmax function is applied to the output of the artificial neural network to compute the first loss function, and the second loss function is further configured such that the output of the artificial neural network approximates an output of the softmax function (O’Connor, ¶ 66-70 teaches a group of neural networks connected together which includes a softmax function as an output layer of a neural network)”.
Regarding claim 9, Ma teaches:
“The method according to claim 8 (Ma teaches the limitations of the parent claim as discussed above)”.
Ma does not, but in related art, O’Connor teaches: “wherein the computer program is stored on a non-transitory machine-readable storage medium (O’Connor, ¶ 97 teaches implementation with a non-transitory computer readable medium)”.
Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Ma and O’Connor, to modify the neural network training system of Ma to include the non-transitory computer readable medium as taught in O’Connor. The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Ghosh (US 2020/0012923 A1).
Regarding claim 4, Ma teaches:
“The method according to claim 1 (Ma teaches the limitations of the parent claim as discussed above)”.
Ma does not, but in related art, Ghosh teaches:
“wherein the second loss function is further configured such that the output of the artificial neural network adds up to 1 (Ghosh, ¶ 41 teaches the loss function adding up to 1)”.
Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Ma and Ghosh, to modify the neural network training system of Ma to include the method to add up the loss function to one as taught in Ghosh. The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen T Gundry whose telephone number is (571) 270-0507. The examiner can normally be reached Monday-Friday 9AM-5PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amir Mehrmanesh can be reached at (571) 270-3351. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEPHEN T GUNDRY/Primary Examiner, Art Unit 2435
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