DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This is an initial office action in response to communication(s) filed on December 18, 2024.
Claims 1-15 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 18, 2024 and January 27, 2025 were filed in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non- structural term having no specific structural meaning) for performing the claimed function;
the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for’) or another linking word or phrase, such as “configured to” or “so that’; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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-AlA 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 acquisition unit”, “a calculation unit” and “a creation unit” of indep. Claim 15.
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. These limitations: “an acquisition unit”, “a calculation unit” and “a creation unit” of indep. Claim 15 are covered by the structure(s) from the original disclosure as following:
“an acquisition unit” (in Specification, in fig. 1, para. 57, disclose “the control unit 101” functions as the acquisition mean),
“a calculation unit” (in Specification, in fig. 1, para. 57, disclose “the control unit 101” functions as the calculation mean), and
“a creation unit” (in Specification, in fig. 1, para. 57, disclose “the control unit 101” functions as the creation mean) of indep. Claim 15.
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-AlA 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-11 and 14-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Oi (U.S. Pub. Nol. 2020/0258230 A1, hereinafter as “Oi”).
With regard to claim 1, the claim is drawn to an information processing apparatus (see Oi, i.e. in fig. 1-2, and etc., disclose the image processing apparatus) comprising:
one or more processors (see Oi, i.e. in fig. 2, para. 45 and etc., disclose the image processing apparatus 100, comprises a CPU 1002); and
one or more memories including instructions that, when executed by the one or more processors (see Oi, i.e. in fig. 2, disclose the HDD 1008, RAM 1006, ROM 1004 and etc.), cause the information processing apparatus to:
acquire first deformation data indicating a deformation included in a first inspection image obtained by capturing a first region, and second deformation data indicating a deformation included in a second inspection image obtained by capturing a second region including at least a part of the first region at a timing newer than a timing at which the first inspection image is captured (see Oi, i.e. in fig. 3-9, para. 57-60 and etc., disclose that “[0057] First, as illustrated in FIG. 3, in step S101, the patch cut unit 12 reads data of an image on which the patch cut step is to be performed. When the patch cut step is performed on the learning image, the patch cut unit 12 reads the learning image from the learning data storing unit 10. On the other hand, when the patch cut step is performed on the inspection image, the patch cut unit 12 reads image data from the inspection data storing unit 20. [0058] Next, in step 5102, the patch cut unit 12 performs a patch cut process on the image read in step 5101. As illustrated in FIG. 4, an image IM read in step 5101 includes an inspection target article T. The image IM is a learning image or an inspection image. When the image IM is a learning image, the inspection target article T included in the image IM is a normal article. When the image IM is an inspection image, the inspection target article T included in the image IM is an article that is to be determined whether it is a normal article or a defect article. [0059] In the patch cut process, as illustrated in FIG. 4, the patch cut unit 12 cuts out a rectangular image having a preset patch size from the read image IM as a patch image IMp, for example, from the left upper of the image IM. Note that the patch size used for cutting the patch image IMp can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the image IM. [0060] Moreover, in the patch cut process, as illustrated in FIG. 5, the patch cut unit 12 cuts out and removes a rectangular image having a preset size from the center part of the cut patch image IMp. In such a way, the patch cut unit 12 creates a center-removed patch image IMr, which is a frame-shape patch image IMp for which an image of the center part has been removed, and creates a center image IMc, which is an image cut out of the center part of the patch image IMp. The patch image IMp obtained before the patch cut process is performed includes the center-removed patch image IMr that is a first region and the center image IMc that is a second region and can be considered to be formed of both images. Note that the size at which the center image IMc is cut out can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the patch image IMp….”);
calculate difference deformation data indicating a difference between the first deformation data and the second deformation data (see Oi, i.e. fig. 7, 10, 12, para. 84, 89-92 and etc.,, disclose that “[0084] As a method for detecting a defect article, a scheme for preparing a template of a normal article to detect a defect article based on a difference between the template and an inspection image without using machine learning is considered. In such a method using a template, however, detection of a defect article may be affected by an individual difference of inspection target articles, that is, an individual difference of inspection images…”); and
create learning data for causing, to learn, a model that predicts an appearance of the deformation based on the difference deformation data (see Oi, i.e. in fig. 1, 7-9, para. 37, and etc., disclose that “[0037] The learning model storage unit 18 stores the trained learning model created as a result of learning performed by the learning unit 16…”).
With regard to claim 2, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is created in units of inspection images based on the difference deformation data (Oi, i.e. in para. 36-37 and etc., disclose that “[0036] The learning unit 16 reads an image pair for a learning image from the patch-processed data storing unit 14 and creates a learning model by using the read image pair. The learning unit 16 performs learning by using a center-removed patch image as learning data and a center image as training data out of an image pair for a learning image and creates a learning model used for restoring a center image from the center-removed patch image. The learning unit 16 stores the created learning model in the learning model storage unit 18. [0037] The learning model storage unit 18 stores the trained learning model created as a result of learning performed by the learning unit 16…”).
With regard to claim 3, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is created in units of regions obtained by dividing an inspection image based on the difference deformation data (see Oi, i.e. in para. 33-34 and etc., disclose that “[0033] The patch cut unit 12 reads learning image data from the learning data storing unit 10 and performs a patch cut step on the learning image. That is, the patch cut unit 12 cuts out a patch size image as a patch image from a learning image. Further, the patch cut unit 12 cuts out an image of the center part as a center image from the patch image. The center image includes at least a predetermined region of an inspection target article. The patch cut unit 12 outputs a pair of a center-removed patch image, which is a patch image from which the center image has been cut out, and the center image thereof in association with each other as an image pair. The patch cut unit 12 stores the image pair output for the learning image in the patch-processed data storing unit 14. [0034] Further, the patch cut unit 12 reads inspection image data from the inspection data storing unit 20 and performs a patch cut process on an inspection image as with the case of the learning image. The patch cut unit 12 outputs a pair of a center-removed patch image and the center image thereof in association with each other as an image pair for the inspection image as with the case of the learning image. The patch cut unit 12 stores the image pair output for the inspection image in the patch-processed data storing unit 14…”).
With regard to claim 4, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is created in units of the difference deformation data based on the difference deformation data (see Oi, i.e. in para. 84 and etc., disclose that “[0084] As a method for detecting a defect article, a scheme for preparing a template of a normal article to detect a defect article based on a difference between the template and an inspection image without using machine learning is considered. In such a method using a template, however, detection of a defect article may be affected by an individual difference of inspection target articles, that is, an individual difference of inspection images….”).
With regard to claim 5, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is classified and created based on the difference deformation data (see Oi, i.e. in para. 31-33, 84 and etc., disclose that “[0031] As illustrated in FIG. 1, an image processing apparatus 100 according to the present example embodiment has a learning data storing unit 10, a patch cut unit 12, and a patch-processed data storing unit 14. Further, the image processing apparatus 100 has a learning unit 16 and a learning model storage unit 18. Moreover, the image processing apparatus 100 has an inspection data storing unit 20, a normal image generation unit 22, a generated normal data storing unit 24, and a defect article detection unit 26. [0032] The learning data storing unit 10 stores a learning image used for learning performed by the learning unit 16. The learning image is an image including a normal article of an inspection target article, that is, an image representing a normal state of the inspection target article.”; also, “[0084] As a method for detecting a defect article, a scheme for preparing a template of a normal article to detect a defect article based on a difference between the template and an inspection image without using machine learning is considered. In such a method using a template, however, detection of a defect article may be affected by an individual difference of inspection target articles, that is, an individual difference of inspection images….”).
With regard to claim 6, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is created based on a type of the second deformation data with respect to the first deformation data associated with the difference deformation data (see Oi, i.e. in para. 90 and etc., disclose that “[0090] Note that, when a difference between two types of center images is calculated, two types of center images of RGB images or other color images can be used not only for calculation of a difference directly but also for calculation of a difference after performing conversion into another type of images or images defined by another color space and a filtering process, for example. For example, the two types of center images can be used for calculation of a difference after converted into another type of images such as gray scale images or binary images, or images defined by another color space such as HSV or YCbCr. Further, two types of center images can be used for calculation of a difference after a filtering process using a preprocessing filter such as an averaging filter, a median filter, or the like or an edge extraction filter such as a Sobel filter or a Laplacian filter, for example is performed thereon…”).
With regard to claim 7, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is classified and created based on a number of pieces of difference deformation data (see Oi, i.e in para. 97 and etc., disclose that “[0097] Next, in step S407, the defect article detection unit 26 detects a defect article and outputs a detection result based on the determination result output in step S406. In detection of a defect article, if the number of center images determined to be defective for an inspection image is zero or less than or equal to a predetermined number, the defect article detection unit 26 determines that the inspection target article included in the inspection image is a normal article. On the other hand, if the number of center images determined as defective for the inspection image exceeds the predetermined number, the defect article detection unit 26 determines that the inspection target article included in the inspection image is a defect article…”).
With regard to claim 8, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is classified and created based on a length of pieces of difference deformation data (see Oi, i.e. in para. 59 and etc., disclose that “[0059] In the patch cut process, as illustrated in FIG. 4, the patch cut unit 12 cuts out a rectangular image having a preset patch size from the read image IM as a patch image IMp, for example, from the left upper of the image IM. Note that the patch size used for cutting the patch image IMp can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the image IM…”).
With regard to claim 9, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is displayed (see Oi, i.e. in para. 43, discloses that “[0043] The defect article detection unit 26 outputs a detection result of a defect article. An output method of the detection result is not particularly limited, and various methods may be used. For example, the defect article detection unit 26 can cause a display device to display a detection result, output the detection result as a voice from an audio output device, and store the detection result in a database stored in a storage device…”).
With regard to claim 10, the claim is drawn to the information processing apparatus according to claim 1, wherein in creation of the learning data, the learning data is edited based on a learning data instruction received from a user (see Oi, i.e. in para. 51 and etc., disclose that “[0051] The input device 1012 is a keyboard, a mouse, or the like, for example. Further, the input device 1012 may be a touch panel embedded in a display device that is the output device 1010. An operator of the image processing apparatus 100 can set the image processing apparatus 100 via the input device 1012 or can input an instruction of performing a process….”).
With regard to claim 11, the claim is drawn to the information processing apparatus according to claim 1, wherein in calculation of the difference deformation data, a misalignment between the first inspection image and the second inspection image is corrected, and the difference deformation data is calculated (see Oi, i.e in para. 84, disclose that “[0084] As a method for detecting a defect article, a scheme for preparing a template of a normal article to detect a defect article based on a difference between the template and an inspection image without using machine learning is considered. In such a method using a template, however, detection of a defect article may be affected by an individual difference of inspection target articles, that is, an individual difference of inspection images…”).
With regard to claim 14, the claim is drawn to an information processing method (see Oi, i.e. abstact, para. 5 and etc., disclose the image processing method) comprising:
acquiring first deformation data indicating a deformation included in a first inspection image obtained by capturing a first region, and second deformation data indicating a deformation included in a second inspection image obtained by capturing a second region including at least a part of the first region at a timing newer than a timing at which the first inspection image is captured (see Oi, i.e. in fig. 3-9, para. 57-60 and etc., disclose that “[0057] First, as illustrated in FIG. 3, in step S101, the patch cut unit 12 reads data of an image on which the patch cut step is to be performed. When the patch cut step is performed on the learning image, the patch cut unit 12 reads the learning image from the learning data storing unit 10. On the other hand, when the patch cut step is performed on the inspection image, the patch cut unit 12 reads image data from the inspection data storing unit 20. [0058] Next, in step 5102, the patch cut unit 12 performs a patch cut process on the image read in step 5101. As illustrated in FIG. 4, an image IM read in step 5101 includes an inspection target article T. The image IM is a learning image or an inspection image. When the image IM is a learning image, the inspection target article T included in the image IM is a normal article. When the image IM is an inspection image, the inspection target article T included in the image IM is an article that is to be determined whether it is a normal article or a defect article. [0059] In the patch cut process, as illustrated in FIG. 4, the patch cut unit 12 cuts out a rectangular image having a preset patch size from the read image IM as a patch image IMp, for example, from the left upper of the image IM. Note that the patch size used for cutting the patch image IMp can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the image IM. [0060] Moreover, in the patch cut process, as illustrated in FIG. 5, the patch cut unit 12 cuts out and removes a rectangular image having a preset size from the center part of the cut patch image IMp. In such a way, the patch cut unit 12 creates a center-removed patch image IMr, which is a frame-shape patch image IMp for which an image of the center part has been removed, and creates a center image IMc, which is an image cut out of the center part of the patch image IMp. The patch image IMp obtained before the patch cut process is performed includes the center-removed patch image IMr that is a first region and the center image IMc that is a second region and can be considered to be formed of both images. Note that the size at which the center image IMc is cut out can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the patch image IMp….”);
calculating difference deformation data indicating a difference between the first deformation data and the second deformation data (see Oi, i.e. fig. 7, 10, 12, para. 84, 89-92 and etc.,, disclose that “[0084] As a method for detecting a defect article, a scheme for preparing a template of a normal article to detect a defect article based on a difference between the template and an inspection image without using machine learning is considered. In such a method using a template, however, detection of a defect article may be affected by an individual difference of inspection target articles, that is, an individual difference of inspection images…”); and
creating learning data for causing, to learn, a model that predicts an appearance of the deformation based on the difference deformation data (see Oi, i.e. in fig. 1, 7-9, para. 37, and etc., disclose that “[0037] The learning model storage unit 18 stores the trained learning model created as a result of learning performed by the learning unit 16…”).
With regard to claim 15, the claim is drawn to a non-transitory computer-readable storage medium storing a computer program that (see Oi, i.e. in para. 115-116 and etc., disclose the storage medium, and etc.), when read and executed by a computer, causes the computer to function as
an acquisition unit that acquires first deformation data indicating a deformation included in a first inspection image obtained by capturing a first region, and second deformation data indicating a deformation included in a second inspection image obtained by capturing a second region including at least a part of the first region at a timing newer than a timing at which the first inspection image is captured (see Oi, i.e. in fig. 3-9, para. 57-60 and etc., disclose that “[0057] First, as illustrated in FIG. 3, in step S101, the patch cut unit 12 reads data of an image on which the patch cut step is to be performed. When the patch cut step is performed on the learning image, the patch cut unit 12 reads the learning image from the learning data storing unit 10. On the other hand, when the patch cut step is performed on the inspection image, the patch cut unit 12 reads image data from the inspection data storing unit 20. [0058] Next, in step 5102, the patch cut unit 12 performs a patch cut process on the image read in step 5101. As illustrated in FIG. 4, an image IM read in step 5101 includes an inspection target article T. The image IM is a learning image or an inspection image. When the image IM is a learning image, the inspection target article T included in the image IM is a normal article. When the image IM is an inspection image, the inspection target article T included in the image IM is an article that is to be determined whether it is a normal article or a defect article. [0059] In the patch cut process, as illustrated in FIG. 4, the patch cut unit 12 cuts out a rectangular image having a preset patch size from the read image IM as a patch image IMp, for example, from the left upper of the image IM. Note that the patch size used for cutting the patch image IMp can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the image IM. [0060] Moreover, in the patch cut process, as illustrated in FIG. 5, the patch cut unit 12 cuts out and removes a rectangular image having a preset size from the center part of the cut patch image IMp. In such a way, the patch cut unit 12 creates a center-removed patch image IMr, which is a frame-shape patch image IMp for which an image of the center part has been removed, and creates a center image IMc, which is an image cut out of the center part of the patch image IMp. The patch image IMp obtained before the patch cut process is performed includes the center-removed patch image IMr that is a first region and the center image IMc that is a second region and can be considered to be formed of both images. Note that the size at which the center image IMc is cut out can be appropriately set in accordance with accuracy or the like required for the inspection within a range of the size that is smaller than the patch image IMp….”),
a calculation unit that calculates difference deformation data indicating a difference between the first deformation data and the second deformation data (see Oi, i.e. fig. 7, 10, 12, para. 84, 89-92 and etc.,, disclose that “[0084] As a method for detecting a defect article, a scheme for preparing a template of a normal article to detect a defect article based on a difference between the template and an inspection image without using machine learning is considered. In such a method using a template, however, detection of a defect article may be affected by an individual difference of inspection target articles, that is, an individual difference of inspection images…”); and
a creation unit that creates learning data for causing, to learn, a model that predicts an appearance of the deformation based on the difference deformation data (see Oi, i.e. in fig. 1, 7-9, para. 37, and etc., disclose that “[0037] The learning model storage unit 18 stores the trained learning model created as a result of learning performed by the learning unit 16…”).
Allowable Subject Matter
With regard to Claims 12-13, claims are 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 and overcoming the corresponding rejections and/or objection (if any) set forth in the Office Action above.
The following is a statement of reasons for the indication of allowable subject matter:
With regard to claim 12, the closest prior arts of record, Oi, do not disclose or suggest, among the other limitations, the additional required limitation of “the information processing apparatus according to claim 1, wherein in calculation of the difference deformation data, the second deformation data existing outside an expansion range where the first deformation data is expanded in a width direction is calculated as the difference deformation data”. These additional features in combination with all the other features required in the claimed invention, are neither taught nor suggested by Oi.
With regard to claim 13, the closest prior arts of record, Oi, do not disclose or suggest, among the other limitations, the additional required limitation of “the information processing apparatus according to claim 6, wherein in creation of the learning data, the type of the second deformation data is set based on an overlap between the first deformation data and the second deformation data”. These additional features in combination with all the other features required in the claimed invention, are neither taught nor suggested by Oi.
Therefore, claims 12-13 are objected to.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dou et al. (U.S. Pat/Pub No. 2020/0074611 A1) disclose an invention relates to an pattern inspection system.
The Art Unit (or Workgroup) location of your application in the USPTO has changed. To aid in correlating any papers for this application, all further correspondence regarding this application should be directed to Art Unit 2681.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jacky X. Zheng whose telephone number is (571) 270-1122. The examiner can normally be reached on Monday - Friday, 9:00 am - 5:00 pm, alt. Friday Off.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached on (571) 272-3438. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JACKY X ZHENG/Primary Examiner, Art Unit 2681
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