DETAILED ACTION
This action is responsive to the amendments filed 1/21/2026.
Claims 1-6, 8-15, 18 and 21-25 are pending. Claims 1-6, 8-15 and 18 are currently amended. Claims 7, 16, 17, 19 and 20 are canceled, and Claims 21-25 are new.
All prior rejections under 35 U.S.C. §§ 102-103 are withdrawn as necessitated by amendment.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8, 10-15 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Greene, et al., U.S. PGPUB No. 2008/0312833 (“Greene”).
With regard to Claim 1, Greene teaches a method for avoiding a collision between a first traffic participant and a second traffic participant, the method comprising:
receiving, with a computing unit, an indication of a first probability cone, wherein the first probability cone represents a probability of a location, in a space comprising at least two geographical dimensions, of the first traffic participant at one or more times in the future ([0106] describes receiving segmented cones for two vehicles, where [0084] describes that segmented cones represent predictions in three dimensions – x and y spatial dimensions and a t time dimension – regarding where a vehicle might travel);
receiving, with the computing unit, an indication of a second probability cone, wherein the second probability cone represents a probability of a location, in the space comprising at least two geographical dimensions, of the second traffic participant at one or more times in the future ([0106] describes receiving segmented cones for two vehicles, where [0084] describes that segmented cones represent predictions in three dimensions – x and y spatial dimensions and a t time dimension – regarding where a vehicle might travel);
superimposing, with the computing unit, the first probability cone and the second probability cone in the space comprising at least two geographical dimensions ([0106] and Fig. 13B show the segmented cones are generated within the space comprising the x and y dimensions as well as the t time dimension); and
determining a collision probability based on the superimposing of the first probability cone and the second probability cone ([0106] describes that the cones intersecting can trigger a preliminary assessment of the collision risk at the intersection).
With regard to Claim 2, Greene teaches that determining the collision probability comprises: determining one or more probable matches of the location of the first traffic participant and the location of the second traffic participant at a common time in the future. [0110] describes that the preliminary assessment includes estimating a probability, time, and location for the collision.
With regard to Claim 3, Greene teaches receiving, with the computing unit, the indication of the first probability cone from a mobile device carried by the first traffic participant. [0061] describes that a reasoning layer of a principal resident in a vehicle can receive information from the vehicle itself, as well as sensors of other principals, thereby indicating the cone from devices including those carried by cyclists and pedestrians.
With regard to Claim 4, Greene teaches that the first probability cone is based on a type of movement of the first traffic participant, and wherein the type of movement of the first traffic participant is indicative of characteristic movement variables and/or measurable environmental variables and/or capabilities of the first traffic participant. [0093]-[0094] describes that a segmented cone is generated based on the context, including the user’s behavioral patterns, state of the vehicle, and information surrounding the intersection.
With regard to Claim 5, Greene teaches receiving, with the computing unit, the indication of the second probability cone from a second mobile device carried by the second traffic participant. [0061] describes that a reasoning layer of a principal resident in a vehicle can receive information from the vehicle itself, as well as sensors of other principals, thereby indicating the cone from devices including those carried by cyclists and pedestrians.
With regard to Claim 6, Greene teaches that the second probability cone is based on a type of movement of the second traffic participant, and wherein the type of movement of the second traffic participant is indicative of characteristic movement variables and/or measurable environmental variables and/or capabilities of the second traffic participant. [0093]-[0094] describes that a segmented cone is generated based on the context, including the user’s behavioral patterns, state of the vehicle, and information surrounding the intersection. [0105] describes multiple cones generated for each of a plurality of vehicles or other principals approaching an intersection.
With regard to Claim 8, Greene teaches receiving, with the computer unit, the indication of the first probability cone and the indication of the second probability cone. [0061] describes that a given vehicle’s reasoning layer can receive information from both the vehicle itself as well as other principals. Infrastructure can also receive this information, thereby allowing for indications from a first and second principal to be received with the computer unit.
With regard to Claim 10, Greene teaches generating, based on the determined collision probability, a visual and/or aural and/or haptic warning; and communicating the visual and/or aural and/or haptic warning to at least one of the first traffic participant and the second traffic participant. [0205] describes exemplary warnings, which include aural and visual types of warnings.
With regard to Claim 11, Greene teaches that the collision probability is determined in real time, in a prioritized manner with the computer unit, after the computer unit receives the first probability cone and the second probability cone. [0053] describes processing the cones many times per second, thereby providing collision warnings that are actionable in real-time while driving a vehicle.
With regard to Claim 12, Greene teaches that the computing unit includes at least one of a transmission mast and a network node. [0061] describes that the architecture allows principals to transmit data to one another, and also transmit to an infrastructure device.
With regard to Claim 13, Greene teaches that the method is iteratively performed and the method further comprises: obtaining one or more learning parameters from a first performance of the method, the one or more learning parameters based on at least one of: the first probability cone, the second probability cone, a type of movement of the first traffic participant, a type of movement of the second traffic participant, and the determined collision probability; and using the one or more learning parameters from the first performance of the method to determine a second collision probability in a second performance of the method. [0153] describes that parameters for vehicles can be learned by observing behavior, such as a driver’s usual speed. [0053] describes iteratively making assessments several times per second.
With regard to Claim 14, Greene teaches using machine learning to determine at least one of the first probability cone and the second probability cone. [0095] describes that a cone is created using a priori information. [0147] describes that a priori models can be of a type where training data and statistical learning is used to update and improve the models.
With regard to Claim 15, Greene teaches that the computing unit comprises a required computing power executed at a defined edge of a communication network. [0061] describes that the reasoning layer can be resident on principals which communicate with one another, thereby requiring computing power at the edges.
With regard to Claim 18, Greene teaches that the computing unit captures a restricted geographical area, and wherein the restricted geographical area comprises an intersection region of a road including the first traffic participant and the second traffic participant. [0100] describes that the system engages as a vehicle approaches an intersection.
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.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Greene.
With regard to Claim 9, Greene does not teach that determining the collision probability comprises: determining the collision probability is determined within a maximum of 100 milliseconds (ms) after receiving, with the computer unit, the first probability cone and the second probability cone.
However, [0053] describes that the system repetitively processes sensor data many times per second to provide near real-time collision warnings. As Greene was filed in 2007, computing power and calculation speed has increased many times over. Therefore, it is well within the capability of modern computing devices to determine a collision probability in less than 100 ms, given that equipment in 2007 was able to carry out calculations many times per second. It would have been obvious to one of ordinary skill in the art at the time this application was filed to implement Greene on modern hardware capable of faster calculations, to improve system functioning by enabling faster warning calculations and earlier warnings to users.
Claims 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Greene, in view of Campbell, et al., U.S. PGPUB No. 2002/0185324 (“Campbell”).
With regard to Claim 21, Greene teaches a collision probability computing system comprising:
a first mobile device having a first position sensor configured to generate position sensor data representing a geographic position of the first mobile device, wherein the first mobile device is configured to: generate a first probability cone in a space comprising at least two geographical dimensions, wherein the first probability cone is based on the position sensor data representing the geographic position of the first mobile device, and wherein the first probability cone represents a probability of a location, in the space comprising at least two geographical dimension, of the first mobile device at one or more times in the future ([0061] describes that multiple principles can each have a reasoning layer resident thereon for assessing collision probability at an intersection. [0084] describes that segmented cones are generated representing predictions in three dimensions – x and y spatial dimensions and a t time dimension – regarding where a vehicle might travel);
a second mobile device having a second position sensor configured to generate position sensor data representing a geographic position of the second mobile device, wherein the second mobile device is configured to: generate a second probability cone in the space comprising at least two geographical dimensions, wherein the second probability cone is based on the position sensor data representing the geographic position of the second mobile device, and wherein the second probability cone represents a probability of a location, in the space comprising at least two geographical dimension, of the second mobile device at one or more times in the future ([0061] describes that multiple principles can each have a reasoning layer resident thereon for assessing collision probability at an intersection. [0084] describes that segmented cones are generated representing predictions in three dimensions – x and y spatial dimensions and a t time dimension – regarding where a vehicle might travel);
a computing unit that receives an indication of the first probability cone and receives an indication of the second probability cone, wherein the computing unit is configured to: superimpose the first probability cone and the second probability cone in the space comprising at least two geographical dimensions; and determine a collision probability based on the superimposition of the first probability cone and the second probability cone ([0106] and Fig. 13B show the segmented cones are generated within the space comprising the x and y dimensions as well as the t time dimension; the cones intersecting can trigger a preliminary assessment of the collision risk at the intersection).
Greene, in view of Campbell teaches a computing unit that receives an indication of the first probability cone from the first mobile device and receives an indication of the second probability cone from the second mobile device. Greene teaches at [0061] that a reasoning layer can receive information and sensor data from devices of other principals. Campbell teaches at [0043] that a second vehicle can calculate its own path and transmit it to a first vehicle, which compares the second path to the first path in order to determine an anticipatory crash event condition using the vehicle paths.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Greene with Campbell. One of skill in the art would have sought the combination, to improve system functioning by enabling vehicles to transmit their own generated probability cones to other vehicles, thereby reducing the computing load at each vehicle by not requiring calculation of probability cones which have already been calculated by other vehicles.
With regard to Claim 22, Greene teaches that the computing unit includes at least one of a transmission mast and a network node. [0061] describes that the architecture allows principals to transmit data to one another, and also transmit to an infrastructure device.
With regard to Claim 23, Greene, in view of Campbell teaches that the computing unit receives the first probability cone and the second probability cone via a communication network. Greene teaches at [0061] that a reasoning layer can receive information and sensor data from devices of other principals. Campbell teaches at [0043] that a second vehicle can calculate its own path and transmit it to a first vehicle, which compares the second path to the first path in order to determine an anticipatory crash event condition using the vehicle paths. As vehicles communicate with one another as separate entities, they make up a communications network.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Greene with Campbell. One of skill in the art would have sought the combination, to improve system functioning by enabling vehicles to transmit their own generated probability cones to other vehicles, thereby reducing the computing load at each vehicle by not requiring calculation of probability cones which have already been calculated by other vehicles.
With regard to Claim 24, Greene teaches that the first mobile device is carried by a first traffic participant and wherein the second mobile device is carried by a second traffic participant. [0061] describes that the device containing the reasoning layer exists in each of the plurality of principals.
With regard to Claim 25, Greene teaches that the position sensor data representing a geographic position of the first mobile device corresponds to a geographic position of the first traffic participant, wherein the position sensor data representing a geographic position of the second mobile device corresponds to a geographic position of the second traffic participant, and wherein the collision probability is indicative of a collision between the first traffic participant and the second traffic participant. [0093]-[0094] describe the positions in relation to the generation of cones.
Response to Arguments
Applicant’s arguments 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/KEITH D BLOOMQUIST/Primary Examiner, Art Unit 2171
3/6/2026