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 .
Examiner notes the entry of the following papers:
Amended claims filed 2/12/2026.
Arguments/remarks made in amendment filed 2/12/2026.
Claims 1, and 4-8 are amended. Claims 2-3 are cancelled. Claim 9 is new. Claims 1, and 4-9 are presented for examination.
Response to Arguments
Applicant presents arguments. Each is addressed.
Applicant remarks “…Claims 2-3 have been canceled without prejudice or disclaimer.” (Remarks, page 6, paragraph 2, line 2.) Examiner notes the cancellation of claim 2. The objection to claim 2 is withdrawn.
Applicant argues “… the present amendments make it clear that the claims do not invoke 35 U.S.C. § 112(f)…” (Remarks, page 6, paragraph 5, line 1.) Examiner agrees. The interpretation under 35 U.S.C. § 112(f) is withdrawn.
Applicant argues “In this case, the claims should not be generally interpreted as a mental process such as learning parameter groups for prediction action in view of the need to create a learning apparatus specifically configured to reduce calculation resources as noted above.” (Remarks, page 9, paragraph 4, line 1.) Applicant’s arguments are persuasive. The rejections under 35 U.S.C. § 101 are withdrawn.
Applicant argues “Kurashima2 does not disclose a process where the parameters (specifically the first, second, and third parameter groups) learned for a first group are fixed and maintained without change, and only the membership rate (first parameter group) for a new, second user group is learned.”(Remarks, page 11, paragraph 3, line 4.) However, Kurushima2 does teach this. In Fig. 2, the parameters of Latent class A, Latent Class B, and Latent Class C remain unchanged. The only thing that changes are the item proportions based on the user’s favorite categories, and the User’s interest in the respective latent classes. Therefore, the rejection is maintained.
Applicant argues “The claimed invention allows for the addition of new users (User Group B) without retraining the entire model (i.e., without changing the parameters already learned for User Group A). This is fundamentally different from the validation method in Kurashima2.” (Remarks, page 11, paragraph 4, line 1.) See response to the argument in section d. Latent classes A through C remain unchanged and therefore, not retrained. Therefore, the rejection is maintained.
Applicant argues “New Claim 9 recites features analogous to those of amended Claim 1.” (Remarks, page 12, paragraph 3, line 1.) However, claim 1 remains rejected. Claim 9 recites the same relevant limitations and is rejected as well. The dependent claims remain rejected at least for depending from rejected base claims.
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, 4-7, and 9 are rejected under 35 U.S.C. § 103 as being unpatentable over Kurashima, T. (JP201783963, Parameter Estimation apparatus, prediction apparatus, method, and program, herein Kurashima), and Kurashima, et al (A Probabilistic Behavior Model for Discovering Unrecognized Knowledge, herein Kurashima2).
Regarding claim 1,
Kurashima teaches A learning apparatus (Kurashima, page 1, paragraph 1, line 1 “The present invention relates to a parameter estimating device, a predicting device, a method, and a program.” And, page 17, paragraph 3, line 3 line “By learning the behavioral model based on the hypothesis that was based on the actual condition that "I did it", it is possible to separate the influence components of "visit from near" and "visit because it is interesting". In other words, learning the behavioral model is learning, and parameter estimating device is learning apparatus.) comprising:
processing circuitry (Kurashima, page 24, paragraph 4, line 1 “In addition, although the above-described prediction apparatus 100 has a computer system” In other words, computer system is processing circuitry.) configured to
acquire action history data indicating action history for each of a plurality of users (Kurashima, page 2, paragraph 4, line 1 “In order to achieve the above object, the parameter estimating apparatus according to the present invention is characterized in that, based on movement history information indicating landmarks visited by the operator for each of a plurality of operators, each of the plurality of operators.” And, page 13, paragraph 8, line 1 “When the movement history information is input, the prediction device 100 stores the movement history information in the movement history information storage unit 22.” In other words, input is acquire, movement history…for each of a plurality of operators is action history for each of a plurality of users.) ; and
[learn a first parameter group and a second parameter group included in a predictive model for predicting an action of each of the plurality of users by using the action history data as training data], wherein
[the first parameter group is a parameter group related to a membership rate of each user for each of a plurality of clusters],
[the second parameter group is a parameter group related to an action tendency of each cluster for each of a plurality of actions] wherein
[the predictive model includes a third parameter group related to an action tendency] of the plurality of users, the processing circuitry is configured to
[learn the third parameter group together with the first parameter group and the second parameter group] and
[perform a second learning process after performing a first learning process,]
[the first learning process is a process of learning the first parameter group, the second parameter group, and the third parameter group for a first user group by using the action history data for the first user group as training data], and
[the second learning process is a process of learning the first parameter group for a second user group that is different from the first user group by using the action history data for the second user group as training data without changing the first parameter group, the second parameter group, and the third parameter group for the first user group learned by the first learning process].
Thus far, Kurashima does not explicitly teach learn a first parameter group and a second parameter group included in a predictive model for predicting an action of each of the plurality of users by using the action history data as training data.
Kurashima2 teaches learn a first parameter group and a second parameter group included in a predictive model for predicting an action of each of the plurality of users by using the action history data as training data (Kurashima2, Fig. 2, Table, 1, and, page 1098, column 1, paragraph 2, line 1 “In summary, the major contributions of this paper include
A probabilistic framework for estimating latent classes of items that have an impact on user item selection but cannot be inferred from current knowledge
A probabilistic behavior model that can jointly learn both the user’s favorite categories and interests in latent classes of items.”
And, page 1099, column 1, paragraph 1, line 7 “ Each user generates chosen-item log data denoted by xu = {xu1, ..., xuMu }, where xum represents the mth chosen item of user u, and xum ∈ I. The notations used in this paper are summarized in Table I. Given user’s log data xu, we want to estimate the probability of user u selecting item i. We make the following two assumptions about item selection: One is that the user’s selection is determined by her/his interests in explicit categories. For example, a user who has many accessories is likely to buy rings and necklaces. Second, latent classes of items also influence the user’s selection. For example, a person with a sunny disposition is likely to buy colorful items regardless of category.
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In other words, probabilistic behavior model is predictive model, jointly learn both the user’s favorite categories and interests in latent classes of items is learn a first parameter group and a second parameter group included in a predictive model, user item selection is predicting an action of each of the plurality of users, and chosen item log data is using action history as training data.)
Kurashima2 teaches the first parameter group is a parameter group related to a membership rate of each user for each of a plurality of clusters (Kurashima2, Fig. 2, Table 1, and, page 1099, column 2, paragraph 2, line 7 “Probability P(i|z, Cu, Φ) can be written as follows:
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where
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is the normalization constant, and γ is a parameter describing the width of each user’s category. As bandwidth parameter γ is decreased, the width of the user’s favorite area in the hierarchy increases.”
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In other words, from Table 1, set of users is for each user, and, from Fig. 2, item proportions based on a user’s favorite categories is a first parameter group, categories is plurality of clusters, and proportions of user’s favorite categories is membership rate of each of a plurality of clusters for the user.)
Kurashima2 teaches the second parameter group is a parameter group related to an action tendency of each cluster for each of a plurality of actions (Kurashima2, Fig. 2. And, page 1099, column 2, paragraph 1, line 4 “Each user has user-dependent latent-class proportions θu = {θuz}Zz=1 which represent the user’s interests in the latent characteristics (classes) of items,
where Σz θuz = 1, θuz ≥ 0, and Z is the number of latent classes. For each selection,
latent class z is chosen according to θu, and then item i is generated depending on both latent-class-specific parameters φz = {φzi}Ii=1, and the set of categories of previously selected
items: Cu = {cxum}Mum=1. Here, φzi represents how likely item i is to be selected given latent class z.”
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In other words, from Fig. 2, latent-class specific parameters is a second parameter group, and user’s interests in latent class is a parameter group related to an action tendency for each cluster of a plurality of actions.)
Kurashima2 teaches the predictive model includes a third parameter group related to an action tendency of the plurality of users the plurality of users (Kurashima2, Fig. 2, and, page 1099, column 2, paragraph 2, line 15 “Parameter φzi is proportional to the log probability that item i is chosen for latent class z. In existing topic models, this parameter corresponds to the word probability for the latent class.”
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In other words, parameter corresponds to the word probability for the latent class is parameter group related to an action of the plurality of users.), the processing circuitry is configured to
Kurashima2 teaches learn the third parameter group together with the first parameter group and the second parameter group (Kurashima2, page 1100, column 1, paragraph 1, line 1 “In summary, the proposed model assumes the following generative process for a set of users U:
1) For each latent class z = 1, ...,Z:
a) Draw trends
φz ∼ Normal(0, β−1I)
2) For each user u = 1, ...,N
a) Draw interests θu ∼ Dirichlet(α)
b) For each selection m = 1, ...,Mu
i) Draw latent class zum ∼ Mult(θu)
ii) Draw item
ium|Cu,φzum ∼ Mult({P(i|Cu, φzum)}I i=1).”
In other words, proposed model is learning unit, generative process is learn, and the above algorithm shows learn a third parameter group together with the first parameter group and the second parameter group.), and
Kurashima2 teaches perform a second learning process after performing a first learning process (Kurashima2, page 1097, column 2, paragraph 1, line 7 “Our model learns the latent characteristics of items that influence the user’s decision as separate from the user’s favorite categories in the site. In this example, we can find the latent class of “fashionable” which groups items across multiple categories in terms of their impression on the user. Starting from current knowledge (the tree-structured categories in the site), our model learns user’s profile at a deeper semantic level that reflects one or underlying properties of the items.” In other words, learns latent characteristics of items that influence the user’s decision separate from the user’s favorite categories remain unchanged after the first learning, and our model learns user’s profile is a second learning based on a second user.),
Kurashima2 teaches the first learning process is a process of learning the first parameter group, the second parameter group, and the third parameter group for a first user group by using the action history data for the first user group as training data (Kurashima2, See above mapping, beginning at office action page 10.) , and
Kurashima2 teaches the second learning process is a process of learning the first parameter group for a second user group that is different from the first user group by using the action history data for the second user group as training data without changing the first parameter group, the second parameter group, and the third parameter group for the first user group learned by the first learning process (Kurashima2, See above mapping, office action page 10. And, page 1100, column 2, paragraph 3, line 1 “We predicted the last viewed item of each user from her/his past item selections. One data was used for testing, and the others were used for training. This was repeated using each of the users in Table II.” And, page 1098, column 2, paragraph 3, line 6 “Inputs are (1) a set of observed usage data, represented by the matrix of user × item and (2) the tree-structured categories that represent the manager’s knowledge about the relationships between items. Each item is associated with a leaf node (category) in the hierarchy. Tree-structured categories are generally organized so that items in the same category are frequently accessed by users with common needs. Outputs are a set of latent classes.” Examiner notes the only thing this limitation claims that is different from the limitations for the first learning process included in claim 1, is the second learning process uses new training data to create a first parameter group for a second group of users, the learning latent characteristics of items that influence the user’s decision is not changed. The first parameter group is the item proportions of the user’s favorite categories. This means that training again is merely inputting new users with their own favorite categories. The latent categories which are created by the tree-structured categories from the manager, and the previous users, remain unchanged. In other words, inputting new users is a second training using a second group of users to produce a first parameter group for the second group of users without changing the first, second and third parameter group for the first group of users.) .
Both Kurashima and Kurashima2 are directed to predicting user actions, among other things. Kurashima teaches a learning apparatus, estimating parameters and a prediction apparatus for predicting user behavior; but does not explicitly teach learning a first parameter group and a second parameter group included in a predictive model for predicting an action of each of the plurality of users by using the action history data as training data, wherein the first parameter group is a parameter group related to a membership rate of each user for each of a plurality of clusters, and the second parameter group is a parameter group related to an action tendency of each cluster for each of a plurality of actions. Kurashima2 teaches learning a first parameter group and a second parameter group included in a predictive model for predicting an action of each of the plurality of users by using the action history data as training data, wherein the first parameter group is a parameter group related to a membership rate of each user for each of a plurality of clusters, and the second parameter group is a parameter group related to an action tendency of each cluster for each of a plurality of actions.
In view of the teaching of Kurashima, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Kurashima2 into Kurashima. This would result in a learning apparatus estimating parameters and a prediction apparatus for predicting user behavior, and learning a first parameter group and a second parameter group included in a predictive model for predicting an action of each of the plurality of users by using the action history data as training data, wherein the first parameter group is a parameter group related to a membership rate of each user for each of a plurality of clusters, and the second parameter group is a parameter group related to an action tendency of each cluster for each of a plurality of actions.
One of ordinary skill in the art would be motivated to do this because developing new methods for predicting collective behavior patterns can provide better marketing techniques for managers by giving them more information about customer behavior. (Kurashima2, abstract, line 1 “Discovering interesting behavior patterns and profiles of users as they interact with E-commerce (EC) sites is an important task for site managers. We propose a probabilistic
behavior model for extracting latent classes of items that impact the users’ item selections but cannot be inferred from the current knowledge of the managers.”)
Regarding claim 4,
The combination of Kurashima and Kurashima2 teaches the learning apparatus according to claim 2, wherein
the action history data for each of the users includes a plurality of records in which a time, a place, and information indicating an action performed by the user at the time and the place are associated with each other (Kurashima, page 2, paragraph 4, line 1 “In order to achieve the above object, the parameter estimating apparatus according to the present invention is characterized in that, based on movement history information indicating landmarks visited by the operator for each of a plurality of operators, each of the plurality of operators.” And, page 13, paragraph 8, line 1 “When the movement history information is input, the prediction device 100 stores the movement history information in the movement history information storage unit 22.” And, page 2, paragraph 4, line 6 “As a parameter of a model representing that the landmark to be visited at time t + 1 is generated in accordance with the probability distribution of the landmark inherent to the landmark being visited at the time t + 1. In other words, history movement information is action history data, visit at time t + 1 is time, landmark is place, movement history is associated with time and place, visit is action performed by a user, and for each of a plurality of operators is for each of a plurality of users.) , and
the predictive model is a model that outputs a probability that each of the plurality of actions is performed by a prediction target user at prediction target time based on the first parameter group (Kurashima, page 2, paragraph 4, line 6 “As a parameter of a model representing that the landmark to be visited at time t + 1 is generated in accordance with the probability distribution of the landmark inherent to the landmark being visited at the time t + 1. An action-specific inherent latent topic appearance probability representing a probability that the operator selects each of the plurality of latent topics, and each of the plurality of latent topics. A latent topic unique landmark appearance probability representing the ease of selecting each of the plurality of landmarks in the latent topic, and a latent topic specific landmark appearance probability for each of the plurality of landmarks, for each of the plurality of landmarks from the landmark. And a parameter estimating unit for estimating a transition probability between landmarks indicating ease of movement to the other landmarks.” In other words, model is predictive model, probability distribution is probability that each plurality of predictions is performed, landmark is prediction target, and at time t + 1 is at prediction target time.),
the parameter group related to the prediction target user included in the second parameter group, and the third parameter group when recent action history data for the prediction target user and the prediction target time are given as input data (Kurashima2, See mapping of claim 1, office action page 11. And, page 1100, column 2, paragraph 3, line 1 “We predicted the last viewed item of each user from her/his past item selections. One data was used for testing, and the others were used for training. This was repeated using each of the users in Table II.” In other words, past item selections is recent action history data given as input data for the target user, and, from prior mapping, time t + 1 is prediction target time. Examiner notes that parameter group, second parameter group, and third parameter group are previously mapped to Kurashima2 in claim 1.) .
Regarding claim 5,
The combination of Kurashima and Kurashima2 teaches the learning apparatus according to claim 2, wherein
the action history data for each of the users includes a plurality of records in which a time, a place, and information indicating an action performed by the user at the time and the place are associated with each other (Kurashima, page 2, paragraph 4, line 1 “In order to achieve the above object, the parameter estimating apparatus according to the present invention is characterized in that, based on movement history information indicating landmarks visited by the operator for each of a plurality of operators, each of the plurality of operators.” And, page 2, paragraph 4, line 6 “As a parameter of a model representing that the landmark to be visited at time t + 1 is generated in accordance with the probability distribution of the landmark inherent to the landmark being visited at the time t + 1.” And, page 13, paragraph 8, line 1 “When the movement history information is input, the prediction device 100 stores the movement history information in the movement history information storage unit 22.” In other words, history movement information is action history data, time t + 1 is time, landmark is place, movement history is the time and place are associated, visit is action performed by the user, and for each of a plurality of operators is for each of a plurality of users.) , and
the predictive model is a model that outputs information in which a probability and a time at which a prediction target action is performed by a prediction target user are associated with each other based on the first parameter group, the parameter group related to the prediction target user included in the second parameter group, and the third parameter group when recent action history data for the prediction target user and information indicating the prediction target action are given as input data (Kurashima2, Fig. 2, Table I, and, page 1099, column 2, paragraph 2, line 1 “In our model, the probability that user u selects item i is calculated by the following equation:
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And, page 1098, column 2, paragraph 3, line 3 “Figure 2 overviews the mining process. We assume that our model is used by a manager, marketer, or operator of EC site. The manager wants to know what latent classes of items are suggested by the observations. Inputs are (1) a set of observed usage data, represented by the matrix of user × item and (2) the tree-structured categories that represent the manager’s knowledge about the relationships between items. Each item is associated with a leaf node (category) in the hierarchy. Tree-structured categories are generally organized so that items in the same category are frequently accessed by users with common needs. Outputs are a set of latent classes.”
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In other words, model is predictive model, probability that u selects i is probability that a prediction target action is performed by a user, and from equation (1), and as shown in prior mapping of claim 1, set of latent classes is first parameter group related to the user included in the second parameter group and third parameter group based on recent history data, and information indicating the prediction target action are given as input data.) .
Regarding claim 6,
The combination of Kurashima and Kurashima2 teaches the learning apparatus according to claim 1, wherein
the learning unit is configured to learn the predictive model after fixing a number of clusters in advance (Kurashima 2, page 1099, column 2, paragraph 1, line 2 “Our model incorporates these assumptions by assuming the following generative process of item selection.
Each user has user-dependent latent-class proportions θu = {θuz}Zz =1 which represent the user’s interests in the latent characteristics (classes) of items, where Σz θuz = 1, θuz ≥ 0, and Z is the number of latent classes.” In other words, classes is clusters, and assuming….Z is the number of classes is fixing the number of clusters in advance.)
Regarding claim 7,
The combination of Kurashima and Kurashima 2 teaches the learning apparatus according to claim 1, wherein
the learning unit is configured to learn the predictive model by using a number of clusters as a variable parameter (Kurashima2, page 1098, column 1, paragraph 1, line 1 “We evaluate our proposed model using item-access log data generated from Web server access logs of an EC site selling women’s clothes. We quantitatively show that our model can extract latent classes of items having similar latent characteristics such as “material”, “pattern”, “color”, and “impression”, that are not contained in the categories of the site.” And, column 1, paragraph 2, subparagraph 2, line 1 “A probabilistic behavior model that can jointly learn both the user’s favorite categories and interests in latent classes of items.” In other words, probabilistic behavior model is learning unit, can jointly learn is configured to learn, and, extract classes…not contained in the categories of the site is additional classes which is a variable number of clusters.) .
Claim 9 is a method claim corresponding to apparatus claim 1. Otherwise, they are not patentably distinct. The combination of Kurashima and Kurashima2 teaches a method (Kurashima, page 1, paragraph 1, line 1 “The present invention relates to a parameter estimating device, a predicting device, a method, and a program.” In other words, method is method.) Therefore, claim 9 is rejected for the same reasons as claim 1.
Claim 8 is rejected under 35 U.S.C. § 103 as being unpatentable over Kurashima, Kurashima2, and He, et al (Learning heterogeneous traffic patterns from travel time prediction of bus journeys, herein He).
Regarding claim 8,
The combination of Kurashima and Kurashima2 teaches the learning apparatus according to claim 7, wherein the learning unit is configured to:
Thus far, the combination of Kurashima and Kurashima2 does not explicitly teach learn a plurality of predictive models having mutually different number of clusters, and acquire an indicator for evaluating goodness of each of the plurality of predictive models; and determine a best predictive model based on the indicator for each of the plurality of predictive models.
He teaches learn a plurality of predictive models having mutually different number of clusters, and acquire an indicator for evaluating goodness of each of the plurality of predictive models; and determine a best predictive model based on the indicator for each of the plurality of predictive models (He, Fig. 1, and, page 1394, abstract, line 3 “We propose a novel method called Traffic Pattern centric Segment Coalescing Framework (TP-SCF) that relies on learned disparate patterns of traffic conditions across different bus line segments for bus journey travel time prediction.” And, page 1395, paragraph 4, line 6 “There are many other advantages for identifying and employing traffic patterns for bus journey time prediction: 1) It only needs to train a few prediction models (one for each cluster of a specific travel time pattern) for all the bus lines, and thus is of lower cost to retrain the prediction models to adapt to the evolving traffic conditions. 2) Some bus lines that lack sufficient training data can benefit from other bus lines that share the same traffic patterns.” And, page 1395, paragraph 5, subparagraph 2, line 1 “We train a separate Long Short Term Memory (LSTM) based prediction model for each cluster that captures the travel time pattern associated with that cluster. The clusters are coalesced to extract journey records of various distance for training the LSTM network…. Our work is the first to demonstrate that bus travel time prediction models do not need to be confined to the spatial connectivity of the bus lines and exploiting common traffic patterns across different bus line segments can lead to better prediction accuracy.”
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In other words, a few prediction models is multiple prediction models, prediction model for each cluster that captures a travel time pattern is each prediction model has mutually different number of clusters, accuracy is an indicator for evaluating goodness, and training the LSTM network is producing the best prediction model based on accuracy.)
Both He and the combination of Kurashima and Kurashima2 are directed to learning predictive models, among other things. The combination of Kurashima and Kurashima2 teach the learning apparatus of claim 7, but does not explicitly teach learn a plurality of predictive models having mutually different number of clusters, and acquire an indicator for evaluating goodness of each of the plurality of predictive models, and determine a best predictive model based on the indicator for each of the plurality of predictive models. He teaches learn a plurality of predictive models having mutually different number of clusters, and acquire an indicator for evaluating goodness of each of the plurality of predictive models, and determine a best predictive model based on the indicator for each of the plurality of predictive models.
In view of the teaching of the combination of Kurashima and Kurashima2, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of He into the combination of Kurashima and Kurashima2. This would result in the learning apparatus of claim 7, and learning a plurality of predictive models having mutually different number of clusters, acquiring an indicator for evaluating goodness of each of the plurality of predictive models, and determining a best predictive model based on the indicator for each of the plurality of predictive models.
One of ordinary skill in the art would be motivated to do this because efficient public transportation is important to cities and accurately predicting travel time is crucial for an efficient transportation system. (He, page 1394, paragraph 2, line 1 “Efficient and easy-to-use public transportation system is an important element in sustainable cities as it can boost the reduction in traffic congestion and lower carbon emissions from vehicles [1] . A key enabler to the success of public transportation system lies in the provision of accurate travel time information for travelers to make reliable journey planning. This is especially vital for bus services which typically account for the majority ridership among all public transport journeys [2] . Travel time prediction is also elementary to dynamic route guidance systems that provide intermodal transport options and recommended routes to travelers based on real-time data.”)
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.
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/B.I.R./Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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