DETAILED ACTION
This is a non-final, first office action on the merits. Claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of Patent No. 19/237,890.
This is a provisional double patenting rejection since the conflicting claims have not yet been patented.
Claims 1-20 of the instant application are substantially similar to claims 1-20 of the patents 12,056,722, 12,026,729, & 12,367,504, & 19/233,801. Accordingly the independent claims of the instant application are obvious variants of those recited in the '12,288,169, US12,443,899, 19/328,019, and 19/543,720 patents. Accordingly the independent claims of the instant application are obvious variants of those recited in the 12,288,169, US12,443,899, 19/328,019, and 19/543,720 patent.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1, 8, and 15 recite an abstract idea. Claims 1, 8, and 15 include “collecting a first set of operator data associated with a first group of vehicle operators, wherein the first set of operator data comprise a first set of sensor data and a first set of user management data associated with a first user management model implemented by a marketplace participant to manage the first group of vehicle operators as users; collecting a second set of operator data associated with a second group of vehicle operators, wherein the second set of operator data comprise a second set of sensor data and a second set of user management data associated with a second user management model implemented by the marketplace participant to manage the second group of vehicle operators as users; determining and updating a first set of telematics inferences based at least on the first set of sensor data; determining and updating a second set of telematics inferences based at least on the second set of sensor data; determining a first model evaluation based at least on the first set of operator data and the first set of telematics inferences; determining a second model evaluation based at least on the second set of operator data and the second set of telematics inferences; and transmitting the first model evaluation and the second model evaluation to the marketplace participant”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and commercial interactions (including advertising, marketing or sales activities or behaviors; business relations) because the elements describe a process for transmitting first and second model evaluations. As a result, claims 1, 8, and 15 recite an abstract idea under Step 2A Prong One.
Claims 2-7, 9-14, and 16-20 further describe the process for transmitting first and second model evaluations. As a result, claims 2-7, 9-14, and 16-20 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 8, and 15.
With respect to Step 2A Prong Two of the framework, claims 1, 8, and 15 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 8, and 15 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 8, and 15 include one or more processors and one or more non-transitory computer-readable media. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 8, and 15 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 4-7, 11-14, and 17-20 do not include any additional elements beyond those recited with respect to claims 1, 8, and 15. As a result, claims 4-7, 11-14, and 17-20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 8, and 15.
Claims 2-3, 9-10, and 16 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2-3, 9-10, and 16 include a common mobile application, a system software application, an entertainment software application, a gaming software application, a navigation software application, and an environment software application. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 2-3, 9-10, and 16 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 1, 8, and 15 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 8, and 15 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 8, and 15 include one or more processors and one or more non-transitory computer-readable media. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 8, and 15 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 4-7, 11-14, and 17-20 do not include any additional elements beyond those recited with respect to claims 1, 8, and 15. As a result, claims 4-7, 11-14, and 17-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 8, and 15.
Claims 2-3, 9-10, and 16 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2-3, 9-10, and 16 include a common mobile application, a system software application, an entertainment software application, a gaming software application, a navigation software application, and an environment software application. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 2-3, 9-10, and 16 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hallac et al. (US Pat No. 11,676,014) (hereinafter Hallac et al.) in view of Bowne et al. (US Pub No. 2013/0179198) (hereinafter Bowne et al.).
Regarding claims 1, 8, and 15, Hallac discloses a computer-implemented method comprising:
Regarding claims 8 and 15, recites additional features one or more processors and one or more non-transitory computer readable media (see Hallac, column 22, lines 16-27, wherein computer processor…..machine executable instructions are stored on memory 610. Non-Transitory Computer Readable Storage Medium).
collecting a first set of operator data associated with a first group of vehicle operators, wherein the first set of operator data comprise a first set of sensor data and a first set of user management data associated with a first user management model implemented by a marketplace participant to manage the first group of vehicle operators as users (see Hallac, column 4, lines 1-39, wherein a unique driver fingerprint for the individual based on the weights for novel telematics data generated at a plurality of sensors of the vehicle. In some embodiments, the telematics data originates at a plurality of vehicle sensors connected to the vehicle's CAN bus……the collecting the telematics data occurs at least every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second, including increments therein. In further embodiments, the collecting the telematics data occurs substantially continuously; column 1, lines 1-4, wherein an operator fingerprint must be able to distinguish unique characteristics among different operators or vehicles to effectively identify an individual operator based on their operating patterns; column 17, lines 360-65, wherein a software module utilizing the driver and/or vehicle fingerprint to determine an insurance pricing factor for the individual. In some embodiments, the example process comprises a software module utilizing the driver and/or vehicle fingerprint to detect changes in driving behavior of the individual; column 3, lines 45-49, wherein authenticate the individual in a payment system, determine an insurance pricing factor for the individual, detect changes in driving behavior of the individual, and personalize vehicle settings for the individual; and column 15, lines 31-41, wherein being able to accurately identify operator behaviors has profound implications for many industries. For example, marketplace participants automobile insurers would be able to give better deals on a usage-based occasional driver insurance policy. Moreover, insurers could evaluate driver behavior and track how often the insured vehicle is driven by the occasional driver right from the telematics data);
collecting a second set of operator data associated with a second group of vehicle operators, wherein the second set of operator data comprise a second set of sensor data and a second set of user management data associated with a second user management model implemented by the marketplace participant to manage the second group of vehicle operators as users (see Hallac, column 7, lines 33-35, wherein periodically collecting, by a computer, telematics data generated at a plurality of sensors of a vehicle…..; column 15, lines 28-30, wherein a new fingerprint is generated at every timestamp in a collected telematics dataset; column 4, lines 1-39, wherein a unique driver fingerprint for the individual based on the weights for novel telematics data generated at a plurality of sensors of the vehicle. In some embodiments, the telematics data originates at a plurality of vehicle sensors connected to the vehicle's CAN bus……the collecting the telematics data occurs at least every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second, including increments therein. In further embodiments, the collecting the telematics data occurs substantially continuously; column 1, lines 1-4, wherein an operator fingerprint must be able to distinguish unique characteristics among different operators or vehicles to effectively identify an individual operator based on their operating patterns; column 17, lines 360-65, wherein a software module utilizing the driver and/or vehicle fingerprint to determine an insurance pricing factor for the individual. In some embodiments, the example process comprises a software module utilizing the driver and/or vehicle fingerprint to detect changes in driving behavior of the individual; and column 15, lines 31-41, wherein being able to accurately identify operator behaviors has profound implications for many industries. For example, marketplace participants automobile insurers would be able to give better deals on a usage-based occasional driver insurance policy. Moreover, insurers could evaluate driver behavior and track how often the insured vehicle is driven by the occasional driver right from the telematics data);
determining and updating a first set of telematics inferences based at least on the first set of sensor data (see Hallac, column 2, lines 43-55, wherein a software module periodically receiving telematics data generated at a plurality of sensors of a vehicle; a software module standardizing the telematics data; a software module aggregating the standardized telematics data….iteratively repeated to update the dynamic component of the driver fingerprint. In some embodiments, the telematics data originates at a plurality of vehicle sensors connected to the vehicle's CAN bus…; and column 3, lines 65-67 & column 4, lines 1-5, wherein inferring, by the computer or the vehicle, a unique driver fingerprint for the individual based on the weights for novel telematics data generated at a plurality of sensors of the vehicle. In some embodiments, the telematics data originates at a plurality of vehicle sensors connected to the vehicle's CAN bus);
determining and updating a second set of telematics inferences based at least on the second set of sensor data (see Hallac, column 18, lines 30-34, wherein continuously generate/update the fingerprints for the operators and/or vehicles as additional telematics data is collected for the respective vehicle sensors…; and column 3, lines 65-67 & column 4, lines 1-5, wherein inferring, by the computer or the vehicle, a unique driver fingerprint for the individual based on the weights for novel telematics data generated at a plurality of sensors of the vehicle. In some embodiments, the telematics data originates at a plurality of vehicle sensors connected to the vehicle's CAN bus);
determining a first model evaluation based at least on the first set of operator data and the first set of telematics inferences (see Hallac, column 15, lines 31-37, wherein being able to accurately identify operator behaviors has profound implications …..Moreover, insurers could evaluate driver behavior and track how often the insured vehicle is driven by the occasional driver right from the telematics data; ; and column 3, lines 65-67 & column 4, lines 1-5, wherein inferring, by the computer or the vehicle, a unique driver fingerprint for the individual based on the weights for novel telematics data generated at a plurality of sensors of the vehicle. In some embodiments, the telematics data originates at a plurality of vehicle sensors connected to the vehicle's CAN bus);
determining a second model evaluation based at least on the second set of operator data and the second set of telematics inferences (see Hallac, column 14, lines 1-14, wherein machine learning algorithms can be trained with, for example, telematics data to determine a fingerprint for an operator of a vehicle, which may be subsequently employed within various decision-making processes…..; and column 18, lines 30-34, wherein continuously generate/update the fingerprints for the operators and/or vehicles as additional telematics data is collected for the respective vehicle sensors…; and column 3, lines 65-67 & column 4, lines 1-5, wherein inferring, by the computer or the vehicle, a unique driver fingerprint for the individual based on the weights for novel telematics data generated at a plurality of sensors of the vehicle. In some embodiments, the telematics data originates at a plurality of vehicle sensors connected to the vehicle's CAN bus); and
transmitting the first model evaluation and the second model evaluation to the marketplace participant (see Hallac, column 5, lines 14-19, wherein receive the transmitted weights; and infer a unique driver fingerprint for the individual based on the transmitted weights for novel telematics data generated at a plurality of sensors of the vehicle, the driver fingerprint comprising a static component and/or a dynamic component; column 15, lines 31-41, wherein being able to accurately identify operator behaviors has profound implications for many industries. For example, marketplace participants automobile insurers would be able to give better deals on a usage-based occasional driver insurance policy. Moreover, insurers could evaluate driver behavior and track how often the insured vehicle is driven by the occasional driver right from the telematics data; and column 17, lines 360-65, wherein a software module utilizing the driver and/or vehicle fingerprint to determine an insurance pricing factor for the individual. In some embodiments, the example process comprises a software module utilizing the driver and/or vehicle fingerprint to detect changes in driving behavior of the individual).
Hallac et al. fails to explicitly disclose transmitting the first model evaluation and the second model evaluation to the marketplace participant.
Analogous art Bowne discloses transmitting the first model evaluation and the second model evaluation to the marketplace participant (see Bowne, para [0209], wherein vehicle users to register their mobile devices 10 and download the application 50 so as to take advantage of use-based insurance products, wireless service providers may discount service provider premiums in exchange for users registering their mobile devices 10 for use-based insurance. Wireless service providers and carriers currently offer a number of discounts and/or subsidizing programs for phone and data plans. Participants in use-based insurance programs may be offered discounts or subsidized programs relative to their phone or data plans; and para [0048], wherein calculate one or more driving behavior metrics and/or scores based on such collected driving data).
Hallac directed to a system for receiving telematics data generated at a plurality of sensors of a vehicle. Bowne directed to determining a vehicle insurance premium based at least in part on collected vehicle operation data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hallac, regarding the System for Applying Machine Learning to Telematics Data to Generate Vehicle Fingerprint, to have included transmitting the first model evaluation and the second model evaluation to the marketplace participant because both inventions teach improving driving safety. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 2, 9, and 16, Hallac discloses the computer-implemented method of claim 1, wherein the first set of sensor data and the second set of sensor data are collected via a common mobile application (see Hallac, column 13, lines 37-41, wherein system employs mobile sensing technologies to collect, store, and process telematics data from built-in or external sensors of a mobile device, such as a smartphone. In some embodiments, the described system collects telematics data when a vehicle in in ).
Regarding claims 3, 10, and 16, Hallac discloses the computer-implemented method of claim 2, wherein the common mobile application comprises one of a system software application, an entertainment software application, a gaming software application, a navigation software application, or an environment software application (see Hallac, column 13, lines 37-41, wherein system employs mobile sensing technologies to collect, store, and process telematics data from built-in or external sensors of a mobile device, such as a smartphone. In some embodiments, the described system collects telematics data when a vehicle in in; and column 13, lines 45-46).
Regarding claims 4, 11, and 17, Hallac discloses the computer-implemented method of claim 1, wherein:
the first user management model and the second user management model have a common type; and the common type is one of pricing models, incentive models, user-retention models, or customer service models (see Hallac, column 1, lines 47-50, wherein an operator fingerprint can be used to verify that a particular operator is operating the vehicle to authenticate a credit card payment using an in-vehicle payment system; and column 3, lines 45-49, wherein authenticate the individual in a payment system, determine an insurance pricing factor for the individual, detect changes in driving behavior of the individual, and personalize vehicle settings for the individual).
Regarding claims 5, 12, and 18, Hallac discloses the computer-implemented method of claim 1 further comprising:
determining one or more model modifications for the first user management model based at least on the first model evaluation (see Hallac, column 5, lines 61-67 & column 6, lines 1-14, wherein the model weights are iteratively updated at least every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second, including increments therein. In further embodiments, the model weights are iteratively substantially continuously; and column 3, lines 45-49, wherein authenticate the individual in a payment system, determine an insurance pricing factor for the individual, detect changes in driving behavior of the individual, and personalize vehicle settings for the individual); and
Hallac et al. fails to explicitly disclose transmitting the one or more model modifications to the marketplace participant.
Analogous art Bowne discloses transmitting the one or more model modifications to the marketplace participant (see Bowne, para [0209], wherein vehicle users to register their mobile devices 10 and download the application 50 so as to take advantage of use-based insurance products, wireless service providers may discount service provider premiums in exchange for users registering their mobile devices 10 for use-based insurance. Wireless service providers and carriers currently offer a number of discounts and/or subsidizing programs for phone and data plans. Participants in use-based insurance programs may be offered discounts or subsidized programs relative to their phone or data plans; and para [0205], wherein based on the collected data, a previously paid insurance premium may be adjusted by providing a rebate for low risk driving behaviors or charging a surcharge for high risk driving behaviors).
One of ordinary skill in the art would have recognized that applying the known technique of Bowne would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claims 6, 13, and 19, Hallac discloses the computer-implemented method of claim 5, wherein the one or more model modifications comprise one or more of increasing a policy premium, reducing a risk allowance, extending a user incentive, or issuing a user challenge (see Hallac, column 1, lines 64-67, wherein determining an operator's or vehicle's fingerprint is a challenge because it must satisfy a number of requirements, while also aggregating large amounts of telematics data into, for example, a low-dimensional representation).
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hallac et al. (US Pat No. 11,676,014) (hereinafter Hallac et al.), in view of Bowne et al. (US Pub No. 2013/0179198) (hereinafter Bowne et al.), and further in view of Khoury et al. (US Pub No. 2017/0255966) (hereinafter Khoury et al.).
Regarding claims 7, 14, and 20, Hallac discloses the computer-implemented method of claim 1, wherein the first set of user management data, as set forth above with claim 1.
Hallac et al. and Bowne et al. combined fail to explicitly disclose comprise one or more of historic customer service expenses, historic user experience costs, historic user acquisition costs, historic user retention costs, historic claim losses, or historic referral revenue.
Analogous art Khoury discloses one or more of historic customer service expenses, historic user experience costs, historic user acquisition costs, historic user retention costs, historic claim losses, or historic referral revenue (see Khoury, para [0011], wherein loss ratio from general expected liability or property claims ... historical claims ... associated claims ... actual loss and claims data being provided to cause the determined insurance risk ... to correspond more closely to an actual insurance risk as calculated from actual loss and claims data provided over time therefore the system obtains historic claim losses to determine/predict loss ratio and risk/profitability; para [0035], wherein total cost from accident and liability claims; para [0045], wherein data provided by products and service providers such as actual profitability of drivers ... claims data; and paras [0135]-[0137], wherein feedback loop provides actual profitability ... using those actual values ... loss ratio ... claims ... the cost potential claim ... predictors of the future insurance cost (loss ratio) and the intermediary cost ... weight ... feedback loop of actual claims ... revised weights ... predictor of the claims).
Hallac directed to a system for receiving telematics data generated at a plurality of sensors of a vehicle. Khoury directed to driving information and classifying drivers and self-driving systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Hallac, regarding the System for Applying Machine Learning to Telematics Data to Generate Vehicle Fingerprint, to have included one or more of historic customer service expenses, historic user experience costs, historic user acquisition costs, historic user retention costs, historic claim losses, or historic referral revenue because both inventions teach improving driving safety. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2017/0057411; US Pat No. 10,089,692; US Pub No. 2006/0053038; US Pub No. 2023/0214916; US Pat No. 11,087,209; US Pub No. 2019/0102840; US Pub No. 2014/0350970; US Pub No. 2009/0109037; US Pub No. 2014/0195272; US Pat No. 10,417,714; US Pat No. 10,127,570; US Pub No. 2022/0136843; US Pub No. 2018/0181860; US Pub No. 2015/0294422; and YL Ma, X Zhu, X Hu, YC Chiu (The use of context-sensitive insurance telematics data in auto insurance rate making) Transportation Research Part A: Policy and Practice, 2018•Elsevier.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAFIZ A KASSIM whose telephone number is (571)272-8534. The examiner can normally be reached 9:00 - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 09/08/2026
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