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 .
DETAILED ACTION
The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below.
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of JP 2022-051137, filed in Japan on 03/28/2022, and PCT/JP2023/012303, filed in Japan on 03/27/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/26/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0002], on lines 1-4, “There are various methods for disease relevance determination such as dementia diagnosis, and amyloid positron emission tomography (PET), cerebrospinal fluid biomarkers, and the like have been proposed.” Is suggested to read “There are various methods for disease relevance determination such as dementia diagnosis, amyloid positron emission tomography (PET), cerebrospinal fluid biomarkers, and the like .”
In paragraph [0006], on lines 3-5, “not only mere face data but also two of a normal face and a smiling face as face data” is suggested to read, “not only mere face data but also including both normal face data and smiling face data” or similar to improve clarity.
Appropriate correction is required.
Abstract
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
[AltContent: arrow][AltContent: arrow]The abstract of the disclosure is objected to because the abstract includes multiple paragraphs/sections and exceeds 150 words in length. In addition, on lines 13 and 14 of the abstract, words appear to be covered by the word “DISTINCTION”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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Claim Objections
Claim 2 is objected to because of the following informalities:
Regarding claim 2, it is unclear what “a specific value of the data A is 0.5 or more” means.
For example, Claim 1 recites, “acquiring data A corresponding to a weighted feature point from face photograph data of the test subject”. Further, Applicant’s specification states, “it is preferable to use the eyes as the weighted feature point in the smiling face photograph data, and further, it is preferable to use the eyes as the weighted feature point in the smiling face photograph data and use the mouth as the weighted feature point in the normal face photograph data.” Therefore, it is unclear how, if for example, the eyes are selected as the weighted feature point, how the value of the data is numerical, and what a value of 0.5 or more indicates.
Appropriate correction is required.
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:
(A) 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;
(B) 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-AIA 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.
Such claim limitation(s) is/are:
“input means” in claim 5; See page 10 of Applicant’s specification, “an image input device such as a keyboard, a mouse, or a camera, a speech input device such as a microphone, a communication input device using a communication device such as Bluetooth (registered trademark), or the like is used as the input means 20.”
“output device” in claim 5; see page 10 of Applicant’s specification, “a display for displaying an evaluation result, a printer for printing, or the like is used as the output means 30.”
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
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.
Claims 3 and 4 are 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 pre-AIA the applicant regards as the invention. The Examiner strongly suggested that appropriate corrections be made to clarify the claim scope.
With respect to Claim 3, the claim recites the following, each of which renders the claim indefinite:
“the feature point” on line 2 (unclear antecedent basis); it is unclear as to whether “the feature point recited on line 2 of claim 3 is the same or different from “a weighted feature point” recited on lines 5 and 10 of claim 1. It is also unclear which (weighted) feature point is being referred to in claim 3, since weighted feature points are acquired in a reference subject (data X, smiling photograph data) and in the test subject (data A, face photograph data).
With respect to Claim 4, the claim recites the following, each of which renders the claim indefinite:
“the smiling photograph data” on lines 2-3 (unclear antecedent basis); it is unclear as to whether “the smiling photograph data” recited on lines 2-3 of claim 4 is the same or different from “default smiling photograph data” recited on lines 6-7 of claim 1.
Claim Rejections - 35 USC § 103
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 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 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 3-6 are rejected under 35 U.S.C. 103(a) as being unpatentable over Peters (U.S. Patent Pub. No. US 20250391027 A1, hereafter referred to as Peters) in view of Tanaka et al. (NPL “Detecting Dementia Through Interactive Computer Avatars”, 2017, hereafter referred to as Tanaka).
Regarding Claim 1, Peters teaches the following:
[AltContent: arrow]A disease relevance determination program (Paragraph [0014], Peters teaches a computer program for noninvasive estimation of dementia progression of a subject.) for causing a computer to perform steps below (Paragraph [0033], Peters teaches a computer program including instructions which, when the program is executed by a computer, cause the computer to carry out the method.) to determine disease relevance in a test subject (Paragraphs [0015], [0002], Figs. 10N-10O, Peters teaches non-invasive estimation of dementia progression using an image of a subject’s head. See Figs. 10N-10O below, the program estimates probability of Alzheimer’s stage 4 diagnosis within 5-10 years, for example.),
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the steps including:
[AltContent: arrow][AltContent: arrow]a feature point acquisition step of acquiring data X corresponding to a weighted feature point in a reference subject (Paragraph [0104], Figs. 8A & 8B, Peters teaches in one use of the Random Forest Classification algorithm, 20 feature importances were extracted to give an idea of which pixels are on average the most important in determining the final prediction of the model (80).) known to have the disease (Paragraph [0104], Fig. 8A, Peters teaches extracting feature importances from an average Alzheimer’s disease (AD) face.)
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by using default smiling face photograph data (Paragraph [0104], Figs. 8A & 8B, Peters teaches a second type of machine learning method, employing a well-known and standard image recognition technique known as Haar-feature-cascade-based classifiers, as represented in Fig. 8B (below). Both ways of looking at feature importances (Random Forest Classification and Haar-feature-cascade-based classifiers) arrived at similar conclusions about what areas (30) of the head (80, 81) are most important in driving the final results. See Fig. 8A (above) and Fig. 8B (below), both Figures depict images of people/subjects smiling, (smiling face photograph data). Fig. 8A appears blurry but a “smiling” face can still be seen.)
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acquired from the reference subject (Paragraph [0103], Peters teaches pictures of 1329 separate people’s heads were used. 223 (16.3%) of these had AD and 1112 did not. 686 men and 643 women did not have AD. 138 of those with AD were women (21.5% of total women), and 79 (11.5% of total men) were men. The Examiner interprets “the reference subject” to be at least one of the 223 people in the picture dataset who had AD. For example, Fig. 8A, “average AD face”.) prior to the feature point acquisition step (Paragraph [0104], Peters teaches feature importances were extracted to give an idea of which pixels are on average most important in determining the final prediction. The Examiner asserts the images are “acquired” prior to extracting the feature points from the image(s) since the image must be present in order to extract feature points from the image.);
a distinction data acquisition step of acquiring data A corresponding to a (Paragraph [0142], Peters teaches an analysis of features of the user’s face may be used to indicate prevalence of genetic makeup of that user, and by extension whether certain of these genetic indications may demonstrate increased risk of dementia. Facial [AltContent: arrow]characteristics are associated with genetic makeup and can be used for genotyping. Knowledge of this genetic predisposition can help segment these users better and therefore more accurately predict progression of dementia.) from face photograph data of the test subject (Paragraph [0112], Peters teaches one or more images of the user’s head are taken by the user using a computer device (22), such as a smartphone or camera. The user may be prompted to take one or more photos from different angles of the user’s head.);
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a determination step of determining disease relevance in the test subject (Paragraph [0018], Fig. 10N, Peters teaches estimating the progress of the dementia symptoms of the subject.)
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by comparing the data A (Paragraph [0067], Peters teaches comparing a plurality of areas of a user’s face based on a recent set of one or more images with a plurality of areas of a user’s face in an older set of one or more images, and using differences between these sets to estimate dementia progression. Changes in image commonalities may further indicate changes to the brain which may be related to the progression of dementia.).
Peters does not explicitly disclose the following:
a distinction data acquisition step of acquiring data A corresponding to a weighted feature point and a determination step of determining disease relevance in the test subject by comparing the data A with the data X.
Tanaka is in the same field of art of automatic dementia detection. Further, Tanaka teaches a distinction data acquisition step of acquiring data A corresponding to a weighted feature point (D. Feature Extraction, 3) Image Features (page 6), Tanaka teaches to analyze the recorded video, facial features were extracted using 2D landmark detection. Distance of the heights of the outer eyebrow, the inner eyebrow, the outer lip, and the inner lip, as well as the eye opening and lip corner distance were calculated. Smiling ratio was also calculated (proportion of smiling frames among all the frames in an utterance). Under BRI and in light of paragraph [0014] of Applicant’s specification, which states, “the feature point relates to a state of the eyes and the mouth, … it is preferable to use the eyes as the weighted feature point in the smiling face photograph data and use the mouth as the weighted feature point in the normal face photograph data”, the Examiner interprets a distance of the heights of the outer lip, inner lip, eye opening, and lip corner distance to be relating to the state of the eyes and the mouth, and therefore is being interpreted as a “weighted feature point”.) and a determination step of determining disease relevance (V. Results, A. Features and Classifiers (page 6), VI. Discussion (page 7), Tanaka teaches features such as smile were found to be significantly different (p < 0.05) between groups (control and dementia groups). Based on the finding, the researchers found that participants in the dementia group tended to smile more than the healthy controls. The Examiner interprets since the feature difference between the two groups was found to be statistically significant, and the researchers asserted that the dementia group tended to smile more than the control group, that determining the groups were “significantly different” regarding a specific feature is determining a “disease relevance” i.e., how relevant the feature is to predicting the presence of the disease.) comparing the data A with the data X (V. Results, A. Features and Classifiers (page 6), D. Feature Extraction, 3) Image Features (pages 5,6), Table 2, Tanaka teaches features (including image features) were studied and compared between groups using the Wilcoxon rank sum test and effect size (Cohen’s D). Features such as h1a3, F0cov, and smile were significantly different (p < 0.05) (between control and dementia patients). Smiling ratio (smile) was determined based on the video by predicting whether the label belongs to the smiling or neutral class in each frame. The Examiner interprets “data A” to be a weighted feature point (i.e., an important feature such as smiling ratio or “smile” collected from the control patients and “data X” to be a feature point collected from the dementia group. The feature points are compared, and a p-value is determined, as shown in Table 2 below.)
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Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Peters by acquiring landmark facial features indicative of disease (dementia) from the video data (image frames) for both groups (AD & control) and determining whether a significant difference exists between the groups by comparing the extracted features that is taught by Tanaka, to make the invention that diagnoses a patient with a disease (dementia), or provides a risk probability of developing the disease based on changes or differences in facial expressions observed in images, such as differences in smiling; thus, one of ordinary skilled in the art would be motivated to combine the references since changes in facial features and areas of a person between different images and/or across time may provide insight into dementia risk of that person or how quickly a dementia risk factor may be progressing (Peters, Paragraphs [0067], [0070], [0142]). In addition, there is an increasing need for cost-effective tools that allow the identification of people with dementia in preclinical or early stages (Tanaka, I. Introduction), therefore, by extracting image features for dementia detection, such as facial landmarks or other audiovisual cues, an easy to use, cost-effective, repeatable, and timely detection system can be realized (Tanaka, VII. Conclusion).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
In regards to Claim 3, Peters in view of Tanaka discloses the program according to claim 1, wherein the feature point relates to a state of eyes and a mouth (D. Feature Extraction, 3) Image Features, Tanaka teaches calculating the distance of the heights of the outer eyebrow, the inner eyebrow, the outer lip, and the inner lip as well as the eye opening and lip corner distance.).
In regards to Claim 4, Peters in view of Tanaka discloses the program according to claim 1, wherein the weighted feature point is based on a state of eyes in the smiling face photograph data (Paragraph [0117], Peters teaches identifying the eyes and shape of head (51) may be based on previously trained machine learning models, from a dataset or persons not related to the AD and dementia dataset.).
In regards to Claim 5, Peters in view of Tanaka discloses:
A disease relevance determination apparatus (Abstract, Peters teaches a system for non-invasive estimation of dementia progression.) including:
one or more computers (Abstract, Peters teaches the system includes a computer device and a server.) that store the disease relevance determination program (Paragraphs [0145], [0063], Peters teaches the invention may take the form of a computer program product on a computer-readable storage medium having computer-usable program code embodied in the medium. The software may be stored in the system memory or on other removable/non-removable volatile/non-volatile computer storage media which is included in or accessible to the computer device.) according to claim 1 (the Examiner states see rejection for Claim 1, method of combining references, Peters and Tanaka.);
an input means for inputting predetermined data to the one or more computers (Paragraph [0109-112], Fig. 2, Fig. 10C, Peters teaches a data collection step (10) is performed. The data collection may be done using a handset that comprises a camera, such as a mobile phone. The user may be prompted to fill out information pertaining to them, such as age, ethnicity, country of residence, country of birth, eye color, weight, height, etc. The Examiner interprets “predetermined data” to be data regarding the patient such as age, eye color, weight, etc. This data is “predetermined”. For example, a subject’s eye color is “predetermined”, it is not changing or going to be changed.);
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an output device that outputs a determination result stored in the one or more computers (Paragraphs [0034], [0109], [0115], Figs. 1 & 10N-10O, Peters teaches results from the automated machine-learning-based data analysis may then be executed through communication step (12) to the user and/or another recipient. The results of the analysis may be sent to the user’s device. See Fig. 1 below, (reference character 12), phone/smartphone shown displaying results.).
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In regards to Claim 6, Peters in view of Tanaka discloses the disease relevance determination apparatus according to claim 5, wherein the one or more computers (Paragraph [0064], Peters teaches the method may be implemented to run on a single computer.) that perform the distinction data acquisition step (Paragraph [0033], Peters teaches a computer program including instructions which, when the program is executed by the computer, cause the computer to carry out the method described.) has an additional function comprising a question-and-answer function (Paragraphs [0124-136], Peters teaches a Cognitive Assessment tool which may be a questionnaire or an assessment to test the reaction of a subject. Patient assessment tools may include questions pertaining to different symptoms of different types and stages of dementia. Examples of questions that the user may be asked to estimate risk and progression of dementia, such as “Do you often struggle to find the right words (e.g., when answering questions, describing events or people)?”) for having the test subject to have a conversation (Section III Interactive System, Tanaka teaches an interactive system which uses a computer avatar, an MMDAgent on a Windows application. Users can operate and communicate with the avatar using speech throughout the interaction. The system asks the user questions such as “1) What’s your name?” and “2) How old are you?”.) with the one or more computers (Section IV. Methods, B. Data Collection, Tanaka teaches using a Windows laptop to record the interactions.) or an image display function for showing an image to the test subject (Under BRI, the Examiner interprets since the claim states “or”, only one of either a question-and-answer function or an image display function is required to meet the claim limitation.).
Claim 2 is rejected under 35 U.S.C. 103(a) as being unpatentable over Peters (U.S. Patent Pub. No. US 20250391027 A1, hereafter referred to as Peters) in view of Tanaka et al. (NPL “Detecting Dementia Through Interactive Computer Avatars”, 2017, hereafter referred to as Tanaka) in further view of Eichler et al. (U.S. Patent Pub. No. 20220044821 A1, hereafter referred to as Eichler).
Regarding Claim 2, Peters in view of Tanaka teaches the program according to claim 1.
Peters in view of Tanaka does not explicitly disclose wherein in the determination step, the determination is performed based on whether or not a specific value of the data A is 0.5 or more.
Eichler is in the same field of art of determining likelihood of disease by extracting features from image data and comparing extracted features to datasets comprising positive (diagnosed) subjects and negative (non-diagnosed) subjects. Further, Eichler teaches wherein in the determination step, the determination is performed based on whether or not a specific value of the data A is 0.5 or more (Paragraph [0055], Eichler teaches an output of the comparison is a quantitative measure to how a particular extracted potential stroke feature matches either one of a positive stroke dataset (182), negative stroke dataset (184), or both (i.e., an intermediate result, e.g., in the case there’s a 50% match to a positive stroke dataset (182) and a 50% match to a negative stroke dataset (184).).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Peters in view of Tanaka by determining the disease relevance or presence of disease based on a majority matching between positive and negative datasets that is taught by Eichler, to make the invention that determines the dementia determination output based on which of the positive or negative datasets is the best/majority match with the image of the subject; thus, one of ordinary skilled in the art would be motivated to combine the references since generally, the use of both a positive (disease) dataset and negative (disease) dataset in the comparison enhances the estimation of likelihood in determining the presence of (disease) condition of the subject (Eichler, Paragraph [0055]).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Peyman (US 20250352104 A1) teaches a system for evaluating cognitive function. Peyman teaches comparing a first reference digital image of a patient at a first time to a second digital image of a patient captured by an image recognition sensor at a second, subsequent time, and to determine if the second digital image matches or substantially matches the first reference digital image of the patient.
Jiang et al. (NPL “Automated analysis of facial emotions in subjects with cognitive impairment”, 2022) teaches a deep-learning based analysis of facial emotion expression in 493 healthy controls and patients with various types of cognitive impairments, which provided evidence that the reference facial emotions expressed in a lab-controlled environment are significantly different in people with cognitive impairment. The research determined that differences in emotional expression can be used to effectively screen for cognitive impairment (Conclusion).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYDNEY L BLACKSTEN whose telephone number is (571)272-7651. The examiner can normally be reached 8:30am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached at 313-446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYDNEY L BLACKSTEN/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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