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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/22/26 has been entered.
Response to Amendment
This action is in response to the amendments and remarks filed on 5/22/2026. The amendments filed on 5/22/2026 have been entered. Accordingly Claims 1-4, 6-14 and 16-22 are pending.
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 1-4, 6-14 and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1 recites, for example, the following abstract ideas: “…establish, based on the ultrasound data, a data model of the fetus…” and “…generate, based on the data model, a visualization of the fetus…” fall within mental processes. That is to say, under broadest reasonable interpretation the recited limitations are typical of the functions of a physician or radiologist performed in the mind, thus the limitations fall within “Mental Processes” grouping of abstract ideas. Further, “…using a generative artificial intelligence…” in the generate limitation uses a generic model that has no specifics to the algorithmic foundation or dimensionality associated with the data model generated and as such can be considered a vector of minimal dimensions that can be “used” by mental process or simple pen and paper computations. Analogous limitations are found in claim 11.
The dependent claims 2-4, 6-10, 12-14 and 16-22 do not sufficiently link the subject matter to a practical application or recite element(s) which constitute significantly more than the abstract ideas identified. The depending claims are directed to additional limitations which encompass abstract ideas consistent with those identified above that are well-understood, routine and/or conventional activity. Further, dependent Claims 2-4, 6-10, 12-14 and 16-22 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
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 1-2,4,6,8-9,10-12,14,16 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin et. al. (WO2022133806, June 30, 2022)(hereinafter, “Lin”).
Regarding Claim 1, Lin teaches: Circuitry for generating a visualization of a fetus (Figs. 1-2, “The fetal facial volume image restoration method and ultrasonic imaging system provided by the present application can be applied to the human body…”), the circuitry being configured to:
obtain ultrasound data representing a fetus in a uterus of its mother (“The ultrasound probe 110 receives the ultrasound echoes returned from the region where the face of the fetus is located through the receiving circuit, so as to obtain ultrasound echo data.”);
establish, based on the ultrasound data, a data model of the fetus (“The first step is to establish a database. This step is to build a database required for the training of the generative model of the deep learning algorithm, so that the generative model can learn the feature space distribution of a large number of fetal facial data. Wherein, the database includes the data that the fetal face can be completely displayed and the data that the fetal face is missing and/or the data that the fetal face is blocked... the database includes several paired or unpaired 3D ultrasound data of the fetal face, wherein the paired 3D ultrasound data means that the low-quality data and the high-quality data are from the same fetus, and the acquisition conditions are similar; The unpaired data refers to a pair of low-quality data and high-quality data in the database that are not from the same fetus. It should be noted that whether the data is paired only affects which generation model is finally used to achieve enhancement, and the two are not mutually exclusive methods, that is, they can exist alone or at the same time…”); and
generate, based on the data model, a visualization of the fetus using by executing a generative artificial intelligence model that has been trained on fetal anatomical data to learn fetal anatomy, wherein the executing of the generative artificial intelligence model includes reconstructing, in the data model, a missing portion of the fetus, the missing portion being a portion of the fetus that is absent from the ultrasound data due to at least one of: occlusion by another structure, and wherein reconstructing includes generating, by the generative artificial intelligence model, anatomically consistent data for the missing portion based on the learned fetal anatomy, to produce a complete visualization of the fetus (“In this step, the restoration processing of the volumetric image data of the fetal face may be performed according to the specific conditions of the volumetric image data. For example, in an embodiment, when part of the data of the fetal face is missing, the The repairing process can be to fill in the missing part of the data; in another embodiment, when the part of the face of the fetus is occluded, the occluded part can be filled, and the data that occludes the face of the fetus can also be removed., so as to expose the occluded area of the fetal face, so as to completely display the fetal face.”; “…the repair method based on the generative model of the deep learning algorithm (hereinafter referred to as the generative model) is to learn the feature space distribution of a large number of volumetric image data of the fetal face through the generative model, and the learned generative model can infer the low quality of the input. The missing part of the volume image data or the occlusion and other influencing factors are generated, and then the high-quality restored volume image data corresponding to the input low-quality volume image data is generated, so as to realize the restoration of the fetal face.”).
Regarding Claim 2, Lin teaches the claim limitations as noted above.
Lin further teaches: wherein the circuitry is further configured to: obtain image data of a portion of a belly of the mother; and determine a position of the fetus relative to the mother (“…ultrasound image acquisition may be performed based on the ultrasound imaging system 100 shown in FIG. 1 . The user moves the ultrasound probe 110 to select an appropriate position and angle, and the transmit circuit in the transmit/receive circuit 120 sends a set of delayed-focused pulses to the ultrasound probe 110, and the ultrasound probe 110 transmits ultrasonic waves along the 2D scanning plane to the face of the fetus.”); and wherein the generating of the visualization of the fetus includes generating the visualization according to the determined position (“…the three-dimensional spatial relationship of the ultrasonic echoes obtained by the ultrasound probe 110 transmitted/received in a series of scanning planes can be integrated, so as to realize the scanning of the fetal face in the three-dimensional space and the reconstruction of the 3D image . Finally, after some or all image post-processing steps such as denoising, smoothing, and enhancement, the volumetric image data of the fetal face is obtained.”).
Regarding Claim 4, Lin teaches the claim limitations as noted above.
Lin further teaches: wherein the circuitry is further configured to determine a position of a display device relative to the mother (“…the three-dimensional spatial relationship of the ultrasonic echoes obtained by the ultrasound probe 110 transmitted/received in a series of scanning planes can be integrated, so as to realize the scanning of the fetal face in the three-dimensional space and the reconstruction of the 3D image . Finally, after some or all image post-processing steps such as denoising, smoothing, and enhancement, the volumetric image data of the fetal face is obtained.”); and wherein the generating of the visualization of the fetus includes generating the visualization of the fetus in a perspective as seen from the position of the display device (“After image drawing, rendering and other post-processing, the visual information is obtained, and then sent to the display for display. Among them, 4D ultrasound repeats the above process in the time dimension to obtain multi-volume volume data and display them one by one.”).
Regarding Claim 6, Lin teaches the claim limitations as noted above.
Lin further teaches: wherein the missing portion includes an organ of the fetus (“In this step, the restoration processing of the volumetric image data of the fetal face may be performed according to the specific conditions of the volumetric image data. For example, in an embodiment, when part of the data of the fetal face is missing, the The repairing process can be to fill in the missing part of the data; in another embodiment, when the part of the face of the fetus is occluded, the occluded part can be filled, and the data that occludes the face of the fetus can also be removed., so as to expose the occluded area of the fetal face, so as to completely display the fetal face.”; “…the repair method based on the generative model of the deep learning algorithm (hereinafter referred to as the generative model) is to learn the feature space distribution of a large number of volumetric image data of the fetal face through the generative model, and the learned generative model can infer the low quality of the input. The missing part of the volume image data or the occlusion and other influencing factors are generated, and then the high-quality restored volume image data corresponding to the input low-quality volume image data is generated, so as to realize the restoration of the fetal face.”).
Regarding Claim 8, Lin teaches the claim limitations as noted above.
Lin further teaches: wherein the data model includes a three-dimensional representation of the fetus (“The first step is to establish a database. This step is to build a database required for the training of the generative model of the deep learning algorithm, so that the generative model can learn the feature space distribution of a large number of fetal facial data. Wherein, the database includes the data that the fetal face can be completely displayed and the data that the fetal face is missing and/or the data that the fetal face is blocked... the database includes several paired or unpaired 3D ultrasound data of the fetal face, wherein the paired 3D ultrasound data means that the low-quality data and the high-quality data are from the same fetus, and the acquisition conditions are similar; The unpaired data refers to a pair of low-quality data and high-quality data in the database that are not from the same fetus. It should be noted that whether the data is paired only affects which generation model is finally used to achieve enhancement, and the two are not mutually exclusive methods, that is, they can exist alone or at the same time…”).
Regarding Claim 9, Lin teaches the claim limitations as noted above.
Lin further teaches: wherein the ultrasound data represent the fetus in respective perspectives as seen from multiple directions (“…a volume of pre-reconstructed (polar coordinate) volume data is obtained after a complete probe sector scan cycle is completed through the above processing, and then the volume data is sent to the 3D reconstruction module to obtain the reconstruction. The latter (Cartesian coordinate system) volume data. After image drawing, rendering and other post-processing, the visual information is obtained, and then sent to the display for display. Among them, 4D ultrasound repeats the above process in the time dimension to obtain multi-volume volume data and display them one by one.”).
Regarding Claim 10, Lin teaches the claim limitations as noted above.
Lin further teaches: wherein the circuitry is further configured to generate an instruction for positioning an ultrasound sensor at a belly of the mother (“…the ultrasound imaging system 100 shown in FIG. 1 . The user moves the ultrasound probe 110 to select an appropriate position and angle, and the transmit circuit in the transmit/receive circuit 120 sends a set of delayed-focused pulses to the ultrasound probe 110, and the ultrasound probe 110 transmits ultrasonic waves along the 2D scanning plane to the face of the fetus.”; “The processor 114 processes the received ultrasound echo data to obtain volumetric image data of the fetal face. As an example, the ultrasound probe 110 transmits/receives ultrasound in a series of scanning planes, and is integrated by the processor 114 according to its three-dimensional spatial relationship, so as to realize the scanning of the fetal face in space and the reconstruction of the image.”).
Regarding Claim 11, Lin teaches: A method for generating a visualization of a fetus (Figs. 1-2, “The fetal facial volume image restoration method and ultrasonic imaging system provided by the present application can be applied to the human body…”), the method comprising:
obtaining ultrasound data representing a fetus in a uterus of its mother (“The ultrasound probe 110 receives the ultrasound echoes returned from the region where the face of the fetus is located through the receiving circuit, so as to obtain ultrasound echo data.”);
establishing, based on the ultrasound data, a data model of the fetus (“The first step is to establish a database. This step is to build a database required for the training of the generative model of the deep learning algorithm, so that the generative model can learn the feature space distribution of a large number of fetal facial data. Wherein, the database includes the data that the fetal face can be completely displayed and the data that the fetal face is missing and/or the data that the fetal face is blocked... the database includes several paired or unpaired 3D ultrasound data of the fetal face, wherein the paired 3D ultrasound data means that the low-quality data and the high-quality data are from the same fetus, and the acquisition conditions are similar; The unpaired data refers to a pair of low-quality data and high-quality data in the database that are not from the same fetus. It should be noted that whether the data is paired only affects which generation model is finally used to achieve enhancement, and the two are not mutually exclusive methods, that is, they can exist alone or at the same time…”); and
generating, based on the data model, a visualization of the fetus using by executing a generative artificial intelligence model that has been trained on fetal anatomical data to learn fetal anatomy, wherein the executing of the generative artificial intelligence model includes reconstructing, in the data model, a missing portion of the fetus in the data model, the missing portion being a portion of the fetus that is absent from not indicated by the ultrasound data due to at least one of: occlusion by another structure, and wherein reconstructing includes generating, by the generative artificial intelligence model, anatomically consistent data for the missing portion based on the learned fetal anatomy, to produce a complete visualization of the fetus (“In this step, the restoration processing of the volumetric image data of the fetal face may be performed according to the specific conditions of the volumetric image data. For example, in an embodiment, when part of the data of the fetal face is missing, the The repairing process can be to fill in the missing part of the data; in another embodiment, when the part of the face of the fetus is occluded, the occluded part can be filled, and the data that occludes the face of the fetus can also be removed., so as to expose the occluded area of the fetal face, so as to completely display the fetal face.”; “…the repair method based on the generative model of the deep learning algorithm (hereinafter referred to as the generative model) is to learn the feature space distribution of a large number of volumetric image data of the fetal face through the generative model, and the learned generative model can infer the low quality of the input. The missing part of the volume image data or the occlusion and other influencing factors are generated, and then the high-quality restored volume image data corresponding to the input low-quality volume image data is generated, so as to realize the restoration of the fetal face.”).
Regarding Claim 12, Lin teaches the claim limitations as noted above.
Claim 12 further recites limitations: wherein the method further comprises: obtaining image data of a portion of a belly of the mother; and determining a position of the fetus relative to the mother; and wherein the generating of the visualization of the fetus includes generating the visualization according to the determined position. These limitations are present in claim 2 and are therefore, rejected under the same rationale.
Regarding Claim 14, Lin teaches the claim limitations as noted above.
Claim 14 further recites limitations: wherein the method further comprises determining a position of a display device relative to the mother; and wherein the generating of the visualization of the fetus includes generating the visualization of the fetus in a perspective as seen from the position of the display device. These limitations are present in claim 4 and are therefore, rejected under the same rationale.
Regarding Claim 16, Lin teaches the claim limitations as noted above.
Lin further teaches: wherein the missing portion includes an organ of the fetus (“In this step, the restoration processing of the volumetric image data of the fetal face may be performed according to the specific conditions of the volumetric image data. For example, in an embodiment, when part of the data of the fetal face is missing, the The repairing process can be to fill in the missing part of the data; in another embodiment, when the part of the face of the fetus is occluded, the occluded part can be filled, and the data that occludes the face of the fetus can also be removed., so as to expose the occluded area of the fetal face, so as to completely display the fetal face.”; “…the repair method based on the generative model of the deep learning algorithm (hereinafter referred to as the generative model) is to learn the feature space distribution of a large number of volumetric image data of the fetal face through the generative model, and the learned generative model can infer the low quality of the input. The missing part of the volume image data or the occlusion and other influencing factors are generated, and then the high-quality restored volume image data corresponding to the input low-quality volume image data is generated, so as to realize the restoration of the fetal face.”).
Regarding Claim 18, Lin teaches the claim limitations as noted above.
Claim 18 further recites limitations: wherein the data model includes a three-dimensional representation of the fetus. These limitations are present in claim 8 and are therefore, rejected under the same rationale.
Regarding Claim 19, Lin teaches the claim limitations as noted above.
Claim 19 further recites limitations: wherein the ultrasound data represent the fetus in respective perspectives as seen from multiple directions. These limitations are present in claim 9 and are therefore, rejected under the same rationale.
Regarding Claim 20, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 20 further recites limitations: wherein the method further comprises generating an instruction for positioning an ultrasound sensor at the belly of the mother. These limitations are present in claim 10 and are therefore, rejected under the same rationale.
Claim Rejections - 35 USC § 103
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 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 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Dickie et. al. (U.S. 20220047241, February 17, 2022)(hereinafter, “Dickie”).
Regarding Claim 3, Lin teaches the claim limitations as noted above.
Lin does not teach: wherein the determining of the position of the fetus includes: identifying, in the ultrasound data, a structure of a body of the mother; and determining, in an image represented by the image data, a position that corresponds to the structure.
Dickie in the field of fetal ultrasound systems teaches: “…FIG. 2, the image areas of the ultrasound frame that are on a distal side 42 of the predicted cut line 40, relative to the fetus 20, may be removed prior to generating the 3D fetal representation.” [0038]; “…FIG. 2, the AI model may aim to predict the cut line 40 so that it is exterior to the imaged fetus 20, fetal head 22, and fetal abdomen 26, and so that it is exterior to (e.g., does not lie within) any imaged placenta, uterine wall 24, amniotic sac, umbilical cord, cervix and bladder.” [0039]; “This second cut line 40a may delineate the fetus 20 from the non-fetal anatomy on the distal side of the fetus 20 relative to the probe head.” [0093]; “When multiple cut lines 40, 40a are identified by the AI model 56 on each ultrasound frame, this may allow only the imaged fetus 20 between the cut lines 40, 40a to be retained when generating the 3D fetal representation. When this fetal ultrasound information is used as slices across various ultrasound image frames, the generated 3D fetal representation may be a 3D fetal volume 64a that shows the 3D contours of the fetus 20 both on the proximate side of the fetus 20 relative to the probe head, and also on the distal side of the fetus 20 relative to the probe head.” [0094]. See Figs. 2 and 8).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the determining of the position of the fetus of Lin to include identifying, in the ultrasound data, a structure of a body of the mother and determining, in an image represented by the image data, a position that corresponds to the structure as taught in Dickie to mask out the imaged non-fetal anatomy and remove for a more accurate 3D fetal representation (Dickie, [0037-0039]).
Regarding Claim 13, Lin teaches the claim limitations as noted above.
Claim 13 further recites limitations: wherein the determining of the position of the fetus includes: identifying, in the ultrasound data, a structure of a body of the mother; and determining, in an image represented by the image data, a position that corresponds to the structure. These limitations are present in claim 3 and are therefore, rejected under the same rationale.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lin as applied to claims 1 and 12, respectively above, and further in view of Kahn et. al. (U.S. 20040260178,December 23, 2004)(hereinafter, “Kahn”).
Regarding Claim 7, Lin teaches the claim limitations as noted above.
Lin does not teach: wherein the ultrasound data represent the fetus at two different dates; and wherein the circuitry is configured to interpolate the fetus for a date between the two different dates.
Kahn in the field of obstetric ultrasound systems teaches: “…FIG. 4, the graphical format of FIG. 3 can be modified to allow the representation of fetal growth trends during the gestation by including data from multiple examinations throughout the gestation. In the graphical display format shown in FIG. 4, each dimension of fetal growth data contains a set of points representing data acquired throughout pregnancy, with the right-most point in each bar representing the data collected at the noted gestational age (30 weeks, 4 days).” [0104]; “In the graphical display format of FIG. 5, biparietal diameter, head circumference, and abdominal circumference are all plotted on the same graph. This graph illustrates expected value (mean) and standard deviations for each of the dimensions of fetal growth data versus gestational age. The "dots" (squares, triangles, and circles) on the graph indicate measurements obtained during the examination at various gestational ages, and each of the dimensions of fetal growth data is normalized with respect to the mean.” [0105].
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the ultrasound data in Lin to represent the fetus at two different dates; and wherein the circuitry is configured to interpolate the fetus for a date between the two different dates as taught in Kahn “…to determine whether the growth of a fetus is consistent with a best estimate of the fetus' age and to determine whether the relative sizes of various anatomical components are in correct proportion.” (Kahn, [0001]).
Regarding Claim 17, Lin teaches the claim limitations as noted above.
Claim 17 further recites limitations: wherein the ultrasound data represent the fetus at two different dates; and wherein the method comprises interpolating the fetus for a date between the two different dates. These limitations are present in claim 7 and are therefore, rejected under the same rationale.
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Lin as applied to claims 12 and 1, respectively above, and further in view of Mahfouz (U.S. 20230165482, June 1, 2023)(hereinafter, “Mahfouz”).
Regarding Claim 21, Lin teaches the claim limitations as noted above.
Lin does not teach: wherein the method further comprises applying a visual indicator to the reconstructed missing portion in the visualization, the visual indicator distinguishing the reconstructed missing portion from portions of the fetus indicated by the ultrasound data.
Mahfouz in the field of reconstruction-based systems teaches: “the reconstructed 3D model is compared to the patient-specific 3D model to identify and record bone missing from the patient-specific 3D model that is present in the reconstructed 3D model. Localization may be carried out in a multitude of fashions including, without limitation, curvature comparison, surface area comparisons, and point cloud area comparisons. Ultimately, in exemplary form, the missing/abnormal bone is localized and the output comprises two lists: (a) a first list identifying vertices corresponding to bone of the reconstructed 3D model that is absent or deformed in the patient-specific 3D model; and, (b) a second list identifying vertices corresponding to bone of the reconstructed 3D model that is also present and normal in the patient-specific 3D model.” [0250]. See fig. 21
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to further modify Lin to apply a visual indicator to the reconstructed missing portion in the visualization, the visual indicator distinguishing the reconstructed missing portion from portions of the fetus indicated by the ultrasound data as taught in Mahfouz for organ, tissue, etc. reconstruction and measurement utilization during procedures (Mahfouz, [0002]).
Regarding Claim 22, Lin teaches the claim limitations as noted above.
Claim 22 further recites limitations: wherein the circuitry is further configured to apply a visual indicator to the reconstructed missing portion in the visualization, the visual indicator distinguishing the reconstructed missing portion from portions of the fetus indicated by the ultrasound data. These limitations are present in claim 21 and are therefore, rejected under the same rationale.
Response to Arguments
With regards to Applicant’s arguments regarding the amendments to claims 1 and 11 overcoming 35 U.S.C. 101 rejection, Examiner respectfully disagrees and maintains the 35 U.S.C. 101 rejections. As provided in the MPEP § 2106.05(f) “using a computer as a tool to perform the abstract idea” is not sufficient to integrate a judicial exception into a practical application as interpreted by the court(s). Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972) “held that simply implementing a mathematical principle on a physical machine, namely a computer, was not a patentable application of that principle and Intellectual Ventures LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016) established that mental processes encompass acts which, absent anything beyond generic computer components, may be “performed by a human, mentally or with pen and paper.”. Applicant states: “The claims are directed to a specific technological improvement: overcoming the physical limitations of ultrasound sensor hardware. Ultrasound imaging is inherently limited by acoustic occlusion (structures blocking8 the signal path), probe geometry (angular coverage limitations), and signal penetration depth (attenuation in dense tissue). These are hardware-rooted constraints, not information-processing abstractions. The claimed generative Al model addresses these hardware limitations directly, producing a complete visualization where the sensor hardware cannot. This is analogous to the eligible claims in Enfish (software improving computer memory indexing) and McRO (specific rules-based process improving animation technology). The improvement here is to the functioning of the medical imaging system itself, not merely to how information is displayed or analyzed on a generic computer.” (Remarks, pg. 2), yet the recited claim limitations are broader than Applicant’s interpretations and do not explicitly provide how the functionality of the computer is improved at a level of particularity well beyond applying a mental process. Claims 1 and 11 disclose usage of a generative artificial intelligence model but not sufficient information regarding how the generation is performed with respect to the recited claim limitations. The obtaining of ultrasound data limitation is broad and can be considered generic data gathering. The generating limitation amendments have narrowed the abstract idea but not sufficient for overcoming the 35 U.S.C. 101 rejection. Review of the 2024 and 2019 Revised Subject Matter Eligibility Guidance is recommended, Example 3, 39 and 47 in particular. As previously provided, Applicant’s recited claims simply uses generative artificial intelligence and provides claims that can be considered collections of intangible data that mathematical operations are performed on to obtain a result. Applicant’s interpretations of the amended claim limitations are respectfully narrower than the recited claims.
With regards to Applicant’s arguments regarding amended claims prior art rejection, the arguments are moot in view of the new grounds of rejections.
Conclusion
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/AMAL ALY FARAG/Primary Examiner, Art Unit 3798
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