Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office action has been issued in response to amendment filed on 07/31/2026, Claims (1-14) are pending. Applicants' arguments have been carefully and respectfully considered and addressed. Accordingly, this action has been made FINAL necessitated by amendment.
Claims (1-14) are presented for examination.
Response to Arguments
Applicants' arguments have been carefully and respectfully considered and addressed. The arguments presented are moot based on amendment.
Regarding Applicant arguments that pertain to the existing claims’ limitations and the amendment, the arguments were fully considered and are moot in view of the new ground rejection wherein Chandrashekhar. US Patent Application Publication US 20210165901 A1 (hereinafter Chandrashekhar) and further in view of Foreign Patent Application Publication WO 2019022085 A1 (hereinafter Sato) in view of Osamu Nonaka et al. Foreign Patent Application Publication WO 2021181634 Al (hereinafter Nonaka) wherein the combination of the new ground rejection teaches the claims’ limitations based on the amendment.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/26/2026 were filed prior to current Office Action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 first recites “an object identified by identification code” and later recites “the target object identified by the identification code.” The phrase “identification code” is introduced without an article such as “an” or “a,” while the later limitation refers to “the identification code.”
Claim 1 is rejected under 35 U.S.C. § 112(b) as indefinite because the claim recites “an object identified by identification code” and subsequently recites “the target object identified by the identification code,” without clearly establishing the antecedent and required relationship of “the identification code” to the target object. It is unclear whether the identification code associated with the training-data object is the same identification code used to identify the target object or whether separate identification codes may be used. Accordingly, the scope of the claimed relationship cannot be determined with reasonable certainty.
Claim Rejections - 35 USC§ 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims (1-7) and (8-14) are rejected under 35 U.S.C. 101 because the claimed
invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an
abstract idea) without significantly more.
Step 1: Claims (1-7) and (8-14) are drawn to a method each of which is within the four statutory categories (e.g., a process, a machine).
Step 2A - Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1.
A system comprising a processor and a memory storing instructions that, when executed by the processor, cause the system to:
input training data from a set of training data in a random order into a plurality of Artificial Intelligence (Al) servers to train an Al engine executed on each of the plurality of Al servers, the training data classifying a level of an event associated with an object identified by identification code;
input target data, associated with a target object for which a class determination of the event is required, into each trained Al engine on the plurality servers; and
receive a plurality of individual classification outputs respectively from the plurality of AI servers in response to the target data;
wherein a final class of the event associated with the target object identified by the identification code is thus determined based on a combination of the plurality of individual classification outputs received from the plurality of AI servers.
The claim is analyzed limitation by limitation to determine whether any limitation recites a judicial exception. The potentially applicable abstract-idea grouping is a mental process involving evaluation, judgment, and classification. The claim does not expressly recite a mathematical equation, formula, calculation, mathematical relationship, or other mathematical operation. Accordingly, no limitation is identified as a mathematical concept merely because AI or random ordering is recited.
Claim 1 recites an abstract idea in the form of a mental process: classifying a level of an event and determining a final class based on multiple individual classification outputs. The claim does not expressly recite a mathematical concept because it does not require a mathematical formula, calculation, relationship, or specific statistical technique.
Limitation 1 — “a system comprising a processor and a memory storing instructions that, when executed by the processor, cause the system to”
This limitation recites physical/computing components and the execution of stored instructions. A processor and memory are not themselves a mental process or mathematical concept. This limitation is therefore treated as an additional element and is reserved for analysis under Step 2A, Prong Two.
Prong One characterization: Additional element; not part of the judicial exception.
Limitation 2 — “input training data from a set of training data in a random order into a plurality of Artificial Intelligence (AI) servers”
This limitation recites the manner in which training data is supplied to multiple AI servers. The act of inputting information is a data-handling operation rather than the substantive classification judgment. The requirement that the data be supplied “in a random order” specifies an ordering technique, but the claim does not recite a mathematical randomization formula, probability calculation, statistical distribution, or other mathematical relationship. Thus, this language is not identified as a mathematical concept on the face of the claim.
The plurality of AI servers and the random-order input are therefore treated as additional elements that implement the claimed process and are evaluated under Prong Two.
Prong One characterization: Additional elements; not part of the judicial exception.
Limitation 3 — “to train an AI engine executed on each of the plurality of AI servers”
This limitation requires training an AI engine that is executed on each of the plurality of AI servers. The limitation does not itself recite a human evaluation, judgment, or mathematical operation. Rather, it specifies the technological environment and the operation of training machine-learning software on multiple servers.
Prong One characterization: Additional element; defer to Prong Two.
Limitation 4 — “the training data classifying a level of an event associated with an object identified by identification code”
This limitation describes the informational content of the training data: the training data assigns or indicates a level/class for an event associated with an identified object. Classification of information into a category or level is the type of evaluation or judgment that can conceptually be performed in the human mind. A person could review information about an event associated with an object and assign that event to a level or class.
Accordingly, to the extent this limitation requires or embodies the act of classifying the level of an event, it recites a mental process involving evaluation and judgment. The mere use of an identification code does not change the nature of the underlying classification.
Prong One characterization: Judicial exception — mental process (classification/evaluation).
Limitation 5 — “input target data, associated with a target object for which a class determination of the event is required, into each trained AI engine on the plurality of AI servers”
This limitation supplies target data to each trained AI engine. Inputting data into trained AI engines is a data-gathering/data-input function and does not itself recite the substantive mental judgment of selecting the final class. The trained AI engines and plurality of AI servers are technological implementation features.
Prong One characterization: Additional elements; defer to Prong Two.
Limitation 6 — “receive a plurality of individual classification outputs respectively from the plurality of AI servers in response to the target data”
This limitation requires receiving multiple classification results generated by the AI servers. The individual outputs represent classifications, but the recited act at this point is receipt of the results. Merely receiving information does not itself constitute the claimed final evaluation. Accordingly, the receipt of the plurality of outputs is treated as an additional element.
The informational content of each output is a classification, which relates to the identified abstract evaluation, but the claimed computer operation of receiving the outputs is considered separately under Prong Two.
Prong One characterization: Additional element; defer to Prong Two.
Limitation 7 — “wherein a final class of the event associated with the target object identified by the identification code is determined based on a combination of the plurality of individual classification outputs received from the plurality of AI servers”
This limitation recites the substantive determination of the final class. At the level of abstraction stated in the claim, the operation requires considering multiple classification results and determining a final category/class for the event. Evaluating several proposed classifications and selecting or determining a final classification is an act of evaluation and judgment that can conceptually be performed in the human mind.
Prong One characterization: Judicial exception — mental process (evaluation/judgment resulting in classification).
Step 2A — Prong Two: Whether the Additional Elements Integrate the Exception Into a Practical Application.
The following limitations are treated as additional elements and are not included within the identified judicial exception: the processor and memory; the plurality of AI servers; inputting training data in a random order; training an AI engine on each AI server; inputting target data to each trained AI engine; and receiving the plurality of individual classification outputs.
The additional elements are now evaluated individually and as an ordered combination to determine whether they integrate the identified mental process into a practical application. At this stage, the additional elements are not disregarded merely because they may be conventional.
Processor and memory
The processor and memory provide the computing platform on which the instructions are executed. Standing alone, these components merely provide a computer environment for performing the claimed operations and do not impose a meaningful limit on the abstract classification.
Plurality of AI servers and AI engines
The claim requires a plurality of AI servers, with an AI engine executed and trained on each server. This is more specific than simply reciting a generic computer. Nevertheless, the claim does not recite a particular server architecture, inter-server communication protocol, neural-network architecture, training algorithm, memory-management technique, processor improvement, or other technological modification to the servers or AI engines themselves.
The servers and AI engines are used to generate classification outputs for the target data. Thus, on the face of claim 1, they function primarily as technological tools for carrying out the underlying classification activity.
Random-order input of training data
The requirement that training data from a set be input “in a random order” limits how the training information is supplied. However, claim 1 does not state that the random ordering changes the functioning of the servers, improves computer resource utilization, reduces processing time, improves memory operation, changes the internal training algorithm, or otherwise produces a specifically claimed technological improvement.
Instead, the random ordering concerns the presentation of information used to train the AI engines. At the breadth of the claim, this limitation facilitates the generation of classification outputs but does not itself require a technological result beyond the classification task.
Inputting the target data and receiving the individual outputs
Supplying target data to the trained AI engines and receiving their respective outputs are data-input and data-output functions that enable the abstract classification to be performed using the claimed computer arrangement. These functions do not, individually, impose a meaningful limitation that transforms the abstract evaluation into a practical application.
Additional elements as an ordered combination
As an ordered combination, claim 1 requires: (1) a processor and memory; (2) random-order input of training data into plural AI servers; (3) training an AI engine on each server; (4) inputting the same target data into each trained AI engine; (5) receiving plural individual classification outputs; and (6) determining a final class based on a combination of those outputs.
This arrangement narrows the manner in which the abstract classification is implemented. However, claim 1 does not require a particular technological mechanism for combining the outputs, does not recite a specific improvement to the AI model or server operation, and does not expressly require the error/noise-elimination or statistical mechanisms described elsewhere in the application. The claim therefore uses the computer/AI components to produce information that is then used to make the abstract classification determination.
Accordingly, the additional elements do not integrate the mental process into a practical application because they amount to implementing the classification using a distributed AI/computing environment without claiming a specific technological improvement to that environment.
Step 2A, Prong Two: NO — the judicial exception is not integrated into a practical application.
Step 2B — Whether the Claim Amounts to Significantly More
Because claim 1 is directed to the judicial exception under Step 2A, the claim is evaluated under Step 2B to determine whether the additional elements, individually or as an ordered combination, amount to significantly more than the identified mental process.
Processor and memory
The processor and memory perform their ordinary functions of storing and executing instructions. Unless the record establishes that their use in the claimed manner was unconventional, these generic computing components do not, by themselves, supply an inventive concept.
AI servers and AI engines
The use of multiple AI servers and AI engines is an additional limitation that must be considered. Nevertheless, the claim does not recite an unconventional server structure or a particular technical interaction among the servers beyond separately training AI engines, processing the target data, and returning classification outputs.
Thus, the inventive concept cannot rest merely on using multiple AI computing instances to perform the underlying classification unless the record demonstrates that this arrangement itself constituted an unconventional technical implementation.
Data input, output, and final use
Inputting target data and receiving classification outputs are ordinary data-processing functions. The final class determination is the identified abstract evaluation itself and therefore cannot supply the inventive concept.
Considering the additional elements as an ordered combination, the claim distributes training information in a random order across multiple AI servers, trains an AI engine on each server, obtains multiple classifications for the same target data, and uses those outputs as the basis for the final class. This is a specific implementation sequence, but claim 1 does not recite a particular technical mechanism for combining the outputs or a specifically claimed improvement in computer or AI operation.
Claim 1 is directed to the abstract idea of evaluating classification information and determining a final class, which constitutes a mental process involving evaluation and judgment. The additional limitations — including the processor and memory, plurality of AI servers, random-order input of training data, training of AI engines, input of target data, and receipt of individual classification outputs — do not, individually or as an ordered combination, integrate the exception into a practical application because the claim does not recite a particular technological improvement to the operation of the processor, memory, AI servers, or AI engines. Further, the additional elements do not amount to significantly more than the judicial exception, provided that any finding that the additional implementation was well-understood, routine, and conventional is properly supported. Accordingly, claim 1 is subject to rejection under 35 U.S.C. § 101.
The same rational applies to claim 8.
Dependent claims (2-7) and (9-14) fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims (2-6) and 17 are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
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 of this title, 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 1-14 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Chandrashekhar. US Patent Application Publication US 20210165901 A1 (hereinafter Chandrashekhar) and further in view of Foreign Patent Application Publication WO 2019022085 A1 (hereinafter Sato) in view of Osamu Nonaka et al. Foreign Patent Application Publication WO 2021181634 Al (hereinafter Nonaka).
Regarding claim 1, Chandrashekhar teaches A system comprising a processor and a memory storing instructions that, when executed by the processor, cause the system to ([0134-0135] wherein Chandrashekhar teaches a system with processor and a memory to execution) input training data from a set of training data in a random order into a plurality of Artificial Intelligence (Al) servers to train an Al engine executed on each of the plurality of Al servers, the training data classifying a level of an event associated with an object identified by identification code (FIGS. 1, 6, Abstract, [0002], [0005-0006], [0023], [0036], [0069-0077], [0081], [0097-0103] wherein Chandrashekhar describes a method training one or more machine learning models to control data accessibility, wherein the models are based on machine learning. Wherein the method leverages machine learning and/or rules engines to provide a common methodology across industries in order to act as a trusted source to retrieve relevant data based on valid requests. Wherein the method utilizes trained models provided by a training server. Although depicted in FIG. 1 as a separate server for conceptual clarity, in some embodiments, the training server 135 and analysis server 110 may operate as a single server. That is, the models may be trained and used by a single server, or may be trained by one or more servers and deployed for use on one or more other servers. Chandrashekhar describes classifying data based on person, names, company or individuals as an identification code).
Chandrashekhar does not teach input target data, associated with a target object for which a class determination of the event is required, into each trained Al engine on the plurality servers; receive a plurality of individual classification outputs respectively from the plurality of AI servers in response to the target data;
However in analogous art of determining events class, Sato teaches input target data, associated with a target object for which a class determination of the event is required, into each trained Al engine on the plurality servers (page. 2, ¶ 2, page. 3, ¶ 2-6, page. 4, ¶ 1-3, page. 7. ¶ 2, page. 9, ¶ 3-7, page. 13, ¶ 1-6, page. 21, ¶ 3, page. 32, ¶ 6, page. 33, ¶ 2, 5-6, page. 34, ¶ 2-5 wherein Sato describes a development of multiple AI engines for processing data based on target objects) receive a plurality of individual classification outputs respectively from the plurality of AI servers in response to the target data (page. 2, ¶ 6, page. 3, ¶ 3-6, page. 4, ¶ 4, page. 5, ¶ 4-5, page. 13, ¶ 6, page. 15, ¶ 2, wherein Sato describes input/output correlation table description file that classifies the types and numerical values of the input data and creates a correlation table of internal states, wherein the input is received from a plurality of AI engines).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to comodule andrashekhar with Sato by incorporating the method of input target data, associated with a target object for which a class determination of the event is required, into each trained Al engine on the plurality servers; receive a plurality of individual classification outputs respectively from the plurality of AI servers in response to the target data of Sato into the method of input training data from a set of training data in a random order into a plurality of Artificial Intelligence (Al) servers to train an Al engine executed on each of the plurality of Al servers, the training data classifying a level of an event associated with an object identified by identification code of Chandrashekhar for the purpose of incorporating a method that verify the accuracy of the state estimated by the inference module and the state estimated by the artificial intelligence module; and an assessment module for switching the priority order of the state estimated by the inference module and the state estimated by the Al module on the basis of a verification result from the verification module, and then outputting same. (Sato: Abstract).
Chandrashekhar does not teach wherein a final class of the event associated with the target object identified by the identification code is thus determined based on a combination of the plurality of individual classification outputs received from the plurality of AI servers.
However in analogous art of determining events class, Nonaka teaches wherein a final class of the event associated with the target object identified by the identification code is thus determined based on a combination of the plurality of individual classification outputs received from the plurality of AI servers (page. 2, ¶ 2-3, page. 5, ¶ 6, page. 12, ¶ 8, page. 14, ¶ 3-5, page. 22, ¶ 4 wherein Nonaka describes classification of outputs and results based on object types).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to comodule Chandrashekhar with Nonaka by incorporating the method of wherein a final class of the event associated with the target object identified by the identification code is thus determined based on a combination of the plurality of individual classification outputs received from the plurality of AI servers of Nonaka into the method of input training data from a set of training data in a random order into a plurality of Artificial Intelligence (Al) servers to train an Al engine executed on each of the plurality of Al servers, the training data classifying a level of an event associated with an object identified by identification code of Chandrashekhar for the purpose of incorporating a method of creating teacher data for generating an inference model (Nonaka: Page. 2, paragraph 1).
Regarding claim 2, Chandrashekhar as modified by Sato and Nonaka teach wherein the training data corresponding to two or more pieces of class determination data pertinent to a predetermined condition is deleted from the entire training data to attain teacher data for Al learning (Page. 21, paragraph 8 wherein Nonaka describes the steps of deleting the teacher data), ([0058], [0087], [0092] wherein Chandrashekhar describes removing data elements wherein an analysis server attempts to find the combination of data elements that passes the rules with the fewest elements removed).
Regarding claim 3, Chandrashekhar as modified by Sato and Nonaka teach wherein the predetermined condition is at least based on dispersion of the two or more pieces of class determination data obtained from each Al process, or at least based on whether or not the dispersion of the two or more pieces of class determination data obtained from each Al process is a predetermined degree or lower (page. 3, paragraphs 1-5, page. 6, paragraph 2, page. 8, paragraph 4, page. 21, paragraph 3 wherein Nonaka describes the determination unit for classifying timeseries biometric information data processed via AI. Wherein the information pertains to a specific patient for heath condition and provides the risk state such as developing or worsening).
Regarding claim 4, Chandrashekhar as modified by Sato and Nonaka teach wherein the predetermined condition is that number of inconsistencies between the class indicated by the training data and the class indicated by the class determination data by the plurality of servers corresponding to the training data is a predetermined number or more (FIGS. 3, 4a-4b page. 3, paragraphs 1-5, page. 6, paragraph 2, page. 8, paragraph 4, page. 9, ¶ 6, page. 21, paragraph 3 wherein Nonaka describes the determination unit for classifying timeseries biometric information data processed via AI. Wherein the information pertains to a specific patient for heath condition and provides the risk state such as developing or worsening), ([0062], [0069], [0077] wherein Chandrashekhar describes the training server that trains one or more machine learning models based on the selected data record. In some embodiments, the training server does so by providing the features (extracted in block 425) as input to a model. This model may be a new model initialized with random weights and parameters or may be partially or fully pre-trained (e.g., based on prior training rounds). Based on the input features, the model-in-training generates some output (e.g., a classification as “pass” or “fail” for one or more access rules. In embodiments, the training server can compare this generated classification with the actual label (included with the data record) to compute a loss based on the difference between the actual result and the generated result. This loss is then used to refine one or more internal weights and parameters of the model (e.g., via backpropagation) such that the model learns to classify individual data elements more accurately), (page. 6, ¶ 8, page. 7, ¶ 1-2, wherein Sato describes input data for accelerating learning of the Al engine 21 and each engine of inference, verification, and optimization. It is positioned as a module. The replica generation module 19 is a tool for, among other things, generating a state (replica) of an internal element that clearly shows comparisons, errors, defects, inconsistencies, defects, damage, and destruction in order to facilitate verification. It is also good. The replica generation module 19 may have a method of regenerating the input replica from the generated internal replica 13b to compare the activity statuses of the functions more and check the inference accuracy, in which case the generation is performed first. The variation of the input replica 13a or the internal replica 13b generated from the input data 11 may be estimated, and the regenerated input replica 13c considered from the variation may be prepared. Compared with actual data (input data) 11, it is possible to increase replicas having high possibility as variations of the internal replica 13b and improve the prediction/ estimation accuracy of the Al engine 21).
Regarding claim 5, Chandrashekhar as modified by Sato and Nonaka teach wherein the class of the event changes according to a lapse of a time period, and is the class of the event for which a class determination of the event to be obtained by the plurality of Al servers is predicted after the lapse of the time period (Page. 5, ¶ 4 wherein Sato incorporates the verification engine 23 that evaluates the meta-model description file 14 with respect to the estimated internal state (internal replica) included in the estimation result 231 of the inference engine 22 or the AI engine 21 (algorithm (algorithm) The accuracy of the predicted or estimated state may be verified according to the equation, the method of least squares, etc.). The accuracy of the estimation result may be verified on the input regenerated replica regenerated from the internal replica. If the accuracy of the estimation result 231 is insufficient and the ambiguity is large, the verification engine 23 may indicate a verification result requiring a medical doctor (MD), a follow-up examination, and a two-week continuous examination. Furthermore, when ambiguity remains in determination, cannot be concluded, or narrowing down is insufficient, verification engine 23 may set a continuous inspection (verification) flag to the estimation result and indicate a verification result of continuing progress audit. That is, if the verification engine 23 determines that the evaluation of the estimation results 231 of the AI engine 21 and / or the inference engine 22 cannot be determined at that time, the verification engine 23 posts those estimation results 231 to the timeline (time capsule), It may also include a function (progressive observation function) that allows verification to be performed after a lapse of time. These options may be included in the meta model description file 14).
Regarding claim 6, Chandrashekhar as modified by Sato and Nonaka teach wherein the class of the event of the object identified by the identification code is determined to be the class to which largest number of the pieces of class determination data obtained from each Al process belong (FIGS. 3, 4a-4b, Abstract, page. 12, paragraph 2, page. 13, paragraph 3, page. 16, paragraph 6 wherein Nonaka incorporates patient information as a code and classifies and identifies for each code based on an identification as illustrated in FIGS. 3, 4a-4b).
Regarding claim 7, Chandrashekhar as modified by Sato and Nonaka teach wherein the object is a person and the event is a severity degree of the person (page. 3, paragraphs 1-6, page. 5, paragraph 6, page. 6 paragraph 1-2, page. 13, paragraph 4, wherein Nonaka describes data that includes patient information and health condition information).
Regarding claim 8, the claim is similar in scope to claim 1 therefore the claim is rejected under similar rationale.
Regarding claim 9, the claim is similar in scope to claim 2 therefore the claim is rejected under similar rationale.
Regarding claim 10, the claim is similar in scope to claim 3 therefore the claim is rejected under similar rationale.
Regarding claim 11, the claim is similar in scope to claim 4 therefore the claim is rejected under similar rationale.
Regarding claim 12, the claim is similar in scope to claim 5 therefore the claim is rejected under similar rationale.
Regarding claim 13, the claim is similar in scope to claim 6 therefore the claim is rejected under similar rationale.
Regarding claim 14, the claim is similar in scope to claim 7 therefore the claim is rejected under similar rationale.
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 extension fee 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 HASSAN MRABI whose telephone number is (571)272-8875. The examiner can normally be reached on Monday-Friday, 7:30am-5pm. Alt, Friday, EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached on 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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/HASSAN MRABI/Examiner, Art Unit 2144
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