CTNF 18/716,053 CTNF 82525 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an input interface, a data processing unit, and an output interface” in claim 14. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 07-30-02 AIA 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 1 recites the limitation "the region" in line 18. There is insufficient antecedent basis for this limitation in the claim. Claims 2-13 depends from claim 1, therefore they are rejected. 07-34-05 AIA Claim 9 recites the limitation " the feature maps " in line 2 . There is insufficient antecedent basis for this limitation in the claim. Claim 14 recites the limitation "the region" in line 21. There is insufficient antecedent basis for this limitation in the claim. Claims 15-18 depends from claim 14, therefore they are rejected. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1-5, 8-10, 12, 14 and 16 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by NPL1 (Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer, Ashnil Kumar et al., arXiv, Oct 2019, Pages 1-21) hereafter NPL1 . 1. Regarding claim 1, NPL1 discloses a method (Page 3 section II “Methods” and Pages 4-5 figs 1-3 shows and discloses a method) for fusing sensor data, comprising the following steps: a) receiving input sensor data, wherein the input sensor data (pages 3-4 section II A. “our dataset comprised 50 FDG PET-CT scans and section C discloses PET and CT images meeting the limitations of receiving input sensor data, examiner notes that the specifics of input sensor data are not required by the current claim) , comprise: - a first representation which comprises a first region of a scene (Fig 1 right side shows PET image (first representation which comprises a first region of a scene/site/location to be diagnosed) as an input to the PET encoder, description of Fig 1 on page 4 discloses “ the input to each modality-specific encoder is a 2D image slice of the corresponding modality ”, examiner notes that the specifics of a first representation and a first region of a scene are not required by the current claim) , and - a second representation which comprises a second region of the scene (Fig 1 left side shows CT image (second representation which comprises a second region of the scene/site/location to be diagnosed) as an input to the CT encoder, description of Fig 1 on page 4 discloses “ the input to each modality-specific encoder is a 2D image slice of the corresponding modality ”, examiner notes that the specifics of a second representation and a second region of the scene are not required by the current claim) , wherein the first and second regions overlap one another, but are not identical (as seen in Fig 1 the first and second regions in each of the CT and PET images overlap (i.e each regions shows the common to one another (overlap one another) portions of the organs and each look different in each of the images (i.e but are not identical) meeting the claim limitations) ; b) determining a first feature map with a first height and width on the basis of the first representation (Page 4, fig 1 and col 1 discloses “as shown in Fig 1. Each encoder comprises four blocks that each contain two convolution layer for feature map generation and a max pooling layer to down sample the feature maps” and page 4 section D discloses “each feature map of size w x h x c with w width and h height meeting the claim limitations determining a first feature map with a first height and width on the basis of the first representation) and determining a second feature map with a second height and width on the basis of the second representation (Page 4, fig 1 and col 1 discloses “as shown in Fig 1. Each encoder comprises four blocks that each contain two convolution layer for feature map generation and a max pooling layer to down sample the feature maps” and page 4 section D discloses “each feature map of size w x h x c with w width and h height meeting the claim limitations of determining a second feature map with a second height and width on the basis of the second representation meeting the claim limitations determining a second feature map with a second height and width on the basis of the second representation) ; c) computing a first output feature map by means of a first convolution of the first feature map (Page 4, fig 1 and col 1 discloses “as shown in Fig 1. Each encoder comprises four blocks that each contain two convolution layer for feature map generation and a max pooling layer to down sample the feature maps (i.e the first convolution of the first feature map (initial (first) feature map in the prior layer) to compute a first output feature map)” from the input PET images and page 4 col 2 discloses “ when the feature map is used as an input for subsequent convolutional layers ” and section D discloses “each feature map of size w x h x c with w width and h height meeting the claim limitations computing a first output feature map by means of a first convolution of the first feature map) , and computing a second output feature map by means of a second convolution of the second feature map (Page 4, fig 1 and col 1 discloses “as shown in Fig 1. Each encoder comprises four blocks that each contain two convolution layer for feature map generation and a max pooling layer to down sample the feature maps (i.e the second convolution of the second feature map (initial (second) feature map in the prior layer) to compute a second output feature map” from the input CT images and page 4 col 2 discloses “ when the feature map is used as an input for subsequent convolutional layers ” and page 4 section D discloses “each feature map of size w x h x c with w width and h height meeting the claim limitations of determining a second feature map with a second height and width on the basis of the second representation meeting the limitations of computing a second output feature map by means of a second convolution of the second feature map) ; d) computing a fused feature map through element-by-element addition of the first and second output feature maps, wherein a position of the first and the second region with respect to one another is taken into consideration used to compute the fused feature map, such that the elements in the region of overlap are added (Pages 4-5, Fig 2 shows performing a 3D convolution on the first output feature map Fpet (PET feature map) and the second output feature map Fct (CT feature map) using the 3D neighborhood defined by the width and height and as seen in page 5 equation 4 stacking operation is a element-wise operation meeting the above claim limitations, Fig 3 and its description underneath the description also shows fused feature map based on the element-wise multiplication ( PNG media_image1.png 60 191 media_image1.png Greyscale ) ; and e) outputting the fused feature map (Page 4 fig 2 shows the fused feature map output) . 2. Regarding claim 2, NPL1 discloses the method according to Claim 1, wherein the first and second output feature maps have the same height and width in the region of overlap (Page 4, fig 2, section D shows and discloses wherein the first and second output feature maps have the same height and width in the region of overlap) . 3. Regarding claim 3, NPL1 discloses the method according to Claim 1, wherein the height and width of the fused feature map are determined by a rectangle which surrounds the first and the second output feature map (Page 4 fig 2 shows the output fused feature map with a pink bounding box (with a height and the width) corresponding to each PET (red box) and the CT (blue box) feature maps meeting the above claim limitations) . 4. Regarding claim 4, NPL1 discloses the method according to Claim 1, wherein the first region is an overview region of the scene and the second region is a partial region of the overview region of the scene (Page 4, figs 1-2 shows wherein the first region is an overview region of the scene and the second region is a partial region of the overview region of the scene meeting the claim limitations, examiner notes that the specifics of the first region, overview region, second region and the partial regions are not required by the current claim) . 5. Regarding claim 5, NPL1 discloses the method according to Claim 1, wherein the first representation has a first resolution and the second representation has a second resolution, wherein the second resolution is higher than the first resolution (Page 3 section A discloses “CT resolution was 512X512 pixels (second resolution higher) and PET resolution was 200X200 pixels (first resolution) meeting the above claim limitations, examiner notes that the specifics of the first and the second resolutions are not required by the current claim) . 6. Regarding claim 8, NPL1 discloses the method according to Claim 4, wherein the second output feature map contains an entire region of overlap and wherein the fused feature map is calculated by element-by-element addition of the second output feature map to the first output feature map by means of suitable starting values only in the region of overlap (pages 4-5, figs 2-3 (description) shows and discloses wherein the second output feature map contains an entire region of overlap and wherein the fused feature map is calculated by element-by-element addition of the second output feature map to the first output feature map by means of suitable starting values only in the region of overlap) . 7. Regarding claim 9 as best understood by the examiner, NPL1 discloses the method according to Claim 1, wherein the feature maps each have a depth which depends on a resolution of at least one of the first representation or the second representation (pages 3-4 and fig 3 shows 3D convolution of the feature maps each have a depth and the resolution, examiner notes that the specifics of a depth and resolution are not required by the current claim) . 8. Regarding claim 10, NPL1 discloses the method according to Claim 1, wherein further comprising determining a determination of ADAS /AD relevant information occurs using the fused feature map (pages 2 and 10 discloses CNN could be extended in computer aided diagnosis (AD (aided diagnosis)) using the fused feature map meeting the above limitations, examiner notes that the specifics of the AD are not required by the current claim, Examiner notes that due to the recital of / only one is required to be met) . 9. Regarding claim 12, NPL1 discloses the method according to Claim 1, wherein the fused feature map is generated in an encoder of an artificial neural network which is configured to determine ADAS /AD-relevant information (fig 3 shows the co learning unit generating the fused feature map and pages 10, 12 col 1 discloses the CNN architectures with the encoder blocks for the computation of the fusion maps in the computer aided diagnosis (aided diagnosis (AD)) meeting the above claim limitations, Examiner notes that due to the recital of / only one is required to be met and the specifics of AD are not required by the current claim) . 10. Regarding claim 14, A system (Page 3 section II “Methods” and “Architecture Design” and Pages 4-5 figs 1-3 shows and discloses a method and a system) for fusing sensor data, comprising an input interface (page 3-4 section B discloses “the input to each encoder is an axial 2D image slices of the corresponding modality (i.e input sensor data)” meeting the claim limitations of the input interface, Also fig 1 shows the 2D image slices as input to modality specific encoders of the CNN architecture, examiner notes that the specifics of the input interface are not required by the current claim) , a data processing unit (figs 1-2, page 4 shows and discloses a multi-modality feature co-learning component which processes the input data meeting the limitations of the data processing unit. Examiner notes that the specifics of the data processing unit are not required by the current claim) and an output interface (page 4 fig 1 shows the “Reconstruction block” and page 5 discloses “the output feature map” from the last reconstruction block meeting the limitations of the output interface, examiner notes that the specifics of the output interface are not required by the current claim) , wherein a) the input interface is configured to receive input sensor data, wherein the input sensor data (page 3-4 section B discloses “the input to each encoder is an axial 2D image slices of the corresponding modality (i.e input sensor data)” meeting the claim limitations of the input interface, Also fig 1 shows the 2D image slices from the PET and CT modality sensors as input to the CNN architecture, pages 3-4 section II A. “our dataset comprised 50 FDG PET-CT scans and section C discloses PET and CT images meeting the limitations of receiving input sensor data, examiner notes that the specifics of input sensor data and the input interface are not required by the current claim) comprise: - a first representation which comprises a first region of a scene (Fig 1 right side shows PET image (first representation which comprises a first region of a scene/site/location to be diagnosed) as an input to the PET encoder, description of Fig 1 on page 4 discloses “ the input to each modality-specific encoder is a 2D image slice of the corresponding modality ”, examiner notes that the specifics of a first representation and a first region of a scene are not required by the current claim) , and - a second representation which comprises a second region of the scene (Fig 1 left side shows CT image (second representation which comprises a second region of the scene/site/location to be diagnosed) as an input to the CT encoder, description of Fig 1 on page 4 discloses “ the input to each modality-specific encoder is a 2D image slice of the corresponding modality ”, examiner notes that the specifics of a second representation and a second region of the scene are not required by the current claim) , wherein the first and second regions overlap one another, but are not identical (as seen in Fig 1 the first and second regions in each of the CT and PET images overlap (i.e each regions shows the common to one another (overlap one another) portions of the organs and each look different in each of the images (i.e but are not identical) meeting the claim limitations) ; b) the data processing unit (figs 1-2) is configured to: determine a first feature map with a first height and width on the basis of the first representation (Page 4, fig 1 and col 1 discloses “as shown in Fig 1. Each encoder comprises four blocks that each contain two convolution layer for feature map generation and a max pooling layer to down sample the feature maps” and page 4 section D discloses “each feature map of size w x h x c with w width and h height meeting the claim limitations determining a first feature map with a first height and width on the basis of the first representation) and determine a second feature map with a second height and width on the basis of the second representation (Page 4, fig 1 and col 1 discloses “as shown in Fig 1. Each encoder comprises four blocks that each contain two convolution layer for feature map generation and a max pooling layer to down sample the feature maps” and page 4 section D discloses “each feature map of size w x h x c with w width and h height meeting the claim limitations of determining a second feature map with a second height and width on the basis of the second representation meeting the claim limitations determining a second feature map with a second height and width on the basis of the second representation) ; c) compute a first output feature map by means of a first convolution of the first feature map (Page 4, fig 1 and col 1 discloses “as shown in Fig 1. Each encoder comprises four blocks that each contain two convolution layer for feature map generation and a max pooling layer to down sample the feature maps (i.e the first convolution of the first feature map (initial (first) feature map in the prior layer) to compute a first output feature map)” from the input PET images and page 4 col 2 discloses “ when the feature map is used as an input for subsequent convolutional layers ” and section D discloses “each feature map of size w x h x c with w width and h height meeting the claim limitations computing a first output feature map by means of a first convolution of the first feature map) , and compute a second output feature map by means of a second convolution of the second feature map (Page 4, fig 1 and col 1 discloses “as shown in Fig 1. Each encoder comprises four blocks that each contain two convolution layer for feature map generation and a max pooling layer to down sample the feature maps (i.e the second convolution of the second feature map (initial (second) feature map in the prior layer) to compute a second output feature map” from the input CT images and page 4 col 2 discloses “ when the feature map is used as an input for subsequent convolutional layers ” and page 4 section D discloses “each feature map of size w x h x c with w width and h height meeting the claim limitations of determining a second feature map with a second height and width on the basis of the second representation meeting the limitations of computing a second output feature map by means of a second convolution of the second feature map) ; and d) compute a fused feature map through element-by-element addition of the first and second output feature maps, wherein a position of the first and the second region with respect to one another is taken into consideration used when computing the fused feature map, such that a elements in the region of overlap are added (Pages 4-5, Fig 2 shows performing a 3D convolution on the first output feature map Fpet (PET feature map) and the second output feature map Fct (CT feature map) using the 3D neighborhood defined by the width and height and as seen in page 5 equation 4 stacking operation is a element-wise operation meeting the above claim limitations, Fig 3 and its description underneath the description also shows fused feature map based on the element-wise multiplication ( PNG media_image1.png 60 191 media_image1.png Greyscale ) ;; and e) the output interface is configured to output the fused feature map (page 5 discloses “the output feature map” from the last reconstruction block meeting the limitations of the output interface, Page 4 fig 2 shows the fused feature map output) . 11. Regarding claim 16, NPL1 discloses the system according to Claim 14, wherein the system comprises a convolutional neural network having an encoder and wherein the input interface, the data processing unit and the output interface are implemented in the encoder such that the encoder is configured to generate the fused feature map (figs 1-2 shows the CNN architecture with an encoder and wherein the input interface (page 3-4 section B discloses “the input to each encoder is an axial 2D image slices of the corresponding modality (i.e input sensor data)” meeting the claim limitations of the input interface, Also fig 1 shows the 2D image slices as input to modality specific encoders of the CNN architecture, examiner notes that the specifics of the input interface are not required by the current claim), the data processing unit (figs 1-2, page 4 shows and discloses a multi-modality feature co-learning component which processes the input data meeting the limitations of the data processing unit. Examiner notes that the specifics of the data processing unit are not required by the current claim) and the output interface (page 4 fig 1 shows the “Reconstruction block” and page 5 discloses “the output feature map” from the last reconstruction block meeting the limitations of the output interface, examiner notes that the specifics of the output interface are not required by the current claim) are implemented in the encoder such that the encoder is configured to generate the fused feature map (figs 1-2)) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over NPL1 in view of NPL2 (Fused-Layer CNN Accelerators, Manoj Alwani et al., IEEE, 2016, Pages 1-12) hereafter NPL2 . 12. Regarding claim 11, NPL1 discloses the method according to Claim 1. NPL1 disclose and shows the method implemented by the convolution neural network (See figs 1-3). NPL1 is silent and however fails to disclose wherein the method is implemented in a hardware accelerator for an artificial neural network. NPL2 discloses wherein the method is implemented in a hardware accelerator for an artificial neural network (Page 2 cols 1-2 discloses the method is implemented in a hardware accelerator for an artificial neural network) . Before the effective filing date of the invention was made, NPL1 and NPL2 are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be an increased throughput, reduced data and maximum performance and highly efficient system/method on page 6 cols 1-2. Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of NPl2 in the method of NPL1 to obtain the invention as specified in claim 11. 13. Regarding claim 15, NPL1 discloses the system according to Claim 14. NPL1 disclose and shows the method implemented by the convolution neural network/hardware (See figs 1-3). NPL1 is silent and however fails to disclose wherein the system comprises a CNN hardware accelerator, wherein the input interface, the data processing unit and the output interface are implemented in the CNN hardware accelerator. NPL2 discloses wherein the system comprises a CNN hardware accelerator, wherein the input interface, the data processing unit and the output interface are implemented in the CNN hardware accelerator (Fig 3, Pages 2-3, page 5 cols 1-2 discloses the method is implemented in a hardware accelerator the for an artificial neural network with the input interface (initial computation layer), data processing unit (intermediate processing layer) and the output interface (i.e output features) meeting the above claim limitations) . Before the effective filing date of the invention was made, NPL1 and NPL2 are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be an increased throughput, reduced data and maximum performance and highly efficient system/method on page 6 cols 1-2. Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of NPl2 in the method of NPL1 to obtain the invention as specified in claim 15 . 07-21-aia AIA Claim s 13, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over NPL1 in view of Sistu Ganesh et al., (DE102018114231A1) hereafter Ganesh . 14. Regarding claim 13, NPL1 discloses the method according to Claim 12. NPL discloses wherein the artificial neural network which is configured to determine fusion map using encoder as seen in Figs 1-2. NPL1 however is silent and fails to disclose ADAS /AD relevant information comprises multiple decoders for different ADAS /AD detection functions. Ganesh discloses wherein the convolutional neural network comprises multiple decoders which are configured to realize different ADAS /AD detection functions (figs 1- 2, pages 5-6 shows and discloses the CNN system 36 with Decoder 40 for detecting objects 16 and decoder 44 for detecting objects 18 configured to realize different ADAS output functions meeting the claim limitations) . Before the effective filing date of the invention was made, NPL1 and Ganesh are combinable because they are from the same field of endeavor and are analogous art of image processing. The suggestion/motivation would be an improved system/method (page 2 lines 34-41) . Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of Ganesh in the method/system of NPL1 to obtain the invention as specified in claim 13. 15. Regarding claim 17, NPL1 discloses the system according to Claim 16 and the CNN with multiple encoders realizing the detection functions on the basis of the fused feature map as seen in Figs 1-2. NPL1 however is silent and fails to disclose wherein the convolutional neural network comprises multiple decoders which are configured to realize different ADAS /AD detection functions at least on the basis of the fused feature map . Ganesh discloses wherein the convolutional neural network comprises multiple decoders which are configured to realize different ADAS /AD detection functions (figs 1- 2, pages 5-6 shows and discloses the CNN system 36 with Decoder 40 for detecting objects 16 and decoder 44 for detecting objects 18 configured to realize different ADAS output functions meeting the claim limitations) . Before the effective filing date of the invention was made, NPL1 and Ganesh are combinable because they are from the same field of endeavor and are analogous art of image processing. The suggestion/motivation would be an improved system/method (page 2 lines 34-41) . Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of Ganesh in the method/system of NPL1 to obtain the invention as specified in claim 17. 16. Regarding claim 18, NPL1 and Ganesh discloses the system according to Claim 17. Ganesh discloses further comprising an ADAS /AD controller, wherein the ADAS /AD controller is configured to realize ADAS /AD functions at least on the basis of the results of the ADAS /AD detection functions (figs 1-2 shows the CNN system 26/36 with ADAS controller 34 configured to realize ADAS detection functions meeting the above claim limitations) . Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAYESH PATEL whose telephone number is (571)270-1227. The examiner can normally be reached IFW Mon-FRI. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at 571-270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JAYESH A PATEL/Primary Examiner, Art Unit 2677 JAYESH PATEL Primary Examiner Art Unit 2677 Application/Control Number: 18/716,053 Page 2 Art Unit: 2677 Application/Control Number: 18/716,053 Page 3 Art Unit: 2677 Application/Control Number: 18/716,053 Page 4 Art Unit: 2677 Application/Control Number: 18/716,053 Page 5 Art Unit: 2677 Application/Control Number: 18/716,053 Page 6 Art Unit: 2677 Application/Control Number: 18/716,053 Page 7 Art Unit: 2677 Application/Control Number: 18/716,053 Page 8 Art Unit: 2677 Application/Control Number: 18/716,053 Page 9 Art Unit: 2677 Application/Control Number: 18/716,053 Page 10 Art Unit: 2677 Application/Control Number: 18/716,053 Page 11 Art Unit: 2677 Application/Control Number: 18/716,053 Page 12 Art Unit: 2677 Application/Control Number: 18/716,053 Page 13 Art Unit: 2677 Application/Control Number: 18/716,053 Page 14 Art Unit: 2677 Application/Control Number: 18/716,053 Page 15 Art Unit: 2677 Application/Control Number: 18/716,053 Page 16 Art Unit: 2677 Application/Control Number: 18/716,053 Page 17 Art Unit: 2677 Application/Control Number: 18/716,053 Page 18 Art Unit: 2677 Application/Control Number: 18/716,053 Page 19 Art Unit: 2677 Application/Control Number: 18/716,053 Page 20 Art Unit: 2677 Application/Control Number: 18/716,053 Page 21 Art Unit: 2677
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