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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 25, 2025 has been entered.
Response to Amendment
This Non-Final Office action is responsive to the communication filed under 37 C.F.R. § 1.111 on January 15, 2026 (hereafter “Response”). The amendments to the claims filed on November 25, 2025 are acknowledged and have been entered.
Claims 16–28 and 30 are now amended.
Claims 16–30 are pending in the application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 16–30 have been considered but are no longer relevant 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.
Accordingly, the Applicant’s request for a notice of allowance is respectfully denied.
Claim Objections
The Office objects to all three independent claims because the amendment to all three claims introduces a new informality: the present continuous tense of “in response to determining” disagrees with “is less than or equal to a distance threshold value.” The verb “is” needs to be replaced with its present continuous form, “being.”
Appropriate correction is required.
Claim Rejections – 35 U.S.C. § 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
I. Liu and Zhang teach claims 16–20 and 26–30.
Claims 16–20 and 26–30 are rejected under 35 U.S.C. § 103 as being unpatentable over U.S. Patent Application Publication No. 2018/0126984 A1 (“Liu”) in view of Chinese Patent Application Publication No. 110488319 A (“Zhang”).
This rejection uses a machine translation of Liu obtained from the EPO’s Espacenet website at <https://translationportal.epo.org/emtp/translate/?ACTION=description-retrieval&COUNTRY=CN&ENGINE=google&FORMAT=docdb&KIND=A&LOCALE=en_EP&NUMBER=110488319&OPS=ops.epo.org/3.2&SRCLANG=zh&TRGLANG=en>. A copy is also attached to this Office Action, coded as REF.OTHER in the file wrapper and bearing the same file date as this document.
Claim 16
Liu teaches:
A method for ascertaining an approximate object position of a dynamic object in surroundings of a vehicle,
“FIG. 4 illustrates a method 400 by which tracklets are created and updated by the controller 102.” Liu ¶ 34. “Each tracklet includes data, or a representation of data, corresponding to an object, where the data is obtained from outputs of the imaging devices 104 and other sensors 106.” Liu ¶ 15.
the vehicle including at least two ultrasonic sensors and at least one vehicle camera,
“The vehicle 300a housing the controller 102 may have forward facing sensors 106a, 106b, such as a LIDAR, RADAR, ultrasonic, or other sensor. The vehicle 300 may further include forward facing cameras 104a, 104b.” Liu ¶ 32.
the method comprising the following steps: detecting sensor data using the at least two ultrasonic sensors;
“The method 400 may include receiving 402 sensor data,” including from sensors 106a and 106b, and “processed simultaneously.” Liu ¶ 34.
ascertaining a present reflection origin position of a static or dynamic object as a function of the detected sensor data;
“The method 400 may include identifying 404 features in the sensor data.” Liu ¶ 35. “The output of step 404 may be a listing of data objects that each represent a detected feature. The data objects may include a location, e.g. a coordinate of a center, of the feature, an extent of the feature, a total volume or facing area or the size or vertex locations of a bounding box or cube, or other data.” Liu ¶ 36.
detecting at least one camera image using the vehicle camera;
Step 402 may further include “receiving data from an individual sensors 104a, 104b.” Liu ¶ 34.
recognizing the dynamic object as a function of the at least one detected camera image;
“Step 404 may include identifying features in the sensor data that are consistent with presence of objects such as vehicles, people, animals, signs, buildings, or any other object that may be present.” Liu ¶ 35. Recall that the sensor data includes data from sensors 104, which are “forward facing cameras.” Liu ¶ 32.
and ascertaining a present estimated position of the recognized dynamic object relative to the vehicle as a function of the at least one detected camera image;
“The output of step 404 may be a listing of data objects that each represent a detected feature. The data objects may include a location, e.g. a coordinate of a center, of the feature, an extent of the feature, a total volume or facing area or the size or vertex locations of a bounding box or cube, or other data.” Liu ¶ 36.
Next, in step 406, the method determines whether the features of the detected object should be assigned to an existing tracklet, or if a new tracklet should be created for the object’s features. Liu ¶ 37. Method 400 does this by executing a feature-matching algorithm, like the method 500 shown in FIG. 5. Liu ¶ 38. In other words, step 406 and method 500 classify the features of the sensor data as belonging to a known object, or a new one.
Method 500 falls within the scope of the “using a trained machine recognition method” claim language because the result of method 500 is that the device executing the method recognizes an object based on its features. To the extent that the claim language includes the word “trained,” that word is recited in the past-tense, and thus describes acts that occurred before the method of claim 16, and therefore, outside the scope of the claimed invention. See MPEP § 2111.04.
and ascertaining the approximate object position of the dynamic object as a function of the reflection origin positions classified as belonging to the dynamic object, using a Kalman filter.
“Updating 410 may include adding the data from the data object of step 404 to the tracklet and performing Kalman filtering with respect to the data from step 404 and from step 404 for previous iterations of the method 400.” Liu ¶ 39.
Based on the foregoing, the difference between Liu and the claimed invention is that Liu’s classification and Kalman filtering steps are not contingent upon “a position distance between the ascertained present estimated position of the recognized dynamic object and the ascertained present reflection origin position is less than or equal to a distance threshold value.”
Zhang, however, teaches a method comprising:
detecting sensor data using the at least two ultrasonic sensors;
In step 1, ultrasonic waves are used to detect obstacles around the vehicle. Zhang ¶ 17. In particular, up to 12 ultrasonic sensors may be used. Zhang ¶ 48.
detecting at least one camera image using the vehicle camera;
Step 1 further includes using cameras to detect obstacles around the vehicle. In particular, up to four surround-view CMOS cameras may be used. Zhang ¶ 48.
in response to a position distance between the ascertained present estimated position of the recognized dynamic object and the ascertained present reflection origin position is less than or equal to a distance threshold value . . . ascertaining the approximate object position of the dynamic object as a function of the reflection origin positions classified as belonging to the dynamic object, using a Kalman filter.
“Based on the obstacles detected by the ultrasonic sensor and camera . . . the coordinates [of the respective obstacles] obtained by the two sensors at the same moment are obtained. If the error between the two coordinates is within the set range, they are fused into the same obstacle.” Zhang ¶ 18. Kalman filtering, which is already disclosed in Liu, is a specific type of sensor fusion method.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve Liu’s object detection method 400 by using the same improvement technique that Zhang used for object detection, i.e., gating further processing of ultrasonic and visual sensor data (including sensor fusion steps) behind a determination of whether or not the discrepancy between the ultrasonic data and the camera data is sufficiently small. One would have been motivated to improve Liu’s method in the same way that Zhang’s method was improved because Zhang’s method “improv[es] the accuracy of relative position detection while enhancing obstacle detection reliability and detection distance.” Zhang ¶ 35.
Claim 17
Liu and Zhang teach the method as recited in claim 16,
wherein the classification of the present reflection origin position as belonging to the recognized dynamic object additionally takes place as a function of the underlying sensor data of reflection origin positions, classified as belonging to the recognized dynamic object, during a predefined time period prior to the present point in time in surroundings of the present reflection origin position.
“Updating 410 may include adding the data from the data object of step 404 to the tracklet and performing Kalman filtering with respect to the data from step 404 and from step 404 for previous iterations of the method 400.” Liu ¶ 39 (emphasis added).
Claim 18
Liu and Zhang teach the method as recited in claim 17, wherein:
the surroundings of the present reflection origin position include those reflection origin positions belonging to the dynamic object whose distance from the present reflection origin position is less than or equal to a distance threshold value,
“The output of step 404 may be a listing of data objects that each represent a detected feature. The data objects may include a location, e.g. a coordinate of a center, of the feature, an extent of the feature, a total volume or facing area or the size or vertex locations of a bounding box or cube, or other data.” Liu ¶ 36 (emphasis added). “The method 400 may include evaluating 406 whether the feature has been assigned to an existing tracklet. Each tracklet may be a data object that contains some or all of the data of the features identified at step 404, e.g. a location and extent.” Liu ¶ 37.
and/or the surroundings of the present reflection origin position include at least one ultrasonic cluster assigned to the dynamic object, the ultrasonic cluster including reflection origin positions classified as belonging to the dynamic object,
The broadest reasonable interpretation of “and/or” includes “or,” and thus, the broadest reasonable interpretation of the above claim element is that it is merely an alternative to the other two claim elements. Since Liu at least discloses the first claimed alternative, and since the claim only requires one of the alternatives to be present for infringement, Liu anticipates the claim.
and/or the surroundings of the present reflection origin position include at least one grid cell in which the present reflection origin position is situated or assigned, a grid of the grid cell subdividing the surroundings of the vehicle.
“There are various sensors 104a, 104b, 106a, 106b by which objects are detected and each may define its own coordinate system. Accordingly, the location data of step 404 may be translated from the coordinate system of the sensor in which the feature was detected into a common coordinate system, such as a coordinate system of one of the sensors 104a, 104b, 106a, 106b that is designated as the common coordinate system.” Liu ¶ 36.
Claim 19
Liu and Zhang teach the method as recited in claim 16, wherein the following step is additionally carried out:
ascertaining a present object speed of the dynamic object and/or a present object movement direction of the dynamic object, as a function of ascertained approximate object positions of the dynamic object at different points in time.
“Each tracklet may be a data object that contains some or all of the data of the features identified at step 404, e.g. a location and extent. The tracklet may further include a trajectory . . . such that a predicted location of the object may be determined from the trajectory.” Liu ¶ 37.
Claim 20
Liu and Zhang teach the method as recited in claim 16, and do not need to disclose any further elements in order to anticipate claim 20, because the broadest reasonable interpretation of claim 20 is that its remaining elements of claim 20 are optional, since they are all contingent upon the unmet condition precedent of “when a number of reflection origin positions classified as belonging to the dynamic object falls below a predefined confidence number.” “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” MPEP § 2111.04 (subsection II.).
Claim 26
Liu and Zhang the method as recited in claim 16,
wherein the classification of the present reflection origin position as belonging to the recognized dynamic object takes place via a second trained machine recognition method.
FIG. 5 illustrates a machine-executed method 500, which is meant to be executed in the context of method 400 when determining whether or not to create a tracklet for a detected feature. The method includes “determining whether the feature is the Nth occurrence of this feature in a set of N contiguous sensor frames, where N is an integer, such as an integer from 10 to 30, preferably 20.” Liu ¶ 46. In this context, a “feature” refers to any distinguishing characteristics of the object in the sensor data, including but not limited to its shape. Liu ¶ 51.
The above machine-executed method 500 falls within the scope of the “using a trained machine recognition method” claim language because the result of method 500 is that the device executing the method recognizes an object based on its features. To the extent that the claim language includes the word “trained,” that word is recited in the past-tense, and thus describes acts that occurred before the method of claims 16 or 26, and therefore, outside the scope of the claimed invention. See MPEP § 2111.04.
Claim 27
Claim 27 recites a non-transitory computer-readable medium that causes a computer to perform exactly the same method as claim 16. Therefore, claim 26 is rejected over the same findings and rationale as provided above for claim 16, taken together with Liu’s disclosure of encoding the same method on a computer readable medium. See Liu ¶ 65.
Claim 28
Claim 28 recites a broader version of the device that is required for performing the method of claim 16. It is broader in the sense that the device for performing the method of claim 16 requires the three sensors recited, whereas the device of claim 28 only needs to receive the data of those sensors, via “inputs.”
Either way, the rejection of claim 16 is written such that all of structures required by the device that performs the method of claim 16 are included among the findings of anticipation. Therefore, all of the findings set forth in the rejection of claim 16 are hereby reincorporated by reference as applied to claim 28, and claim 28 is therefore rejected for the same reasons as claim 16.
Claim 29
Liu and Zhang teach the device as recited in claim 28, further comprising:
a signal output, the signal output being configured to generate a control signal for a display device and/or a braking device and/or a steering device and/or a drive motor, as a function of the ascertained approximate object position of the dynamic object.
“The collision prediction module 110c predicts which obstacles are likely to collide with the vehicle based on its current trajectory or current intended path and the trajectory of the obstacles. The decision module 110d may make a decision to stop, accelerate, turn, etc. in order to avoid obstacles.” Liu ¶ 19. “The decision module 110d may control the trajectory of the vehicle by actuating one or more actuators 114 controlling the direction and speed of the vehicle. For example, the actuators 114 may include a steering actuator 116a, an accelerator actuator 116b, and a brake actuator 116c.” Liu ¶ 20.
Additionally or alternatively, “an alert to a driver may be generated if the trajectory of the vehicle 300a and the object indicate collision will occur absent a change in bearing.” Liu ¶ 44.
Claim 30
Claim 30 is functionally identical to claim 28, and therefore rejected over all of the same findings as claim 28. To the extent claim 30 requires the vehicle and claim 28 does not, Liu explicitly discloses that its system 100 and controller 102 are “housed within a vehicle.”
II. Liu, Zhang, and Boydston teach claim 21.
Claim 21 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu as applied to claim 16 above, and further in view of U.S. Patent No. 12,172,649 B1 (“Boydston”).
Claim 21
Liu and Zhang teach the method as recited in claim 16, further comprising:
determining a statistical uncertainty as a function of the detected sensor data, and/or of the ascertained present reflection origin position, and/or of the reflection origin positions classified as belonging to the dynamic object, and/or of the ascertained approximate object position,
“The method 400 may include updating 412 the probability of the tracklet that is updated at step 410 or created at step 408. In some embodiments, updating 412 may include processing the features identified at step 404 according to a Bayesian statistical model wherein each sensor output is processed to determine an impact on a probability that an object represented by the tracklet exists.” Liu ¶ 40.
“In some embodiments, distance to a feature may be a factor in updating 412 the probability, such that the probability is increased more for features that are closer to the vehicle 300a as compared to features that are farther away from the vehicle 300a.” Liu ¶ 41.
“[O]ther data may be incorporated into the statistical model, such as the variance of the sensor in which the feature of step 404 was identified, the variation of the location data with respect to a Kalman filtered trajectory, or other factors.” Liu ¶ 41.
Since Liu does not gate its tracklet determinations behind a distance threshold, Liu does not disclose “adapting the distance threshold value as a function of the determined statistical uncertainty.” Zhang improves upon this, but its distance threshold still is not adaptive.
Boydston, however, teaches a method that does require sensor data to be within a distance threshold for further classification and processing, and further teaches:
adapting the distance threshold value as a function of the determined statistical uncertainty.
“[S]ensor data within a threshold distance of the ground profile may be associated with the ground classification and other sensor data that doesn't meet this criteria and has an elevation greater than the ground profile may be classified as being associated with an object. In some examples, the threshold distance may include . . . a distance that is set based at least in part on a confidence or residual error associated with the ground profile. For example, residual error(s) determined as part of fitting the ground profile to the sensor data may be used to determine the threshold distance.” Boydston col. 15 ll. 30–41.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve Liu and Zhang’s combined object tracking method with Boydston’s technique of requiring a threshold distance to further process tracked objects. One would have been motivated to improve Liu with Boydston’s thresholds because a threshold helps “avoid false negative detections of small objects.” Boydston col. 15 ll. 43–44.
III. Liu, Zhang, and Herman teach claim 22.
Claim 22 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu and Zhang as applied to claim 16 above, and further in view of U.S. Patent Application Publication No. 2020/0409361 A1 (“Herman”).
Claim 22
Liu teaches the method as recited in claim 16, but since Liu does not gate its tracklet determinations behind a distance threshold, Liu does not disclose a distance threshold value in a range between 0.1 meter and 5 meters. Zhang improves upon this by providing such a gate, but does not explicitly disclose how big of a range is allowed.
Herman, however, teaches a vehicle computer 105 that determines a “risk level” of a current situation, and activate or deactivate vehicle sensors based on the amount of risk present. Herman ¶ 63. Notably, in this method, Herman teaches that the risk level may be based on a distance threshold,
wherein the distance threshold value is in a range between 0.1 meter and 5 meters.
“The computer 105 can determine the risk level based on a number of objects within a distance threshold of the vehicle 101. That is, each risk level has a corresponding threshold number of objects, and the computer 105 can deactivate one or more sensors 110 and/or components 120 when the detected number of objects is below one of the thresholds. The distance threshold can be a distance within which the objects are able to interact with the vehicle 101, e.g., 3 meters.” Herman ¶ 63.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve Liu’s and Zhang’s combined monitoring system with Herman’s technique of using a 3-meter threshold. One would have been motivated to improve Liu with Herman’s technique because, by requiring a specific distance threshold, a vehicle can “reduce power consumption” at times when the shorter-range sensors are not needed. Herman ¶ 63.
IV. Liu, Zhang, and Breed teach claim 23.
Claim 23 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu and Zhang as applied to claim 16 above, and further in view of U.S. Patent Application Publication No. 2001/0037903 A1 (“Breed”).
Claim 23
Liu and Zhang teach the method as recited in claim 16, but do not explicitly disclose “normalizing at least a portion of the sensor data for ascertaining the present reflection origin position with regard to their amplitude, based on an angular position of the ascertained present reflection origin position for a detection range of a particular one of the ultrasonic sensors.” Liu’s classification does “take place” as a function of the sensor data for the reasons given in the rejection in claim 16, but it is ambiguous as to whether or not that sensor data is normalized.
Breed, however, teaches a method (FIGS. 6 and 7(a)) comprising:
normalizing at least a portion of the sensor data for ascertaining the present reflection origin position with regard to their amplitude, based on an angular position of the ascertained present reflection origin position for a detection range of a particular one of the ultrasonic sensors, wherein the classification of the present reflection origin position as belonging to the recognized dynamic object takes place as a function of the normalized sensor data and as a function of at least one amplitude threshold value.
“As shown in FIG. 7(a), measured data is normalized by making the peaks of the reflected wave pulses P1–P4 equal (step S5).” Breed ¶ 132. The measured data is necessarily based on the angle between the source of the pulses and its target, since there is necessarily an angle between them (even if that angle is 90 degrees).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve the accuracy of Liu and/or Zhang’s ultrasonic sensors with Breed’s technique of normalizing measured ultrasonic data to their peaks. One would have been motivated to do so because this technique “eliminates the effects of different reflectivities of different objects and people depending on the characteristics of their surfaces such as their clothing,” Breed ¶ 132, thereby improving the overall system’s ability to distinguish different objects from one another.
V. Liu, Zhang, and Derom teach claim 24.
Claim 24 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu and Zhang as applied to claim 16 above, and further in view of U.S. Patent Application Publication No. 2016/0025836 A1 (“Derom”).
Claim 24
Liu and Zhang teach the method as recited in claim 16, but do not explicitly use a correlation coefficient to classify origin positions as belonging to the respective tracklets.
Derom, however, teaches a method comprising:
ascertaining a correlation coefficient between at least a portion of the detected sensor data underlying the reflection origin position and a sensor signal emitted by a particular one of the ultrasonic sensors, wherein the classification of the present reflection origin position as belonging to the recognized dynamic object takes place as a function of the ascertained correlation coefficient and as a function of a threshold value for the correlation coefficient.
“To obtain a cross correlated signal 402 that is useful for determining the distance to the first object 104a, it is desirable that the reference signal 114 exhibits good autocorrelation properties. Autocorrelation is a measure of how well a signal correlates with an earlier or later version of itself. The degree of auto correlation can be described by a correlation coefficient, which, when normalised, varies between 0 and 1 (1 representing perfect correlation).” Derom ¶ 74.
Note that while this paragraph does not call out a particular correlation coefficient, the scope of claim 24 covers any and all possible threshold values for the correlation coefficient—as long as one is chosen for classification. To that end, Derom directs the person of ordinary skill in the art to select a particular auto correlation based on “the level of accuracy required and the how noisy the surrounding environment is. For any application, the auto correlation peak should be easily identifiable over the noise. As greater accuracy is required, a higher correlation coefficient is required.” Derom ¶ 74.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve Liu’s method of classifying ultrasonic data into respective tracklets around a vehicle using Derom’s technique of characterizing the ultrasonic reflections of objects based on their correlation coefficients. One would have been motivated to utilize Derom’s technique because it “processing requirements because only a small part of a received signal needs to be analysed,” while leveraging auto correlation properties to “provide a high degree of accuracy.” Derom ¶ 5.
VI. Liu, Zhang, and Cohen teach claim 25.
Claim 22 is rejected under 35 U.S.C. § 103 as being unpatentable over Liu and Zhang as applied to claim 16 above, and further in view of U.S. Patent Application Publication No. 2019/0391250 A1 (“Cohen”).
Claim 25
Liu and Zhang teach the method as recited in claim 16, further comprising:
ascertaining a number of reflections for a sensor signal emitted by a particular one of the ultrasonic sensors as a function of at least a portion of the detected sensor data underlying the
“In some embodiments, a tracklet is created for a detected feature only if certain conditions are met, such as repeated detection of the feature for a minimum number of data frames.” Liu ¶ 41.
The only difference between this embodiment of Liu’s disclosure and claim 25 is that, in this embodiment, Liu ascertains and uses as its classification criteria the number of reflections from a reflection origin position received over time, rather than a “present” reflection origin position (i.e., the number of reflections at a single time in a cluster of reflections from an object at that particular point in time).
Cohen, however, teaches a method comprising:
ascertaining a number of reflections for a sensor signal emitted by a particular one of the ultrasonic sensors as a function of at least a portion of the detected sensor data underlying the present reflection origin position, wherein the classification of the present reflection origin position as belonging to the recognized dynamic object taking place as a function of the ascertained number of reflections and as a function of a number threshold value.
“The clustering component 332 can include functionality to cluster points, e.g. from radar scans, to better identify objects.” Cohen ¶ 48. Please note that although this example refers to radar, Cohen also explicitly teaches that sonar may be used instead of radar in the same technique. See Cohen ¶ 28.
In any case, Cohen further teaches that “the clustering component 332 may receive sensor data comprising a plurality of points and information associated with the points. For example, the clustering component may receive position information, signal strength information, velocity information, or the like about points, and determine similarities between the points based on some or all of that information. For example, the clustering component 332 may determine points having close positional proximity and a signal strength within a threshold amount of neighboring points are indicative of an object and should be clustered.” Cohen ¶ 48.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add Cohen’s clustering component 332 to Liu’s tracklet system and method, such that tracklets would be based on the clusters of sonar points detected in the ultrasonic data. One would have been motivated to improve Liu’s tracklet system with Cohen’s clustering component because clustering the point data results in better identification of the objects around the vehicle. Cohen ¶ 48.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Justin R. Blaufeld whose telephone number is (571)272-4372. The examiner can normally be reached M-F 9:00am - 4:00pm ET.
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Justin R. Blaufeld
Primary Examiner
Art Unit 2151
/Justin R. Blaufeld/Primary Examiner, Art Unit 2151
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