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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,412,471 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following:
Claims 1 and 19 of the present application appear to be similar to claims 1 and 18 of U.S. Patent No. 12,412,471 B2 with the exception of the following:
logged trajectory being replaced by observed trajectory
adjusting based on map information and the logged trajectory of the logged road user, respective headings of a first and second set of candidate lane segments, the first set of candidate lane segments being associated with wrong-way driving is not recited in claim 1 of the current application.
using the selected candidate lane segment to train a model to provide a likelihood of a road user being engaged in wrong-way driving is also not recited in claim 1 of the current application.
Accordingly, claims 1 and 19 of the present application appear to be a broader version of claims 1 and 18 of U.S. Patent No. 12,412,471 B2.
Claims 2-12 of the present application appear to be similar to claims 2-12 of U.S. Patent No. 12,412,471 B2.
Claim 13 of the present application appear to be similar to the training step of claim 1 of U.S. Patent No. 12,412,471 B2.
Claim 14 of the present application appear to be similar to claims 13-14 of U.S. Patent No. 12,412,471 B2.
Claims 15-16 and 18 of the present application appear to be similar to claims 15-17 of U.S. Patent No. 12,412,471 B2.
Claims 17 and 20 of the present application appear to be similar to the adjusting step of claim 1 of U.S. Patent No. 12,412,471 B2.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) accessing log data including an observed trajectory, identifying a first set of candidate lane segments for wrong-way driving, identifying a second set of candidate lane segments for not wrong-way driving, for each candidate lane segment, determining a distance cost between the candidate lane and the observed trajectory, selecting a candidate lane segment based on the distance cost, determining a likelihood of a second user being engaged in wrong-way driving, and providing data based on the likelihood to a second vehicle.
The limitations of accessing log data including an observed trajectory, identifying a first set of candidate lane segments for wrong-way driving, identifying a second set of candidate lane segments for not wrong-way driving, for each candidate lane segment, determining a distance cost between the candidate lane and the observed trajectory, selecting a candidate lane segment based on the distance cost, and determining a likelihood of a second user being engaged in wrong-way driving, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by one or more processors,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by one or more processors” language, the recited limitations in the context of the claims encompasses user observing the data and mentally making a decision or a conclusion of the likelihood of a second user being engaged in wrong-way driving using observation, evaluation, judgment, and opinion. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of using one or more processors to perform the recited limitations. The one or more processors are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Regarding the additional limitations of providing data based on the likelihood to a second vehicle, the examiner submits that this limitation is insignificant extra-solution activities. In particular, providing the data is recited at a high level of generality (i.e. as a general means of transmitting data from the determining step), and amounts to mere post solution output, which is a form of insignificant extra-solution activity. 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 limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using one or more processors to perform the recited steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of providing the data is well-understood, routine, and conventional activities. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claims are not patent eligible.
Dependent claim(s) 2-18, and 20 do not recite any further limitations that cause the claim(s) 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. Claims 2-14 and 17-18 recite additional steps that can be performed mentally using observation, evaluation, judgment, and opinion and fall under the mental process grouping of abstract ideas. Claims 15 and 16 recite a decision tree model and a deep neural network. These limitations are not indicative of integration into a practical application, because they are recited at a high level of generality and are considered mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Therefore, dependent claims 2-18, and 20 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1 and 19.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kashihara et al US 2023/0360533 A1 (hence Kashihara) in view of Nayak et al US 11,551,548 B1 (hence Nayak).
In re claims 1 and 19, Kashihara discloses a wrong-way driving determination apparatus that determines whether a vehicle is traveling the wrong way (Abstract) and teaches the following:
accessing, by one or more processors of one or more server computing devices (Paragraph 0021 “apparatus 10 may partially be implemented on a server installed outside the subject vehicle”), log data associated with a first vehicle and including an observed trajectory of a first road user (Paragraphs 0029 and 0040, “information on a location and a direction of travel of the subject vehicle on the road on which the subject vehicle is traveling”, and Paragraphs 0070-0072);
identifying, by the one or more processors, from map information, a first set of candidate lane segments for wrong-way driving (Fig.2, Paragraph 0024 “a proper direction of travel of each lane of the road on which the subject vehicle is traveling”, Paragraph 0026 “the road information generation unit 11 may determine the proper directions of travel of the lanes using a result of recognition”, Paragraph 0039, and Paragraphs 0070-0072);
identifying, by the one or more processors, from the map information, a second set of candidate lane segments for not wrong-way driving (Paragraph 0025 “Information on proper directions of travel (D1 to D4 in FIG. 3) of the respective lanes can be generated based on travel direction markings 52 (road surface markings indicated by arrows) painted on road surfaces of the lanes and directions of travel of non-subject vehicles 53 traveling in the lanes”, Paragraph 0039, and Paragraphs 0070-0072);
for each candidate lane segment in the first set and in the second set, determining, by the one or more processors, a distance cost between the candidate lane segment and the observed trajectory (Paragraph 0031 “the road information may further include information on the width and the location of the centerline of each lane and the like. In this case, accuracy of determination (i.e., accuracy of mapping) of the lane in which the subject vehicle is traveling can be improved by the wrong-way driving determination unit 13 determining the lane in which the subject vehicle is traveling while taking the information on the width and the location of the centerline of each lane and the like into consideration”, Paragraph 0042 “calculates the orientation difference Y between the proper direction of travel of the lane and the direction of travel of the subject vehicle (step S201). In this case, when the orientation difference Y is 90° or more (YES in step S202), the wrong-way driving determination unit 13 calculates the wrong-way driving possibility P1 based on the orientation difference Y (step S203). On the other hand, when the orientation difference Y is less than 90° (NO in step S202), the wrong-way driving determination unit 13 calculates the wrong-way driving possibility P1 as zero (step S204)”, and Paragraph 0075);
selecting, by the one or more processors, a candidate lane segment from at least one of the first set or the second set based on the determined distance costs (Paragraph 0042 as recited above and Paragraph 0075 “the wrong-way driving determination unit 13 determines the lane in which the subject vehicle is traveling from the location of each lane included in the map information and the absolute location of the subject vehicle to map the location of the subject vehicle onto the lane represented by the map information. The wrong-way driving determination unit 13 calculates the difference (orientation difference Y) between the proper direction of travel of the lane included in the map information and the direction of travel of the subject vehicle known from a change in location of the subject vehicle, and calculates the wrong-way driving possibility P3 based on the orientation difference Y”);
determining, by the one or more processors and using the selected candidate lane segment, a first likelihood of a second road user being engaged in wrong-way driving in a lane (Paragraph 0030 “the wrong-way driving determination unit 13 determines whether the subject vehicle is traveling the wrong way based on a value of the calculated wrong-way driving possibility P1, and outputs a result of determination”);
However, Kashihara doesn’t explicitly teach the following:
a first autonomous vehicle
and providing, by the one or more processors to a second autonomous vehicle, data based on the first likelihood, the data enabling the second autonomous vehicle to determine a second likelihood of a third road user, observed by the second autonomous vehicle, being engaged in wrong-way driving
Nevertheless, Nayak discloses an apparatus configured to training and using a machine learning model to predict wrong-way-driving (WWD) events associated with vehicles (Abstract) and teaches the following:
a first autonomous vehicle (Col.4, lines 33-63 “the vehicle 105 is an autonomous vehicle”)
and providing, by the one or more processors to a second autonomous vehicle, data based on the first likelihood, the data enabling the second autonomous vehicle to determine a second likelihood of a third road user, observed by the second autonomous vehicle, being engaged in wrong-way driving (Col.18, lines 19-44 “The notification module 305 may cause the notification to: (1) UE 101; (2) one or more other UEs associated with one or more passengers of the vehicle 105; (3) one or more passengers of the vehicle 105 via interior speakers within the cabin of the vehicle 105; (4) one or more vehicles proximate to the vehicle 105 or cause the vehicle 105 to provide the notification to the one or more vehicles via vehicle-to-vehicle (V2V) communication; or (5) a combination thereof”)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Kashihara reference to include an autonomous vehicle and causing the notification to one or more other UEs associated with one or more passengers of the vehicle, as taught by Nayak, with a reasonable expectation of success, in order to improve overall safety within a road network (Nayak, Col.22, lines 14-20).
In re claim 2, Nayak teaches the following:
wherein identifying the first set includes using a first threshold distance from the observed trajectory (Col.8, lines 25-57 “The WWD assessment platform 123 is capable of identifying whether a WWD event has occurred. In one embodiment, the WWD assessment platform 123 may detect that a WWD event has occurred when the vehicle 105:” and “(5) detects that a relative distance between the front of the vehicle and another object (e.g., a vehicle, a barrier, etc.) is less than a threshold distance (e.g., the relative distance becomes less than 4.2 meters) at the location”; motivation to combine has been provided supra)
In re claim 3, Nayak teaches the following:
wherein the first threshold distance is a radial distance (Col.8, lines 37-41 “detects that a relative distance between the front of the vehicle and another object (e.g., a vehicle, a barrier, etc.) is less than a threshold distance (e.g., the relative distance becomes less than 4.2 meters) at the location”)
In re claim 4, Nayak teaches the following:
wherein identifying the second set includes using a second threshold distance from the observed trajectory (Col.8, lines 37-41 “detects that a relative distance between the front of the vehicle and another object (e.g., a vehicle, a barrier, etc.) is less than a threshold distance (e.g., the relative distance becomes less than 4.2 meters) at the location”)
In re claim 6, Kashihara teaches the following:
assigning a value to the selected candidate lane segment based on whether the selected candidate lane segment is from the first set or the second set (Fig.2, Fig.3, and Paragraph 0025 “Information on proper directions of travel (D1 to D4 in FIG. 3) of the respective lanes can be generated based on travel direction markings 52 (road surface markings indicated by arrows) painted on road surfaces of the lanes and directions of travel of non-subject vehicles 53 traveling in the lanes”, Paragraph 0031 “The wrong-way driving determination unit 13 determines a lane in which the subject vehicle is traveling from the location of each lane included in the road information and the location of the subject vehicle included in the travel state information to map the location of the subject vehicle represented by the travel state information onto the lane represented by the road information.”)
In re claim 7, Kashihara teaches the following:
wherein the value indicates that wrong-way driving occurred when the selected candidate lane segment is from the first set (Paragraph 0031 “The wrong-way driving determination unit 13 determines a lane in which the subject vehicle is traveling from the location of each lane included in the road information and the location of the subject vehicle included in the travel state information to map the location of the subject vehicle represented by the travel state information onto the lane represented by the road information.”)
In re claim 8, Kashihara teaches the following:
wherein the value indicates that wrong-way driving did not occur when the selected candidate lane segment is from the second set (Paragraph 0031 “The wrong-way driving determination unit 13 determines a lane in which the subject vehicle is traveling from the location of each lane included in the road information and the location of the subject vehicle included in the travel state information to map the location of the subject vehicle represented by the travel state information onto the lane represented by the road information.”)
In re claim 9, Kashihara teaches the following:
wherein the observed trajectory includes one or more locations, and each distance cost for a particular candidate lane segment of the first set or the second set is determined by taking an average of distances between each location of the observed trajectory and a closest location on the particular candidate lane segment (Paragraph 0031 “As described above, while the road information is only required to at least include the information on the location and the proper direction of travel of each lane of the road on which the subject vehicle is traveling, the road information may further include information on the width and the location of the centerline of each lane and the like. In this case, accuracy of determination (i.e., accuracy of mapping) of the lane in which the subject vehicle is traveling can be improved by the wrong-way driving determination unit 13 determining the lane in which the subject vehicle is traveling while taking the information on the width and the location of the centerline of each lane and the like into consideration”)
In re claim 10, Kashihara teaches the following:
wherein each distance cost for the first set is determined further based on a heading cost (Paragraph 0024 “The road information at least includes information on a location and a proper direction of travel of each lane of the road on which the subject vehicle is traveling.”)
In re claim 11, Kashihara discloses calculates a wrong-way driving possibility P1 of the subject vehicle based on the road information generated by the road information generation unit 11 and the travel state information of the subject vehicle (Paragraphs 0030-0033) but doesn’t explicitly teach the following:
wherein the heading cost for a given candidate lane segment of the first set is determined based on a cosine of an angular difference between a heading of the observed trajectory and a heading of the given candidate lane segment adjusted 180 degrees
It would have been an obvious of design choice, to one having ordinary skills in the art at the time the invention was filed, to have modified the Kashihara reference to include a cosine of an angular difference between a heading of the observed trajectory and a heading of the given candidate lane segment adjusted 180 degrees, since applicant has not disclosed that this specific function solves any stated problem or is for any particular purpose and it appears that the invention would perform equally well with the method recited in Kashihara.
In re claim 12, Kashihara discloses calculates a wrong-way driving possibility P1 of the subject vehicle based on the road information generated by the road information generation unit 11 and the travel state information of the subject vehicle (Paragraphs 0030-0033) but doesn’t explicitly teach the following:
wherein the heading cost for a given candidate lane segment of the second set is determined based on a cosine of an angular difference between a heading of the observed trajectory and a heading of the given candidate lane segment
It would have been an obvious of design choice, to one having ordinary skills in the art at the time the invention was filed, to have modified the Kashihara reference to include a cosine of an angular difference between a heading of the observed trajectory and a heading of the given candidate lane segment adjusted 180 degrees, since applicant has not disclosed that this specific function solves any stated problem or is for any particular purpose and it appears that the invention would perform equally well with the method recited in Kashihara.
In re claim 13, Kashihara discloses the invention as recited above but doesn’t explicitly teach the following:
wherein the data includes a model configured to provide likelihoods
Nevertheless, Nayak discloses an apparatus configured to training and using a machine learning model to predict wrong-way-driving (WWD) events associated with vehicles (Abstract) and teaches the following:
wherein the data includes a model configured to provide likelihoods (Col.1, line 34 – Col.2, line 13)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Kashihara reference to include training a machine learning model to perform similar determination, as taught by Nayak, with a reasonable expectation of success, in order to predict the wrong-way-driving event based on one or more attributes associated with wrong-way-driving events (Nayak, Col.1, lines 48-64).
In re claim 14, Kashihara teaches the following:
wherein the model is further configured to provide the likelihoods for at least one of bicyclists or vehicles (Abstract)
In re claim 15, Nayak teaches the following:
wherein the model is a decision tree model (Col.18, lines 16-18)
In re claim 16, Nayak teaches the following:
wherein the model is a deep neural network (Col.18, lines 16-18)
In re claims 17 and 20, Kashihara teaches the following:
wherein identifying the first set includes adjusting, based on map information and the observed trajectory of the first road user, respective headings of the first set (Fig.2, Paragraph 0025 “information on lanes (L1 to L4 in FIG. 3) of the road information can be generated by extracting regions between two parallel lane markings 51. Information on proper directions of travel (D1 to D4 in FIG. 3) of the respective lanes can be generated based on travel direction markings 52 (road surface markings indicated by arrows) painted on road surfaces of the lanes and directions of travel of non-subject vehicles 53 traveling in the lanes”)
In re claim 18, Kashihara teaches the following:
wherein the selected candidate lane segment has a lowest of the determined distance costs (Paragraph 0031 “a lane in which the subject vehicle is traveling from the location of each lane included in the road information and the location of the subject vehicle included in the travel state information to map the location of the subject vehicle represented by the travel state information onto the lane represented by the road information”; matching corresponds to cost near 0)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Beaurepaire et al US 2023/0182775 A1 discloses processing mapping data, sensor data, or a combination thereof to develop a map of an area within a threshold distance of an autonomous vehicle in response to a detection of an oncoming vehicle that is driving in a wrong direction towards the autonomous vehicle and using a decision tree of a plurality of candidate strategies for avoiding the oncoming vehicle to select a strategy based on one or more attributes of the map.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMI KHATIB whose telephone number is (571)270-1165. The examiner can normally be reached M-F: 9:00am-5:30pm.
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, Erin M Piateski can be reached at 571-270 7429. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAMI KHATIB/ Primary Examiner, Art Unit 3669
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