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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claim limitation “an input”, “a processing unit”, “an output” and “an image acquisition device”, in claims 1 – 13 and 16 has/have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a placeholder “input”, “unit”, “output” and “device” coupled with functional language “obtain”, “generate”, “determine”, “output”, and “acquire” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 1 – 13 and 16 has/have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: the Specification discloses the physical structure about above units are part of processor or computer.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 – 16 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 1 and 13 - 15:
Step 1:
Claims 1 and 13 – 15 are directed towards a process, machine, manufacture or composition of matter which is/are statutory subject matter.
Step 2A:
Prong 1:
Claims 1 and 13 – 15 are directed an idea for generate and determine deviations of an estimation image which is an abstract idea.
Consideration of the claimed elements:
Regarding claims 1 and 13 – 15:
The claims in the instant application include:
an input configured to obtain a series of two or more images and a reference image of an object of interest of a subject, wherein the input images have been acquired at different points in time and the reference image has been acquired at a later point in time than the input images;
a processing unit configured to
generate an estimation image that represents an estimated representation of the object of interest at an estimation point in time by applying, onto the input images, a trained algorithm or computing system that has been trained on a plurality of training images showing objects of the same type as the object of interest at different points in time to let the object of interest shown in the input images artificially age by a desired period of time, wherein the estimation point in time corresponds to the point in time at which the reference image has been acquired, and
determine deviations of the estimation image from the reference image; and
an output configured to output the generated estimation image and the determined deviations.
Regarding “
generate an estimation image that represents an estimated representation of the object of interest at an estimation point in time by applying, onto the input images, a trained algorithm or computing system that has been trained on a plurality of training images showing objects of the same type as the object of interest at different points in time to let the object of interest shown in the input images artificially age by a desired period of time, wherein the estimation point in time corresponds to the point in time at which the reference image has been acquired”,
it is considered as generating an estimated representation using an algorithm boils down to mathematical relationships, formulas, and calculations, or estimating what an object looks like at a certain point in time using trained parameters/rules is something that can theoretically be performed entirely in the human mind. Thus, it is categorized as Mathematical Concepts or Mental Processes.
Regarding “determine deviations of the estimation image from the reference image,” it is considered a fundamental mathematical algorithm or the basic manipulation of information, or simply visually determine the similarity of two images. Thus, it is categorized as Mathematical Concepts or Mental Processes.
As analyzed above, the above claimed limitations are Mathematical Concepts or Mental Processes.
Prong 2:
The claims include additional elements of
an input configured to obtain a series of two or more images and a reference image of an object of interest of a subject, wherein the input images have been acquired at different points in time and the reference image has been acquired at a later point in time than the input images;
an output configured to output the generated estimation image and the determined deviations;
an image acquisition device configured to acquire a series of two or more first images and a second image of an object of interest of a subject, wherein the two or more first images are acquired at different first points in time and the second image is acquired at a second point in time later than the two or more first images;
a non-transitory computer readable medium.
Regarding “
an input configured to obtain a series of two or more images and a reference image of an object of interest of a subject, wherein the input images have been acquired at different points in time and the reference image has been acquired at a later point in time than the input images;
an image acquisition device configured to acquire a series of two or more first images and a second image of an object of interest of a subject, wherein the two or more first images are acquired at different first points in time and the second image is acquired at a second point in time later than the two or more first images”,
it is considered as data gathering of adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g).
.
Regarding “an output configured to output the generated estimation image and the determined deviations”, it is considered as general data outputting of adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g).
Regarding “a non-transitory computer readable medium”, they are computer hardware / software. It is considered as “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
They are mere instructions to implement an abstract idea uses a computer as a tool to perform an abstract idea.
Moreover, the claim limitations that are not indicative of integration into a practical application.
Thus, the recited generic additional element (e.g., data gathering, outputting, non-transitory computer readable medium) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a computational algorithm, a generic memory and processor are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Accordingly, the claims are directed to an idea of itself, and therefore not patent eligible.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception such as improvements to another technology or technical field, or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Moreover, the claim language that may be separate from the abstract idea (i.e., additional elements) include computer processors, computer-readable storage media.
The additional element (e.g., data gathering, outputting, non-transitory computer readable medium) simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see MPEP 2106.05(d) and 2106.07(a)III.
Thus, the recited generic additional elements (e.g., data gathering, outputting, non-transitory computer readable medium) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a generic memory are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Consequently, the identified additional elements taken into consideration individually or in combination fails to amount of significantly more than the abstract idea above.
Regarding claims 2 – 12 and 16, the rejection is based on the same rationale described for claim because the claims include/inherit the same/similar type of problematic limitation(s) as claim 1 , wherein limitations regarding additional aspect for process; "use ... ", “analyzing …”, “generating …”, “quantify …”, “qualify …”, “acquired …”, “perform …”, “train …”, “additionally using …” and “includes …”, is/are of sufficient breadth that it would be substantially directed to or reasonably interpreted as a part of the “mental processes” as the abstract idea (similar to claim as stated above). It is noted that further additional limitation is merely generic/conventional computer component/steps to implement the abstract idea, which is, individually or in combination, not sufficient to amount to significantly more than the judicial exception. Therefore, the claimed invention as a whole is directed to an ineligible subject matter.
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.
Claim(s) 1 – 16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wegmayr et al. (“Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression”, IDS), hereinafter referred as Wegmayr.
Regarding claim 1, Wegmayr discloses an image processing device (Fig. 1) comprising:
an input configured to obtain a series of two or more images (Fig. 1, input baseline image; section 3.3, Brain data) and a reference image of an object of interest of a subject (page 256, ground-truth image), wherein the input images have been acquired at different points in time and the reference image has been acquired at a later point in time than the input images (page 256, ground-truth image used in classifier (critic as discriminator) is processed at a later point in time than the input images in generator in Fig. 1);
a processing unit configured to
generate an estimation image that represents an estimated representation of the object of interest at an estimation point in time by applying, onto the input images, a trained algorithm or computing system that has been trained on a plurality of training images showing objects of the same type as the object of interest at different points in time to let the object of interest shown in the input images artificially age by a desired period of time (section 3, generating aged images), wherein the estimation point in time corresponds to the point in time at which the reference image has been acquired (Fig. 3, aligning ground-truth images with generated images), and
determine deviations of the estimation image from the reference image (pages 253 – 254, section “Image Prediction Error”); and
an output configured to output the generated estimation image and the determined deviations (Fig. 3 and 5, display the generated estimation image and the determined deviations).
Regarding claim 2 (depends on claim 1), Wegmayr disclose the device wherein the processing unit is configured to use a learning system, a neural network, a convolutional neural network or a U-net network as a trained algorithm or a computing system (page 249).
Regarding claim 3 (depends on claim 1), Wegmayr disclose the device wherein applying the trained algorithm or computing system onto the input images by the processing unit includes analyzing, by a convolutional neural network, the input images to derive image features of the object of interest (Fig. 1).
generating, by a generator network, the reference image based on the derived image features and information regarding image features evolution over time (Fig. 3, ground-truth images evolution over time).
Regarding claim 4 (depends on claim 1), Wegmayr disclose the device wherein the processing unit is configured to generate the estimation image by letting the object of interest as shown in the input images age by a period of time corresponding to the time difference between the points in time at which the respective input image and the reference image have been acquired, between the points in time at which the last input image and the reference image have been acquired (Fig. 3).
Regarding claim 5 (depends on claim 1), Wegmayr disclose the device wherein applying the trained algorithm or computing system to the input images by the processing unit includes analyzing, by a convolutional neural network, the input images to derive aging information indicating how the object of interest shown in the input images has been aging over time spanned by the different points in time at which they have been acquired, generating, by a generator network, the estimation image based on the derived aging information and the time differences between the points in time at which the input images and the reference image have been acquired (Fig. 1 and 3).
Regarding claim 6 (depends on claim 1), Wegmayr disclose the device wherein the processing unit is configured to quantify and/or qualify the deviations and wherein the output unit is configured to output the determined quantification and/or qualification (page 253 - 254, Image Prediction Error: scores).
Regarding claim 7 (depends on claim 1), Wegmayr disclose the device wherein the time differences between the points in time at which the respective input image and the reference image have been acquired and the time differences between the points in time at which the respective input image has been acquired and the estimation image has been estimated are more than one week or more than one month or more than six months or more than one year (Fig. 3, years).
Regarding claim 8 (depends on claim 1), Wegmayr discloses the device wherein the processing unit is configured to perform a registration of the input images before generating the estimation image and/or to perform a registration of the estimation image after generating the estimation image (registration in page 251, 252, 254).
Regarding claim 9 (depends on claim 1), Wegmayr disclose the device wherein the processing unit is configured to train the algorithm or computing system on a plurality of training images showing objects of the same type as the object of interest at different points in time, wherein the plurality of training image comprises real images and/or synthetic images (section 3.4).
Regarding claim 10 (depends on claim 9), Wegmayr disclose the device wherein the processing unit is configured to train the algorithm or computing system by analyzing, by a convolutional neural network, the plurality of training images to derive image features describing normal appearance of the object over time to be trained (section 3, Fig. 3, HC).
Regarding claim 11 (depends on claim 1), Wegmayr disclose the device wherein the processing unit is configured to perform a generic training phase for a neural network and a personalization phase as a preprocessing step before generating the estimation image, in which a part of the network is retrained (page 255, pre-trained, and then trained (retrained)).
Regarding claim 12 (depends on claim 11), Wegmayr disclose the device wherein the processing unit is configured to additionally using the age of the subject of the respective image of the plurality of training images to train the algorithm or computing system (page 255, trained for Δ years).
Regarding claim 13, Wegmayr discloses a system (Fig. 1) comprising:
an image acquisition device configured to acquire a series of two or more first images and a second image of an object of interest of a subject, wherein the two or more first images are acquired at different first points in time (Fig. 1, input baseline image; section 3.3, Brain data) and the second image is acquired at a second point in time later than the two or more first images (page 256, ground-truth image used in classifier (critic as discriminator) is processed at a later point in time than the input images in generator in Fig. 1); and
an image processing device as claimed in claim 1 (see claim 1 rejection).
Regarding claims 14 and 15, they are corresponding to claim 1, thus, they are interpreted and rejected for a same reason set forth for claim 1.
Regarding claim 16 (depends on claim 3), Wegmayr disclose the device wherein the evolution includes age-related atrophy (page 251).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QIAN YANG whose telephone number is (571)270-7239. The examiner can normally be reached on Monday-Thursday 8am-6pm.
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/QIAN YANG/
Primary Examiner, Art Unit 2677
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