DETAILED ACTION
Status of Claims
The status of the claims is as follows:
(a) Claims 1-7, 9-15, and 17-22 remain pending.
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 .
Response to Amendments
The Examiner accepts the amendments received on 12/30/2025.
Response to Arguments
The Examiner has considered the Applicant’s submitted Remarks, filed on 12/30/2025. The Examiner below proceeds with a bona fide attempt to respond properly to each argument raised by the Applicant.
To begin, Applicant asserts that “[t]he claims do not ‘recite’ mathematical formulas or purely mental processes,” and instead are directed to “a specific, technological solution to a lane-identification problem in connected-vehicle environments.” Applicant further asserts that the claims use “vehicular sensor data from multiple connected vehicles to determine lane identification in real-world driving environments.”
The Examiner respectfully disagrees. Claims 1, 9, and 17 recite identifying lane-level patterns, assigning vehicles to lane-level patterns to form clusters, determining lane identifications for the clusters, generating a lane identification distribution, estimating a lane identification, and outputting the result. These limitations recite collecting, analyzing, classifying, and evaluating information. More specifically, the claims recite mathematical concepts, including classification, clustering, generating a distribution, and estimation.
Applicant additionally asserts that “‘lane-level patterns’ are not abstract ‘patterns’ in the vacuum,” because the specification describes examples such as acceleration profile patterns, suspension patterns, speed patterns, detected object patterns, road condition patterns, traffic patterns, direction patterns, and driving patterns.
The Examiner respectfully disagrees. The independent claims do not require the specific examples identified by Applicant, do not recite a specific algorithm for identifying those patterns, do not recite particular rules or thresholds for assigning vehicles to clusters, and do not recite a specific technological mechanism for generating the lane identification distribution. On the contrary, the claims broadly recite analyzing vehicle-related data to identify patterns, form clusters, generate a distribution, estimate a lane identification, and output the result.
Applicant further asserts that the claims “recite automatic vehicle actuation based on the lane identification outputs,” including maintaining a lane above a probability threshold and performing lane changes to avoid obstructions.
The Examiner respectfully disagrees as to independent claims 1, 9, and 17. These claims do not recite transmitting control inputs to a vehicle actuation system, actuating steering, throttle, or braking, maintaining a lane, or performing a lane change. Instead, claims 1, 9, and 17 end with “outputting the lane identification distribution and the estimated lane identification of the first vehicle.” Therefore, Applicant’s vehicle-actuation argument is not within the scope with independent claims 1, 9, and 17.
Applicant asserts that, even if the claims recite an abstract idea, the claims integrate the alleged exception into a practical application because the claimed process improves lane-identification technology and is tied to vehicle systems.
The Examiner respectfully disagrees. The additional elements of claims 1, 9, and 17 include generic computer implementation, vehicle sensor data, a road-segment environment, generic processor/memory components, a non-transitory machine-readable medium, and outputting the result. These elements do not integrate the abstract idea into a practical application. The sensor data merely provides input data for the analysis, the vehicle/road context limits the abstract idea to a particular field of use, and the outputting step merely reports the result of the abstract analysis.
Applicant also asserts that the claims recite an “unconventional ordered combination” that improves lane-identification technology itself and amounts to “significantly more.”
The Examiner respectfully disagrees. The features relied upon by Applicant, including identifying patterns, clustering vehicles, generating a lane identification distribution, and estimating lane identification, are part of the abstract idea itself. These limitations cannot supply the inventive concept. The remaining additional elements are generic computer components, vehicle sensor data used as input, and outputting the result. Viewed individually and as an ordered combination, these elements do not amount to significantly more than the judicial exception.
Accordingly, Applicant’s arguments are not persuasive, and the rejection of independent claims 1, 9, and 17 under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7, 9-15, and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is not directed to patent eligible subject matter. Specifically, the claimed invention is directed to a judicial exception without significantly more.
Analysis for Independent Claims 1, 9, and 17:
Step 1: Determining if claim(s) are directed a statutory class of invention (i.e., process, machine, manufacture, or composition of matter).
Independent claims 1, 9, and 17 are directed to statutory categories. (Step 1: yes)
Step 2A Prong One: Determining if the claim(s) recite a judicial exception (e.g., mathematical concepts, certain method of organizing human activity, or a mental processes (MPEP 2106.04).
Under Step 2A, Prong One, claims 1, 9, and 17 recite an abstract idea. Independent claim 1 recites, in relevant part: identifying lane-level patterns based on sensor data from a plurality of other vehicles; assigning each vehicle to one of the lane-level patterns to sort the vehicles into one or more clusters; determining a lane identification for each cluster; generating a lane identification distribution for a first vehicle based on sensor data of the first vehicle and the lane identification for each cluster; and estimating a lane identification for the first vehicle based on the generated lane identification distribution.
The Examiner finds these limitations recite collecting, analyzing, classifying, and evaluating information to determine an estimated lane identification. More specifically, the claim recites analyzing vehicle sensor data, identifying patterns, assigning vehicles to clusters, generating a distribution, and estimating a lane identification based on that distribution. These operations are directed to mathematical concepts, including mathematical calculations, statistical analysis, classification, clustering, and estimation. The limitations also recite mental processes because they involve observation, evaluation, judgment, and determination of information, even though performed using a computer.
Accordingly, claim 1 recites an abstract idea. Claims 9 and 17 recite substantially similar limitations in system and non-transitory machine
Step 2A Prong Two: Determining if additional limitations within the claim(s) integrate the judicial exception into a practical application.
Claim 1 recites the method as “computer implemented” and based on “sensor data.” These recitations describe the use of generic computing elements and conventional sensors to provide input data, which does not amount to integrating the abstract idea into a practical application. The claim does not improve computer functionality or another technical field but merely applies the abstract idea in a routine computer environment.
Claim 9 specifies “processors” and “memory” configured to carry out the abstract data analysis of claim 1. Such components are conventional hardware performing their ordinary functions of executing instructions and storing data. The recitation of generic computing elements does not integrate the abstract idea into a practical application.
Claim 17 recites a “non-transitory machine-readable medium” storing instructions to carry out the abstract steps. Merely specifying a storage medium as the carrier of instructions does not provide a meaningful limitation, nor does it improve the technology of computer memory. The Examiner finds this continues to fail integrating the abstract idea into a practical application.
Step 2B: Determining if the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the judicial exception.
Claim 1 does not recite additional elements that amount to significantly more than the abstract idea. The reliance on generic computing implementation and conventional sensors represents well-understood, routine, and conventional components. These do not provide an inventive concept that transforms the abstract idea into patent-eligible subject matter.
Claim 9 likewise does not recite significantly more. The processors and memory represent conventional components and are recited at a high level of generality. They perform no more than their routine functions in executing the abstract data analysis. This fails to amount to an inventive concept.
Claim 17 similarly lacks an inventive concept. The recitation of a machine-readable medium is a conventional computer storage component, and its said carrier for instructions implementing abstract data analysis does not transform the claim into patent-eligible subject matter.
Conclusion:
The independent claim(s) are directed to the abstract idea of a mental process. Accordingly, claims 1, 9, and 17 are not patent eligible under 35 U.S.C. 101.
Analysis for Dependent Claims 2-7, 10-15, and 17-21:
Step 1: Determining if the claim(s) are directed a statutory class of invention (i.e., process, machine, manufacture, or composition of matter).
The dependent claims are properly directed to claims 1, 9, and 17. As a result, the dependent claims are properly directed to statutory classes. (Step 1: yes)
Step 2A Prong One: Determining if the claim(s) recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity, fundamental economic practices, and “an idea ‘of itself’”).
The dependent claims continue to encompass the mental process established in the independent claim(s). The same analysis of Step 2A Prong One for the independent claim(s) applies. Therefore, the dependent claims are directed to the judicial exception of a mental process.
Step 2A Prong Two: Determining if additional limitations within the claim(s) integrate the judicial exception into a practical application.
The dependent claims recite additional limitations, these limitations, when viewed both individually and in combination for the claim, fail to integrate the judicial exception into a practical application. As a result, the dependent claims are not integrated into a practical application.1
Step 2B: Determining if the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the judicial exception.
The additional elements in the dependent claims fail to recite any additional elements, viewed both individually (i.e., within a claim) and as a whole (i.e., claim set), that amount to significantly more than the judicial exception. The same analysis applies in this step 2B as discussed in Step 2A Prong Two (see independent claim analysis). As a result, the dependent claims fail to claim anything significantly more than the judicial exception and fail to integrate said claims into a practical application.
Conclusion:
The dependent claims are directed to the abstract idea of a mental process. Accordingly, claims 1-7, 9-15, and 17-21 are not patent eligible.
Claim Rejections - 35 USC § 102
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 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 1-7, 9-15, and 17-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jammoussi et al. U.S. P.G. Publication 2017/0316684A1 (hereinafter, Jammoussi).
Regarding Claim 1, Jammoussi describes a computer implemented method for lane identification of a first vehicle on a road segment (lane identification for a vehicle, Jammoussi, Paragraphs 0007, 0027, and Figures 2 and 3), the method comprising:
-identifying, based on sensor data from a plurality of other vehicles that traveled on the road segment, a plurality of lane-level patterns for the plurality of other vehicles (identifying a plurality of other vehicles traveling the same road segment, wherein the other vehicles are determined to be in lane patterns (see lane mapping and lane boundary detection), Jammoussi, Paragraphs 0012-0013, 0027, and 0007-0008 and Figures 2 and 3);
-assigning each vehicle of the plurality of other vehicles to one of the lane-level patterns to sort the vehicles of the plurality of other vehicles into one or more clusters of vehicles (sorting the other vehicles into “clusters” based in part on the lanes for which the vehicles are traveling, Jammoussi, Paragraphs 0007-0008 and Figure 2);
-determining a lane identification for each cluster of vehicles (determining lane position of the clusters of vehicles, Jammoussi, Paragraphs 0027, 0007-0008, and Figures 2 and 3);
-generating a lane identification distribution for the first vehicle based on sensor data of the first vehicle and the lane identification for each cluster of vehicles (generating lane identification and distribution of the vehicles, Jammoussi, Paragraphs 0007-0008, 0027, and Figures 2 and 3);
-estimating a lane identification for the first vehicle based on the generated lane identification distribution (estimating lane identification, wherein the lane identification can be based in part on the distribution of the other vehicles, Jammoussi, Paragraphs 0026-0027 and Figures 2, 3, and 5), and outputting the lane identification distribution and the estimated lane identification of the first vehicle (outputting the lane identification distribution / estimation of the lane identification (320)), Jammoussi, Paragraphs 0027-0030, 0038, 0041-0045 and Figure 3).
Regarding Claim 2, Jammoussi describes the method of claim 1, wherein the sensor data of a vehicle is obtained from a sensor of the vehicle, the sensor comprising at least one of a camera, image sensor, radar sensor, light detection and ranging (LiDAR) sensor, position sensor, audio sensor, infrared sensor, microwave sensor, optical sensor, haptic sensor, magnetometer, communication system and global positioning system (GPS) (sensors for detection can be a plurality of different sensors, such as a camera, Lidar, etc.), Jammoussi, Paragraph 0012 and Figure 1).
Regarding Claim 3, Jammoussi describes the method of claim 1, wherein the sensor data of a vehicle comprises information of an environmental condition, road condition, map, location, lane marker type, traffic, speed, direction, and object encountered by the vehicle (sensor can detect objects encountered by the vehicle, Jammoussi, Paragraph 0012).
Regarding Claim 4, Jammoussi describes the method of claim 3, wherein the object comprises at least one of a pothole, crack, tire marking, faded road marking, debris, occlusion, road reflection, flooding, ice, fire, oil leak, uneven pavement, erosion, raveling, sign, pole, building, structure, pedestrian, animal, and vehicle (object detected can be another vehicle, Jammoussi, Paragraph 0012 and Figures 2 and 3).
Regarding Claim 5, Jammoussi the method of claim 1, wherein the lane-level pattern of a vehicle comprises at least one of an acceleration profile pattern, suspension pattern, speed pattern, detected object pattern, road condition pattern, traffic pattern, direction pattern, and driving pattern (lane determination can be based on detected object pattern (e.g., lane markers or vehicles being a certain distance near the current vehicle (see DR, DL, and CO), Jammoussi, Paragraphs 0012-0013 and Figure 2).
Regarding Claim 6, Jammoussi describes the method of claim 1, wherein the plurality of other vehicles are sorted into clusters of vehicles so that each vehicle in a cluster has a similar lane-level pattern to other vehicles in the cluster (vehicles sorted into cluster based on similar lane level pattern (see again DR, DL, and CO), Jammoussi, Paragraphs 0014-0016 and Figure 2).
Regarding Claim 7, Jammoussi describes the method of claim 1, further comprising updating the lane identification distribution as the first vehicle traverses the road segment according to the sensor data of the first vehicle and the lane identifications for the one or more clusters of vehicles (updating in real-time lane identification, Jammoussi, Paragraphs 0012-0016, 0028, and Figures 2-5).
Regarding Claim 9, the Applicant’s claim has similar limitations to claim 1 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 10, the Applicant’s claim has similar limitations to claim 2 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 11, the Applicant’s claim has similar limitations to claim 3 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 12, the Applicant’s claim has similar limitations to claim 4 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 13, the Applicant’s claim has similar limitations to claim 5 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 14, the Applicant’s claim has similar limitations to claim 6 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 15, the Applicant’s claim has similar limitations to claim 7 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 16, the Applicant’s claim has similar limitations to claim 8 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 17, the Applicant’s claim has similar limitations to claim 1 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 18, the Applicant’s claim has similar limitations to claim 6 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 19, the Applicant’s claim has similar limitations to claim 5 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 20, the Applicant’s claim has similar limitations to claim 8 and therefore are rejected for similar reasons set forth by the Examiner in the rejection of said claim.
Regarding Claim 21, Jammoussi describes the computer implemented method of claim 1, wherein each lane-level pattern comprises a position-indexed sequence of vehicle behaviors along the road segment derived from sensor data of the plurality of other vehicles and of the first vehicle, wherein the behaviors modeling how vehicles of the plurality of other vehicles accelerate, steer, and maneuver in view of lane context and roadway features detected along the road segment (use of sensors to determine vehicle behaviors, such as acceleration, steering, maneuvering, Jammoussi, Paragraphs 0036, 0027, and 0028).
Regarding Claim 22, Jammoussi describes the computer implemented method of claim ,1 wherein outputting the lane identification distribution and the estimated lane identification of the first vehicle comprises automatically transmitting control inputs to a vehicle actuation system of the first vehicle to actuate at least one of a steering system, throttle, and brake to maintain the first vehicle within a lane of the road segment corresponding to the estimated lane identification when a lane identification probability for that lane in the lane identification distribution exceeds a threshold, or execute a lane change to another lane of the road segment to avoid an obstruction indicated by one or more of the lane-level patterns (controlling of the vehicle (e.g., steering, throttle, braking) to allow for the vehicle to continue to travel in said lane or a desired lane, Jammooussi, Paragraphs 0032 and 0006).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. U.S. P.G. Publication 2016/0167582A1 (hereinafter, Chen)
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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1 Claim 22 encompasses a practical application, thus is not included in the 35 U.S.C. 101 rejection.
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