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 .
Claim Status
Claims 1-11 are pending for examination in the application filed 04/29/2026. Claims 1-3, 5, 8, and 10 are currently amended.
Priority
Acknowledgement is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent application JP2021-079992 filed on 05/10/2021. Acknowledgement is additionally made of the present application as a national stage entry of PCT/JP2022/019602, international filing date: 05/06/2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/30/2026 and 05/07/2026 have been considered by the examiner.
Response to Arguments and Amendments
The 35 U.S.C. 112(f) interpretation is withdrawn in view of the amendments. The 35 U.S.C. 112(b) rejections of claims 3-6, 8-9, and 10-11 are withdrawn in view of the amendments. Applicant’s arguments with to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument, as facilitated by the newly added amendments.
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.
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 1-4, 6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Mantyjarvi (US20210142526A1) in view of Kwon (US20150314785A1).
Regarding claim 1, Mantyjarvi teaches an observation device comprising: a sensor configured to observe a predetermined area ([0019] Some embodiments of the apparatus further may include: a set of sensors; a blind area prediction module configured to identify potential blind areas; a driving mode selection module configured to select a driving mode responsive to a comparison of the potential blind areas with a visibility area threshold; a communication module configured to receive vehicle-to-vehicle (V2V) messages; and an augmented reality (AR) display device. [0028] FIG. 2 is a picture illustration depicting an example illustration of an autonomous vehicle sensing a surrounding environment according to some embodiments. [0077] Modern self-driving cars may use Bayesian simultaneous localization and mapping (SLAM) algorithms, which fuse data from multiple sensors and an off-line map into current location estimates and map updates. SLAM may be combined with detection and tracking of other moving objects (DATMO), which handles the recognition of objects such as cars and pedestrians. Simpler systems may use roadside real-time locating system (RTLS) beacon systems to aid localization. [0071] The peripherals 138 may include one or more sensors, the sensors may be one or more of a camera);
storage configured to store a visibility distance, the visibility distance being a maximum distance at which an observation target is identifiable within the predetermined area ([0088] Typical detection ranges, for example, may be 150 m for vehicles or up to 50 m for pedestrians, although other ranges and distances may be used. Detection ranges may vary according to the size and reflectivity of the object and current environmental factors (e.g., humidity, rain, fog, snow, or hail, as just some examples). [0104] the limited sensor visibility prediction area module 502 computes a level of reduction of effective sensor range due to e.g., road geometry, sensor placement, and other visibility factors like the weather and lighting conditions. [0134] A visibility requirements determination may use the information about oncoming traffic and other information related to ability to maneuver (which may have been gathered previously). In some embodiments, determining minimum visibility requirements may include receiving minimum visibility requirements (or minimum sensor visibility requirements) from another networked entity and retrieving minimum visibility requirements from a storage device (e.g., a database). In some embodiments, determining may include, e.g., calculating locally minimum visibility requirements prior to travel and/or automatically calculating (and updating) during travel);
and a controller configured to identify the distance to the position of the observation target on a basis of an observation result from the observation unit, and use the identified distance as a basis for updating the visibility distance stored in the storage ([0077] Autonomous vehicles (which may include cars) generally may have control systems that are capable of analyzing sensory data to distinguish between different cars, motorcycles, bikes, and pedestrians on the road, which is very useful in planning a safe path to a desired destination. Modern self-driving cars may use Bayesian simultaneous localization and mapping (SLAM) algorithms, which fuse data from multiple sensors and an off-line map into current location estimates and map updates. SLAM may be combined with detection and tracking of other moving objects (DATMO), which handles the recognition of objects such as cars and pedestrians. Simpler systems may use roadside real-time locating system (RTLS) beacon systems to aid localization. [0103] The sensor and communication module 508 may send 510 data comprising a present sensor field-of-view, effective range, vehicle location, vehicle speed, and any local dynamic map updates to the limited sensor visibility area module 502. [0104] Furthermore, the limited sensor visibility prediction area module 502 computes a level of reduction of effective sensor range due to e.g., road geometry, sensor placement, and other visibility factors like the weather and lighting conditions. [0110] The limited sensor visibility area prediction module 602 may continually evaluate 620 blind areas along the route. The evaluation may take into account a real-time stream of sensor data from the sensor information module 608 and the evaluation may be carried out continually in order to keep the blind area prediction up to date).
Mantyjarvi does not explicitly teach use the identified distance as a basis for updating the visibility distance stored in the storage by replacing a current value of the visibility distance stored in the storage with a replacement distance that the controller approves based on a maximum value among distances detected with respect to the observation target.
Kwon, in the same field of endeavor of vehicle observation devices, teaches use the identified distance as a basis for updating the visibility distance stored in the storage by replacing a current value of the visibility distance stored in the storage with a replacement distance that the controller approves based on a maximum value among distances detected with respect to the observation target ([0041] The updating unit 22 updates the maximum detection distance between the stationary object monitored by the monitoring unit 21 and the host vehicle. The maximum detection distance is included in the vehicle-ahead information detected by the radar 10. When there are a plurality of stationary objects in the vehicle-ahead information, the updating unit 22 determines the longest one of detection distances for the plurality of stationary objects as the maximum detection distance and performs updating by using the determined maximum detection distance).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the device of Mantyjarvi with the teachings of Kwon to use the identified distance as a basis for updating the visibility distance by replacing a current value of the visibility distance with a replacement distance approved by the controller based on a maximum value of distances detected with respect to the observation target to "determine that a vertical angle abnormality occurs in the radar when a vertical angle of the radar which is estimated by using the updated maximum detection distance is out of a preset required mount angle range" [0021].
Regarding claim 2, Mantyjarvi and Kwon teach the device of claim 1. Mantyjarvi further teaches wherein the controller is configured to identify a type of the observation target on a basis of an observation result from the sensor ([0077] Autonomous vehicles (which may include cars) generally may have control systems that are capable of analyzing sensory data to distinguish between different cars, motorcycles, bikes, and pedestrians on the road, which is very useful in planning a safe path to a desired destination. Modern self-driving cars may use Bayesian simultaneous localization and mapping (SLAM) algorithms, which fuse data from multiple sensors and an off-line map into current location estimates and map updates. SLAM may be combined with detection and tracking of other moving objects (DATMO), which handles the recognition of objects such as cars and pedestrians. Simpler systems may use roadside real-time locating system (RTLS) beacon systems to aid localization).
Regarding claim 3, Mantyjarvi and Kwon teach the device of claim 2. Mantyjarvi further teaches wherein the controller is configured to identify the observation target by classifying the observation target into one of a plurality of types ([0077] Autonomous vehicles (which may include cars) generally may have control systems that are capable of analyzing sensory data to distinguish between different cars, motorcycles, bikes, and pedestrians on the road, which is very useful in planning a safe path to a desired destination), and the storage is configured to store the visibility distance associated with a respective one of categories that are based on the types ([0088] Typical detection ranges, for example, may be 150 m for vehicles or up to 50 m for pedestrians, although other ranges and distances may be used. Detection ranges may vary according to the size and reflectivity of the object and current environmental factors (e.g., humidity, rain, fog, snow, or hail, as just some examples). [0104] the limited sensor visibility prediction area module 502 computes a level of reduction of effective sensor range due to e.g., road geometry, sensor placement, and other visibility factors like the weather and lighting conditions. [0134] A visibility requirements determination may use the information about oncoming traffic and other information related to ability to maneuver (which may have been gathered previously). In some embodiments, determining minimum visibility requirements may include receiving minimum visibility requirements (or minimum sensor visibility requirements) from another networked entity and retrieving minimum visibility requirements from a storage device (e.g., a database). In some embodiments, determining may include, e.g., calculating locally minimum visibility requirements prior to travel and/or automatically calculating (and updating) during travel).
Regarding claim 4, Mantyjarvi and Kwon teach the device of claim 3. Mantyjarvi further teaches wherein the categories include speed categories classified on a basis of a speed anticipated for the observation target ([0092] A driver of an AV may benefit from, e.g., knowing if a vehicle's sensors are not able to properly observe a foreseeable traffic situation. In dangerous situations, for example, the driver may not know that the range or field-of-view may be reduced in one or several directions—or that range or FoV is reduced only for specific types of objects, such as (fast) approaching vehicles (which may include cars, trucks, and motorcycles, for example). Slower-approaching pedestrians may be detected with shorter detection distances).
Regarding claim 6, Mantyjarvi and Kwon teach the device of claim 3. Mantyjarvi further teaches wherein when the visibility distance updated for a first category of an identified type of the observation target is less than a visibility distance associated with the first category stored in the storage, the controller is configured to execute a common update process to also update a visibility distance associated with a second category different from the first category, the visibility distance associated with the second category being reduced to the visibility distance updated for the first category ([0123] For some embodiments, at each step, an analysis may calculate areas that are viewable by a vehicle's sensors at that location and orientation, using an HD 3D map, and recorded locations, orientations, FoVs, and ranges of the vehicle's sensors. FIG. 10 shows one example of a visualization of a single step of such an analysis. For some embodiments, at each step, viewable areas may be calculated for one or more object types that the sensors are able to recognize. The calculation may take into account the current lighting and weather conditions, e.g., by referencing a manufacturer specified look-up table indicating lab-measured sensor performance in various conditions. [0148] FIG. 14 depicts an example of a style of AR visualization of sensor range and field-of-view on a decline, in accordance with at least one embodiment. FIG. 14 depicts an environment with a decline, followed by a flat area, and followed by another decline that may impact object detection by a vehicle's sensors. The sensors may be oriented along the decline, and the sensors' FoVs may max out in the vertical direction. In the flat section, the sensors may be unable to detect objects past the intersection because the road section following the intersection declines steeply and the sensors may overshoot the road. In FIG. 14's augmented view 1400 a blind area AR visualization 1404 and a region of trusted sensor coverage 1402 are shown. The AVs current sensor coverage is indicated by the horizontal slashes 1402 (which may be shown in green in some embodiments). Based on high-definition map data, the AV may determine that due to the steep decline, the AV's sensors are unable to detect objects past the flat section in the forthcoming intersection. The potential blind area may be indicated (or highlighted) to the driver by overlaying (or projecting) vertical slashes (in some embodiments, red may be used instead) over the blind areas of the AR visualization of a map).
Regarding claim 10, Mantyjarvi and Kwon teach the device of claim 2. Mantyjarvi further teaches wherein the controller is configured to generate notification information including an indication of the presence or absence of the observation target in another observation result, and when the observation target is not present in the other observation result, the controller is configured to create and include information pertaining to the visibility distance in the notification information on a basis of the type of the observation target and the visibility distance associated with the relevant type ([0082] Some embodiments determine deficiencies in the effective range and viewing angles of vehicle sensors in traffic situations with respect to road angles, junctions, and fixed obstacles. Some embodiments of systems make these determinations based on predicted sensor range decreases (such as decreases due to weather conditions, lighting conditions, and equipment age). The deficiencies may be displayed to the driver or user (such as with user (or driver) head tracking and AR projection of the sensor range onto the road using AR goggles, an HUD, or an AR display mechanism). In a real driving environment, the driver may see areas or situations in which sensors may be unable to detect other traffic users/objects or may produce unreliable measurements to warrant automated driving, and an advanced driver-assistance systems (ADAS) may be engaged to assist the driver. For some embodiments, if a situation occurs in which vehicle sensors are measuring degraded measurements (which, e.g., may affect an ADAS), this situation may be communicated to the driver (such as by displaying a warning message and/or playing a sound, for example). [0105] If regions of limited sensor visibility are detected the limited sensor visibility area prediction module 502 sends 516 a hazard warning to the driving mode module. In some embodiments, the driving mode module 504 may receive 518 vehicle-to-vehicle (V2V) messages from another nearby vehicle or from a sensor and communication module 508. The messages may include information about another vehicle such as its location, speed, driving direction, and any planned courses of action. The driving mode module 504 may receive the V2V message along with a hazard warning indicating the areas of potentially limited sensor capabilities and may determine 520 if other drivers are likely in a limited sensor visibility area. The driving mode module 504 may determine 522 a change to an operable driving mode. The UI module 506 may receive 524 a warning from the driving mode module 504 and may execute 526 a blind area AR visualization. Various examples of AR visualizations of blind areas and user prompts are presented in the descriptions of FIGS. 13A, 13B, 14, 15, and 16).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Mantyjarvi in view of Kwon and Xue (US20220044023A1).
Regarding claim 5, Mantyjarvi and Kwon the device of claim 3. Mantyjarvi further teaches wherein the sensor is configured to detect a temperature of the target ([0071] The peripherals 138 may include one or more sensors, the sensors may be one or more of a camera, a RADAR, a LIDAR, a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and/or a humidity sensor).
Mantyjarvi does not teach the categories include temperature categories classified on a basis of a temperature anticipated for the observation target.
Xue, in the same field of endeavor of vehicle image analysis, teaches the categories include temperature categories classified on a basis of a temperature anticipated for the observation target (See Fig. 4. [0006] The invention concerns an apparatus comprising an interface and a processor. The interface may be configured to receive pixel data generated by a capture device and a temperature measurement generated by a thermal sensor. The processor may be configured to receive the pixel data and the temperature measurement from the interface, generate video frames in response to the pixel data, perform computer vision operations on the video frames to detect objects, perform a classification of the objects detected based on characteristics of the objects, detect a temperature anomaly in response to the temperature measurement and the classification, and generate a control signal in response to the temperature anomaly. The control signal may provide a warning based on the temperature anomaly. The classification may provide a normal temperature range for the objects detected).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the device of Mantyjarvi with the teachings of Xue to include temperature categories classified on a basis of a temperature anticipated for the observation target because "each of the object types 250a-250n may have similar but different normal operating temperatures" [Xue 0133].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mantyjarvi in view of Kwon and Fujita (WO2020012212A1).
Regarding claim 7, Mantyjarvi and Kwon teach the device of claim 6. Fujita, in the same field of endeavor of vehicle object detection, teaches wherein the controller is configured to execute the common update process upon meeting a first condition regarding a decrease in a number of observation targets passing through the predetermined area ([pg. 2 para. 9] The radar 12 is a distance measuring sensor that realizes, as functions required for automatic driving, a function of detecting the presence of an object around the vehicle and a function of detecting the distance to an object around the vehicle. [pg. 22 para. 3] The predetermined distance may be changed based on the surrounding traffic volume from the own vehicle surrounding information. For example, the predetermined distance is set shorter as the traffic volume increases, and the predetermined distance is set longer as the traffic volume decreases).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the device of Mantyjarvi with the teachings of Fujita to execute a common update based on a decrease in the number of observation targets because "As a result, when the own vehicle V enters the roundabout RA (annular road CR), it is possible to provide a driving support method for transmitting an action plan of the own vehicle V to other vehicles in the vicinity of the own vehicle according to a direction instruction" [Fujita pg. 19 para. 4].
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Mantyjarvi in view of Kwon, Fujita, and Murayama (US20150310313A1).
Regarding claim 8, Mantyjarvi, Kwon, and Fujita teaches the device of claim 7. Murayama, in the same field of endeavor of vehicle visibility processing, teaches wherein when the first condition is met (within judgement threshold) after the common update process and the visibility distance updated for the category of the type of the observation target identified in a new observation result (calculated detection distance of a specific landmark) is greater than the visibility distance associated with the relevant category stored in the storage, the controller is configured to update the visibility distance associated with the relevant category, and does not update the visibility distance associated with a category other than the relevant category ([0053] In the detection distance record unit 22 in the information storage unit 2b, a distance from a vehicle position where a landmark is detected for the first time to the landmark is recorded as a detection history used for visibility estimation. The distance is used as a reference detection distance (detection distance in the past) being a comparison target for a detection distance in the following driving. The reference detection distance is calculated as follows. When obtaining an image recognition result of a landmark from the image recognition unit 1 for the first time, the detection distance record unit 22 obtains vehicle position information as well as a position where the detected landmark is actually situated from the landmark position record unit 21 and comparers the information with the position so as to calculate a distance from the vehicle position to the landmark. For example, if the image recognition unit 1 detects a road sign situated in a vehicle traveling direction and outputs an image analysis result of “speed-limit sign” and “40 km/h”, the detection distance record unit 22 obtains position information of the road sign from the landmark position record unit 21. By comparing the obtained position of the road sign with the current vehicle position, the detection distance record unit 22 calculates a distance, e.g. “25 m”. That is, the fact that the vehicle can detect the road sign 25 m before the sign is recorded. [0055] On receiving the image analysis result from the image recognition unit 1, the visibility judgment unit 3b receives the vehicle position information at that time. The visibility judgment unit 3b calculates a distance from the vehicle to the landmark by using the inputted vehicle position information and the landmark position information. That is, a detection distance showing how short a distance is which is needed to detect the landmark this time is calculated. By comparing the calculated detection distance with the reference detection distance obtained from the information storage unit 2b, it is determined whether the former is shorter than a reference detection distance recorded in the past, i.e. whether or not the detection is made at a closer distance from the landmark. When making the comparison, a judgment threshold is used similar to the case in Embodiment 1. For example, if the reference detection distance is “25 m”, the detection distance calculated this time is “20 m”, and the threshold is “3 m”, the difference of 5 m between the reference detection distance and the detection distance calculated this time, i.e. a moving distance toward the landmark, exceeds the threshold, and thus it is determined as “visibility decreased”. On the other hand, if the detection distance of this time is “23 m”, a moving distance to the landmark of 2 m does not exceed the threshold, and thus the visibility judgment result is determined as “visibility normal”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the device of Mantyjarvi with the teachings of Murayama to only update the visibility distance associated with the relevant category because "for each type of various landmarks, the detection distance record unit 22 records a distance detected for the first time for each type of the landmarks as the reference detection distance" [Murayama 0059].
Regarding claim 9, Mantyjarvi, Kwon, Fujita, and Murayama teach the device of claim 8. Murayama teaches wherein when the first condition is not met (exceeds judgement threshold), the controller is configured to update the visibility distance only for the category of the identified type of observation target (calculated detection distance of a specific landmark) ([0053] In the detection distance record unit 22 in the information storage unit 2b, a distance from a vehicle position where a landmark is detected for the first time to the landmark is recorded as a detection history used for visibility estimation. The distance is used as a reference detection distance (detection distance in the past) being a comparison target for a detection distance in the following driving. The reference detection distance is calculated as follows. When obtaining an image recognition result of a landmark from the image recognition unit 1 for the first time, the detection distance record unit 22 obtains vehicle position information as well as a position where the detected landmark is actually situated from the landmark position record unit 21 and comparers the information with the position so as to calculate a distance from the vehicle position to the landmark. For example, if the image recognition unit 1 detects a road sign situated in a vehicle traveling direction and outputs an image analysis result of “speed-limit sign” and “40 km/h”, the detection distance record unit 22 obtains position information of the road sign from the landmark position record unit 21. By comparing the obtained position of the road sign with the current vehicle position, the detection distance record unit 22 calculates a distance, e.g. “25 m”. That is, the fact that the vehicle can detect the road sign 25 m before the sign is recorded. [0055] On receiving the image analysis result from the image recognition unit 1, the visibility judgment unit 3b receives the vehicle position information at that time. The visibility judgment unit 3b calculates a distance from the vehicle to the landmark by using the inputted vehicle position information and the landmark position information. That is, a detection distance showing how short a distance is which is needed to detect the landmark this time is calculated. By comparing the calculated detection distance with the reference detection distance obtained from the information storage unit 2b, it is determined whether the former is shorter than a reference detection distance recorded in the past, i.e. whether or not the detection is made at a closer distance from the landmark. When making the comparison, a judgment threshold is used similar to the case in Embodiment 1. For example, if the reference detection distance is “25 m”, the detection distance calculated this time is “20 m”, and the threshold is “3 m”, the difference of 5 m between the reference detection distance and the detection distance calculated this time, i.e. a moving distance toward the landmark, exceeds the threshold, and thus it is determined as “visibility decreased”. On the other hand, if the detection distance of this time is “23 m”, a moving distance to the landmark of 2 m does not exceed the threshold, and thus the visibility judgment result is determined as “visibility normal”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the device of Mantyjarvi with the teachings of Murayama to only update the visibility distance associated with the relevant category because "for each type of various landmarks, the detection distance record unit 22 records a distance detected for the first time for each type of the landmarks as the reference detection distance" [Murayama 0059].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Mantyjarvi in view of Kwon and Murayama.
Regarding claim 11, Mantyjarvi and Kwon teach the device of claim 10. Murayama teaches wherein when the visibility distance for a certain category is less than or equal to a predetermined value, the controller is configured to not include visibility distance corresponding to the certain category in the notification information ([0057] Note that, while the detection distance when a landmark is detected for the first time is recorded in the detection distance record unit 22 as a reference value in the above-described explanation, the reference detection distance recorded in the detection distance record unit 22 may be updated every time when a landmark is detected. By employing such a configuration, determination whether visibility is better or worse than that at the previous time can be made. Also, the reference detection position may be obtained by averaging a plurality of detection distances. In addition, while a detection distance when visibility is good is recorded, update may not be made when visibility is estimated to be poor. [0036] On the other hand, when visibility is poor, for example, due to fog, etc., detection of a road sign should be made from a closer position than usual).
Therefore, it would have been obvious to a person of ordinary skill in the art before the time of filing to modify the device of Mantyjarvi with the teachings of Murayama to not include visibility distance less than or equal to a threshold because the "reference detection position information (detection position in the past) and is used as a determination criterion when estimating visibility" [Murayama 0031].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JACQUELINE R ZAK/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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