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 .
Priority
Applicant claims benefit of prior filed German Patent Application No. 20 2022 104 805.3, filed August 25, 2022, that is acknowledged by the examiner.
Drawings
The drawings were received on 01/06/2023. These drawings are acceptable.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/06/2023 has been considered by the examiner.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1-20, the claims include numbers in parenthesis that make the claim indefinite because one of ordinary skill is unable to determine the intended scope of the claimed invention. Specially, the information associated with the numbers or intended to be interpreted by the notation is unclear in the claim limitation. Thus, the claims are rendered indefinite. Examiner suggests that the numbers in parenthesis be removed or replaced with words that express clearly the intended claim limitations.
Examiner interprets the notation broadly as non-limiting and gives them no patentable weight.
Regarding claim 1, the limitation “the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8)” renders the claim indefinite as it is unclear how one ascertains the intended scope. Specifically, how can one quantify or measure or detect the claimed difference. Does a “data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs” mean that there is no difference given that all the pairs are uniform? How does a function/feature/detection remains uniform and yet different, as claimed? The specification, appears to mirror the claim language and thus the specification provides no insight regarding how the limitations should be interpreted. Examiner interprets any data pair for training a neural network as within the scope of the claim limitation.
Regarding claim 11, the claim recites similar limitations to claim 1 and rejected under the same rationale.
Regarding the dependent claims that depend on claims 1 and 11 respectively, the claims fail to resolve the noted issue and are rejected for the noted rationale above.
Regarding claim 2, the limitation “generating the second datum at least using a convolution (12) of the first datum (9) with a lateral response function (13)” renders the claim indefinite because the limitation is unclear making one of ordinary skill in the art would be unable to ascertain the intended scope. Specifically, the convolution is a mathematical process for combining mathematical operations usually a input kernel and a weight kernel by sliding them over each other and multiplying the function values, (e.g. generally the known operations associated with a convolutional neural network) it is unclear what is meant by “using a convolution (12) of the first datum (9) with a lateral response function” such that resulting data pair meets the unclear requirement noted in claim 1 limitation, from which claim 2 depends, “wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10) and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8)]”. Thus, the claim is indefinite. Examiner interprets any data set with the scope of the claim limitation.
Regarding claim 3, the limitation recited limitations for generating the first datum (i.e. generating the first datum (9) using a convolution (14) of a synthetic rough profile (15) with a mapping function (16)) and again it is unclear how the limitation fails within the scope of the limitation noted in claim 1, from which claim 2 depends, “wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10) and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8)]”. Thus, the claim is indefinite. Examiner interprets any data set with the scope of the claim limitation.
Regarding claim 6, the limitation “further comprising using a plurality of at least one of a) said synthetic rough profiles (15), b) said first data (9), or said second data (10) within a single data set for training the artificial neural network (4)” includes the terms in “b) said first data (9), or said second data (10)” and “synthetic rough profiles” that lack antecedent basis. There is no recitation of a first or a second data. Being the data and profiles are plural forms and the prior stated first and second datum and synthetic rough profile do not serve as sufficient antecedent basis. Thus, the claim is rendered indefinite. Examiner notes that any data for training the neural network is within the scope of the claim limitation.
Regarding claims 3-6 and 8-9 the term “synthetic rough profile” renders the claim indefinite as it is unclear what the term refers to and the specification appears to mirror claim language. The term is not a term of art in computer science and interpretation is unclear are recited in the claimed invention or specification. The claims are rendered indefinite. The examiner notes that any data is within the scope of the claimed term.
Regarding claims 11-20 the clams recite similar limitations as claims 1-10, and are rejected under the same rationale.
The examiners notes that dependent claims of noted claims are also rejected. The applicant should review the claims for further issues as the examiner has attempted to cover most of the issues with the claimed invention.
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 a judicial exception (i.e. an abstract idea) without significantly more.
Regarding claims 11-20 ,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 preamble is noted as a “detection arrangement” which is not considered a system, method, or artificial of manufacturer. Specifically, the claimed inventions appears to directed to a category that is denoted in MPEP 2106.03, that enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. The claimed detector and units are not sufficient to determine the intended statutory category as enumerated in MPEP 2106.03.
Regarding claims 22,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 preamble is noted as a “a phantom comprising the detection arrangement” which is not considered a system, method, or artificial of manufacturer. Specifically, the claimed inventions appears to directed to a category that is denoted in MPEP 2106.03, that enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. The claimed detector and units are not sufficient to determine the intended statutory category as enumerated in MPEP 2106.03.
Claim 1: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
detecting a result (Considered directed to a Mental Process: Making observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
… using a detector (2); initially training the artificial neural network (4) with synthetic data pairs (8),... (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10); and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8). Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 2: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea recited in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
further comprising generating the second datum at least using a convolution (12) of the first datum (9) with a lateral response function (13). (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements considered mere instructions to apply the judicial exception using a computer/computing environment as a tool. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 3: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
generating the first datum (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
further comprising generating the first datum (9) using a convolution (14) of a synthetic rough profile (15) with a mapping function (16). (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 4: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 3.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions, or b) the synthetic rough profile (15) is a piecemeal linear function. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 5: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 4.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein at least one of the synthetic rough profile (15), the mapping function (16), or the first datum (9) is randomly generated, with at least individual parameters of the at least one of the rough profile (15), the mapping function (16), or the first datum (9) being randomly selected. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 6: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 4.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
further comprising using a plurality of at least one of a) said synthetic rough profiles (15), b) said first data (9), or said second data (10) within a single data set for training the artificial neural network (4). (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
… first learnable parameter associated with the first neural network …(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 7: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 4.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the at least one of the lateral response function (13) or the mapping function (16) are distribution functions, and standard deviations of the distribution functions that are restricted to at least one of fixed values or ranges of values. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 8: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 3.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
further comprising furnishing at least one of the synthetic rough profile (15), the first datum (9), or the second datum (10) with randomly generated noise that is greater than an expected noise for the detected results (5) to be corrected. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 9 Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 3.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
further comprising recalculating at least one of the synthetic rough profile (15), the first datum (9), or the second datum (10) locally at a changed resolution by at least one of a sampling-rate conversion or downsampling. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 10: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
further comprising correcting the detected result (5) in a region of a penumbra of a beam profile. (Considered directed to a Mental Process: Making observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
… detected result (5) in a region of a penumbra of a beam profile.. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 11: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
…correct a result (5), detected (Considered directed to a Mental Process: Making observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
a detector (2); an arithmetic logic unit (3) with an artificial neural network (4), the artificial neural network (4) being configured to … using a detector... (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… using the detector (2), of a radiation-physics process pertaining to a radiation-source (6); wherein the artificial neural network (4) is initially trained with synthetic data pairs (8), the data pairs (8) comprising a first datum (9) and a second datum (10), and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8). Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 12: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 11.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured such that the second datum (10) has been generated at least using a convolution (12) of the first datum (9) with a lateral response function (13). (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 13: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 12.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured such that the first datum (9) has been generated using a convolution (14) of a synthetic rough profile (15) with a mapping function (16).. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 14: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 13.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured such that at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions, or b) the synthetic rough profile (15) is a piecemeal linear function. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 15: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 14.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured such that at least one of the synthetic rough profile (15), the mapping function (16), or the first datum (9) was randomly generated, and at least individual parameters of at least one of the rough profile (15), the mapping function (16), or of the first datum (9) were randomly selected within a respective specified parameter range.. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 16: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 14.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured with at least one of a plurality of synthetic rough profiles (15), said first data (9), or second data (10) stored within a single data set for training the artificial neural network (4). (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 17: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 14.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured such that standard deviations of the distribution functions have been restricted to at least one of fixed values or ranges of values. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 18: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 14.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured such that at least one of the synthetic rough profile (15), the first datum (9), of the second datum (10) has been furnished with randomly generated noise that is greater than to be expected for the detected results (5) to be corrected. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 19: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 14.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured such that at least one of the synthetic rough profile (15), the first datum (9), of the second datum (10) has been recalculated locally at a changed resolution by at elast one of a sampling-rate conversion or downsampling. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 20: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
corrected detected result (19) is corrected … (Considered directed to a Mental Process: Making observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the arrangement is configured such that a corrected detected result (19) is corrected in a region of a penumbra of a beam profile. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 21: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
The rejection of claim 1 is incorporated
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 22: Does claim fall within a statutory category? No, but can be amended to fall within a statutory category.
The rejection of claim 11 is incorporated.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception and does not recite, when claim elements are examined or as an ordered combination, that are directed to what have the courts have identified as "significantly more”, than the identified abstract idea, see MPEP 2106.05.
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.
Claims 1-3, 11-13, and 21 -22 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Wang et al. (US 20200273215, hereinafter ‘Wang’).
Regarding independent claim 1, Wang teaches method for correcting a result (5) of a radiation-physics process pertaining to a radiation-source (6) by an artificial neural network (4), the method comprising: (in [0099] Embodiments of the operations described herein may be implemented in a computer-readable storage device having stored thereon instructions that when executed by one or more processors perform the methods. The processor may include, for example, a processing unit and/or programmable circuitry….; And as depicted in Fig. 1)
detecting a result using a detector (2); (in [0050] Training data 130 may include one or more training data sets. In one example, a training data set may correspond to a dual energy training data set. In another example, a training data set may not correspond to a dual energy data set, Each training data set may include a training monochromatic projection data set, training CT image data [detecting a result using a detector] corresponding to a training CT image and/or training measured projection data…; [0064] Operations of flowchart 200 may begin with acquiring training data at operation 202. In one example, acquiring training data may include generating a training data set from simulation data and/or experimental data [detecting a result using a detector]. The simulation data may correspond to a training data set that includes training CT image data and the training projection data sets. In another example, acquiring training data may include generating the training data set using dual energy CT techniques [detecting a result using a detector]…)
initially training the artificial neural network (4) with synthetic data pairs (8), wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10); and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8). (in [0065] A neural network for tomographic imaging may be trained with a relatively large training data set to optimize its performance. In one nonlimiting example, a training data set may include CT scanning data (e.g., measured projection data and corresponding measured CT image data) [initially training the artificial neural network (4) with synthetic data pairs (8), wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10) and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8)] of ten objects (e.g., patients) and CT scanning data of three objects (e.g., patients) for testing. The training data set may be generated from simulation data and/or experimental data Simulation data may be utilized to establish big training data [initially training the artificial neural network (4) with synthetic data pairs (8), wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10) and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8)]. For example, simulated big training data may be configured to provide a pre-trained network that may then be fine-tuned with experimental data…)
Regarding claim 2, the rejection of claim 1 is incorporated and Wang further teaches the method as claimed in claim 1, further comprising generating the second datum at least using a convolution (12) of the first datum (9) with a lateral response function (13). (in [0038] Deep learning may be utilized in image analysis including, for example, image classification, identification and segmentation. For example, convolutional neural network (CNN) techniques [using a convolution (12) of the first datum (9) with a lateral response function (13)] may be utilized for image denoising in low-dose CT. The deep learning CT denoising methods may be configured to learn a function between a low-dose image patch and the corresponding patch of high quality from a large training data set [generating the second datum at least using a convolution (12) of the first datum (9) with a lateral response function (13)]…)
Regarding claim 3, the rejection of claim 2 is incorporated and Wang further teaches the method as claimed in claim 2, further comprising generating the first datum (9) using a convolution (14) of a synthetic rough profile (15) with a mapping function (16). (in [0039] Generally, the present disclosure relates to a machine learning-based image reconstruction method configured to reduce or eliminate beam-hardening artifacts in a reconstructed CT image. A neural network is configured to learn a nonlinear transform from a training data set to map CT images reconstructed [further comprising generating the first datum (9) using a convolution (14) of a synthetic rough profile (15) with a mapping function (16)] from a single current-integrating data set to monochromatic projections at a pre-specified energy level, realizing monochromatic imaging and overcoming beam hardening effectively on a common clinic CT scanner without any dual-energy function… For example, convolutional neural network (CNN) techniques [further comprising generating the first datum (9) using a convolution (14) of a synthetic rough profile (15) with a mapping function (16)] may be applied for image denoising in low-dose CT. To train the neural network, the training data can be obtained from a state of the art dual-energy CT scanner,…)
Regarding independent claim 11, Wang teaches detection arrangement (1), comprising: a detector (2); an arithmetic logic unit (3) (in [0099] Embodiments of the operations described herein may be implemented in a computer-readable storage device having stored thereon instructions that when executed by one or more processors perform the methods. The processor may include, for example, a processing unit and/or programmable circuitry….; And as depicted in Fig. 1; And in [0097] “Circuitry” [detection arrangement (1), comprising: a detector (2); an arithmetic logic unit (3)], as used in any embodiment herein, may include, for example, singly or in any combination, hardwired circuitry, programmable circuitry such as computer processors including one or more individual instruction processing cores, state machine circuitry, and/or firmware that stores instructions executed by programmable circuitry. The logic may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic device (PLD), a complex programmable logic device (CPLD), a system on-chip (SoC), etc.)
with an artificial neural network (4), the artificial neural network (4) being configured to correct a result (5), (in [0043] FIG. 1 illustrates a functional block diagram of a computed tomography (CT) image system 100 consistent with several embodiments of the present disclosure. CT image system 100 includes CT scanner 102 and correction circuitry 104. In an embodiment, correction circuitry 104 may be coupled to CT scanner 102. In another embodiment, correction circuitry 104 may be included in CT scanner 102. CT scanner 102 is configured to generate CT images of an object from detected attenuated x-ray beams. Correction circuitry 104 is configured to determine a monochromatic projection data set based, at least in part, on measured projection data using a trained artificial neural network [with an artificial neural network (4), the artificial neural network (4) being configured to correct a result].)
detected using the detector (2), of a radiation-physics process pertaining to a radiation-source (6); (2); (in [0050] Training data 130 may include one or more training data sets. In one example, a training data set may correspond to a dual energy training data set. In another example, a training data set may not correspond to a dual energy data set, Each training data set may include a training monochromatic projection data set, training CT image data [detected using the detector (2), of a radiation-physics process pertaining to a radiation-source] corresponding to a training CT image and/or training measured projection data…; [0064] Operations of flowchart 200 may begin with acquiring training data at operation 202. In one example, acquiring training data may include generating a training data set from simulation data and/or experimental data. The simulation data may correspond to a training data set that includes training CT image data and the training projection data sets. In another example, acquiring training data may include generating the training data set using dual energy CT techniques [detected using the detector (2), of a radiation-physics process pertaining to a radiation-source]…)
initially training the artificial neural network (4) with synthetic data pairs (8), wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10); and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8). (in [0065] A neural network for tomographic imaging may be trained with a relatively large training data set to optimize its performance. In one nonlimiting example, a training data set may include CT scanning data (e.g., measured projection data and corresponding measured CT image data) [initially training the artificial neural network (4) with synthetic data pairs (8), wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10) and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8)] of ten objects (e.g., patients) and CT scanning data of three objects (e.g., patients) for testing. The training data set may be generated from simulation data and/or experimental data Simulation data may be utilized to establish big training data [initially training the artificial neural network (4) with synthetic data pairs (8), wherein the synthetic data pairs (8) comprise a first datum (9) and a second datum (10) and the data (9, 10) of one said data pair (8) differ by a detector-specific transformation (11) that is uniform for all said data pairs (8)]. For example, simulated big training data may be configured to provide a pre-trained network that may then be fine-tuned with experimental data…)
Regarding claims 12-13, the claims are similar to claims 2-3 and are thus rejected under the same rationale.
Regarding claim 21, Wang further teaches a method for measuring radiation with a phantom (21), the method comprising carrying out the method as claimed in claim 1. Examiner incorporates the rejection of claim 1.
Regarding claim 22, Wang further teaches a phantom (21) comprising the detection arrangement (1) as claimed in claim 11. Examiner incorporates the rejection of claim 11.
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, 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.
Claims 4-7 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200273215, hereinafter ‘Wang’) in view of Gilbert (US 20220245793, hereinafter ‘Gil’).
Regarding claim 4, the rejection of claim 3 is incorporated and Wang further teaches the method as claimed in claim 3, wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions, or b) the synthetic rough profile (15) is a piecemeal linear function. (in [0039] Generally, the present disclosure relates to a machine learning-based image reconstruction method configured to reduce or eliminate beam-hardening artifacts in a reconstructed CT image. A neural network is configured to learn a nonlinear transform from a training data set to map CT images reconstructed [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] from a single current-integrating data set to monochromatic projections at a pre-specified energy level, realizing monochromatic imaging and overcoming beam hardening effectively on a common clinic CT scanner without any dual-energy function… For example, convolutional neural network (CNN) techniques [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] may be applied for image denoising in low-dose CT. To train the neural network, the training data can be obtained from a state of the art dual-energy CT scanner,…; And in [0065] A neural network for tomographic imaging may be trained with a relatively large training data set to optimize its performance. In one nonlimiting example, a training data set may include CT scanning data (e.g., measured projection data and corresponding measured CT image data) [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] of ten objects (e.g., patients) and CT scanning data of three objects (e.g., patients) for testing. The training data set may be generated from simulation data and/or experimental data Simulation data may be utilized to establish big training data [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions]. For example, simulated big training data may be configured to provide a pre-trained network that may then be fine-tuned with experimental data…)
Wang teaches the process of image data pairs for training a convolutional neural network. The processing in Wang is considered a use of a distribution function for projecting the data pairs as noted above.
Alternatively, Gil teaches the use of Gaussian distribution for process the image projections on an heatmap, in [0039] The heat map(s) 213 produced by the heat map generator 206 may be paired with the medical image 202 to create a heat map/medical image input pair 212 [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] to be inputted into the CNN 210. For example, if there are two user-selected measurement points on the medical image, the heat map/medical image input pair 212 may include two heat maps (e.g., one for each user-selected measurement point) and the medical image, or the heat map/medical image input pair 212 may include a single heat map with an encoding of both user-selected measurement points and the medical image. Prior to being inputted into the CNN 210, the heat map/medical image input pair 212 may be inputted into an image pre-processor 208, which may pre-process the heat map/medical image input pair 212 via one or more pre-processing routines…; And in [0073] At 602, method 600 includes encoding the one or more user-selected measurement points as heat maps. In one example, each set of x/y coordinate points is encoded as a heat map by generating a 2D normal Gaussian probability density function [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] with a standard deviation (e.g., 10 pixels) centered on the set of x/y coordinate points. The heat map may be generated in a first resolution (e.g., 512×256 pixels), where the first resolution matches a resolution of the medical image (e.g., raw data of the medical image). Once the heat map is generated, the heat map may be appended to the medical image, where the heat map and the medical image represent inputs [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] into different channels of a CNN…)
Wang and Gil are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for retrieving information from images using automated machine learning techniques and models disclosed by Gil with the method of developing information retrieval and processing techniques using machine learning-based image reconstruction methods as disclosed by Wang
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Wang and Gil as discussed above; Doing so allows the automated measurement system to predict one or more segmentation measurement points along the portion of a boundary that is not clearly visible, (Gil, Abstract & 0058).
Regarding claim 5, the rejection of claim 4 is incorporated and Wang in combination with Gil further teaches the method as claimed in claim 4, wherein at least one of the synthetic rough profile (15), the mapping function (16), or the first datum (9) is randomly generated, with at least individual parameters of the at least one of the rough profile (15), the mapping function (16), or the first datum (9) being randomly selected. (in [0087] 274 CT images as input data were reconstructed from a single spectral current-integrating projection data set (i.e., measured projection data set) synthesized by the multi-energy data set [with at least individual parameters of the at least one of the rough profile (15)]. A corresponding 274 monochromatic images (i.e., training monochromatic CT images) in the 80 keV energy channel were used as labeled images of training data set for the example MLP neural network. The 274 image pairs generated 122 million x-ray paths for the training data set [with at least individual parameters of the at least one of the rough profile (15)]. The training procedure was programmed in Python on the TensorFlow on a PC computer with a NVIDIA Titan XP GPU and 12 GB memory. The neural network was trained using an Adam optimization algorithm, Data was sampled randomly over the training data set [the first datum (9) is randomly generated, with at least individual parameters of the at least one of the rough profile (15)], to facilitate finding the global minimum. As illustrated in FIG. 4, the MLP neural network training, as described herein, showed an excellent converging behavior, and its cost function decreased towards a global minimum.)
Regarding claim 6, the rejection of claim 4 is incorporated and Wang in combination with Gil further teaches the method as claimed in claim 4, further comprising using a plurality of at least one of a) said synthetic rough profiles (15), b) said first data (9), or said second data (10) within a single data set for training the artificial neural network (4). (in [0065] A neural network for tomographic imaging may be trained with a relatively large training data set to optimize its performance. In one nonlimiting example, a training data set may include CT scanning data (e.g., measured projection data and corresponding measured CT image data) [using a plurality of at least one of a) said synthetic rough profiles (15), b) said first data (9), or said second data (10) within a single data set for training the artificial neural network] of ten objects (e.g., patients) and CT scanning data of three objects (e.g., patients) for testing. The training data set may be generated from simulation data and/or experimental data Simulation data may be utilized to establish big training data [using a plurality of at least one of a) said synthetic rough profiles (15), b) said first data (9), or said second data (10) within a single data set for training the artificial neural network)]. For example, simulated big training data may be configured to provide a pre-trained network that may then be fine-tuned with experimental data…)
Regarding claim 7, the rejection of claim 4 is incorporated and Wang in combination with Gil further teaches the method as claimed in claim 4, wherein the at least one of the lateral response function (13) or the mapping function (16) are distribution functions, (in [0039] Generally, the present disclosure relates to a machine learning-based image reconstruction method configured to reduce or eliminate beam-hardening artifacts in a reconstructed CT image. A neural network is configured to learn a nonlinear transform from a training data set to map CT images reconstructed [or the mapping function (16) are distribution functions,] from a single current-integrating data set to monochromatic projections at a pre-specified energy level, realizing monochromatic imaging and overcoming beam hardening effectively on a common clinic CT scanner without any dual-energy function… For example, convolutional neural network (CNN) techniques [or the mapping function (16) are distribution functions,] may be applied for image denoising in low-dose CT. To train the neural network, the training data can be obtained from a state of the art dual-energy CT scanner,…; And in [0065] A neural network for tomographic imaging may be trained with a relatively large training data set to optimize its performance. In one nonlimiting example, a training data set may include CT scanning data (e.g., measured projection data and corresponding measured CT image data) [or the mapping function (16) are distribution functions,] of ten objects (e.g., patients) and CT scanning data of three objects (e.g., patients) for testing. The training data set may be generated from simulation data and/or experimental data Simulation data may be utilized to establish big training data. For example, simulated big training data may be configured to provide a pre-trained network that may then be fine-tuned with experimental data…)
Wang teaches the process of image data pairs for training a convolutional neural network. The processing in Wang is considered a use of a distribution function for projecting the data pairs as noted above. Wang does not expressly teach and standard deviations of the distribution functions that are restricted to at least one of fixed values or ranges of values.
Gil teaches the use of Gaussian distribution for process the image projections on an heatmap, and standard deviations of the distribution functions that are restricted to at least one of fixed values or ranges of values, in [0039] The heat map(s) 213 produced by the heat map generator 206 may be paired with the medical image 202 to create a heat map/medical image input pair 212 [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] to be inputted into the CNN 210. For example, if there are two user-selected measurement points on the medical image, the heat map/medical image input pair 212 may include two heat maps (e.g., one for each user-selected measurement point) and the medical image, or the heat map/medical image input pair 212 may include a single heat map with an encoding of both user-selected measurement points and the medical image. Prior to being inputted into the CNN 210, the heat map/medical image input pair 212 may be inputted into an image pre-processor 208, which may pre-process the heat map/medical image input pair 212 via one or more pre-processing routines…; And in [0073] At 602, method 600 includes encoding the one or more user-selected measurement points as heat maps. In one example, each set of x/y coordinate points is encoded as a heat map by generating a 2D normal Gaussian probability density function [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] with a standard deviation (e.g., 10 pixels) [and standard deviations of the distribution functions that are restricted to at least one of fixed values or ranges of values.] centered on the set of x/y coordinate points. The heat map may be generated in a first resolution (e.g., 512×256 pixels), where the first resolution matches a resolution of the medical image (e.g., raw data of the medical image). Once the heat map is generated, the heat map may be appended to the medical image, where the heat map and the medical image represent inputs [wherein at least one of a) at least one of the lateral response function (13) or the mapping function (16) are distribution functions] into different channels of a CNN…)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Wang and Gil for the same reasons disclosed above.
Regarding claims 14-17, the claims are similar to claims 4-7 and are thus rejected under the same rationale.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200273215, hereinafter ‘Wang’) in view of Hagiwara et al. (US 20230097283, hereinafter ‘Hagi’)
Regarding claim 8, the rejection of claim 3 is incorporated and Wang further teaches the method as claimed in claim 3, further comprising furnishing at least one of the synthetic rough profile (15), the first datum (9), or the second datum (10) with randomly (in [0092] Qualitatively, in this example, the trained neural network delivered relatively high-quality monochromatic projection data in the testing phase, with a relative error less than 0.2%. CT image reconstruction was performed from monochromatic projection data using a simultaneous algebraic reconstruction technique (SART). Structural information was relatively well-preserved in the reconstructed monochromatic CT image including, for example, texture features, thus providing relatively superior image quality. The calculated peak-to-noise ratio (PSNR) [further comprising furnishing at least one of the synthetic rough profile (15), the first datum (9), or the second datum (10) with ] and structural similarity (SSIM) for the reconstructed monochromatic image were 44.18 and 0.9698, respectively.)
Wang does not expressly teaches the use of noise as noted in the limitation randomly generated noise that is greater than an expected noise…
Hagi does expressly teach the use of noise as noted in the limitation randomly generated noise that is greater than an expected noise…, in [0128] FIG. 14 is a diagram for describing an example of a method of changing an amplification gain of a noise pixel value in an ultrasonic image obtained by the electronic endoscope system of an embodiment. An echo signal becomes a digital echo signal by passing through the amplifier circuit 120, the integration circuit 122, and the A/D converter 124. The noise detection unit 34 sets a threshold of a noise level in advance [randomly generated noise that is greater than an expected noise] and determines a signal equal to or higher than the threshold [randomly generated noise that is greater than an expected noise] as a noise component. A cycle length detection unit 150 measures a cycle (T1, T2, T3, . . . ) of a digital echo signal detected as a noise component. At this time, the digital echo signal may be a part of random noise [randomly generated noise that is greater than an expected noise], and it is preferable to exclude a short cycle that is shorter than or equal to a certain cycle.
Wang and Hagi are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for retrieving information from images using machine learning to learn a relationship between: operation environment information of the captured image processor and a feature amount of the image as disclosed by Hagi with the method of developing information retrieval and processing techniques using machine learning-based image reconstruction methods as disclosed by Wang.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Wang and Hagi as discussed above; Doing so improves the image quality of an image by suppressing a random noises including harmonic components, (Hagi, 0152).
Regarding claim 18 the claim is similar to claim 8 and thus rejected under the same rationale.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200273215, hereinafter ‘Wang’) in view of Kanazawa et al. (US 20220301227, hereinafter ‘Kan’).
Regarding claim 9, the rejection of claim 3 is incorporated and Wang further teaches the method as claimed in claim 3, further comprising recalculating at least one of the synthetic rough profile (15), the first datum (9), or the second datum (10) locally at a changed resolution. (in [0039] Generally, the present disclosure relates to a machine learning-based image reconstruction method configured to reduce or eliminate beam-hardening artifacts in a reconstructed CT image. A neural network is configured to learn a nonlinear transform from a training data set to map CT images reconstructed [further comprising recalculating at least one of the synthetic rough profile (15), the first datum (9), or the second datum (10) locally at a changed resolution …] from a single current-integrating data set to monochromatic projections at a pre-specified energy level, realizing monochromatic imaging and overcoming beam hardening effectively on a common clinic CT scanner without any dual-energy function. Machine learning has been successful in image classification, identification, segmentation, and super resolution [further comprising recalculating at least one of the synthetic rough profile (15), the first datum (9), or the second datum (10) locally at a changed resolution …] …)
Wang does not expressly teach the limitation a changed resolution by at least one of a sampling-rate conversion or downsampling.
Hagi expressly teaches a changed resolution by at least one of a sampling-rate conversion or downsampling, in [0018] In some implementations, the intermediate colorized image has a lower resolution than a resolution of the grayscale image [a changed resolution by at least one of a ]. In these implementations generating the colorized image can further include resizing the intermediate colorized image to the resolution of the grayscale image and after the resizing, combining the intermediate colorized image and a network output image of an output layer of the convolutional neural network to obtain the colorized image. And in [0128] At block 604, the grayscale image is downsampled. Downsampling the grayscale image may include reducing the image resolution. For example, the image resolution may be changed to 256×256 pixels, 352×352 pixels, or other suitable resolution [a changed resolution by at least one of a ], while the received grayscale image may be of a higher resolution, e.g., 1024×1024 pixels, 2048×2048 pixels, or any other resolution. Downsampling [a changed resolution by at least one of a ] the image can reduce the computational complexity of colorizing the image, e.g., colorizing the downsampled image may require lower processor resources, lower memory, etc…
Wang and Kan are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for achieving a the target resolution after downsampling based on a configuration of a colorizer convolutional neural network as disclosed by Kan with the method of developing information retrieval and processing techniques using machine learning-based image reconstruction methods as disclosed by Wang
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Wang and Kan as discussed above; Doing so allows processing an image that can reduce the computational complexity of colorizing the image, e.g., colorizing the downsampled image may require lower processor resources, lower memory, etc., (Kan, 0128).
Regarding claim 19 the claim is similar to claim 9 and thus rejected under the same rationale.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200273215, hereinafter ‘Wang’) in view of Kanazawa et al. (US 20220301227, hereinafter ‘Kan’) in further view of Besson (US 20040264628, hereinafter ‘Bess’).
Regarding claim 10, the rejection of claim 9 is incorporated and Wang in combination with Gil further teaches claim 9, further comprising correcting the detected result (5) in a region of a . (in [0009] In some embodiments, the monochromatic CT image may be reconstructed using a reconstruction technique selected from the group comprising filtered back projection (FBP), an iterative technique and/or a dictionary learning based technique. In some embodiments, the monochromatic projection data set may be configured to reduce beam hardening artifacts[further comprising correcting the detected result (5) in a region of a ] in the monochromatic CT image compared to the measured CT image.; And in [0051] In an embodiment, correction circuitry 104 may be configured to receive measured CT image data 132 corresponding to a measured CT image from CT scanner 102. In this embodiment, CT image reconstruction circuitry 116 may be configured to reconstruct the measured CT image based, at least in part, on measured projection data 134 received from projection data measurement circuitry 114…. )
Wang does not expressly teach the limitation correcting the detected result (5) in a region of a penumbra of a beam profile.
Bess expressly teaches correcting the detected result (5) in a region of a penumbra of a beam profile, in [0247] The model may also allow optimization of beam width for intended applications. Results show that a 10-mm beam width with a 25-mm detector width will allow penumbra imaging, automatic beam tracking, and scatter detection and correction [correcting the detected result (5) in a region of a penumbra of a beam profile]…
Bess, Kan and Wang are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for processing digital image data from digital x-ray systems using an automated software analysis package as disclosed by Bess with the method of developing information retrieval and processing techniques using machine learning-based image reconstruction methods as collectively disclosed by Kan and Wang
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Bess, Kan and Wang as discussed above; Doing so eliminates the need for a pre-scan or pre-exposure, and enables improved image quality at lower dose, (Bess, 0209).
Regarding claim 20 the claim is similar to claim 10 and thus rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lu et al. (US 20200234471): teaches a neural network being a trained neural network, apply the first plurality of energy-resolved projection images together with the second plurality of energy-resolved projection images to the trained neural network to estimate an X-ray scatter flux of the projection data, and remove, using the estimated X-ray scatter flux, a scatter component from the projection data to generate corrected projection data representing an intensity of a primary X-ray beam isolated from the X-ray scatter flux.
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/OLUWATOSIN ALABI/ Primary Examiner, Art Unit 2129
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