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 .
This office action is in response to applicant’s arguments/remarks and amendments filed on 05/22/2026. Claims 1-3, 13-15, and 19-20 have been amended. No Claims have been cancelled. No Claims have been newly added. Accordingly, claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. With respect to claim 5, the applicant claims “output the determined traffic congestion status on the first lane, wherein the determined traffic congestion status on the first lane corresponds to one of: an enqueuing of the traffic congestion on the first lane, a dequeuing of the traffic congestion on the first lane, or stagnant traffic congestion on the first lane”. It is not clear to the examiner if said traffic congestion status are the same statuses recited above with respect to claim 1 and different. The metes and bounds of the claimed limitation are vague and ill-defined rendering the claim indefinite. According to the examiner’s best knowledge, the claim limitation will be treated as said traffic congestion status being the same as said traffic congestion status of claim 1.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) (claims 1, 13, and 20) recite(s) estimating a first accelerator metric, obtain first probe data, generate a plurality of motion components, apply a machine learning model, generate a set of clusters, determine traffic congestion status, and output the generated set of clusters.
The limitation of “estimating a first accelerator metric, generate a plurality of motion components, generate a set of clusters, and determine traffic congestion”, 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 “one or more processors and a memory”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “one or more processors and a memory” language, “generating” in the context of this claim encompasses the user manually or mentally calculate or identify a motion component and divide the data into a set of clusters. 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. The limitation “apply a machine learning model”, implicitly recite performing mathematical calculation because the specification recites in Paragraph 0093, that the ML model may implement an algorithm that may be, for example, but not limited to, a K-means algorithm. Because the recited machine learning implicitly recites performing mathematical calculations, the limitation falls within the “mathematical concepts” 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 an additional element, “one or more processors and a memory”, to perform the generating steps. The “one or more processors and the memory” in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Regarding the additional limitations of “obtain first probe data, and output the generated set of clusters”, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (one or more processors and a memory) to perform the process. In particular, the “obtain first probe data” step is recited at a high level of generality (i.e. as a general means of gathering vehicle data for use in the generating steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The “output the generated set of clusters” is also recited at a high level of generality (i.e. as a general means of outputting the set of clusters from the generating step), and amounts to mere post solution data 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 claims are directed to an 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 both the generating 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 “obtain first probe data” is well-understood, routine, and conventional activities because the background recites that traditional methods of monitoring and managing traffic often rely on manual observations or fixed sensors, which may provide real-time and comprehensive data on traffic conditions on a road link (Paragraph 0003). 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. The additional limitation of “outputting…,” is also a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying or outputting of data is a well understood, routine, and conventional function. Hence, the claims are not patent eligible.
Dependent claim(s) 2-12, and 14-19 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 2-10, 12, and 14-19 recite additional steps that fall under the mental process. Claim 11 recite additional elements of using one or more sensors. The sensors are all conventional sensors mounted on a vehicle and do not impose any meaningful limits on practicing the abstract idea. Therefore, dependent claims 2-12, and 14-19 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1 and 13.
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.
Claim(s) 1-6, 9, 11-17, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sheynman et al US 2020/0349834 A1 (hence Sheynman).
In re claims 1, 13, and 20, Sheynman discloses detecting changes in road traffic conditions based on vehicle probe data (Abstract) and teaches the following:
memory configured to store a computer-executable instruction; and one or more processors coupled to the memory, wherein the one or more processors (Fig.2, and Paragraph 0036) are configured to:
obtain, from a first vehicle of a set of vehicles associated with a road segment, first probe data associated with a first plurality of probe points (Paragraph 0034 “The processing server 102 may receive probe data from a mobile device 114”, and Paragraph 0026 “The mobile device 114 may be associated, coupled, or otherwise integrated with a vehicle”);
estimate a first accelerator metric associated with the first vehicle based on the obtained first probe data (Paragraph 0048 “Consecutive probe data points are combined at 265 to establish the travel time between points, and this travel time is allocated to all of the road segments the vehicle traversed as established through the path identification”, and Paragraph 0049 “constant computation of vehicle travel speeds along all road segments from all vehicle probes”)
generate a plurality of motion components for each of the first plurality of probe points based on the obtained first probe data and the estimated first accelerator metric (Paragraph 0035 “probe data (e.g., collected by mobile device 114) is representative of the location of a vehicle at a respective point in time and may be collected while a vehicle is traveling along a route”, and Paragraph 0044 “derived vehicle travel times, or vehicle speed. The derived travel time and speed are computed from vehicle reported GPS locations and time stamps”);
apply a machine learning (ML) model implemented as a clustering technique on the generated plurality of motion components for the first plurality of probe points (As recited above, the examiner is interpreting said limitation as analyzing the generated plurality of motion components using a generic processor, Fig.3, Paragraph 0046 “a top-down model based on the clustering of reported speeds along road segments during time epochs”, and Paragraph 0048 “aggregate the travel times/speeds for a particular road segment during a single epoch (e.g., for one minute of time) using most commonly a combination of weighing, windowing, and averaging”);
generate a set of clusters based on the application of the ML model on the generated plurality of motion components, wherein each probe point is assigned within one cluster of the set of clusters (Fig.6, and Paragraph 0060 “the probe data points fall into two distinct clusters” and “the two clusters are formed of spatial subgroups according to the cluster groupings”);
determine traffic congestion status on the road segment based on the generated set of clusters, wherein the determined traffic congestion status corresponds to one of: an enqueuing of a traffic congestion, a dequeuing of the traffic congestion, or stagnant traffic congestion (Paragraph 0060 “providing an indication to a driver or user of a vehicle that congestion exists along a portion of the candidate road”, Fig.11, #550, and Paragraph 0067 “A road traffic condition change message is generated and provided in response to a difference between centroid speeds along the road exceeding a predefined threshold”)
and output the determined traffic congestion status (Fig.6, and Paragraph 0060 “When the candidate road represented in FIG. 6 is displayed on a map, road segments RS1 and RS2, and road segments RS4 and RS5 may appear in green, while road segment RS3 appears in red, providing an indication to a driver or user of a vehicle that congestion exists along a portion of the candidate road” indicates that the clusters are being used, i.e. they are outputted in order to be used)
In re claims 2 and 14, Sheynman teaches the following:
determine the traffic congestion status by comparing an average acceleration of a first set of vehicles within a first cluster of the set of clusters to at least one predefined threshold (Paragraph 0042 “These algorithms are commonly based on some type of averaging of vehicle speeds within a temporal window. The averaging may be used to stop the algorithm from overreacting to the speed of outliers”, Paragraph 0048)
In re claims 3 and 15, Sheynman teaches the following:
wherein the determined traffic congestion status on the road segment corresponds to the enqueuing of the traffic congestion on the road segment based on determining that the average acceleration is less than or equal to a first threshold, a dequeuing of the traffic congestion on the road segment based on determining that the average acceleration is greater than or equal to the second threshold, and corresponds to the stagnant traffic congestion based on determining that the average acceleration is between the first threshold and a second threshold (Paragraph 0005 “ establishing centroid speeds corresponding to clusters of probe speeds; spatially grouping said road segments according to probe-to-cluster mapping; and providing a road traffic condition change message in response to a difference between centroid speeds along the candidate road exceeding a predefined threshold, where the road traffic condition change message includes at least information about said road segment groups that correspond to said clusters”)
In re claims 4 and 16, Sheynman teaches the following:
wherein the first probe data associated with each probe point of the first plurality of probe points comprises of: speed information of the first vehicle at the corresponding probe point, location information of the first vehicle at the corresponding probe point, and timestamp associated with the corresponding probe point (Paragraph 0005 “each probe data point received from a probe apparatus of a plurality of probe apparatuses, each probe apparatus including one or more sensors and being onboard a respective vehicle, where each probe data point includes location information associated with the respective probe apparatus and having at least speed and timestamp information associated with the respective probe apparatus”)
In re claims 5 and 19, Sheynman teaches the following:
the first vehicle is associated with a first lane of a set of lanes within the road segment, and wherein the one or more processors are further configured to: determine traffic congestion status on the first lane based on the generated set of clusters (Paragraph 0029 “lane level vehicle speed profiles”);
and output the determined traffic congestion status on the first lane, wherein the determined traffic congestion status on the first lane corresponds to one of: an enqueuing of the traffic congestion on the first lane, a dequeuing of the traffic congestion on the first lane, or stagnant traffic congestion on the first lane (Fig.6 “RS3” and Paragraph 0060 “road segment RS3 appears in red, providing an indication to a driver or user of a vehicle that congestion exists along a portion of the candidate road”)
In re claims 6 and 17, Sheynman teaches the following:
receive, from a user device, an input associated with a count of the set of clusters (Fig.7 and Paragraph 0062 “precompiled binary tables that may be used for establishing clusters based on maximizing the GVF”);
and generate the set of clusters based on the application of the ML model on the generated plurality of motion components and the received input (Fig.9, and Paragraph 0064 “FIG. 9 illustrates the variance based clustering of the epoch of 11:18:XX of FIG. 8 using the complementary binary tables of FIG.7”)
In re claim 9, Sheynman teaches the following:
estimate a first accelerator metric associated with the first vehicle based on the obtained first probe data; and generate the plurality of motion components for the first plurality of probe points based on the obtained first probe data and the estimated first accelerator metric (Paragraph 0037 “a hardware accelerator”)
In re claim 11, Sheynman teaches the following:
wherein the first probe data is captured using one or more sensors associated with the first vehicle, and wherein the one or more sensors comprises at least one of: a Global Navigation Satellite System (GNSS) sensor, or a speed sensor (Paragraph 0034 “The mobile device 114 may also include a system for tracking mobile device movement, such as rotation, velocity, or acceleration”)
In re claim 12, Sheynman teaches the following:
wherein the one or more processors are further configured to store the first probe data and the generated set of clusters for the first probe data in one or more databases (Fig.9, and Paragraph 0064 “FIG. 9 illustrates the variance based clustering of the epoch of 11:18:XX of FIG. 8 using the complementary binary tables of FIG.7”)
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.
Claim(s) 7 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheynman in view of Karri et al US 2022/0391712 A1 (hence Karri).
In re claims 7 and 18, Sheynman discloses the claimed invention as recited above using speed, and including wherein the determined traffic congestion status corresponds to one of: an enqueuing of a traffic congestion in the first portion of the road segment, a dequeuing of the traffic congestion in the first portion of the road segment, or stagnant traffic congestion in the first portion of the road segment (Paragraph 0029 “lane level vehicle speed profiles”, and Fig.6 “RS3” and Paragraph 0060 “road segment RS3 appears in red, providing an indication to a driver or user of a vehicle that congestion exists along a portion of the candidate road”), but doesn’t explicitly teach the following:
determine an average acceleration of a first set of vehicles of the set of vehicles within a first cluster of the set of clusters; and determine a traffic congestion status in a first portion of the road segment based on the determined average acceleration
Nevertheless, Karri discloses data processing systems, and more specifically, to artificial intelligence systems (Paragraph 0001) and teaches the following:
determine an average acceleration of a first set of vehicles of the set of vehicles within a first cluster of the set of clusters; and determine a traffic congestion status in a first portion of the road segment based on the determined average acceleration (Paragraph 0070 “Examples of vehicle traffic-related contextual information include, but are not limited to… average acceleration rates of vehicles); traffic deceleration rates (e.g., average deceleration rates of vehicles))
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Sheynman reference to include using the acceleration value of the vehicle, as taught by Karri, with a reasonable expectation of success, in order to measure vehicle traffic-related parameters at different geographic locations along various roadways (Karri, Paragraph 0070).
Response to Arguments
Applicant's arguments filed 05/22/2026 have been fully considered but they are not persuasive.
With respect to applicant’s arguments/remarks with respect to the rejection of claims 1-20 under 35 U.S.C. 101 and that ML model limitation involving ML training do not recite a judicial exception and comparing it to example 39, the examiner respectfully disagrees with that statement. Example 39 is about modification and use of digital image data, so that is not analogous to just vehicle position and time which is much simpler. The claims here are more analogous to example 47, i.e. the limitation implicitly set forth, a mathematical calculation, i.e. a clustering technique.
With respect to applicant’s arguments/remarks that claim limitations that encompass AI in a way that cannot be practically performed in a human mind do not fall under the mental process as recited in the claim, the examiner respectfully disagrees with that statement. The limitations, 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. For example, a person can consider an accelerator metric (e.g. predicting whether each vehicle is being accelerated by its driver) and can think about or draw a map of cluster/congestion information. These limitations may be practically performed in the human mind using observation, evaluation, judgment, and opinion, and therefore fall under the mental process bucket of abstract ideas.
With respect to applicant’s arguments/remarks that the claim limitations represents a concrete improvement to the technical field of traffic monitoring, the examiner respectfully disagrees with that statement. As recited above, 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. The additional limitation of “outputting the determined traffic congestion status,” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying or outputting of data is a well understood, routine, and conventional function. Hence, the claims are not patent eligible.
With respect to applicant’s arguments/remarks with respect to the rejection of claims 1-6, 9, 11-17, and 19-20 under 35 U.S.C. 102(a)(1) as being anticipated by Sheynman, and that Sheynman fails to disclose estimating an accelerator metric from the probe data and generating motion components based on that accelerator metric, the examiner respectfully disagrees with that statement. Sheynman discloses in Paragraph 0049 that the approach described with respect to FIG. 3 results in constant computation of vehicle travel speeds along all road segments from all vehicle probes.
With respect to applicant’s argument/remark that Sheynman does not disclose applying a ML clustering technique to such motion components or outputting the specific enqueuing/dequeuing congestion statuses based on those clusters, the examiner respectfully disagrees with that statement. Sheynman discloses applying a ML clustering technique in at least Paragraph 0046 “a top-down model based on the clustering of reported speeds along road segments during time epochs”. Furthermore, the claim recites a stagnant traffic congestion in addition to enqueuing/dequeuing congestion statuses and the BRI of the limitation stagnant traffic congestion is congestion in general and Sheynman discloses that in Fig.6, and Paragraph 0060 “When the candidate road represented in FIG. 6 is displayed on a map, road segments RS1 and RS2, and road segments RS4 and RS5 may appear in green, while road segment RS3 appears in red, providing an indication to a driver or user of a vehicle that congestion exists along a portion of the candidate road” indicates that the clusters are being used, i.e. they are outputted in order to be used.
Conclusion
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.
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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