DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 09/15/2024. 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 Status
Claim(s) 1, 3-7, 10, 12-13, 15-18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schmer (US 20210097300 A1).
Claim(s) 2, 9, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Schmer (US 20210097300 A1) in view of Silva (US 20160300119 A1).
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Schmer (US 20210097300 A1) in view of Nijhuis (US 20190066492 A1).
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-7, 10, 12-13, 15-18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schmer (US 20210097300 A1).
Regarding claim 1, Schmer discloses A method for data processing, comprising: (¶2 “This disclosure relates to systems and methods for automatically identifying and verifying vehicle license plate data.”)
receiving, at a server (¶43 “In particular embodiments, one or more data storages may be communicatively linked to one or more servers via one or more links.”) and from a computing device (¶3 “computing systems”) associated with a camera, at least one image of a vehicle detected in a vehicle detection zone during a first time period; (¶7 “cause the processor to: receive, from a digital camera system, one or more images associated with a vehicle,”)
generating, using the at least one image, a prediction of at least one data object associated with the vehicle; (¶7 “predict, based on the applied machine learning and prediction application, at least one of make and model information of the vehicle that is represented in the one or more received images,”)
determining whether the prediction of the at least one data object matches a vehicle profile from a vehicle profile data store; and (¶7 “compare the make and model information that is obtained from the second database to the make and model information that is predicted based on the applied machine learning and prediction application,”)
communicating a result of determining whether the prediction of the at least one data object matches the vehicle profile. (¶52 “The comparison score may indicate a likelihood that the license plate ID obtained from the ALPR system is the same as the license plate ID that is represented in the one or more received images.” ¶52 discloses a threshold that determines whether the predicted make/model information matches the information from the image)
Regarding claim 3, Schmer discloses wherein generating the prediction of the at least one data object comprises: (¶7 “predict, based on the applied machine learning and prediction application, at least one of make and model information of the vehicle that is represented in the one or more received images,”)
transmitting, to one or more vehicle identification services, the at least one image of the vehicle; and (¶7 “cause the processor to: receive, from a digital camera system, one or more images associated with a vehicle,”)
generating, by the one or more vehicle identification services, a predicted license plate string identified from the at least one image, wherein the prediction comprises the predicted license plate string (¶20 “In one embodiment, the license plate ID that is received from the ALPR system comprises at least one of numbers, letters, numerals, and characters.”) and a confidence score associated with the prediction of the predicted license plate string. (¶31 “The analysis system 120 may provide predicted license plate data and associated confidence level to the toll agency 170 via the network 150.”)
Regarding claim 4, Schmer discloses wherein determining whether the prediction of the at least one data object matches the vehicle profile comprises: (¶21 “If the make/model information matches or matches within a threshold range,”)
determining, based at least in part on the confidence score (¶21 “, from the ALPR system, a recognition confidence score that is associated with the received license plate ID, the recognition confidence score identifying a probability that the license plate ID identified by the ALPR system is the same as the license plate ID of the vehicle that is represented in the one or more received images”) being greater than a threshold, whether the predicted license plate string matches a license plate string in a plurality of vehicle profiles in the vehicle profile data store. (¶52 “The comparison score may indicate a likelihood that the license plate ID obtained from the ALPR system is the same as the license plate ID that is represented in the one or more received images. If the make/model information matches or matches within a threshold range, then the license plate information may be sent to a billing department associated with the toll agency 170.”)
Regarding claim 5, Schmer discloses wherein determining whether the prediction of the at least one data object matches the vehicle profile comprises: (¶7 “compare the make and model information that is obtained from the second database to the make and model information that is predicted based on the applied machine learning and prediction application,”)
determining, that the confidence score is less than a threshold; (¶61 “If the threshold is not met, then a human operator may be notified for involvement in determining the license plate information.”)
identifying a subset of vehicle profiles of a plurality of vehicle profiles in the vehicle profile data store (¶36 “The DMV database 140 refers to a database that maintains a vehicle identification number (VIN) that is associated with one or more license plate numbers. It may be used to look up a vehicle identification number (VIN) that is associated with a license plate ID that is recognized by the ALPR system 130.”) that have a license plate string length that matches a license plate string length of the predicted license plate string; and (¶34 “In an embodiment, machine learning models may be employed to identify the characters, numbers, and/or letters that license plate IDs comprise. In an embodiment, the ALPR system 130 may output the characters, numerals, and/or letters that may be identified from a license plate ID.” Schmer discloses matching the strings between the license plate in the database and identified license plate)
determining whether a threshold quantity of characters are matched between the predicted license plate string (¶34 “In an embodiment, machine learning models may be employed to identify the characters, numbers, and/or letters that license plate IDs comprise. In an embodiment, the ALPR system 130 may output the characters, numerals, and/or letters that may be identified from a license plate ID.” Schmer discloses matching the strings between the license plate in the database and identified license plate) and respective license plate strings of the subset of vehicle profiles. (¶36 “The DMV database 140 refers to a database that maintains a vehicle identification number (VIN) that is associated with one or more license plate numbers. It may be used to look up a vehicle identification number (VIN) that is associated with a license plate ID that is recognized by the ALPR system 130.”)
Regarding claim 6, Schmer discloses wherein determining, based at least in part on a threshold quantity of characters being matched, whether at least one additional data object prediction from the at least one image of the vehicle and comprising a make, model, or color, matches corresponding data objects of the subset of vehicle profiles, wherein the at least one additional data object is received from the one or more vehicle identification services. (¶7 “predict, based on the applied machine learning and prediction application, at least one of make and model information of the vehicle that is represented in the one or more received images,”)
Regarding claim 7, Schmer discloses wherein the threshold quantity is based on the license plate string length. (¶34 “In an embodiment, machine learning models may be employed to identify the characters, numbers, and/or letters that license plate IDs comprise. In an embodiment, the ALPR system 130 may output the characters, numerals, and/or letters that may be identified from a license plate ID.” Schmer discloses matching the number of characters between the license plate in the database and identified license plate)
Regarding claim 10, Schmer discloses wherein generating the prediction comprises:
detecting, based at least at in part on one or more heat signatures included in the at least one image that comprises a thermal image captured by the camera that comprises a thermal camera, identifiable features of the vehicle; and (¶32 “generating the prediction based at least in part on the identifiable features of the vehicle…The digital representation may be…thermal heat signature data”)
generating the prediction based at least in part on the identifiable features of the vehicle. (¶35 “The identifying information may include… make and/or model information… color, special badging regarding engine size, for example, or trim level, etc”)
Regarding claim 12, Schmer discloses wherein communicating the result comprises: (¶52 “The comparison score may indicate a likelihood that the license plate ID obtained from the ALPR system is the same as the license plate ID that is represented in the one or more received images.” ¶52 discloses a threshold that determines whether the predicted make/model information matches the information from the image)
communicating, to the computing device, an identifier for the vehicle, wherein the computing device is configured to perform one or more actions based at least in part on the identifier. (¶35 “The machine learning system 122 may recognize identifying information from the digital representation data. The machine learning system 122 may provide results to the comparison module 124.”)
Regarding claim 13, Schmer discloses An apparatus for data processing, comprising: (¶2 “This disclosure relates to systems and methods for automatically identifying and verifying vehicle license plate data.”)
one or more memories storing processor-executable code; and (¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to: (¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
receive, at a server (¶43 “In particular embodiments, one or more data storages may be communicatively linked to one or more servers via one or more links.”) and from a computing device (¶3 “computing systems”) associated with at least one camera, at least one image of a vehicle detected in a vehicle detection zone during a first time period; (¶7 “cause the processor to: receive, from a digital camera system, one or more images associated with a vehicle,”)
generate, using the at least one image, a prediction of at least one data object associated with the vehicle; (¶7 “predict, based on the applied machine learning and prediction application, at least one of make and model information of the vehicle that is represented in the one or more received images,”)
determine whether the prediction of the at least one data object matches a vehicle profile from a vehicle profile data store; and (¶7 “compare the make and model information that is obtained from the second database to the make and model information that is predicted based on the applied machine learning and prediction application,”)
communicate a result of determining whether the prediction of the at least one data object matches the vehicle profile. (¶52 “The comparison score may indicate a likelihood that the license plate ID obtained from the ALPR system is the same as the license plate ID that is represented in the one or more received images.” ¶52 discloses a threshold that determines whether the predicted make/model information matches the information from the image)
Regarding claim 15, Schmer discloses wherein to generate the prediction of the at least one data object, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to: (¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
transmit, to one or more vehicle identification services, the at least one image of the vehicle; and (¶7 “cause the processor to: receive, from a digital camera system, one or more images associated with a vehicle,”)
generating, by the one or more vehicle identification services, a predicted license plate string identified from the at least one image, wherein the prediction comprises the predicted license plate string (¶20 “In one embodiment, the license plate ID that is received from the ALPR system comprises at least one of numbers, letters, numerals, and characters.”) and a confidence score associated with the prediction of the predicted license plate string. (¶31 “The analysis system 120 may provide predicted license plate data and associated confidence level to the toll agency 170 via the network 150.”)
Regarding claim 16, Schmer discloses wherein to determine whether the prediction of the at least one data object matches the vehicle profile, (¶21 “If the make/model information matches or matches within a threshold range,”) the one or more processors are individually or collectively operable to execute the code to cause the apparatus to: (¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
determine, based at least in part on the confidence score(¶21 “, from the ALPR system, a recognition confidence score that is associated with the received license plate ID, the recognition confidence score identifying a probability that the license plate ID identified by the ALPR system is the same as the license plate ID of the vehicle that is represented in the one or more received images”) being greater than a threshold, whether the predicted license plate string matches a license plate string in a plurality of vehicle profiles in the vehicle profile data store. (¶52 “The comparison score may indicate a likelihood that the license plate ID obtained from the ALPR system is the same as the license plate ID that is represented in the one or more received images. If the make/model information matches or matches within a threshold range, then the license plate information may be sent to a billing department associated with the toll agency 170.”)
Regarding claim 17, Schmer discloses wherein to determine whether the prediction of the at least one data object matches the vehicle profile, (¶7 “compare the make and model information that is obtained from the second database to the make and model information that is predicted based on the applied machine learning and prediction application,”) the one or more processors are individually or collectively operable to execute the code to cause the apparatus to: (¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
determine, that the confidence score is less than a threshold; (¶61 “If the threshold is not met, then a human operator may be notified for involvement in determining the license plate information.”)
identify a subset of vehicle profiles of a plurality of vehicle profiles in the vehicle profile data store (¶36 “The DMV database 140 refers to a database that maintains a vehicle identification number (VIN) that is associated with one or more license plate numbers. It may be used to look up a vehicle identification number (VIN) that is associated with a license plate ID that is recognized by the ALPR system 130.”) that have a license plate string length that matches a license plate string length of the predicted license plate string; and (¶34 “In an embodiment, machine learning models may be employed to identify the characters, numbers, and/or letters that license plate IDs comprise. In an embodiment, the ALPR system 130 may output the characters, numerals, and/or letters that may be identified from a license plate ID.” Schmer discloses matching the strings between the license plate in the database and identified license plate)
determine whether a threshold quantity of characters are matched between the predicted license plate string (¶34 “In an embodiment, machine learning models may be employed to identify the characters, numbers, and/or letters that license plate IDs comprise. In an embodiment, the ALPR system 130 may output the characters, numerals, and/or letters that may be identified from a license plate ID.” Schmer discloses matching the strings between the license plate in the database and identified license plate) and respective license plate strings of the subset of vehicle profiles. (¶36 “The DMV database 140 refers to a database that maintains a vehicle identification number (VIN) that is associated with one or more license plate numbers. It may be used to look up a vehicle identification number (VIN) that is associated with a license plate ID that is recognized by the ALPR system 130.”)
Regarding claim 18, Schmer discloses A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to: (¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
receive, at a server and from a computing device (¶43 “In particular embodiments, one or more data storages may be communicatively linked to one or more servers via one or more links.”) and from a computing device (¶3 “computing systems”) associated with at least one camera, at least one image of a vehicle detected in a vehicle detection zone during a first time period; (¶7 “cause the processor to: receive, from a digital camera system, one or more images associated with a vehicle,”)
generate, using the at least one image, a prediction of at least one data object associated with the vehicle; (¶7 “predict, based on the applied machine learning and prediction application, at least one of make and model information of the vehicle that is represented in the one or more received images,”)
determine whether the prediction of the at least one data object matches a vehicle profile from a vehicle profile data store; and (¶7 “compare the make and model information that is obtained from the second database to the make and model information that is predicted based on the applied machine learning and prediction application,”)
communicate a result of determining whether the prediction of the at least one data object matches the vehicle profile. (¶52 “The comparison score may indicate a likelihood that the license plate ID obtained from the ALPR system is the same as the license plate ID that is represented in the one or more received images.” ¶52 discloses a threshold that determines whether the predicted make/model information matches the information from the image)
Regarding claim 20, Schmer discloses wherein the code to generate the prediction of the at least one data object are executable by the one or more processors to: (¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
transmit, to one or more vehicle identification services, the at least one image of the vehicle; and (¶7 “cause the processor to: receive, from a digital camera system, one or more images associated with a vehicle,”)
receive, from the one or more vehicle identification services, a prediction of a license plate string identified from the at least one image, wherein the prediction comprises the license plate string (¶20 “In one embodiment, the license plate ID that is received from the ALPR system comprises at least one of numbers, letters, numerals, and characters.”) and a confidence score associated with the prediction of the license plate string. (¶31 “The analysis system 120 may provide predicted license plate data and associated confidence level to the toll agency 170 via the network 150.”)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 2, 9, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Schmer (US 20210097300 A1) in view of Silva (US 20160300119 A1).
Regarding claim 2, Schmer discloses the claimed invention except for wherein creating, based at least in part on determining that the prediction of the at least one data object fails to match the vehicle profile, a new vehicle profile for the vehicle comprising at least one descriptor corresponding to the at least one data object; and
assigning a vehicle token to the vehicle profile, wherein communicating the result comprises storing the vehicle profile in a data store.
In related art, Silva discloses creating, based at least in part on determining that the prediction of the at least one data object fails to match the vehicle profile, a new vehicle profile for the vehicle comprising at least one descriptor corresponding to the at least one data object; and (Silva: ¶53 “Responsive to determining that the confidence threshold has been met (i.e., exceeded), the service 102 may generate a new database entry 310 based at least in part on the received roadside data 114,”)
assigning a vehicle token to the vehicle profile, wherein communicating the result comprises storing the vehicle profile in a data store. (Silva: ¶53 “Dynamic integrations of constantly received roadside data (e.g., roadside data 114), enables the continuous generation of new database entries 310, and the constant access to updated information.” )
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate updating the vehicle database disclosed by Silva into the method of identifying license plate, make, and model information disclosed by Schmer to create a more robust database containing license plate information.
Regarding claim 9, Schmer discloses the claimed invention except for wherein communicating the result of comprises:
transmitting, to the computing device, at least one of an identifier for the matched vehicle profile and an identifier for a generated new vehicle profile.
In related art, Silva discloses communicating the result of comprises: (Silva: ¶90 “the process 900 then includes determining…”)
transmitting, to the computing device, at least one of an identifier for the matched vehicle profile (Silva: ¶90 “after combining the recognized characters, if a database entry corresponding to the recognized characters already exists, a new database entry may not be necessary,”) and an identifier for a generated new vehicle profile. (Silva: ¶90 “the process 900 then includes determining if the received roadside data should be stored as a new database entry,”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate the identifying a profile match or generating a new profile disclosed by Silva into the method of identifying license plate, make, and model information disclosed by Schmer to update the vehicle database with new profiles without including duplicate information within the database.
Regarding claim 14, Schmer discloses wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to: (Schmer: ¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
Schmer fails to specifically disclose create, based at least in part on determining that the prediction of the at least one data object fails to match the vehicle profile, a new vehicle profile for the vehicle comprising at least one descriptor corresponding to the at least one data object; and
assign a vehicle token to the vehicle profile, wherein communicating the result comprises storing the vehicle profile in a data store.
In related art, Silva discloses create, based at least in part on determining that the prediction of the at least one data object fails to match the vehicle profile, a new vehicle profile for the vehicle comprising at least one descriptor corresponding to the at least one data object; and (Silva: ¶53 “Responsive to determining that the confidence threshold has been met (i.e., exceeded), the service 102 may generate a new database entry 310 based at least in part on the received roadside data 114,”)
assign a vehicle token to the vehicle profile, wherein communicating the result comprises storing the vehicle profile in a data store. (Silva: ¶53 “Dynamic integrations of constantly received roadside data (e.g., roadside data 114), enables the continuous generation of new database entries 310, and the constant access to updated information.” )
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate updating the vehicle database disclosed by Silva into the method of identifying license plate, make, and model information disclosed by Schmer to create a more robust database containing license plate information.
Regarding claim 19, Schmer discloses wherein the code is further executable by the one or more processors to: (¶7 “a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:”)
Schmer fails to specifically disclose create, based at least in part on determining that the prediction of the at least one data object fails to match the vehicle profile, a new vehicle profile for the vehicle comprising at least one descriptor corresponding to the at least one data object; and
assign a vehicle token to the vehicle profile, wherein communicating the result comprises storing the vehicle profile in a data store.
In related art, Silva discloses create, based at least in part on determining that the prediction of the at least one data object fails to match the vehicle profile, a new vehicle profile for the vehicle comprising at least one descriptor corresponding to the at least one data object; and (Silva: ¶53 “Responsive to determining that the confidence threshold has been met (i.e., exceeded), the service 102 may generate a new database entry 310 based at least in part on the received roadside data 114,”)
assign a vehicle token to the vehicle profile, wherein communicating the result comprises storing the vehicle profile in a data store. (Silva: ¶53 “Dynamic integrations of constantly received roadside data (e.g., roadside data 114), enables the continuous generation of new database entries 310, and the constant access to updated information.” )
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate updating the vehicle database disclosed by Silva into the method of identifying license plate, make, and model information disclosed by Schmer to create a more robust database containing license plate information.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Schmer (US 20210097300 A1) in view of Nijhuis (US 20190066492 A1).
Regarding claim 11, Schmer discloses the claimed invention except for wherein the identifiable features comprises hood structure of a hood of the vehicle.
In related art, Nijhuis discloses the identifiable features comprises hood structure of a hood of the vehicle. (Nijhuis: ¶27 “In another embodiment the trusted database includes non-character data that can uniquely identify the vehicle. Non-character data includes the…ornamentation on the vehicle, including bumpers and hood”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate using the hood of the vehicle as an identifiable feature disclosed by Nijhuis into the method of identifying license plate, make, and model information disclosed by Schmer to have more identifying information that could be used to recognize a vehicle’s make or model.
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hwang (US 20240412634 A1) discloses An edge device generates an exit event for a vehicle exiting a parking facility. The edge device determines whether the exit event matches with an entry event. Responsive to determining that the exit event does not match to an entry event, the edge device inputs images of the vehicle into a supervised machine learning model and receives, as output from the model, an exit feature vector. The edge device retrieves entry feature vectors corresponding to hanging entry events. A hanging entry event is an entry event for a vehicle with an unknown vehicle identifier. Edge device inputs the exit feature vector and the entry feature vectors into an unsupervised machine learning model and receives, as output from the model, matching scores for each entry feature vector. Edge device matches the exit event to one of the hanging entry events based on the matching scores.
Wilbert (US 10867327 B1) discloses A system and method is provided for automatically identifying a vehicle and facilitating a transaction related to the vehicle. The system includes a first computing apparatus having an image sensor that captures optical images of a vehicle and an interface that transmits the captured optical image. The system further includes a remote server that receives the transmitted and captured optical image, automatically scans the captured optical image to identify one or more distinguishing features of the vehicle, automatically compares the identified distinguishing features with a unique feature database that includes respective vehicle identification information associated with unique vehicle features, automatically identify the vehicle identification information that corresponds to the vehicle upon determining a match, automatically identify vehicle configuration information based on the identified vehicle identification information, and automatically transmit the identified vehicle configuration information to the first computing apparatus to be displayed thereon.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL KIM MAIDEN whose telephone number is (703)756-1264. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Koziol can be reached at 4089187630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL KIM MAIDEN/ Examiner, Art Unit 2665
/Stephen R Koziol/ Supervisory Patent Examiner, Art Unit 2665
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