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 .
Acknowledgement of Amendment
The following office action is in response to the applicant’s amendment filed on 03/18/2026. Claims 1-9 are pending. Claims 2-4 have been cancelled. Claims 8 and 9 are newly added. Claims 1, and 5-9 are rejected under 35 U.S.C. 102 and 35 U.S.C. 103 for the reasons stated in the Response to Arguments, 35 U.S.C. 102 and 35 U.S.C. 103 sections below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The examiner acknowledges that the Applicant has provided a certified copy of Japanese application JP 2023-200491 filed 11/29/2023.
Response to Arguments
Applicant’s arguments, see Remarks page 5, filed 03/18/2026, with respect to the objections to the drawings have been fully considered and are persuasive. The objections to the drawings in the non-final rejection of 10/01/2025 has been withdrawn.
Applicant’s arguments, see Remarks page 5, filed 03/18/2026, with respect to the rejection of the claim 5 under 35 U.S.C. 112(b) have been fully considered and are persuasive. The rejection of the claim 5 under 35 U.S.C. 112(b) in the non-final rejection of 10/01/2025 has been withdrawn.
Applicant’s arguments, see Remarks page 5-6, filed 03/18/2026, with respect to the rejection of the claims under 35 U.S.C.102(a)(2) have been fully considered and are not persuasive.
Regarding claim 1, the claim has been amended to include: “wherein the hardware processor determines whether the image data based on the reception signal of the ultrasound probe is saturated, and when it is determined to be saturated, the hardware processor estimates and generates, based on the first learned data, the image data without a saturated region from the image data”.
The examiner notes that independent claims 6 and 7 have been similarly amended. This amendment is drawn in part from claim 4, now cancelled.
The Applicant notes that Ghani discloses a technology that analyzes the image quality of ultrasound images using a machine learning model to output a set of adjustment parameters, and then uses these adjustment parameters to appropriately acquire additional ultrasound images. This adjustment parameter automatically adjusts depth settings, lateral angle settings, 3D elevational plane setting, or gain settings. The Applicant respectfully submits that Ghani discloses a technology for adjusting the parameters of phasing addition using a beamformer.
Furthermore, the Applicant submits that there is not any description in Ghani of the configuration that the “image data of saturated regions” within the image data after phasing addition by the beamformer is replaced with “estimated image data without a saturated region generated by the machine learning model” rather than adjusting the parameters of beamformer, as claims 4-5 of the present application, as described in paragraph [0061] of the specification as filed.
The examiner respectfully agrees that Ghani discloses a technology that analyzes the image quality of ultrasound images using a machine learning model to output a set of adjustment parameters, and then uses these adjustment parameters to appropriately acquire additional ultrasound images. This adjustment parameter automatically adjusts depth settings, lateral angle settings, 3D elevational plane setting, or gain settings. The examiner respectfully agrees that Ghani discloses a technology for adjusting the parameters of phasing addition using a beamformer.
However, the examiner disagrees that there is not any description in Ghani of the configuration that the “image data of saturated region” within the image data after phasing addition by the beamformer is replaced with “estimated image data without a saturated region generated by the machine learning model” rather than adjusting the parameters of the beamformer.
The examiner respectfully notes that claim 1 as written does not require replacing the saturated image data with image data without saturation. Rather, it requires that the hardware processor estimate and generate image data without a saturated region.
In this case, the examiner respectfully notes that Ghani discloses “The image 410 may be an ultrasound image displaying a view of the subject anatomy including the kidney 312, the diaphragm 316, and a spleen 414. In the example shown in FIG. 4, the ultrasound image 410 may display a view of the subject anatomy with a gain setting that is too high. A gain setting being too high may correspond to an oversaturation of an image. Such an oversaturation may be exemplified in the image 410. When an image is over saturated, such as the image 410, it may be more difficult for a user or a processor circuit of the ultrasound imaging system to identify anatomical features within the image. To remedy the issue, the gain setting may be adjusted. In the case shown in FIG. 4, the gain setting may be decreased. Ultrasound images may be obtained with improper gain settings due to any of the factors described with reference to FIG. 3” [0057]; “In some examples, the processor circuit 210 may be configured to implement and/or train a machine learning algorithm to determine any of the measurements 854, the scores 856, or the recommended setting adjustments 858. For example, the images 710, 730, and 750 of FIG. 7 may be example images of annotated images of a database of training images. For example, multiple training images may be annotated by experts in the field to identify measurements of the image, including lateral presence, axial presence, elevational presence, and saturation or gain measurements, corresponding scores, and/or recommended setting adjustments to remedy any deficiencies. An AI algorithm may then be trained to determine any of these values based on the training image set” [0090]; “In an example in which the processor circuit uses a machine learning algorithm to analyze, measure, classify, and/or score a received image, the machine learning algorithm may be of any suitable type. For example, a classification process may include a random forest algorithm, a classification tree approach, a convolutional neural network (CNN) or any other type of deep learning network” [0091].
In this case, since an AI algorithm is trained to analyze, measure, classify and/or score received images (i.e. the measurement being a saturation or gain measurements, see [0090]), such that recommended settings can be output and used to adjust the gain (i.e. which corresponds to saturation) of an image (i.e. see [0057]), the hardware processor determines whether the image data based on the reception signal of the ultrasound probe is saturated (i.e. oversaturated), and when it is determined to be saturated (i.e. oversaturated), the hardware processor estimates and generates, based on the first learned data, the image data (see [0057] and FIG. 4) without a saturated region from the image data.
Therefore, the examiner respectfully maintains that Ghani teaches the newly added claim limitations for the reasons stated above. The rejections of claims 1, 6 and 7 have been updated to reflect the newly added claim language as stated in the 35 U.S.C. 102 section below.
Regarding newly added claims 8 and 9, the examiner respectfully refers the Applicant to the 35 U.S.C. 102 and 35 U.S.C. 103 sections below.
Claim Objections
Claims 6-7 are objected to because of the following informalities:
Regarding claims 6 and 7, as written they read “determining whether the image data based on the reception signal of the ultrasound probe is saturated, and when it is determined to be saturated, estimating and generating, based on the first learned data, the image data of without a saturated region from the image data” (Claim 6); “determines whether the image data based on the reception signal of the ultrasound probe is saturated, and when it is determined to be saturated, estimates and generates, based on the first learned data, the image data of without a saturated region from the image data” (Claim 7). However, the examiner believes that “of” (i.e. bolded and underlined above), is a typo which should be removed.
Appropriate correction is required.
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.
(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) 1-8 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ghani et al. US 2023/0329674 A1 “Ghani”.
Regarding claims 1, 6 and 7, Ghani teaches “An ultrasound diagnostic apparatus comprising:” (Claim 1) (“FIG. 1 is a schematic diagram of an ultrasound imaging system 100, according to aspects of the present disclosure. The system 100 is used for scanning an area or volume of a subject's body. […] The probe 110 may include a transducer array 112, a beamformer 114, a processor circuit 116, and a communication interface 118. The host 130 may include a display 132, a processor circuit 134, a communication interface 136, and a memory 138 storing subject information” [0033]. Therefore, the system shown in Ghani FIG. 1 represents an ultrasound diagnostic apparatus.);
“An ultrasound image generating method comprising:” (Claim 6) (“FIG. 5 is a flow diagram of a method 500 of automatically assigning an image quality score to an ultrasound image, according to aspects of the present disclosure” [0058]; “At step 520, the method 500 includes displaying the ultrasound image received. […] The display of the ultrasound images at step 520 may be performed directly after the ultrasound image is received at step 510 or may occur at a later time” [0062]. Therefore, Ghani discloses an ultrasound image generating method.);
“A non-transitory computer-readable recording medium storing a program that causes a computer to:” (Claim 7) (“In some instances, the memory 264 includes a non-transitory computer-readable medium. The memory 264 may store instructions 266. The instructions 266 may include instructions that, when executed by the processor 260, cause the processor 260 to perform the operations described herein with reference to the probe 110 and/or the host 130 (FIG. 1)” [0051]. Therefore, Ghani discloses a non-transitory computer-readable recording medium storing a program (i.e. instructions 266) that causes a computer to perform specific processes.);
“a hardware processor” (Claim 1) (“The processor 134 may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a controller, a Field-Programmable Gate Array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. […] The processor 134 can be configured to generate image data from the image signals received from the probe 110. The processor 134 can apply advanced signal processing and/or image processing techniques to the image signals” [0044]. In this case, processor 134 in FIG. 1 is equivalent to processor circuit 210 of FIG. 2 (See [0060]). Therefore, the processor 134 and the processor circuit 210 represent hardware processors. Thus, the ultrasound diagnostic apparatus includes a hardware processor.);
“wherein the hardware processor generates image data without saturation from image data with saturation using a first learned data of a model that has undergone machine learning using the image data with saturation and the image data without saturation, which are based on a reception signal of an ultrasound probe and the image data without saturation” (Claim 1); “generating image data without saturation from image data with saturation using a first learned data of a model that has undergone machine learning using the image data with saturation and the image data without saturation, which are based on a reception signal of an ultrasound probe” (Claim 6); “as a controller, generate image data without saturation from image data with saturation using a first learned data of a model that has undergone machine learning using the image data with saturation and the image data without saturation, which are based on a reception signal of an ultrasound probe” (Claim 7) (“The processor 134 may utilize deep learning-based prediction networks to identify parameters of an ultrasound image, including an anatomical scan window, probe orientation, subject position, and/or other parameters” [0049]; “In some examples, the processor circuit 210 may be configured to implement and/or train a machine learning algorithm to determine any of the measurements 854, the scores 856, or the recommended setting adjustments 858. […] For example, multiple training images may be annotated by experts in the field to identify measurements of the image, including lateral presence, axial presence, elevational presence, and saturation or gain measurements, corresponding scores, and/or recommended setting adjustments to remedy any deficiencies. An AI algorithm may then be trained to determine any of these values based on the training image set” [0090]; “In an example in which the processor circuit uses a machine learning algorithm to analyze, measure, classify, and/or score a received image, the machine learning algorithm may be of any suitable type” [0091]; “The recommended setting adjustments 858 shown in the table 850 of FIG. 8 may additionally be calculated by the processor circuit 210 and may indicate how the measurements 854 may be improved to improve image quality” [0092]; “In the example shown in FIG. 4, the ultrasound image 410 may display a view of the subject anatomy with a gain setting that is too high. A gain setting being too high may correspond to an oversaturation of an image. Such an oversaturation may be exemplified in the image 410. When an image is over saturated, such as the image 410, it may be more difficult for a user or a processor circuit of the ultrasound imaging system to identify anatomical features within the image. To remedy the issue, the gain setting may be adjusted. In the case shown in FIG. 4, the gain setting may be decreased” [0057]; “At the step 1010, the processor circuit 210 may then either increase or decrease the gain setting by incremental amounts” [0118].
In this case, the processor 134 and the processing circuit 210 utilize deep learning-based prediction networks and trained machine learning algorithms, respectively, to 1) identify imaging parameters of an ultrasound image (see [0049]) and 2) measure, classify and/or score a received image (See [0091]). The machine learning algorithm is trained to determine recommended setting adjustments 858 (i.e. such as a change in gain, see [0057]) which are applied to ultrasound images (see FIG. 4, 410). Therefore, the hardware processor (i.e. 134 or 210) generates image data without saturation (i.e. image in which gain is decreased, see 410 in FIG. 4) from image data with saturation (i.e. received by the probe 110) using a first learned data of a model that has undergone machine learning (i.e. trained machine-learning algorithm configured to determine recommended setting adjustments 858, see [0090]) using the image data with saturation based on a reception signal of an ultrasound probe (i.e. image received from the probe 110) and the image data without saturation (i.e. training images annotated by experts, for example).
Additionally, the method carried out by the ultrasound diagnostic apparatus includes generating image data without saturation (i.e. image in which gain is decreased, see 410 in FIG. 4) from image data with saturation using learned data of a model that has undergone machine learning (i.e. trained machine-learning algorithm configured to determine recommended setting adjustments 858, see [0090]) using the image data with saturation based on a reception signal of an ultrasound probe (i.e. image received from the probe 110) and the image data without saturation (i.e. training images annotated by experts, for example).
Finally, the non-transitory computer-readable recording medium causes a computer to, as a controller, generate image data without saturation (i.e. image in which gain is decreased, see 410 in FIG. 4) from image data with saturation using learned data of a model that has undergone machine learning (i.e. trained machine-learning algorithm configured to determine recommended setting adjustments 858, see [0090]) using the image data with saturation (i.e. image received from the probe 110) based on a reception signal of an ultrasound probe and the image data without saturation (i.e. training images annotated by experts, for example).);
“wherein the hardware processor determines whether the image data based on the reception signal of the ultrasound probe is saturated, and when it is determined to be saturated, the hardware processor estimates and generates, based on the first learned data, the image data without a saturated region from the image data” (Claim 1); “determining whether the image data based on the reception signal of the ultrasound probe is saturated, and when it is determined to be saturated, estimating and generating, based on the first learned data, the image data of without a saturated region from the image data” (Claim 6) (see [0090], [0091] and [0057] as discussed above. In this case, In this case, since an AI algorithm is trained to analyze, measure, classify and/or score received images (i.e. the measurement being a saturation or gain measurements, see [0090]), such that recommended settings can be output and used to adjust the gain (i.e. which corresponds to saturation) of an image (i.e. see [0057]), the hardware processor determines whether the image data based on the reception signal of the ultrasound probe is saturated (i.e. oversaturated), and when it is determined to be saturated (i.e. oversaturated), the hardware processor estimates and generates, based on the first learned data, the image data (see [0057] and FIG. 4) without a saturated region from the image data.
Furthermore, the method involves determining whether the image data based on the reception signal of the ultrasound probe is saturated, and when it is determined to be saturated, estimating and generating, based on the first learned data, the image data or without a saturated region. Finally, the non-transitory computer-readable recording medium storing a program that causes a computer to as a controller, determine whether the image data based on the reception signal of the ultrasound probe is saturated, and when it is determined to be saturated, estimates and generates , based on the first learned data, the image data of without a saturated region from the image data.).
Regarding claim 2, Ghani discloses all features of the claimed invention as discussed with respect to claim 1 above, and Ghani further teaches “wherein the hardware processor determines, based on the learned data, whether the image data based on the reception signal of a transducer of the ultrasound probe is saturated, and when it is determined to be saturated, the hardware processor generates the image data by reducing a gain of a variable gain amplifier that amplifies the reception signal at any degree so as not to be saturated” (See [0090], [0091] and [0057] as discussed in claim 1 above. Since the gain is decreased (see FIG. 4 and [0057]) to avoid oversaturation in an image, a variable gain amplifier is inherently present within the system. In this case, since the processor circuit 210 may be configured to implement and/or train a machine learning algorithm to determine recommended setting adjustments 858, corresponding to saturation or gain measurements (See [0090]), and an AI algorithm may then be trained and thus utilized to analyze, measure and classify received images to determine these values (i.e. saturation and gain), such that the gain can be decreased (see [0057]), the hardware processor determines, based on the learned data (i.e. training images annotated by experts, see [0090]), whether the image data based on the reception signal of a transducer of the ultrasound probe (i.e. received image, see [0091]) is saturated (i.e. oversaturated, see [0057]), and when it is determined to be saturated, the hardware processor generates the image data by reducing a gain of a variable gain amplifier that amplifies the reception signal at any degree so as not to be saturated (i.e. decreases the gain such that anatomical features can be identified, see [0057]).).
Regarding claim 3, Ghani discloses all features of the claimed invention as discussed with respect to claim 2 above, and Ghani further teaches “wherein the hardware processor determines, based on the learned data, whether the image data based on the reception signal of the ultrasound probe includes a saturated region, and when it is determined that there is the saturated region, the hardware processor generates the image data by decreasing, so as not to saturate, the gain of the region of the variable gain amplifier that amplifies the reception signal at any degree” (See [0090], [0091] and [0057] as discussed in claim 1 above. Since the gain is decreased (see FIG. 4 and [0057]) to avoid oversaturation in an image, a variable gain amplifier is inherently present within the system. In this case, since the processor circuit 210 may be configured to implement and/or train a machine learning algorithm to determine recommended setting adjustments 858, corresponding to saturation or gain measurements (See [0090]), and an AI algorithm may then be trained and thus utilized to analyze, measure and classify received images to determine these values (i.e. saturation and gain), such that the gain can be decreased (see [0057]), the hardware processor determines, based on the learned data (i.e. training images annotated by experts, see [0090]), whether the image data based on the reception signal of the ultrasound probe (i.e. received image, see [0091]) includes a saturated region (i.e. region that is oversaturated), and when it is determined that there is the saturated region, the hardware processor generates the image data by decreasing, so as not to saturate, the gain of the region (see [0057]) of the variable gain amplifier that amplifies the reception signal at any degree.).
Regarding claim 4, Ghani discloses all features of the claimed invention as discussed with respect to claim 1 above, and Ghani further teaches “wherein the hardware processor determines, based on the learned data, whether the image data based on the reception signal of the ultrasound probe is saturated, and when it is determined to be saturated, the hardware processor estimates and generates, based on the learned data, the image data of a non-saturated region from the image data” (See [0090], [0091] and [0057] as discussed in claim 1 above. In this case, since the processor circuit 210 may be configured to implement and/or train a machine learning algorithm to determine recommended setting adjustments 858, corresponding to saturation or gain measurements (See [0090]), and an AI algorithm may then be trained and thus utilized to analyze, measure and classify received images to determine these values (i.e. saturation and gain), such that the gain can be decreased (see [0057]), the hardware processor determines, based on the learned data (i.e. training images annotated by experts, see [0090]), whether the image data based on the reception signal of the ultrasound probe (i.e. received image, see [0091]) is saturated (i.e. oversaturated, see [0057]), and when it is determined to be saturated, the hardware processor estimates and generates, based on the learned data (i.e. training images annotated by experts, see [0090], the image data of a non-saturated region from the image data (i.e. adjusting the gain setting in image 410).).
Regarding claim 5, Ghani discloses all features of the claimed invention as discussed with respect to claim 1 above, and Ghani further teaches “wherein the hardware processor determines, whether the image data based on the reception signal of the ultrasound probe includes a saturated region, and when it is determined that there is the saturated region, estimates and generates, with the first learned data, the image data of the non-saturated region from the image data of the saturated region, and generates the image data including the non-saturated region” (See [0090], [0091] and [0057] as discussed in claim 1 above. In this case, since the processor circuit 210 may be configured to implement and/or train a machine learning algorithm to determine recommended setting adjustments 858, corresponding to saturation or gain measurements (See [0090]), and an AI algorithm may then be trained and thus utilized to analyze, measure and classify received images to determine these values (i.e. saturation and gain), such that the gain can be decreased (see [0057]), the hardware processor determines, based on the learned data (i.e. training images annotated by experts, see [0090]), whether the image data based on the reception signal of the ultrasound probe (i.e. received image, see [0091]) includes the saturated region (i.e. oversaturated, see [0057]), and when it is determined that there is the saturated region, estimates and generates, with the learned data (i.e. training images annotated by experts, see [0090], the image data of a non-saturated region from the image data of the region, and generates the image data including the non-saturated region (i.e. adjusting the gain setting in image 410).).
Regarding claim 8, Ghani discloses all features of the claimed invention as discussed with respect to claim 1 above, and Ghani further teaches "wherein: the hardware processor determines, based on a second learned data of a model that has undergone machine learning using the image data with saturation based on a reception signal of an ultrasound probe and the image data without saturation, whether the image data based on the reception signal of the ultrasound probe includes the saturated region” (See [0057], [0090] and [0091] as discussed with respect to claim 1 above and “generating, using a machine learning algorithm, a score associated with the first ultrasound image, wherein the machine learning algorithm is trained using a reference ultrasound image; comparing the score to a threshold score” [0012]; “In some instances, the ultrasound images 710, 730, and 750 can be considered references images to which ultrasound images acquired during an imaging procedure are compared. In some instances, the ultrasound images 710, 730, and 750 are part of a training data set (e.g., ground truth data) that is used to train a machine learning/AI algorithm” [0087]. Therefore, since ultrasound images 710, 730 and 750 are reference images which are part of a training data set to train a machine learning algorithm, the hardware processor determines, based on a second learned data of a model that has undergone machine learning using the image data with saturation (i.e. oversaturation, see [0057]) based on a reception signal of an ultrasound probe and the image data without saturation, whether the image data based on the reception signal of the ultrasound probe includes the saturated region (i.e. oversaturated region).).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ghani et al. US 2023/0329674 A1 “Ghani” and further in view of Ott US 2022/0066004 A1 “Ott”.
Regarding claim 9, Ghani discloses all features of the claimed invention as discussed with respect to claim 1 above, however, Ghani does not teach “wherein the hardware processor replaces the saturated region with a non-saturated region generated by the model”.
Ott is within the same field of endeavor as the claimed invention because it involves field of a motion or saturation determination apparatus (see [Abstract]).
Ott teaches “wherein the hardware processor replaces the saturated region with a non-saturated region generated by the model” (“In another embodiment, the system is an imaging system and comprises a statistical processing engine that uses the excess amplitude, L.sub.sat−a.sub.1*, as an input therefor in order to identify saturated pixels or areas of saturation at an image level. […] In another example, saturated regions of a first depth map image can be replaced by non-saturated corresponding regions of a second depth map image acquired over a second frame cycle following, for example immediately following, the first frame cycle. Furthermore, for some applications, the statistical processing can comprise filtering and/or algorithmic processing in order to ignore small clusters of saturated pixels and/or lines of saturation and avoid applying compensation for these pixels, because the performance of the application, for example image recognition, is unaffected by the saturation of such small numbers of pixels” [0113]. Therefore, since algorithmic processing is used to identify and ignore small clusters of saturated pixels and saturated regions of a first depth map image are replaced by non-saturated corresponding regions of a second depth map image, the hardware processor replaces the saturated region with a non-saturated region generated by the model.).
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 ultrasound diagnostic apparatus of Ghani such that the hardware processor replaces the saturated region with a non-saturated region generated by the model as disclosed in Ott in order to improve the quality of an ultrasound image by removing the saturated region. Replacing a saturated region with a non-saturated region is one of a finite number of techniques which can be used to improve ultrasound image quality with a reasonable expectation of success. Thus, modifying the ultrasound diagnostic apparatus of Ghani such that the hardware processor replaces the saturated region with a non-saturated region generated by the model as disclosed in Ott would yield the predictable result of improving the quality of an ultrasound image by removing the saturated region.
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 KAITLYN E SEBASTIAN whose telephone number is (571)272-6190. The examiner can normally be reached Mon.- Fri. 7:30-4:30 (Alternate Fridays Off).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne M Kozak can be reached at (571) 270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KAITLYN E SEBASTIAN/Examiner, Art Unit 3797
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