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 .
Status of Claims
This office action is in response to the patent application 19/060,763 originally filed on February 23, 2025. Claims 1-20 are presented for examination. Claim 1 is independent.
Information Disclosure Statement
The Information Disclosure Statement filed on February 23, 2025 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Priority
This application is a continuation-in-part of PCT/IL2023/050892, filed August 22, 2023. This application claims priority of US Provisional Application 63/373,286, filed August 23, 2022.
Drawings
Regarding FIG. 10, 37 CFR 1.84(m), stated in part, prefers the use of shading when parts are shown in perspective. In the present case, the drawings use dashed lines and shading in an otherwise non-perspective view that would not be of sufficient quality so that all details in the drawings are reproducible in the printed patent. Therefore, the use of shading in an otherwise non-perspective view prevents FIG. 10 from complying with 37 CFR 1.84(m).
Claim Objections
Claims 1-20 are objected to because of the following informalities: typographical errors.
Claim 1 recites subgroupings of claim limitations under labels “1)”, “2)” “4)”, and “5)”. However, “3)” appears to be missing from the claim. The Examiner reasonably believes this is a typographical error and should be corrected, either by removing the numbered groupings entirely, or by changing “4)” and “5)” to “3)” and “4)”, respectively. Appropriate correction is required.
Dependent claims 2-20 are also objected to based on their respective dependencies to claim 1.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is directed to “a system” (i.e. a machine), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
However, the claims are drawn to an abstract idea of “driver monitoring” either in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Claims that require a computer may also recite a mental process, as described in MPEP 2106.04(a)(2)(III)(C).
Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
“storing:
i) an alert dataset defining alert codes corresponding to signals that vehicle telematics and driver monitoring sensors are configured to generate,
ii) a sequence dataset storing predefined alert patterns, each pattern including a specific ordering of alert codes, an associated maximum time duration constraint for pattern completion, and a numeric severity indicator, and
iii) a category dataset defining categories of sequences,
iv) an instructional dataset of instructional text associated with each of the sequence categories, and… instructions that … implement steps of:
1) receiving, for a given driver, streams of sensor-generated alert signals from one or more vehicle telematics systems, each alert signal comprising an alert code and an associated timestamp;
2) applying multiple sliding time windows over the alert signal streams to identify alert sequences, applying a pattern-matching algorithm to detect an occurrence of the identified alert sequences in the predefined alert patterns, according to the associated maximum time duration constraint for each respective pattern, and identifying a sequence category of the detected pattern;
4) according to a predefined severity of the detected pattern, increasing by a proportional amount a priority score of the identified sequence category, to generate an aggregate priority score for each sequence category for the given driver over a preset period of time;
5) for each of the sequence categories having priority scores above a preset threshold, extracting instructional text associated with said sequence category from the text dataset and providing said instructional text to the driver.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “a system,” “one or more processors,” and “non-transient memory” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “driver monitoring,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g., “a system,” “one or more processors,” and “non-transient memory” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Applicant’s specification discloses them in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “system” comprising “one or more processors” and “non-transient memory” is described in instant specification paragraph [0041] as follows: “The process is typically performed on a general purpose computer having one or more processors and non-transient memory communicatively coupled to the one or more processors.”
These elements are reasonably interpreted as a generic computer which provides no details of anything beyond ubiquitous standard equipment. As such, the claimed limitation of “a system” is reasonably understood as not providing anything significantly more. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-20 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-20 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to independent claim 1.
Therefore, claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
The factual inquiries 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 1-5, 13, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gaudin et al. (hereinafter “Gaudin,” US 2021/0053579) in view of (Li et al. hereinafter “Li,” US 2020/0126423).
Regarding claim 1, Gaudin discloses a system for driver monitoring (Gaudin [0050], “evaluate driving behavior for a particular driving activity”) comprising:
one or more processors and non-transient memory coupled to the one or more processors and storing (Gaudin [0052], “Data analysis unit 74 may include one or more processors, or software instructions that are executed by one or more processors of computing system 16, and may be configured to process the data collected by data collection unit 70 and stored in memory 72 for various purposes”):
i) an alert dataset defining alert codes corresponding to signals that vehicle telematics and driver monitoring sensors are configured to generate (Gaudin [0060], “Driving behavior identification unit 80 may generally configured to receive and/or process historical driving data received from stored historical data 78. The historical driving data may include a history of at least one driving activity aided by activation of an alert from the ADAS feature that has been deactivated for the operator of vehicle 12. It may contain records of measurement data that have caused ADAS alerts to have been generated in the past.”),
ii) a sequence dataset storing predefined alert patterns, each pattern including a specific ordering of alert codes, an associated maximum time duration constraint for pattern completion, and a numeric severity indicator (Gaudin [0070-0071], “each of one or more driving behaviors characterized by driving behavior identification unit 80 may be associated with tags or other metadata indicating the circumstances in which the driving behavior occurred. As briefly described above, driving behavior identification unit 80 may identify the contextual data generated by ADAS 22 as the data associated with a tagged first timestamp at the time ADAS 22 activated an alert or autonomous action, and may identify the reaction data generated by subsystems 40, 42, and/or 44 as the data associated with a tagged second timestamp that is just after the first timestamp (within a pre-determinable threshold)… Driving behavior identification unit 80 may use other tags or other metadata associated with the contextual data and reaction data in order to pair them as a set to characterize driving behavior in response to an ADAS alert. For example, driving behavior identification unit 80 may identify contextual data and reaction data that are both associated with an operator's name or other identifier, in addition to the first and second timestamps, to character the particular driver's driving behavior in response to an ADAS alert. As another example, driving behavior identification unit 80 may identify contextual data and reaction data that are both associated with a location, in addition to the first and second timestamps, to character the general driving behavior (i.e., not to a particular driver) in response to an ADAS alert at the location,” showing specific ordering of alert codes and a threshold time duration constraint; also Gaudin [0108], “When ADAS likelihood indicator 88 calculates the likelihood level, various profile information types and/or categories may be more heavily weighted than others. For example, responsiveness to ADAS alerts may be weighted more heavily than weather-specific windshield wiper usage,” quantifying severity), and
iii) a category dataset defining categories of sequences (Gaudin [0062], “Contextual data, which may be generated by subsystems like subsystems 40, 42, 44, and/or 46 of FIG. 1, may indicate driving behaviors (e.g., speeding, accelerating, braking, lane shifting, weaving patterns, etc.) that caused either activation of the alert,” wherein the driving behaviors are defined categories of sequences),
iv) an instructional dataset of instructional text associated with each of the sequence categories (Gaudin [0100], “Feature usage information 154 may include forward collision warning feature usage, a blind spot indication feature usage, a cruise control feature usage, a lane departure warning feature usage, automatic high beam usage, and/or other ADAS feature usage,” instructional text from identified categories), and
v) computer-readable instructions that when executed cause the system to implement steps of (Gaudin [0008], “a non-transitory, computer-readable medium (or media) stores instructions that, when executed by one or more processors, cause the one or more processors to…”):
1) receiving, for a given driver, streams of sensor-generated alert signals from one or more vehicle telematics systems, each alert signal comprising an alert code and an associated timestamp (Gaudin [0070], “each of one or more driving behaviors characterized by driving behavior identification unit 80 may be associated with tags or other metadata indicating the circumstances in which the driving behavior occurred. As briefly described above, driving behavior identification unit 80 may identify the contextual data generated by ADAS 22 as the data associated with a tagged first timestamp at the time ADAS 22 activated an alert or autonomous action, and may identify the reaction data generated by subsystems 40, 42, and/or 44 as the data associated with a tagged second timestamp that is just after the first timestamp (within a pre-determinable threshold)”);
2) applying multiple sliding time windows over the alert signal streams to identify alert sequences, applying a pattern-matching algorithm to detect an occurrence of the identified alert sequences in the predefined alert patterns, according to the associated maximum time duration constraint for each respective pattern, and identifying a sequence category of the detected pattern (Gaudin [0093], “driving behaviors identification unit 80, or another unit of data analysis unit 74, may determine acceleration, braking, weaving patterns, and ADAS usage patterns by analyzing acceleration, braking, weaving data, and ADAS data from operational data 102 of FIG. 2 over a period of time”; also Gaudin [0096], “driving behaviors identification unit 80, or another unit of data analysis unit 74, may correlate the sensor data 104 of FIG. 2 to the speed, acceleration, braking, weaving, ADAS data and other data from operational data 102 of FIG. 2 over a period of time”);
…
5) for each of the sequence categories having priority scores above a preset threshold, extracting instructional text associated with said sequence category from the text dataset and providing said instructional text to the driver (Gaudin [0079], “Based upon the comparison, ADAS likelihood indicator 88 may determine a likelihood level for the operator in accordance with a threshold tolerance configured in ADAS likelihood indicator 88. For instance, the likelihood level may indicate that it was highly likely that the vehicle 12 may have provided a lane departure alert had ADAS 22 been activated (e.g., 100 is above a threshold tolerance). ADAS likelihood indicator 88 may send the likelihood level to the profile generation/update unit 82 for the profile of the operator to update or be set with the likelihood level accordingly. In some embodiments, the ADAS likelihood indicator 88 may generate a notification including the likelihood level (e.g., that the number of alerts exceeded the threshold tolerance) for display, such as at a monitor (not shown in FIG. 1) of computer system 16 and/or on-board system 14.”).
Gaudin does not explicitly teach every limitation of according to a predefined severity of the detected pattern, increasing by a proportional amount a priority score of the identified sequence category, to generate an aggregate priority score for each sequence category for the given driver over a preset period of time.
However, Li discloses according to a predefined severity of the detected pattern, increasing by a proportional amount a priority score of the identified sequence category, to generate an aggregate priority score for each sequence category for the given driver over a preset period of time (Li [0033-0034], “a ranking or score of the drive summary can be generated based on the contents of the summary such as the frequency of potential hazards, near misses, speeding, contact or potential contact with nearby objects, tailgating, or any other alerts that can be subject to the driver summary discussed above. The score can also be based on the magnitude of each type of alert. For example, an alert for a potential contact between the vehicle and another vehicle can have a different score depending on how close, in distance the potential contact was detected. A closer distanced near miss can have a different score than a further distanced near miss. In one example, both the frequency of alerts and the magnitude of the alerts can be calculated to generate the score of the drive summary… the summary can include multiple scores based on different types of alerts. For example, the frequency and magnitude of near misses (discussed in the paragraph above) can determine a score of the driver's ability to keep a safe distance in the driving session. The frequency or magnitude of speeding above a predetermined speed in a given location or above or below another type of threshold can determine a separate score of the driver's ability to keep a safe speed. Other types of alerts can be used to generate other types of scores based on that specific type of alert.”).
Li is analogous to Gaudin, as both are drawn to the art of driver monitoring. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Gaudin, to include according to a predefined severity of the detected pattern, increasing by a proportional amount a priority score of the identified sequence category, to generate an aggregate priority score for each sequence category for the given driver over a preset period of time, as taught by Li, since it applies a known technique of driver scoring to a known system ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 2, Gaudin does not explicitly teach wherein the instructional text is provided in an order, according to the aggregate priority score of the associated sequence category.
However, Li discloses wherein the instructional text is provided in an order, according to the aggregate priority score of the associated sequence category (Li [0033], “a ranking or score of the drive summary can be generated based on the contents of the summary such as the frequency of potential hazards, near misses, speeding, contact or potential contact with nearby objects, tailgating, or any other alerts that can be subject to the driver summary discussed above. The score can also be based on the magnitude of each type of alert.”).
Li is analogous to Gaudin, as both are drawn to the art of driver monitoring. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Gaudin, to include wherein the instructional text is provided in an order, according to the aggregate priority score of the associated sequence category, as taught by Li, since it applies a known technique of driver scoring to a known system ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 3, Gaudin does not explicitly teach wherein the severity of each alert sequence is a value correlated with a risk of accident or with a risk of traffic violation.
However, Li discloses wherein the severity of each alert sequence is a value correlated with a risk of accident or with a risk of traffic violation (Li [0033], “The score can also be based on the magnitude of each type of alert. For example, an alert for a potential contact between the vehicle and another vehicle can have a different score depending on how close, in distance the potential contact was detected. A closer distanced near miss can have a different score than a further distanced near miss.”).
Li is analogous to Gaudin, as both are drawn to the art of driver monitoring. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Gaudin, to include wherein the severity of each alert sequence is a value correlated with a risk of accident or with a risk of traffic violation, as taught by Li, since it applies a known technique of driver scoring to a known system ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 4, Gaudin does not explicitly teach wherein generating the aggregate priority score for each sequence category further comprises calculating driver progress parameters on a recurring basis for the given driver.
However, Li discloses wherein generating the aggregate priority score for each sequence category further comprises calculating driver progress parameters on a recurring basis for the given driver (Li [0042], “the mobile device 102 and/or server 104 may track the improvement of a particular driver over time. For example, the reduction in the number of alerts and potential hazards may be tracked longitudinally to determine that the number is decreasing from earlier driving sessions to more recent driving sessions.”).
Li is analogous to Gaudin, as both are drawn to the art of driver monitoring. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Gaudin, to include wherein generating the aggregate priority score for each sequence category further comprises calculating driver progress parameters on a recurring basis for the given driver, as taught by Li, since it applies a known technique of driver scoring to a known system ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 5, Gaudin in view of Li discloses wherein calculating the aggregate priority score for each sequence category further comprises determining a driver profile with a weighted distribution of attribute categories according to preset correlations between the sequence categories and the attribute categories (Gaudin [0108], “When ADAS likelihood indicator 88 calculates the likelihood level, various profile information types and/or categories may be more heavily weighted than others. For example, responsiveness to ADAS alerts may be weighted more heavily than weather-specific windshield wiper usage. Generally, specific types of profile information may be used to determine the likelihood level if it is known a priori (e.g., from past correlations with driver actions) or believed that those types of information are probative of how trustworthy or responsible the operator is.”).
Regarding claim 13, Gaudin does not explicitly teach wherein a subset of the alert sequences in the sequence dataset are close-call events having a high correlation with accident risk and the steps further comprise providing the close-call events to the driver with the instructional text.
However, Li discloses wherein a subset of the alert sequences in the sequence dataset are close-call events having a high correlation with accident risk and the steps further comprise providing the close-call events to the driver with the instructional text (Li [0034], “the summary can include multiple scores based on different types of alerts. For example, the frequency and magnitude of near misses (discussed in the paragraph above) can determine a score of the driver's ability to keep a safe distance in the driving session. The frequency or magnitude of speeding above a predetermined speed in a given location or above or below another type of threshold can determine a separate score of the driver's ability to keep a safe speed. Other types of alerts can be used to generate other types of scores based on that specific type of alert.”).
Li is analogous to Gaudin, as both are drawn to the art of driver monitoring. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Gaudin, to include wherein a subset of the alert sequences in the sequence dataset are close-call events having a high correlation with accident risk and the steps further comprise providing the close-call events to the driver with the instructional text, as taught by Li, since it applies a known technique of driver scoring to a known system ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 15, Gaudin in view of Li discloses wherein the alerts include one or more of vehicle speed, a vehicle-to-vehicle distance, an intersection approach, a pedestrian distance, a severe steering alert, a severe braking alert, a severe bypassing alert, a traffic light or traffic sign violations, a forward collision, an accident, or an impact (Gaudin [0041], “ADAS 22 may also generate contextual data that describes characteristics of driving behavior (e.g., speeding, accelerating, braking, lane shifting, weaving patterns, cornering, etc.) that led to either activation of the alert or the autonomous action”).
Regarding claim 17, Gaudin in view of Li discloses wherein the stream of alerts is associated with a location of occurrence, wherein each alert sequence in the sequence dataset includes one or more road parameters, and wherein identifying each alert sequence further comprises acquiring one or more road parameters associated with the location of occurrence (Gaudin [0040], “the GPS subsystem 48 may generate data indicative of a current location of vehicle 12”; also Gaudin [0071], “driving behavior identification unit 80 may identify contextual data and reaction data that are both associated with a location”; also Gaudin [0090], “third party data 114 may include data indicative of traffic conditions, speed limits, and/or road conditions, which may be obtained from a governmental entity, an entity that provides a mapping service, or another entity.”).
Regarding claim 18, Gaudin in view of Li discloses wherein the alerts are acquired in post processing, with video analysis of a video log of a drive configured to generate at least some of the alerts.
However, Li discloses wherein the alerts are acquired in post processing, with video analysis of a video log of a drive configured to generate at least some of the alerts (Li [0019], “Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event.”).
Li is analogous to Gaudin, as both are drawn to the art of driver monitoring. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Gaudin, to include wherein the alerts are acquired in post processing, with video analysis of a video log of a drive configured to generate at least some of the alerts, as taught by Li, since it applies a known technique of driver scoring to a known system ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 19, Gaudin in view of Li discloses wherein the alerts are acquired as a log history including multiple sequential alerts (Gaudin [0076], “The historical driving data includes a history of at least one driving activity aided by activation of the alert from the ADAS feature that has been deactivated (or inoperable) at vehicle 12, as indicated in the measurements data, for the operator and/or a plurality of drivers.”).
Regarding claim 20, Gaudin in view of Li discloses wherein accessing predefined datasets further comprises adding sequences to the dataset by determining that an alert sequence not stored in the sequence dataset is relevant as an indicator of driving behavior, by determining by a pattern matching algorithm that the alert sequence includes combinations of alerts and external events similar to combinations in existing sequences (Gaudin [0092], “FIG. 3 depicts exemplary profile information categories 150 that may be determined by the system 10 of FIG. 1 (e.g., by data analysis unit 74) when identifying driving behavior patterns and/or generating or modifying an operator profile, such as an operator profile included in driver profiles database 76 of computer system 16.”).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Gaudin in view of Li, and in further view of Srivastava et al. (hereinafter “Srivastava,” US 2022/0013006).
Regarding claim 16, Gaudin in view of Li does not teach wherein the alerts include one or more driver distraction indicators including phone use, drowsiness, smoking, eating and a drinking.
However, Srivastava discloses wherein the alerts include one or more driver distraction indicators including phone use, drowsiness, smoking, eating and a drinking (Srivastava [0104], “For example, alerts may be generated from Driver Monitoring Systems (DMS) that detect driver behavioral events, such as distracted-driver events, impaired-driver events, phone usage, smoking, and drowsiness.”).
Srivastava is analogous to Gaudin in view of Li, as both are drawn to the art of driver monitoring. It would be obvious to try by one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as taught by Gaudin in view of Li, to include wherein the alerts include one or more driver distraction indicators including phone use, drowsiness, smoking, eating and a drinking, as taught by Srivastava, since it would have combined prior art elements of particular types of alerts according to known methods to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen Alvesteffer whose telephone number is (571)272-8680. The examiner can normally be reached M-F 8:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat can be reached at 571-270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEPHEN ALVESTEFFER/Examiner, Art Unit 3715
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