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
This action is in response to Applicant’s remarks, filed on 2/16/2026. The amendments to claim(s) 1-14 have been entered. Claim(s) 5-6 and 8-14 were withdrawn by Applicant. No new claims have been entered. Accordingly, claim(s) 1-4 and 7 remain pending for examination.
Response to Arguments
Applicant’s arguments, see p. 7-10, with respect to the rejection of claim(s) 1-4 and 7 have been fully considered.
After review of the amendments and Applicant’s remarks regarding the objections to claim(s) 1 and 3, Examiner respectfully agrees with Applicant and the objections to the claims are withdrawn.
After review of the amendment and Applicant’s remarks regarding the rejections under 35 U.S.C. §112(b) to claim(s) 1-4 and 7, Examiner respectfully disagrees with the Applicant; the 35 USC §112(b) rejections have been revised in view the amended claim language and are maintained.
Applicant’s arguments with respect to claim(s) 1-4 and 7 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. New grounds of rejection are made in view of the following: new amendments provided by Applicant and attached remarks; updated search and review of pertinent, eligible prior art; and/or different interpretation of the previously applied references.
Examiner respectfully notes that Applicant’s arguments only address independent claim(s) 1, and no remarks regarding the subject matter of the dependent claim(s) have been presented. Accordingly, the rejections to dependent claims 2-4 and 7 are modified to address Applicant’s amendments and the new rejection to independent claim(s) 1 and are sustained. The rejections of claim(s) 1-4 and 7 under 35 U.S.C. §102 are maintained.
Claim Rejections - 35 USC § 112
35 USC § 112(b)
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.
Claim(s) 1-4 and 7 is/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. Claims 2-4 and 7 are also rejected at least by virtue of dependency upon a rejected base claim.
Claim 1 recites the limitation “a different image style that conforms to the visual properties of ultrasound images of the type produced by a cart- based ultrasound system” which renders the claim indefinite. As discussed in the prior office action, the recitation is unclear and renders the scope of the claim limitations indefinite. There is no definition or description within the claim of the specific ‘type’ of ultrasound images “produced by a cart- based ultrasound system” indicating what type of ‘ultrasound images’ the ‘neural filter’ generates. As known in the art there are many different qualities which may be associated with ultrasound imaging (e.g., resolution, noise, number of scanlines, etc.); additionally, the ‘ultrasound images’ may be interpreted to be produced by distinct ultrasound imaging modalities (e.g., a ‘stream’ of B-mode images/M-mode imaging, Doppler imaging, etc.). Upon review of the Applicant’s written description, the instant disclosure provides:
“Additionally, cart-based devices typically have larger image display screens as well as different image display resolutions. Resolution as it is used in this embodiment includes, but is not limited to, a visual characteristic of an image which defines the amount of detail which an image holds such that an image which holds more detail is categorized as having a higher level of resolution. It will be clear to those skilled in the art that in other embodiments this term can encompass other elements based on the application at hand. Further, some cart-based devices have specialized graphics processors for more sophisticated image processing. As such, the clinician can experience a different visual aesthetic for the images produced by a cart-based ultrasound device as compared to a handheld ultrasound device. Moreover, the images generated by cart-based systems may have sharper features and a cleaner, less cluttered appearance.”
[0006] (emphasis added)
“In the embodiment depicted in FIG. 1, the application 109 may include a neural filter module that operates to transform image data generated by the probe 102 so that the distribution of the images presented to the clinician matches, or substantially matches, the distribution of images generated by a cart-based ultrasound imaging probe. In this way, the application 109 applies the neural filter to generate a transformed image, or stream of images, wherein the transformed images have the look and feel of the type of images generated by cart-based ultrasound imaging probes. Thus, a clinician more familiar with cart-based ultrasound imaging systems can activate the neural filter and thereby be presented within window 110 with images that are more familiar in look and feel and that provide sharper and cleaner images. Moreover, the sharper features and a cleaner, less cluttered image generated with the active neural filter may help clinicians find the anatomical structure of interest more quickly and allow the clinician to make a more accurate and faster diagnosis. In this embodiment a neural filter can be understood to encompass, but is not limited to, an AI system trained to take image data, or a stream of image data, and process it in such a way that it can output image data or a stream of image data which when processed by a standard image processor, produce visual images which are in a different visual style as defined by common properties such as resolution, sharpness, and noise, without losing the essential components of the original image. For example, if you trained an AI system to act as a neural filter which translates photographs into images which resemble Rembrandt oil paintings, this system would be able to take a photograph of a sunset and generate as output an image which looks like a Rembrandt oil painting of a sunset, the original content of a sunset is still represented in the output image, but the style of the image now resembles Rembrandt's style of painting in that shapes of individual elements may be altered or the color palate may be different. Additionally, such filtered images may increase the ease and efficiency of the clinician locating the probe 102 on the habitus of the patient. As used herein the term cart-based ultrasound imaging device encompasses, but is not limited to, ultrasound systems of the type that are typically large enough to be carried on a cart and powered by wall power, and having an image or video display that is mounted to the cart to allow the clinician to move the ultrasound probe while viewing the display. However, it will be understood by those of skill in the art that the systems and methods described herein are not limited to any specific set of cart-based imaging devices or systems. Moreover, it will be understood by those of skill in the art for the systems and methods described herein that the term cart-based ultrasound imaging device will encompass any ultrasound imaging device that generated images that were used, or collected for use, as a part of a training set of images employed to train the neural filter applied by application 109.”
[0036] (emphasis added)
Aside from merely suggesting that the ‘sharpness’ of the image changes and the ‘clutter’ in the image is reduced, the specification fails to provide an explicit explanation of the ‘visual properties’ and/or ‘image style’ of “the type produced by a cart- based ultrasound system”; indeed, the specification does not limit the system to any specific set of ‘cart-based imaging devices or systems’, as claimed by Applicant. Similarly, the instant figures 6 and 7 in the drawings provided by the Applicant fail to describe the precise image processing changes being performed by the ‘neural filter’. The instant claim language does not preclude the ‘system of generating ultrasound images’ from being itself a ‘cart-based ultrasound system’, i.e., the ‘handheld ultrasound imaging device’ may be part of one ‘cart-based ultrasound system’ which applies the “neural filter” to produce the ultrasound images of another distinct ‘cart-based ultrasound system’.
Accordingly, it is unclear what sort of ‘processing’ is being performed by the ‘neural filter’. It is suggested to amend the claims to particularly point out the subject matter being claimed in view of the disclosure of the Applicant’s instant specification. For the purposes of examination, any type of image processing performed by an artificial intelligence algorithm which alters the resolution, sharpness and noise between input and output ultrasound images may read on the ‘neural filter’, and the ‘image style’ of ‘cart-based ultrasound systems’ are any type of ultrasound image.
Claim 1 further recites the limitation “wherein the image style includes visual properties of sharpness, resolution and noise of the ultrasound images, and”. There is insufficient antecedent basis for this limitation in the claim, rendering the claim indefinite. It is not clear which ‘image style’ (e.g., ‘initial image style’, ‘different image style’, etc.) is being referred to (and whose visual properties are represented). For the purposes of examination the broadest reasonable interpretation is either the ‘initial’ or ‘different image style’.
Claim 2 recites the limitations “wherein the UI switch comprises a preset configuration for configuring the handheld ultrasound imaging device to generate image data for an associated image study requirement associated with the preset and for processing generated image data with the neural filter to conform the visual properties of the generated image data to have the different image style of ultrasound images of the type produced by the cart-based ultrasound system for the respective preset image study requirements” which renders the claim indefinite. As discussed in the prior office action, there is insufficient antecedent basis for use of the term “the preset”; in an interpretation it may refer to the ‘preset configuration’, and in another interpretation ‘the preset’ may refer to a new, distinct ‘preset’. It is suggested to amend the claim to refer to ‘the preset configuration’ if that is the Applicant’s intended interpretation. The use of the phrase “to generate image data for an associated image study requirement associated with the preset” is unclear because of the ‘associated’ and ‘associated with’ language. It appears that the ‘preset configuration’ is already ‘associated’ with ‘an image study requirement’, so it is not clear what the ‘image study requirement’ is further ‘associated with’. Finally, the use of “have the different image style of ultrasound images of the type produced by the cart-based ultrasound system for the respective preset image study requirements” is unclear. The ‘cart-based ultrasound systems’ language is unclear mutatis mutandis in view of the discussion regarding claim 1 above. Furthermore, the ‘respective preset image study requirements’ is unclear and there is insufficient antecedent basis for the limitation in the claim. There is no prior recitation of a plurality of ‘preset image study requirements’ that clearly points out what is being claimed, and it is not clear if the recitation is interpreted as the ‘preset configuration’, the ‘image study requirement’, or to a new and distinct ‘respective preset image study requirement’. For the purposes of examination, the broadest reasonable interpretation – including those discussed above – is applied to the limitations.
Claim 3 recites the limitations “wherein adjusting the different image style to conform to the visual properties of the type produced by the cart-based ultrasound system includes adjusting the image data wherein the measures of the visual properties of sharpness, resolution, and noise of the resulting output image conform to the measures of the visual properties of sharpness, resolution and noise of the different image style of ultrasound images produced by cart-based ultrasound imaging system” which renders the claim indefinite. There is no prior recitation of any ‘adjustment’ of the ‘different image style’ that clearly points out what the ‘adjusting’ step is referring to, and the instant claim language appears to conflate with the prior recitation to “generate a new stream of image data” as provided in claim 1. Similarly, there is insufficient antecedent basis for “the resulting output image”, because it is not clear if the ‘output image’ is a new image or is referring to the “new stream of image data […] having a different image style” recited in independent claim 1. The use of the phrase “ultrasound images produced by cart-based ultrasound imaging system” also lacks sufficient antecedent basis and should be amended to clarify which ‘cart-based ultrasound system’ is being referred to. Furthermore, it appears that the functions performed by the instant claim 3 are already inherently being performed in the independent claim 1. Accordingly, it is not clear if the ‘generation’ and ‘adjustment’ are the same function or different functions; it is not clear what structure of the system (e.g., the image processor, ‘neural filter’, a new structure, etc.) performs the ‘adjustment’; and it is not clear what ‘image data’ (i.e., the first ‘stream’ of image data, the ‘new stream’ of image data, etc.) is being referenced. For the purposes of examination, the broadest reasonable interpretation of the claim language – including those discussed above – is applied to the limitations.
Claim 4 recites the limitations “the neural filter includes a mapping function for translating image data produced by a handheld ultrasound imaging device into image data of the type produced by the cart-based ultrasound imaging system by employing a training module to define for the neural filter visual properties of image data produced by handheld ultrasound imaging device and the cart-based ultrasound imaging system”, which renders the claim indefinite. There is insufficient antecedent basis for the limitations “image data produced by a handheld ultrasound imaging device”, “image data of the type produced by the cart-based ultrasound imaging system” and “visual properties of image data”, because it is not clear what any of the instances of ‘image data’ are referring to (e.g., whether the first ‘stream of image data’, the ‘new stream of image data’, etc.). Furthermore, the use of “a handheld ultrasound imaging device” is unclear because it is uncertain if it is referring to the “handheld ultrasound imaging device” introduced in claim 1 or to a new and distinct ‘device’. Similarly, the last clause of the claim limitation “image data produced by handheld ultrasound imaging device and the cart-based ultrasound imaging system” has insufficient antecedent basis for the ‘handheld ultrasound imaging device’, and it is unclear if the ‘image data’ may be produced by both the ‘handheld’ and ‘cart-based’ ultrasound or is referring to individual data. For the purposes of examination, the broadest reasonable interpretation of the claim language – including those discussed above – is applied to the limitations.
Claim 7 recites the limitations “the training module employs a cycle-consistent adversarial network to evaluate unpaired images translated from a first image distribution having the initial image style into a second image distribution having the different image style to determine the accuracy of the translation, and evaluating images translated from a first image distribution into a second image distribution and then back into the first image distribution to determine the content lost across the translation to generate the mapping function” which renders the claim indefinite, and there is insufficient antecedent basis for these limitations in the claim. As discussed in the prior office action, the limitation “translated from a first image distribution into a second image distribution and then back into the first image distribution” lacks sufficient antecedent basis because it is unclear if the ‘first image distribution’ refer to the ‘first image distribution’ in the ‘unpaired images translated’ clause of the instant claim 7, if it refers to either the “stream of image data” or “new stream of image data” recited in claim 1, or if it refers to a new and distinct ‘first image distribution’. Similarly, it is not clear if “the content lost across the translation” refers to the ‘unpaired images’ or the ‘evaluated images’. The claim language does not describe how the ‘mapping function’ is actually generated (e.g., is the ‘mapping function’ generated by the ‘training modules’ cycle-consistent adversarial network, is the ‘mapping function’ a difference in the ‘content’, etc.). Furthermore, use of “the content lost” lacks sufficient antecedent basis, because there is no prior recitation of what the ‘content’ actually is. Aside from merely suggesting that a ‘translation occurs’ between two (or potentially more) ‘image distributions’, there is no specific description of the ‘translation process’ which allows one of ordinary skill in the art to determine what the ‘content’ is (e.g., image noise, image resolution, unprocessed RF signals, etc.). Finally, use of the ‘unpaired images’ and the ‘evaluating images’ phrases is unclear because it is not certain if they refer to the same set of images, if each refers to distinct and new sets of images, or some combination. For the purposes of examination, the broadest reasonable interpretation of the claim language includes any machine learning algorithm applying an adversarial network which improves the image quality of an ultrasound image distribution.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4 and 7 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lee (US20220061816A1, 2022-03-03; hereinafter “Lee”).
Regarding claim 1, Lee teaches a system of generating ultrasound images during a medical imaging procedure (“An image processing system […] generate a resolution mapped ultrasound image” [clm 18]; “systems and methods for enhancing elevational resolution in ultrasound images using a generative neural network” [0001]; [fig. 1-4B]) comprising,
a handheld ultrasound imaging device for generating a stream of image data from the body habitus of a patient (“receive an ultrasound image of an anatomical region of a subject, the ultrasound image having a first resolution;” [clm 18]; “a transmit beamformer 101 and a transmitter 102 that drives elements (e.g., transducer elements) 104 within a transducer array, herein referred to as probe 106, to emit pulsed ultrasonic signals (referred to herein as transmit pulses) into a body” [0026]; “The echoes are converted into electrical signals, or ultrasound data, by the elements 104 and the electrical signals are received by a receiver 108. The electrical signals representing the received echoes are passed through a receive beamformer 110 that outputs ultrasound data.” [0027]; “Probe 106 may comprise a handheld probe in electronic communication with the handheld ultrasound imaging device to collect raw ultrasound data.” [0032]; [0020-0037], [fig. 1-4B]),
an image processing application being executed on a processor for processing the stream of image data to produce ultrasound images having an initial image style (“a processor communicably coupled to the display device, the user input device, and a non-transitory memory storing the trained resolution mapping network […] receive an ultrasound image of an anatomical region of a subject, the ultrasound image having a first resolution; generate a resolution mapped ultrasound image using the trained resolution mapping network,” [clm 18]; “The ultrasound imaging system may be communicatively coupled to an image processing system, […] The image processing system may include one or more neural network models, such as generative neural network models and generative adversarial network models, stored in non-transitory memory.” [0025]; “The processor 116 may control the probe 106 to acquire data” [0029]; The image processing system includes a processor and memory storing a trained resolution mapping network, and receives ultrasound data from the ultrasound probe to generate ultrasound images with a first resolution profile (i.e., initial image style) to input to the trained resolution mapping network [0020-0047], [fig. 1-4B]),
the processing application including a neural filter which receives the stream of image data from the handheld ultrasound imaging device and processes it to generate a new stream of image data that produces ultrasound images having a different image style that conforms to the visual properties of ultrasound images of the type produced by a cart- based ultrasound system, wherein the image style includes visual properties of sharpness, resolution and noise of the ultrasound images (“generate a resolution mapped ultrasound image using the trained resolution mapping network, the resolution mapped ultrasound image having a second resolution greater than the first resolution; and” [clm 18]; “Trained neural network models may be deployed by an image processing system, […] to map one or more ultrasound images from the first resolution profile to the corresponding resolution-mapped ultrasound images with the target resolution profile,” [0025]; “if a portion of anatomical structure is visible at a first lower resolution in an image acquired via a 1D probe, and after passing the image through the trained neural network algorithm, a generated image is obtained in which the portion of anatomical structure has a second higher resolution and an additional portion of the anatomical structure is indicated in the generated image,” [0109]; The trained resolution mapping network receives input ultrasound images having a first resolution and outputs ultrasound images with a second resolution (i.e., cart-based), wherein the resolution, sharpness and noise of the resultant ultrasound images having the second resolution are improved over the ultrasound images having the first resolution [0038-0077], [fig. 1-4B]), and
a user interface control for controlling operation of the neural filter and having a UI switch for activating the neural filter (“a display device; a user input device;” [clm 18]; “A user interface 115 may be used to control operation of the ultrasound imaging system 100, including to control the input of patient data (e.g., patient clinical history), to change a scanning or display parameter,” [0028]; “user input device 232 may enable a user to make a selection of an ultrasound image to use in training a machine learning model, […] or for further processing using a trained machine learning model.” [0045]; “the processor may determine the operating mode, the processing mode, and the type of transducer based on user input via an ultrasound imaging interface on a display portion of a display coupled to the ultrasound system.” [0100]; The user may input selections (i.e., UI switch) for neural network processing of the ultrasound images using the user interface, including control of input patient data and changing scanning parameters [0028-0077], [fig. 1-4B]).
Regarding claim 2, Lee teaches the system of Claim 1
Lee further teaching wherein the UI switch comprises a preset configuration for configuring the handheld ultrasound imaging device to generate image data for an associated image study requirement associated with the preset and for processing generated image data with the neural filter to conform the visual properties of the generated image data to have the different image style of ultrasound images of the type produced by the cart-based ultrasound system for the respective preset image study requirements (“A user interface 115 may be used to control operation of the ultrasound imaging system 100, including to control the input of patient data (e.g., patient clinical history), to change a scanning or display parameter, to initiate a probe repolarization sequence, and the like. The user interface 115 may include one or more of the following: […] hard keys linked to specific actions, soft keys that may be configured to control different functions, and/or a graphical user interface displayed on a display device 118.” [0028]; “user input device 232 may enable a user to make a selection of an ultrasound image to use in training a machine learning model, to indicate or label a position of an interventional device in the ultrasound image data 214, or for further processing using a trained machine learning model” [0045]; “the processor may determine the operating mode, the processing mode, and the type of transducer based on user input via an ultrasound imaging interface” [0100]; “As a non-limiting example, one or more graphical indications, such as outlines of one or more areas on the generated image may be provided so as to enable to user to identify areas that were modified. The user may then make a decision if additional scan with a 2D probe is desired or if the image acquired via the 1D probe and/or the generated image may has desired resolution/clarity for diagnosis. The annotations (that is, graphical indications) may be turned on or off based on user input request, for example.” [0108]; [0028-0077], [fig. 1-4B], [see claim 1 rejection]).
Regarding claim 3, Lee teaches the system of Claim 1
Lee further teaching wherein adjusting the different image style to conform to the visual properties of the type produced by the cart-based ultrasound system includes adjusting the image data wherein the measures of the visual properties of sharpness, resolution, and noise of the resulting output image conform to the measures of the visual properties of sharpness, resolution and noise of the different image style of ultrasound images produced by cart-based ultrasound imaging system (“generate a resolution mapped ultrasound image using the trained resolution mapping network, the resolution mapped ultrasound image having a second resolution greater than the first resolution; and” [clm 18]; “Trained neural network models may be deployed by an image processing system, […] to map one or more ultrasound images from the first resolution profile to the corresponding resolution-mapped ultrasound images with the target resolution profile,” [0025]; “the processor may determine the operating mode, the processing mode, and the type of transducer based on user input via an ultrasound imaging interface” [0100]; “if a portion of anatomical structure is visible at a first lower resolution in an image acquired via a 1D probe, and after passing the image through the trained neural network algorithm, a generated image is obtained in which the portion of anatomical structure has a second higher resolution and an additional portion of the anatomical structure is indicated in the generated image,” [0109]; [0038-0077], [fig. 1-4B], [see claim 1 rejection]).
Regarding claim 4, Lee teaches the system of Claim 1
wherein Lee further teaches the neural filter includes a mapping function for translating image data produced by a handheld ultrasound imaging device into image data of the type produced by the cart-based ultrasound imaging system by employing a training module to define for the neural filter visual properties of image data produced by handheld ultrasound imaging device and the cart-based ultrasound imaging system respectively (“the trained resolution mapping network is trained with a first training set and/or a second training set;” [clm 20]; “Non-transitory memory 206 may store a neural network module 208, a network training module 210, an inference module 212, and ultrasound image data 214.” [0040]; “resolution mapping network training system 300 includes a resolution mapping network 302, to be trained, and a training module 304 that includes a training dataset comprising a plurality of image pairs divided into training image pairs 306 and test image pairs 308.” [0048]; “receiving a training image pair (e.g., comprising an input image acquired via a 1D ultrasound probe and a ground truth image acquired via a 2D ultrasound probe) from a training set. In an embodiment, the training set may be stored in a training module of an image processing system,” [0093]; “the weights and biases of the resolution mapping network are adjusted based on the difference between the output image and the ground truth image from the relevant data pair. The difference (or loss), as determined by the loss function, may be back-propagated through the neural learning network to update the weights (and biases) of the convolutional layers.” [0097]; The resolution mapping network is a neural network which is trained by a training module to map a 1D ultrasound image acquired by the handheld ultrasound probe to a 2D resolution mapped ultrasound image [0038-0098], [fig. 1-6]).
Regarding claim 7, Lee teaches the system of Claim 4
wherein Lee further teaches the training module employs a cycle-consistent adversarial network to evaluate unpaired images translated from a first image distribution having the initial image style into a second image distribution having the different image style to determine the accuracy of the translation, and evaluating images translated from a first image distribution into a second image distribution and then back into the first image distribution to determine the content lost across the translation to generate the mapping function (“The image processing system may include one or more neural network models, such as generative neural network models and generative adversarial network models, stored in non-transitory memory. […] training the generative neural network algorithm to learn a mapping from a first resolution profile to a target resolution profile, wherein training image pairs comprising 1D probe images and corresponding ground-truth 2D probe images are used to adjust parameters of the generative neural network according to a backpropagation algorithm.” [0025]; “neural network module 208 may include at least a deep learning model (e.g., a generative neural network or generative adversarial network), and instructions for implementing the deep learning model to reconstruct an ultrasound image acquired via a 1D probe as an image with higher resolution in a broader range of elevations typical of a 2D probe,” [0040]; “resolution mapping network training system 300 includes a resolution mapping network 302, to be trained, and a training module 304 that includes a training dataset comprising a plurality of image pairs divided into training image pairs 306 and test image pairs 308.” [0048]; “the weights and biases of the resolution mapping network are adjusted based on the difference between the output image and the ground truth image from the relevant data pair. The difference (or loss), as determined by the loss function, may be back-propagated through the neural learning network to update the weights (and biases) of the convolutional layers.” [0097]; [0038-0098], [fig. 1-6]).
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.
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JAMES FRANKLIN MCDONALD III
Examiner
Art Unit 3797
/CHRISTOPHER KOHARSKI/ Supervisory Patent Examiner, Art Unit 3797
Prosecution Insights