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 .
Response to Amendment
In response to the amendment filed 1/28/2026; claims 1-3,5-20 and 22-33 are pending; claims 4 and 21 have been cancelled.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8,10,12-15,18-25,27 and 29-32 are rejected under 35 U.S.C. 103 as being unpatentable over Desmet et al. (US 2023/0037923 A1) in view of Wang et al. (US 2024/0412377 A1).
Re claims 1, 18:
Desmet teaches 1. An ultrasound image simulator producing simulated ultrasound images for a medical application, the simulator (Desmet, Abstract; [0010]) comprising:
one or more computer processors (Desmet, [0034], “The processor”);
a generative neural network running on said one or more computer processors and configured to … based on a received 3D ultrasound probe position and orientation (Desmet, Abstract, “Automated machine learning models”; [0009]; [0052], “tracking system 14 can track the position and/or orientation of the probe”; Abstract, “Automated machine learning models, trained on a dataset of labelled training images associated with different imaging device positions”; [0033] – [0034]),
said generative neural network being trained on a multiplicity of training samples from body parts of a plurality of subjects … its associated probe position and orientation (Desmet, [0033] – [0034]), and a multiplicity of parameters (Desmet, [0033], “an imaging probe being positioned at different positions relative to a training manikin, a human or an animal”; [0063]; [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types … Other possible tracking systems include MEMS, gyros, accelerometers, magnetometers, and image-based tracking system”; [0060], “where the artefacts prevent measurement and/or assessment of the organ(s), or portions thereof, in the anatomical region”; [0062], “set for a first organ”),
said generative neural network to receive at least one of said multiplicity of parameters and to generate at least one said simulated ultrasound image or clip for said at least one parameter of said multiplicity of parameters (Desmet, [0033], “an imaging probe being positioned at different positions relative to a training manikin, a human or an animal”; [0063]; [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types … Other possible tracking systems include MEMS, gyros, accelerometers, magnetometers, and image-based tracking system”; [0060], “where the artefacts prevent measurement and/or assessment of the organ(s), or portions thereof, in the anatomical region”; [0062], “set for a first organ”; parameters – a probe /tracking device type, manikin, human, animal, position, orientation, acceleration of the probe handled by the trainee or set of organs), and
a unit to provide said at least one 2D simulated ultrasound image or clip to said medical application (Desmet, [0038], “the training platform comprises one or more displays for displaying the generated image as the user moves the imaging probe”; [0052]; figs. 1 - 2).
Desmet 18. A method for generating simulated ultrasound images for a medical application (Desmet, Abstract; [0010]), the method being executed by one or more computer processors (Desmet, [0034], “The processor”) and comprising:
generating at least one simulated 2D ultrasound image or clip of a body part of a subject … based on a received 3D ultrasound probe position and orientation (Desmet, Abstract, “Automated machine learning models”; [0009]; [0052], “tracking system 14 can track the position and/or orientation of the probe”; Abstract, “Automated machine learning models, trained on a dataset of labelled training images associated with different imaging device positions”; [0033] – [0034]),
said generative neural network being trained on a multiplicity of training samples from body parts of a plurality of subjects … its associated probe position and orientation (Desmet, [0033] – [0034]), and a multiplicity of parameters (Desmet, [0033], “an imaging probe being positioned at different positions relative to a training manikin, a human or an animal”; [0063]; [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types … Other possible tracking systems include MEMS, gyros, accelerometers, magnetometers, and image-based tracking system”; [0060], “where the artefacts prevent measurement and/or assessment of the organ(s), or portions thereof, in the anatomical region”; [0062], “set for a first organ”),
said generative neural network receiving at least one of said multiplicity of parameters and generating at least one said simulated ultrasound image or clip for said at least one parameter of said multiplicity of parameters (Desmet, [0033], “an imaging probe being positioned at different positions relative to a training manikin, a human or an animal”; [0063]; [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types … Other possible tracking systems include MEMS, gyros, accelerometers, magnetometers, and image-based tracking system”; [0060], “where the artefacts prevent measurement and/or assessment of the organ(s), or portions thereof, in the anatomical region”; [0062], “set for a first organ”; parameters – a probe /tracking device type, manikin, human, animal, position, orientation, acceleration of the probe handled by the trainee or set of organs), and
providing said at least one 2D simulated ultrasound image or clip to said medical application (Desmet, [0038], “the training platform comprises one or more displays for displaying the generated image as the user moves the imaging probe”; [0052]; figs. 1 - 2).
Desmet does not explicitly disclose
a generative neural network running on said one or more computer processors and configured to generate at least one simulated 2D ultrasound image or clip of a body part of a subject based on a received 3D ultrasound probe position and orientation,
said generative neural network being trained on a multiplicity of training samples from body parts of a plurality of subjects, where each training sample comprises a real 2D ultrasound image or clip of said body part of one of said plurality of subjects, its associated probe position and orientation.
Wang et al. (US 2024/0412377 A1) teaches an invention generally relates to image processing. More particularly, the present invention relates to reconstruction of high-resolution images using a neural radiance field encoded into a machine learning model. Wang teaches
a generative neural network running on said one or more computer processors and configured to generate at least one simulated 2D ultrasound image or clip of a body part of a subject based on a received 3D ultrasound probe position and orientation (Wang, [0024], “The machine learning model can render high-resolution images of the objects in orientations and/or perspectives that are different from orientations and/or perspectives of the plurality of low-resolution images”; [0026], “train a machine learning model to volumetrically render high-resolution images of the objects … in computer vision or computer graphics, voxels are elements of volume (e.g., units of volume) that constitute a three-dimensional space .. various high-resolution image of the objects in new orientations or perspectives can be volumetrically rendered”; [0031], “the machine learning model 250 is to estimate a density volume v of the objects depicted in the plurality of images captured from a plurality of angles, orientations, and/or perspectives”),
said generative neural network being trained on a multiplicity of training samples from body parts of a plurality of subjects, where each training sample comprises a real 2D ultrasound image or clip of said body part of one of said plurality of subjects, its associated probe position and orientation (Wang, fig. 4, “Obtain a plurality of images of an object from a plurality of orientations at a plurality of times”; [0020], “a training process to optimize a neural network module”; [0002], “The machine learning model can be trained using the plurality of images”; [0029], “the neural radiance module 254 can be based on a multi-layer perceptron (MLP) to handle the plurality of images acquired in various orientations or perspectives”; [0031], “the plurality of images can be magnetic resonance images (MRIs).”; fig. 26; [0026], “FIG. 2A illustrates a scenario 200 in which a plurality of images 202a-202c depicting objects (e.g., biological samples, cell samples, etc.) is obtained to train a machine learning model”; [0030], “the at least one data store 260 can store training data to train the machine learning model 250 for reconstruction of high-resolution images. The training data can include, for example, images, videos, and/or looping videos depicting objects”; [0040], “the processor 402 can train the machine learning model using the plurality of images”).
Therefore, in view of Wang, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method and simulator described in Desmet, by providing the machine learning model as taught by Wang, in order to provide volumetrically rendering high-resolution images of objects in orientations and/or perspectives selected by the user (Wang, [0024]).
Re claims 2, 19:
2. The simulator of claim 1 wherein said training samples are generated by an ultrasound guidance system. 19. The method of claim 18 and comprising generating training samples by an ultrasound guidance system (Desmet, Abstract, “trained on a dataset of labelled training images associated with different imaging device positions”; [0051], “The platform 100 can be used for training or for evaluating users in capturing images of an internal anatomical region for the analysis of organs. Users can be trainees, learning how to use a simulated or real imaging system, or medical staff, such as clinicians or veterinarians, that are using an imaging system in the normal course of their work on patients, and want feedback or guidance while manipulating an imaging probe”; Wang, fig. 4, “Obtain a plurality of images of an object from a plurality of orientations at a plurality of times”; fig. 26; [0026], “FIG. 2A illustrates a scenario 200 in which a plurality of images 202a-202c depicting objects (e.g., biological samples, cell samples, etc.) is obtained to train a machine learning model”; [0040], “the processor 402 can train the machine learning model using the plurality of images”).
Re claims 3, 20:
3. The simulator of claim 1 wherein said associated probe position and orientation is generated by a probe motion sensing system. 20. The method of claim 18 and comprising generating said associated probe position and orientation by a probe motion sensing system (Desmet, [0052]).
Re claim 4 – 5, 21 – 22:
4. The simulator of claim 1 wherein said training samples are associated with a multiplicity of parameters. 5. The simulator of claim 4 wherein said parameters comprise at least one of: information about said one of said plurality of subjects, probe parameters, an operator parameter and noise parameter. 21. The method of claim 18 wherein said training samples are associated with a multiplicity of parameters. 22. The method of claim 21 wherein said parameters comprise at least one of: information about said one of said plurality of subjects, probe parameters, at least one operator parameter and at least one noise parameter (Desmet, [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types”; [0060], “where the artefacts prevent measurement and/or assessment of the organ(s), or portions thereof, in the anatomical region”; [0062], “set for a first organ”).
Re claims 6, 23:
6. The simulator of claim 5 wherein said at least one simulated 2D ultrasound image or clip has an artificial or non-human element therein. 23. The method of claim 22 wherein said at least one simulated 2D ultrasound image or clip has an artificial or non-human element therein (Desmet, [0053], “The internal anatomical region can be part of a living human or animal”; [0042], “the simulated anatomical image”; [0007], “capturing an image of an anatomical region, where the image is for organ analysis”; [0060], “recording a video sequence of the images”; Wang, fig. 4, “Obtain a plurality of images of an object from a plurality of orientations at a plurality of times”; fig. 26; [0026], “FIG. 2A illustrates a scenario 200 in which a plurality of images 202a-202c depicting objects (e.g., biological samples, cell samples, etc.) is obtained to train a machine learning model”; [0040], “the processor 402 can train the machine learning model using the plurality of images”).
Re claims 12, 29:
12. A simulator producing synthetic data for training an ultrasound based machine learning unit, the simulator comprising: the simulator according to claim 4 to receive at least one of said parameters and to generate at least one said simulated ultrasound image or clip for said at least one parameter of said multiplicity of parameters. 29. A method for producing synthetic data for training an ultrasound based machine learning unit, the method comprising: receiving at least one of said multiplicity of parameters; and generating at least one said simulated ultrasound image or clip for said at least one parameter of said multiplicity of parameters with said generative neural network according to claim 21 (Desmet, [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types”; [0060], “where the artefacts prevent measurement and/or assessment of the organ(s), or portions thereof, in the anatomical region”; [0062], “set for a first organ”).
Re claims 13, 30:
13. The simulator according to claim 12 and wherein said ultrasound based machine learning unit is one of: a classifier, a segmenter and a regressor. 30. The method according to claim 29 and wherein said ultrasound based machine learning unit is one of: a classifier, a segmenter and a regressor (Desmet, [0033], “classifying the training images as valid or invalid images by assigning a label thereto”; [0034], “machine learning models trained on a dataset of labelled training images associated with different imaging device positions”; [0010], “the image is an ultrasound image representing a cross sectional view of (an) organ(s) within the anatomical region”).
Re claims 14, 31:
14. The simulator according to claim 12 and wherein said ultrasound based machine learning unit comprises
a navigation neural network under training to generate probe movement instructions for an ultrasound probe on a virtual subject (Desmet, [0038], “the training platform comprises one or more displays for displaying the generated image as the user moves the imaging probe, and for displaying a probe score and an image score, the probe score being indicative of a positional similarity between the position of the probe and (a) position(s) previously determined”; [0051], “The platform 100 is adapted and configurable to evaluate any type of images that can be captured or generated by an imaging probe which is moved or positioned relative to a body”; [0053]) and
a probe position updater to convert said movement instructions to position and orientation of said ultrasound probe (Desmet, [0052]).
31. The method according to claim 29 and wherein said ultrasound based machine learning unit comprises a navigation neural network under training which generates probe movement instructions for an ultrasound probe on a virtual subject (Desmet, [0038], “the training platform comprises one or more displays for displaying the generated image as the user moves the imaging probe, and for displaying a probe score and an image score, the probe score being indicative of a positional similarity between the position of the probe and (a) position(s) previously determined”; [0051], “The platform 100 is adapted and configurable to evaluate any type of images that can be captured or generated by an imaging probe which is moved or positioned relative to a body”; [0053]) and the method comprises converting said movement instructions to position and orientation of said ultrasound probe (Desmet, [0052]).
Re claims 15, 32:
15. A simulator producing synthetic data for training a sonographer to perform ultrasound scans of an artificial body with an ultrasound probe, the simulator comprising:
the simulator according to claim 4 to receive at least one of said multiplicity of parameters and to generate at least one said 2D simulated ultrasound image or clip for said at least one parameter of said multiplicity of parameters (Desmet, [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types”); and
sonographer training software to receive said at least 2D one simulated ultrasound image or clip and to provide instructions to said sonographer (Desmet, [0051], “The platform 100 can be used for training or for evaluating users in capturing images of an internal anatomical region for the analysis of organs. Users can be trainees, learning how to use a simulated or real imaging system, or medical staff, such as clinicians or veterinarians, that are using an imaging system in the normal course of their work on patients, and want feedback or guidance while manipulating an imaging probe”; [0054], “The graphical user interface displays the images generated (real or simulated) and provide feedback to the user regarding its performance”).
32. A method for producing synthetic data for training a sonographer to perform ultrasound scans of an artificial body with an ultrasound probe, the method comprising: receiving at least one of said multiplicity of parameters; generating at least one said 2D simulated ultrasound image or clip for said at least one parameter of said multiplicity of parameters with said generative neural network according to claim 21 (Desmet, [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types”); and a sonographer training software providing instructions to said sonographer (Desmet, [0051], “The platform 100 can be used for training or for evaluating users in capturing images of an internal anatomical region for the analysis of organs. Users can be trainees, learning how to use a simulated or real imaging system, or medical staff, such as clinicians or veterinarians, that are using an imaging system in the normal course of their work on patients, and want feedback or guidance while manipulating an imaging probe”; [0054], “The graphical user interface displays the images generated (real or simulated) and provide feedback to the user regarding its performance”).
Re claims 7 – 8:
7. The simulator of claim 1 and wherein said generative neural network also comprises a latent variable provider (Desmet, [0057]; [0061]). 8. The simulator of claim 7 and wherein said generative neural network is trained with a latent variable neural network receiving training samples (Desmet, [0061], “Normalizing and standardizing the image raw data allows for all images to share a common scale, without distorting the variations of the different attributes. The normalizing and standardizing of the training image data (step 416) can be performed before or after other image-processing operations but prior to being fed to the automated machine learning models.”; [0057], “the processing device 20 is configured to determine whether the imaging probe 10 is correctly positioned for generating one of the predetermined views by comparing the position and/or the orientation of the imaging probe to previously determined valid probe positions for said one predetermined view”; probe’s position and orientation is a variable).
Re claims 24 – 25:
24. The method of claim 18 and also comprising providing latent variables to said generative neural network (Desmet, [0057]; [0061]). 25. The method of claim 24 and comprising training said generative neural network with a latent variable neural network receiving said training samples (Desmet, [0061], “Normalizing and standardizing the image raw data allows for all images to share a common scale, without distorting the variations of the different attributes. The normalizing and standardizing of the training image data (step 416) can be performed before or after other image-processing operations but prior to being fed to the automated machine learning models.”; [0057], “the processing device 20 is configured to determine whether the imaging probe 10 is correctly positioned for generating one of the predetermined views by comparing the position and/or the orientation of the imaging probe to previously determined valid probe positions for said one predetermined view”; probe’s position and orientation is a variable).
Re claims 10, 27:
10. The simulator of claim 1 wherein said generative neural network comprises:
a volumetric neural network to receive said ultrasound probe position and orientation and generating volume data of a body part for said ultrasound probe position and orientation (Desmet, [0057], “the processing device 20, and is associated to a given target cut plane or view, such as X, Y, Z coordinates and/or an orientation or attitude angles (pitch, row, yaw), relative to a reference frame or point. The tracking system 14 detect signals emitted or received by the imaging probe 10 or manikin 12 to determine the position of the probe 10”; fig. 2);
a slicer to extract a slice of said volume associated with the position and orientation of said ultrasound probe (Desmet, fig. 2; [0053], “the images can be simulated, by generating a given cut plane of a virtual 3D model of body or of a region of a body, as a function of the position of the imaging probe 10”; [0010], “the image is an ultrasound image representing a cross sectional view of (an) organ(s) within the anatomical region”); and
a rendering neural network to render said slice as a simulated ultrasound image of said body part (Desmet, [0012], “The method comprises rendering the simulated anatomical image associated with the determined position of the imaging probe, where the simulated anatomical image is extracted from a virtual 3D model of a human or animal”; [0053], “comprises an image generating (or rendering) module 40, typically part of the software application 200 and running on device 20, for generating images of an internal anatomical region”).
27. The method of claim 18 wherein said generating comprises: generating volume data of a body part for said ultrasound probe position and orientation with a volumetric neural network in response to said ultrasound probe position and orientation (Desmet, [0057], “the processing device 20, and is associated to a given target cut plane or view, such as X, Y, Z coordinates and/or an orientation or attitude angles (pitch, row, yaw), relative to a reference frame or point. The tracking system 14 detect signals emitted or received by the imaging probe 10 or manikin 12 to determine the position of the probe 10”; fig. 2); extracting a slice of said volume associated with the position and orientation of said ultrasound probe (Desmet, fig. 2; [0053], “the images can be simulated, by generating a given cut plane of a virtual 3D model of body or of a region of a body, as a function of the position of the imaging probe 10”; [0010], “the image is an ultrasound image representing a cross sectional view of (an) organ(s) within the anatomical region”); and rendering said slice as a simulated ultrasound image of said body part with a rendering neural network (Desmet, [0012], “The method comprises rendering the simulated anatomical image associated with the determined position of the imaging probe, where the simulated anatomical image is extracted from a virtual 3D model of a human or animal”; [0053], “comprises an image generating (or rendering) module 40, typically part of the software application 200 and running on device 20, for generating images of an internal anatomical region”).
Claims 9 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Desmet and Wang as applied to claims 1 and 18 above, and further in view of Saharia et al. (US 2023/0067841 A1).
Re claims 9, 26:
Desmet does not explicitly disclose a diffusion model neural network. Saharia teaches a method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image (Saharia, Abstract). Saharia teaches 9. The simulator of claim 1 wherein said generative neural network comprises a diffusion model neural network. 26. The method of claim 18 wherein said generative neural network comprises a diffusion model neural network (Saharia, [0042]; from [0045]). Therefore, in view of Saharia, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/system described in Desmet, by providing the diffusion model as taught by Saharia, since a primary advantage of diffusion models is the ease of training with simple and efficient loss functions and their ability to generate highly realistic images (Saharia, [0042]).
Claims 11 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Desmet and Wang as applied to claims 10 and 27 above, and further in view of van Rensburg et al. (US 10217029 B1).
Re claims 11, 28:
Desmet does not explicitly disclose an empirical risk minimization training procedure and utilize the same loss function. van Rensburg teaches an invention related to the field of image analysis and transformation (van Rensburg, Abstract). van Rensburg teaches 11. The simulator of claim 10 and wherein both said volumetric neural network and said rendering neural network are trained with an empirical risk minimization training procedure and utilize the same loss function. 28. The method of claim 27 and also comprising training both said volumetric neural network and said rendering neural network with an empirical risk minimization training procedure that utilizes the same loss function (van Rensburg, col. 21, lines 15 - 30). Therefore, in view of van Rensburg, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/system described in Desmet, by providing empirical risk minimization as taught by van Rensburg, since van Rensburg suggests adjust the classifier by applying empirical risk minimization or structural risk minimization (in order to prevent overfitting by incorporating a regularization penalty into the optimization) to the calculated loss function. The optimization may be performed using one or more
optimization algorithms or by applying an iterative optimization technique (Col.21, lines 15 - 30).
Allowable Subject Matter
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Claims 16, 17 and 33 includes the phrase “and/or” is ambiguous; the phrase believed to be “or”. Claims 16 – 17 and 33 would be allowable if rewritten or amended to overcome the objection above.
Response to Arguments
Applicant's arguments filed 1/28/2026 have been fully considered but they are not persuasive.
Applicant argues:
The combination of Desmet and Wang fails to teach or suggest "a generative neural network trained on "a real 2D ultrasound image . . . its associated probe position and orientation, and a multiplicity of parameters", the neural network "to generate at least one said simulated ultrasound image or clip for said at least one parameter of said multiplicity of parameters"; and "a unit to provide said at least one 2D simulated ultrasound image or clip to said medical application", as recited in claim 1 (with similar language in claim 18).
Claims 1 and 18 now require that the generative neural network is trained on training samples that include not only the image and probe position but also a "multiplicity of parameters." Crucially, claims 1 and 18 further require that the network generates "at least one said simulated ultrasound image or clip for said at least one parameter."
As supported by the specification in paragraph [0071], these parameters can include patient-specific data such as BMI, age, or gender. This limitation defines a system capable of dynamic, conditional generation-it can simulate how a specific body part would appear under different conditions or in different patients (see paragraphs [0089], [0090]) …
Neither Desmet nor Wang teaches or suggests this capability. Desmet discloses a system for evaluating a user's probe handling skills against a static anatomical model. Its goal is to determine if a user can find a predefined view. While Desmet mentions parameters, these relate to the probe's position or the alterations of the images ([0060])-they are not used as conditional inputs to dynamically alter the underlying anatomy being simulated.
According to MPEP 2111 [R-5], during patent examination, the pending claims must be “given their broadest reasonable interpretation consistent with the specification.” The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 75 USPQ2d 1321 (Fed. Cir. 2005) expressly recognized that the USPTO employs the “broadest reasonable interpretation” standard. According to claim 5, “said parameters comprise at least one of: information about said one of said plurality of subjects, probe parameters, an operator parameter and a noise parameter”. The claimed “a multiplicity of parameters” can be any of the following: information about subject, probe parameters, operator parameter or noise parameter. Desmet teaches the claimed multiplicity of parameters (Desmet, [0033], “an imaging probe being positioned at different positions relative to a training manikin, a human or an animal”; [0063]; [0008], “the method further comprises determining the position of the imaging probe; and generating the image based on the position of the imaging probe”; [0052], “tracking system 14 can track the position and/or orientation of the probe, generally relative to a reference point, as is common in existing ultrasound training platforms. The tracking device 14 can be of different types … Other possible tracking systems include MEMS, gyros, accelerometers, magnetometers, and image-based tracking system”; [0060], “where the artefacts prevent measurement and/or assessment of the organ(s), or portions thereof, in the anatomical region”; [0062], “set for a first organ”; parameters – a probe /tracking device type, manikin, human, animal, position, orientation, acceleration of the probe handled by the trainee or set of organs).
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 JACK YIP whose telephone number is (571)270-5048. The examiner can normally be reached Monday thru Friday; 9:00 AM - 5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, XUAN THAI can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JACK YIP/Primary Examiner, Art Unit 3715
Prosecution Insights