DETAILED ACTION
Claims 2 – 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 .
EXAMINER’S COMMENT
Claims 2 and 12 recite ‘a natural language query’ in their first limitations and then in their third limitations, recite ‘the received query’ which appears to refer to the receiving of the earlier ‘a natural language query’ from their first limitation. These claims then again refer to ‘the natural language query’ in their same third limitation. The Examiner chooses not to provide a claim objection to these different uses of ‘the received query’ and ‘the natural language query’ as ‘the received query’ seems to be what is processed by the NLU while ‘the natural language query’ seems to refer to the content of the user’s actual utterance.
Claim Objections
Claims 10 and 20 are objected to because of the following informalities:
Claims 10 and 20 both end without a punctuation. The Examiner suggests placing a full-stop at the end of the claims.
Appropriate correction is required.
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 therefor, subject to the conditions and requirements of this title.
Claims 2 – 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Claims 2 and 12 recite the limitations of receiving a natural language query at an interface, applying a natural language understanding model to execute the received query, determine a confidence level of understanding the query by first generating a system (first) response to the query, receiving a user input as a second response to the system response, and then comparing the user input to a user input threshold to be able to determine the confidence level of the query, if the confidence level is below a threshold, reprocessing the query using a rule-based natural language processing, and finally generating a response to the query making use of the NLU processing of the query and the reprocessing.
Nothing in the claims precludes them from being performed in the human mind. The entire process involves data gathering through receiving a query and the user input after the system’s response; data processing through executing the NLU model, determining the confidence level, determining that a confidence level is below a threshold, and determining to reprocess the query; and data generation by generating a response to the query. A human may listen to a spoken query or read a textual query as received from a user, perform human reasoning on the query and determine a confidence level that shows the human’s understanding of the query by providing a first response to the user’s query and getting a feedback from the user, the human compares this feedback to available feedbacks and determines if the feedback meets a cut-off, as a way of determining the confidence of understanding the user’s query. The human then determines that the confidence level of understanding the query is below a threshold by reasoning that an understanding of the query wasn’t met, the user applies the information from the initial reasoning to a second rule-based reasoning process, and finally provides a response to the user’s query. The NLU model presented here is interpreted as the human’s brain which performs the necessary reasoning, and the interfaces are just media which the user and human employ for communication. The mentioned control circuitry is provided simply as a tool to be applied to performing the claimed method. Claims 2 and 12 hereby recite a mental process.
This judicial exception is not integrated into a practical application as the claims merely teach of gathering data, data processing and data generation, all of which, as provided here, can be performed in the human mind through reasoning and with the aid of a pen and paper. While claim 12 recites a control circuitry, it is presented in generic terms.
The invention is not tied to any particular defining structure and simply provides instructions to apply the judicial exception. The techniques can be performed by a generic computer which would be presented as a tool to implement the abstract idea (classifiable as automation of the mental process steps). The Specification in [0033] provides that the control circuitry could be based on one or more microprocessors, microcontrollers, digital signal processors, etc. The natural language understanding model and the rule-based natural language processing presented here are not well-specified in the Specification either, leading the Examiner to believe that these can be addressed by any generic NLU/NLP model. The NLU model and the rule-based NLP presented in the claims can be performed by a human applying mental reasoning to determine approximate confidence levels, which can be compared to a threshold that also can be mental level of understanding, to know when a confidence is either strong or weak. These limitations can simply be generic models performed on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the invention is not tied to a practical application.
The claims provide techniques that amount to no more than mere instructions that apply the judicial exception which can be performed by a generic device. Merely mentioning storage memory and a processor amount to no more than general-purpose hardware used as tools to implement the abstract idea and do not provide any particular application other than applying them for the purpose of implementing a judicial exception. While the claims make mention of machine learning models, the machine learning models do not recite specifics on how the models are performed, and therefore still do not amount to significantly more than the mentioned judicial exception. Mere instructions to apply an exception using a generic device cannot provide an inventive concept. Claims 2 and 12 are not eligible.
Claims 3 and 13 provide determining an average confidence score of a plurality of previous NL queries, comparing the difference between the average confidence and the earlier obtained confidence level, to a threshold value. This recites a purely mathematical process. This does not integrate any practical application, nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 4 and 14 provide a rule-based NLP, through a sentiment analysis that analyses linguistic terms to classify them as either positive or negative. A human may mentally analyse linguistic terms in a query to classify the sentiment as either positive or negative. This does not integrate any practical application, nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 5 and 15 provide a rule-based NLP that applies linguistic patterns that are determined based on previous queries to identify a sentence structure. A human may observe previous queries and analyse their linguistic patterns to identify sentence structures. This does not integrate any practical application, nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 6 and 16 provide that the user input includes one or more selection inputs received at a media guidance application. This simply provides a user selection of items or icons on a display, which can be presented to a user on a piece of paper to choose from, which can be easily performed as a mental task. This does not integrate any practical application, nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 7 and 17 provide that the user input is used if received within a period. A human may decide to make use of user feedback if received within a period. This does not integrate any practical application, nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 8 and 18 provide determining a new confidence level of understanding the query based on the response generated to the query after processing the query by the first NLU model and reprocessing the query. A human may obtain a confidence in understanding a query based on a series of steps involving generating a response to the query after initially processing the query to initially understand it, and again processing it a second time. This does not integrate any practical application, nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 9 and 19 provide that the reprocessing involves processing using a second NLU, and determining a new confidence level of understanding the query in response to processing the query using the NLU. A human may try to understand the query by passing it to a second human who processes it also, this second human being taken as the second NLU model. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 10 and 20 provide that the reprocessing of the query involves increasing a size of the dataset used for processing the query using the NLU model. A human may perform reprocessing by collecting more information regarding the query, to attempt to understand the query better. This does not integrate any practical application, nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 11 and 21 provide updating the NLU model, based on reprocessing the query. A human may re-analyse the query to gain more information about be able to analyse such queries, thereby gaining more knowledge. This does not integrate any practical application, nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
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 2, 7, 8, 9, 12, 17, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Pasko et al. (US 11,132,509 B1: hereafter — Pasko) in view of Taubman et al. (US 2020/0082829 A1: hereafter — Taubman).
For claim 2, Pasko discloses a method comprising:
receiving a natural language query at a user interface (Pasko: Col 2 lines 28–30 — user speech/input data (query) is received at a speech interface device to be processed by the Natural Language Understanding component executed on the speech interface device);
executing the received natural language query using a natural language understanding (NLU) model (Pasko: Col 2 lines 28–30 — processing an input at an NLU component; Col 15 lines 8–10 — NLU processing of user input);
determining, for the NLU model used to execute the received query, a confidence level of understanding of the natural language query (Pasko: Col 2 lines 47–65 — obtaining an NLU confidence score in order to address the user input (natural language query)),
in response to the determined confidence level being below a confidence level threshold, reprocessing the natural language query using rule-based natural language processing (Pasko: Col 18 line 61 – Col 19 line 3 — when a first confidence threshold isn’t met, the system may continue the evaluation (reprocessing the natural language when the first confidence level is below a threshold); Col 15 lines 7–8, 63–65 — rules-based NLU processing); and
generating a response to the natural language query based on the processing of the natural language query by the NLU model and the reprocessing of the natural language query (Pasko: Col 27 lines 29–47 — the selection of an NLU result to the user’s input based on a computed final confidence score that is based on the prior NLU confidence scores (the initial confidence score and the reprocessing, as provided by FIG. 3 Steps 312 – 320, for the processing regarding the use of the first NLU and going through Step 322 for the situation where there NLU score threshold is not met)).
The reference of Pasko teaches the above but fails to teach the determination of the confidence level through receiving a user input after generating a response to the natural language query, the user input which then gets compared to a user input threshold.
This isn’t new to the art as the reference of Taubman is now introduced to teach:
wherein determining the confidence level comprises:
receiving user input into the user interface subsequent to generating a response to the natural language query (Taubman: FIG. 8 Steps 805–820 — a user provides an input query which gets a response from the device, and the user then provides a feedback to the response (the feedback being the user input); [0027] — a user interface which a user may use for providing input; [0068] — the system receives negative user feedback);
comparing the received user input to a user input threshold to determine the confidence level (Taubman: [0068] — the system receives negative user feedback (negative in this sense means that the user input does not meet a user input threshold resulting in its classification as being negative) and then adjusting a question-answer pair score; [0101] — adjusting a confidence score of the initial natural language query to be lower based on the negative feedback).
The reference of Pasko provides teaching for computing a confidence level associated with a natural language query input, but differs from the claimed invention in that the claimed invention further provides the calculation of the confidence level based on further user input received after generating a response to the initial query. This isn’t new to the art as the reference of Taubman is seen to teach above.
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the technique of Pasko which provides the computing of a confidence score associated with a natural language query, by applying the known technique of Taubman which computes the confidence based on a further received user feedback after a system response has been provided, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of updating an NLU’s comprehension of a user query based on the user’s feedback to the system response, so that negative associations would not be made between the query and the provided response again in the future, leading to a better understanding of the natural language query. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
For claim 7, claim 2 is incorporated and the combination of Pasko in view of Taubman discloses the method, wherein the received user input is used if it is received within a predetermined monitoring period (Taubman: [0135] – [0136] — the device outputs a follow-up answer after a particular duration has elapsed that the user provided feedback).
For claim 8, claim 2 is incorporated and the combination of Pasko in view of Taubman discloses the method, further comprising, determining a new confidence level of the understanding of the natural language query based on the response generated to the natural language query based on the processing of the natural language query by the first NLU model and the reprocessing of the natural language query (Taubman: [0068] — the system receives negative user feedback (which is a user’s response to the response generated by the system to the user’s initial natural language query after processing the natural language query) and then adjusting a question-answer pair score; [0101] — adjusting a confidence score of the initial natural language query to be lower based on the negative feedback (indicating a reprocessing of the natural language query)
Pasko in Col 2 lines 28–30 provides a suitable NLU).
For claim 9, claim 2 is incorporated and the combination of Pasko in view of Taubman discloses the method, wherein reprocessing the natural language query comprises:
processing the natural language query using a second NLU model (Pasko: FIG. 3 Step 312 — the processing of the input through another NLU model (after having come from Step 322)); and
determining a new confidence level of the understanding of the natural language query in response to processing the natural language query using the second NLU model (Pasko: FIG. 3 Step 318 — obtaining a new confidence score using the second NLU (after having come from Step 322)).
As for claim 12, system claim 12 and method claim 2 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Pasko in Col 31 lines 43–52 provides a processor, suitable to read upon the claimed control circuitry in this claim. Accordingly, claim 12 is similarly rejected under the same rationale as applied above with respect to method claim 2.
As for claim 17, system claim 17 and method claim 7 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 17 is similarly rejected under the same rationale as applied above with respect to method claim 7.
As for claim 18, system claim 18 and method claim 8 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 18 is similarly rejected under the same rationale as applied above with respect to method claim 8.
As for claim 19, system claim 19 and method claim 9 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to method claim 9.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Pasko (US 11,132,509 B1) in view of Taubman (US 2022/0208197 A1) as applied to claim 2, further in view of Oktem et al. (US 2018/0308491 A1: hereafter — Oktem) and further in view of Balchandran et al. (US 2008/0312904 A1: hereafter — Balchandran).
For claim 3, claim 2 is incorporated and the combination of Pasko in view of Taubman provides teaching for the computation of an NLU confidence level. This combination however differs from the claimed invention in that the claimed invention further provides teaching for computing an average confidence score.
The reference of Oktem is now introduced to teach this as:
the method, wherein determining the confidence level further comprises:
determining an average confidence score from a plurality of previous natural language queries (Oktem: [0118] — computing the average over previous confidence score and an initial confidence score for a current utterance).
The combination of Pasko in view of Taubman provides teaching for the computation of an NLU confidence level, but differs from the claimed invention in that the claimed invention further provides teaching for obtaining an average confidence score for the current confidence score and previous natural language queries. This isn’t new to the art as the reference of Oktem is seen to teach above.
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the general confidence score calculation of the combination of Pasko in view of Taubman, by applying the known technique of Oktem which computes an average confidence score over past natural language queries, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of a computation of an average score giving insightful statistical information on the performance of the NLU to be able to make better-informed decisions. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
The combination of Pasko in view of Taubman further in view of Oktem provides teaching for calculating an average confidence score over the current query and previous natural language queries, but differs from the claimed invention in that the claimed invention further provides computing a difference of the confidence scores to be compared to a threshold value.
The reference of Balchandran is now introduced to teach this as:
comparing a difference between the confidence level and the average confidence score to a threshold value (Balchandran: [0039] — a difference between confidence scores being determined not to exceed a difference threshold).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to incorporate the known technique of Balchandran which determines if a difference between threshold values meets a threshold, into improving upon the teaching of computing an average confidence score as taught by the combination of Pasko in view of Taubman further in view of Oktem, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result that checking if a difference threshold is met would lead to a classification information that can then lead to the invoking of a further process/action (Balchandran: [0039]).
As for claim 13, system claim 13 and method claim 3 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 13 is similarly rejected under the same rationale as applied above with respect to method claim 3.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Pasko (US 11,132,509 B1) in view of Taubman (US 2022/0208197 A1) as applied to claim 2, further in view of TRUONG et al. (US 2022/0191154 A1: hereafter — Truong).
For claim 4, claim 2 is incorporated and the combination of Pasko in view of Taubman provides teaching for a rules-based natural language processing to determine if a user provides negative or positive feedback. This combination however particularly fails to teach of performing a sentiment analysis to determine the presence of linguistic terms for positive or negative classification.
The reference of Truong is now introduced to teach this as:
the method, wherein using the rule-based natural language processing includes performing a sentiment analysis that analyses linguistic terms to classify them as positive or negative terms (Truong: [0027] — providing an input for sentiment analysis to obtain a positive or negative sentiment within a word or phrase or sentence (as linguistic terms)).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Pasko in view of Taubman which provides for a rules-based natural language processing to determine if a user provides negative or positive feedback, by applying the known technique of Truong which provides linguistic term analysis to be able to determine the sentiment associated with a user input, to thereby come up with the claimed invention. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 14, system claim 14 and method claim 4 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 14 is similarly rejected under the same rationale as applied above with respect to method claim 4.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Pasko (US 11,132,509 B1) in view of Taubman (US 2022/0208197 A1) as applied to claim 2, further in view of Hewitt et al. (US 2020/0026755 A1: hereafter — Hewitt).
For claim 5, claim 2 is incorporated and the combination of Pasko in view of Taubman provides teaching for a rules-based natural language processing being performed on a natural language query. This combination however particularly fails to teach of the rules-based natural language processing as including the application of linguistic patterns to identify sentence structure.
The reference of Hewitt is now introduced to teach this as:
the method, wherein using the rule-based natural language processing includes applying linguistic patterns determined based on previous natural language queries to identify a structure of a sentence (Hewitt: [0059] — natural language processing to detect patterns in linguistic style, the patterns including sentence structure, as present in posts and reposts published by a user (indicating previous natural language queries)).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Pasko in view of Taubman which provides for a rules-based natural language processing in an attempt to further understand the natural language query, by applying the known technique of Hewitt, which detects linguistic style patterns that include sentence structures in a user’s previously published posts, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of being able to better recognise the intent behind queries and to be able to better match them with personalised responses. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 15, system claim 15 and method claim 5 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to method claim 5.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Pasko (US 11,132,509 B1) in view of Taubman (US 2022/0208197 A1) as applied to claim 2, further in view of Sreedhara (US 2019/0102481 A1).
For claim 6, claim 2 is incorporated and the combination of Pasko in view of Taubman provides teaching for receiving a user input at a user interface. This combination fails to teach that the user input particularly includes a selection input received in a media guidance application.
This isn’t new to the art as the reference of Sreedhara is now introduced to teach this as:
the method, wherein the user input includes one or more selection inputs received in a media guidance application (Sreedhara: [0061], [0068], [0069] — a user navigating a media guidance application through input speech; [0075] — user selection of navigational icons on the media guidance application).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to combine the teaching of the combination of Pasko in view of Taubman which provides the receiving of a user input at a user interface, with the known teaching of Sreedhara which provides the application of the user input as selection of one or more inputs received in a media guidance application, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of giving a user an ease of access to navigate and choose on-screen results/responses that are visually presented to a user, so that the user can more quickly achieve an intended result. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 16, system claim 16 and method claim 6 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 16 is similarly rejected under the same rationale as applied above with respect to method claim 6.
Claims 10, 11, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Pasko (US 11,132,509 B1) in view of Taubman (US 2022/0208197 A1) as applied to claim 2, further in view of KO et al. (US 2020/0349940 A1: hereafter — Ko).
For claim 10, claim 2 is incorporated and the combination of Pasko in view of Taubman provides teaching for the reprocessing of a natural language query after an initial NLU processing. This combination however differs from the claimed invention in that the claimed invention further provides teaching for the reprocessing being performed by increasing the size of a dataset used for the processing of the NL query using the NLU model.
This isn’t new to the art as the reference of Ko is now introduced to teach this as:
the method, wherein reprocessing of the natural language query is performed by increasing a size of a dataset used for the processing the natural language query using the NLU model (Ko: [0196] — a second NLU model obtaining results from a first NLU model (the second NLU model receiving information from the first NLU, indicating an increase in the size of the dataset used for processing the natural language query)).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Pasko in view of Taubman which teaches the reprocessing of a natural language query after an initial NLU processing, by applying the known teaching of Ko which increases the dataset at the second NLU processing by making use of the data obtained from the first NLU processing, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of providing as much dataset as possible from the first NLU processing to the reprocessing stage so that the reprocessing contains as much information as possible needed to make a more informed NLU decision/result while comparing current results with previous ones. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
For claim 11, claim 2 is incorporated and the combination of Pasko in view of Taubman provides teaching for the reprocessing of a natural language query after an initial NLU processing. This combination however differs from the claimed invention in that the claimed invention further provides teaching for updating the NLU model through reprocessing the query.
This isn’t new to the art as the reference of Ko is now introduced to teach this as
the method, further comprising, updating the NLU model, wherein the update is based on the reprocessing of the natural language query (Ko: [0193] — updating the NLU model through learning).
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the teaching of the combination of Pasko in view of Taubman which reprocesses a natural language query after an initial NLU processing, by applying the known technique of Ko which updates a first NLU model through learning, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of improving upon the processing of the first NLU by have it learn new information obtained from a reprocessing, so that it can properly address such a query, or a similar query again, if encountered. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 20, system claim 20 and method claim 10 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 20 is similarly rejected under the same rationale as applied above with respect to method claim 10.
As for claim 21, system claim 21 and method claim 11 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 21 is similarly rejected under the same rationale as applied above with respect to method claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Ocher et al. (US 2019/0372794 A1) provides teaching for a user feeedback being used to determine whether a previous request was successful or not [0037].
Kahan et al. (US 11,430,435 B1) provides teaching for performing sentiment analysis on NLU results and identify sentiments associated with the user inputs (Col 11 lines 49–60).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to OLUWADAMILOLA M. OGUNBIYI whose telephone number is (571)272-4708. The Examiner can normally be reached Monday – Thursday (8:00 AM – 5:30 PM Eastern Standard Time).
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, PARAS D. SHAH can be reached at (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLUWADAMILOLA M OGUNBIYI/Examiner, Art Unit 2653
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