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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims (1, 3-6, 8-10, 12-18, 20) are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more.
Claims (1, 3-6, 8-10, 12-18, 20) are directed to the abstract idea of Mental processes – concepts performed in the human mind (including an observation, evaluation, judgement, opinion). Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations.
“processing an image of tissue; inputting image data comprising a plurality of pixels into a first trained model, the first trained model generating a value corresponding to each of a plurality of pixels representing a feature relevant to disease diagnosis; wherein the output of the first filter and the second filter are combined and input into the subsequent layer”.
This judicial exception is not integrated into a practical application. The claims recite additional limitations such “processing an image of tissue; inputting image data comprising a plurality of pixels into a first trained model, the first trained model generating a value corresponding to each of a plurality of pixels representing a feature relevant to disease diagnosis; wherein the output of the first filter and the second filter are combined and input into the subsequent layer”. However, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims recite additional limitations which are “convolutional neural network; first filter; second filter; a system; processor; microscope; imaging device”. However, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, since there are no limitations in the claims (1, 3-6, 8-10, 12-18, 20) that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as a combination and as an ordered combination adds nothing that is not already present when looking at the elements taken individually, claims (1, 3-6, 8-10, 12-18, 20) are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to software per se.
1) a carrier medium is not a process because it is not a serial steps, 2) a carrier medium has no physical structure, thus it does not fit within the definition of a machine, 3) a carrier medium is not a matter and is not a composition of matter, and 4) a carrier medium does not fit the definition of manufacture.
Claim Rejections - 35 USC § 112
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 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation "the modified data" in line 2. There is insufficient antecedent basis for this limitation in the claim.
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. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); 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); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims (1, 3-8, 10, 12-20) are rejected on the ground of nonstatutory double patenting as being unpatentable over claims (1-2, 8-10, 13-17) of U.S. Patent No. 12,175,661 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following reasons:
Instant application
US Patent
1. A computer implemented method of processing an image of tissue, comprising: inputting image data comprising a plurality of pixels into a first trained model comprising a convolutional neural network, the first trained model generating a value corresponding to each of a plurality of pixels representing a feature relevant to disease diagnosis, wherein the first trained model comprises a convolutional neural network comprising a layer in which a first filter and a second filter are applied, at least one of the first filter and the second filter comprising a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer.
1. A computer implemented method of processing an image of tissue, comprising: inputting image data corresponding to the image of tissue into a first trained model comprising a convolutional neural network, the first trained model generating a first set of output data, wherein the first set of output data represents a feature of the image relevant to disease diagnosis; inputting the image data corresponding to the image of tissue into a second trained model comprising a convolutional neural network, the second trained model generating a second set of output data, wherein the second set of output data represents a feature of the image relevant to disease diagnosis; combining the first set of output data and the second set of output data; generating diagnostic information from the combined data using a third trained model; and generating a diagnosis from the diagnostic information and context information using a fourth trained model.
6. The method according to claim 1, wherein the convolutional neural network of the first trained model comprises a layer in which a first filter and a second filter are applied, at least one of the first filter and the second filter comprising a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer, and wherein the convolutional neural network of the second trained model comprises a layer in which a third filter and a fourth filter are applied, at least one of the third filter and the fourth filter comprising a dilated convolution, wherein the output of the third filter and the fourth filter are combined and input into the subsequent layer.
10. A computer implemented method of training a system for processing an image of tissue, comprising: inputting training image data comprising a plurality of pixels into a first model, the first model generating a value corresponding to each of a plurality of pixels representing a feature relevant to disease diagnosis, wherein the first model comprises a convolutional neural network comprising a layer in which a first filter and a second filter are performed, at least one of the first filter and the second filter being a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer; and training the first model using training data labels.
14. A computer implemented method of training a system for processing an image of tissue, comprising: inputting training image data into a first model comprising a convolutional neural network, the first model generating a first set of output data, wherein the first set of output data represents a feature of the image relevant to disease diagnosis; inputting training image data into a second model comprising a convolutional neural network, the second model generating a second set of output data, wherein the second set of output data represents a feature of the image relevant to disease diagnosis; generating diagnostic information from training data using a third model; generating a diagnosis from the diagnostic information and context information using a fourth model; training the first model using training data labels; training the second model using training data labels; training the third model using training data labels; and training the fourth model using training data labels.
6. The method according to claim 1, wherein the convolutional neural network of the first trained model comprises a layer in which a first filter and a second filter are applied, at least one of the first filter and the second filter comprising a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer, and wherein the convolutional neural network of the second trained model comprises a layer in which a third filter and a fourth filter are applied, at least one of the third filter and the fourth filter comprising a dilated convolution, wherein the output of the third filter and the fourth filter are combined and input into the subsequent layer.
17. A system for processing an image of tissue, comprising: an input; an output; a processor configured to: input received image data comprising a plurality of pixels into a first trained model, the first trained model generating a value corresponding to each of a plurality of pixels representing a feature relevant to disease diagnosis, wherein the first trained model comprises a convolutional neural network comprising a layer in which a first filter and a second filter are performed, at least one of the first filter and the second filter being a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer.
15. A system for processing an image of tissue, comprising: an input for receiving image data corresponding to the image of tissue; an output for outputting diagnostic information; a processor configured to: input received image data corresponding to the image of tissue into a first trained model comprising a convolutional neural network, the first trained model generating a first set of output data, wherein the first set of output data represents a feature of the image relevant to disease diagnosis; input the received image data corresponding to the image of tissue into a second trained model comprising a convolutional neural network, the second trained model generating a second set of output data, wherein the second set of output data represents a feature of the image relevant to disease diagnosis; combine the first set of output data and the second set of output data; generate diagnostic information from the combined data using a third trained model; and generate a diagnosis from the diagnostic information and context information using a fourth trained model.
6. The method according to claim 1, wherein the convolutional neural network of the first trained model comprises a layer in which a first filter and a second filter are applied, at least one of the first filter and the second filter comprising a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer, and wherein the convolutional neural network of the second trained model comprises a layer in which a third filter and a fourth filter are applied, at least one of the third filter and the fourth filter comprising a dilated convolution, wherein the output of the third filter and the fourth filter are combined and input into the subsequent layer.
The instant application differs from the US Patent based on the underlined portion. However, the underlined portion is taught/suggested in claim 6. Therefore, it would have been obvious to one of ordinary skills in the art to incorporate this obvious variation into the US Patent, in the manner as claimed and as taught by the instant application, for the benefit of achieving disease diagnosis. Claims (3-8, 12-16, 18-20) have been analyzed and rejected w/r to claims (8-10, 13-17).
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.
Claim(s) (1-3, 5, 8, 10-12, 14, 16-18, 20) are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (hereinafter Wang)(US Publication 2020/0085290 A1) in view of Claessen et al. (hereinafter Claessen)(US Publication 2021/0174543 A1)
Re claim 1, Wang discloses a computer implemented method of processing an image of tissue, comprising: inputting image data comprising a plurality of pixels into a first trained model comprising a convolutional neural network (See fig. 3: 10A; ¶ 48-49, 83-85 where it teaches inputting medical images into a CNN.), the first trained model generating a value corresponding to each of a plurality of pixels representing a feature relevant to disease diagnosis. (See ¶ 84, 98-102 where it teaches generating values that may be interpreted as the probability of being classified into a particular category.)
While these features may be known in the art, the reference of Wang fails to explicitly teach wherein the first trained model comprises a convolutional neural network comprising a layer in which a first filter and a second filter are applied, at least one of the first filter and the second filter comprising a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer.
However, Claessen does. (See fig. 5; ¶ 111-114) In the same field of endeavors, the reference of Claessen discloses and fairly suggests wherein the first trained model (See ¶ 111 where it teaches a 3D deep neural network architecture.) comprises a convolutional neural network comprising a layer in which a first filter and a second filter are applied (See ¶ 112 where it teaches the 3D CNN de-CNN layers may vary in their amount of filters, filter sizes and subsampling parameters.), at least one of the first filter and the second filter comprising a dilated convolution (See ¶ 112 where it teaches a variety of 3D NN layers, such as dilated convolutional layers (3D CNNs).), wherein the output of the first filter and the second filter are combined and input into the subsequent layer. (See fig. 5: 520, 526, 532; ¶ 114 where it teaches by combining 520, 526, 532 data resulting from such 3D de-CNN layers 518, 524, 534 with the data from the ‘last’ 3D CNN layers operating on the same resolution (512 to 520, 508 to 526 and 504 to 532), highly accurate predictions may be achieved.)
Therefore, taking the combined teachings of Wang & Claessen as a whole, it would have been obvious to one of ordinary skills in the art to incorporate these features into the system of Wang, in the manner as claimed and as taught by Claessen, for the benefit of achieving accurate predictions. (See ¶ 114)
Re claim 2, the combination of Wang & Claessen discloses wherein the first trained model performs image segmentation, whereby each pixel is classified into one of a set of categories, the categories corresponding features relevant to disease diagnosis. (In Wang, see fig. 3; ¶ 44-49)
Re claim 3, the combination of Wang & Claessen discloses wherein the first filter is a dilated convolution having a first dilation factor and the second filter is a dilated convolution having a second dilation factor. (In Claessen, see fig. 5; ¶ 112)
Re claim 5, the combination of Wang & Claessen discloses wherein the convolutional neural network comprises a first layer configured to generate an output having a dimension smaller than the output of a previous layer, and a second layer subsequent to the first layer, wherein the input to the second layer is generated from the input to the first layer or a layer prior to the first layer and the output of the layer prior to the second layer. (In Claessen, see fig. 5; ¶ 111-114)
Re claim 8, the combination of Wang & Claessen discloses wherein the value indicates one of the following features: regions of interest in the image, coordinates of dividing cells, or segments of various tissues. (In Wang, see fig. 3; ¶ 84-85, 95-96)
Re claim 10, Wang discloses a computer implemented method of training a system for processing an image of tissue, comprising: inputting training image data comprising a plurality of pixels into a first model (See fig. 3: 10A; ¶ 48-49, 83-85 where it teaches inputting medical images into a CNN.), the first model generating a value corresponding to each of a plurality of pixels representing a feature relevant to disease diagnosis (See ¶ 84, 98-102 where it teaches generating values that may be interpreted as the probability of being classified into a particular category.), and training the first model using training data labels. (See ¶ 105-112 where it teaches training the CNN using training data.)
While these features may be known in the art, the reference of Wang fails to explicitly teach wherein the first model comprises a convolutional neural network comprising a layer in which a first filter and a second filter are performed, at least one of the first filter and the second filter being a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer.
However, Claessen does. (See fig. 5; ¶ 111-114) In the same field of endeavors, the reference of Claessen discloses and fairly suggests wherein the first model (See ¶ 111 where it teaches a 3D deep neural network architecture.) comprises a convolutional neural network comprising a layer in which a first filter and a second filter are performed (See ¶ 112 where it teaches the 3D CNN de-CNN layers may vary in their amount of filters, filter sizes and subsampling parameters.), at least one of the first filter and the second filter being a dilated convolution (See ¶ 112 where it teaches a variety of 3D NN layers, such as dilated convolutional layers (3D CNNs).), wherein the output of the first filter and the second filter are combined and input into the subsequent layer. (See fig. 5: 520, 526, 532; ¶ 114 where it teaches by combining 520, 526, 532 data resulting from such 3D de-CNN layers 518, 524, 534 with the data from the ‘last’ 3D CNN layers operating on the same resolution (512 to 520, 508 to 526 and 504 to 532), highly accurate predictions may be achieved.)
Therefore, taking the combined teachings of Wang & Claessen as a whole, it would have been obvious to one of ordinary skills in the art to incorporate these features into the system of Wang, in the manner as claimed and as taught by Claessen, for the benefit of achieving accurate predictions. (See ¶ 114)
Re claim 11, the combination of Wang & Claessen discloses wherein the first model performs image segmentation, whereby each pixel is classified into one of a set of categories, the categories corresponding features relevant to disease diagnosis. (In Wang, see fig. 3; ¶ 44-49)
Re claim 12, the combination of Wang & Claessen discloses wherein the first filter is a dilated convolution having a first dilation factor and the second filter is a dilated convolution having a second dilation factor. (In Claessen, see fig. 5; ¶ 112)
Re claim 14, the combination of Wang & Claessen discloses wherein the convolutional neural network comprises a first layer configured to generate an output having a dimension smaller than the output of a previous layer, and a second layer subsequent to the first layer, wherein the input to the second layer is generated from the input to the first layer or a layer prior to the first layer and the output of the layer prior to the second layer. (In Claessen, see fig. 5; ¶ 111-114)
Re claim 16, the combination of Wang & Claessen discloses wherein the value indicates one of the following features: regions of interest in the image, coordinates of dividing cells, or segments of various tissues. (In Wang, see fig. 3; ¶ 84-85, 95-96)
Claim 17 has been analyzed and rejected w/r to claim 1 above.
Re claim 18, the combination of Wang & Claessen discloses wherein the input comprises a microscope and a digital imaging device configured to capture images of tissue through the microscope. (In Wang, see ¶ 48)
Claim 20 has been analyzed and rejected w/r to claim 1 above.
Claim(s) (6-7, 15, 19) are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (hereinafter Wang)(US Publication 2020/0085290 A1) in view of Claessen et al (hereinafter Claessen)(US Publication 2021/0174543 A1), as applied to claims (1, 10, 17,20) above, and further in view of Garnavi. (US Patent 10,002,311 B1)
Re claim 6, the combination of Wang & Claessen fails to teach generating multiple versions of the modified data; and adapting the first trained model using the multiple versions.
However, Garnavi does. (See fig. 2; col. 9, lines 22-67) In the same field of endeavors, the reference of Garnavi discloses and fairly suggests generating multiple versions of the modified data; and adapting the first trained model using the multiple versions.
Therefore, taking the combined teachings of Wang, Claessen, and Garvavi as a whole, it would have been obvious to one of ordinary skills in the art to incorporate these features into the system of Wang, as modified by Claessen, in the manner as claimed and as taught by Garnavi, for the benefit of training the CNN based on user input.
Claims (7, 15, 19) have been analyzed and rejected w/r to claim 6 above.
Claim(s) (4, 13) are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (hereinafter Wang)(US Publication 2020/0085290 A1) in view of Claessen et al (hereinafter Claessen)(US Publication 2021/0174543 A1), as applied to claims (1, 10, 17,20) above, and further in view of Schulz-Trieglaff et al. (hereinafter Shulz-Trieglaff) (US Publication 2019/0220704 A1)
Re claim 4, the combination of Wang & Claessen fails to teach wherein the convolutional neural network comprises at least one skip connection.
However, Shulz-Trieglaff does. (See ¶ 194) In the same field of endeavors, the reference of Garnavi discloses and fairly suggests wherein the convolutional neural network comprises at least one skip connection.
Therefore, taking the combined teachings of Wang, Claessen, and Shulz-Trieglaff as a whole, it would have been obvious to one of ordinary skills in the art to incorporate these features into the system of Wang, as modified by Claessen, in the manner as claimed and as taught by Shulz-Trieglaff, for the benefit of optimizing the CNN architecture.
Claim 13 has been analyzed and rejected w/r to claim 4 above.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (hereinafter Wang)(US Publication 2020/0085290 A1) in view of Claessen et al (hereinafter Claessen)(US Publication 2021/0174543 A1), as applied to claims (1, 10, 17,20) above, and further in view of Yu. (US Publication 2019/0147592 A1)
Re claim 9, the combination of Wang & Claessen fails to teach obtaining a whole side image; and splitting the whole slide image into contiguous tiles, wherein each tile is taken as input to the first trained model one at a time.
However, Yu does. (See ¶ 30) In the same field of endeavors, the reference of Garnavi discloses and fairly suggests obtaining a whole side image; and splitting the whole slide image into contiguous tiles, wherein each tile is taken as input to the first trained model one at a time.
Therefore, taking the combined teachings of Wang, Claessen, and Yu as a whole, it would have been obvious to one of ordinary skills in the art to incorporate these features into the system of Wang, as modified by Claessen, in the manner as claimed and as taught by Yu for the benefit of analyzing large amount of image information. (See ¶ 5)
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON FLORES whose telephone number is (571)270-1201. The examiner can normally be reached M-F 8am - 6pm.
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, HENOK SHIFERAW can be reached at 571-272-4637. 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.
/LEON FLORES/Primary Examiner, Art Unit 2676 August 27, 2026
Prosecution Insights