Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Final Office action is responsive to the communication filed under 37 C.F.R. § 1.111 on March 4, 2026 (hereafter “Response”). The amendments to the claims are acknowledged and have been entered.
The specification and claims 1, 4–9, 11, and 14 are now amended.
Claims 2, 3, 12, and 13 are now canceled.
Claims 1, 4–11, and 14–20 are pending in the application.
Response to Arguments
All prior objections to the claims and specification are hereby withdrawn.
Claim(s) 1, 4, 5, 9, 10, 11, and 14–20 stand rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Timo Schick and Hinrich Schütze, Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference, Proceedings of the 16th conference of the European chapter of the association for computational linguistics (submitted Jan 21, 2020, last revised Jan 25, 2021), https://doi.org/10.48550/arXiv.2001.07676 (hereafter “Schick”).
The amendment, which merely rolls-up claims 2 and 3 into claim 1, and likewise rolls-up claims 12 and 13 into claim 11, fails to overcome the rejection, because the rejections of claims 2, 3, 12, and 13 remain correct. The Applicant’s arguments have been considered, but are not persuasive for the following reasons.
The Applicant’s argument that “Schick’s mask token is singular” (Response 12) is not persuasive, because the claims only require “at least one sample semantic category filling field.”
The Applicant’s argument that Schick’s mask token “functions merely as a placeholder in a natural language processing framework” (Response 12) is unpersuasive because it is untrue. The mask token is a structure that is used to train the model to answer a cloze question, such that the model, once trained using the mask tokens, is able to categorize its inputs. This argument from the Applicant is also unpersuasive because it fails to explain where the mask token falls short with respect to the claim element to which it was mapped. That is, the Applicant hasn’t pointed out anything that the sample semantic category filling field has that is also missing from the mask token. Indeed, they are the same, both functionally and structurally.
The Applicant’s argument that “[t]he training sample (x,l) for the masked language model M are composed of a sequence of phrases and the corresponding label of that sequence” is unpersuasive because it is incomplete. The Applicant hasn’t actually explained why this matters, relative to what the claim recites, and how it was mapped to the elements of Schick’s disclosure.
Likewise, the Applicant’s argument that “[t]he PVP is a pattern-verbalizer pair and does not constitute a training sample itself” is unpersuasive for the same reason. The phrase “training sample” is never mentioned in claims 1 or 11, so it is unclear what this has to do with the rejection.
The Applicant’s argument that “the masked language model M produces a probability distribution over labels as its output” (Response 12) is unpersuasive for two reasons. For one, the claims do not say anything about the output of the model, so this argument appears to be irrelevant. Second, outputting probability distributions for the categories is exactly how the Applicant’s invention functions. See Spec. ¶¶ 126–128.
The Applicant’s arguments for claim 11 are the same as for claim 1, and therefore, so too are the Examiner’s responses to those arguments.
Accordingly, since each and every claim stands rejected over the prior art, the Applicant’s request for a notice of allowance is respectfully denied.
Information Disclosure Statement
The information disclosure statement filed on March 20, 2026 complies with the provisions of 37 C.F.R. § 1.97, 1.98, and MPEP § 609, and therefore has been placed in the application file. The information referred to therein has been considered as to the merits.
Claim Objections
The Examiner objects to claims 1 and 11 for having the following informalities:
Both claims tack all of the new limitations onto the end of the claim, rather than adding each new limitation in their appropriate place. This results in a claim narrative that is extremely difficult to follow, because the claims jump back and forth in time and sequence of steps. For example, claim 1 begins by reciting the whole method, but then looks backwards to the first step to give further details about the first step after the second and third steps have already been performed. The amendment then repeats this pattern internally, referencing a step, giving limitations about the completion of that step, but then looking backwards to describe the internal sub-steps of that step.
For instance, claim 1 should have been amended to read as follows:
A semantic classification model training method, comprising:
constructing, for each system of at least one provided system to which at least one category to be predicted belongs, a sample system filling clause comprising at least one sample semantic category filling field in each system, wherein a number of the at least one sample semantic category filling field in each system is equal to a number of the at least one category to be predicted in each system, and wherein the at least one sample semantic category filling field is used for filling a sample semantic category corresponding to the at least one category to be predicted;
constructing a sample category filling statement according to sample system filling clauses in all of the at least one system;
constructing a sample query template according to a sample query statement and the sample category filling statement;
inputting the sample query template to a semantic classification model to be pre-constructed to obtain a sample semantic category of the at least one category to be predicted; and
training the semantic classification model according to the sample semantic category and a category label of the at least one category to be predicted.
A similar suggestion is made for claim 11.
Appropriate correction is required.
Claim Rejections – 35 U.S.C. § 102
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, 4, 5, 9, 10, 11, and 14–20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Timo Schick and Hinrich Schütze, Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference, Proceedings of the 16th conference of the European chapter of the association for computational linguistics (submitted Jan 21, 2020, last revised Jan 25, 2021), https://doi.org/10.48550/arXiv.2001.07676 (hereafter “Schick”).
For clarity, the following terms from the claims are mapped to the prior art as follows:
Claim Term
Prior Art Term
semantic classification model
language model M
sample category filling statement orprediction category filling statement
input representation
P
x
, obtained by applying the pattern function P to input statement
x
sample category filling field orprediction category filling field
mask token ---- ∈ V
sample query template orprediction query template
a pattern-verbalizer pair (“PVP”)
p
=
P
,
v
, which comprises a pattern function P and a verbalizer v.
sample query statement orprediction query statement
input sequence of phrases
x
category label
label
y
∈
L
(where
L
is the set of labels)
a “system” to which the at least one category to be predicted belongs
the target classification task A, for which a particular set of labels
L
are defined
Claim 1
Schick discloses:
A semantic classification model training method, comprising:
Schick discloses a method for training a masked language model M (such as RoBERTa) to predict categories for text. Schick Abstract and p. 2.
acquiring a sample query template and a category label of at least one category to be predicted in the sample query template,
Regarding the sample query template, a “pattern-verbalizer pair (PVP)” for a particular task is defined as
P
,
v
, which comprises a pattern function P and a verbalizer v. Schick p. 2–3. The pattern function P is a function that maps an input sequence x into a “pattern” comprising phrases from the input sequence x and a mask token. Schick p. 2–3. The verbalizer v that maps a predefined set of labels
y
∈
L
to words in the model’s vocabulary. Schick p. 3. For example, in the “AG News” example on page 5, the verbalizer maps four pre-defined classifications for news articles to human-readable categories, such as World, Business, Sports, etc.
Regarding the claimed category label, this corresponds to the ground truth label
y
provided for each given training example during PVP training. See Schick p. 3.
wherein the sample query template is constructed according to a sample query statement
“Given an input
x
we apply
P
to obtain an input representation
P
x
.” Schick p. 2.
and a number of the at least one category to be predicted;
The output of
P
is predefined to “contain[] exactly one mask token, i.e., its output can be viewed as a cloze question.” Schick p. 2.
inputting the sample query template to the semantic classification model to be pre-constructed to obtain a sample semantic category of the at least one category to be predicted; and
As understood by the Examiner, the phrase “to be pre-constructed to obtain a sample semantic category of the at least one category to be predicted” refers to the intended purpose for inputting the sample query template into the semantic classification model.
Likewise, Schick discloses that, as an initial step for fine tuning (i.e. training)
M
on a given PVP
p
=
P
,
v
, we input the components of
p
into the model
M
using
M
v
y
P
x
. Schick p. 3.
training the semantic classification model according to the sample semantic category and the category label of the at least one category to be predicted.
The above yields a score
s
p
for every word in model M’s vocabulary V, which are run through the softmax function to obtain a probability distribution
q
p
so that we can “use the cross-entropy between
q
p
y
x
and the true (one-hot) distribution of training example
x
,
y
—summed over all
x
,
y
∈
T
—as loss for finetuning
M
on
p
.” Schick p. 3.
wherein the sample query template is constructed in the following manner: constructing a sample category filling statement comprising at least one sample semantic category filling field,
“We define a pattern to be a function
P
that takes as input a sequence of phrases
x
=
s
1
,
…
,
s
k
with
s
i
∈
V
*
and outputs a single phrase
P
x
∈
V
*
that contains exactly one mask token.” Schick p. 2.
wherein a number of the at least one sample semantic category filling field is equal to the number of the at least one category to be predicted, and the at least one sample semantic category filling field is used for filling a sample semantic category corresponding to the at least one category to be predicted;
Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
for which
v
y
is the most likely candidate at the masked position. Schick p. 2–3.
and constructing the sample query template according to the sample query statement and the sample category filling statement;
Schick then defines a “pattern-verbalizer pair (PVP)” with
P
,
v
, which includes both the pattern function P and a verbalizer v, where P further comprises the mask token. Schick p. 2.
wherein at least one system to which the at least one category to be predicted belongs is provided; and
A “target classification task A” may also be defined for which the labels
L
discussed in the rejections of claims 1 and 2 are defined. Schick p. 2 § 3. Five different examples of target classification tasks are given on pages 5–6 of the reference.
constructing the sample category filling statement comprising the at least one sample semantic category filling field, comprises: for each system of the at least one system, constructing a sample system filling clause comprising at least one of sample semantic category filling field in each system,
For each target classification task A, there will be a separate set of labels used by the verbalizer, and each task A further gets its own pattern function P that takes as input a sequence of phrases
x
=
s
1
,
…
,
s
k
with
s
i
∈
V
*
and outputs a single phrase
P
x
∈
V
*
that contains exactly one mask token. Schick p. 2.
wherein a number of the at least one sample semantic category filling field in each system is equal to a number of at least one category to be predicted in each system; and
“Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
for which
v
y
is the most likely candidate at the masked position.” Schick p. 2–3. Recall that
L
is defined separately for each task A.
determining the sample category filling statement according to sample system filling clauses in all of the at least one system.
Schick then defines a “pattern-verbalizer pair (PVP)” with
P
,
v
, which includes both the pattern function P and a verbalizer v, where P further comprises the mask token. Schick p. 2.
Claim 4
Claim scope is not limited by optional claim language, particularly in the case of method claims with contingent steps that have unmet conditions precedent. See MPEP § 2111.04. In this case, claim 4 says that a clause delimiter is placed “between” sample system filling clauses in different systems, and/or a field delimiter is placed “between” sample semantic category filling fields, but claim 4 only requires up to one of each. Since claim 4 does not require more than one sample category filing statement, claim 4 also does not require a clause delimiter, because a clause delimiter only exists in cases when the place of “between” exists. If there is only one sample category filing statement (which is allowed by the claim language), then there is no second sample category filing statement to serve as the other side of the “between.”
Likewise, since claim 4 does not require more than one sample semantic category filling field, claim 4 also does not require a field delimiter, because a field delimiter is only required when there are enough sample semantic category filling fields for the place of “between” to even exist.
Accordingly, Schick anticipates claim 4 at least because Schick discloses each and every required element of claim 4.
Claim 5
The limitations of claim 5 are contingent upon the existence of field delimiters. However, since neither parent claim 4 nor claim 5 require field delimiters in every instance of the method, the prior art does not need to disclose the further limitations of those field delimiters in order to anticipate claim 5. See MPEP § 2111.04 (subsection II.).
Claim 9
Schick discloses the method of claim 1, wherein inputting the sample query template to the pre-constructed semantic classification model to obtain the sample semantic category of the category to be predicted, comprises:
inputting the sample query template to the pre-constructed semantic classification model to obtain at least one sample semantic character of the category to be predicted; and
“Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
for which
v
y
is the most likely candidate at the masked position.” Schick p. 2–3.
combining the at least one sample semantic character in a prediction sequence to obtain the sample semantic category of the category to be predicted.
After the model predicts the label y, the verbalizer maps the label to corresponding category
v
y
, and uses it to fill in the mask. Schick p. 3.
Claim 10
Schick discloses the method of claim 9, wherein inputting the sample query template to the pre-constructed semantic classification model to obtain the at least one sample semantic character of the category to be predicted, comprises:
inputting the sample query template to the pre-constructed semantic classification model to extract a sample semantic feature in the sample query template; and
“Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
.” Schick p. 2–3.
performing feature transformation on the sample semantic feature to obtain the at least one sample semantic character of the category to be predicted.
After the model predicts the label y, the verbalizer maps the label to corresponding category
v
y
, and uses it to fill in the mask. Schick p. 3. Verbalizer v is “an injective function
v
:
L
→
V
that maps each label to a word from M’s vocabulary,” hence, the feature transformation. Schick p. 2.
Claim 11
Schick discloses:
A semantic classification method, comprising:
Schick discloses a method for training and using a masked language model M (such as RoBERTa) to predict categories for text. Schick Abstract and p. 2.
acquiring a prediction query template,
A “pattern-verbalizer pair (PVP)” for a particular task is defined as
P
,
v
, which comprises a pattern function P and a verbalizer v. Schick p. 2–3. The pattern function P is a function that maps an input sequence x into a “pattern” comprising phrases from the input sequence x and a mask token. Schick p. 2–3. The verbalizer v that maps a predefined set of labels
y
∈
L
to words in the model’s vocabulary. Schick p. 3. For example, in the “AG News” example on page 5, the verbalizer maps four pre-defined classifications for news articles to human-readable categories, such as World, Business, Sports, etc.
wherein the prediction query template is constructed according to a prediction query statement
“Given an input
x
we apply
P
to obtain an input representation
P
x
.” Schick p. 2.
and a number of at least one category to be predicted; and
The output of
P
is predefined to “contain[] exactly one mask token, i.e., its output can be viewed as a cloze question.” Schick p. 2.
obtaining a prediction semantic category of the at least one category to be predicted according to the prediction query template.
“Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
for which
v
y
is the most likely candidate at the masked position,” which is also the predicted category. Schick p. 2–3.
wherein the prediction query template is constructed in the following manner: constructing a prediction category filling statement comprising at least one prediction semantic category filling field,
“We define a pattern to be a function
P
that takes as input a sequence of phrases
x
=
s
1
,
…
,
s
k
with
s
i
∈
V
*
and outputs a single phrase
P
x
∈
V
*
that contains exactly one mask token.” Schick p. 2.
wherein a number of the at least one prediction semantic category filling field is equal to the number of the at least one category to be predicted, and the at least one prediction semantic category filling field is used for filling a prediction semantic category corresponding to the at least one category to be predicted;
“Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
for which
v
y
is the most likely candidate at the masked position.” Schick p. 2–3.
and constructing the prediction query template according to the prediction query statement and the prediction category filling statement;
Schick then defines a “pattern-verbalizer pair (PVP)” with
P
,
v
, which includes both the pattern function P and a verbalizer v, where P further comprises the mask token. Schick p. 2.
wherein at least one system to which the at least one category to be predicted belongs is provided; and
A “target classification task A” may also be defined for which the labels
L
discussed in the rejections of claims 1 and 2 are defined. Schick p. 2 § 3. Five different examples of target classification tasks are given on pages 5–6 of the reference.
constructing the prediction category filling statement comprising the at least one prediction semantic category filling field, comprises: for each system of the at least one system, constructing a prediction system filling clause comprising at least one of prediction semantic category filling field in each system,
For each target classification task A, there will be a separate set of labels used by the verbalizer, and each task A further gets its own pattern function P that takes as input a sequence of phrases
x
=
s
1
,
…
,
s
k
with
s
i
∈
V
*
and outputs a single phrase
P
x
∈
V
*
that contains exactly one mask token. Schick p. 2.
wherein a number of the at least one prediction semantic category filling field in each system is equal to a number of at least one category to be predicted in each system; and
“Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
for which
v
y
is the most likely candidate at the masked position.” Schick p. 2–3. Recall that
L
is defined separately for each task A.
determining the prediction category filling statement according to prediction system filling clauses in all of the at least one system.
Schick then defines a “pattern-verbalizer pair (PVP)” with
P
,
v
, which includes both the pattern function P and a verbalizer v, where P further comprises the mask token. Schick p. 2.
Claim 14
Claim scope is not limited by optional claim language, particularly in the case of method claims with contingent steps that have unmet conditions precedent. See MPEP § 2111.04. In this case, claim 14 says that a clause delimiter is placed “between” prediction system filling clauses in different systems, and/or a field delimiter is placed “between” prediction semantic category filling fields, but claim 14 only requires up to one of each. Since claim 14 does not require more than one prediction category filing statement, claim 14 also does not require a clause delimiter, because a clause delimiter only exists in cases when the place of “between” exists. If there is only one prediction category filing statement (which is allowed by the claim language), then there is no second prediction category filing statement to serve as the other side of the “between.”
Likewise, since claim 14 does not require more than one prediction semantic category filling field, claim 14 also does not require a field delimiter, because a field delimiter is only required when there are enough prediction semantic category filling fields for the place of “between” to even exist.
Accordingly, Schick anticipates claim 14 at least because Schick discloses each and every required element of claim 14.
Claim 15
The limitations of claim 15 are contingent upon the existence of field delimiters. However, since neither parent claim 14 nor claim 15 require field delimiters in every instance of the method, the prior art does not need to disclose the further limitations of those field delimiters in order to anticipate claim 15. See MPEP § 2111.04 (subsection II.).
Claim 16
Schick discloses the method of claim 11, wherein obtaining the prediction semantic category of the at least one category to be predicted according to the prediction query template, comprises:
determining at least one prediction semantic character of the at least one category to be predicted according to the prediction query template; and
“Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
for which
v
y
is the most likely candidate at the masked position.” Schick p. 2–3.
combining the at least one prediction semantic character in a prediction sequence to obtain the prediction semantic category of the at least one category to be predicted.
After the model predicts the label y, the verbalizer maps the label to corresponding category
v
y
, and uses it to fill in the mask. Schick p. 3.
Claim 17
Schick discloses the method according to claim 16, wherein
the at least one prediction semantic character comprises at least two prediction semantic characters having a same prediction sequence; and
Given an input
x
,
Schick obtains an unweighted score
s
p
l
∣
x
for every label l in the set of labels denoting each respective label’s likelihood of fitting at that mask position. Schick p. 3 § 3.1.
combining the at least one prediction semantic character in the prediction sequence to obtain the prediction semantic category of the at least one category to be predicted, comprises:
combining prediction semantic characters having different prediction sequences in the prediction sequence to obtain at least one candidate semantic category;
To obtain the score sp of each potential label l, we input a combined sequence into the model,
M
v
l
P
x
(in other words, the template version of P(x) is filled in with the verbalization v(l) of each label l). Schick p. 3 § 3.1
determining a category prediction probability of the at least one candidate semantic category according to character prediction probabilities of different prediction semantic characters in the at least one candidate semantic category; and
Next, we obtain a probability distribution
q
p
l
x
over all of the labels based on their unweighted score, using the softmax function. Schick p. 3 § 3.1
selecting the prediction semantic category from the at least one candidate semantic category according to the category prediction probability and a matching result between the at least one candidate semantic category and each standard semantic category in a standard semantic category library.
Whichever label is deemed “the most likely substitute for the mask” is chosen as the output label. Schick p. 3 (top 5 lines of left column).
Claim 18
Shick discloses the method of claim 16, wherein determining the at least one prediction semantic character of the at least one category to be predicted according to the prediction query template, comprises:
extracting a prediction semantic feature in the prediction query template; and
“Given an input
x
, we apply
P
to obtain an input representation
P
x
, which is then processed by
M
to identify the
y
∈
L
.” Schick p. 2–3.
performing feature transformation on the prediction semantic feature to obtain the at least one prediction semantic character of the at least one category to be predicted.
After the model predicts the label y, the verbalizer maps the label to corresponding category
v
y
, and uses it to fill in the mask. Schick p. 3. Verbalizer v is “an injective function
v
:
L
→
V
that maps each label to a word from M’s vocabulary,” hence, the feature transformation. Schick p. 2.
Claims 19 and 20
The evidence from the rejection of claim 1 is hereby reincorporated by reference to claims 19 and 20 respectively, along with further evidence that the performance of the algorithms therein were tested experimentally, which necessarily implies the use of general purpose computer components like the ones recited in claims 19 and 20.
Claim Rejections – 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 6–8 are rejected under 35 U.S.C. 103 as being unpatentable over Schick as applied to claim 1 above, and further in view of U.S. Patent Application Publication No. 2019/0205794 A1 (“Hsu”).
Claim 6
Schick teaches the method of claim 1, but does not explicitly disclose the label correction steps of claim 6.
Hsu, however, teaches an improvement technique, comprising:
determining a label anomaly type according to the sample semantic category and the category label;
A “correction diagnosis unit 960” is provided with a cluster of erroneous predictions made by a model, “to identify the cause of the cluster as being noise . . . or a defect in the heuristic labeling model.” Hsu ¶ 89.
adjusting the category label according to a label correction manner corresponding to the label anomaly type; and
As shown in FIG. 9A, a correction control unit 970 subsequently provides one of corrections 980a–980c, depending on the type of error diagnosed. See Hsu ¶¶ 89–93. For example, “the label correction unit 360 upon performing the correction processes as described above may activate a label corrector 990 that is configured to change the wrongly labeled data record. Specifically, the label corrector 990 corrects for each data record, the associated first label (determined by the labeling heuristic model) based on the corresponding second label (i.e., the predicted label).” Hsu ¶ 93.
training the semantic classification model according to the sample semantic category and the adjusted category label.
“The set of wrongly labeled data records are input to the label correction unit 360, which is configured by one embodiment to correct the wrongly labeled cross-validation data records.” Hsu ¶ 49.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve Schick’s labels with Hsu’s method of discovering potential label errors. One would have been motivated to improve Schick with Hsu’s technique because “it is often extremely difficult to decipher which labels in the training data set are potentially incorrect, let alone correct the labeled data.” Hsu ¶ 3.
Claims 7 and 8
The additional elements of claims 7 and 8 consist of conditional limitations with unmet conditions precedent: they say what to do if a particular case arises, but do not require any of the particular cases to actually occur in every iteration of the method. “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” MPEP § 2111.04. Therefore, the prior art does not need to show the optional contingent limitations of claims 7 and 8.
Conclusion
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 Justin R. Blaufeld whose telephone number is (571)272-4372. The examiner can normally be reached M-F 9:00am - 4:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James K Trujillo can be reached at (571) 272-3677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Justin R. Blaufeld
Primary Examiner
Art Unit 2151
/Justin R. Blaufeld/Primary Examiner, Art Unit 2151
Prosecution Insights