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 amendment filed on October 29, 2025. Claims 1-20 are pending. Claims 1, 14 and 20 are independent.
Response to Arguments
Applicants’ arguments have been fully considered. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
1. A method, at a processing unit of a first vehicle, the method comprising:
obtaining, from one or more sensors, data representing a sensed position of the first vehicle and a sensed feature in a proximity of the first vehicle;
defining a first probability density function (PDF) based on the obtained data, the first PDF providing a first probability distribution representing likelihood of a future position of the first vehicle;
defining a second PDF based on the obtained data, the second PDF providing a second probability distribution representing likelihood related to a proxemic risk presented by the sensed feature;
computing a risk metric representing a likelihood of the proxemic risk to the first vehicle based on an overlap between the first probability distribution of the first PDF and the second probability distribution of the second PDF; and
in response to the risk metric exceeding a defined risk threshold, control at least one haptic output unit, embedded in the first vehicle, to provide haptic output indicative of the proxemic risk.
The examiner submits that the foregoing bolded limitations constitute a “mathematical concept” because under its broadest reasonable interpretation, the claim covers gathering and analyzing data. Specifically, the “defining a first probability density function,” “computing a risk metric” . . . computing “an overlap between” probability distributions are mathematical concepts. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
1. A method, at a processing unit of a first vehicle, the method comprising:
obtaining, from one or more sensors, data representing a sensed position of the first vehicle and a sensed feature in a proximity of the first vehicle;
defining a first probability density function (PDF) based on the obtained data, the first PDF providing a first probability distribution representing likelihood of a future position of the first vehicle;
defining a second PDF based on the obtained data, the second PDF providing a second probability distribution representing likelihood related to a proxemic risk presented by the sensed feature;
computing a risk metric representing a likelihood of the proxemic risk to the first vehicle based on an overlap between the first probability distribution of the first PDF and the second probability distribution of the second PDF; and
in response to the risk metric exceeding a defined risk threshold, control at least one haptic output unit, embedded in the first vehicle, to provide haptic output indicative of the proxemic risk.
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “one or more sensors” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the sensors are insignificant extra-solution activity (mere data gathering) that are still directed to an abstract idea. Regarding the control of at least one haptic output unit, the examiner submits that this limitation is merely outputting the result of the mental process.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Dependent claims 2-13 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-13 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claims 1-13 are ineligible under 35 USC §101. Claims 14-19 and 20 are ineligible under 35 USC §101 for at least the same reasons of claims 1-13.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2003/0055563 to Lars et al. (hereinafter “Lars”) in view of U.S. Patent Publication No. 2007/0244641 to Altan et al. (hereinafter “Altan”).
With respect to independent claims 1, 14 and 20, Lars discloses obtaining, from one or more sensors, data representing a sensed position of the first vehicle and a sensed feature in a proximity of the first vehicle (see paragraph [0002]: Several methods have been developed for collision avoidance utilizing sensors to obtain values such as distance, speed and direction of objects and vehicles.);
defining a first probability density function (PDF) based on the obtained data, the first PDF providing a first probability distribution representing likelihood of a future position of the first vehicle (see paragraph [0015] and [0034]: The inventive method also comprises the step of predicting the probability density function for at least one additional object at several future occasions. The object could for example be another vehicle the probable position of which is predicted at each of a plurality of times subsequent to detection synchronized with the predicted instants of the vehicle. Thus, the probability of collision for the vehicle and each of the surrounding objects should be calculated for a sufficient number of future occasions. Based on this, rules are set in the probability domain on when to take evasive action or brake. The probability density function can for example be calculated by using the extended Kalman filter to predict the vehicles and surrounding objects future positions as well as their associated covariance matrix.);
defining a second PDF based on the obtained data, the second PDF providing a second probability distribution representing likelihood related to a proxemic risk presented by the sensed feature (see paragraph [0015], [0029] and [0034]: The inventive method also comprises the step of predicting the probability density function for at least one additional object at several future occasions. The object could for example be another vehicle the probable position of which is predicted at each of a plurality of times subsequent to detection synchronized with the predicted instants of the vehicle. The four future occasions are denoted 11, 12, 13 and 14, for the vehicle equipped with the collision avoidance system, with the same time interval between the future occasions. Thus, the probability of collision for the vehicle and each of the surrounding objects should be calculated for a sufficient number of future occasions. Based on this, rules are set in the probability domain on when to take evasive action or brake. The probability density function can for example be calculated by using the extended Kalman filter to predict the vehicles and surrounding objects future positions as well as their associated covariance matrix.);
computing a risk metric representing a likelihood of the proxemic risk to the first vehicle based on an overlap between the first probability distribution of the PDF and the second probability distribution of the second PDF (see paragraph [0015], [0029] and [0034]: The inventive method also comprises the step of predicting the probability density function for at least one additional object at several future occasions. The object could for example be another vehicle the probable position of which is predicted at each of a plurality of times subsequent to detection synchronized with the predicted instants of the vehicle. For the other vehicle the four future occasions are denoted 21, 22, 23 and 24. Thus, the probability of collision for the vehicle and each of the surrounding objects should be calculated for a sufficient number of future occasions. Based on this, rules are set in the probability domain on when to take evasive action or brake. The probability density function can for example be calculated by using the extended Kalman filter to predict the vehicles and surrounding objects future positions as well as their associated covariance matrix.); and
in response to the risk metric exceeding a defined risk threshold (see paragraphs [0032] and [0049]: If, for example, the time interval had been twice as long (FIGS. 4b and 4 d) the probability density functions would pass each other, which then would result in a possible danger not being discovered. However, the calculations are repeated continuously with a frequency large enough to avoid such risks. The threshold for collision avoidance maneuver can be set to alarm when the probability Px and Py are greater than some values Tx and Ty. Tx and Ty are design parameters who should be dependent on the velocity of the vehicle.),
Lars discloses an alarm signal in a normal situation like this when the probability of collision is very low would be most annoying to the driver. Lars does not explicitly teach control at least one haptic output unit, embedded in the first vehicle, to provide haptic output indicative of the proxemic risk.
Altan Changes in the frequency and/or amplitude of vibration with time could also be used to indicate a change in the probability or imminence of a threat from cautionary up through truly imminent. It is also comprehended that the use of active materials as haptic feedback devices has potentially wide application. (see paragraph [0022]).
It would have been obvious to one skilled in the art before the effective filing date of the invention, to combine the collision probability of Lars with the haptic output units embedded in the vehicle seat and steering wheel of Altan that is driven by collision threat detection in order to provide when and how a driver is warned by using a trigger for existing haptics.
With respect to dependent claim 2, Lars discloses wherein the first PDF is a 1D Gaussian distribution having a mean defined by an estimated stopping distance of the first vehicle relative to the sensed position and a standard deviation defined by a variation in sensed speed of the first vehicle )see paragraph [0014], [0017] and [0018]: This probability can be illustrated with a three dimensional plot of a probability distribution, for example a Gaussian normal distribution, where the peak corresponds to the most probable position of the vehicle at a certain time. The probability of collision is thereby calculated and can be used as a measure for basing decisions upon. The result from the calculation is a value between 0 and 1 where 0 means 0% probability of collision and 1 means 100% probability of collision. One of the actions to avoid collision could be braking, but depending on the speed an evasive action might be more suitable. The distance needed to brake to a full stop increases radically with increased speed.).
With respect to dependent claim 3, Lars discloses wherein the sensed feature is a sensed location of another vehicle in the proximity of the first vehicle, and the second PDF represents likelihood of a future position of the other vehicle (see paragraph [0015] and [0029]: The inventive method also comprises the step of predicting the probability density function for at least one additional object at several future occasions. The object could for example be another vehicle the probable position of which is predicted at each of a plurality of times subsequent to detection synchronized with the predicted instants of the vehicle. For the other vehicle the four future occasions are denoted 21, 22, 23 and 24.);
wherein the second PDF is a 1D Gaussian distribution having a mean defined by the sensed location of the other vehicle and a standard deviation defined by a variation in relative distance between the first vehicle and the other vehicle (see paragraphs [0014] and [0034]: Current velocity, change in velocity, position, size, direction and rate of direction change are taken into consideration when predicting the probability density function of the vehicle. This probability can be illustrated with a three dimensional plot of a probability distribution, for example a Gaussian normal distribution, where the peak corresponds to the most probable position of the vehicle at a certain time. The probability of collision for the vehicle and each of the surrounding objects should be calculated for a sufficient number of future occasions. Based on this, rules are set in the probability domain on when to take evasive action or brake. The probability density function can for example be calculated by using the extended Kalman filter to predict the vehicles and surrounding objects future positions as well as their associated covariance matrix. The following is an example describing such a calculation. Calculating the probability density function using the Kalman filter is a relatively simple method.); and
wherein the risk metric is computed based on an area of the overlap between the first PDF and the second PDF (see paragraph [0032]: FIG. 4a shows the probability density functions 11 and 21, the closest in time to the present positions of the vehicles. Hence, the diagram in FIG. 4b shows the probability density functions 12 and 22, the diagram in FIG. 4c shows the probability density functions 13 and 23 and the diagram in FIG. 4d shows the probability density functions 14 and 24. Preferably the time intervals are chosen short. In FIG. 4c the probability density functions of the vehicles partly overlap each other. If, for example, the time interval had been twice as long (FIGS. 4b and 4 d) the probability density functions would pass each other, which then would result in a possible danger not being discovered. However, the calculations are repeated continuously with a frequency large enough to avoid such risks.).
With respect to dependent claim 5, Lars discloses wherein the standard deviation of the second PDF is defined based on data about historical safe trajectories associated with the lane (see paragraph [0034]: the probability of collision for the vehicle and each of the surrounding objects should be calculated for a sufficient number of future occasions. Based on this, rules are set in the probability domain on when to take evasive action or brake. The probability density function can for example be calculated by using the extended Kalman filter to predict the vehicles and surrounding objects future positions as well as their associated covariance matrix. The following is an example describing such a calculation. Calculating the probability density function using the Kalman filter is a relatively simple method.).
With respect to dependent claims 6 and 17, Lars discloses wherein the risk metric is computed using a binary logarithm and the risk metric is represented using bits (see paragraph [0017]: The next step according to the inventive method is to integrate the joint probability density function over the area in which the vehicle and the object are in physical conflict. The probability of collision is thereby calculated and can be used as a measure for basing decisions upon. The result from the calculation is a value between 0 and 1 where 0 means 0% probability of collision and 1 means 100% probability of collision.).
With respect to dependent claim 7, Lars discloses wherein the one or more sensors include at least one of: a camera unit, a radar unit, a global navigation satellite system (GNSS) unit, a LIDAR unit or an ultrasound unit (see paragraph [0015]: The detection could be made by for example radar or laser.).
With respect to dependent claim 8, Lars dose not explicitly teach wherein the at least one haptic unit is controlled to output vibrations at a frequency and intensity based on a magnitude of the risk metric.
Altan discloses active material based haptic alerts can be used in connection with alerting/awakening the driver of/from his drowsiness, alerting of excessive distraction from the driving function due to excessive workload (for example vibration intensity increase as workload factors such as cell phone use increase), alerting of the need to turn headlights on and/or the turn signal off, alerting of the presence of a vehicle in one's blind spot, for example, when one activates the turn signal or starts to turn the wheel for a lane change, low fuel levels, and the like. (See paragraph [0057]).
It would have been obvious to one skilled in the art before the effective filing date of the invention, to combine
It would have been obvious to one skilled in the art before the effective filing date of the invention, to combine the collision probability of Lars that gives a continuous collision probability value and mapping a vibration amplitude or patten to reflect risk severity more finely with different patterns and intensities of vibration for different warning types and severities of Altan in order to provide when and how a driver is warned by using a trigger for existing haptics.
With respect to dependent claims 9 and 18, Lars does not explicitly teach wherein a direction of the likely proxemic risk is determined based on the sensed feature, and wherein the at least one haptic unit is controlled to provide haptic output indicative of the direction of the likely proxemic risk.
Altan discloses the area of the seat cushion that is vibrated is spatially mapped to the corresponding direction of the collision threat, as indicated below:
Direction of Collision Threat
General Area
(Degrees offset from driver using 0°
of Seat Cushion
as straight ahead reference point)
That is Vibrated
Forward-Straight Ahead (0°)
Front (A, C)
Forward-Left Side (−45°)
Front-Left (A)
Forward-Right Side (+45°)
Front-Right (C)
Side-Left of Vehicle (−90°)
Left Side-Center (D)
Side-Right of Vehicle (+90°)
Right Side-Center (F)
Rearward-Straight Back (180°)
Rear-Center (H)
Rearward-Left Side (−135°)
Rear-Left (G)
Rearward-Right Side (+135°)
Rear-Right (I)
In this example, seat vibration collision alerts corresponding to the four cardinal and four oblique directions in the haptic seat 208 are represented. The letters in parenthesis represent the partition, or matrix, locations as labeled in the haptic seat 208 illustrated in FIG. 2. A picture of a seat pan portion 210 of a seat cushion 212 with the partition locations marked is depicted in FIG. 3 (See paragraphs [0068] and [0069]).
With respect to dependent claims 10 and 19, Lars does not explicitly teach wherein there is a plurality of haptic units embedded in a respective plurality of locations in the first vehicle, and at least one selected haptic unit is selected from the plurality of haptic units to provide the haptic output, the at least one selected haptic unit being embedded in a respective location in the first vehicle corresponding to the direction of the likely proxemic risk.
Altan discloses active material devices that can also be located in specific locations in the seat, the steering wheel, pedals, and the like, and actuated in a certain sequence or just in select locations to convey additional feedback to the driver, for example, as to direction of the condition. Expanding on this, activation of just a section on the left side of the seat, for example, could indicate detection of a condition from the left direction. (See paragraph [0022]). Within each section an active material actuator can be disposed in operative communication with seat surface to provide seat vibrotactile sensation to the seat occupant. For example, a piezoelectric patch 214 can be disposed within the seat cushion and in close proximity to the seat surface. (See paragraphs [0069]).
With respect to dependent claim 11, Lars does not explicitly teach wherein the direction of the likely proxemic risk is from a front of the first vehicle, and the at least one selected haptic unit is embedded in a steering wheel of the first vehicle.
Altan discloses another exemplary embodiment utilizes steering wheel vibration as a haptic collision alert to indicate to the driver of a vehicle the presence, direction, and urgency of a collision threat in a vehicle equipped with multiple collision avoidance (or warning) systems as illustrated in FIG. 1. The driver experiences collision alerts, or cues, through the steering wheel where the driver's hands contact the steering wheel. (See paragraph [0073]).
With respect to dependent claim 12, Lars does not explicitly teach wherein the direction of the likely proxemic risk is from a side of the first vehicle, and the at least one selected haptic unit is embedded in a side of a driver's seat of the first vehicle.
Altan discloses active material devices that can also be located in specific locations in the seat, the steering wheel, pedals, and the like, and actuated in a certain sequence or just in select locations to convey additional feedback to the driver, for example, as to direction of the condition. Expanding on this, activation of just a section on the left side of the seat, for example, could indicate detection of a condition from the left direction. (See paragraph [0022]).
With respect to dependent claim 13, Lars does not explicitly teach wherein the direction of the likely proxemic risk is from a rear of the first vehicle, and the at least one selected haptic unit is embedded in a back of a driver's seat of the first vehicle.
Altan discloses the area of the seat cushion that is vibrated is spatially mapped to the corresponding direction of the collision threat, as indicated below:
Direction of Collision Threat
General Area
(Degrees offset from driver using 0°
of Seat Cushion
as straight ahead reference point)
That is Vibrated
Forward-Straight Ahead (0°)
Front (A, C)
Forward-Left Side (−45°)
Front-Left (A)
Forward-Right Side (+45°)
Front-Right (C)
Side-Left of Vehicle (−90°)
Left Side-Center (D)
Side-Right of Vehicle (+90°)
Right Side-Center (F)
Rearward-Straight Back (180°)
Rear-Center (H)
Rearward-Left Side (−135°)
Rear-Left (G)
Rearward-Right Side (+135°)
Rear-Right (I)
In this example, seat vibration collision alerts corresponding to the four cardinal and four cardinal and four oblique directions in the haptic seat 208 are represented. The letters in parenthesis represent the partition, or matrix, locations as labeled in the haptic seat 208 illustrated in FIG. 2. (See paragraphs [0068] and [0069]).
With respect to dependent claims 9-13, 18 and 19, It would have been obvious to one skilled in the art before the effective filing date of the invention, to combine the collision probability of Lars that provides when and where a collision is likely to take place to the host vehicle with the seat of Altan that is divided into zones and provides vibrations that correspond with the zones in which the zone is related to the collision threat area of the vehicle in order to provide risk direction immediately clear to a driver.
With respect to dependent claim 15, Lars discloses wherein the first PDF is a 1D Gaussian distribution having a mean defined by an estimated stopping distance of the first vehicle relative to the sensed position and a standard deviation defined by a variation in sensed speed of the first vehicle (see paragraph [0018]: One of the actions to avoid collision could be braking, but depending on the speed an evasive action might be more suitable. The distance needed to brake to a full stop increases radically with increased speed. However, the distance needed to make an evasive maneuver increases linearly with the speed and at speeds higher than approximately 40 km/h (˜25 mph) the distance needed to make an evasive maneuver is shorter than the distance needed to brake in order to avoid a collision.);
wherein the sensed feature is a sensed location of another vehicle in the proximity of the first vehicle, and the second PDF represents likelihood of a future position of the other vehicle (see paragraph [0015] and [0029]: The inventive method also comprises the step of predicting the probability density function for at least one additional object at several future occasions. The object could for example be another vehicle the probable position of which is predicted at each of a plurality of times subsequent to detection synchronized with the predicted instants of the vehicle. For the other vehicle the four future occasions are denoted 21, 22, 23 and 24.);
wherein the second PDF is a 1D Gaussian distribution having a mean defined by the sensed location of the other vehicle and a standard deviation defined by a variation in relative distance between the first vehicle and the other vehicle (see paragraphs [0014] and [0034]: Current velocity, change in velocity, position, size, direction and rate of direction change are taken into consideration when predicting the probability density function of the vehicle. This probability can be illustrated with a three dimensional plot of a probability distribution, for example a Gaussian normal distribution, where the peak corresponds to the most probable position of the vehicle at a certain time. The probability of collision for the vehicle and each of the surrounding objects should be calculated for a sufficient number of future occasions. Based on this, rules are set in the probability domain on when to take evasive action or brake. The probability density function can for example be calculated by using the extended Kalman filter to predict the vehicles and surrounding objects future positions as well as their associated covariance matrix. The following is an example describing such a calculation. Calculating the probability density function using the Kalman filter is a relatively simple method.); and
wherein the risk metric is computed based on an area of the overlap between the first PDF and the second PDF (see paragraph [0032]: FIG. 4a shows the probability density functions 11 and 21, the closest in time to the present positions of the vehicles. Hence, the diagram in FIG. 4b shows the probability density functions 12 and 22, the diagram in FIG. 4c shows the probability density functions 13 and 23 and the diagram in FIG. 4d shows the probability density functions 14 and 24. Preferably the time intervals are chosen short. In FIG. 4c the probability density functions of the vehicles partly overlap each other. If, for example, the time interval had been twice as long (FIGS. 4b and 4 d) the probability density functions would pass each other, which then would result in a possible danger not being discovered. However, the calculations are repeated continuously with a frequency large enough to avoid such risks.).
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lars in view of Altan as applied to claim 1 above, and further in view of U.S. Patent Publication No. 2020/0086859 to McGill et al. (hereinafter “McGill”).
With respect to dependent claims 4 and 16, Lars discloses wherein the first vehicle is moving within a lane, and the second PDF represents a distribution of safe trajectories within the lane (see paragraph [0034]: the probability of collision for the vehicle and each of the surrounding objects should be calculated for a sufficient number of future occasions. Based on this, rules are set in the probability domain on when to take evasive action or brake. The probability density function can for example be calculated by using the extended Kalman filter to predict the vehicles and surrounding objects future positions as well as their associated covariance matrix. The following is an example describing such a calculation. Calculating the probability density function using the Kalman filter is a relatively simple method.);
wherein the second PDF is a 1D Gaussian distribution having a mean defined by a midpoint of a width of the lane (see paragraph [0030]: The probability density functions 11 and 21 are the ones closest in time to the present location and thus the peaks are higher than for the functions 12, 13, 14, 22, 23 and 24, i.e. the probabilities are high for the vehicles to be in this area. Contrary, the peaks of the probability density functions 14 and 24 are lower but the functions are on the other hand wider, i.e. the further away in the future the more alternative positions. The probability that the vehicle ends up in a specific position is lower since the time difference between the present position and the future position is long and therefore larger changes can occur, for example changes in direction and velocity.); and
wherein the risk metric is computed based on a complement of the overlap between the first PDF and the second PDF (see paragraph [0032]: the probability density functions of the vehicles partly overlap each other. If, for example, the time interval had been twice as long (FIGS. 4b and 4 d) the probability density functions would pass each other, which then would result in a possible danger not being discovered.).
Lars does not explicitly teach the sensed feature is a sensed boundary of the lane.
McGill discloses a risk estimation module also includes instructions to discretize the at least one other lane into a plurality of segments. The risk estimation module also includes instructions to determine a trajectory along which the vehicle will travel relative to the intersection. The risk estimation module also includes instructions to estimate a probability density function for whether a road agent external to the vehicle is present in the respective segments in the plurality of segments based, at least in part, on the sensor data. In connection with estimating the risk of a vehicular maneuver by vehicle 100 (sometimes referred to herein as an “ego vehicle”) and using that information to assist vehicle 100 in navigating intersections, intersection management system 170 can store geometric representation data 250 for intersections (discussed further below) and various kinds of model data 260 in database 240. Risk estimation module 220 discretizes the at least one other lane into a plurality of segments. (See paragraphs [0005], [0028] and [0060]).
It would have been obvious to one skilled in the art before the effective filing date of the invention to combine probability density function driven by speed, acceleration and uncertainty of Lars with the lane geometry and shape of McGill in order to provide probability distributions over lane segments for effectively containing the collision probability calculations for accurate risk metrics.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEMETRA R SMITH-STEWART whose telephone number is (571)270-3965. The examiner can normally be reached 10am - 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan can be reached at 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DEMETRA R SMITH-STEWART/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661