DETAILED ACTION
This action is in response to the application filed 29 May 2024.
Claims 1 – 20 are pending and have been examined.
This action is Non-Final.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 29 May 2024 has been considered by the examiner.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 2A – 1: The claims recite a Judicial Exception. Exemplary independent claim 1 recites the limitations of: A method for training a network model comprising:
training a network model iteratively using cost-optimized gradient descent, wherein, responsive to successive iterations of the training, parameters of the gradient descent are adjusted in a cost-optimized manner to sequentially increase data precision.
This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers " Enter Abstract Category" but for the recitation of generic computer components.
The training algorithm is a backpropagation algorithm and a gradient descent algorithm. When given their broadest reasonable interpretation in light of the background, the backpropagation algorithm and gradient
descent algorithm are mathematical calculations. The plain meaning of these terms are optimization algorithms, which compute neural network parameters using a series of mathematical calculation, and as such encompasses mathematical concepts. See MPEP 2106.04(a)(2) I.
Step 2A – 2: This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In respect to claim 1, there are no additional elements claimed; in respect to independent claim 8, it recites the additional elements of a system comprising at least one processor and a storge device storing instructions that are executable by the at least one processor (in the preamble), however, these are recited at a high level of generality, and amount to no more than mere instructions to apply the exception using a generic computer, see MPEP 2106.05(f); and independent claim 15 recites the additional element of a computer program product comprising a non-transient storage medium storing instructions (in the preamble), however, these are recited at a high level of generality, and amount to no more than mere instructions to apply the exception using a generic computer, see MPEP 2106.05(f).
Further, the claims do not provide for or recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO). The claim is directed to the abstract idea. (Step 2A: YES).
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims, and it has been held that “[i]n defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow.” (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350. )
Turning to the dependent claims, none of the claimed features of the dependent claims further limit the claimed invention in such a way to direct the claimed invention to statutory subject matter (e.g. change the scope of the claimed invention as to no longer be directed towards an abstract idea, or include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements or combination of elements in the claims other than the abstract idea per se), nor do they add limitations that, when taken as a combination, result in the claim as a whole amounting to significantly more than the judicial exception. In respect to exemplary dependent claims 2 – 7:
Claims 2, 3, 4, and 5 merely further describe the parameters used in the mathematical algorithms;
Claims 6 and 7 merely describe additional model training steps.
In additional respect to dependent claims 16 – 20, they merely describe the type of model being trained and the intended use of the model.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, explained with respect to Step 2A, Prong Two, the additional elements or combination of elements in the claims other than the abstract idea per se amount to no more than mere instructions to implement the idea on a computer, or the recitation of generic computer structure that serves to perform generic computer functions previously known to the industry1 [e.g. performing repetitive calculations; receiving, processing, and storing data; electronically scanning or extracting data from a physical document; electronic recordkeeping; automating mental tasks; receiving or transmitting data over a network, e.g., using the Internet to gather data] .
Applicant’s specification, at, e.g., paragraphs [0009], [0040]-[0042], provides evidence of generic computer hardware performing generic, well-known, computer functions.
Viewed as a whole, these additional claim elements, both individually and in combination, do not provide meaningful limitations to transform the above identified abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more (e.g. improvements to another technology or technical fields, improvements to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment) than the abstract idea itself. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation2.
Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, 573 U.S. No. 13–298.
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 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)(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.
Claims 1, 6, 8, 13, and 15 are rejected under 35 U.S.C. 102(a)(1) as being disclosed by JP 7,555,429 (hereinafter ‘429 document).
In respect to claim 1, ‘429 document discloses a method for training a network model comprising:
training a network model iteratively using cost-optimized gradient descent, wherein, responsive to successive iterations of the training, parameters of the gradient descent are adjusted in a cost-optimized manner to sequentially increase data precision (page 4: “Returning again to FIG. 2 , in block 240, a parameter w of the machine learning structure is updated to be equal to the determined parameter value w .sub.i for the i th iteration. Updating the parameter w may cause the machine learning structure to output a different result for a given input, which iteratively reduces or optimizes the cost function of the machine learning structure over several iterations of the gradient descent algorithm. In this way, the machine learning structure may converge toward an optimal or relatively optimal set of parameters that minimizes or relatively reduces the cost function, resulting in more accurate decisions by the machine learning structure for future data inputs”; “The benefit of avoiding division-by-zero errors when the learning rate has a value of zero or near-zero can be seen when considering another parameter value function that is inversely proportional to the learning rate. When the learning rate is zero or close to zero, the other function returns a division-by-zero error. To avoid the effects of a learning rate near-zero in such a parameter value function, it is necessary to start the learning rate at a value greater than near-zero. However, starting the machine learning process with a relatively large learning rate prevents the machine learning algorithm from converging to a good solution, i.e., to a set of parameter values that minimize the cost function. Thus, to ensure that the gradient descent algorithm converges toward a good solution, another method is needed to reduce the adjustments to the parameters of the machine learning structure. One method is to slow down the processing of the training data so that less data is considered during each iteration of the gradient descent algorithm. This may be achieved by limiting the number of processor cores that are allocated to process the training data with the machine learning structure. Reducing the processing ensures that the adjustments to the parameters at each iteration are smaller. Eventually, you may want to speed things up after the gradient descent algorithm starts moving towards a good solution, but this won't happen until after several iterations of the gradient descent algorithm”).
Further regarding “wherein” clauses, according to the MPEP 2111.04, a wherein “clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited” (Minton v. Nat'l Ass'n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003))". Also, a wherein clause that merely states the result of the limitations in the claim adds nothing to the patentability or substance of the claim ((Texas Instruments Inc. v. International Trade Commission 26, USPQ2d 1010 (Fed. Cir. 1993); Griffin v. Bertina, 62 USPQ2d 1431 (Fed. Cir. 2002); Amazon.com Inc. v. Barnesandnoble.com Inc., 57 USPQ2d 1747 (CAFC 2001)).
Claims 8 and 15 recite a system and medium reciting the same limitation as found in claim 1, and are rejected using the same rationale.
In respect to claim 6, ‘429 document discloses the method of claim 1, comprising performing goal-oriented training of the model for each of a plurality of respective training goals, and wherein the step of training the network model iteratively using cost-optimized gradient descent is performed in-turn for each respective training goal (page 2: “The instructions may include a gradient descent algorithm 130 programmed to instruct the one or more processors 110to receive training data 140, to instruct the one or more processors 110 to process the training data 140 according to one or more specified goals of the gradient descent algorithm 130, and to instruct the one or more processors 110 to incrementally adjust the gradient descent algorithm 130 based on whether the processing results were successful or unsuccessful in order to better achieve the specified goals when processing new data in the future. The gradient descent algorithm 130 may be used in a supervised learning algorithm, whereby whether the processing results were successful or unsuccessful may be determined according to known properties of the training data 140. Alternatively, the gradient descent algorithm 130 may be used in a fully or partially unsupervised learning algorithm, whereby at least a portion of whether the processing results were successful or unsuccessful may be derived from the processing results themselves, such as through a clustering algorithm or an associative algorithm. In the case of a fully or partially unsupervised learning algorithm, at least a portion of the training data 140 has unknown properties, as may new data that will be processed by the gradient descent algorithm in the future”).
Claims 13 recites a system reciting the same limitation as found in claim 1, and is rejected using the same rationale.
Subject Matter not Rejected Over Prior Art
Claims 2 – 5, 7, 9 – 12, and 16 – 20 do not stand rejected over prior art.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Kaoudi, Zoi, et al. "A cost-based optimizer for gradient descent optimization." Proceedings of the 2017 ACM International Conference on Management of Data. 2017.
Hashem, Ibrahim Abaker Targio, et al. "Adaptive stochastic conjugate gradient optimization for backpropagation neural networks." IEEE Access 12 (2024): 33757-33768.
Zaki; Tamer et al. US 20240143970 A1 EVOLUTIONAL DEEP NEURAL NETWORKS
Gural; Albert T. et al. US 11586908 B1 System and method for implementing neural networks in integrated circuits
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN S MILLER whose telephone number is (571)270-5288. The examiner can normally be reached on M-F 10am-6pm. Examiner’s fax phone number is (571) 270-6288.
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/ALAN S MILLER/Primary Examiner, Art Unit 3625
1 “It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea. Rather, the components must involve more than performance of “‘well understood, routine, conventional activit[ies]’ previously known to the industry.” Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)”. Id, pages 10-11. “Likewise, the server fails to add an inventive concept because it is simply a generic computer that “administer[ s]” digital images using a known “arbitrary data bank system.” Id. at col. 5 ll. 45–46. But “[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of ‘well-understood, routine, [and] conventional activities previously known to the industry.’” Content Extraction, 776 F.3d at 1347–48 (quoting Alice, 134 S. Ct at 2359). “These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a ‘communications controller’ and a ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”); Content Extraction, 776 F.3d at 1345, 1348 (“storing information” into memory, and using a computer to “translate the shapes on a physical page into typeface characters,” insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324–25 (generic computer components such as an “interface,” “network,” and “database,” fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a “database” and “a communication medium” “are all generic computer elements”); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.”)”. TLI Communications LLC v. AV Automotive L.L.C., (No. 15-1372, (Fed. Cir. May 17, 2016)), at *12-13.
See additionally MPEP 2106.05(d).
2 “Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted))”. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 U.S.P.Q.2d 1636 (Fed. Cir. 2015).
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