Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . See 35 U.S.C. § 100 (note).
Oath/Declaration
The receipt of Oath/Declaration is acknowledged.
Drawings
The drawing(s) filed on March 3, 2022 are accepted by the Examiner.
Status of Claims
Claims 1–5 are pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 29, 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Patent Subject Matter Eligibility
Claim Rejection - 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 1–5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g., an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the claims are directed toward non-statutory subject matter, as shown below:
STEP 1: Do the claims fall within one of the statutory categories? Yes.
Claim 1: Machine (device/apparatus with processor and storage medium).
Claim 2: Machine (device, depends from claim 1).
Claim 3: Machine (device/apparatus).
Claim 4: Process (method).
Claim 5: Manufacture (non-transitory computer-readable medium).
All claims fall within a statutory category.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claims are directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claims 1–5 are a mathematical concepts, that is mathematical relationships, mathematical formulas or equations, mathematical calculations and, therefore, an abstract idea. With regard to independent claims 1, 3, and 4, the method/device/computer-readable media (or computer implemented functionality) recites the steps of:
Execute an image reconstruction model that is a mathematical model including: fidelity processing that is processing of generating, on the basis of the learning data, a tensor in which a solution is a tensor closest to a tensor to be processed by solving an inverse problem (mathematical concept: inverse problem solution; tensor computations).
Regularization processing that is processing of generating, on the basis of the learning data, image data of an image having a property close to a statistical property satisfied by the image to be captured (mathematical/statistical property; regularization).
The tensor to be processed by the regularization processing is a combination of the tensors generated by the fidelity processing, and the fidelity processing and the regularization processing are alternately executed (mathematical data structuring; alternating optimization reminiscent of ADMM/iterative algorithms).
Acquire learning data including image data…captured through a filter and filter state information (mere data acquisition without specific technological operation).
These limitations, under their broadest reasonable interpretation, cover applying mathematical algorithms and/or calculations. The use of a computer or processing device include no more than applying the exception using a generic computer or computer component. The limitations are not directed to an improvement in the computer itself or a computer component and therefore cannot provide an inventive concept.
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 2B), if so, ask “what else is there in the claims?’ to determine whether the additional elements transform the nature of the claim into a patent eligible application.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses 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.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1, 3 and 4 recites the additional limitations of a “one or more processors”. These one or more processors is simply a computer recited at a high level of generality. The generic computer is used to perform the abstract idea. Using a computer as a tool to perform the abstract idea does not integrate the exception into a practical application. Data gathering is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Claim 1 recites a processor and a storage medium executing instructions to perform mathematical operations (fidelity and regularization processing). These elements represent generic computer components that merely provide a computational environment for executing the abstract idea. The acquisition of learning data, including image data and filter-state information, constitutes conventional data gathering.
The claimed alternating execution of fidelity and regularization processing, and the use of tensors differing in size and number, merely define how the mathematical computation is structured not how the underlying machine is improved. The filter and its state information are used only as input data to the mathematical model and are not claimed to produce a tangible transformation or improved operation of an imaging system.
Accordingly, the additional elements (processor, storage medium, and filter) amount to nothing more than generic computer implementation of the mathematical model and do not integrate the judicial exception into a practical application. See Alice Corp. v. CLS Bank, 573 U.S. 208 (2014); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016).
Claims 3 and 4 are drawn to substantially the same concept as claim 1, differing only in format: claim 3 recites a learning device operating on a signal, and claim 4 recites a learning method performing the same steps. These claims merely express the abstract mathematical model in different statutory categories (device and method). The underlying concept — acquiring data and executing a mathematical model comprising fidelity and regularization processing — remains the same and thus is not integrated into a practical application.
Claim 2 specifies that the filter state information is a spatial distribution of optical constants. This merely describes the content of the data used in the computation, not an improvement to how the computer or filter operates. Thus, it does not meaningfully limit the abstract idea.
Claim 5 recites a computer-readable medium storing instructions that cause a computer to execute the same mathematical operations. Implementing an abstract idea as software stored on a generic storage medium is not a practical application of that idea. See Digitech Image Techs. v. Electronics for Imaging, 758 F.3d 1344 (Fed. Cir. 2014).
Accordingly, none of the claims integrate the abstract idea into a practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
The following computer functions have been recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality): receiving or transmitting data over a network. See MPEP 2106.05(d)(II).
The claims also fail to recite additional elements that amount to “significantly more” than the judicial exception itself. The recited processor, storage medium, and filter are all described at a high level of generality and perform their well-understood, routine, and conventional functions (data collection, computation, and storage). The fidelity and regularization processing steps represent standard mathematical techniques long known in image reconstruction and compressed sensing.
There is no indication that the claim elements, individually or in combination, perform any function beyond the ordinary use of a computer executing an algorithm. The claims do not provide any unconventional architecture, sensor arrangement, or computer operation that would transform the abstract idea into a patent-eligible application.
Accordingly, the claims do not recite any additional elements sufficient to amount to “significantly more” than the abstract idea itself. See Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018); SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018).
Conclusion
Thus, since claims 1, 3, and 4 are: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1, 3, and 4 are directed towards non-statutory subject matter.
Further, dependent claims 2 and 5 further limit the abstract idea without integrating the abstract idea into practical application or adding significantly more. Each of the claimed limitations either expand upon or add either 1) new mathematical process, 2) a new additional element, 3) previously presented mathematical process, and/or 4) a previously presented additional element. As such, claims 2 and 5 are similarly rejected as being directed towards non-statutory subject matter.
ALLOWABLE SUBJECT MATTER
Objection
Claims 1–5 would be allowed provided the 101 rejection is overcome.
Examiner's Statement of Reason for Allowance
Claims 1–5 are allowed. Claims 1, 3 and 4 are independent claim. Claims 2 and 5 depend on claim 1.
Claims 1, 3 and 4. are allowable over the prior art because the Examiner found neither prior art cited in its entirety, nor based on the prior art, found any motivation to combine any of the said prior art that teaches the features of claims 1, 3 and 4.
Claims 1, 3 and 4 recites the following specific features as shown in the excerpt below.
[1] “acquire learning data including image data of an image to be captured that has been captured through a filter and filter state information indicating a state of the filter; and
execute an image reconstruction model that is a mathematical model including: fidelity processing that is processing of generating, on the basis of the learning data, a tensor in which a solution is a tensor closest to a tensor to be processed by solving an inverse problem; and regularization processing that is processing of generating, on the basis of the learning data, image data of an image having a property close to a statistical property satisfied by the image to be captured,
wherein the number of tensors to be processed by the fidelity processing is larger than the number of tensors to be processed by the regularization processing, each tensor to be processed by the fidelity processing is smaller in size than a tensor to be processed by the regularization processing, and the tensor to be processed by the regularization processing is a combination of the tensors generated by the fidelity processing, and
the fidelity processing and the regularization processing are alternately executed.”
[3] “acquire learning data including a signal obtained by imaging an imaging target through a filter and filter state information indicating a state of the filter; and execute an image reconstruction model that is a mathematical model including: fidelity processing that is processing of generating, on the basis of the learning data, a tensor in which a solution is a tensor closest to a tensor to be processed by solving an inverse problem; and regularization processing that is processing of generating, on the basis of the learning data, a signal having a property close to a statistical property satisfied by the signal obtained by imaging the imaging target,
wherein the number of tensors to be processed by the fidelity processing is larger than the number of tensors to be processed by the regularization processing, each tensor to be processed by the fidelity processing is smaller in size than a tensor to be processed by the regularization processing, and the tensor to be processed by the regularization processing is a combination of the tensors generated by the fidelity processing, and
the fidelity processing and the regularization processing are alternately executed.”
[4] “a learning step of executing an image reconstruction model that is a mathematical model including: fidelity processing that is processing of generating, on the basis of the learning data, a tensor in which a solution is a tensor closest to a tensor to be processed by solving an inverse problem; and regularization processing that is processing of generating, on the basis of the learning data, image data of an image having a property close to a statistical property satisfied by the image to be captured,
wherein the number of tensors to be processed by the fidelity processing is larger than the number of tensors to be processed by the regularization processing, each tensor to be processed by the fidelity processing is smaller in size than a tensor to be processed by the regularization processing, and the tensor to be processed by the regularization processing is a combination of the tensors generated by the fidelity processing, and
the fidelity processing and the regularization processing are alternately executed.”
These features, considered in combination with the remainder of the claim’s limitations are not fairly disclosed, thought or suggested by the cited prior art. Specifically, the closest prior art, Image Reconstruction: From Sparsity to Data-adaptive Learning (S. Ravishankar et al., 2019), An Efficient Image Reconstruction Framework Using Total Variation Regularization (F. Lin et al., 2019), Prospective Regularization Design in Prior-Image-Based Reconstruction (H. Dang et al., 2015), and Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems (F. Schirrmacher et al., 2018), as further discussed in Table 1 below, fails to either anticipate or render obvious the above underlined limitations. Accordingly, claims 1, 3 and 4 are allowable over the prior art of record. It follows that claims 2 and 5 are then inherently allowable for depending on an allowable base claim.
ADDITIONAL CITATIONS
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
Image Reconstruction: From Sparsity to Data-adaptive Learning (S. Ravishankar et al., 2019)
Reviews major image reconstruction methods based on inverse problems, including fidelity + regularization term models (sparsity, low-rank) and learning/adaptive priors.Relevance: The application under review uses an inverse problem, tensor fidelity processing, regularization processing. The review shows that the notion of combining data‐fidelity and regularization is well known.
An Efficient Image Reconstruction Framework Using Total Variation Regularization (F. Lin et al., 2019)
Proposes a method where the image reconstruction is formulated by a fidelity term, a regularization term (TV and group gradient sparsity) and uses accelerated ADMM to reduce computation cost. Relevance: The application claims the benefit of dividing into smaller tensors (block tensors) to reduce computation in the fidelity part. The prior art shows techniques for trading off fidelity vs regularization and using iterative optimization to reduce cost.
Prospective Regularization Design in Prior-Image-Based Reconstruction (H. Dang et al., 2015)
In prior image based reconstruction (PIBR) for CT, they propose tuning regularization parameters spatially to admit changes in the image while balancing fidelity and prior image information. Relevance: The application also uses a filter state information update to suppress certain objects (concealment condition) and the regularization is tuned. This prior art addresses spatially varying regularization.
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems (F. Schirrmacher et al., 2018)
A method for inverse imaging problems using an adaptive prior (quantile filter) in the regularization step. Relevance: The application discloses regularization processing generating image data of an image having a statistical property (e.g., sparsity) and uses alternate fidelity/regularization processing. This paper shows adaptive priors in inverse problems, quite similar conceptually.
Table 1
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENOK A SHIFERAW whose telephone number is (571)272-4637. The examiner can normally be reached Monday-Friday, 8:30AM - 5:00PM, (EST).
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
HENOK A. SHIFERAW
Supervisory Patent Examiner
Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676