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
In the previous Office Action issued February 19, 2026 (hereinafter “the previous Office Action”), claims 1-20 were pending.
This action is in response to the amendment and remarks filed May 19, 2026. In the amendment, claims 1, 11-12, and 16 were amended, claim 7 was canceled, and no claims were added. Thus, claims 1-6 and 8-20 are pending.
The objections to the drawings and the specification, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The objections of claims 16-19, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The rejections of claims 11-19 under 35 U.S.C. § 112(b), set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 10, 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
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 8-12, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Groha et al. ("A General Framework for Survival Analysis and Multi-State Modelling"), hereinafter Groha, in view of Andersen et al. ("Inference for Outcome Probabilities in Multi-State Models"), hereinafter Andersen.
Regarding Claim 1:
Groha discloses:
A computer-implemented method for machine-learning a function that is configured…to output a distribution of transition-specific probabilities for each interval of a set of intervals
Groha, pg. 2, col. 2, “The aim of multi-state models is a more granular analysis of time-to-event phenomena…Such models and their state-space can be described by a directed graph as shown in Figure 1(b).”
Pg. 4, col. 2, “With this model, we also have direct access to the hazard rate (the instantaneous risk for a given transition) over time.”
On pg. 2, Groha discloses a multi-state model [machine-learning a function that is configured to…output a distribution of transition-specific probabilities…]. Pg. 4 further specifies that the model works with data over time [for each interval of a set of intervals]. Lastly, as Groha’s models are implemented on a computer, it is construed as disclosing a computer-implemented method.
…based on input covariates representing medical characteristics of a patient with respect to a multi-state model of an illness having states and transitions between the states…
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Groha, pg. 3, col. 1, “Figure 1. Example graphs corresponding to the 2-state competing risks model (a) and the illness-death model (b), a popular multi-state model. Competing risks models are a special case of multi-state models that only have one non-absorbing state, whereas in general multi-state models can have arbitrary, even cyclical connections.”
Pg. 4, col. 2, “The initial conditions are encoded by the covariates of the patient
m
(
0
)
=
f
(
x
)
, where
f
is given by a neural net.”
Pg. 13, C. Algorithm 1
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On pg. 3 and in view of FIG. 1, Groha discloses 1(b), a general multi-state model [a multi-state model of an illness having states and transitions between the states]. On pg. 4, Groha discloses patient covariates [covariates representing medical characteristics of a patient], and further on pg. 13, Algorithm 1 discloses the covariates are used as input [input covariates…].
the set of intervals forming a subdivision of a follow-up period
Groha, pg. 3, col. 2, “For each individual we will observe the process
Y
(
t
)
in the form of discrete jumps over the relevant time interval
[
0
,
T
]
.”
On pg. 3, Groha discloses a relevant time interval
[
0
,
T
]
from the time intervals [the set of intervals forming a subdivision of a follow-up period].
the method comprising: obtaining an input dataset of covariates and time-to-event data of a set of patients
Groha, pg. 15, “we choose three covariates, where one of the covariates has time varying coefficients to model a proportional hazards violation. We choose all coefficients to be of O(1), with a saw-tooth time dependence for the time dependent covariate. We sample 2048 patients for the training set and 1024 patients for the validation and test set respectively with event times between 0 and 100.”
Groha discloses choosing three covariates with time varying coefficients [obtaining an input dataset of covariates] with event times between 0 and 100 [time-to-event data] for a set of 2048 patients for training and 1024 patients for validation [a set of patients].
training the function based on the input dataset
As cited above on pg. 15, Groha discloses that the sampled patient data is for the training set [training the function based on the input dataset].
the training further including minimizing a loss function which includes at least one of:
a likelihood term, or
a regularization term that penalizes at least one of:
in weight matrices, first order differences of weights associated with two adjacent time intervals, or
in bias vectors, first order differences of biases associated with two adjacent time intervals
Groha, pg. 13, “For training the model minimizing the negative log-likelihood”
Groha discloses training the model [training further including] minimizing the negative log-likelihood [minimizing a loss function which includes at least one of: a likelihood term].
Groha does not explicitly disclose:
the distribution of transition-specific probabilities being piecewise constant
However, in the same field, analogous art Andersen teaches:
the distribution of transition-specific probabilities being piecewise constant
Andersen, p. 412, “For the parametric regression model with piecewise constant transition intensities, (3) leads to so-called Poisson regression…Here, the
h
→
j
transition intensity
α
h
j
l
,
v
is a piecewise constant function of time and, in the multiplicative Poisson model,
log
α
h
j
l
,
v
is linear in the covariates”
Groha, Andersen, and the instant application are analogous art because they are all directed to survival analysis.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Groha with Andersen because “[for] large samples, this sufficiency reduction of the follow-up data may lead to considerable simplification of the inference without seriously losing the flexibility of the non-parametric baseline hazard in the Cox model (though, of course, a choice of time intervals needs to be made). Furthermore, Poisson regression has the advantages that non-proportional hazards is simply an interaction between time and covariates and, further, that non-homogeneous semi-Markov models are easily handled by splitting follow-up time according to both (“calendar”) time t and duration, t − T. This means that, when analyzing such models, a choice of “baseline time-variable” (calendar time or duration) is not needed since both appear in the log-hazard model on equal footings. Finally, the parameters for effects of such time variables will be part of the standard regression output from any computer package in contrast to the Breslow estimates from the Cox models which will usually appear in the output, in either tabular or graphical form, only when specifying the relevant options” (Andersen, p. 412).
Regarding Claim 8:
As discussed above, Groha in view of Andersen teach [the] computer-implemented method of claim 1, and Groha further discloses:
wherein the multi-state model is an illness-death model
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Groha, pg. 3, col. 1, “Figure 1. Example graphs corresponding to the 2-state competing risks model (a) and the illness-death model (b), a popular multi-state model. Competing risks models are a special case of multi-state models that only have one non-absorbing state, whereas in general multi-state models can have arbitrary, even cyclical connections.”
Regarding Claim 9:
As discussed above, Groha in view of Andersen teach [the] computer-implemented method of claim 1, and Groha further discloses:
wherein the illness is a cancer disease including a breast cancer or a disease having an intermediate state and a final state
Groha, pg. 5, col. 2, “To benchmark our proposed model against various survival frameworks, we examine the performance of SURVNODE on the METABRIC breast cancer data set…”
Regarding Claim 10:
As discussed above, Groha in view of Andersen teach [the] computer-implemented method of claim 1, and Groha further discloses:
wherein the medical characteristics comprise characteristics representing a general state of the patient and/or characteristics representing a condition of the patient with respect to the illness
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Groha, pg. 7, col. 2, Figure 5 depicts medical characteristics of a patient representing a condition of the patient with respect to the illness.
Regarding Claim 11:
Groha discloses:
A method for applying a function machine-learnt by machine-learning the function that is configured to output a distribution of transition-specific probabilities for each interval of a set of intervals
Groha, pg. 2, col. 2, “The aim of multi-state models is a more granular analysis of time-to-event phenomena…Such models and their state-space can be described by a directed graph as shown in Figure 1(b).”
Pg. 4, col. 2, “With this model, we also have direct access to the hazard rate (the instantaneous risk for a given transition) over time.”
On pg. 2, Groha discloses a multi-state model [machine-learning a function that is configured to…output a distribution of transition-specific probabilities…]. Pg. 4 further specifies that the model works with data over time [for each interval of a set of intervals].
the set of intervals forming a subdivision of a follow-up period
Groha, pg. 3, col. 2, “For each individual we will observe the process
Y
(
t
)
in the form of discrete jumps over the relevant time interval
[
0
,
T
]
.”
On pg. 3, Groha discloses a relevant time interval
[
0
,
T
]
from the time intervals [the set of intervals forming a subdivision of a follow-up period].
the method comprising: obtaining an input dataset of covariates and time-to-event data of a set of patients
Groha, pg. 15, “we choose three covariates, where one of the covariates has time varying coefficients to model a proportional hazards violation. We choose all coefficients to be of O(1), with a saw-tooth time dependence for the time dependent covariate. We sample 2048 patients for the training set and 1024 patients for the validation and test set respectively with event times between 0 and 100.”
Groha discloses choosing three covariates with time varying coefficients [obtaining an input dataset of covariates] with event times between 0 and 100 [time-to-event data] for a set of 2048 patients for training and 1024 patients for validation [a set of patients].
training the function based on the input dataset
As cited above on pg. 15, Groha discloses that the sampled patient data is for the training set [training the function based on the input dataset].
the training further including minimizing a loss function which includes at least one of:
a likelihood term, or
a regularization term that penalizes at least one of:
in weight matrices, first order differences of weights associated with two adjacent time intervals, or
in bias vectors, first order differences of biases associated with two adjacent time intervals
Groha, pg. 13, “For training the model minimizing the negative log-likelihood”
Groha discloses training the model [training further including] minimizing the negative log-likelihood [minimizing a loss function which includes at least one of: a likelihood term].
obtaining the input covariates representing medical characteristics of the patient
Groha, pg. 4, col. 2, “The initial conditions are encoded by the covariates of the patient
m
(
0
)
=
f
(
x
)
, where
f
is given by a neural net.”
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503
812
media_image3.png
Greyscale
On pg. 4, Groha discloses patient covariates [covariates representing medical characteristics of a patient], and further on pg. 13, Algorithm 1 discloses the covariates are used as input [input covariates…]
applying the function to the input covariates
Groha, pg. 2, col. 2, “The aim of multi-state models is a more granular analysis of time-to-event phenomena…Such models and their state-space can be described by a directed graph as shown in Figure 1(b).”
On pg. 2 and in view of Algorithm 1, Groha discloses inputting the covariates and the multi-state model [applying the function to the input covariates].
Groha does not explicitly disclose:
the distribution of transition-specific probabilities being piecewise constant
However, in the same field, analogous art Andersen teaches:
the distribution of transition-specific probabilities being piecewise constant
Andersen, p. 412, “For the parametric regression model with piecewise constant transition intensities, (3) leads to so-called Poisson regression…Here, the
h
→
j
transition intensity
α
h
j
l
,
v
is a piecewise constant function of time and, in the multiplicative Poisson model,
log
α
h
j
l
,
v
is linear in the covariates”
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Groha with Andersen because “[for] large samples, this sufficiency reduction of the follow-up data may lead to considerable simplification of the inference without seriously losing the flexibility of the non-parametric baseline hazard in the Cox model (though, of course, a choice of time intervals needs to be made). Furthermore, Poisson regression has the advantages that non-proportional hazards is simply an interaction between time and covariates and, further, that non-homogeneous semi-Markov models are easily handled by splitting follow-up time according to both (“calendar”) time t and duration, t − T. This means that, when analyzing such models, a choice of “baseline time-variable” (calendar time or duration) is not needed since both appear in the log-hazard model on equal footings. Finally, the parameters for effects of such time variables will be part of the standard regression output from any computer package in contrast to the Breslow estimates from the Cox models which will usually appear in the output, in either tabular or graphical form, only when specifying the relevant options” (Andersen, p. 412).
Regarding Claim 12:
As discussed above, Groha in view of Andersen teach [the] method of claim 11, and Groha further discloses:
computing one or more transition-specific cumulative incidence functions (CIF)
Groha, pg. 15, “Figure 8. Cumulative incidence functions for the two competing outcomes for all benchmarked models on the top…Risk 1 is shown in blue for the estimates and purple for the ground truth, whereas risk 2 is color coded in green for estimates and teal for ground truth.”
Groha discloses cumulative incidence functions for two competing outcomes.
optionally displaying said one or more transition-specific cumulative incidence functions
Groha, pg. 15, Figure 8,
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959
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Groha depicts in Figure 8, the cumulative incidence functions (CIF) [optionally displaying said one or more transition-specific cumulative incidence functions]
identify relapse risk and/or death risk associated with the patient
Groha, pg. 7, Figure 5,
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As cited above on pg. 15, Groha discloses estimated risks and Figure 5 depicts identifying states such as relapse and death.
and/or
determining a treatment, a treatment adaptation, a follow-up visit, and/or a surveillance with diagnostic tests, including based on the identified relapse risk and/or death risk
Groha, pg. 1, col. 2, “For example, in the case of acute myeloid leukemia, individualized genetic prediction based on a sophisticated multi-stage model was used to tailor personalized treatment within first complete remission…”
Groha discloses determining a personized treatment for a patient [determining a treatment] using the multi-stage model [based on the identified relapse risk and/or death risk].
Regarding Claim 16:
Groha discloses:
A device comprising: a processor; and a non-transitory data storage medium having recorded thereon a data structure having a computer program including instructions for machine-learning a function configured to output a distribution of transition-specific probabilities for each interval of a set of intervals that when executed by the processor, cause the processor to be configured to
Groha, pg. 2, col. 2, “The aim of multi-state models is a more granular analysis of time-to-event phenomena…Such models and their state-space can be described by a directed graph as shown in Figure 1(b).”
Pg. 4, col. 2, “With this model, we also have direct access to the hazard rate (the instantaneous risk for a given transition) over time.”
On pg. 2, Groha discloses a multi-state model [machine-learning a function that is configured to…output a distribution of transition-specific probabilities…]. Pg. 4 further specifies that the model works with data over time [for each interval of a set of intervals]. Lastly, as Groha’s models are implemented on a computer, it is construed as disclosing a device with a processor, non-transitory data storage medium, computer program, and machine instructions.
the set of intervals forming a subdivision of a follow-up period
Groha, pg. 3, col. 2, “For each individual we will observe the process
Y
(
t
)
in the form of discrete jumps over the relevant time interval
[
0
,
T
]
.”
On pg. 3, Groha discloses a relevant time interval
[
0
,
T
]
from the time intervals [the set of intervals forming a subdivision of a follow-up period].
obtain an input data set of covariates and time-to-event data of a set of patients
Groha, pg. 15, “we choose three covariates, where one of the covariates has time varying coefficients to model a proportional hazards violation. We choose all coefficients to be of O(1), with a saw-tooth time dependence for the time dependent covariate. We sample 2048 patients for the training set and 1024 patients for the validation and test set respectively with event times between 0 and 100.”
Groha discloses choosing three covariates with time varying coefficients [obtaining an input dataset of covariates] with event times between 0 and 100 [time-to-event data] for a set of 2048 patients for training and 1024 patients for validation [a set of patients].
train the function based on the input data set
As cited above on pg. 15, Groha discloses that the sampled patient data is for the training set [training the function based on the input dataset].
the training further including minimizing a loss function which includes at least one of:
a likelihood term, or
a regularization term that penalizes at least one of:
in weight matrices, first order differences of weights associated with two adjacent time intervals, or
in bias vectors, first order differences of biases associated with two adjacent time intervals
Groha, pg. 13, “For training the model minimizing the negative log-likelihood”
Groha discloses training the model [training further including] minimizing the negative log-likelihood [minimizing a loss function which includes at least one of: a likelihood term].
and/or instructions for a function machine-learnt according to the machine-learning that when executed by the processor causes the processor to be configured to: obtain the input covariates representing medical characteristics of the patient, and apply the function to the input covariates,
and/or a function machine-learnt according to the machine-learning
Groha does not explicitly disclose:
the distribution of transition-specific probabilities being piecewise constant
However, in the same field, analogous art Andersen teaches:
the distribution of transition-specific probabilities being piecewise constant
Andersen, p. 412, “For the parametric regression model with piecewise constant transition intensities, (3) leads to so-called Poisson regression…Here, the
h
→
j
transition intensity
α
h
j
l
,
v
is a piecewise constant function of time and, in the multiplicative Poisson model,
log
α
h
j
l
,
v
is linear in the covariates”
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Groha with Andersen because “[for] large samples, this sufficiency reduction of the follow-up data may lead to considerable simplification of the inference without seriously losing the flexibility of the non-parametric baseline hazard in the Cox model (though, of course, a choice of time intervals needs to be made). Furthermore, Poisson regression has the advantages that non-proportional hazards is simply an interaction between time and covariates and, further, that non-homogeneous semi-Markov models are easily handled by splitting follow-up time according to both (“calendar”) time t and duration, t − T. This means that, when analyzing such models, a choice of “baseline time-variable” (calendar time or duration) is not needed since both appear in the log-hazard model on equal footings. Finally, the parameters for effects of such time variables will be part of the standard regression output from any computer package in contrast to the Breslow estimates from the Cox models which will usually appear in the output, in either tabular or graphical form, only when specifying the relevant options” (Andersen, p. 412).
Regarding Claim 20:
As discussed above, Groha in view of Andersen teach [the] method according to claim 1, and Groha further discloses:
A non-transitory computer readable medium having stored thereon a program that when executed by a computer causes the computer to implement the method according to claim 1.
Groha, pg. 2, col. 2, “The aim of multi-state models is a more granular analysis of time-to-event phenomena…Such models and their state-space can be described by a directed graph as shown in Figure 1(b).”
Since Groha’s models are implemented on a computer, it is construed as disclosing a device with a non-transitory computer readable medium, a program, and a computer.
Claims 2-6, 13-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Groha in view of Andersen as applied to claims 1, 11, and 16 above, respectively, and further in view of Lee et al. ("DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks"), hereinafter Lee.
Regarding Claim 2:
As discussed above, Groha in view of Andersen teach [the] computer-implemented method of claim 1, but do not explicitly disclose:
wherein the function further comprises a covariate-shared network and/or a transition-specific subnetwork per transition
However, in the same field, analogous art Lee teaches:
wherein the function further comprises a covariate-shared network and/or a transition-specific subnetwork per transition
Lee, pg. 3, Figure 2, and pg. 3-4, “The shared sub-network takes as inputs the clinical covariates x…Each cause-specific sub-network takes as inputs the pairs
z
=
(
f
s
(
x
)
,
x
)
and produces as output a vector
f
c
k
(
z
)
, which corresponds to the probability of the first hitting time of a specific cause k.”
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Lee discloses a shared sub-network [a covariate-shared subnetwork] and cause-specific sub-networks [transition-specific subnetwork per transition].
Groha, Andersen, Lee, and the instant application are analogous art because they are all directed to survival analysis.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Groha and Andersen with Lee to use a shared sub-network and cause-specific sub-networks in order to maintain the input for each cause-specific sub-network. “Second, we maintain a residual connection (He et al. 2016) from the input covariates into the input of each cause-specific sub-network” (Lee, pg. 3, col. 2).
Regarding Claim 3:
As discussed above, Groha in view of Andersen teach [the] computer-implemented method of claim 2, and Lee further teaches:
wherein the covariate-shared subnetwork comprises a respective fully connected neural network and/or at least one transition-specific subnetwork comprises a fully connected neural network
Lee, pg. 3, Figure 2,
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Figure 2 depicts the shared sub-network [covariate-shared subnetwork] and the cause-specific sub-networks [transition-specific subnetwork] as fully-connected.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Groha and Andersen with Lee for at least the same reasons as given with respect to claim 2.
Regarding Claim 4:
As discussed above, Groha in view of Andersen teach [the] computer-implemented method of claim 2, and Lee further teaches:
wherein the covariate-shared subnetwork comprises a respective non-linear activation function and/or at least one transition-specific subnetwork comprises a respective non-linear activation function
Lee, pg. 3, col. 2, “First, we utilize a single softmax layer as the output layer of DeepHit in order to ensure that the network learns the joint distribution of K competing events not the marginal distributions of each event.”
Lee discloses using a single softmax layer as the output layer [at least one transition-specific subnetwork comprises a respective non-linear activation function].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Groha and Andersen with Lee in order to learn non-linear relationships. “The output of the softmax layer is a probability distribution…given a patient with covariates x, an output element
y
k
,
s
is the (estimated) probability
P
^
(
s
,
k
|
x
)
that the patient will experience the event
k
at time
s
. This architecture drives the network to learn potentially non-linear, even non-proportional, relation ships between covariates and risks” (Lee, pg. 4, col. 1).
Regarding Claim 5:
As discussed above, Groha in view of Andersen teach [the] computer-implemented method of claim 2, and Lee further teaches:
wherein each transition-specific subnetwork is followed by a softmax layer
Lee, pg. 3, Figure 2,
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Lee, pg. 3, col. 2, “First, we utilize a single softmax layer as the output layer of DeepHit in order to ensure that the network learns the joint distribution of K competing events not the marginal distributions of each event.”
Lee discloses using a softmax layer as the output layer after the cause-specific sub-networks [each transition-specific subnetwork is followed by a softmax layer].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Groha and Andersen with Lee in order to learn non-linear relationships. “The output of the softmax layer is a probability distribution…given a patient with covariates x, an output element
y
k
,
s
is the (estimated) probability
P
^
(
s
,
k
|
x
)
that the patient will experience the event
k
at time
s
. This architecture drives the network to learn potentially non-linear, even non-proportional, relation ships between covariates and risks” (Lee, pg. 4, col. 1).
Regarding Claim 6:
As discussed above, Groha in view of Andersen teach [the] computer-implemented method of claim 2, and Lee further teaches:
wherein the multi-state model further comprises competing transitions, and transition-specific subnetworks of the competing transitions share a common softmax layer
Lee, pg. 3, Figure 2,
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Lee, pg. 3, col. 2, “First, we utilize a single softmax layer as the output layer of DeepHit in order to ensure that the network learns the joint distribution of K competing events not the marginal distributions of each event.”
Lee discloses using a softmax layer as the output layer after the competing cause-specific sub-networks [comprises competing transitions, and transition-specific subnetworks of the competing transitions share a common softmax layer].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Groha and Andersen with Lee in order to learn joint distributions. “First, we utilize a single softmax layer as the output layer of DeepHit in order to ensure that the network learns the joint distribution of K competing events not the marginal distributions of each event.” (Lee, pg. 3, col. 2).
Regarding Claims 13-15:
Claims 13-15 are method claims corresponding to the computer-implemented method claims 2-4 and is rejected for at least the same reasons as given in the rejection of claim 2-4. In particular, 13:2, 14:3, 15:4.
Regarding Claims 17-19:
Claims 17-19 are device claims corresponding to the computer-implemented method claims 2-4 and is rejected for at least the same reasons as given in the rejection of claim 2-4. In particular, 17:2, 18:3, 19:4.
Response to Arguments
Applicant's arguments filed May 19, 2026 (“Remarks”) have been fully considered but they are not persuasive.
35 U.S.C. § 102/35 U.S.C. § 103:
Remarks, pp. 11-14. In particular, Applicant argues Groha and Raghavan do not teach the claimed limitations including (1) the piecewise constant and (2) minimizing the loss function with a regularization term that penalizes first order differences of weights [or biases] associated with two adjacent time intervals.
Applicant’s arguments with respect to claim limitation (1) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments with respect to claim limitation (2) have been fully considered and are persuasive. The rejection involving the reference Raghavan has been withdrawn. However, upon further consideration, a new rejection is made over Groha in view of Andersen.
Conclusion
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 STEVEN PHUNG whose telephone number is (703) 756-1499. The examiner can normally be reached Monday-Thursday: 9:00AM-4:00PM ET.
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, KAMRAN AFSHAR can be reached at (571) 272-7796. 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.
/S.H.P./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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