DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The present application is being examined under the claims filed 05/15/2026.
Claims 1-5, 8-9, and 13 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/16/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
This Office Action is in response to Applicant’s communication filed 05/15/2026 in response to office action mailed 02/24/2026. The Applicant’s remarks and any amendments to the claims or specification have been considered with the results that follow.
Response to Arguments
Regarding objections and 35 U.S.C. 112
In Remarks page 5, Argument 1
(Examiner summarizes Applicant’s arguments) Applicant argues that the claims are amended to overcome objections.
Examiner’s response to Argument 1
Applicant’s amendments overcome the objections.
In Remarks page 5, Argument 2
(Examiner summarizes Applicant’s arguments) Applicant argues that the claims are amended to overcome 35 U.S.C. 112(b) rejections.
Examiner’s response to Argument 2
Applicant’s amendments overcome the rejections under 35 U.S.C. 112(b).
In Remarks pages 6-7, Argument 3
(Examiner summarizes Applicant’s arguments) Applicant argues that the claims as amended are not taught by the prior art of record.
Examiner’s response to Argument 3
Examiner found it necessary to apply new art to teach the amended claim limitations. Particularly the new amendments specify that the timer must measure units of time and that the first time period is predetermined and stored in association with the timer. An updated search revealed new art which is used in the rejections under 35 U.S.C. 103 below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over NPL reference Yan et al. “HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition” herein referred to as Yan in view of Yau et al. (PGPUB no. US 20220327467 A1) herein referred to as Yau, and Perumallapalli “Autonomous Vehicles: Real-Time AI for Safer Transportation Networks” herein referred to as Perumallapalli.
Regarding Claim 1
Yan teaches:
A method comprising: […], by a controller of a machine comprising a first artificial intelligence unit, a second artificial intelligence unit, a sensor
(page 2741 column 2 last line) “performs end-to-end classification, as illustrated in Fig 1(b). It mainly comprises four parts: (i) shared layers, (ii) a single component B to handle coarse categories[*Examiner notes: mapped to first AI unit], (iii) multiple components {Fk}Kk=1 (one for each group) for fine classification[*Examiner notes: mapped to second AI unit], and (iv) a single probabilistic averaging layer.”
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inputting, by […] an input value to the first artificial intelligence unit and the second artificial intelligence unit, wherein the input value comprises measured value detected by the sensor
(page 2741 column 1 paragraph 2) “Shared layers (left of Fig 1 (b)) receive raw image pixels as input[*Examiner notes: inputting input values to at least a first AI unit and a second AI unit] and extract low-level features. The configuration of shared layers is set to be the same as the preceding layers in the building block CNN. On the top of Fig 1(b) are independent layers of coarse category component B, which reuses the configuration of rear layers from the building block CNN and produces an intermediate fine prediction {Bfij}Cj=1 for an image xi[*Examiner notes: measured values detected by one or more sensors].”; (page 2745 column 2 section 7.3) “The ILSVRC-2012 ImageNet dataset consists of about 1.2 million training images,”; [*Examiner notes: ImageNet dataset contains images taken by cameras, a type of sensor]
and the second artificial intelligence unit comprises a first structure that defines more classes than a second structure of the first artificial intelligence unit
(page 2741 column 2 last line) “performs end-to-end classification, as illustrated in Fig 1(b). It mainly comprises four parts: (i) shared layers, (ii) a single component B to handle coarse categories[*Examiner notes: mapped to first AI unit], (iii) multiple components {Fk}Kk=1 (one for each group) for fine classification[*Examiner notes: mapped to second AI unit], and (iv) a single probabilistic averaging layer.”; [*Examiner notes: second AI unit defines more classes (components) for fine-grained classification than the first AI unit]
obtaining, […], a first output value from the first artificial intelligence unit;
(page 2741 column 1 paragraph 2) “To produce a coarse category prediction {Bik}Kk=1, we append a fine-to-coarse aggregation layer (not shown in Fig 1(b)), which reduces fine predictions into coarse using a mapping[*Examiner notes: mapped to obtaining first output values of the first AI unit] P : [1, C] → [1, K].”
forming a modulation function based on the first output value of the first artificial intelligence unit;
(page 2742 column 1 paragraph 3 line 8) “The coarse category probabilities serve two purposes. First, they are used as weights for combining the predictions[*Examiner notes: modulation function] made by fine category components {Fk}Kk=1.”; (page 2742 column 1 last paragraph) “On the right side of Fig 1 (b) is the probabilistic averaging layer[*Examiner notes: mapped to modulation function], which receives fine as well as coarse category predictions and produces a final prediction based on weighted average [Equation 1] where Bik is the probability of coarse category k for image xi predicted by the coarse category component B[*Examiner notes: output of the first artificial intelligence unit].”
applying the modulation function to one or more parameters of the second artificial intelligence unit, wherein the one or more parameters influence a processing of the input value by the second artificial intelligence unit;
(page 2742 column 1 last paragraph) “On the right side of Fig 1 (b) is the probabilistic averaging layer[*Examiner notes: applying the formed modulation functions to one or more parameters], which receives fine as well as coarse category predictions and produces a final prediction based on weighted average[*Examiner notes: obtaining second output values of the second AI unit]”
obtaining, at a second time subsequent to the first time period, a second output value from of the second artificial intelligence unit; and
(page 2742 column 1 last paragraph) “On the right side of Fig 1 (b) is the probabilistic averaging layer[*Examiner notes: applying the formed modulation functions to one or more parameters], which receives fine as well as coarse category predictions and produces a final prediction based on weighted average[*Examiner notes: obtaining second output values of the second AI unit]”; [*Examiner notes: Obtaining the second output occurs at a subsequent time to the first time because the second output depends on the first output via the weighted average]
Yan does not explicitly teach:
a timer configured to measure units of time, and an actuator
setting the first artificial intelligence unit as a currently dominant artificial intelligence entity;
obtaining, at a first time within a first time period defined by the timer, a first output value, wherein the first time period is a predetermined period of time that is stored in association with the timer;
providing a first direct or indirect control signal to the actuator based on the currently dominant artificial intelligence entity and the first output value;
in response to detecting, by the timer, that the first time period has elapsed, setting the second artificial intelligence unit as the currently dominant artificial intelligence entity;
providing a second direct or indirect control signal to the actuator based on the currently dominant artificial intelligence entity and the second output value
However, Yau teaches:
a timer configured to measure units of time
(paragraph [0049]) “In examples, the predetermined time period is a plurality of days. Other examples are possible.”
setting the first artificial intelligence unit as a currently dominant artificial intelligence entity;
(paragraph [0049]) “ (1) select a model from the plurality of models 104 with a different model is selected at each of the different times[*Examiner notes: “first artificial intelligence unit” mapped to first model selected at first time in the sequence]”
obtaining, at a first time within a first time period defined by the timer, a first output value, wherein the first time period is a predetermined period of time that is stored in association with the timer;
[*Examiner notes: Within the context of computers, the time periods taught by Yau are stored as data in association with the timer because computer software is stored as data.]; (paragraph [0049]) “The control circuit 110 is further configured to, at a sequence of different times over a predetermined time period[*Examiner notes: first time in the sequence is mapped to the first time, and the first time period is the interval up to the first time], and until the PI value for the product is adjusted or the predetermined time period has expired: (1) select a model from the plurality of models 104 with a different model is selected at each of the different times[*Examiner notes: “first artificial intelligence unit” mapped to first model selected at first time]; (2) apply the determined features of the product and the features of the store 102 to the selected model 104 and responsively receive an out-of-stock probability[*Examiner notes: first output value from first AI unit] from the selected model”
in response to detecting, by the timer, that the first time period has elapsed, setting the second artificial intelligence unit as the currently dominant artificial intelligence entity;
(paragraph [0049]) “The control circuit 110 is further configured to, at a sequence of different times over a predetermined time period[*Examiner notes: first time in the sequence is mapped to the first time, and the first time period is up to the first time], and until the PI value for the product is adjusted or the predetermined time period has expired: (1) select a model from the plurality of models 104 with a different model is selected at each of the different times[*Examiner notes: “second artificial intelligence unit” mapped to second model selected at second time in the sequence]”
Yan, Yau, and the instant application are analogous because they are all directed to artificial intelligence and neural networks.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the artificial intelligence units of Yan in with the timer measuring the elapse of a predetermined time period taught by Yau because (Yau paragraph [0041]) “The models 104 may be grouped into different sets based, for example, on department of the store 102. For instance, a first group of models 104 may relate to the produce department and a second group of models 104 may relate to the meat department. Model selection may include selecting the correct group of models (meat department models or produce department models) and if the approach is implemented over several days, the model for the correct day (module for day 1, model for day 2, and so forth) is also selected. It will be appreciated that model selection is an affirmative step whereby the control circuit 110 makes a decision based upon a type of product (e.g., as indicated by a scanned code) and the timing (e.g., day 1, 2, etc.)” and (Yau paragraph [0050]) “Model selection can be made based on a variety of different factors. For example, if the approach is performed over a multiple day period, a different model is selected based upon the day. Day 1 may have a day 1 model (trained on data of that age), day 2 may have a different model (trained on data of that age), and so forth”. Stated differently, it would have been obvious because data is often time-specific and thus using a timer to control model choice will ensure that the correct model is used for the data specified at each predefined period of time.
And Perumallapalli teaches:
and an actuator
(page 64 section 3.5) “The control system uses control theory to convert the planned path into commands that can be used by the vehicle's actuators (braking, steering, acceleration).”
providing a first direct or indirect control signal to the actuator based on the currently dominant artificial intelligence entity and the first output value;
(page 64 section 3.5) “The control system uses control theory to convert the planned path[*Examiner notes: based on the dominant artificial intelligence entity and the first output value] into commands that can be used by the vehicle's actuators (braking, steering, acceleration).”
providing a second direct or indirect control signal to the actuator based on the currently dominant artificial intelligence entity and the second output value.
(page 64 section 3.5) “The control system uses control theory to convert the planned path[*Examiner notes: based on the dominant artificial intelligence entity and the second output value] into commands that can be used by the vehicle's actuators (braking, steering, acceleration).”
Yan, Yau, Perumallapalli, and the instant application are analogous because they are all directed to artificial intelligence and neural networks.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the artificial intelligence units of Yan in view of Yau with the actuators of Perumallapalli because (Perumallapalli page 1 abstract) “Using real-time AI algorithms, AVs can predict and respond to unpredictable traffic events and road conditions, resulting in safer transportation networks. The results of the study demonstrate the potential of AI-driven solutions to raise vehicle safety and provide the framework for fully autonomous, green transportation systems.”
Regarding Claim 2
Yan in view of Yau and Perumallapalli teaches:
The method of claim 1
(see rejection of claim 1)
Yan further teaches:
wherein the first artificial intelligence unit comprises a neural network having a plurality of nodes
(page 2740 abstract line 8) “However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifiers […] In this paper, we introduce hierarchical deep CNNs (HD-CNNs)[*Examiner notes: mapped to neural network] by embedding deep CNNs into a two-level category hierarchy”; (page 2741 column 2 last line) “It mainly comprises four parts: (i) shared layers, (ii) a single component B to handle coarse categories, (iii) multiple components {Fk}K k=1 (one for each group) for fine classification[*Examiner notes: mapped to plurality of nodes], and (iv) a single probabilistic averaging layer.”
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and wherein the one or more parameters is at least one of: a weighting (wi) for a first node of the neural network, an activation function (fakt) of a second node of the neural network, an output function (fout) of a third node of the neural network, or a propagation function of a fourth node of the neural network.
(page 2742 column 1 last paragraph) “On the right side of Fig 1 (b) is the probabilistic averaging layer, which receives fine as well as coarse category predictions and produces a final prediction[*Examiner notes: output function] based on weighted average[*Examiner notes: weighting] [Equation 1] where Bik is the probability of coarse category k for image xi predicted by the coarse category component B.”; Equation 1
Regarding Claim 3
Yan in view of Yau and Perumallapalli teaches:
The method according to claim 1
(see rejection of claim 1)
Yan further teaches:
wherein a classification memory is associated with the first artificial intelligence unit
(page 2745 column 1 “Parameter compression”) “As fine category CNNs have independent layers from conv2 to cccp6, we compress them and reduce the memory footprint from 447MB to 286MB[*Examiner notes: classification memory associated with each of the artificial intelligence units]”
wherein the first artificial intelligence unit performs a classification of the input value into one or more classes (Ki,K2, ..., K., Km) which are stored in the classification memory, the one or more classes each being structured in one or more dependent levels
(page 2741 column 2 section 3.1) “The dataset consists of a set of pairs {xi, yi}, where xi is an image and yi its category label. C denotes the number of fine categories, which will be automatically grouped into K coarse categories[*Examiner notes: one or more classes being structured in one or more dependent levels].”
and a number of first classes (n) in the classification memory is less than a number of second classes (m) in a second classification memory of the second artificial intelligence unit.
(page 2742 column 1) “(ii) a single component B[*Examiner notes: first AI unit] to handle coarse categories, (iii) multiple components {Fk}Kk=1 (one for each group)[*Examiner notes: second AI unit] for fine classification”; (page 2741) “The dataset consists of a set of pairs {xi, yi}, where xi is an image and yi its category label. C denotes the number of fine categories, which will be automatically grouped into K coarse categories.”; [*Examiner notes: The coarse component classifies input data into K coarse categories (1 level) and the fine classifier classifies input data into C fine categories based on the K coarse categories (2 categories, see figure 1(a) below). C is divided into K categories (i.e. C is a multiple of K) so C is larger than K.]
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Regarding Claim 4
Yan in view of Yau and Perumallapalli teaches:
The method according to claim 1,
(see rejection of claim 1)
And Yan further teaches:
wherein applying the modulation function causes a time-dependent superposition of parameters of the second artificial intelligence unit
[*Examiner notes: The superposition principle is a mathematical and scientific principle stating that the cumulative effect is equal to a summation of the effects. Therefore, a weighted summation of the fine classifiers can be interpreted as a superposition.]; (page 2743 column 2 last paragraph) “At test time[*Examiner notes: mapped to time-dependent], for a given image, it is not necessary to evaluate all fine category classifiers, as most of them have insignificant weights Bik, […] Those fine category classifiers with Bik = 0 are not evaluated.”; (page 2742 column 1 last paragraph) “On the right side of Fig 1 (b) is the probabilistic averaging layer, which receives fine as well as coarse category predictions and produces a final prediction based on weighted average[*Examiner notes: mapped to superposition] [equation 1] where Bik is the probability of coarse category k”
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and wherein the modulation function (fmoa f, fmoaw) comprises one of: a periodic function, a step function, a function with briefly increased amplitudes, a damped oscillation function, a beat function as a superposition of several periodic functions, a continuously increasing function, or a continuously decreasing function.
(page 2744 column 1 paragraph 1) “Conditional execution of top relevant fine components can accelerate the HD-CNN classification. Therefore, we threshold Bik using a parametric variable Bt = (βK)−1 and reset Bik to zero when Bik < Bt[*Examiner notes: mapped to step function]. Those fine category classifiers with Bik = 0 are not evaluated.”
Regarding Claim 5
Yan in view of Yau and Perumallapalli teaches:
The method of claim 1
(see rejection of claim 1)
Yan further teaches:
wherein the second artificial intelligence unit comprises a second neural network having a plurality of nodes
(page 2741 column 1 paragraph 2) “Third, we make the HD-CNN scalable by compressing the layer parameters and conditionally executing the fine category classifiers.”; (2743 column 1 last paragraph) “Fine category components {Fk}Kk=1[*Examiner notes: second neural network] can be independently pretrained in parallel […] . For each Fk, we initialize all the rear layers[*Examiner notes: mapped to plurality of nodes] except the last convolutional layer by copying the learned parameters from the pretrained model Fp.”; [*Examiner notes: Yan teaches on a second artificial intelligence unit having a plurality of fine category components which are convolutional neural networks. Each of these neural networks have a plurality of layers, which are nodes in the neural network.]
and wherein applying the one or more modulation function causes deactivation of at least a portion of the plurality of nodes.
(page 2742 column 1 paragraph 3 line 11) “The coarse category probabilities[*Examiner notes: applying modulation function] serve two purposes. First, they are used as weights for combining the predictions made by fine category components {Fk}Kk=1. Second, when thresholded, they enable conditional execution of fine category components whose corresponding coarse probabilities are sufficiently large.”; (page 2743 column 2 last paragraph) “At test time, for a given image, it is not necessary to evaluate all fine category classifiers, as most of them have insignificant weights Bik, as in Eqn 1. Their contributions to the final prediction are negligible. Conditional execution of top relevant fine components can accelerate the HD-CNN classification. Therefore, we threshold Bik using a parametric variable Bt = (βK)−1 and reset Bik to zero when Bik < Bt. Those fine category classifiers with Bik = 0 are not evaluated[*Examiner notes: applying modulation function causes deactivation of at least a portion of the nodes]”
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Yan in view of Yau and Perumallapalli, and further in view of Pfeil et al. (PGPUB no. US20220019874A1) herein referred to as Pfeil.
Regarding Claim 8
Yan in view of Yau and Perumallapalli teaches:
The method according to claim 1
(see rejection of claim 1)
Yan in view of Yau and Perumallapalli does not explicitly teach:
further comprising determining a comparison between the input value and a previous input value and, if the comparison results in a deviation that is above a predetermined input threshold, setting the first artificial intelligence unit to the currently dominant artificial intelligence entity
However, Pfeil teaches:
further comprising determining a comparison between the input value and a previous input value and, if the comparison results in a deviation that is above a predetermined input threshold, setting the first artificial intelligence unit to the currently dominant artificial intelligence entity
[*Examiner notes: Specification page 2 recites “The input values or features can
also be considered as layers.” Specification page 4 further recites “An important application of neural networks is the classification of input data or inputs into certain categories or classes, i.e. the recognition of correlations and assignments.”; Therefore, the broadest reasonable interpretation of “input value” includes representations of inputs processed by neural networks. The output variables of Pfeil are representations of input variables and thus may reasonably interpreted as input variables in view of Applicant’s specification.]; (paragraph [0075]) “Preferably, the criterion therefore characterizes a threshold value, for example a confidence or variance of the output variable (y) and/or the resource contingent. In addition or alternatively, the criterion can characterize a change in the respectively outputted output variables of the different paths[*Examiner notes: different paths mapped to previous and current]. If, for example, the output variable[*Examiner notes: mapped to input] differs by less than 5%,[*Examiner notes: mapped to threshold] the criterion is met[*Examiner notes: first AI unit is the dominant unit].”; (paragraph [0082]) “If the criterion is met in step S35, there follows step S36. In step S36, for example, an at least partly autonomous robot can be controlled as a function of the output variable of the deep neural network.”
Yan, Yau, Perumallapalli, Pfeil, and the instant application are analogous because they are all directed to artificial intelligence and neural networks.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the artificial intelligence units of Yan with the determining of a currently dominating unit taught by Pfeil because (Pfeil paragraph [0004]) “Because mobile terminal devices usually have a limited energy budget, it is desirable for as little energy as possible to be consumed during operation of deep neural networks on these terminal devices. The advantage of the method according to the present invention is that the deep neural networks can be carried out at least partly successively in order to save energy while nonetheless providing accurate results. A further advantage of the method according to the present invention is that a deep neural network that is executed successively requires less memory than a plurality of deep neural networks that are optimized for respective (energy or time) budgets. An advantage of the at least partly successive execution of the deep neural network is a low latency of input signals to output signals. The first “coarse” result is then refined through the addition of further paths.”
Regarding Claim 9
Yan in view of Yau and Perumallapalli teaches:
The method of claim 1
(see rejection of claim 1)
Yan in view of Yau and Perumallapalli does not explicitly teach:
further comprising determining a comparison between the first output value of the first artificial intelligence unit and a previous first output value of the first artificial intelligence unit, and, if the comparison results in a deviation that is above a predetermined output threshold, setting the first artificial intelligence unit to the currently dominant artificial intelligence entity.
However, Pfeil teaches:
further comprising determining a comparison between the first output value of the first artificial intelligence unit and a previous first output value of the first artificial intelligence unit, and, if the comparison results in a deviation that is above a predetermined output threshold, setting the first artificial intelligence unit to the currently dominant artificial intelligence entity.
(paragraph [0075]) “Preferably, the criterion therefore characterizes a threshold value, for example a confidence or variance of the output variable (y) and/or the resource contingent. In addition or alternatively, the criterion can characterize a change in the respectively outputted output variables of the different paths[*Examiner notes: different paths mapped to previous and current]. If, for example, the output variable[*Examiner notes: mapped to output values] differs by less than 5%,[*Examiner notes: mapped to threshold] the criterion is met[*Examiner notes: first AI unit is the dominant unit].”; (paragraph [0082]) “If the criterion is met in step S35, there follows step S36. In step S36, for example, an at least partly autonomous robot can be controlled as a function of the output variable of the deep neural network.”
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Yan in view of Yau and Perumallapalli, and further in view of Han et al. (US 20190050736 A1) herein referred to as Han.
Regarding Claim 13
Yan in view of Yau and Perumallapalli teaches:
The method according to claim 1
(see rejection of claim 1)
Yan in view of Yau and Perumallapalli does not explicitly teach:
wherein the input values (Xi) further comprise at least one of data detected by a user interface, data retrieved from a memory, data received via a communication interface, and data output by a computing unit.
However, Han teaches:
wherein the input value further comprises at least one of data detected by a user interface, data retrieved from a memory, data received via a communication interface, or data output by a computing unit.
(paragraph [0007]) “Another example aspect of the present disclosure provides an example method for data pruning at a maxout layer of a neural network. The example method may include retrieving, by a load/store unit, input data from a storage module, wherein the input data is formatted as a three-dimensional vector that includes one or more feature values stored in a feature dimension of the three-dimensional vector”
Yan, Yau, Perumallapalli, Han, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the artificial intelligence units of Yan in view of Yau and Perumallapalli with the storage taught by Han because (Han paragraph [0023]) “For example, the data conversion unit 108 may be configured to prioritize the reading/writing of the feature values by adjusting the read/write sequence. That is, feature values in the feature dimension of the input data may be read from or written into the register unit 104 or other storage components prior to the reading or writing of data in other dimensions of the input data.” That is, a storage can be used for reading and writing of 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ezra J Baker whose telephone number is (703)756-1087. The examiner can normally be reached Monday - Friday 10:00 am - 8:00 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.J.B./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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