DETAILED ACTION
The United States Patent & Trademark Office appreciates the response filed for the current application that is submitted on 04/09/2026. The United States Patent & Trademark Office reviewed the following documents submitted and has made the following comments below.
Amendment
Applicant submitted amendments on 04/09/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Priority
Acknowledgment is made that this application has the parent application PRO 63/426,489 filed on 11/18/2022.
Information Disclosure Statement
The IDS dated 07/09/2024 has been considered and placed in the application file.
Overview
Claims 1-20 are pending in this application and have been considered below.
Claims 1-20 are rejected.
Applicant Arguments:
In regards to the argument on Argument 1, Applicant/s state/s “Claims 10 and 12 are canceled.” therefore, the pending claims are 1-9, 11, and 13-20. (See Remarks, page 1, paragraph 1).
In regards to the argument on Argument 2, Applicant/s state/s “The cited references Lad and Mulyukov each fail to disclose alone or in their combination, "generating gradient severity score vector from the baseline ML model for a second data set" and "training a second ML model using the second data set, wherein at least one of the first severity score label and the second severity score label is used (i) for diagnosis or (ii) as labels for the second data set as a training data set for the second ML model or the baseline ML model," as recited in Claim 1, as amended.” therefore, the 35 USC 103 rejections for Claim 1 should be withdrawn (See Remarks, page 3, paragraph 3).
In regards to the argument on Argument 3, Applicant/s state/s “Lad is silent as to the output of the contrastive learning model being used as labels in another training operation.” therefore, the 35 U.S.C. 103 rejection of Claim 1 should be withdrawn (See Remarks, page 3, paragraph 4).
In regards to the argument on Argument 4, Applicant/s state/s “Similarly, Mulyukov appears to disclose training a machine learning model from preexisting labeled data, rather than labels generated from contrastive learning.” therefore, the 35 U.S.C. 103 rejection for Claim 1 should be withdrawn (See Remarks, page 4, paragraph 1).
In regards to the argument on Argument 5, Applicant/s state/s “Lad and Mulyukov each fail to disclose alone or in combination, "generating gradient severity score vector from the baseline ML model for a second data set" and "training a second ML model using the second data set, wherein at least one of the first severity score label and the second severity score label is used (i) for diagnosis or (ii) as labels for the second data set as a training data set for the second ML model or the baseline ML model," therefore, the 35 U.S.C. 103 rejection for Claim 1 should be withdrawn (See Remarks, page 5, paragraph 2).
In regards to the argument on Argument 6, Applicant/s state/s “Lad and Mulyukov also fail to disclose using severity class as labels (for a labeled data set) generated from contrastive learning for training a machine learning model. Dependent claims 2-7, 8, 11-14, and 17-19 should be allowable per their dependencies on Claims 1, 10, and 16, respectively." therefore, the 35 U.S.C. 103 rejection for Claims 10 and 16 and the dependent claims should be withdrawn (See Remarks, page 5, paragraph 3).
Examiner’s Responses:
In response to Argument 1, Applicant’s arguments, see Remarks, filed 04/09/2026, with respect to the Applicants remarks stating that Claims 10 and 12 are cancelled, the Examiner will be basing the analysis of the claims on the claim set submitted 04/09/2026, which does not have claims 10 and 12 cancelled.
In response to Argument 2, Applicant’s arguments, see Remarks, filed 04/09/2026, with respect to claim 1 have been considered but are not persuasive. The rejection is maintained for Claim 1 under 35 U.S.C. 103 in view of Lad et al (US Patent Publication US 2022/0351373 A1, hereafter referred to as Lad) in view of Mulyukov et al (WO Patent Publication WO 2021/220138 A1, hereafter referred to as Mulyukov).
The Examiner finds that Mulyukov teaches on the claim language “training a second ML model using the second data set, wherein at least one of the first severity score label and the second severity score label is used as labels for the second data set as a training data set for the second ML model or the baseline ML model,” in amended claim 1 with the amendment changing the scope of the “generating gradient severity score vector from the baseline ML model for a second data set” which is taught by Lad.
Specifically, Mulyukov, teaches training a second ML model using the second data set as disclosed in ¶0047, ¶0051, ¶0054, ¶0072, ¶00162, ¶00179. Mulyukov further teaches that the second dataset has indicators of severity level of the disease labeled in ¶00143, and ¶00065. We determine claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). The Examiner interprets that under broadest reasonable interpretation “severity level” has no special definition in the claims, and therefore can be interpreted as disease activity, which is what is labeled in Mulyukov. Lad specifically teaches generating a gradient of an input image with a severity score vector from the baseline ML model for a second data set in Fig 9, ¶0101, Fig 18, ¶0114, ¶0155, ¶0179, ¶0183and ¶0102. The Examiner interprets that the output itself is a dataset that is different from the first dataset, therefore it can be interpreted as a second dataset. We determine claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). The Examiner interprets that under broadest reasonable interpretation “severity vector score” has no special definition in the claims, and therefore can be interpreted as classifying the severity according to a grading criteria as taught by Lad. Applicant argues that "training a second ML model using the second data set, wherein at least one of the first severity score label and the second severity score label is used (i) for diagnosis or (ii) as labels for the second data set as a training data set for the second ML model or the baseline ML model," and "generating gradient severity score vector from the baseline ML model for a second data set”. The Examiner is only going to address generating gradient severity score vector from the baseline ML model for a second data set and the second ML model and second dataset with the severity labels as that is what is present in the claim set submitted on 04/09/2026. Mulyukov does disclose training a second ML model using the second dataset. Mulyukov does not disclose the specific limitation of generating gradient severity score vector from the baseline ML model for a second data set, as recited in Claim 1. However, the Examiner interprets that Lad teaches the main concept of generating gradient severity score vector from the baseline ML model for a second data set, the additional details of the function and characteristics of the main concepts as stated above by the applicant in the amendments is taught by Mulyukov in the details of the rejection below. The Examiner will maintain prior art Lad and Mulyukov and details of the rejection are below.
In response to Argument 3, Applicant’s arguments, see Remarks, filed 04/09/2026, with respect to claim 1 have been considered but are not persuasive. The rejection is maintained for Claim 1 under 35 U.S.C. 103 in view of Lad et al (US Patent Publication US 2022/0351373 A1, hereafter referred to as Lad) in view of Mulyukov et al (WO Patent Publication WO 2021/220138 A1, hereafter referred to as Mulyukov).
Applicant argues that " Lad is silent as to the output of the contrastive learning model being used as labels in another training operation”. However the Examiner finds that this limitation is not present in the claim. The claim states that in “in the contrastive learning operation generating gradient severity score vector from the baseline ML model for a second data set”. In response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., the output of the contrastive learning model being used as labels in another training operation) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to Argument 4, Applicant’s arguments, see Remarks, filed 04/09/2026, with respect to claim 1 have been considered but are not persuasive. The rejection is maintained for Claim 1 under 35 U.S.C. 103 in view of Lad et al (US Patent Publication US 2022/0351373 A1, hereafter referred to as Lad) in view of Mulyukov et al (WO Patent Publication WO 2021/220138 A1, hereafter referred to as Mulyukov).
In response to the applicants argument “Similarly, Mulyukov appears to disclose training a machine learning model from preexisting labeled data, rather than labels generated from contrastive learning.”, The Examiner respectfully disagrees. The Examiner finds that Lad teaches outputting severity classes from the contrastive learning operation in Fig 9, ¶10101, ¶0042, ¶0069 and Mulyukov teaches using labeled data as input for the machine training model in Fig 10 ¶00189-¶00191. Therefore the combination of the references teaches the claimed invention. The Examiner made a proper determination of obviousness under 35 U.S.C. §103, and also provided an appropriate supporting rationale in view of the decision by the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007). The Examiner’s rational are based on the Office’s current understanding of the law, and are believed to be fully consistent with the binding precedent of the Supreme Court. Furthermore, the Examiner supported the rejection under 35 U.S.C. §103 via making the clear articulation of the reason(s) why the claimed invention would have been obvious by citing the specific areas in the prior art references. Further the Examiner, clearly stating the modification of the inventions, supported the rejection under 35 U.S.C. §103 by making the analysis explicit. Last, the Examiner did not make conclusory statements. The Court quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006), stated that “‘[R]ejections on obviousness cannot be sustained by mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness.’” KSR, 550 U.S. at ___, 82 USPQ2d at 1396. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection with Lad in view of Mulyukov, which is disclosed in detail below.
In response to Argument 5, Applicant’s arguments, see Remarks, filed 04/09/2026, with respect to claim 1, 10 and 16 have been considered but are not persuasive. The rejection is maintained for Claim 1, 10, and 16 under 35 U.S.C. 103 in view of Lad et al (US Patent Publication US 2022/0351373 A1, hereafter referred to as Lad) in view of Mulyukov et al (WO Patent Publication WO 2021/220138 A1, hereafter referred to as Mulyukov).
The Examiner finds that Mulyukov teaches on the amended claim language “training a second ML model using the second data set, wherein at least one of the first severity score label and the second severity score label is used as labels for the second data set as a training data set for the second ML model or the baseline ML model,"” in amended claim 1 with the amendment changing the scope of the “generating gradient severity score vector from the baseline ML model for a second data set” which is taught by Lad.
Specifically, Mulyukov, teaches training a second ML model using the second data set as disclosed in ¶0047, ¶0051, ¶0054, ¶0072, ¶00162, ¶00179. Mulyukov further teaches that the second dataset has indicators of severity level of the disease labeled in ¶00143, and ¶00065. We determine claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). The Examiner interprets that under broadest reasonable interpretation “severity level” has no special definition in the claims, and therefore can be interpreted as disease activity. Lad specifically teaches generating a gradient of an input image with a severity score vector from the baseline ML model for a second data set in Fig 9, ¶0101, Fig 18, ¶0114, ¶0155, ¶0179, ¶0183and ¶0102. The Examiner interprets that the output itself is a dataset that is different from the first dataset, therefore it can be interpreted as a second dataset. We determine claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). The Examiner interprets that under broadest reasonable interpretation “severity vector score” has no special definition in the claims, and therefore can be interpreted as classifying the severity according to a grading criteria. Applicant argues that "training a second ML model using the second data set, wherein at least one of the first severity score label and the second severity score label is used (i) for diagnosis or (ii) as labels for the second data set as a training data set for the second ML model or the baseline ML model," and "generating gradient severity score vector from the baseline ML model for a second data set”. The Examiner is only going to address generating gradient severity score vector from the baseline ML model for a second data set and the second ML model and second dataset with the severity labels as that is what is present in the claim set submitted on 04/09/2026. Mulyukov does disclose training a second ML model using the second dataset. Mulyukov does not disclose the specific limitation of generating gradient severity score vector from the baseline ML model for a second data set, as recited in Claim 1. However, the Examiner interprets that Lad teaches the main concept generating gradient severity score vector from the baseline ML model for a second data set, the additional details of the function and characteristics of the main concepts as stated above by the applicant in the amendments is taught by Mulyukov in the details of the rejection below. The Examiner will maintain prior art Lad and Mulyukov and details of the rejection are below.
In response to Argument 6, Applicant’s arguments, see Remarks, filed 04/09/2026, with respect to claims 10 and 16 have been considered but are not persuasive. The rejection is maintained for Claim 10 and 16 under 35 U.S.C. 103 in view of Lad et al (US Patent Publication US 2022/0351373 A1, hereafter referred to as Lad) in view of Mulyukov et al (WO Patent Publication WO 2021/220138 A1, hereafter referred to as Mulyukov).
Specifically, Mulyukov, teaches severity classes associated with severity score labels in Fig 10 and ¶00189 and using a second dataset for training a second model in ¶0047, ¶0051, ¶0054, ¶0072, ¶00162, ¶00179. While Lad teaches the contrastive learning in Fig 9 ¶0101. In response to the applicants argument “Lad and Mulyukov also fail to disclose using severity class as labels (for a labeled data set) generated from contrastive learning for training a machine learning model.”, The Examiner respectfully disagrees. The combination of the references teaches the claimed invention. The Examiner made a proper determination of obviousness under 35 U.S.C. §103, and also provided an appropriate supporting rationale in view of the decision by the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007). The Examiner’s rational are based on the Office’s current understanding of the law, and are believed to be fully consistent with the binding precedent of the Supreme Court. Furthermore, the Examiner supported the rejection under 35 U.S.C. §103 via making the clear articulation of the reason(s) why the claimed invention would have been obvious by citing the specific areas in the prior art references. Further the Examiner, clearly stating the modification of the inventions, supported the rejection under 35 U.S.C. §103 by making the analysis explicit. Last, the Examiner did not make conclusory statements. The Court quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006), stated that “‘[R]ejections on obviousness cannot be sustained by mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness.’” KSR, 550 U.S. at ___, 82 USPQ2d at 1396. Therefore, the Examiner has established a proper 35 U.S.C. §103 rejection with Lad in view of Mulyukov, which is disclosed in detail below for Clams 1, 10, 16 and its dependent claims.
Claim Interpretation
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.
Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability
of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
Claim 1 recite “or ” then listing “wherein at least one of the first severity score label and the second severity score label is used as labels for the second data set as a training data set for a second ML model or the baseline ML model.” Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim 3 and 12 recite “or ” then listing “training the second ML model or the baseline ML model via the selected portion of the second data set.”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim 10 and 16 recite “or ” then listing “the determined presence or severity value.”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim 9 recite “at least one of ” then listing “Intraretinal Fluid (IRF), Diabetic Macular Edema (DME), and Intra-Retinal Hyper- Reflective Foci (IRHRF).”. Since “at least one of” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
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 (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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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 1-7, 9-14, 16-19 are rejected under 35 U.S.C. 103 as unpatentable over Lad et al
(US Patent Publication US 2022/0351373 A1, hereafter referred to as Lad) in view of Mulyukov et al (WO Patent Publication WO 2021/220138 A1, hereafter referred to as Mulyukov).
Regarding Claim 1, Lad teaches a method of training a machine learning model (Lad Fig 1 discloses a method for training a machine learning model), the method comprising:
in a contrastive learning operation (Lad Fig 9, ¶0101 discloses a contrastive learning manner), training a baseline ML model via a first data set (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline), the first data set consisting of data for a non-anomalous, normal, or healthy set (Lad ¶0116 discloses uses auto fluorescent images of healthy patients with GA to training a multilayer CNN);
in the contrastive learning operation (Lad Fig 9, ¶0101 discloses a contrastive learning manner), generating gradient (Lad Fig 18 discloses the output of the image with the gradient label) severity score vector (Lad ¶0114 discloses classifying the severity according to a grading criteria) from the baseline ML model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) for a second data set (Lad ¶0102 discloses using a second OCT dataset), the second data set comprising data for anomalous or unhealthy set (Lad ¶0102 discloses a second OCT dataset that consisted of images that were classified as having CNV, DME, and drusen), wherein the second data set is unlabeled with respect to severity (Lad ¶0102 discloses the secondary dataset being labeled for presence of disease not severity);
in the contrastive learning operation (Lad Fig 9, ¶0101 discloses a contrastive learning manner), tiering the severity score vector (Lad ¶0114 discloses classifying the MD severity according to a grading criteria) into a plurality of severity classes (Lad ¶0042, ¶0069, discloses determining the level of severity of the degeneration using percentage labels),
as labels for the second data set as a training data set (Lad ¶0102 discloses using a second OCT dataset that has been labeled with classifications of the presences of disease)
or the baseline ML model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline).
Lad does not explicitly teach including a first severity class associated with a first severity score label and a second severity class associated with a second severity score label;
and
training a second ML model, using the second data set, wherein at least one of the first severity score label and the second severity score label is used for the second ML model.
Mulyukov is in the same field of medical eye disease detection image processing. Further, Mulyukov teaches including a first severity class associated with a first severity score label (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being less than a threshold and being labeled low) and a second severity class associated with a second severity score label (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high);
training a second ML model (Mulyukov ¶0047, ¶0051, ¶0054, ¶0072, ¶00162, ¶00179 discloses a second neural network and training the second algorithm), using the second data set (Mulyukov ¶00162 discloses a second neural network that uses the second dataset D(0)) ¶00051-¶00052 discloses training the second algorithm using an additional set of historical data different from the first set, which the Examiner is interpreting to be equivalent to a second data set ¶00143 discloses labelling the disease activity level as a target attribute in the training data and training the machine learning algorithms using that data set, the Examiner interprets the disease activity level to be equivalent with severity level ¶00065 discloses a set of input data fed to the second algorithm that consists of current and previous Snellen chart measurements; patient longitudinal data on physiological characteristics, e.g., current and previous age, weight etc.; previous disease activity scores, the Examiner interprets that all of these could indicate the severity class of the disease ¶00160 discloses a second input dataset that is comprised of values of the anatomical variables, ¶00118-¶00123 discloses assessing the severity of the disease based on evaluated anatomical variables which are then included in the dataset), wherein at least one of the first severity score label and the second severity score label is used (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high),
for the second ML model (Mulyukov ¶00044, ¶00045, ¶00047 discloses applying a second algorithm to the variable identified).
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 Lad by incorporating the scoring of the severity of disease to be used an input for the second machine learning model for training as taught by Mulyukov, to make an invention that can more accurately identify and classify the disease present in the patient image; thus, one of ordinary skilled in the art would be motivated to combine the references since an object of the present invention is to address the need for a method that reliably and accurately assesses disease activity of w-AMD and/or of other retinopathies and that provides patient-specific anti-VEGF treatment regimen models, such as customized dosing frequency models (Mulyukov, ¶00017).
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.
Regarding Claim 2, Lad in view of Mulyukov teaches the method of claim 1, wherein the step of tiering the severity score vector (Lad ¶0114 discloses classifying the MD severity according to a grading criteria) into a plurality of severity classes (Lad ¶0042, ¶0069, discloses determining the level of severity of the degeneration using percentage labels) comprises:
ordering the severity score vector by rank (Mulyukov Fig 10 and Fig 11, ¶00023 disclose the severity of the disease activity being ranked as high medium or low) to generate a ranked list of vector elements of the severity score vector (Mulyukov Fig 4 and ¶00134 disclose analyzing feature vales to rank out they affect the disease activity in patients); and
arranging the ranked list of vector elements of the severity score vector (Mulyukov Fig 4 and ¶00134 disclose analyzing feature vales to rank out they affect the disease activity in patients) into a plurality of bins (Mulyukov Fig 9, ¶00182 discloses soring the scoring of the patient disease activity in high and low bins), wherein a first bin corresponds to the first severity class (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being less than a threshold and being labeled low), and wherein the second bin corresponds to the second severity class (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high). See Claim 1 for rationale, its parent claim.
Regarding Claim 3, Lad in view of Mulyukov teaches the method of claim 1, further comprising:
selecting a portion (Mulyukov ¶00075 discloses splitting the dataset and selecting one of the branches) of the second data set (Lad ¶0102 discloses using a second OCT dataset) based on the gradient labels (Lad Fig 18 discloses the output of the image with the gradient label); and
training the second ML model (Mulyukov ¶00044, ¶00045, ¶00047 discloses applying a second algorithm to the variable identified) or the baseline ML (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) model via the selected portion (Mulyukov ¶00075 discloses splitting the dataset and selecting one of the branches) of the second data set (Lad ¶0102 discloses using a second OCT dataset). See Claim 1 for rationale, its parent claim.
Regarding Claim 4, Lad in view of Mulyukov teaches the method of claim 1, wherein the second data set (Lad ¶0102 discloses using a second OCT dataset) comprises candidate biomarker data (Lad ¶0087, ¶0092,¶0117 discloses biomarkers being used to determine AMD progression) for anomalous or unhealthy set (Lad ¶0102 discloses a second OCT dataset that consisted of images that were classified as having CNV, DME, and drusen), and wherein the method further comprising:
training the second ML model (Mulyukov ¶00044, ¶00045, ¶00047 discloses applying a second algorithm to the variable identified) or the baseline ML model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) via the second data set (Lad ¶0102 discloses using a second OCT dataset), wherein the gradient labels (Lad Fig 18 discloses the output of the image with the gradient label) are used as ground truth for a set of biomarkers (Lad ¶0087, ¶0092,¶0117 discloses biomarkers being used to determine AMD progression) identified in the second data set (Lad Fig 12 and 13 and Fig 19 discloses how the ground truth lesion masks are used to determine the success rate of the gradient labeling). See Claim 1 for rationale, its parent claim.
Regarding Claim 5, Lad in view of Mulyukov teaches the method of claim 1, further comprising:
outputting, via a report or display (Lad ¶0065 discloses outputting the result, ¶0056 discloses displays), respective gradient label (Lad Fig 18 discloses the output of the image with the gradient label) and classifier output of the baseline ML model (Lad ¶0116, ¶0134, discloses the output of the model being the classifier outputting the presence of GA), wherein the respective gradient label (Lad Fig 18 discloses the output of the image with the gradient label) and classifier output (Lad ¶0116, ¶0134, discloses the output of the model being the classifier outputting the presence of GA), is used for diagnosis of a disease or a medical condition (Lad ¶0003 discloses the output being from the GA detection algorithm which uses both the gradient and classifier algorithm to output the detection GA and if it is likely to occur). See Claim 1 for rationale, its parent claim.
Regarding Claim 6, Lad in view of Mulyukov teaches the method of claim 1, wherein the first data set comprises image data from a medical scan (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline). See Claim 1 for rationale, its parent claim.
Regarding Claim 7, Lad in view of Mulyukov teaches the method of claim 1, wherein the first data set (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) comprises image data from a sensor (Mulyukov ¶00049 discloses sensors collecting patient data and the patient data consisting of retinal images). See Claim 1 for rationale, its parent claim.
Regarding Claim 9, Lad in view of Mulyukov teaches the method of claim 4, wherein the candidate biomarker data (Lad ¶0087, ¶0092,¶0117 discloses biomarkers being used to determine AMD progression) includes at least one of:
Intraretinal Fluid (IRF) (Mulyukov ¶00118 discloses measuring a key number of biomarker data including IRF), Diabetic Macular Edema (DME) (Lad ¶0102 discloses the images labeled for diabetic macular edema), and Intra-Retinal Hyper- Reflective Foci (IRHRF) (Lad Fig 12,14 and 15 shows the small bright spots seen in the OCT images which are IRHRF). See Claim 1 for rationale, its parent claim.
Regarding Claim 10, Lad teaches a method comprising:
receiving a data set (Lad ¶0003 discloses receiving a set of OCT volume can images as an input);
determining, via a trained machine learning model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline), a presence or severity value associated with a disease or medical condition (Lad ¶0003 discloses a machine learning algorithm determining the presence of detecting geographic atrophy) using the data set (Lad ¶0003 discloses receiving a set of OCT volume can images as an input);
outputting, via a report or graphical user interface (Lad ¶0065 discloses outputting the result, ¶0056 discloses displays, and interfaces), the determined presence or severity value (Lad ¶0003 discloses a machine learning algorithm determining the presence of detecting geographic atrophy), wherein the trained machine learning model was trained in a contrastive learning operation (Lad Fig 9, ¶0101 discloses a contrastive learning manner), the contrastive learning operation comprising:
training a baseline ML model via a first data set (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline), the first data set consisting of data for a non-anomalous, normal, or healthy set (Lad ¶0116 discloses uses auto fluorescent images of healthy patients with GA to training a multilayer CNN);
generating gradient (Lad Fig 18 discloses the output of the image with the gradient label) severity score vector (Lab ¶0114 discloses classifying the severity according to a grading criteria) from the baseline ML model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) for a second data set (Lad ¶0102 discloses using a second OCT dataset), the second data set comprising data for anomalous or unhealthy set (Lad ¶0102 discloses a second OCT dataset that consisted of images that were classified as having CNV, DME, and drusen), wherein the second data set is unlabeled with respect to severity (Lad ¶0102 discloses the secondary dataset being labeled for presence of disease not severity);
tiering the severity score vector (Lad ¶0114 discloses classifying the MD severity according to a grading criteria) into a plurality of severity classes (Lad ¶0042, ¶0069, discloses determining the level of severity of the degeneration using percentage labels).
Lad does not explicitly teach including a first severity class associated with a first severity score label and a second severity class associated with a second severity score label and
generating the trained machine learning model using the first severity score label and the second severity class as labels for the second training data set as a training data set for the trained machine learning model.
Mulyukov is in the same field of medical eye disease detection image processing. Further, Mulyukov teaches including a first severity class associated with a first severity score label (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being less than a threshold and being labeled low) and a second severity class associated with a second severity score label (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high); and
generating the trained machine learning model (Mulyukov ¶00082 and Fig disclose generating a machine learning model based on the level of disease activity) using the first severity score label (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being less than a threshold and being labeled low) and the second severity class (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high) as labels for the second training data set (Mulyukov ¶00143 discloses labelling the disease activity level as a target attribute in the training data and training the machine learning algorithms using that data set, the Examiner interprets the disease activity level to be equivalent with severity level ¶00065 discloses a set of input data fed to the second algorithm that consists of current and previous Snellen chart measurements; patient longitudinal data on physiological characteristics, e.g., current and previous age, weight etc.; previous disease activity scores, the Examiner interprets that all of these could indicate the severity class of the disease ¶00160 discloses a second input dataset that is comprised of values of the anatomical variables, ¶00118-¶00123 discloses assessing the severity of the disease based on evaluated anatomical variables which are then included in the dataset ) as a training data set for the trained machine learning model (Mulyukov ¶00051-¶00052 discloses training the second algorithm using an additional set of historical data different from the first set, which the Examiner is interpreting to be equivalent to a second data set).
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 Lad by incorporating the scoring of the severity of disease to be used an input for the second machine learning model for training the model as taught by Mulyukov, to make an invention that can more accurately identify and classify the disease present in the patient image; thus, one of ordinary skilled in the art would be motivated to combine the references since an object of the present invention is to address the need for a method that reliably and accurately assesses disease activity of w-AMD and/or of other retinopathies and that provides patient-specific anti-VEGF treatment regimen models, such as customized dosing frequency models (Mulyukov, ¶00017).
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.
Regarding Claim 11, Lad in view of Mulyukov teaches the method of claim 10, wherein the step of tiering the severity score vector (Lad ¶0114 discloses classifying the MD severity according to a grading criteria) into a plurality of severity classes (Lad ¶0042, ¶0069, discloses determining the level of severity of the degeneration using percentage labels) comprises:
ordering the severity score vector by rank (Mulyukov Fig 10 and Fig 11, ¶00023 disclose the severity of the disease activity being ranked as high medium or low) to generate a ranked list of vector elements of the severity score vector (Mulyukov Fig 4 and ¶00134 disclose analyzing feature vales to rank out they affect the disease activity in patients); and
arranging the ranked list of vector elements of the severity score vector (Mulyukov Fig 4 and ¶00134 disclose analyzing feature vales to rank out they affect the disease activity in patients) into a plurality of bins (Mulyukov Fig 9, ¶00182 discloses soring the scoring of the patient disease activity in high and low bins) , wherein a first bin corresponds to the first severity class (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being less than a threshold and being labeled low) , and wherein the second bin corresponds to the second severity class (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high). See Claim 10 for rationale, its parent claim.
Regarding Claim 12, Lad in view of Mulyukov teaches the method of claim 10, wherein the second data set (Lad ¶0102 discloses using a second OCT dataset) comprises candidate biomarker data (Lad ¶0087, ¶0092,¶0117 discloses biomarkers being used to determine AMD progression) for anomalous or unhealthy set (Lad ¶0102 discloses a second OCT dataset that consisted of images that were classified as having CNV, DME, and drusen), and wherein the method further comprising:
training the second ML model (Mulyukov ¶00044, ¶00045, ¶00047 discloses applying a second algorithm to the variable identified) or the baseline ML model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) via the second data set (Lad ¶0102 discloses using a second OCT dataset), wherein the gradient labels (Lad Fig 18 discloses the output of the image with the gradient label) are used as ground truth for a set of biomarkers(Lad ¶0087, ¶0092,¶0117 discloses biomarkers being used to determine AMD progression) identified in the second data set (Lad Fig 12 and 13 and Fig 19 discloses how the ground truth lesion masks are used to determine the success rate of the gradient labeling). See Claim 10 for rationale, its parent claim.
Regarding Claim 13, Lad in view of Mulyukov teaches the method of claim 10, wherein the first data set comprises image data from a medical scan (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline). See Claim 10 for rationale, its parent claim.
Regarding Claim 14, Lad in view of Mulyukov teaches the method of claim 10, wherein the first data set (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) comprises image data from a sensor (Mulyukov ¶00049 discloses sensors collecting patient data and the patient data consisting of retinal images). See Claim 10 for rationale, its parent claim.
Regarding Claim 16, Lad teaches a system (Lad ¶0056-¶0058 disclose the working of a system) comprising:
a processor (Lad ¶0056-¶0057 discloses a processor); and
a memory (Lad ¶0057 discloses a memory) having instructions stored thereon, wherein execution of the instructions by the processor causes the processor (Lad ¶0056-¶0057 discloses a processor in communication with memory that performs instructions) to:
receive a data set (Lad ¶0003 discloses receiving a set of OCT volume can images as an input);
determine, via a trained machine learning model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline), a presence or severity value associated with a disease or medical condition (Lad ¶0003 discloses a machine learning algorithm determining the presence of detecting geographic atrophy) using the data set (Lad ¶0003 discloses receiving a set of OCT volume can images as an input);
output, via a report or graphical user interface (Lad ¶0065 discloses outputting the result, ¶0056 discloses displays, and interfaces), the determined presence or severity value (Lad ¶0003 discloses a machine learning algorithm determining the presence of detecting geographic atrophy), wherein the trained machine learning model was trained in a contrastive learning operation(Lad Fig 9, ¶0101 discloses a contrastive learning manner), the contrastive learning operation comprising:
training a baseline ML model via a first data set (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline), the first data set consisting of data for a non-anomalous, normal, or healthy set (Lad ¶0116 discloses uses auto fluorescent images of healthy patients with GA to training a multilayer CNN);
generating gradient (Lad Fig 18 discloses the output of the image with the gradient label) severity score vector (Lab ¶0114 discloses classifying the severity according to a grading criteria) from the baseline ML model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) for a second data set (Lad ¶0102 discloses using a second OCT dataset), the second data set comprising data for anomalous or unhealthy set (Lad ¶0102 discloses a second OCT dataset that consisted of images that were classified as having CNV, DME, and drusen), wherein the second data set is unlabeled with respect to severity (Lad ¶0102 discloses the secondary dataset being labeled for presence of disease not severity);
tiering the severity score vector (Lad ¶0114 discloses classifying the MD severity according to a grading criteria) into a plurality of severity classes (Lad ¶0042, ¶0069, discloses determining the level of severity of the degeneration using percentage labels).
Lad does not explicitly teach including a first severity class associated with a first severity score label and a second severity class associated with a second severity score label; and
generating the trained machine learning model using the first severity score label and the second severity class as labels for the second training data set as a training data set for the trained machine learning model.
Mulyukov is in the same field of medical eye disease detection image processing. Further, Mulyukov teaches including a first severity class associated with a first severity score label (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being less than a threshold and being labeled low) and a second severity class associated with a second severity score label (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high); and
generating the trained machine learning model (Mulyukov ¶00082 and Fig disclose generating a machine learning model based on the level of disease activity) using the first severity score label (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being less than a threshold and being labeled low) and the second severity class (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high) as labels for the second training data set (Mulyukov ¶00143 discloses labelling the disease activity level as a target attribute in the training data and training the machine learning algorithms using that data set, the Examiner interprets the disease activity level to be equivalent with severity level ¶00065 discloses a set of input data fed to the second algorithm that consists of current and previous Snellen chart measurements; patient longitudinal data on physiological characteristics, e.g., current and previous age, weight etc.; previous disease activity scores, the Examiner interprets that all of these could indicate the severity class of the disease ¶00160 discloses a second input dataset that is comprised of values of the anatomical variables, ¶00118-¶00123 discloses assessing the severity of the disease based on evaluated anatomical variables which are then included in the dataset ) as a training data set for the trained machine learning model (Mulyukov ¶00051-¶00052 discloses training the second algorithm using an additional set of historical data different from the first set, which the Examiner is interpreting to be equivalent to a second data set).
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 Lad by incorporating the scoring of the severity of disease to be used an input for the second machine learning model to be trained on as taught by Mulyukov, to make an invention that can more accurately identify and classify the disease present in the patient image; thus, one of ordinary skilled in the art would be motivated to combine the references since an object of the present invention is to address the need for a method that reliably and accurately assesses disease activity of w-AMD and/or of other retinopathies and that provides patient-specific anti-VEGF treatment regimen models, such as customized dosing frequency models (Mulyukov, ¶00017).
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.
Regarding Claim 17, Lad in view of Mulyukov teaches the system of claim 16, wherein the instructions (Lad ¶0056-¶0057 discloses a processor in communication with memory that performs instructions) to tier the severity score vector into the plurality of severity classes comprises:
instructions (Lad ¶0056-¶0057 discloses a processor in communication with memory that performs instructions) to order the severity score vector by rank (Mulyukov Fig 10 and Fig 11, ¶00023 disclose the severity of the disease activity being ranked as high medium or low) to generate a ranked list of vector elements of the severity score vector (Mulyukov Fig 4 and ¶00134 disclose analyzing feature vales to rank out they affect the disease activity in patients); and
instructions (Lad ¶0056-¶0057 discloses a processor in communication with memory that performs instructions) to arrange the ranked list of vector elements of the severity score vector (Mulyukov Fig 4 and ¶00134 disclose analyzing feature vales to rank out they affect the disease activity in patients) into a plurality of bins (Mulyukov Fig 9, ¶00182 discloses soring the scoring of the patient disease activity in high and low bins) , wherein a first bin corresponds to the first severity class (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being less than a threshold and being labeled low) , and wherein the second bin corresponds to the second severity class (Mulyukov Fig 10, ¶00189- ¶00191 discloses the first severity score being above than a threshold and being labeled high). See Claim 10 for rationale, its parent claim.
Regarding Claim 18, Lad in view of Mulyukov teaches the system of claim 16, wherein the first training data set comprises image data from a medical scan (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline). See Claim 10 for rationale, its parent claim.
Regarding Claim 19, Lad in view of Mulyukov teaches the method of claim 16, wherein the first training data (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline) comprises image data from a sensor (Mulyukov ¶00049 discloses sensors collecting patient data and the patient data consisting of retinal images). See Claim 10 for rationale, its parent claim.
Claims 8, 15, 20 are rejected under 35 U.S.C. 103 as unpatentable over Lad
in view of Mulyukov in further view of Paschalakis et al (US Patent No US 10719936 B2, hereafter referred to as Paschalakis).
Regarding Claim 8, Lad in view of Mulyukov teaches the method of claim 1, wherein the baseline ML model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline).
Lad in view of Mulyukov does not explicitly teach comprises an auto-encoder.
Paschalakis is in the same field of medical eye image processing. Further, Paschalakis teaches comprises an auto-encoder (Paschalakis Col 5 Lines 5-6 disclose the layer of the model being trained as autoencoders).
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 Lad in view of Mulyukov by incorporating ana autoencoder as part of the machine learning model as taught by Paschalakis, to make an invention that is more efficient in identifying and classifying the disease present in the patient image; thus, one of ordinary skilled in the art would be motivated to combine the references since an object of the present invention is to reduce the necessity of the time-consuming hand-crafting of features that would otherwise be required to pre-process the images with application-specific filters or by calculating computable features (Paschalakis, Col 2, Lines 55-60).
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.
Regarding Claim 15, Lad in view of Mulyukov teaches the method of claim 10, wherein the baseline ML model (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline).
Lad in view of Mulyukov does not explicitly teach comprises an auto-encoder.
Paschalakis is in the same field of medical eye image processing. Further, Paschalakis teaches comprises an auto-encoder (Paschalakis Col 5 Lines 5-6 disclose the layer of the model being trained as autoencoders).
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 Lad in view of Mulyukov by incorporating ana autoencoder as part of the machine learning model as taught by Paschalakis, to make an invention that is more efficient in identifying and classifying the disease present in the patient image; thus, one of ordinary skilled in the art would be motivated to combine the references since an object of the present invention is to reduce the necessity of the time-consuming hand-crafting of features that would otherwise be required to pre-process the images with application-specific filters or by calculating computable features (Paschalakis, Col 2, Lines 55-60).
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.
Regarding Claim 20, Lad in view of Mulyukov teaches the method of claim 16, wherein the baseline ML model comprises an auto-encoder (Lad ¶0155, ¶0179, ¶0183 discloses using a CNN trained on 3D OCT inputs as a baseline).
Lad in view of Mulyukov does not explicitly teach comprises an auto-encoder.
Paschalakis is in the same field of medical eye image processing. Further, Paschalakis teaches comprises an auto-encoder (Paschalakis Col 5 Lines 5-6 disclose the layer of the model being trained as autoencoders).
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 Lad in view of Mulyukov by incorporating ana autoencoder as part of the machine learning model as taught by Paschalakis, to make an invention that is more efficient in identifying and classifying the disease present in the patient image; thus, one of ordinary skilled in the art would be motivated to combine the references since an object of the present invention is to reduce the necessity of the time-consuming hand-crafting of features that would otherwise be required to pre-process the images with application-specific filters or by calculating computable features (Paschalakis, Col 2, Lines 55-60).
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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL ROBERTS whose telephone number is (571)272-6413. The examiner can normally be reached Monday- Friday 7:30am- 5:00pm.
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/RACHEL L ROBERTS/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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