DETAILED ACTION
This final office action is responsive to the amendment filed on July 1, 2026. Claims 1-7 and 9-10 are pending. Claims 1, 9, and 10 are independent. Claim 8 is canceled.
The objection to the specification is withdrawn in light of applicant’s amendment to the specification.
The objection to claims 9 and 10 are withdrawn in light of applicant’s amendment to claims 9 and 10.
Claim rejection of claims 1-4, 7, and 9-10 under 35 USC §101 are withdrawn in light of applicant’s amendment and arguments. See section Response to Arguments below.
Claim rejections of claims 1-10 under 35 USC §103 are withdrawn, however, a new grounds of rejection has been made in light of applicant’s amendment. See sections Claim Rejections – 35 USC §103 and Response to Arguments below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 22, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1, 2, 5, 6, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Tsang et al. (Decision Trees for Uncertain Data), hereinafter Tsang, in view of Quinlan (Induction of Decision Trees), hereinafter Quinlan, in view of Fredrikson et al. (Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures), hereinafter Fredrikson.
Regarding claim 1, Tsang teaches the processing device:
a memory containing program instructions; and a processor connected to the memory, wherein the processor is configured to execute the program instructions to: (Tsang, page 74, column 2, paragraph 2: “The algorithms described above have been implemented8 in Java using JDK 1.6 and a series of experiments was performed on a PC with an Intel Core 2 Duo 2.66 GHz CPU and 2 GB of main memory, running Linux kernel 2.6.22 i686.”)
on a basis of the acquired score information, specify a possible range that a value of an unknown feature takes, the unknown feature being a part of a plurality of features included in the training data; and (Tsang, page 70, column 1, paragraph 1: “We model uncertainty information by fitting appropriate error models on to the point data. For each tuple
t
i
and for each attribute
A
j
, the point value
v
i
,
j
reported in a data set is used as the mean of a pdf
f
i
,
j
, defined over an interval
a
i
,
j
,
b
i
,
j
. The range of values for
A
j
(over the whole data set) is noted and the width of
a
i
,
j
,
b
i
,
j
is set to
w
∙
A
j
, where
A
j
denotes the width of the range for
A
j
and
w
is a controlled parameter.” – The range of values for each attribute A is analogous to the possible range that an unknown feature takes where the attributes A are analogous to the features of the training data. Where the uncertainty information from the pdf is analogous to the score information that was acquired.)
the specifying includes specifying the possible range that the value of the unknown feature takes by excluding a value of a candidate determined that a possibility of the candidate constituting the training data is low, ... (Tsang, page 66: “We can take the average of the pdf of the attribute in question over the tuples, where the value is present. The result is a pdf, which can be used as a “guess” distribution of the attribute’s value in the missing tuples. Then, we can proceed with decision tree construction.” page 69, column 1, last paragraph: “This is because a tuple
t
i
’s pdf spans a continuous range
a
i
,
j
,
b
i
,
j
. Moving the split point from
a
i
,
j
to
b
i
,
j
continuously changes the probability
p
L
=
∫
a
i
,
j
n
z
n
f
i
,
j
n
x
d
x
(and likewise for
p
R
). This changes the fractional tuples
t
L
and
t
R
, and thus, changes the resulting tree. If we model a pdf by
s
sample values, we are approximating the pdf by a discrete distribution of
s
points. In this case, as the split point moves from one end point
a
i
,
j
to another end point
b
i
,
j
of the interval, the probability
p
L
changes in
s
steps. With
m
tuples, there are in total
m
s
sample points. So, there are at most
m
s
-
1
possible split points to consider. Considering all
k
attributes, to determine the best (attribute, split point) pair thus requires us to examine
k
(
m
s
-
1
)
combinations of attributes and split points.” – The split point being approximated over the range indicates that when the probability is 0, since there isn’t any training data, then the split point would be changed and, therefore, the specifying excludes values.)
Tsang does not explicitly teach:
acquire, from a decision tree that is a learned model and includes a plurality of nodes, score information representing a value according to a number of pieces of data that fell to each of the nodes, among a plurality of pieces of training data used for training of the decision tree; and
perform risk assessment of the decision tree on a basis of a result of specifying,
wherein the acquiring includes acquiring the score information by acquiring a plurality of inference results that are inferred as a result of inputting a plurality of pieces of candidate data to the decision tree, respectively, the candidate data being created on a basis of information representing a value of a known feature having been held and information representing a candidate value of the unknown feature among the features constituting the training data used for training the decision tree, or by acquiring structure data from the decision tree having white box setting, and
…the determination being able to be made using at least one of a confidence score that is the score information and the structure data.
However, Quinlan teaches:
acquire, from a decision tree that is a learned model and includes a plurality of nodes, score information representing a value according to a number of pieces of data that fell to each of the nodes, among a plurality of pieces of training data used for training of the decision tree; and (Quinlan, page 98, paragraph 3: “Conceptually, suppose that, along with the object to be classified, we have been passed a token with some value T. In the situation above, each branch of Ai is then explored in turn, using a token of value T • ratio i i.e. the given token value is distributed across all possible values in proportion to the ratios above. The value passed to a branch may be distributed further by subsequent tests on other attributes for which this object has unknown values.” – The token is analogous to the score information that is taught by Tsang above. The value passed to branches is analogous to the nodes of the decision tree which is used to determine the attribute of the unknown values.)
Quinlan is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Tsang, which already teaches a processing device to specify a range of values an unknown feature can take from a decision tree but does not explicitly teach acquiring the score information from the nodes of the decision tree, to include the teachings of Quinlan which does teach acquiring the score information from the nodes of the decision tree in order to better classify unknown values with a decision tree at higher rates. (Quinlan, page 99, paragraph 1)
Tsang and Quinlan do not explicitly teach:
perform risk assessment of the decision tree on a basis of a result of specifying,
wherein the acquiring includes acquiring the score information by acquiring a plurality of inference results that are inferred as a result of inputting a plurality of pieces of candidate data to the decision tree, respectively, the candidate data being created on a basis of information representing a value of a known feature having been held and information representing a candidate value of the unknown feature among the features constituting the training data used for training the decision tree, or by acquiring structure data from the decision tree having white box setting, and
…the determination being able to be made using at least one of a confidence score that is the score information and the structure data.
However, Fredrikson teaches:
perform risk assessment of the decision tree on a basis of a result of specifying, (Fredrikson, page 6, column 1, paragraph 2: “To evaluate the risk posed by existing, publicly-available models, we used trees obtained from the BigML service [4]. Because the sensitive feature in each dataset is binary-valued, we use precision and recall to measure the effectiveness of the attack. In our setting, precision measures the fraction of predicted “Yes” responses that are correct, and recall measures the fraction of “Yes” responses in the dataset that are predicted by the adversary. We also measure the accuracy which is defined as the fraction of correct responses.”)
wherein the acquiring includes acquiring the score information by acquiring a plurality of inference results that are inferred as a result of inputting a plurality of pieces of candidate data to the decision tree, respectively, the candidate data being created on a basis of information representing a value of a known feature having been held and information representing a candidate value of the unknown feature among the features constituting the training data used for training the decision tree, or by acquiring structure data from the decision tree having white box setting, and (Fredrikson, page 5, column 1, paragraphs 3-4: “Recall from before that in the white-box setting, we not only assume that the attacker knows each
ϕ
i
, but also the number of training samples
n
i
that correspond to
ϕ
i
. From this, he also knows
N
=
∑
i
=
1
m
n
i
,
the total number of samples in the training set. The known values
x
K
induce a set of paths
S
=
s
i
1
≤
i
≤
m
:
S
=
ϕ
i
,
n
i
|
∃
x
'
∈
R
d
.
x
'
K
=
x
K
∧
ϕ
i
(
x
'
)
. Each path corresponds to a basis function
ϕ
i
and a sample count
n
i
. We let
p
i
denote
n
i
/
N
, and note that each
p
i
gives us some information about the joint distribution on features used to build the training set. In the following, we write
s
i
as shorthand for the event that a row drawn from the joint prior traverses the path
s
i
, i.e., Pr[
s
i
] corresponds to the probability of drawing a row from the joint prior that traverses
s
i
. Observe that
p
i
is an empirical estimate for this quantity, derived from the draws used to produce the training set.” – The probability is analogous to the score information, where the probability is acquired using the structure data of the training samples, paths, and basis function of the white-box setting.)
…the determination being able to be made using at least one of a confidence score that is the score information and the structure data. (Fredrikson, page 2, column 1, last paragraph: “We give a new MAP estimator that uses the confidence information in the white-box setting to infer sensitive information with no false positives when tested against two different BigML decision tree models.” – The MAP estimator to infer the sensitive information is analogous to the specifying which is already done by Tsang above. Thus, the confidence interval being used is analogous to the determination being made based on the confidence interval.)
Fredrikson is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Tsang and Quinlan, which already teaches a processing device that acquires score information and specifies a possible range of values for an unknown feature from a decision tree but does not explicitly teach that the result is used to perform risk assessment or that the specifying is done using a confidence score, to include the teachings of Fredrikson which does teach that the result is used to perform risk assessment or that the specifying is done using a confidence score in order to “avoid these kinds of MI attacks with negligible degradation to utility.” (Fredrikson, abstract)
Regarding claim 2, Tsang, Quinlan, and Fredrikson teach the processing device of claim 1, as cited above.
Tsang further teaches:
create a plurality of pieces of candidate data on a basis of information representing a value of a known feature having been held and information representing a candidate value of the unknown feature; and (Tsang, page 70, column 1, paragraph 1: “To generate the pdf
f
i
,
j
, we consider two options. The first is uniform distribution, which implies
f
i
,
j
x
=
b
i
,
j
-
a
i
,
j
-
1
. The other option is Gaussian distribution5 for which we use
1
4
b
i
,
j
-
a
i
,
j
as the standard deviation. In both cases, the pdf is generated using
s
sample points in the interval.” – The pdf generated using s sample points is analogous to the candidate data on a basis of information representing a value of a known feature and information representing a candidate of an unknown feature.)
acquire the score information by acquiring a plurality of inference results that are inferred as a result of inputting the plurality of pieces of created candidate data to the decision tree, respectively. (Tsang, page 70, column 1, paragraph 2: “As we have explained, under our uncertainty model, classification results are probabilistic. Following [3], we take the class label of the highest probability as the final class label.” – Tsang, Quinlan, and Fredrikson already teach acquiring the score information, while the class label of the highest probability as the final class label is analogous to using the inference results to acquire the score information.)
Regarding claim 5, Tsang, Quinlan, and Fredrikson teach the processing device of claim 1, as cited above.
Tsang further teaches:
specify a leaf node corresponding to the score information including a value that becomes equal to or smaller than a given threshold, and (Tsang, page 66, column 2, paragraph 3: “Each internal node
n
of a decision tree is associated with an attribute
A
j
n
and a split point
z
n
∈
dom
A
j
n
, giving a binary test
v
0
,
j
n
≤
z
n
. An internal node has exactly two children, which are labeled “left” and “right,” respectively. Each leaf node
m
in the decision tree is associated with a discrete probability distribution
P
m
over
C
. For each
c
∈
C
,
P
m
(
c
)
gives a probability reflecting how likely a tuple assigned to leaf node
m
would have a class label of
c
.” – The leaf nodes specifying a probability is analogous to specifying a leaf node corresponding to the score information, while the binary test is analogous to the value being equal to or smaller than a given threshold.)
specify the possible range that the value of the unknown feature takes on a basis of the score information corresponding to the node existing on a route between the specified leaf node and a root node that is a first branch in the decision tree. (Tsang, page 66, column 2, paragraph 4: “To determine the class label of a given test tuple
t
0
=
v
0.1
,
⋯
,
v
0
.
k
,
?
, we traverse the tree starting from the root node until a leaf node is reached. When we visit an internal node
n
, we execute the test
v
0
,
j
n
≤
z
n
and proceed to the left child or the right child accordingly. Eventually, we reach a leaf node
m
. The probability distribution
P
m
associated with
m
gives the probabilities that
t
0
belongs to each class label
c
∈
C
. For a single result, we return the class label
c
∈
C
that maximizes
P
m
(
c
)
.” – Following the internal node to a leaf node to return the class label that maximizes P is analogous to the possible range of the value the unknown feature takes by a route between a specified leaf node to a root node.)
Tsang does not explicitly teach:
acquire the score information by acquiring structure information of the decision tree corresponding to each of the nodes included in the decision tree,
the score information representing a value corresponding to a ratio of a number of pieces of data corresponding to each label to the training data, on the node; and
However, Quinlan further teaches:
acquire the score information by acquiring structure information of the decision tree corresponding to each of the nodes included in the decision tree, (Quinlan, page 86, paragraph 3: “Leaves of a decision tree are class names, other nodes represent attribute-based tests with a branch for each possible outcome. In order to classify an object, we start at the root of the tree, evaluate the test, and take the branch appropriate to the outcome. The process continues until a leaf is encountered, at which time the object is asserted to belong to the class named by the leaf. Taking the decision tree of Figure 2, this process concludes that the object which appeared as an example at the start of this section, and which is not a member of the training set, should belong to class P.”)
the score information representing a value corresponding to a ratio of a number of pieces of data corresponding to each label to the training data, on the node; and (Quinlan, page 98, paragraph 1: “One strategy which has been found to work well is as follows. Let A be an attribute with values {A1, A2, ... Av}. For some collection C of objects, let the numbers of objects with value Ai of A be pi and ni, and let pu and nu denote the numbers of objects of class P and N respectively that have unknown values of A. When the information gain of attribute A is assessed, these objects with unknown values are distributed across the values of A in proportion to the relative frequency of these values in C. Thus the gain is assessed as if the true value of Pi were given by pi + pu • ratioi where
ratio
i
=
p
i
+
n
i
∑
i
p
i
+
n
i
and similarly for ni.”)
Regarding claim 6, Tsang, Quinlan, and Fredrikson teach the processing device of claim 5, as cited above.
Tsang further teaches:
specify the possible range that the value of the unknown feature takes by checking whether or not there is a node serving as a branch by the unknown feature among the nodes existing on the route between the leaf node and the root node. (Tsang, page 66, column 2, paragraph 4: “To determine the class label of a given test tuple
t
0
=
v
0.1
,
⋯
,
v
0
.
k
,
?
, we traverse the tree starting from the root node until a leaf node is reached. When we visit an internal node
n
, we execute the test
v
0
,
j
n
≤
z
n
and proceed to the left child or the right child accordingly. Eventually, we reach a leaf node
m
. The probability distribution
P
m
associated with
m
gives the probabilities that
t
0
belongs to each class label
c
∈
C
. For a single result, we return the class label
c
∈
C
that maximizes
P
m
(
c
)
.” – Traversing the decision tree from the root node until a leaf node is reached is analogous to checking if there is a node existing on the route between the leaf node and the route node as each internal node being visited and proceeding left or right accordingly will check if there is a node to follow from there. Where specifying the possible range of values based off this is already taught by Tsang above.)
Regarding claim 9, claim 9 has all the same limitations of claim 1 which are taught by Tsang, Quinlan, and Fredrikson – see claim 1 above.
Regarding claim 10, claim 10 has all the same limitations of claim 1 which are taught by Tsang, Quinlan, and Fredrikson – see claim 1 above.
Tsang also teaches:
A non-transitory computer-readable medium storing thereon a program comprising instructions for causing an information processing device to execute processing to: (Tsang, page 74, column 2, paragraph 2: “The algorithms described above have been implemented8 in Java using JDK 1.6 and a series of experiments was performed on a PC with an Intel Core 2 Duo 2.66 GHz CPU and 2 GB of main memory, running Linux kernel 2.6.22 i686.”)
Claims 3, 4, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Tsang in view of Quinlan in view of Fredrikson in view of Rahman et al. (Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques), hereinafter Rahman.
Regarding claim 3, Tsang, Quinlan, and Fredrikson teach the processing device of claim 2, as cited above.
Tsang further teaches:
the training data includes a plurality of feature values and labels, (Tsang, page 66, column 2, paragraph 3: “In our model, a data set consists of
d
training tuples,
t
1
,
t
2
,
⋯
,
t
d
, and
k
numerical (real-valued) feature attributes,
A
1
,
⋯
,
A
k
. The domain of attribute
A
j
is dom
A
j
. Each tuple
t
i
is associated with a feature vector
V
i
=
v
i
,
1
,
v
i
,
2
,
⋯
,
v
i
,
k
and a class label
c
i
, where
v
i
,
j
∈
dom
A
j
and
c
i
∈
C
, the set of all class labels.”)
Tsang does not explicitly teach:
the inference result represents a value according to a ratio of a number of pieces of data corresponding to each of the labels to the training data, on a leaf node to which candidate data belongs among the nodes included in the decision tree, and
the processor is configured to execute the program instructions to specify the possible range that the value of the unknown feature takes by excluding the candidate value on a basis of a value according to a label corresponding to the candidate data in the inference result.
However, Quinlan further teaches:
the inference result represents a value according to a ratio of a number of pieces of data corresponding to each of the labels to the training data, on a leaf node to which candidate data belongs among the nodes included in the decision tree, and (Quinlan, page 98, paragraph 1: “One strategy which has been found to work well is as follows. Let A be an attribute with values {A1, A2, ... Av}. For some collection C of objects, let the numbers of objects with value Ai of A be pi and ni, and let pu and nu denote the numbers of objects of class P and N respectively that have unknown values of A. When the information gain of attribute A is assessed, these objects with unknown values are distributed across the values of A in proportion to the relative frequency of these values in C. Thus the gain is assessed as if the true value of Pi were given by pi + pu • ratioi where
ratio
i
=
p
i
+
n
i
∑
i
p
i
+
n
i
and similarly for ni.”)
Tsang, Quinlan, and Fredrikson do not explicitly teach:
the processor is configured to execute the program instructions to specify the possible range that the value of the unknown feature takes by excluding the candidate value on a basis of a value according to a label corresponding to the candidate data in the inference result.
However, Rahman teaches:
the processor is configured to execute the program instructions to specify the possible range that the value of the unknown feature takes by excluding the candidate value on a basis of a value according to a label corresponding to the candidate data in the inference result. (Rahman, page 64, column 2, paragraph 1: “Whereas the proposed techniques (DMI and SiMI) impute a missing value by using only a group of records (instead of all records) that are similar to the record having the missing value. Another existing technique IBLLS also imputes a missing value by using the similar records. However, it also uses only a subset of all the attributes where the correlation between the attribute having the missing value and each attribute belonging to the subset is high. It completely ignores all other attributes having a correlation lower than a threshold value.” – The missing values being imputed when the correlation is high and ignoring the other attributes with a correlation below a threshold is analogous to specifying the possible range based on excluding candidate values.)
Rahman is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Tsang, Quinlan, and Fredrikson, which already teaches specifying a range of values an unknown feature takes using a decision tree but does not explicitly teach excluding candidate values based on inference results, to include the teachings of Rahman which does teach excluding candidate values based on inference results in order to "improve the imputation accuracy." (Rahman, page 64, column 1, paragraph 3)
Regarding claim 4, Tsang, Quinlan, Fredrikson, and Rahman teach the processing device of claim 3, as cited above.
Tsang, Quinlan, and Fredrikson do not explicitly teach:
specify the possible range that the value of the unknown feature takes by excluding the candidate value in which the value according to the label corresponding to the candidate data in the inference result becomes equal to or smaller than a given threshold.
However, Rahman further teaches:
specify the possible range that the value of the unknown feature takes by excluding the candidate value in which the value according to the label corresponding to the candidate data in the inference result becomes equal to or smaller than a given threshold. (Rahman, page 64, column 2, paragraph 1: “Whereas the proposed techniques (DMI and SiMI) impute a missing value by using only a group of records (instead of all records) that are similar to the record having the missing value. Another existing technique IBLLS also imputes a missing value by using the similar records. However, it also uses only a subset of all the attributes where the correlation between the attribute having the missing value and each attribute belonging to the subset is high. It completely ignores all other attributes having a correlation lower than a threshold value.”)
Regarding claim 7, Tsang, Quinlan, and Fredrikson teach the processing device of claim 1, as cited above.
Tsang, Quinlan, and Fredrikson do not explicitly teach:
instruct how to output the score information by the decision tree on a basis of a result of specifying.
However, Rahman teaches:
instruct how to output the score information by the decision tree on a basis of a result of specifying. (Rahman, page 55, algorithm 1: second line and Step 5 show that the algorithm to impute missing values provides instructions on how that information is output.)
Rahman is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Tsang, Quinlan, and Fredrikson, which already teaches acquiring score information using a decision tree but does not explicitly teach instructions on how to output the score information, to include the teachings of Rahman which does teach instructions on how to output the score information. Since a user is able to compare imputation accuracies, being able to view the output of the score information would allow them to compare the results. (Rahman, page 54, bottom of column 1 and top of column 2)
Response to Arguments
Applicant’s arguments, filed July 1, 2026, with respect to the rejection(s) of claim(s) 1-10 under 35 USC 103 have been fully considered. Applicant argues on page 12 that since Tsang does not teach the “acquiring” feature that it therefore cannot teach the newly added “wherein the acquiring includes…” limitation, likewise that since Quinlan does not teach the “specifying” feature that it therefore cannot teach the newly added “the specifying includes specifying…” limitation, examiner agrees.
However, Tsang, which does teach the “specifying” feature also teaches, in combination with Fredrikson, the “the specifying includes specifying…” limitation. Tsang’s decision tree construction uses a split point method to determine the branches and nodes of the decision tree and further is able to handle missing values by excluding them from the ranges, see claim rejection above. While Tsang teaches that the determination is made using probability, it does not specifically discuss the confidence score. However, this feature is taught by Fredrikson.
Examiner further agrees with the argument on page 12 that Quinlan does not teach the newly added “wherein the acquiring includes…” limitation, however this is taught by Fredrikson as acquiring the score information is done by acquiring structure data from the decision tree having white box setting.
Therefore, claims rejections under 35 USC §103 of claims 1-7, and 9-10 have been updated in light of applicants amendment. See section Claim Rejections – 35 USC §103 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Muhlbacher et al. (TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees)
Katz et al. (ConfDTree: Improving Decision Trees Using Confidence Intervals)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 JACQUELINE MEYER whose telephone number is (703)756-5676. The examiner can normally be reached M-F 8:00 am - 4:30 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.C.M./Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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