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 .
Drawings
The drawings were received on 12/29/2025. These drawings are acceptable.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 - 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “ascertaining a setpoint value for at least one control parameter that serves to control an acquisition processing or representation of medical image data” based on information derived from an obtained “measurement dataset that is based on an acquisition by at least one sensor of brain activity of a reference person, while the reference person observes at least one representation of medical image data, wherein the at least one representation is based on a medical image dataset”. Claim 14 recites substantially similar features.
Applicant’s specification has not disclosed the necessary algorithm for how to ascertain the recited setpoint value in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the claimed invention, including how to program a computer to achieve the claimed invention. For example, although the specification provides flowcharts in figs. 2 - 4, rather than providing the necessary details of the logic by which the measurement dataset is used to determine the setpoint value, the specification refers to non-patent literature (NPL) in which applicant asserts “algorithms trained by machine learning in each case are used in order to reconstruct the originally observed image from measurement data of a functional magnetic resonance imaging” ([0013], as published). Applicant further discusses the NPL by stating that “… images generated directly on the basis of the brain activity are, as a rule, blurred, but the semantic content of the image, i.e. for example a keyword relating to the image content, or a header of the image content, may be robustly ascertained” ([0014]). Applicant also refers to the NPL in [0071], and further states that “information may be obtained on the basis of the measurement dataset 40 by way of suitable algorithms, for example those trained by machine learning, which image contents the reference person 41 sees or identifies in the representation and how robustly and unambiguously this identification takes place. Supplementary implementation details may also be found in the articles cited in the introduction by Yu Takagi et al. and Zijiao Chen et al” ([0100]). The NPL is cited again in [0133].
However, these references to the NPL do not constitute sufficient disclosure of the missing details of the algorithm that are necessary to implement the claimed invention, at least because the NPL does not specifically and clearly describe how to program a computer to ascertain ‘a setpoint value for at least one control parameter that serves to control an acquisition, processing, or representation of medical image data’ based on information derived from an obtained “measurement dataset that is based on an acquisition by at least one sensor of brain activity of a reference person, while the reference person observes at least one representation of medical image data, wherein the at least one representation is based on a medical image dataset”. Similarly, no other portion of the specification provides the necessary algorithmic details.
As explained in MPEP 2161.01(I), simply specifying a desired outcome (i.e., ‘ascertaining the setpoint’) without sufficiently describing how the functions necessary to achieve the outcome are performed or how the result is achieved (i.e., the processing steps necessary to analyze the measurement dataset and consequently arrive at any specific setpoint value based on the measurement dataset), is insufficient to fulfill the written description requirement; the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. MPEP 2161.01(I) further states that:
It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
Applicant’s specification has not disclosed the necessary algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the claimed invention, including how to program a computer to analyze the measurement dataset and consequently ascertain any specific setpoint value based on the measurement dataset.
Claims 1 - 14 thus lack adequate written description support. See MPEP 2161.01.I.
Claim 2 recites “ascertaining the setpoint value comprises determining the setpoint value as a function of the reference value associated with the at least one representation”.
As explained above, applicant’s specification does not disclose the necessary algorithm for how to ascertain the recited setpoint value in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the claimed invention, including how to program a computer to achieve the claimed invention. There is thus insufficient written description of the subject matter of claim 2.
Claim 4 recites “at least one measurement dataset obtained while multiple reference persons observe the selected representation”.
The specification does not disclose a measurement dataset that is obtained “while multiple reference persons observe the selected representation”.
There is thus insufficient written description of the subject matter of claim 4.
Claim 4 recites “wherein ascertaining the setpoint value comprises determining the setpoint value as a function of the quality measure”.
As explained above, applicant’s specification does not disclose the necessary algorithm for how to ascertain the recited setpoint value in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the claimed invention, including how to program a computer to achieve the claimed invention. There is thus insufficient written description of the subject matter of claim 4.
Claim 5 recites “the setpoint value is specified based on the reference value associated with the selected representation or with the selected subset of representations”.
As explained above, applicant’s specification does not disclose the necessary algorithm for how to ascertain the recited setpoint value in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the claimed invention, including how to program a computer to achieve the claimed invention. There is thus insufficient written description of the subject matter of claim 5.
Claim 7 recites “wherein ascertaining the setpoint value comprises determining the setpoint value as a function of whether the perception condition is satisfied”.
As explained above, applicant’s specification does not disclose the necessary algorithm for how to ascertain the recited setpoint value in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the claimed invention, including how to program a computer to achieve the claimed invention. There is thus insufficient written description of the subject matter of claim 7.
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 - 14 are 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.
Claim 1 is indefinite for the following reasons:
There is unclear antecedent basis for “at least one representation” in line 6. It is unclear if this is the “representation” in line 3 or not.
There is unclear antecedent basis for “medical image data” in line 6. It is unclear if this is the “medical image data” in line 3 or not.
There is unclear antecedent basis for “a medical image dataset” in line 7. It is unclear if this is the “medical image data” or not.
It is unclear how the setpoint value is ascertained. As explained in the 112(a) rejections above, applicant has not provided the algorithm necessary to ascertain the setpoint value.
Claim 2 is indefinite because it is unclear if “the at least one representation is generated” attempts to set forth that the claimed method comprises a step of generating the representation, or merely to characterize the measurement dataset acquisition process. Examiner suggests amending the claim to recite “further comprising generating the representation…”, if this is what is intended.
Claim 2 is indefinite because it is unclear how the setpoint value is ascertained. As explained in the 112(a) rejections above, applicant has not provided the algorithm necessary to ascertain the setpoint value.
Claim 3 is indefinite because it is unclear what is meant by “the at least one representation [of the medical image dataset] is generated using a reference value” (claim 2) wherein “generating the at least one representation comprises … representing the medical image dataset … using the reference value”. The logic appears to be circular. It is unclear what is required.
Claim 4 is indefinite because it is unclear how a measurement dataset that is obtained “while multiple reference persons observe the selected representation” is used in the algorithm of the claimed invention. As explained in the 112(a) rejections above, the specification does not disclose such a dataset. For the purposes of examination, any determination of the setpoint value as a function of a quality measure will be interpreted as meeting the claim.
Claim 4 is indefinite because it is unclear how the setpoint value is ascertained. As explained in the 112(a) rejections above, applicant has not provided the algorithm necessary to ascertain the setpoint value.
Claim 5 is indefinite for the following reasons:
It is unclear if “a plurality of representations of the medical image dataset are generated” attempts to set forth that the claimed method comprises a step of generating the representations, or merely to characterize the measurement dataset acquisition process. Examiner suggests amending the claim to recite “further comprising generating a plurality of representations …”, if this is what is intended.
It is unclear if “one representation or a subset of the plurality of representations is selected” attempts to set forth that the claimed method comprises the ‘selecting’ step, or if the claim merely narrates such activity.
It is unclear how “the setpoint value is specified” relates to the previously recited step of “ascertaining the setpoint value”. It is unclear if these are two different steps.
It is unclear how the setpoint value is ascertained. As explained in the 112(a) rejections above, applicant has not provided the algorithm necessary to specify the setpoint value.
Claim 6 is indefinite because it is unclear if “the medical image dataset is acquired” attempts to set forth that the claimed method comprises a step of acquiring the medical image dataset or not. Similarly, it is unclear if “the ascertained setpoint value is used to control acquisition, processing, or representation” attempts to set forth that the claimed method comprises a ‘control’ step as recited, or to narrate activity.
Claim 6 is indefinite because there is unclear antecedent basis for “medical image data”. It is unclear if these are the medical image data in claim 1 or some other medical image data.
Claim 7 is indefinite because it is unclear how the setpoint value is ascertained. As explained in the 112(a) rejections above, applicant has not provided the algorithm necessary to ascertain the setpoint value.
Claim 11 is indefinite because there is insufficient antecedent basis for “the trained function.” It is unclear what is required.
Claim 12 is indefinite because there is unclear antecedent basis for “the measurement dataset”. It is unclear which of the “at least one measurement dataset” is being referred to.
Claim 13 is indefinite because there is insufficient antecedent basis for “the at least one control parameter set to the ascertained setpoint value.” It is unclear what is required.
Claim 14 is indefinite for the following reasons:
There is unclear antecedent basis for “at least one representation” (lines 7 - 8). It is unclear if this is the “representation” (line 3) or not.
There is unclear antecedent basis for “a representation” (line 8). It is unclear if this is the “representation”, the “at least one representation”, or some other representation.
There is unclear antecedent basis for “a medical image dataset” (line 8). It is unclear if this is the “medical image data” (line 4) or not.
It is unclear how the setpoint value is ascertained. As explained in the 112(a) rejections above, applicant has not provided the algorithm necessary to ascertain the setpoint value.
Claims 8 - 10 are indefinite by virtue of dependency.
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 - 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a mental-process type abstract idea) without significantly more.
Independent claim 1:
With regard to Step 1, the claim is directed to one of the four statutory categories of invention, i.e., a method.
With regard to Step 2A: Prong 1, the claim recites a limitations directed towards “ascertaining a setpoint value [for at least one control parameter that serves to control an acquisition and/or processing and/or a representation of medical image data] based on information derived from a measurement dataset”.
As drafted, the limitation amounts to nothing more than a step that can practically be performed in the human mind and/or with the aid of pen/paper. For example, a human can ‘ascertain the setpoint value based on information derived from a measurement dataset’ by thinking about the information, and/or values derived by processing the information in some way, such as an image/graphic characterizing the information, and/or with the aid of pen and paper. Therefore, the limitation recites a mental-process type abstract idea. See MPEP 2106.04(a)(2).
With regard to Step 2A: Prong 2, the claim recites an additional element of ‘obtaining a measurement dataset’ which is pre-solution activity, and reads on simply opening a file containing the measurement dataset. The limitations specifying that the measurement dataset is “based on an acquisition by at least one sensor of brain activity of a reference person while the reference person observes a representation of medical image data, wherein the representation is based on a medical image dataset”, merely limits the judicial exception to a particular technological environment or field of use. Therefore, the recited additional elements do not, either individually or as a whole, integrate the judicial exception into a practical application.
With regard to Step 2B, as explained above, the additional limitations are directed towards pre-solution activity, and limiting the judicial exception to a particular technological environment. Therefore, when considered separately and in combination, the additional limitations do not result in the claim, as a whole, amounting to significantly more than the judicial exception.
Dependent Claims
Dependent claim 2 recites additional limitations directed towards generating the representation using a reference value, which is extra-solution activity. The claim further recites that the “ascertaining” is as a function of the reference value, which merely modifies details of the abstract idea, and does not preclude the step from being performed mentally and/or with the aid of pen/paper.
Dependent claim 3 recites additional limitations directed towards the generation of the representation, and thus merely modifies extra-solution activity.
Dependent claim 4 recites additional limitations directed towards ascertaining a quality measure for a selected representation, which reads on a mental step. The claim further recites that ‘ascertaining the quality measure’ is “based on at least one measurement dataset obtained while multiple reference persons observe the selected representation”, which merely limits the judicial exception to a particular technological environment or field of use. The claim further recites that “ascertaining the setpoint value” is as a function of the quality measure, which merely modifies details of the abstract idea, and does not preclude the step from being performed mentally and/or with the aid of pen/paper.
Dependent claim 5 recites additional limitations directed towards generating representation using different reference values, which is extra-solution activity. The additional limitations directed towards the representations differing from one another merely modifies extra-solution activity. The claim further recites limitations directed towards selecting a representation, which reads on a mental step. The claim further recites that the setpoint value is “based on the reference value associated with the selected representation or with the selected subset of representations”, which merely modifies details of the abstract idea, and does not preclude the step from being performed mentally and/or with the aid of pen/paper.
Dependent claim 6 recites additional limitations directed towards acquisition of the medical image dataset, which is extra-solution activity. The claim further recites limitations directed towards ‘controlling acquisition, processing, or representation of medical image data’, which also encompasses extra-solution activity.
Dependent claim 7 recites additional limitations directed towards determining whether a perception condition is fulfilled, which reads on a mental step. The claim further recites that the “ascertaining the setpoint value comprises determining the setpoint value as a function of whether the perception condition is satisfied”, which merely modifies details of the abstract idea, and does not preclude the step from being performed mentally and/or with the aid of pen/paper.
Dependent claim 8 and 9 recite additional limitations directed towards the nature of the control/control parameter, which merely limit the judicial exception to a particular technological environment.
Dependent claim 10 recites additional limitations directed towards training a trained function for processing medical image data using supervised machine learning, which reads on a mathematical process (i.e., an abstract idea).
Dependent claim 11 recites additional limitations directed towards performing semantic enhancement or segmentation, which reads on a mental step. The claim further recites limitations directed towards use of a training function, which amounts to no more than an instruction to implement the judicial exception on a computer, as the training function is recited at a high level of generality
Dependent claim 12 recites additional limitations directed towards applying a function trained by machine learning, which amounts to no more than an instruction to implement the judicial exception on a computer, as the training function is recited at a high level of generality.
Dependent claim 13 recites additional limitations directed towards parametrization of the acquisition, processing, or representation, which merely limit the judicial exception to a particular technological environment.
Therefore, when considered separately and in combination, the additional limitations of the dependent claims do not integrate the judicial exception into a practical application, or result in the claims amounting to significantly more than the judicial exception.
Independent claim 14:
With regard to Step 1, the claims is directed to one of the four statutory categories of invention, i.e., a non-transitory computer implemented storage medium.
With regard to Step 2A: Prong 1, the claim recites limitations directed towards “ascertaining a setpoint value for at least one control parameter that serves to control an acquisition and/or processing and/or a representation of medical image data” as a function of an obtained measurement dataset.
As drafted, the limitation amounts to nothing more than a step that can practically be performed in the human mind and/or with the aid of pen/paper. For example, a human can ‘ascertain the setpoint value as a function of the measurement dataset’ by thinking about the measurement dataset, and/or values derived by processing the measurement dataset in some way, and/or with the aid of pen and paper. Therefore, the limitation recites a mental-process type abstract idea. See MPEP 2106.04(a)(2).
With regard to Step 2A: Prong 2, the claims recite that computer-executable instructions for performing the “ascertaining” step, which amounts to no more than an instruction to implement the judicial exception on a computer. The claim also recites an additional element of ‘obtaining the measurement dataset’ which is pre-solution activity, and reads on simply opening a file containing the measurement dataset. The claim further recites that the measurement dataset is “based on the acquisition by way of sensors of a brain activity of a reference person while the reference person observes at least one representation, wherein the representation is based on a medical image dataset”, which merely limits the judicial exception to a particular technological environment or field of use. Therefore, the recited additional elements do not, either individually or as a whole, integrate the judicial exception into a practical application.
With regard to Step 2B, as explained above, the additional limitations are directed towards than an instruction to implement the judicial exception on a computer, pre-solution activity, and limiting the judicial exception to a particular technological environment. Therefore, when considered separately and in combination, the additional limitations do not result in the claim, as a whole, amounting to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 - 3, 6 - 7, 9, 11, and 13 - 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Douglas (US 11,003,342).
Regarding claims 1 and 14, Douglas shows a computer implemented method for ascertaining a setpoint value for a control parameter that serves to control acquisition, processing, or representation of medical image data (“workflow for a radiologist… triggering events which cause a precise timing system to exactly determine the appropriate amount of time to spend on each image…”, abstract; col. 1, lines 26 - 33; “… timer-dependent image refresh rate; an image-dependent viewing parameter; and, image-dependent reporting parameter…”, col. 9, lines 36 - 45. The “setpoint value” is mapped to the refresh rate, viewing parameter, and/or reporting parameter), the method comprising:
obtaining a measurement dataset that is based on an acquisition by a sensor of brain activity of a reference person (“ … EEG analysis of the user … utilized as a triggering event …”, col. 36, lines 47 - 64), while the reference person observes a representation (presenting images to a user, col. 9, lines 40 - 50 and step 303 in fig. 3), wherein the representation is based on a medical image dataset (“viewing of medical images by medical personnel”, col. 10, lines 1 - 15; displayed image is a chest x-ray, col. 33, lines 49 - 60 and fig. 33); and
ascertaining the setpoint value based on information derived from the measurement dataset (“…performing the predetermined image adjustment(s) matched to the triggering event…”, col. 9, lines 50 - 57 and step 307 in fig. 3; “… EEG analysis of the user … utilized as a triggering event…”, col. 36, line 47 - col 37, line 10).
The method is implemented using a non-transitory computer implemented storage medium that stores machine-readable instructions to perform the method (col. 9, lines 60 - 67).
Regarding claim 2, Douglas discloses the claimed invention substantially as noted above. Douglas further shows the at least one representation is generated using a reference value of the at least one control parameter, because the value of the parameter (“… timer-dependent image refresh rate; an image-dependent viewing parameter; and, image-dependent reporting parameter…”, col. 9, lines 36 - 45) that is adjusted is at a “reference value” prior to its adjustment. The setpoint value is determined “as a function of the reference value associated with the at least one representation” at least because the application of the setpoint value (“…performing the predetermined image adjustment(s) matched to the triggering event…”, col. 9, lines 50 - 57 and step 307 in fig. 3) adjusts the control parameter from the reference value to the setpoint value.
Regarding claim 3, Douglas discloses the claimed invention substantially as noted above. Douglas further shows generating the at least one representation comprises at least one of acquiring the medical image dataset using the reference value of the at least one control parameter, processing the medical image dataset using the reference value of the at least one control parameter to generate a processing result, or representing the medical image dataset or the processing result using the reference value of the at least one control parameter (“…performing the predetermined image adjustment(s) matched to the triggering event…”, col. 9, lines 50 - 57 and step 307 in fig. 3).
Regarding claim 6, Douglas discloses the claimed invention substantially as noted above. Douglas further shows the medical image dataset is acquired during an imaging sequence comprising repeated (prior imaging examination, col. 26, lines 7 - 50 and step 2407 in fig. 24C) or continuous acquisition of medical image data from a same examination object (col. 9, lines 59 - 62: continuous acquisition at least of individual frames/slices is understood), and wherein the ascertained setpoint value is used to control acquisition, processing, or representation of medical image data acquired after acquisition of the medical image dataset during the imaging sequence (“…performing the predetermined image adjustment(s) matched to the triggering event…”, col. 9, lines 50 - 57 and step 307 in fig. 3).
Regarding claim 7, Douglas discloses the claimed invention substantially as noted above. Douglas further shows comprising determining, based on the at least one measurement dataset (“ … EEG analysis of the user … utilized as a triggering event …”, col. 36, lines 47 - 64), whether a perception condition is satisfied for at least one object or at least one anatomical feature depicted in the at least one representation, wherein ascertaining the setpoint value comprises determining the setpoint value as a function of whether the perception condition is satisfied (“… deserves a disproportionate amount of attention by a radiologist…appearance event is an example of a triggering event”, col. 11, lines 10 - 15. Note that the amount of attention ‘deserved by a radiologist’ is a “perception condition”. Also refer to “metrics relating to attentiveness is an example of a triggering event”, col. 11, lines 65 - 67).
Regarding claim 9, Douglas discloses the claimed invention substantially as noted above. Douglas further shows the at least one control parameter (“… timer-dependent image refresh rate; an image-dependent viewing parameter; and, image-dependent reporting parameter…”, col. 9, lines 36 - 45) controls an enhancement of the medical image data with an item of semantic information (“color schematic … triggering event has caused the predetermined response of the image-dependent viewing parameter to perform false color”, col. 15, line 45 - col. 16, line 2).
Regarding claim 11, Douglas discloses the claimed invention substantially as noted above. Douglas further shows the trained function is configured to perform at least one of semantic enhancement (“color schematic … triggering event has caused the predetermined response of the image-dependent viewing parameter to perform false color”, col. 15, line 45 - col. 16, line 2) or segmentation (segmented, col. 5, lines 14- 23) of the medical image data.
Regarding claim 13, Douglas discloses the claimed invention substantially as noted above. Douglas further shows the acquisition, processing, or representation of the medical image data is parameterized using the at least one control parameter set to the ascertained setpoint value (“…performing the predetermined image adjustment(s) matched to the triggering event…”, col. 9, lines 50 - 57 and step 307 in fig. 3).
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 - 5 are rejected under 35 U.S.C. 103 as being unpatentable over Douglas in view of Dhatt et al. (US 2024/0122579).
Regarding claims 4 - 5, Douglas discloses the claimed invention substantially as noted above.
Douglas fails to show determining the setpoint value as a function of a quality measure, wherein the setpoint value is specified based on a reference value associated with a selected representation.
Dhatt discloses ultrasound systems. Dhatt teaches determining a setpoint value as a function of a quality measure (“neural network can determine, based on the ultrasound data (e.g., an ultrasound image) that an imaging parameter should be adjusted to improve the quality of the ultrasound image … adjust the imaging parameter according to the recommended adjustment from the neural network, [0062]), wherein the setpoint value is specified based on a reference value associated with a selected representation (implicit).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Douglas to include determining the setpoint value as a function of a quality measure, wherein the setpoint value is specified based on a reference value associated with a selected representation, as taught by Dhatt, in order to improve image quality, as suggested by Dhatt ([0062]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Douglas in view of Hyung (US 2015/0139382 ).
Regarding claim 8, Douglas discloses the claimed invention substantially as noted above.
Douglas fails to show the control parameter specifies an X-ray dose irradiated onto an examination object for acquisition of the medical image data and/or imaging rate.
Hyung discloses an X-ray imaging apparatus. Hyung teaches a control parameter specifies an X-ray dose irradiated onto an examination object for acquisition of medical image data and/or imaging rate (“imaging parameters … X-ray imaging apparatus … exposure control…, automatic dose control, or automatic dose rate control”, [0122]; “… frame rate, … dose per frame… controlling the imaging parameters”, [0126]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Douglas to have the control parameter specify an X-ray dose irradiated onto an examination object for acquisition of the medical image data and/or imaging rate., as taught by Hyung, in order to reduce a radiation dose of the X-rays radiated onto the subject, as suggested by Hyung ([0007]).
Claims 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Douglas in view of Poltorak (US 2021/0041953).
Regarding claim 10, Douglas discloses the claimed invention substantially as noted above.
Douglas fails to show training a trained function for processing the image data using supervised machine learning, wherein the at least one control parameter comprises a parameter of the trained function, wherein a plurality of training datasets is used for the supervised machine learning, and wherein each training dataset of the plurality of training datasets is based at least in part on a corresponding measurement dataset.
Poltorak discloses communicating brain activity to an imaging device. Poltorak teaches training a trained function (machine learning, [0130]; artificial intelligence, [0240]; “Artificial neural networks have been employed to analyze EEG signals”, [0411] and subsequent cited references; AI and ML, [0790) for processing image data (“ … brain activity data may be used to control image capture or capture mode, zoom settings, depth of field, shutter speed, aperture, image stabilization, autofocus and focal object selection, storage, compression type, or other imaging device function…capture the most interesting aspects of the scene…”, [0713]) using supervised machine learning (“artificially intelligent system… supervised…”, [0993]), wherein the at least one control parameter comprises a parameter of the trained function, wherein a plurality of training datasets is used for the supervised machine learning, and wherein each training dataset of the plurality of training datasets is based at least in part on a corresponding measurement dataset (implicit - see description of training throughout the reference, including at least [0549] - [0551], [0781], [0790] - [0796], etc. It is understood in the art of artificial intelligence that ‘each training dataset is based on a corresponding measurement dataset’).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Douglas to include training a trained function for processing the image data using supervised machine learning, wherein the at least one control parameter comprises a parameter of the trained function, wherein a plurality of training datasets is used for the supervised machine learning, and wherein each training dataset of the plurality of training datasets is based at least in part on a corresponding measurement dataset, as taught by Poltorak, in order to facilitate robust analysis of the measurement data by using artificial intelligence techniques, as is understood in the art.
Regarding claim 12, Douglas discloses the claimed invention substantially as noted above.
Douglas fails to show ascertaining the setpoint value comprises applying a function trained by machine learning to the measurement dataset.
Poltorak discloses communicating brain activity to an imaging device. Poltorak teaches ascertaining a setpoint value (“ … brain activity data may be used to control image capture or capture mode, zoom settings, depth of field, shutter speed, aperture, image stabilization, autofocus and focal object selection, storage, compression type, or other imaging device function…capture the most interesting aspects of the scene…”, [0713]) comprises applying a function trained by machine learning to a measurement dataset (machine learning, [0130]; artificial intelligence, [0240]; “Artificial neural networks have been employed to analyze EEG signals”, [0411] and subsequent cited references; AI and ML, [0790)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Douglas to have ascertaining the setpoint value comprises applying a function trained by machine learning to the measurement dataset, as taught by Poltorak, in order to facilitate robust analysis of the measurement data by using artificial intelligence techniques, as is understood in the art.
Response to Arguments
Applicant's arguments filed 12/29/2025 have been fully considered but they are not persuasive.
CLAIM REJECTIONS 35 U.S.C. § 112
Applicant argues on page 9, regarding the 112(a) rejections, that the specification provides examples of “adjusting X-ray dose based on attention or perception conditions, selecting reference values that maximize a quality measure, and iteratively offsetting control parameters based on robustness metrics derived from brain activity analysis”.
This argument is not persuasive because the specification does not specifically explain the algorithm used to process the measurement data to arrive at any specific value of the control parameter. In contrast, the specification refers the reader to NPL for the missing information that is needed to relate specific features of the measurement data to the desired control parameters. In this sense, the disclosure essentially specifies a desired outcome (i.e., ‘ascertaining the setpoint’) without sufficiently describing how the functions necessary to achieve the outcome are performed or how the result is achieved (i.e., the processing steps necessary to analyze the measurement dataset and consequently arrive at any specific setpoint value based on the measurement dataset), which is insufficient to fulfill the written description requirement. See MPEP 2161.01(I).
Applicant further argues on page 10, regarding the 112(a) rejections, that the cited “passages describe multiple mechanisms by which a setpoint value is ascertained as a function of the measurement dataset”.
Examiner respectfully disagrees because the cited passages do not specifically explain how the measurement data are processed to arrive at any specific value of the control parameter.
Applicant further argues on page 10, regarding the 112(a) rejections, that the “claims do not recite a specific algorithm, and therefore possession is not limited to possession of a particular algorithmic structure. Instead, possession of the functional relationship between brain activity-derived data and control-parameter setpoint determination is sufficient.”
However, the claimed invention requires implementation of a specific algorithm; and the specification has not adequately disclosed the specific algorithm needed to implement the claimed invention. Examiner disagrees with applicant’s assertion that “possession of the functional relationship between brain activity-derived data and control-parameter setpoint determination is sufficient” because an algorithm (i.e., not just “the functional relationship”) is required to implement the claimed invention.
Applicant further argues on page 10, regarding the 112(a) rejections, that the “specification … identifying multiple classes of algorithms suitable for performing the claimed function, including trained machine learning functions, neural networks, convolutional neural networks, transformers, and classification-based robustness metrics, and by describing how outputs of such algorithms are used to select or adjust control parameters”.
Examiner respectfully disagrees because the specification merely refers the reader to NPL at the point necessary to understand the algorithm needed to implement the claimed invention.
Applicant further argues on page 10, regarding the 112(a) rejections, that the “absence of a single, narrowly defined algorithm does not equate to lack of possession of the claimed invention, particularly given the breadth of the claims and the state of the art in, for example, machine learning-based signal interpretation.”
Examiner respectfully disagrees. Those of ordinary skill in the art would not understand from applicant’s disclosure the algorithm or steps/procedure that the inventor intended to be taken to perform the claimed function of ‘ascertaining the setpoint value’. Moreover, the breadth of the claims specifically very broadly encompasses the genus of any parameter “that serves to control an acquisition and/or processing and/or a representation of medical image data”, whereas the specification contemplates only a limited number of species of such parameters, which are not sufficient to demonstrate possession of the entire genus.
CLAIM REJECTIONS 35 U.S.C. § 101
Applicant argues on page 12, regarding the 101 rejections, that the “claim does not recite a mental process” because the “claim requires derivation of information from a dataset that is itself the product of sensor-based brain activity acquisition”.
This is not persuasive because the claim recites a mental step for reasons explained in the 101 rejections above.
Applicant argues on page 12, regarding the 101 rejections, that the sensor-based acquisition is not data-gathering.
Examiner respectfully disagrees because the use of a sensor is insufficient to cause data-gathering steps to integrate the judicial exception into a practical application, or result in the claim amounting to significantly more than the judicial exception.
Applicant argues on page 12, regarding the 101 rejections, that ‘Applicants disagree with the Office Action's assertion that "obtaining the measurement dataset" reads on "simply opening a file’.
This is not persuasive because the claim reads on opening a file containing the measurement dataset.
Applicant argues on page 12, regarding the 101 rejections, that the additional limitations “define a sensor-driven control method for medical imaging systems”.
This is not persuasive because the additional limitations do not integrate the judicial exception into a practical application, or result in the claim amounting to significantly more than the judicial exception, for reasons explained in the 101 rejections above.
CLAIM REJECTIONS 35 U.S.C. § 102
Applicant argues on page 13, regarding the 102 rejections, that in Douglas, “There is no disclosure of determining a setpoint value as an intermediate control variable that is then used to control acquisition, processing, or representation.”
Examiner respectfully disagrees. The triggering event changes the control
parameter from a reference value to a setpoint value, as explained in the abstract. The setpoint value is necessarily “ascertained” in some way as the triggering event changes the parameter.
Applicant argues on page 13, regarding the 102 rejections, that “Douglas mentions EEG analysis as one possible triggering event among many, but the EEG analysis is described only at a high level”.
This is not persuasive of error. Regardless of the detail provided by Douglas, the reference meets the claim for reasons explained in the 102 rejection above.
Applicant argues on page 13, regarding the 102 rejections, that “Douglas does not disclose obtaining a measurement dataset of brain activity as an input to a
method step”.
Examiner respectfully disagrees. The EEG dataset is “measurement dataset of brain activity”. The data is used as input when it is “utilized as a triggering event…” (col. 36, line 47 - col 37, line 10).
Applicant argues on page 14, regarding the 102 rejections, that ‘Douglas further does not disclose that the reference person observes "at least one representation of medical image data" while the brain-activity dataset is acquired, as a coupled condition’.
Examiner respectfully disagrees. The EEG dataset is used as “utilized as a triggering event…” (col. 36, line 47 - col 37, line 10) while the clinician is scrolling (abstract), inter alia.
Applicant argues on page 14, regarding the 102 rejections, that ‘Douglas further does not teach or suggest "ascertaining the setpoint value based at least in part on information derived from the at least one measurement dataset."’
Examiner respectfully disagrees for reasons explained above.
Applicant argues on page 14, regarding the 102 rejections, that “Douglas does not disclose controlling an acquisition of medical image data at all.”
The argument is moot because the limitation is required only in the alternative.
Applicant’s argument regarding claim 7 are moot in view of amendments. Douglas meets the claim for reasons explained above in the rejections.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMELIE R DAVIS whose telephone number is (571)270-7240. The examiner can normally be reached Monday-Friday, 9:30 - 6:00 PST.
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/AMELIE R DAVIS/Primary Examiner, Art Unit 3798
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