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 .
Response to Arguments
Applicant's arguments filed on 06/18/2026 have been fully considered but they are not persuasive.
Step 2A Prong 1, Applicant argued that claims do not recite any mathematical concept or mental process. However, Examiner respectfully disagrees.
The claim recites a sequence of mathematical/statistical operations such as (i) compute a probability distribution from an input, (ii) sample a value from that distribution, (iii) apply a statistical mapping (entropy coding) to a sequence of such values and its inverse, and (iv) repeat steps (i)-(ii) using a second computed distribution based on the first sampled value. This is analogous to claims that recite a series of mathematical calculations or statistical analyses performed on data, which the courts have consistently found to fall within the mathematical concepts grouping. For example: SAP AMERICA, INC. v. INVESTPIC, LLC (Fed. Cir. 2018) and DIGITECH IMAGE TECHNOLOGIES v. ELECTRONICS FOR IMAGING, INC. (Fed. Cir. 2014). Therefore, the claim recites a process that deals with mathematical calculations because claims are directed to generating a sample value based on determined values. (See MPEP 2106.04(a)).
Applicant argues that the claimed steps determining a probability distribution via a first machine learning model, sampling to obtain a first sample value, entropy-encoding a sequence of such values, entropy-decoding to reconstruct the sequence, determining a second probability distribution via a second machine learning model, and sampling to obtain a second sample value, reflect a technical improvement in data compression. However, Examiner respectfully disagrees.
The claimed steps, viewed individually and as an ordered combination, recite the abstract idea itself (a mathematical process of computing probability distributions and sampling from them, coupled with the generic data processing operations of encoding and decoding) rather than any additional element that applies, relies on, or uses the exception in a manner that imposes a meaningful limit. Here, “using a first/second machine learning model” recites the model only as a black-box tool invoked to perform the abstract mathematical operation (probability computation). No architecture, parameters, training methodology, or technical mechanism internal to the model is recited that would constitute an additional element separate from the exception. “Entropy-encode” and “entropy-decode” are recited at high level of generality as the generic, well-understood function of a coder/decoder converting data to and from a compressed bitstream. No specific entropy-coding technique, table structure or adaptation mechanism is claimed. Sampling “the quantized values in the value range” is itself a mathematical/statistical operation (selecting an outcome according to a distribution), not a technical implementation detail. Accordingly, the claim as a whole amounts to no more than performing the abstract mathematical/statistical process using generic machine learning and generic entropy-coding components for their known, generic functions. The claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment.
Applicant relies on paragraph 0058, 0061, 0063 and 0067-0070 as support that the claimed system reduce divergence between input and output values, improving reconstruction accuracy, and on paragraph 0004 as identifying the technical problem solved (i.e. in the auto-encoder, the image feature amount is quantized but the loss function cannot be differentiated by a quantized image feature amount, which is an image feature amount after quantization. Accordingly, the differential value of the quantized image feature amount with respect to the image feature amount before quantization was assumed to be 1.). Therefore, the parameter set obtained by learning does not necessarily converge to an optimum solution, and this has caused deterioration in the quality of the reconstructed image represented by the reconstructed image data. However, Examiner respectfully disagrees.
Firstly, USPTO guidance memorandum December 4, 2025 confirm that the specification need not explicitly label something as “an improvement” so long as the improvement would be apparent to one of ordinary skill in the art from the disclosure, the guidance still requires that the claim itself recite the technical means that achieves the asserted improvement, it is not enough that the specification describes an improvement somewhere if the claim language does not capture it. (MPEP 2106.05(a).
Secondly, the technical problem identifying in paragraph 0004 concerns the training process of an auto-encoder, specifically, the non-differentiability of the quantization operation during backpropagation, which prevents the learned parameter set from converging to an optimal solution. However, claims are directed to an inference-time encode/decode/sampling procedure (determining a probability distribution, sampling a value, entropy-coding, and repeating with a second model). No step of training the first or second machine learning model, let alone a step that addresses the differentiability problem of paragraph 0004 is recited. Because the claims do not include the operative steps that the specification identifies as solving the stated technical problem, the “improvement” Applicant relies on is not commensurate in scope with what is claimed.
With respect to claims 18-19, claim 18 adds a training step: recursively updating model parameters based on a loss function computed from the input value and the second sample value. “Recursively updating parameters based on a loss function” describes the mathematical process of iterative optimization. This is a mathematical concept not a technical mechanism. The claim does not recite how the parameters are updated. Training a model by minimizing a loss function via parameter updates is a generic, foundational machine-learning technique; reciting it at this level of generality does not itself constitute an improvement to the machine learning technology or to computer functionality. Claim 19 recites mathematical composition of a loss function which does not transform the underlying operation from an abstract mathematical calculation into a technical implementation. “Jointly trained”, “recursively updating…parameters” and “using training data” recite training in generic terms, without any specific technical detail of how the training is technically achieved. The claim does not recite the specific technical means by which this loss function addresses the differentiability problem identified in paragraph 0004 of the specification. Absent such a recited mechanism, claim 19 merely specifies the mathematical consent of the objective function rather than a technical solution to the stated technical problem and does not by itself integrate the abstract idea into a practical application.
Therefore, the rejection is maintained.
Status of Claims
Claims 1, 3-8 and 12-13 and 17-19 have been examined.
Claims 2, 9-11 and 14-16 have been canceled by the Applicant.
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, 3-8, 12-13 and 17-19 are rejected under 35 U.S.C. 101 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.
In the instant case, claims 1, 3-8, 17-19 are directed to a system, claim 12 is directed to a non-transitory storage medium and claim 13 is directed to a method. Therefore, these claims fall within the four statutory categories of invention.
The claims are directed to generating a sample value based on determined values which is an abstract idea. Specifically, the claims recite “determine a first probability distribution of quantized values…; sample the quantized values…; entropy-encode a first sample value…; entropy-decode the code sequence…; determine a second probability distribution…; and sample the quantized values…” grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See MPEP 2106) because the claims involve a series of steps of compute a probability distribution from an input, sample a value from that distribution, apply a statistical mapping (entropy coding) to a sequence of such values and its inverse, and repeat steps compute and sample using a second computed distribution based on the first sampled value which is a process that deals with mathematical calculations because claims are directed to generating a sample values based on determined values. Accordingly, the claims recite an abstract idea (See MPEP 2106.04(a)).
Additionally, the steps/functions of method and system, under their broadest reasonable interpretation, recite concepts that are performed in the human mind, including observations, evaluations and judgements. In particular, generating a sample value based on determined values that may be performed in the human mind or with pen and paper. Therefore, the claim is directed to an abstract idea, as it has been held that a combination of abstract ideas, in this case mathematical concept and mental processes, is still an abstract idea. See FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See MPEP 2106.04(d)), the additional elements of the claims such as, at least one processor, non-transitory storage medium and machine learning models merely use a computer as a tool to perform an abstract idea. Specifically, at least one processor, non-transitory storage medium and machine learning models perform the steps or functions of compute a probability distribution from an input, sample a value from that distribution, apply a statistical mapping (entropy coding) to a sequence of such values and its inverse, and repeat steps compute and sample using a second computed distribution based on the first sampled value. The use of a processor/computer as a tool to implement the abstract idea does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See MPEP 2106), the additional elements of at least one processor, non-transitory storage medium and machine learning models, to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of generating sample values based on determined values. As discussed above, taking the claim elements separately, at least one processor, non-transitory storage medium and machine learning models perform the steps or functions of compute a probability distribution from an input, sample a value from that distribution, apply a statistical mapping (entropy coding) to a sequence of such values and its inverse, and repeat steps compute and sample using a second computed distribution based on the first sampled value. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of generating sample values based on determined values. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claims are not patent eligible.
Dependent claims 3-8 and 17-19 further describe the abstract idea of generating sample values based on determined values. Specifically, claims 2-8 describing determining different values and further describing additional elements which are part of the abstract idea. Claims 17-19, describing “recursively updating parameters based on a loss function” describes the mathematical process of iterative optimization. This is a mathematical concept not a technical mechanism. Claim 19 recites mathematical composition of a loss function which does not transform the underlying operation from an abstract mathematical calculation into a technical implementation. The dependent claims do not include additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZESHAN QAYYUM whose telephone number is (571)270-3323. The examiner can normally be reached Monday-Friday 9:00AM-6:00PM EST.
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/ZESHAN QAYYUM/Primary Examiner, Art Unit 3697
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