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 .
Earliest Priority Date
The earliest priority date for this application is 11/20/2024.
Status of Claims
Claims 1-20 have been presented and examined in this action.
Claims 1-20 have been rejected using under 35 U.S.C. 101.
Claims 1-2, 5-12, 15-20 have been rejected using 35 U.S.C. 103 as being unpatentable by Balakrishnan (US 2020/0074326) in view of Kroeller (US 2017/0146353).
Claims 3-4, and 13-14 have been rejected under 35 U.S.C. 103 as being unpatentable over Balakrishnan in view of Kroeller in view of Modica (US 2014/0324748).
Response to Arguments
The applicant has argued that the independent claims overcome the rejections under 35 U.S.C. 101. These arguments are not persuasive and the claim rejections under 35 U.S.C. 101 are being maintained as is described in the rejection section of this action.
The applicant has argued that Balakrishnan (US 2020/0074326) does not disclose “associate each of the one or more stop events with one of: a traffic label or a non-traffic label based on the corresponding reference location” or “generate traffic data associated with the trajectory based on at least one of the one or more stop events associated with the traffic label in accordance with U.S.C. 102(a)(1) under which the independent claims are currently rejected. These arguments are moot, however, as the rejection of the independent claims is being replaced with a rejection under 35 U.S.C. 103 as necessitated by amendments made to the independent claims. However, the argued limitations are at least rendered obvious by Balakrishnan in Paragraph [0047] as is described in the rejection section of this action.
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 non-statutory subject matter. A step-by-step analysis of the independent claims according to the Subject Matter Eligibility Test For Products and Processes is provided below:
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes, Claim 1 is directed towards a machine, Claim 11 is directed towards a process, and Claim 20 is directed towards an article of manufacture.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, the claims recite, “determine event data…”, “associate each of the one or more stop events with a reference location…”, “associate each of the one or more stop events…”, “generate traffic data associated with the trajectory…”, “generate…a navigation recommendation…”.These elements represent an abstract idea as they can be performed solely within the human mind.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No, because the additional elements recited represent generic computing components performing route functions upon which the recited limitations are merely performed, or they represent insignificant extra-solution activity (pre-selection activity in the case of obtaining trajectory and post-solution activity in the case of outputting generated data).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the additional elements represent mere generic computing components or extra-solution activity and do not contribute to the inventive concept presented in the claims and are therefore insufficient to cause the claims as-a-whole to amount to significantly more than the judicial exception.
Dependent Claims: the dependent claims do not add any additional elements that are sufficient to either integrate the abstract idea into a practical application or cause the claim as-a-whole to amount to significantly more than the abstract idea.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-2, 5-12, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Balakrishnan in view of Kroeller. These references are related as they both are related to identification of traffic status.
As per Claim 1:
“A system comprising: a memory configured to store computer executable instructions; and one or more processors configured to execute the instructions to: obtain trajectory data associated with a trajectory of a vehicle, the trajectory data received from one or more sensors associated with the vehicle”
Balakrishnan Paragraph [0081] discloses obtaining a trajectory of an instant vehicle. [Abstract] teaches vehicle telematic data from vehicle sensors.
“determine event data associated with each of one or more stop events in the trajectory based on the trajectory data; associate each of the one or more stop events with a reference location based on the corresponding event data;”
Balakrishnan Paragraph [0047] discloses identifying the location of a stop event of a vehicle as a specific road segment.
“associate each of the one or more stop events with one of: a traffic label or a non-traffic label based on the corresponding reference location; generate traffic data associated with the trajectory based on at least one of the one or more stop events associated with the traffic label, and excluding the one or more stop events associated with the non-traffic label, the generated traffic data being indicative of a real-time traffic condition on a road segment; output the generated traffic data.”
Balakrishnan Paragraph [0047] discloses associating a sort of action label with a stop event as being related to traffic or not based on the context and location of the stop event. Specifically, the location of a vehicle that is stopped can cause the stop action to be identified as parking and therefore is excluded from traffic data whereas when a stop event is associated with a location associated with movement then it is included as traffic data.
Balakrishnan does not disclose the following limitations that Kroeller teaches:
“generate, based on the generated traffic data, a navigation recommendation indicating a route for a vehicle; and output the navigation recommendation”
Kroeller Figure 3 teaches presenting navigation instructions based on traffic information.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed Balakrishnan with the navigation display taught by Kroeller. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by providing a means of navigating via the traffic data.
With regards to Claim 2, Balakrishnan in view of Kroeller discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the trajectory comprises a plurality of datapoints, and wherein the trajectory data comprises at least one of: speed value associated with each of the plurality of datapoints, location data associated with each of the plurality of datapoints, and timestamp associated with each of the plurality of datapoints.”
Balakrishnan Paragraph [0047] discloses identifying location and speed and time window that a stop event occurs. In order to detect whether something occurs within a time window continuous awareness of time associated with measurements must be tracked.
With regards to Claim 5, Balakrishnan in view of Kroeller teaches all of the limitations of Claim 1 and further discloses the following limitations:
“Wherein the reference location corresponds to one of: a Point of Interest (POI)…or a road segment.”
Balakrishnan Paragraph [0047] discloses identifying the location of a stop event of a vehicle as a specific road segment, one of ordinary skill in the art would find it obvious that a road segment can represent the broadest reasonable interpretation of a point of interest.
Balakrishnan further suggests the following limitations:
“a signalized intersection, a non-signalized intersection”
Balakrishnan Paragraph [0047] discloses identifying locations based on stop event timing and other data, one of ordinary skill in the art would find it obvious that such an approach could be used to identify any arbitrary feature of a road network that has an identifiable effect of traffic characteristics which both signalized intersections and non-signalized intersections do.
With regards to Claim 6, Balakrishnan in view of Kroeller teaches and suggests all of the limitations of Claim 5 and further suggests:
“wherein the reference location of a stop event of the one or more stop events corresponds to the signalized intersection, and wherein the one or more processors are further configured to: associate the stop event with the traffic label; determine turn time data associated with the stop event; and generate the traffic data based on the determined turn time data, wherein the turn time data is indicative of an operation of traffic signal at the signalized intersection.”
Balakrishnan Paragraph [0047] discloses identifying locations based on stop event timing and other data, one of ordinary skill in the art would find it obvious that such an approach could be used to identify any arbitrary feature of a road network that has an identifiable effect of traffic characteristics which signalized intersections do and that effect would concern the turn time as that is a core function of a signalized intersection.
With regards to Claim 7, Balakrishnan in view of Kroeller teaches and suggests all of the limitations of Claim 5 and further suggests:
“wherein the reference location of a stop event of the one or more stop events corresponds to the non-signalized intersection, and wherein the one or more processors are further configured to: associate the stop event with the traffic label; determine waiting time data associated with the stop event; and generate the traffic data based on the determined waiting time data, wherein the waiting time data is indicative of an operation of traversing the non-signalized intersection.”
Balakrishnan Paragraph [0047] discloses identifying locations based on stop event timing and other data, one of ordinary skill in the art would find it obvious that such an approach could be used to identify any arbitrary feature of a road network that has an identifiable effect of traffic characteristics which non-signalized intersections do and that effect would concern the dwell time as that is a core function of a non-signalized intersection.
With regards to Claim 8, Balakrishnan in view of Kroeller teaches and suggests all of the limitations of Claim 5 and further suggests:
“wherein the reference location of a stop event of the one or more stop events corresponds to the POI, and wherein the one or more processors are further configured to: associate the stop event with the non-traffic label; determine dwell time data associated with the stop event; and update a map database based on the determined dwell time data, wherein the dwell time data is indicative of a standstill operation at the POI.”
Balakrishnan Paragraph [0047] discloses identifying locations based on stop event timing and other data, one of ordinary skill in the art would find it obvious that such an approach could be used to identify any arbitrary feature of a road network that has an identifiable effect of traffic characteristics. As a road segment represents a POI given its broadest reasonable interpretation, Balakrishnan discloses this limitation.
With regards to Claim 9, Balakrishnan in view of Kroeller teaches all of the limitations of Claim 5 and further discloses the following limitations:
“wherein the reference location of a stop event of the one or more stop events corresponds to a location on the road segment, and wherein the one or more processors are further configured to: associate the stop event with the traffic label; determine congestion time data associated with the stop event; and generate the traffic data based on the determined congestion time data, wherein the congestion time data is indicative of a congestion event on the road segment.”
Balakrishnan Paragraph [0048] discloses generating traffic data based on congestion and time data.
With regards to Claim 10, Balakrishnan in view of Kroeller teaches all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the one or more processors are further configured to: receive the trajectory data from one or more sensors associated with the vehicle, and wherein the one or more sensors comprises at least one of: a Global Navigation Satellite System (GNSS) sensor, or a speed sensor.”
Balakrishnan Paragraph [0019] discloses accelerometers (which represent speed sensors) and GPS sensors (which represent GNSS sensors).
As per Claim 11: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
With regards to Claim 12, this claim is substantially similar to Claim 2 and is therefore rejected using the same references and rationale.
With regards to Claim 15, this claim is substantially similar to Claim 5 and is therefore rejected using the same references and rationale.
With regards to Claim 16, this claim is substantially similar to Claim 6 and is therefore rejected using the same references and rationale.
With regards to Claim 17, this claim is substantially similar to Claim 7 and is therefore rejected using the same references and rationale.
With regards to Claim 18, this claim is substantially similar to Claim 8 and is therefore rejected using the same references and rationale.
With regards to Claim 19, this claim is substantially similar to Claim 9 and is therefore rejected using the same references and rationale.
As per Claim 20: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
Claims 3-4, and 13-14 have been rejected under 35 U.S.C. 103 as being unpatentable over Balakrishnan in view of Kroeller in view of Modica (US 2014/0324748). The references are analogous as they all relate to the field of identifying features using trajectory data (See Paragraph [0003] of Balakrishnan and Paragraph [0001] of Modica).
With regards to Claim 3, Balakrishnan discloses all of the limitations of Claim 2 and further discloses the following limitations:
“wherein the one or more processors are further configured to: input the trajectory data to a trained Machine Learning (ML) model, wherein the ML model is trained to partition the plurality of datapoints based on the speed value;”
Balakrishnan Paragraph [0110] discloses associating position and location datapoints with a machine learning model for identifying contextual information.
Balakrishnan does not disclose the following limitations that Modica teaches:
“and generate, using the trained ML model, one or more clusters based on the speed value, wherein each of the one or more clusters comprises at least one of the plurality of datapoints, and wherein the one or more clusters correspond to the one or more stop events.”
Modica Figure 5 identifies clusters of data points in order to identify points of interest.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Balakrishnan with the identification of features based on clusters of trajectory data. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by utilizing historical data to improve identification accuracy.
With regards to Claim 4, Balakrishnan in view of Modica discloses all of the limitations Claim 3 and further discloses all of the following limitations:
“wherein to associate each of the one or more stop events with the reference location, the one or more processors are further configured to: obtain map data comprising information associated with a plurality of reference locations; determine center location data for each of the one or more clusters; and associate each of the one or more clusters with at least one of the plurality of reference locations based on the corresponding center location data and the map data.”
Modica Paragraphs [0060]-[0062] teaches identifying a cluster and using it, including the center of said cluster, to identify location features.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Balakrishnan with the identification of features based on clusters of trajectory data. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by utilizing historical data to improve identification accuracy.
With regards to Claim 13, this claim is substantially similar to Claim 3 and is therefore rejected using the same references and rationale.
With regards to Claim 14, this claim is substantially similar to Claim 4 and is therefore rejected using the same references and rationale.
Relevant References
Lee (US 8,831,862)
Related to providing traffic information using trajectory information.
Whited (US 2020/0076878)
Related to identifying traffic features and identifying road features.
Camhi (US 2020/0055402)
Related to identifying road segments based on speed measurements.
Grokop (US 2015/0120336)
Related to recording vehicle data to identify traffic information.
Armitage (US 2011/0281564)
Related to identifying road features based on stop events.
Horvitz (US 2008/0004793)
Related to identifying road based on stop events.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Godfrey Maciorowski, whose telephone number is (571) 272-4652. The examiner can normally be reached on Monday-Friday from 7:30am to 5:00pm EST.
Examiner interviews are available via telephone 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 examiner by telephone are unsuccessful the examiner’s supervisor, Thomas Worden can be reached on (571) 272-4876. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/GODFREY ALEKSANDER MACIOROWSKI/Examiner, Art Unit 3658
/THOMAS E WORDEN/Supervisory Patent Examiner, Art Unit 3658
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