DETAILED ACTION
This action is in response to the amendments filed on 07/29/2026.
Claims 1, 12-13, 15, and 18 have been amended. Claims 1-13 and 15-21 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Rejections Under 35 USC § 112(a):
In light of applicant’s arguments and explanations regarding the written description, the rejections under 35 USC § 112(a) have been withdrawn.
Prior Art Rejections:
Applicant's arguments regarding the prior art rejections have been fully considered but they are not persuasive.
Applicant argues (pg. 12-14) that the cited references fail to disclose or suggest the limitations of the independent claim, and that there is no motivation to modify the collective teachings of the references to arrive at the claimed invention. Examiner respectfully notes that the rejection under 35 USC § 103 below identifies the disclosure of the claimed features by the cited references, and explains why one of ordinary skill in the art would have been motivated to combine them. Applicant does not provide any specific reason why said disclosure or motivation might be insufficient, and thus applicant’s arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
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, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over
Poornachandran et al. (US 2022/0078601 A1, hereinafter referred to as Poornachandran) in view of
Hunter (US 11,132,403 B2, hereinafter referred to as Hunter) and
Goenka et al. (US 2022/0007437 A1, hereinafter referred to as Goenka).
As to claim 1, Poornachandran teaches a method, comprising:
in an information processing system with at least a first node and a second node separated from the first node, and each of the first node and the second node are configured to execute an application the first node determining that with at least one entity is moving from a geographic proximity of the first node to a geographic proximity of the second node by listening to one or more network notifications associated with the at least one entity in the information processing system, and in response to the determining that the at least one entity is moving to the geographic proximity of the second node (paragraphs [0031], the vUE 125 is connected to one evolved nodeB (eNB) that acts as a master node (MN) and one 5G or next generation nodeB (gNB) that acts as a secondary node (SN); [0038], the vUE 121/125 may determine to travel from a geographic location A to another geographic location B. The vUE may request or trigger a DA application to provide a route from the geographic location A to the geographic location B based on some algorithm of the application; [0071] The DA activity may be associated with assisting a user and/or vehicle in traveling from a geographic location A to another geographic location B, or based on a particular geographic route from the geographic location A to the geographic location B…; wherein Examiner interprets “a user and/or vehicle” as entity because the specification does not define one “entity” recited in the summary; [0136] These implementations may be used in follow-me clouds (FMC), where cloud services running at distributed data centers follow the UEs 721 as they roam throughout the network (i.e. Listening to the notifications sent from the network); [0070], the MEC-O 321 may decide to select one or more new MEC servers to act as a master node, and initiates the transfer of an application instance or application related state information from the one or more source MEC servers to the one or more target MEC servers. An MEC server may be an MSP edge server 136 in FIG. 2; [0078], once the vUE is authenticated and corresponding SLA is verified, the vUE can initiate the handshake prior to arriving to a dead zone that requires the supplemental wireless service. The vUE can initiate a query with the MNOA when it is approaching or in the proximity of the dead zone);
determining, by the first node, an input model from a plurality of selectable input models associated with the application, wherein the application comprises an artificial intelligence model used for one of inference and training (paragraphs [0035] The direct communication over a reference point may be based on a consumer (or subscriber)/producer model (interpreted by Examiner as an input model) where a consumer is configured with a producer's profile and directly communicates with that producer. The indirect communication over a reference point may also be based on a consumer (or subscriber)/producer model where a consumer queries an intermediate node, selects an end point (or termination point), and the consumer sends the request to the selected producer via the intermediate node; [0056] RNI may be also used by the MEP 337 to optimize the mobility procedures required to support service continuity, such as when a certain MEA 336 requests a single piece of information using a simple request-response model (e.g., using RESTful mechanisms) while other MEAs 336 subscribe to multiple different notifications regarding information changes (e.g., using a pub/sub mechanism and/or message broker mechanisms; [0069] The application requirements may be rules and requirements associated to/with one or more MEAs 336, such as deployment model (interpreted by Examiner as an input model) of the application (e.g., whether it is one instance per user, one instance per host, one instance on each host, etc.); wherein using the broadest reasonable interpretation, Examiner interprets the “consumer (or subscriber)/producer model,” the “simple request-response model,” and the “deployment model” as “selectable input models”; [0057] “In various embodiments, a MEC server acting as a master node for distributed machine learning (ML) may access RNI of individual UEs via a MEA 336 and/or the MEP 337 using the RNI API for the purposes of evaluating the channel conditions and/or link quality for partitioning training datasets and/or for assigning computational tasks to the individual UEs” (the system performs distributed machine learning, i.e., the application comprises an AI model for inference or training).);
determining based on the artificial intelligence model, by the first node, an artificial intelligence framework from a plurality of selectable artificial intelligence frameworks associated with the at least one entity (paragraph [0043], edge computing technology provides an end-to-end system architecture framework that enables distribution of computation processes over localized networks. In this infrastructure framework, localized data collected via local networks and wide area data stored in the cloud are integrated in the edge computing architecture to provide real-time information necessary for the services of connected vUE; [0137] FIG. 8 is an example of application instances in a V2X service with MEC V2X APL. In the framework of V2X services, a vehicle 821 (which may be the same or similar as vehicle system 121 and/or UE 125 of FIG. 1) is hosting a client application, and is connected to a certain MEC host (and a related MEC application; [0129] This virtualized framework allows the freed-up processor cores of the RAN nodes 731/732 to perform other virtualized applications; wherein Examiner interprets an end-to-end system architecture framework and infrastructure framework to include a plurality of selectable frameworks such as the framework of V2X services and the virtualized framework).
Poornachandran does not appear to explicitly disclose the remaining features of claim 1.
However, Hunter, in combination with Poornachandran, teaches:
maintaining, by the first node, an entity context associated with the at least one entity at the first node, the entity context comprising at least a set of computations associated with a computation graph obtained based on the determined input model and the determined artificial intelligence framework, each computation bound to respective ones of a set of inputs with a set of status indicators representing at least a portion of the execution of the application at the first node, wherein the set of status indicators indicate an execution state of each computation of the set of computations on the first node (col. 74; lines 9-24, determining a set of transaction paths between a first entity and a second entity based on the transaction graph, as indicated by block 1636; col. 90, lines 47-67 to col. 91, lines 1-15, In some embodiments, the set of confirmation messages may include one or more authentication frameworks to authenticate a confirmation message. For example, the set of confirmation messages may include a set of passkey values. For example, each respective message of a set of confirmation messages may include a respective passkey value of the set of passkey values, where each message of the set of confirmation messages may be associated with a respective 55 entity of the set of selected entities; col. 33, lines 44-65, some embodiments may use a set of multi-iteration scores as weights of a neural network, where the training inputs of the neural network may be outcome scores and the training outputs of the neural network may be events, indicators representing activated outcome subroutines, or activated patterns; col. 76, lines 18-30, a smart contract may include an entity representing a first asset. The first entity may include or otherwise be associated with a first entity property that includes a first list of indicators, where each of the first list of indicators points to an owner of the first asset. The asset may include or otherwise be associated with a second entity property that includes a second list of indicators, where each of the second list of indicators points to another entity owned by the first entity);
determining, by the first node, a time in which to transfer the entity context based on the one or more network notifications associated with the at least one entity in the information processing system and the movement of the at least one entity (col. 13; lines 7-20, a set of condition expiration thresholds, where the set of condition expiration thresholds may include specific dates, specific datetimes, durations from a starting point, other measurements of time, other measurements of time intervals, or the like. The system may check the set of condition expiration thresholds to determine if any of the condition expiration thresholds have been satisfied; col. 59, lines 52-67, some embodiments may include a determination that a program state has reached its expiration threshold of 30 years (“30 years” interpreted as a transfer time), where each program state has an associated time point based on a timestamp associated with the satisfaction of the norms that would result in that program state. For example, a program state may include an obligation by the first entity to allocate an amount of computing resources to a second entity every month (“every month” interpreted as a transfer time), and determining a set of events that satisfies these obligation norms may include advancing the simulated time by one month for each event that satisfies an obligation norm. If an expiration threshold of the symbolic AI model is one year (“one year” interpreted as a transfer time), the system may determine that an end state has been reached once the one-year expiration threshold has been reached; wherein using the broadest reasonable interpretation, Examiner interprets the “condition expiration thresholds may include specific dates, specific datetimes, durations from a starting point, other measurements of time, other measurements of time intervals “ and “expiration threshold of every month , one and 30 years” to teach “a time in which to transfer”);
causing, by the first node, the transfer of the entity context from the first node to the second node based on the set of status indicators bound to the set of computations to enable the second node to continue execution of one or more computations of the set of computations of the application using the transferred entity context from the first node within the determined time (col. 10, lines 20-55 for example, the event message may be transmitted from a first node of a distributed computing system (e.g., a blockchain platform) to a second node of the distributed computing system, where the first node and second node may be at different geographic locations (e.g., different nodes executing on different computing devices) or share a same geographic location (e.g., different nodes executing on a same computing device. Furthermore, an event message may be sent by a first smart contract executing on a first computing distributed platform to a second smart contract executing on a same or different distributed computing platform…, the event may include satisfying a condition expiration threshold; col. 13; lines 7-20, a set of condition expiration thresholds, where the set of condition expiration thresholds may include specific dates, specific datetimes, durations from a starting point, other measurements of time, other measurements of time intervals, or the like. The system may check the set of condition expiration thresholds to determine if any of the condition expiration thresholds have been satisfied; col. 59, lines 52-67; wherein using the broadest reasonable interpretation, Examiner interprets the “condition expiration thresholds may include specific dates, specific datetimes, durations from a starting point, other measurements of time, other measurements of time intervals “and “expiration threshold of every month, one and 30 years” to teach the time in which to transfer.);
wherein the first node comprises at least one processor and at least one memory storing computer program instructions wherein, when the at least one processor executes the computer program instructions, the first node performs the method (col. 10, lines 20-55, the event message may be transmitted from a first node of a distributed computing system (e.g., a blockchain platform) to a second node of the distributed computing system, wherein using the broadest reasonable interpretation, Examiner interprets the “first node of a distributed computing system” to teach the limitation).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the system of Poornachandran to add a transfer time, as taught by Hunter above. The modification would have been obvious because one of ordinary skill would be motivated to reduce the computational load on the distributed computing platform, as suggested by Hunter (col. 82, lines 9-15).
Poornachandran and Hunter do not appear to explicitly disclose the remaining features of claim 1.
However, Goenka, in combination with Poornachandran and Hunter, teaches:
wherein the information processing system comprises an edge computing environment and the first node and second node respectively comprise first and second edge nodes of the edge computing environment, and the at least one entity comprises cellular-based user equipment moving from a geographic proximity of the first edge node to a geographic proximity of the second edge node (paragraphs [0099] In accordance with at least one embodiment, each edge cloud server ( e.g., the edge cloud server 308A, 308B, etc.) maintains its own QoS-related information and periodically provides a snapshot of its QoS-related information to the core cloud server (e.g., the core cloud server 304A). In some cases, an edge cloud server can periodically provide a snapshot of its QoS-related information to the core cloud server as well as other edge cloud servers (e.g., other edge cloud servers within a certain geographic proximity); [0116]-[0117] As discussed, the user device 314 can be a mobile device that is capable of physically moving from one cellular base station to another. By way of a non-limiting example, the physical location of user device 314 can move from a geographic area (e.g., a cell) serviced by cellular base station 306A to the geographic area (e.g., a cell) serviced by the cellular base station 306B. In such a case, the disclosed systems and methods cause the local device instance to move with the user device 314 from the edge cloud server 308A (at the cellular base station 306A) to the edge cloud server 308B (at the cellular base station 306B).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the system of Poornachandran and Hunter to add a moving cellular-based user equipment, as taught by Goenka above. The modification would have been obvious because one of ordinary skill would be motivated to provide a user device capable of physically shifting from one cellular base station to another to avoid latency issue and optimize the network transmission reliability, as suggested by Goenka, ([0006]).
Claim 15 is a system claim containing substantially the same elements as method claim 1. Poornachandran, Hunter, and Goenka teach the elements of claim 1, as shown above.
Poornachandran also teaches An apparatus, comprising: at least one processor and at least one memory storing computer program instructions wherein, when the at least one processor executes the computer program instructions, the apparatus is configured as a first node (Examiner notes that this limitation is interpreted as implementation of the disclosed method in a generic computing environment. Paragraph [0133], “the server(s) 750 may include an operating system (OS) that provides executable program instructions for the general administration and operation of the individual server computer devices, and may include a computer-readable medium storing instructions that, when executed by a processor of the servers, may allow the servers to perform their intended functions.”)
Claim 18 is a product claim containing substantially the same elements as method claim 1. Poornachandran, Hunter, and Goenka teach the elements of claim 1, as shown above.
Poornachandran also teaches A computer program product stored on a non-transitory computer-readable medium and comprising machine executable instructions, the machine executable instructions, when executed, causing a processing device to perform steps of a first node (Examiner notes that this limitation is interpreted as implementation of the disclosed method in a generic computing environment. Paragraph [0133], “the server(s) 750 may include an operating system (OS) that provides executable program instructions for the general administration and operation of the individual server computer devices, and may include a computer-readable medium storing instructions that, when executed by a processor of the servers, may allow the servers to perform their intended functions.”)
Claims 2-7, 9-13, 16-17 and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Poornachandran in view of Hunter and Goenka, and further in view of
Tucker et al. (US 2020/0302302 A1, hereinafter referred to as Tucker).
As to claim 2, Poornachandran, Hunter, and Goenka teach The method of claim 1, as shown above.
Poornachandran, Hunter, and Goenka do not appear to explicitly disclose the remaining features of claim 2.
However, Tucker, in combination with Poornachandran, Hunter and Goenka, teaches wherein the maintaining step further comprises setting each of the set of status indicators for the set of computations to one of a plurality of statuses based on the indicated execution state of each of the computations (paragraphs [0035] After the devices perform the operations allocated by the placer 108 generate outputs, the executor 106 can retrieve the outputs. The executor 106 can generate an appropriate response to the request, e.g., an output or an indication that the processing has been completed. Then, the executor 106 can return the response to the client 102…; [0039]- [0040] ...The system can maintain a status of each device, e.g., using the executor 106 of FIG. 1. Each device can be either busy or available. A device is busy if the device is currently performing other operations and cannot be assigned further operations or is otherwise unavailable to perform graph processing operations. The device is available if the device can be assigned further operations, e.g., the further operations can be queued for operation by the device).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to modify the combination system of Poornachandran, Hunter and Goenka to add a set of computations to statuses, as taught by Tucker above. The modification would have been obvious because one of ordinary skill would be motivated to have each node containing information specifying an operation type, a name, and a list of incoming and outgoing edges to the node, as suggested by Tucker ([0039]).
As to claim 3, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 2, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein a first status of the plurality of statuses represents that a given computation is completed (paragraph [0040], wherein using the broadest reasonable interpretation, Examiner interprets “The device is available if the device can be assigned further operations, e.g., the further operations can be queued for operation by the device” to teach the limitation).
As to claim 4, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 3, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein a second status of the plurality of statuses represents that the given computation has started but not yet completed (paragraph [0040], wherein using the broadest reasonable interpretation, Examiner interprets “A device is busy if the device is currently performing other operations and cannot be assigned further operations” to teach the limitation.”).
As to claim 5, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 4, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein a third status of the plurality of statuses represents that the given computation has not yet started (paragraph [0040], wherein using the broadest reasonable interpretation, Examiner interprets “the further operations can be queued for operation by the device” to teach the limitation”).
As to claim 6, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 5, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein the entity context is transferred from the first node to the second node after each computation with the second status is completed (paragraph [0009] An outgoing edge from a node represents a flow of an output of the operation represented by the node to be used as an input to an operation represented by another node. Thus, a directed edge connecting a first node in the graph to a second node in the graph indicates that an output generated by the operation represented by the first node is used as an input to the operation represented by the second node.).
As to claim 7, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 5, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein the entity context transferred to the second node includes one or more computations with the third status (paragraphs [0009] An outgoing edge from a node represents a flow of an output of the operation represented by the node to be used as an input to an operation represented by another node. Thus, a directed edge connecting a first node in the graph to a second node in the graph indicates that an output generated by the operation represented by the first node is used as an input to the operation represented by the second node; [0040] …The system can connect to numerous devices, e.g., in a data center. The system can maintain a status of each device, e.g., using the executor 106 of FIG. 1.).
As to claim 9, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 5, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein the transferred context further comprises parameters associated with the set of computations (paragraphs [0008] …. the computational graph can be more easily partitioned for parallel operations than the conventional neural network representation. By way of illustration, subgraphs of the computational graph can be assigned to unique devices, each of which performs operations in the respective subgraph, to reduce an overall time required to perform operations of the neural network…; [0019] …operations necessary to train the neural network by performing a neural network training procedure to adjust the values of the parameters of the neural network, e.g., to determine trained values of the parameters from initial values of the parameters…; [0029]-[0030] …. Client 102 can request an indication that the requested neural network training operations have been completed and, optionally, trained values of the parameters of the neural network or an indication of a memory location from which the trained values can be accessed by the client 102...).
As to claim 10, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 9, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein the parameters for the given computation comprise at least one of model parameters for the given computation and outputs from other computations (paragraphs [0028]-[0029] Client 102 can request an indication that the requested neural network training operations (i.e., model parameters) have been completed and, optionally, trained values of the parameters of the neural network (i.e., model parameters) or an indication of a memory location from which the trained values can be accessed by the client 102...).
As to claim 11, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 10, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein parameters that are outputs of other computations that serve as inputs to computations with the third status are transferred as part of the entity context (paragraphs [0016] …An outgoing edge from a node represents a flow of an output of the operation represented by the node to be used as an input to an operation represented by another node. Thus, a directed edge connecting a first node in the graph to a second node in the graph indicates that an output generated by the operation represented by the first node is used as an input to the operation represented by the second node; [0040] ...The system can maintain a status of each device, e.g., using the executor 106 of FIG. 1. Each device can be either busy or available. A device is busy if the device is currently performing other operations and cannot be assigned further operations or is otherwise unavailable to perform graph processing operations. The device is available if the device can be assigned further operations, e.g., the further operations can be queued for operation by the device); wherein using the broadest reasonable interprets “connect to numerous devices”).
As to claim 12, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 9, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein, when the artificial intelligence model is used for inference, no model parameters are necessarily part of the transferred entity context (paragraphs [0008] …Operations, e.g., an operation to generate an inference from an input, of a neural network can be represented as a computational graph of nodes and directed edges…; [0019] …the operations represented by the computational graph may be operations necessary for the neural network to compute an inference, i.e., to process an input through the layers of the neural network to generate a neural network output for the input…; [0027]-[0029]…a computational graph representing an inference for a particular neural network and can identify an input on which the inference should be performed.)
As to claim 13, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 9, as shown above.
Tucker, in combination with Poornachandran, Hunter and Goenka, also teaches wherein, when the artificial intelligence model is used for training, model parameters of at least computations with the first status and the third status are part of the transferred entity context (paragraphs [0028]-[0029] Client 102 can request an indication that the requested neural network training operations have been completed and, optionally, trained values of the parameters of the neural network or an indication of a memory location from which the trained values can be accessed by the client 102...; [0032] …devices are configured to only perform a particular type of operation, e.g., inference operations.).
Claims 16-17 are system claims containing substantially the same elements as method claims 2-5. Poornachandran, Hunter, Goenka, and Tucker teach the elements of claims 2-5, as shown above.
Claims 19-21 are product claims containing substantially the same elements as method claims 2-7. Poornachandran, Hunter, Goenka, and Tucker teach the elements of claims 2-7, as shown above.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Poornachandran in view of Hunter, Goenka and Tucker, and further in view of
Suzuki (US 5,884,077, hereinafter referred to as Suzuki).
As to claim 8, Poornachandran, Hunter, Goenka, and Tucker teach The method of claim 5, as shown above.
Poornachandran, Hunter, Goenka, and Tucker do not appear to explicitly disclose the remaining features of claim 8.
However, Suzuki, in combination with Poornachandran, Hunter, Goenka, and Tucker, teaches wherein the maintaining step further comprises changing one or more computations with the second status to the third status prior to the one or more computations being completed, based on a timing demand associated with the entity context transfer (col. 10, lines 26-57…In step S103, the control of a process which is being operated and a process which is scheduled to be operated in the processor 303 is transferred to another processor….; col. 14, lines 16-34…The processor migration decision processing is started at the on-demand timing of page securing and at the timing of a change in load on the processor (a change in the number of processes in a ready state) upon process scheduling or upon generation/deletion of a process…; col. 15, lines 37-42…When a process is generated and started and physical pages are to be secured at on-demand timings, if no more pages can be secured on the computer on which the process runs, a computer from which physical pages are to be secured is determined on the basis of the acquired memory scheduling information).
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the combination system of Poornachandran, Hunter, Goenka and Tucker to add a timing demand, as taught by Suzuki above. The modification would have been obvious because one of ordinary skill would be motivated to have a processor migration decision processing checks which is more efficient to proceed with execution of the process by the current processor or to migrate the control of the processor of another computer and to execute the process by the migrated processor, as suggested by Suzuki (col.15, lines 43-55).
Conclusion
THIS ACTION IS MADE FINAL. 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 BENJAMIN M ROHD whose telephone number is (571)272-6445. The examiner can normally be reached Mon-Thurs 8:00-6:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached at (571) 270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.M.R./Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
