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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970);and, In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321[Times New Roman font/0x38] may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-12 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1-10, of U.S. Patent No. 12,190,567. Each of the limitation set forth in the claims of the instant application is defined in the claims of the patent.
As an example, consider claim 1, of current application, compared to claim 1, of patent application, it discloses:
Current Application Patent Application
1. A method for digital contrast post-processing of microscopy images, comprising: driving at least one imaging device in order to capture a plurality of measurement images of a biological sample by means of microscopic imaging during an observation period, after a staining process performed on the biological sample for the purpose of staining the biological sample with a marker for microscopic fluorescence imaging has been carried out after the observation period: driving the at least one imaging device in order to capture a plurality of reference images of the biological sample stained with the marker by means of the microscopic fluorescence imaging after the observation period during a calibration period, driving the at least one imaging device in order to capture further measurement images of the biological sample stained with the marker by means of the microscopic imaging during the calibration period,
1. A method for digital contrast post-processing of microscopy images, comprising: driving at least one imaging device in order to capture a plurality of measurement images of a biological sample by means of microscopic imaging during an observation period, after a staining process performed on the biological sample for the purpose of staining the biological sample with a marker for microscopic fluorescence imaging has been carried out after the observation period: driving the at least one imaging device in order to capture a plurality of reference images of the biological sample stained with the marker by means of the microscopic fluorescence imaging after the observation period during a calibration period, driving the at least one imaging device in order to capture further measurement images of the biological sample stained with the marker by means of the microscopic imaging during the calibration period
determining synthetic fluorescence images on the basis of the measurement images and on the basis of a prediction algorithm, and determining further synthetic fluorescence images on the basis of at least one portion of the further measurement images and on the basis of the prediction algorithm, and carrying out a validation of the synthetic fluorescence images on the basis of a comparison between the reference images and the further synthetic fluorescence images.
determining synthetic fluorescence images on the basis of the measurement images and on the basis of a prediction algorithm, wherein the prediction algorithm is machine-learned, determining further synthetic fluorescence images on the basis of at least one portion of the further measurement images and on the basis of the prediction algorithm, carrying out a validation of the synthetic fluorescence images on the basis of a comparison between the reference images and the further synthetic fluorescence images, driving the at least one imaging device in order to capture, by means of the microscopic fluorescence imaging, a plurality of training images of a further sample stained with the marker for the microscopic fluorescence imaging by means of the staining process, driving the at least one imaging device in order to capture a plurality of further measurement images by means of the microscopic imaging during a further calibration period, and carrying out a training of parameters of the machine-learned prediction algorithm on the basis of the training images as ground truth and the further measurement images captured during the further calibration period.
Although the conflicting claims are not identical, they are not patentably distinct from each other because all of the “features of the current application are covered” in the patented parent application.
The other claims have similar correspondence to the patent application.
DETAILED ACTION
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)(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(e), 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 1-2 and 5-12 are rejected under 35 U.S.C. 102(a) (2) based upon a public use or sale or other public availability of the invention as being anticipated by Ozcan et al (Pub. No.: U.S. 2023/0030424 A1).
Regarding claim 1, Ozcan discloses a method for digital contrast post-processing of microscopy images, comprising: driving at least one imaging device in order to capture a plurality of measurement images of a biological sample by means of microscopic imaging during an observation period (see page 1, paragraph, [0006] in one embodiment, a method of generating a virtually stained microscopic image of a sample includes providing a trained, deep neural network that is executed by image processing software using one or more processors of a computing device, wherein the trained, deep neural network is trained with a plurality of matched immunohistochemistry (IHC) stained microscopy images or image patches and their corresponding fluorescence lifetime (FLIM) microscopy images or image patches of the same sample(s) obtained prior to immunohistochemistry (IHC) staining. A fluorescence lifetime (FLIM) image of the sample is obtained using a fluorescence microscope and at least one excitation light source and the fluorescence lifetime (FLIM) image of the sample is input to the trained, deep neural network. The trained, deep neural network outputs the virtually stained microscopic image of the sample that is substantially equivalent to a corresponding image of the same sample that has been immunohistochemistry (IHC) stained.
Also, page 10, paragraph, [0079] The digitally/virtually-stained output images 40 from the trained, deep neural network 10 were compared to the standard histochemical staining images 48 for diagnosing multiple types of conditions on multiple types of tissues, which were either Formalin-Fixed Paraffin-Embedded (FFPE) or frozen sections. The results are summarized in Table 1 below. The analysis of fifteen (15) tissue sections by four board certified pathologists (who were not aware of the virtual staining technique) demonstrated 100% non-major discordance, defined as no clinically significant difference in diagnosis among professional observers. The “time to diagnosis” varied considerably among observers, from an average of 10 seconds-per-image for observer 2 to 276 seconds-per-image for observer 3. However, the intra-observer variability was very minor and tended towards shorter time to diagnosis with the virtually-stained slide images 40 for all the observers except observer 2 which was equal, i.e., ˜10 seconds-per-image for both the virtual slide image 40 and the histology-stained slide image 48. These indicate very similar diagnostic utility between the two image modalities);
after a staining process performed on the biological sample for the purpose of staining the biological sample with a marker for microscopic fluorescence imaging has been carried out after the observation period: driving the at least one imaging device in order to capture a plurality of reference images of the biological sample stained with the marker by means of the microscopic fluorescence imaging after the observation period during a calibration period (see page 3, paragraphs, [0031-0035] FIG. 14 illustrates a demonstration of the refocusing capability of the post imaging computational autofocusing method for various imaging planes of the samples, where the value of z signifies the focal distance from the focused plane, which resides in z=0 (as a reference plane). The refocusing capability of the network can be assessed both qualitatively and quantitatively, using the structural similarity index, which appears on the upper left corner of every panel, and compares that image to the reference in-focus image (at z=0). FIG. 17 illustrates an example of stain micro structuring. A diagnostician can manually label sections of the unstained tissue. These labels are used by the network to stain different areas of the tissue with the desired stains. A co-registered image of the histochemically stained H&E tissue is shown for comparison. FIGS. 18A-18G illustrates example of stain blending. FIG. 18A illustrates the autofluorescence image used as the input to the machine learning algorithm. FIG. 18B illustrates a co-registered image of the histochemically stained H&E tissue for comparison. FIG. 18C.
Also, page 5, paragraphs, [0048-0049] The trained, deep neural network 10 in response to the input image 20 outputs or generates a digitally stained or labelled output image. The digitally stained output image has “staining” that has been digitally integrated into the stained output image using the trained, deep neural network. In some embodiments, such as those involved tissue sections, the trained, deep neural network appears to a skilled observer (e.g., a trained histopathologist) to be substantially equivalent to a corresponding brightfield image of the same tissue section sample that has been chemically stained. Indeed, as explained herein, the experimental results obtained using the trained, deep neural network show that trained pathologists were able to recognize histopathologic features with both staining techniques (chemically stained vs. digitally/virtually stained) and with a high degree of agreement between the techniques, without a clear preferable staining technique (virtual vs. histological). This digital or virtual staining of the tissue section sample appears just like the tissue section sample had undergone histochemical staining even though no such staining operation was conducted. FIG. 2 microscope and generates a fluorescence image. This fluorescence image is then input to a trained, deep neural network 10 that then promptly outputs a digitally stained image of the tissue section sample. This digitally stained image closely resembles the appearance of a brightfield image of the same tissue section sample had the actual tissue section sample be subject to histochemical staining.
Finally, page 12, paragraph, [0083] beyond the visual comparison provided in FIGS. 3A-3H, 4A-4H, 5A-5P, the results of the trained deep neural network were quantified by first calculating the pixel-level differences between the brightfield images of the chemically stained samples and the digitally/virtually stained images that are synthesized using the deep neural network without the use of any labels/stains. Table 4 below summarizes this comparison for different combinations of tissue types and stains, using the YCbCr color space, where the chroma components Cb and Cr entirely define the color, and Y defines the brightness component of the image. The results of this comparison reveal that the average difference between these two sets of images is <˜5% and <˜16%, for the chroma (Cb, Cr) and brightness (Y) channels, respectively. Next, a second metric was used to further quantify the comparison, i.e., the structural similarity index, which is in general used to predict the score that a human observer will give for an image, in comparison to a reference image. SSIM ranges between 0 and 1, where 1 defines the score for identical images. The results of this SSIM quantification are also summarized in Table 4, which very well illustrates the strong structural similarity between the network output images 40 and the brightfield images 48 of the chemically stained samples.
driving the at least one imaging device in order to capture further measurement images of the biological sample stained with the marker by means of the microscopic imaging during the calibration period (see above, also page 6, paragraph, [0050] the fluorescence microscope, obtains a fluorescence lifetime image of an unstained tissue sample and outputs an image that well matches a bright-field image of the same field-of-view after IHC staining. Fluorescence lifetime imaging (FLIM) produces an image based on the differences in the excited state decay rate from a fluorescent sample. Thus, FLIM is a fluorescence imaging technique where the contrast is based on the lifetime or decay of individual fluorophores. The fluorescence lifetime is generally defined as the average time that a molecule or fluorophore remains in an excited state prior to returning to the ground state by emitting a photon. Among all the intrinsic properties of unlabeled tissue samples, fluorescent lifetime of endogenous fluorophore(s) is one of the most informative channels that measures the time a fluorophore stays in excited stated before returning to ground state.
Also, page 12, paragraph, [0083] beyond the visual comparison provided in FIGS. 3A-3H, 4A-4H, 5A-5P, the results of the trained deep neural network 10 were quantified by first calculating the pixel-level differences between the brightfield images of the chemically stained samples and the digitally/virtually stained images 40 that are synthesized using the deep neural network without the use of any labels/stains. Table 4 below summarizes this comparison for different combinations of tissue types and stains, using the YCbCr color space, where the chroma components Cb and Cr entirely define the color, and Y defines the brightness component of the image. The results of this comparison reveal that the average difference between these two sets of images is <˜5% and <˜16%, for the chroma (Cb, Cr) and brightness (Y) channels, respectively. Next, a second metric was used to further quantify the comparison, i.e., the structural similarity index, which is in general used to predict the score that a human observer will give for an image, in comparison to a reference image (Equation 8 herein). SSIM ranges between 0 and 1, where 1 defines the score for identical images. The results of this SSIM quantification are also summarized in Table 4, which very well illustrates the strong structural similarity between the network output images and the brightfield images of the chemically stained samples.
determining synthetic fluorescence images on the basis of the measurement images and on the basis of a prediction algorithm, and determining further synthetic fluorescence images on the basis of at least one portion of the further measurement images and on the basis of the prediction algorithm, and (see page 6, paragraph, [0055] post-imaging computational autofocusing is performed using a trained, deep neural network for incoherent imaging modalities. Thus, it may be used in connection with images obtained by fluorescence microscopy as well as other imaging modalities. Examples include a fluorescence microscope, a widefield microscope, a super-resolution microscope, a confocal microscope, generation fluorescence microscope, a light-sheet microscope, a FLIM microscope, a brightfield microscope, a darkfield microscope, a structured illumination microscope, a total internal reflection microscope, a computational microscope, a ptychographic microscope, a synthetic aperture-based microscope, or a phase contrast microscope. In some embodiments, the output of the trained, deep neural network generates a modified input image that is focused or more focused than the raw input image. This modified input image 20a with improved focus is then input into a separate trained, deep neural network described herein that transforms from a first image modality to a second image modality (e.g., fluorescence microscopy to brightfield microscopy). In this regard, the trained, deep neural networks 10a, 10 are coupled together in a “daisy chain” configuration with the output of the trained, autofocusing neural network 10a being the input to the trained, deep neural network 10 for digital/virtual staining. In another embodiment, the “machine learning algorithm” that is used for the trained, deep neural network 10 combines the functionality of autofocusing with that functionality described herein of transforming images from one microscope modality to another. In this latter embodiment, there is no need for two separate trained, deep neural networks. Instead, a single trained, deep neural network 10a is provided that performs virtual autofocusing as well as digital/virtual staining. The functionality of both networks 10, 10a are combined into a single network 10a. This deep neural network 10a follows the architecture(s) as described herein.
Also, page 12, paragraph, [0083] next, beyond the visual comparison provided in FIGS. 3, the results of the trained deep neural network were quantified by first calculating the pixel-level differences between the brightfield images of the chemically stained samples and the digitally/virtually stained images that are synthesized using the deep neural network without the use of any labels/stains. Table 4 below summarizes this comparison for different combinations of tissue types and stains, using the YCbCr color space, where the chroma components Cb and Cr entirely define the color, and Y defines the brightness component of the image. The results of this comparison reveal that the average difference between these two sets of images is <˜5% and <˜16%, for the chroma (Cb, Cr) and brightness (Y) channels, respectively. Next, a second metric (calibration), was used to further quantify the comparison, i.e., the structural similarity index (SSIM), which is in general used to “predict” the score that a human observer will give for an image, in comparison to a reference image (Equation 8 herein). SSIM ranges between 0 and 1, where 1 defines the score for identical images. The results of this SSIM quantification are also summarized in Table 4, which very well illustrates the strong structural similarity between the network output images 40 and the brightfield images 48 of the chemically stained samples);
carrying out a validation of the synthetic fluorescence images on the basis of a comparison between the reference images and the further synthetic fluorescence images (see page 3, paragraphs, [0031-0032] FIG. 14 illustrates a demonstration of the refocusing capability of the post imaging computational autofocusing method for various imaging planes of the samples, where the value of z signifies the focal distance from the focused plane, which resides in z=0 (as a reference plane). The refocusing capability of the network can be assessed both qualitatively and quantitatively, using the structural similarity index, which appears on the upper left corner of every panel, and compares that image to the reference in-focus image (at z=0). FIG. 15 illustrates a schematic of the machine learning-based approach that uses class condition to apply multiple stains to a microscope image to create a virtually stained image.
Also, page 12, paragraph, [0083] beyond the visual comparison provided in FIGS. 3A-3H, 4A-4H, 5A-5P, the results of the trained deep neural network 10 were quantified by first calculating the pixel-level differences between the brightfield images of the chemically stained samples and the digitally/virtually stained images that are synthesized using the deep neural network without the use of any labels/stains. Table 4 below summarizes this comparison for different combinations of tissue types and stains, using the YCbCr color space, where the chroma components Cb and Cr entirely define the color, and Y defines the brightness component of the image. The results of this comparison reveal that the average difference between these two sets of images is <˜5% and <˜16%, for the chroma (Cb, Cr) and brightness (Y) channels, respectively. Next, a second metric was used to further quantify the comparison, i.e., the structural similarity index (SSIM), which is in general used to predict the score that a human observer will give for an image, in comparison to a reference image (Equation 8 herein). SSIM ranges between 0 and 1, where 1 defines the score for identical images. The results of this SSIM quantification are also summarized in Table 4, which very well illustrates the strong structural similarity between the network output images and the brightfield images of the chemically stained samples.
Also, page 14, paragraph, [0092] An important part of the training process involves matching the fluorescence images of label-free tissue samples and their corresponding brightfield images after the histochemical staining process (i.e., chemically stained images). One should note that during the staining process and related steps, some tissue constitutes can be lost or deformed in a way that will mislead the loss/cost function in the training phase. This, however, is only a training and validation related challenge and does not pose any limitations on the practice of a well-trained deep neural network for virtual staining of label-free tissue samples. To ensure the quality of the training and validation phase and minimize the impact of this challenge on the network's performance, a threshold was established for an acceptable correlation value between the two sets of images (i.e., before and after the histochemical staining process) and eliminated the non-matching image pairs from the training/validation set to make sure that the deep neural network learns the real signal, not the perturbations to the tissue morphology due to the chemical staining process. In fact, this process of cleaning the training/validation image data can be done iteratively: one can start with a rough elimination of the obviously altered samples and accordingly converge on a neural network that is trained. After this initial training phase, the output images of each sample in the available image set can be screened against their corresponding brightfield images to set a more refined threshold to reject some additional images and further clean the training/validation image set. With a few iterations of this process, one can, not only further refine the image set, but also improve the performance of the final trained deep neural network.
Regarding claim 2, Ozcan discloses the method according to Claim 1, wherein the prediction algorithm is machine-learned, wherein the method furthermore comprises: driving the at least one imaging device in order to capture training images of at least the biological sample stained with the fluorescence marker by means of the microscopic fluorescence imaging during the calibration period, and carrying out a training of parameters of the machine-learned prediction algorithm on the basis of the training images as ground truth and at least one portion of the further measurement images (see claim 1, also page 12, paragraph, [0083] beyond the visual comparison provided in FIGS. 3A-3H, 4A-4H, 5A-5P, the results of the trained deep neural network 10 were quantified by first calculating the pixel-level differences between the brightfield images of the chemically stained samples and the digitally/virtually stained images that are synthesized using the deep neural network without the use of any labels/stains. Table 4 below summarizes this comparison for different combinations of tissue types and stains, using the YCbCr color space, where the chroma components Cb and Cr entirely define the color, and Y defines the brightness component of the image. The results of this comparison reveal that the average difference between these two sets of images is <˜5% and <˜16%, for the chroma (Cb, Cr) and brightness (Y) channels, respectively. Next, a second metric was used to further quantify the comparison, i.e., the structural similarity index (SSIM), which is in general used to predict the score that a human observer will give for an image, in comparison to a reference image (Equation 8 herein). SSIM ranges between 0 and 1, where 1 defines the score for identical images. The results of this SSIM quantification are also summarized in Table 4, which very well illustrates the strong structural similarity between the network output images and the brightfield images of the chemically stained samples.
Regarding claim 4, Ozcan discloses the method according to Claim 1, wherein the prediction algorithm is machine-learned, wherein the method furthermore comprises: driving the at least one imaging device in order to capture, by means of the microscopic fluorescence imaging, a plurality of training images of a further sample stained with the marker for the microscopic fluorescence imaging by means of the staining process, driving the at least one imaging device in order to capture a plurality of further measurement images by means of the microscopic imaging during the further calibration period, and carrying out a training of parameters of the machine-learned prediction algorithm on the basis of the training images as ground truth and the further measurement images captured during the further calibration period (see claim 1, also page 6, paragraph, [0050] the fluorescence microscope, obtains a fluorescence lifetime image of an unstained tissue sample and outputs an image that well matches a bright-field image of the same field-of-view after IHC staining. Fluorescence lifetime imaging (FLIM) produces an image based on the differences in the excited state decay rate from a fluorescent sample. Thus, FLIM is a fluorescence imaging technique where the contrast is based on the lifetime or decay of individual fluorophores. The fluorescence lifetime is generally defined as the average time that a molecule or fluorophore remains in an excited state prior to returning to the ground state by emitting a photon. Among all the intrinsic properties of unlabeled tissue samples, fluorescent lifetime of endogenous fluorophore(s) is one of the most informative channels that measures the time a fluorophore stays in excited stated before returning to ground state.
Also, page 12, paragraph, [0083] beyond the visual comparison provided in FIGS. 3A-3H, 4A-4H, 5A-5P, the results of the trained deep neural network 10 were quantified by first calculating the pixel-level differences between the brightfield images of the chemically stained samples and the digitally/virtually stained images 40 that are synthesized using the deep neural network without the use of any labels/stains. Table 4 below summarizes this comparison for different combinations of tissue types and stains, using the YCbCr color space, where the chroma components Cb and Cr entirely define the color, and Y defines the brightness component of the image. The results of this comparison reveal that the average difference between these two sets of images is <˜5% and <˜16%, for the chroma (Cb, Cr) and brightness (Y) channels, respectively. Next, a second metric was used to further quantify the comparison, i.e., the structural similarity index, which is in general used to predict the score that a human observer will give for an image, in comparison to a reference image (Equation 8 herein). SSIM ranges between 0 and 1, where 1 defines the score for identical images. The results of this SSIM quantification are also summarized in Table 4, which very well illustrates the strong structural similarity between the network output images and the brightfield images of the chemically stained samples).
Regarding claim 6, Ozcan discloses the method according to Claim 5, wherein the validation is carried out in a temporally resolved manner depending on the time position in the observation period (see claim 1, also pages 9-10, paragraphs, [0077] and [0079], the system 2 and methods described herein was tested and demonstrated using different combinations of tissue section samples 22 and stains. Following the training of a CNN-based deep neural network 10 its inference was blindly tested by feeding it with the auto-fluorescence images 20 of label-free tissue sections 22 that did not overlap with the images that were used in the training or validation sets. FIGS. 4A-4H illustrates the results for a salivary gland tissue section, which was digitally/virtually stained to match H&E stained brightfield images 48 (i.e., the ground truth images) of the same sample 22. These results demonstrate the capability of the system 2 to transform a fluorescence image 20 of a label-free tissue section 22 into a brightfield equivalent image 40, showing the correct color scheme that is expected from an H&E stained tissue, containing various constituents such as epithelioid cells, cell nuclei, nucleoli, stroma, and collagen. Evaluation of both FIGS. 3C and 3D show the H&E stains demonstrate a small island of infiltrating tumor cells within subcutaneous fibro-adipose tissue. The digitally/virtually-stained output images 40 from the trained, deep neural network 10 were compared to the standard histochemical staining images 48 for diagnosing multiple types of conditions on multiple types of tissues, which were either Formalin-Fixed Paraffin-Embedded (FFPE) or frozen sections. The results are summarized in Table 1 below. The analysis of fifteen (15) tissue sections by four board certified pathologists (who were not aware of the virtual staining technique) demonstrated 100% non-major discordance, defined as no clinically significant difference in diagnosis among professional observers. The “time to diagnosis” varied considerably among observers, from an average of 10 seconds-per-image for observer 2 to 276 seconds-per-image for observer 3. However, the intra-observer variability was very minor and tended towards shorter time to diagnosis with the virtually-stained slide images 40 for all the observers except observer 2 which was equal, i.e., ˜10 seconds-per-image for both the virtual slide image 40 and the histology stained slide image 48. These indicate very similar diagnostic utility between the two image modalities).
Regarding claim 7, Ozcan discloses the method according to claim 1, wherein the biological sample is unstained or is stained with a further fluorescence marker during the observation period (see claim 1, also page 10, paragraph, [0079] The digitally/virtually-stained output images 40 from the trained, deep neural network 10 were compared to the standard histochemical staining images 48 for diagnosing multiple types of conditions on multiple types of tissues, which were either Formalin-Fixed Paraffin-Embedded (FFPE) or frozen sections. The results are summarized in Table 1 below. The analysis of fifteen (15) tissue sections by four board certified pathologists (who were not aware of the virtual staining technique) demonstrated 100% non-major discordance, defined as no clinically significant difference in diagnosis among professional observers. The “time to diagnosis” varied considerably among observers, from an average of 10 seconds-per-image for observer 2 to 276 seconds-per-image for observer 3. However, the intra-observer variability was very minor and tended towards shorter time to diagnosis with the virtually-stained slide images 40 for all the observers except observer 2 which was equal, i.e., ˜10 seconds-per-image for both the virtual slide image 40 and the histology stained slide image 48. These indicate very similar diagnostic utility between the two image modalities).
With regard to claims 5 and 8-12 the arguments analogous to those presented above for claims 1, 2, 4, 6 and 7, are respectively applicable to claims 5 and 8-12.
Allowable Subject Matter
Claims 3, is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Seyed Azarian whose telephone number is (571) 272-7443. The examiner can normally be reached on Monday through Thursday from 6:00 a.m. to 7:30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Bella, can be reached at (571) 272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application information Retrieval (PAIR) system. Status information for published application may be obtained from either Private PAIR or Public PAIR.
Status information about the PAIR system, see http:// pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/SEYED H AZARIAN/Primary Examiner, Art Unit 2667
July 8, 2026
Prosecution Insights