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 .
Information Disclosure Statement
The information disclosure statement (IDS)s submitted on 10/17/2024 and 07/23/2026 have been considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority based on Korean Patent Application No KR10-2024-0018522, filed on February 07, 2024.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a processing module and context module as in SPEC (paras 49 and 185) in claim 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-10 and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated in view of Afshin et al (“AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control”).
Regarding claim 1, Afshin discloses a pre-learning method for a traffic signal optimization, the pre-learning method comprising: (see Afshin abstract and pages “3” and “5-6” “Reinforcement Learning (RL) algorithm for the problem of traffic signal control… The first attention model is introduced to handle different numbers of roads-lanes; and the second attention model is intended for enabling decision-making with any number of phases in an intersection” and “A modification of FRAP was proposed on [35]. This new algorithm is called MetaLight, where the key idea is to use meta-learning strategy proposed in [10] to make a more universal model. However, MetaLight still needs to re-train its model parameter for any new intersection. In [30], a multi-agent RL algorithm is proposed to control the traffic signals for multiple intersections with arterial streets”),
converting first dynamic traffic information extracted based on a first type of state information corresponding to a first intersection type and second dynamic traffic information extracted based on a second type of state information corresponding to a second intersection type into a common format (see Afshin table 1 and pages “2-3”, “5-6” and “13-15” “We consider a single-intersection traffic signal control problem (TSCP). An intersection is defined as a junction of a few roads, where it can be in the form of 3-way, 4-way or it can have a more complex structure with five or more approaching roads. Each road might have one or two direction(s) and each direction includes one lane or more” and “We consider 11 intersection topologies, where they vary in terms of the number of approaching roads (i.e., 3-way or 4-way), and the number of lanes in each road. Further, each of these 11 intersections may have a different number of phases and traffic-data. Table 1 summarizes the properties of all intersections.” And “The proposed AttendLight framework uses the state-attention to produce a unified state representation from a varying number of lane-traffic information.…”),
extracting at least one static characteristic information corresponding to at least one of the first intersection type or the second intersection type based on context information (see Afshin table 1 and pages “3” and “5-6” “An intersection is defined as a junction of a few roads, where it can be in the form of 3-way, 4-way or it can have a more complex structure with five or more approaching roads…”, “We consider 11 intersection topologies, where they vary in terms of the number of approaching roads (i.e., 3-way or 4-way), and the number of lanes in each road” and “AttendLight is our proposed algorithm and has two major responsibilities: i) extracting meaningful phase representations z! for every phase p, and ii) deciding on the next active phase”),
based on the first dynamic traffic information and the at least one static characteristic information, outputting a first type of action information for an optimized traffic signal for the first intersection type (see Afshin pages “4” and “7” “Action. At each time step t, we define the action as the active phase at time t + 1.” And “In TSCP, we are interested in learning a policy 1r, which for a given state st of an intersection suggests the phase for the next time-step in order to optimize the long-term cumulative rewards. We design a unified model for approximating 1r that fits to every intersection configuration. The AttendLight model that we present in Section 4.2 instantiates such a policy 1r that achieves this universality by appropriate use of two attention mechanisms.”),
and based on the second dynamic traffic information and the at least one static characteristic information, outputting a second type of action information for an optimized traffic signal for the second intersection type (see Afshin pages “5-6”, “9” and “13-15” “Hence, AttendLight provides an appropriate mapping of the states to probability of taking actions for any intersection configuration, regardless of the number of roads, lanes, traffic movements, and 5 number of phases. We would like to emphasize that AttendLight is invariant to the order of lanes or phases, so how to enumerate these components will result in the same control decisions.…” and “we consider the traffic signal control problem, and for the first time, we propose a universal RL model, called AttendLight, which is capable of providing efficient control for any type of intersections. To provide such capability to the model, we propose a framework including two attention mechanisms to make the input and output of the model, independent of the intersection structure. The experimental results on a variety of scenarios verify the effectiveness of the AttendLight. First, we consider the single-environment regime. In this case, AttendLight outperforms existing methods in the literature. Next, we consider AttendLight in the multi-environment regime in which we train it over a set of distinct intersections. The trained model is tested in new intersections verifying the generalizability of AttendLight.”).
Regarding claim 2, Afshin discloses wherein a dimension of the first type of state information and a dimension of the second type of state information are different (see Afshin abstract and pages “3”, “5-6” and “26” “We consider 11 intersection topologies, where they vary in terms of the number of approaching roads (i.e., 3-way or 4-way), and the number of lanes in each road. Further, each of these 11 intersections may have a different number of phases and traffic-data. Table 1 summarizes the properties of all intersections.” And “For example, in Figure 1 phase-I and phase-3 involve two traffic movements, while there are three traffic movements in phase-2. Further, phase-I and phase-3 involve six participating lanes while phase-2 includes nine lanes. This results in different size of the input/output of the model among different intersection instances. Therefore, building a universal model which handles such complexity is not straightforward using conventional deep RL algorithms. To address this issue, we design AttendLight which uses a special attention mechanism as described in the next section”).
Regarding claim 3, Afshin discloses wherein a dimension of the first dynamic traffic information and a dimension of the second dynamic traffic information converted into the common format are the same (see Afshin abstract and pages “3”, “5-6” and “26” “our goal is to design a mechanism with satisfactory performance across a group of intersections. Attentional mechanisms are a natural choice, since they allow unified system representations by handling variable-length inputs. We propose the AttendLight framework, a reinforcement learning algorithm, to train a "universal" model which can be used for any intersection, with any number of roads, lanes, phase, traffic distribution, and type of sensory data to measure the traffic. In other words, once the model is trained under”).
Regarding claim 4, Afshin discloses wherein the state information is defined as a vector of a length which is based on a combination of state information elements (see Afshin abstract and pages “3-6” and “26” “In this section we present our end-to-end RL framework for solving the TSCP. To formulate this problem into an RL context, we first require to identify state, action, and reward. State. The state at time tis the traffic characteristics sf for all lanes l E £, i.e., i = {sf, l E £}. Action. At each time step t, we define the action as the active phase at time t + 1. Reward. Following the discussion in [31], the reward in each time step is set to be the negative of intersection pressure.”).
Regarding claim 5, Afshin discloses wherein the state information elements include at least one of a number of intersection lanes, a number of vehicles, a speed, or a traffic light state (see Afshin abstract and pages “5-6” and “26” “In all experiments, we choose the number of moving and waiting vehicles to represent traffic characteristic sf. To this order, first, for lane l we consider a segment of 300 meters from the intersection and split it into three chunks of 100 meters. Then, af c for c = 1, 2, 3 is the number of moving vehicles in chunk c of the lane l at time t. Also, we define /3{ as the number of waiting vehicles at lane l at time t. Now, we represent the traffic characteristic of lane”).
Regarding claim 6, Afshin discloses wherein the context information element includes at least one of a layout of an intersection, a position of a traffic light, or a road configuration (see Afshin abstract and pages “3-6” and “26” “An intersection is defined as a junction of a few roads, where it can be in the form of 3-way, 4-way or it can have a more complex structure with five or more approaching roads… Each road might have one or two direction(s) and each direction includes one lane or more. Let M be a set of intersections, where each intersection m E M is associated with a known intersection topology and traffic-data”).
Regarding claim 7, Afshin discloses wherein first static characteristic information for the first intersection type and second static characteristic information for the second intersection type are different information having a same format (see Afshin abstract and pages “3”, “5-6” and “26” “We consider 11 intersection topologies, where they vary in terms of the number of approaching roads (i.e., 3-way or 4-way), and the number of lanes in each road. Further, each of these 11 intersections may have a different number of phases and traffic-data. Table 1 summarizes the properties of all intersections.”, “The combination of intersection topologies, their available phases, and traffic-data allows us to construct the set M with 112 unique intersection instances.” And “For example, in Figure 1 phase-I and phase-3 involve two traffic movements, while there are three traffic movements in phase-2. Further, phase-I and phase-3 involve six participating lanes while phase-2 includes nine lanes. This results in different size of the input/output of the model among different intersection instances. Therefore, building a universal model which handles such complexity is not straightforward using conventional deep RL algorithms. To address this issue, we design AttendLight which uses a special attention mechanism as described in the next section”).
Regarding claim 8, Afshin discloses wherein first static characteristic information for the first intersection type and second static characteristic information for the second intersection type are same information (see Afshin abstract and pages “3”, “5-6” and “26” “Rather than specializing on a single intersection, our goal is to design a mechanism with satisfactory performance across a group of intersections.” And “We propose the AttendLight framework, a reinforcement learning algorithm, to train a "universal" model which can be used for any intersection, with any number of roads, lanes, phase, traffic distribution, and type of sensory data to measure the traffic”).
Regarding claim 9, Afshin discloses wherein an output dimension of the first type of action information and an output dimension of the second type of action information are the same (see Afshin abstract and pages “5-6” “AttendLight is our proposed algorithm and has two major responsibilities: i) extracting meaningful phase representations z! for every phase p, and ii) deciding on the next active phase. To add universality to these responsibilities, the input and output dimension of the model needs to be independent of intersection configuration. To this order, we propose two attention mechanisms-as introduced in Section 4.1-called state-attention and action-attention for handling the phase representation from the raw-state and for choosing the next phase, respectively. The policy model that we used in AttendLight is visualized in Figure 2. Next, we explain how AttendLight achieves these goals”).
Regarding claim 10, Afshin discloses wherein a dimension of a first action generated based on the first type of action information, and a dimension of a second action generated based on the second type of action information are different (see Afshin abstract and pages “5-6” part 3 and 5 and see table 1).
Regarding claim 13, Afshin discloses a device for performing pre-learning for a traffic signal optimization, the device comprising: at least one transceiver; at least one processor; and at least one memory operably connected to the at least one processor, and storing an instruction to make the device perform an operation when executed by the at least one processor, wherein the processor is configured to: (see Afshin abstract and pages “3” and “5-6” “Reinforcement Learning (RL) algorithm for the problem of traffic signal control… The first attention model is introduced to handle different numbers of roads-lanes; and the second attention model is intended for enabling decision-making with any number of phases in an intersection” and “A modification of FRAP was proposed on [35]. This new algorithm is called MetaLight, where the key idea is to use meta-learning strategy proposed in [10] to make a more universal model. However, MetaLight still needs to re-train its model parameter for any new intersection. In [30], a multi-agent RL algorithm is proposed to control the traffic signals for multiple intersections with arterial streets”),
convert first dynamic traffic information extracted based on a first type of state information corresponding to a first intersection type input through the at least one transceiver, and second dynamic traffic information extracted based on a second type of state information corresponding to a second intersection type input through the at least one transceiver into a common format (see Afshin table 1 and pages “2-3”, “5-6” and “13-15” “We consider a single-intersection traffic signal control problem (TSCP). An intersection is defined as a junction of a few roads, where it can be in the form of 3-way, 4-way or it can have a more complex structure with five or more approaching roads. Each road might have one or two direction(s) and each direction includes one lane or more” and “We consider 11 intersection topologies, where they vary in terms of the number of approaching roads (i.e., 3-way or 4-way), and the number of lanes in each road. Further, each of these 11 intersections may have a different number of phases and traffic-data. Table 1 summarizes the properties of all intersections.” And “The proposed AttendLight framework uses the state-attention to produce a unified state representation from a varying number of lane-traffic information.…”),
extract at least one static characteristic information corresponding to at least one of the first intersection type or the second intersection type based on context information input through the at least one transceiver (see Afshin table 1 and pages “3” and “5-6” “An intersection is defined as a junction of a few roads, where it can be in the form of 3-way, 4-way or it can have a more complex structure with five or more approaching roads…”, “We consider 11 intersection topologies, where they vary in terms of the number of approaching roads (i.e., 3-way or 4-way), and the number of lanes in each road” and “AttendLight is our proposed algorithm and has two major responsibilities: i) extracting meaningful phase representations z! for every phase p, and ii) deciding on the next active phase”),
based on the first dynamic traffic information and the at least one static characteristic information, output, through the at least one transceiver, a first type of action information for an optimized traffic signal for the first intersection type (see Afshin pages “4” and “7” “Action. At each time step t, we define the action as the active phase at time t + 1.” And “In TSCP, we are interested in learning a policy 1r, which for a given state st of an intersection suggests the phase for the next time-step in order to optimize the long-term cumulative rewards. We design a unified model for approximating 1r that fits to every intersection configuration. The AttendLight model that we present in Section 4.2 instantiates such a policy 1r that achieves this universality by appropriate use of two attention mechanisms.”),
and based on the second dynamic traffic information and the at least one static characteristic information, output, through the at least one transceiver, a second type of action information for an optimized traffic signal for the second intersection type (see Afshin pages “5-6”, “9” and “13-15” “Hence, AttendLight provides an appropriate mapping of the states to probability of taking actions for any intersection configuration, regardless of the number of roads, lanes, traffic movements, and 5 number of phases. We would like to emphasize that AttendLight is invariant to the order of lanes or phases, so how to enumerate these components will result in the same control decisions.…” and “we consider the traffic signal control problem, and for the first time, we propose a universal RL model, called AttendLight, which is capable of providing efficient control for any type of intersections. To provide such capability to the model, we propose a framework including two attention mechanisms to make the input and output of the model, independent of the intersection structure. The experimental results on a variety of scenarios verify the effectiveness of the AttendLight. First, we consider the single-environment regime. In this case, AttendLight outperforms existing methods in the literature. Next, we consider AttendLight in the multi-environment regime in which we train it over a set of distinct intersections. The trained model is tested in new intersections verifying the generalizability of AttendLight.”).
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable in view of Afshin et al (“AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control”) in view of Meng (US 2018/0253967 A1).
Regarding claim 11, Afshin fails to explicitly teach wherein: the first action includes a time distribution ratio for a first number of signal indications of the first intersection type, and the second action includes a time distribution ratio for a second number of signal indications of the second intersection type.
However, Meng teaches wherein: the first action includes a time distribution ratio for a first number of signal indications of the first intersection type, and the second action includes a time distribution ratio for a second number of signal indications of the second intersection type (see Meng para “0042” “FIG. 4: 1—the two numbers #/# in square brackets are for the period remainder and period-complement of the intersection at their lower right, 2—an oval on an intersection is for running signals, horizontal oval is for East-West green lights, vertical oval is for South-North green lights, inwhere the number # is for signal time# of the intersection, e.g., the signal time#=time-switched 60−rest time 80 of the greenwave there =−20, the negative number means there still is 20 seconds to run for finishing the current greenwave in order to run the wi-interim-period and recover mode RATIO, 3—signal time # bigger than 0 means that the wd-interim-period has run for the time#, 4—signal time # bigger than wd-interim-period means that the intersection has run for the time# of RATIO mode, wherein /negative value −8 means that the value had been decreased from the last half period of the greenwave, dotted line ovals mean that the wd-interim-period of the intersection has run out; the FIG. shows that the signals distribution at the 420.sup.th second when the DIFF-mix mode which had run for 360 seconds, then been instructed to recover mode RATIO, and the wd-interim-period has run for 60 seconds, the origin intersection(6,0) has no time-offset, need no wd-interim-period, has run original period 60 seconds”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Afshin for Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control “to modify the traffic signal control method with the signal timing distribution” as taught by Meng (para. [0042]) to provide more efficient allocation of signal timing among different traffic signal indications and thereby improving traffic flow through the intersections.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable in view of Afshin et al (“AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control”) in view of Cohn et al (US 2023/0012236 A1).
Regarding claim 12, Afshin fails to explicitly teach wherein: based on a backpropagation of a first action generated in response to the first type of action information, a parameter of at least one of a first type of action output layer, a first type of action decoder block, a processing module, a context module, a first type of encoder block, or a first type of state input layer is updated, and based on a backpropagation of a second action generated in response to the second type of action information, a parameter of at least one of a second type of action output layer, a second type of action decoder block, the processing module, the context module, a second type of encoder block, or a second type of state input layer is updated.
However, Cohn teaches wherein: based on a backpropagation of a first action generated in response to the first type of action information, a parameter of at least one of a first type of action output layer, a first type of action decoder block, a processing module, a context module, a first type of encoder block, or a first type of state input layer is updated, and based on a backpropagation of a second action generated in response to the second type of action information, a parameter of at least one of a second type of action output layer, a second type of action decoder block, the processing module, the context module, a second type of encoder block, or a second type of state input layer is updated (see Cohn paras “0136” and “0157-0159” “a method involving use of multiple convolutional neural networks and multiple segmentation masks… determining the respective loss value for the respective batch based on summing up loss values determined based on the comparisons of the calculated class probability values for the pixels of the respective digital images with the encoded truth masks, updating one or more parameters of the first convolutional neural network, such updating comprising calculating a gradient of a matrix of the calculated class probability values, starting from this calculated gradient of the matrix of the calculated class probability values, backpropagating through layers of the first convolutional neural network and calculating gradients for parameters associated with these layers, including weight parameters and bias parameters associated with these layers, performing, for each respective parameter of a set of one or more parameters of the first convolutional neural network, a parameter update based on a corresponding calculated gradient for that respective parameter and a step size value; training, using a second plurality of digital images of diapers, a second convolutional neural network to identify an area of a digital image that corresponds to a diaper, such training comprising, for each respective batch of digital images of the second plurality of digital images, for each of a plurality of iterations, calculating, by the second convolutional neural network for each respective digital image of the respective batch of digital images, a class probability value for each pixel of the respective digital image, each class probability value being calculated based on one or more parameters associated with one or more layers of the second convolutional neural network…”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Afshin for Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control “to update parameters of the first and second encoder blocks based on backpropagation” as taught by Cohn (para. [0136] – [0157]) to optimize the parameters of the neural networks based on calculated gradients to improve the accuracy of the trained networks.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOSSAM M ABD EL LATIF whose telephone number is (571)272-5869. The examiner can normally be reached M-F 8 am-5 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rachid Bendidi can be reached on (571) 272-4896. 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.
/HOSSAM M ABD EL LATIF/Examiner, Art Unit 3664
Prosecution Insights