Notice of Pre-AIA or AIA Status
1. 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 Arguments
2. Applicant’s arguments filed May 12, 2026 regarding rejection of claims 1, 11, and 21 under 35 USC 101 have been fully considered and are unpersuasive.
Applicant’s arguments filed May 12, 2026 regarding rejection of claims 1, 7, 11, 12, 17, and 21 under 35 USC 102 have been fully considered and are persuasive. However, the claims remain rejected under 35 USC 103.
Applicant’s arguments filed May 12, 2026 regarding rejection of claims 3 and 13 under 35 USC 103 have been fully considered and are unpersuasive.
3. Applicant argues that the amended independent claims 1, 11, and 21 with processing telematics data originating from a plurality of telematics devices installed in a plurality of vehicles, identifying vehicle stop points and vehicle stop clusters that share one or more vehicle stop points, and applying at least one machine learning model trained to classify vehicle stop zones based on vehicle stop features are not practically performable in the human mind. The applicant argues that there may be “hundred of thousands, or even millions, of data points to process” for identifying commercial domiciles and that telematics data requires a computer to electronically “transmit, receive, interpret, process, and/or store” data.
However, the amended independent claims 1, 11, and 21, first do not require a particular data volume and one of ordinary skill cannot conclude that the claim to be limited to large datasets. Rather, the amended independent claims also encompass smaller datasets for which a person to be able to identify stop points, form and merge clusters, evaluate stop features, and classify stop zones. In addition, electronic transmission, receipt, interpret, process, and storage of telematics data merely gather and provide information to be analyzed and is an insignificant data-gathering and generic computer activity as the claims do not recite improvement to telematics transmission, data storage, an/or computer functionality, but uses generic computer components to perform abstract analysis. Therefore, applicant’s arguments regarding the rejection under 35 USC 101 are unpersuasive.
Applicant argues that the amended independent claims 1, 11, and 21 are not result-oriented mental association as the claims recite a particular computer-implemented geospatial/telematics processing technique for resolving stop clusters into a more accurate vehicle stop zone before machine-learning classification.
However, the claims merely results in identifying a vehicle stop zone as a commercial domicile without reciting the particular details of how that result is achieved beyond what results are to be found from mental processes from generically gathered data. The claims only generically applies an already trained machine learning model and does not recite how the model is trained, adapts, or achieves greater accuracy. Therefore, applicant’s arguments regarding the rejection under 35 USC 101 are unpersuasive.
Applicant argues that the amended independent claims 1, 11, and 21 recites machine learning model that employs large scale processing of telematics data capable of identifying and classifying vehicle stop zones across large geographical areas and large vehicle fleets. The applicant also argues that applying a trained machine learning model constitutes a practical application as it classifies stop zones as commercial domiciles more accurately than conventional rule-based techniques and may adapt over time based on trends in telematics data.
However, the claims do not require any particular data volume, fleet size, geographical area, algorithm, or technical mechanism that allows for large scale processing or improvements on how stop zones are generated. In addition, the claims merely show using an already trained machine learning model and do not specify how the model is trained to produce greater accuracy. Merely using the machine learning model to obtain results is not training the machine learning model in a particular manner to achieve particular results.
Therefore, applicant’s arguments regarding the rejection under 35 USC 101 are unpersuasive.
4. Applicant argues that claims 1, 2, 7, 11, 12, 17, and 21 are amended to overcome rejection under 35 USC 102. However, the amended claims need further search and consideration and now rejected under 35 USC 103.
5. Applicant argues that claims 3 and 13 are incorporated into independent claims 1, 11, and 21 and argues that Choudhry et al. (“Inferring truck activities using privacy-preserving truck trajectories data”, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10175394) teaches merging close by points rather than merging vehicle stop clusters that share one or more stop points.
However, Choudhry’s disclosure of points or clusters within a defined distance encompasses overlapping, i.e. shared, points of distance of zero (see [pg 23-24]). Further, the claim does not define a specific size of a stop point and Choudhry shows hub areas, i.e. a stop point, shared between clusters (see Fig. 5 in [pg 23]). Hence, Choudhry does indeed teach merging of shared stop point in which vehicles share a hub area.
Therefore, applicant’s arguments regarding the rejection under 35 USC 103 are unpersuasive.
6. Applicant argues that claims 4-6, 8-10, 14-16, and 18-20 are non-obvious over relevant cited combination of references.
However, as mentioned in previous Office Action, the claims are rejected over the cited references in obvious combination of references and in dependence of independent claims 1, 11, and 21.
Therefore, applicant’s argument regarding claims 4-6, 8-10, 14-16, and 18-20 regarding the rejection under 35 USC 103 is unpersuasive.
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.
7. Claim 1 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a system (i.e., a machine). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A system for identifying commercial domiciles, the system comprising:
at least one data storage operable to store telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; and
at least one processor in communication with the at least one data storage, the at least one processor operable to:
identify, using the telematics data, a vehicle stop zone, each vehicle stop zone comprising a vehicle stop cluster, [mental process/step] by;
defining a search zone corresponding to a geographical area within which the vehicle stop zone is to be identified [mental process/step];
identifying, using the telematics data, a plurality of vehicle stop points located within the search zone, each of the vehicle stop points representing a location at which a vehicle stopped [mental process/step];
within each search zone, identifying one or more vehicle stop clusters, each vehicle stop cluster comprising at least one of the vehicle stop points [mental process/step];
merging vehicle stop clusters that share one or more of the vehicle stop points [mental process/step]; and
identifying the vehicle stop zone based at least in part on each of the one or more vehicle stop clusters [mental process/step]; and
identify the vehicle stop zone as a commercial domicile [mental process/step] by applying to the vehicle stop cluster of the vehicle stop zone at least one machine learning model trained to classify vehicle stop zones based on one or more vehicle stop features thereof.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” and “mathematical concept” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind and mathematical operations. For example, “identify…vehicle stop zone…”, “identifying…a plurality of vehicle stop points…”, and “identifying one or more vehicle stop clusters…” in the context of the claim encompasses a person looking at gathered data, i.e. telematics data, and associating the data into zones and clusters of locations. For example, “defining a search zone…” in the context of the claim encompasses selecting and/or drawing an area on a map. For example, “merging vehicle stop clusters…” in the context of the claim compares groups of stop points and combining groups that share stop points, which can be performed in a human mind. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A system for identifying commercial domiciles, the system comprising [generic linking to technical field, 2106.05(h)]:
at least one data storage operable to store telematics data originating from a plurality of telematics devices installed in a plurality of vehicles [insignificant pre-solution activity (data gathering) 2106.05(g)]; and
at least one processor in communication with the at least one data storage, the at least one processor operable to [generic linking to technical field, 2106.05(h)]:
identify, using the telematics data, a vehicle stop zone, each vehicle stop zone comprising a vehicle stop cluster [mental process/step] by;
defining a search zone corresponding to a geographical area within which the vehicle stop zone is to be identified [mental process/step];
identifying, using the telematics data, a plurality of vehicle stop points located within the search zone, each of the vehicle stop points representing a location at which a vehicle stopped [mental process/step];
within each search zone, identifying one or more vehicle stop clusters, each vehicle stop cluster comprising at least one of the vehicle stop points [mental process/step];
merging vehicle stop clusters that share one or more of the vehicle stop points [mental process/step]; and
identifying the vehicle stop zone based at least in part on each of the one or more vehicle stop clusters [mental process/step]; and
identify the vehicle stop zone as a commercial domicile [mental process/step] by applying to the vehicle stop cluster of the vehicle stop zone at least one machine learning model trained to classify vehicle stop zones based on one or more vehicle stop features thereof [applying the abstract idea using generic computing module, “apply it” 2106.05(f)].
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of “a system for identifying commercial domiciles” and “at least one processor in communication with the at least one data storage”, the examiner submits that this is recited at a high level of generality and serves only to link the particular abstract concept to a broad technical field. Regarding the “at least one data storage operable to store telematics data originating from a plurality of telematics devices installed in a plurality of vehicles”, specifically, this is merely an insignificant pre-solution activity of data gathering. Regarding the “applying to the vehicle stop cluster of the vehicle stop zone at least one machine learning model trained to classify vehicle stop zones based on one or more vehicle stop features thereof”, specifically, this merely is an instruction to apply the abstract concept identifying a vehicle stop zone using generic computer.
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. see 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.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using “storage” and “processor” to perform the storing, identifying, and applying amounts to nothing 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. And, as discussed above, the additional limitations related to acquiring and transmitting data, the examiner submits that these limitation is insignificant extra-solution activity.
Dependent claim(s) 4-10 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. The dependent claims 4-10 only recites defining, identifying, and generating a search zone and vehicle stop zone based on vehicle stop points and clusters, merging clusters and subclusters, and based on stop features. They also only recite generating and identifying polygons and sub-polygons that encompasses a commercial domicile. All of the recited defining, identifying and generating are abstract mental process of a person looking at data gathered and associating them to zones, clusters, and polygons and the person writing them on a paper.
Independent claim 11 and 21 recites similar limitations to independent claim 1 and therefore requires a similar rejection.
Dependent claims 14-20 recites similar limitations to dependent claims 4-10 and therefore requires a similar rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
8. Claim 1, 7, 11, 17 and 21 are rejected under pre-35 U.S.C. as being unpatentable over Sarti et al. (“Stop Purpose Classification from GPS Dara if Commercial Vehicle Fleets”, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8215675) in view of Choudhry et al. (“Inferring truck activities using privacy-preserving truck trajectories data”, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10175394).
Regarding claim 1, Sarti teaches a system for identifying commercial domiciles (see [pg 281] where, based on labeled dataset, a model can automatically classify a stop between non-work stops and work stops, i.e. commercial domiciles.), the system comprising:
at least one data storage operable to store telematics data originating from a plurality of telematics devices installed in a plurality of vehicles (see [pg 281] where it mentions datasets that there are more than 55 million GPS pings from different vehicles of 98 different business companies are collected over one year and where a model can automatically classify a stop, which must include a computer to perform automatic classification and have memory storage within to collect and analyze the 55 million GPS pings collected over one year.); and
at least one processor in communication with the at least one data storage, the at least one processor operable to (see [pg 281] where a model can automatically classify a stop, which must include a computer with a processor and memory to perform the automatic classification. Note also in [pg 286] that features, such as stop features, are used to train a Random Forest model, which cannot be done without a use of a computer with a processor.):
identify, using the telematics data, a vehicle stop zone, each vehicle stop zone comprising a vehicle stop cluster (see [pg 282] where GPS pings, i.e. telematics data, are used to determine stops, i.e. vehicle stop zone, where each stops are determined using spatio-temporal clustering procedure of having cluster of pings that are sent before engine are turned off and cluster of pings that are sent while stopped, but idling with engines not turned off.) by;
defining a search zone corresponding to a geographical area within which the vehicle stop zone is to be identified (see [pg 281] where search zone is within USA, i.e. country borders, where data of fleet of vehicles with GPS pings are collected to determine stops, i.e. stop zones. Note also in [pg 282] where sequence of GPS pings are considered with distance between consecutive pings less than 150m and speed less than 1.4 m/s for idling to impose spatio-temporal constraints, i.e. search zone.);
identifying, using the telematics data, a plurality of vehicle stop points located within the search zone, each of the vehicle stop points representing a location at which a vehicle stopped (see [pg 282] where vehicle stop points within a search zone are determined using GPS pings, i.e. telematics data, and each classification of vehicle stop points determined through GPS pings of when engine is off and idling that satisfies a spatio-temporal constraints as shown previously shows a location at which a vehicle stopped.);
within each search zone, identifying one or more vehicle stop clusters, each vehicle stop cluster comprising at least one of the vehicle stop points (see [pg 282] where a stop is defined as a group of chronologically consecutive pings which are either idling or engine off and satisfy spatio-temporal constraints as indicated previously, i.e. clusters of consecutive stop pings representing vehicle stop points.); and
identifying the vehicle stop zone based at least in part on each of the one or more vehicle stop clusters (see [pg 283] where stop-wise feature includes shape of a stop, which are stop width, stop height, stop area, and stop ratio, that is computed from a bounding box of GPS pings, i.e. a vehicle stop zone based on vehicle stop clusters.); and
identify the vehicle stop zone as a commercial domicile by applying to the vehicle stop cluster of the vehicle stop zone at least one machine learning model trained to classify vehicle stop zones based on one or more vehicle stop features thereof (see [pg 283] where a Random Forest model is trained, i.e. machine learning model, to classify work and non work stops using stop cluster features and stop-wise features, i.e. stop features, where it collects data of stop duration, stop shape, number of pings in a stop, number of engine off pings in the stop, etc. to determine if a truck was in a work order area to receive and transfer materials, i.e. a commercial domicile, rather than a non work stop such as a congested area.).
Sarti does not teach: wherein the at least one processor is further operable to identify the vehicle stop zone by merging vehicle stop clusters that share one or more of the vehicle stop points.
However, Choudhry teaches density based clustering where cluster of stops are merged into a singular stop, i.e. vehicle stop zone, as well as merging stop events located close by depending on distance, which includes clusters that share points (see [pg 23-24] and [pg 18]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters, of Sarti by incorporating teaching of Choudhry such that clusters of stop pings that are within a distance are merged together to help form a vehicle stop zone.
The motivation to merge clusters of stop pings that share and are within certain distance to identify a vehicle stop zone is that, as indicated by Choudhry, this would allow for prevention of merging too many stop locations from merging in urban areas and obtain compact clusters while controlling for maximum size of clusters (see [pg 29]).
Regarding claim 7, modified Sarti in view of Choudhry teaches the system of claim 1, wherein the one or more vehicle stop features comprise stop time features, stop count features, stop time period features, trip features, operator features, or combinations thereof (see [pg 283] where stop-wise features include total count in a stop, i.e. trip features, and number of engine off pings in a stop, i.e. stop count features.).
Regarding claim 11, Sarti teaches a method for identifying commercial domiciles, the method comprising operating at least one processor to (see [pg 281] where, based on labeled dataset, a model can automatically classify a stop between non-work stops and work stops, i.e. commercial domiciles, which must also include a computer with a processor and memory to perform the automatic classification. Note also in [pg 286] that features, such as stop features, are used to train a Random Forest model, which cannot be done without a use of a computer with a processor.):
receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles (see [pg 281] where it mentions datasets that there are more than 55 million GPS pings from different vehicles of 98 different business companies are collected over one.);
identify, using the telematics data, a vehicle stop zone, each vehicle stop zone comprising a vehicle stop cluster (see [pg 282] where GPS pings, i.e. telematics data, are used to determine stops, i.e. vehicle stop zone, where each stops are determined using spatio-temporal clustering procedure of having cluster of pings that are sent before engine are turned off and cluster of pings that are sent while stopped, but idling with engines not turned off.) by;
define a search zone corresponding to a geographical area within which the vehicle stop zone is to be identified (see [pg 281] where search zone is within USA, i.e. country borders, where data of fleet of vehicles with GPS pings are collected to determine stops, i.e. stop zones. Note also in [pg 282] where sequence of GPS pings are considered with distance between consecutive pings less than 150m and speed less than 1.4 m/s for idling to impose spatio-temporal constraints, i.e. search zone.);
identify, using the telematics data, a plurality of vehicle stop points located within the search zone, each of the vehicle stop points representing a location at which a vehicle stopped (see [pg 282] where vehicle stop points within a search zone are determined using GPS pings, i.e. telematics data, and each classification of vehicle stop points determined through GPS pings of when engine is off and idling that satisfies a spatio-temporal constraints as shown previously shows a location at which a vehicle stopped.);
within each search zone, identify one or more vehicle stop clusters, each vehicle stop cluster comprising at least one of the vehicle stop points (see [pg 282] where a stop is defined as a group of chronologically consecutive pings which are either idling or engine off and satisfy spatio-temporal constraints as indicated previously, i.e. clusters of consecutive stop pings representing vehicle stop points.); and
identify the vehicle stop zone based at least in part on each of the one or more vehicle stop clusters (see [pg 283] where stop-wise feature includes shape of a stop, which are stop width, stop height, stop area, and stop ratio, that is computed from a bounding box of GPS pings, i.e. a vehicle stop zone based on vehicle stop clusters.); and
identify the vehicle stop zone as a commercial domicile by applying to the vehicle stop cluster of the vehicle stop zone at least one machine learning model trained to classify vehicle stop zones based on one or more vehicle stop features thereof (see [pg 283] where a Random Forest model is trained, i.e. machine learning model, to classify work and non work stops using stop cluster features and stop-wise features, i.e. stop features, where it collects data of stop duration, stop shape, number of pings in a stop, number of engine off pings in the stop, etc. to determine if a truck was in a work order area to receive and transfer materials, i.e. a commercial domicile, rather than a non work stop such as a congested area.).
Sarti does not teach: wherein the identifying of the vehicle stop zone further comprises operating the at least one processor to merge vehicle stop clusters that share one or more of the vehicle stop points.
However, Choudhry teaches density based clustering where cluster of stops are merged into a singular stop, i.e. vehicle stop zone, as well as merging stop events located close by depending on distance, which includes clusters that share points (see [pg 18] and [pg 23-24]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters, of Sarti by incorporating teaching of Choudhry such that clusters of stop pings that are within a distance are merged together to help form a vehicle stop zone.
The motivation to merge clusters of stop pings that share and are within certain distance to identify a vehicle stop zone is that, as indicated by Choudhry, this would allow for prevention of merging too many stop locations from merging in urban areas and obtain compact clusters while controlling for maximum size of clusters (see [pg 29]).
Regarding claim 17, modified Sarti in view of Choudhry teaches the method of claim 11, wherein the one or more vehicle stop features comprise stop time features, stop count features, stop time period features, trip features, operator features, or combinations thereof (see Sarti [pg 283] where stop-wise features include total count in a stop, i.e. trip features, and number of engine off pings in a stop, i.e. stop count features.).
Regarding claim 21, Sarti teaches a non-transitory computer-readable medium having instructions stored thereon executable by at least one processor to implement a method for identifying commercial domiciles (see [pg 281] where, based on labeled dataset, a model can automatically classify a stop between non-work stops and work stops, i.e. commercial domiciles, which must also include a computer with a processor and memory to perform the automatic classification. Note also in [pg 286] that features, such as stop features, are used to train a Random Forest model, which cannot be done without a use of a computer with a processor.), the method comprising operating at least one processor to:
receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles (see [pg 281] where it mentions datasets that there are more than 55 million GPS pings from different vehicles of 98 different business companies are collected over one.);
identify, using the telematics data, a vehicle stop zone, each vehicle stop zone comprising a vehicle stop cluster (see [pg 282] where GPS pings, i.e. telematics data, are used to determine stops, i.e. vehicle stop zone, where each stops are determined using spatio-temporal clustering procedure of having cluster of pings that are sent before engine are turned off and cluster of pings that are sent while stopped, but idling with engines not turned off.) by;
define a search zone corresponding to a geographical area within which the vehicle stop zone is to be identified (see [pg 281] where search zone is within USA, i.e. country borders, where data of fleet of vehicles with GPS pings are collected to determine stops, i.e. stop zones. Note also in [pg 282] where sequence of GPS pings are considered with distance between consecutive pings less than 150m and speed less than 1.4 m/s for idling to impose spatio-temporal constraints, i.e. search zone.);
identify, using the telematics data, a plurality of vehicle stop points located within the search zone, each of the vehicle stop points representing a location at which a vehicle stopped (see [pg 282] where vehicle stop points within a search zone are determined using GPS pings, i.e. telematics data, and each classification of vehicle stop points determined through GPS pings of when engine is off and idling that satisfies a spatio-temporal constraints as shown previously shows a location at which a vehicle stopped.);
within each search zone, identify one or more vehicle stop clusters, each vehicle stop cluster comprising at least one of the vehicle stop points (see [pg 282] where a stop is defined as a group of chronologically consecutive pings which are either idling or engine off and satisfy spatio-temporal constraints as indicated previously, i.e. clusters of consecutive stop pings representing vehicle stop points.); and
identify the vehicle stop zone based at least in part on each of the one or more vehicle stop clusters (see [pg 283] where stop-wise feature includes shape of a stop, which are stop width, stop height, stop area, and stop ratio, that is computed from a bounding box of GPS pings, i.e. a vehicle stop zone based on vehicle stop clusters.); and
identify the vehicle stop zone as a commercial domicile by applying to the vehicle stop cluster of the vehicle stop zone at least one machine learning model trained to classify vehicle stop zones based on one or more vehicle stop features thereof (see [pg 283] where a Random Forest model is trained, i.e. machine learning model, to classify work and non work stops using stop cluster features and stop-wise features, i.e. stop features, where it collects data of stop duration, stop shape, number of pings in a stop, number of engine off pings in the stop, etc. to determine if a truck was in a work order area to receive and transfer materials, i.e. a commercial domicile, rather than a non work stop such as a congested area.).
Sarti does not teach: wherein the identifying of the vehicle stop zone further comprises operating the at least one processor to merge vehicle stop clusters that share one or more of the vehicle stop points.
However, Choudhry teaches density based clustering where cluster of stops are merged into a singular stop, i.e. vehicle stop zone, as well as merging stop events located close by depending on distance, which includes clusters that share points (see [pg 18] and [pg 23-24]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters, of Sarti by incorporating teaching of Choudhry such that clusters of stop pings that are within a distance are merged together to help form a vehicle stop zone.
The motivation to merge clusters of stop pings that share and are within certain distance to identify a vehicle stop zone is that, as indicated by Choudhry, this would allow for prevention of merging too many stop locations from merging in urban areas and obtain compact clusters while controlling for maximum size of clusters (see [pg 29]).
9. Claim 4-6 and 14-16 are rejected under pre-35 U.S.C. as being unpatentable over Sarti in view of Choudhry in further view of Zhang et al. (CN 114186619A).
Regarding claim 4, modified Sarti in view of Choudhry teaches the system of claim 2, wherein the at least one processor is further operable to identify the vehicle stop zone (see Sarti [pg 281] where a model can automatically classify a stop, which must include a computer with a processor and memory to perform the automatic classification as shown in claim 1.).
Modified Sarti in view of Choudhry does not teach: identifying an oversized vehicle stop cluster that occupies an area greater than a predetermined threshold;
reclustering the oversized vehicle stop cluster into a plurality of vehicle stop subclusters; and
identifying the vehicle stop zone based on each of the plurality of vehicle stop subclusters.
However, Zhang does teach clustering algorithm where first it is determined that two adjacent high density area are converged into one cluster that does not satisfy requirement, i.e. oversized vehicle stop cluster that is greater than a threshold, then clusters are divided into grid clustering, i.e. plurality of subclusters, and connectivity segmentation calculates similarity between grids to either combine grid clusters that are similar, i.e. identifying vehicle stop zone based on vehicle stop clusters (see [pg 5/26]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters, of modified Sarti in view of Choudhry by incorporating teaching of Zhang such that vehicle stop pings, which are clusters, converged into one oversized vehicle stop cluster beyond a threshold is divided into grid clustering and recombined to form a vehicle stop zone based on similarity of grid clusters.
The motivation to divide a large cluster beyond a threshold into grid clustering and recombined to form vehicle stop zones is that, as indicated by Zhang, this would allow for obtaining an area boundary of a vehicle stop zone that is closer to a real situation and it saves mapping and manual marking of point of interest boundary (see [pg 5/26]).
Regarding claim 5, modified Sarti in view of Choudhry and Zhang teaches the system of claim 4,
Zhang further teaches clustering algorithm where converged clusters that do not meet a threshold are divided into grid clustering, i.e. plurality of subclusters, and connectivity segmentation calculates similarity between grids to combine grid clusters that are similar, such as clustering center and parking sequence (see Zhang [pg 5/26]).
Choudhry still further teaches density based clustering where cluster of stops are merged into a singular stop, i.e. vehicle stop zone, as well as merging stop events located close depending on distance, which includes clusters that share points (see [pg 18] and [pg 23-24]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a converged cluster larger than required, which are divided into grid clustering and recombined to subclusters of similarity of modified Sarti in view of Choudhry and Zhang by incorporating further teaching of Zhang and Choudhry such that clusters that are within a distance, i.e. sharing one or more of a vehicle stop points, are merged together to form a vehicle stop zone.
The motivation to divide a large cluster beyond a threshold into grid clustering and recombined to form vehicle stop zones is that, as indicated by Zhang, this would allow for obtaining an area boundary of a vehicle stop zone that is closer to a real situation and it saves mapping and manual marking of point of interest boundary (see [pg 5/26]).
The motivation to merge clusters of stop pings that share and are within certain distance to identify a vehicle stop zone is that, as indicated by Choudhry, this would allow for prevention of merging too many stop locations from merging in urban areas and obtain compact clusters while controlling for maximum size of clusters (see [pg 29]).
Regarding claim 6, modified Sarti in view of Choudhry and Zhang teaches the system of claim 4, wherein the at least one processor is further operable to identify the vehicle stop zone by defining a reclustering search zone that encompasses at least the oversized vehicle stop zone (see Sarti [pg 281] where search zone is within USA, i.e. country borders, where data of fleet of vehicles with GPS pings are collected to determine stops, i.e. stop zones. Note also in [pg 282] where sequence of GPS pings are considered with distance between consecutive pings less than 150m and speed less than 1.4 m/s for idling to impose spatio-temporal constraints, i.e. search zone. As long as the search zone continues to remain the same before and after reclustering of an oversized vehicle stop cluster, then a reclustering search zone is defined and includes the oversized vehicle stop zone.).
Regarding claim 14, modified Sarti in view of Choudhry teaches the method of claim 12, wherein identifying of the vehicle stop zone further comprises operating at least one processor to (see Sarti [pg 281] where a model can automatically classify a stop, which must include a computer with a processor and memory to perform the automatic classification as shown in claim 1.):
Modified Sarti in view of Choudhry does not teach: identify an oversized vehicle stop cluster that occupies an area greater than a predetermined threshold;
recluster the oversized vehicle stop cluster into a plurality of vehicle stop subclusters; and
identify the vehicle stop zone based on each of the plurality of vehicle stop subclusters.
However, Zhang does teach clustering algorithm where first it is determined that two adjacent high density area are converged into one cluster that does not satisfy requirement, i.e. oversized vehicle stop cluster that is greater than a threshold, then clusters are divided into grid clustering, i.e. plurality of subclusters, and connectivity segmentation calculates similarity between grids to either combine grid clusters that are similar, i.e. identifying vehicle stop zone based on vehicle stop clusters (see [pg 5/26]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters, of modified Sarti in view of Choudhry by incorporating teaching of Zhang such that vehicle stop pings, which are clusters, converged into one oversized vehicle stop cluster beyond a threshold is divided into grid clustering and recombined to form a vehicle stop zone based on similarity of grid clusters.
The motivation to divide a large cluster beyond a threshold into grid clustering and recombined to form vehicle stop zones is that, as indicated by Zhang, this would allow for obtaining an area boundary of a vehicle stop zone that is closer to a real situation and it saves mapping and manual marking of point of interest boundary (see [pg 5/26]).
Regarding claim 15, modified Sarti in view of Choudhry and Zhang teaches the method of claim 14,
Zhang further teaches clustering algorithm where converged clusters that do not meet a threshold are divided into grid clustering, i.e. plurality of subclusters, and connectivity segmentation calculates similarity between grids to combine grid clusters that are similar, such as clustering center and parking sequence (see Zhang [pg 5/26]).
Choudhry still further teaches density based clustering where cluster of stops are merged into a singular stop, i.e. vehicle stop zone, as well as merging stop events located close depending on distance, which includes clusters that share points (see [pg 18] and [pg 23-24]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a converged cluster larger than required, which are divided into grid clustering and recombined to subclusters of similarity of modified Sarti in view of Choudhry and Zhang by incorporating further teaching of Zhang and Choudhry such that clusters that are within a distance, i.e. sharing one or more of a vehicle stop points, are merged together to form a vehicle stop zone.
The motivation to divide a large cluster beyond a threshold into grid clustering and recombined to form vehicle stop zones is that, as indicated by Zhang, this would allow for obtaining an area boundary of a vehicle stop zone that is closer to a real situation and it saves mapping and manual marking of point of interest boundary (see [pg 5/26]).
The motivation to merge clusters of stop pings that share and are within certain distance to identify a vehicle stop zone is that, as indicated by Choudhry, this would allow for prevention of merging too many stop locations from merging in urban areas and obtain compact clusters while controlling for maximum size of clusters (see [pg 29]).
Regarding claim 16, modified Sarti in view of Choudhry and Zhang teaches the method of claim 14, wherein the identifying of the vehicle stop zone based each of the plurality of vehicle stop subclusters further comprises operating the at least one processor to define a reclustering search zone that encompasses at least the oversized vehicle stop zone (see Sarti [pg 281] where search zone is within USA, i.e. country borders, where data of fleet of vehicles with GPS pings are collected to determine stops, i.e. stop zones. Note also in [pg 282] where sequence of GPS pings are considered with distance between consecutive pings less than 150m and speed less than 1.4 m/s for idling to impose spatio-temporal constraints, i.e. search zone. As long as the search zone continues to remain the same before and after reclustering of an oversized vehicle stop cluster, then a reclustering search zone is defined and includes the oversized vehicle stop zone.).
10. Claim 8 and 18 are rejected under pre-35 U.S.C. as being unpatentable over Sarti in view of Choudhry in further view of Patel et al. (“A cluster-driven classification approach to truck stop location identification using passive GPS data”, https://link.springer.com/article/10.1007/s10109-022-00380-y).
Regarding claim 8, modified Sarti in view of Choudhry teaches the system of claim 7,
Modified Sarti in view of Choudhry does not teach: wherein the one or more vehicle stop features comprise an operator stop duration proportion, a vehicle stop duration proportion, a distinct operator vehicle stop count, a median operator trip duration, a weekend trip stop proportion, an operator round trip proportion, a median stop duration, or a combination thereof.
However, Patel teaches a number of trucks belonging to a carrier in a cluster in the calculation of specialized index for a given cluster (see [pg 668]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone comprising of vehicle stop ping clusters classified through stop-wise feature, which includes stop duration, total count of pings, and number of engine off pints in a stop of modified Sarti in view of Choudhry by incorporating teaching of Patel such that stop features also include a number of vehicles belonging to a carrier or operator within a cluster, i.e. a distinct operator vehicle stop count which indicates a total number of distinct vehicles of an owner or operator that stop in a vehicle stop zone.
The motivation to include into a stop feature a number of trucks or vehicles belonging to a carrier or an operator within a cluster is that, as indicated by Patel, this would allow for a determination of whether or not a location is specialized or more specific to a carrier or if it is general and classify them to primary stop or secondary stop for trucks (see [pg 668] and [pg 657]).
Regarding claim 18, modified Sarti in view of Choudhry teaches the method of claim 17,
Modified Sarti in view of Choudhry does not teach: wherein the one or more vehicle stop features comprise an operator stop duration proportion, a vehicle stop duration proportion, a distinct operator vehicle stop count, a median operator trip duration, a weekend trip stop proportion, an operator round trip proportion, a median stop duration, or a combination thereof.
However, Patel teaches a number of trucks belonging to a carrier in a cluster in the calculation of specialized index for a given cluster (see [pg 668]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone comprising of vehicle stop ping clusters classified through stop-wise feature, which includes stop duration, total count of pings, and number of engine off pints in a stop of modified Sarti in view of Choudhry by incorporating teaching of Patel such that stop features also include a number of vehicles belonging to a carrier or operator within a cluster, i.e. a distinct operator vehicle stop count which indicates a total number of distinct vehicles of an owner or operator that stop in a vehicle stop zone.
The motivation to include into a stop feature a number of trucks or vehicles belonging to a carrier or an operator within a cluster is that, as indicated by Patel, this would allow for a determination of whether or not a location is specialized or more specific to a carrier or if it is general and classify them to primary stop or secondary stop for trucks (see [pg 668] and [pg 657]).
11. Claim 9-10 and 19-20 are rejected under pre-35 U.S.C. as being unpatentable over Sarti in view of Choudhry in further view of Chen et al. (Land use classification in construction areas based on volunteered geographic information”, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7577633).
Regarding claim 9, modified Sarti in view of Choudhry teaches the system of claim 1, wherein the at least one processor is further operable to:
Modified Sarti in view of Choudhry does not teach: generate an initial polygon encompassing the commercial domicile;
identify, using map data, one or more road segments that intersect the polygon;
generate from the initial polygon a plurality of sub-polygons based on the one or more road segments that intersect the polygon; and
generating a domicile polygon based on one or more of the sub-polygons within which a vehicle stop point is located.
However, Chen teaches first a research area, which can be any determined area that is of interest, i.e. initial polygon of interest that includes commercial domicile. Then, Chen teaches main and entire road to map first and second class plot, which generates multiple polygons with roads as boundaries, and it is combined with point of interest in a hierarchical grading classification process to determine which polygons belongs to different classification, which includes industrial and warehouses, i.e. commercial domicile (see [pg 2/4-3/4] and Figs 2 and 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters of Sarti by incorporating teaching of Chen such that initial polygon encompassing a commercial domicile, which is an area or city or district or country that has cluster of stop pings from Sarti, are separated into multiple polygons with road segments as boundaries, where multiple polygons are combined and classified based on point of interests, which would include vehicle stop points to classify a work stop zone of industrial and warehouses, i.e. commercial domicile.
The motivation to have a polygon divided into multiple polygons, using road segments, to determine a domicile polygon through points of interest that includes industrial and warehouses is that, as indicated by Chen, this would allow for more efficient way to divide space distribution of different current land use types by having different layers for different levels of classifications based on using points of interest with main road and with entire road (see [2/4-3/4] and Fig. 1).
Regarding claim 10, modified Sarti in view of Choudhry and Chen teaches the system of claim 9, wherein the at least one processor is operable to generate the domicile polygon by:
filtering any sub-polygons within which a vehicle stop point is not located; and
merging any sub-polygons within which a vehicle stop point is located.
Chen teaches [pg 3/4] overlay and merging of results of point of interest assigned and layered in first and second land parcels where land, which are bounded by roads to form sub-polygons, are divided into different classifications that includes industrial and warehouse and commercial facilities. In [pg 2/4] it also indicates that different levels of classification results should be merged together and reunion. In the final classification results shown in Fig. 4, it can be seen that there are polygons and areas merged together that share the same classifications.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters of modified Sarti in view of Choudhry and Chen by incorporating further teaching of Chen such that multiple polygons are combined and classified based on point of interests, which would include vehicle stop points to classify a work stop zone of industrial and warehouses, i.e. commercial domicile, and filtering of any sub-polygons within which a vehicle stop point is not located is already done by classification to indicate what is considered vehicle stop zone of commercial, industrial and warehouse classification and what is not, i.e. all other classification.
The motivation to have a polygon divided into multiple polygons, using road segments, to determine a domicile polygon through points of interest that includes industrial and warehouses is that, as indicated by Chen, this would allow for more efficient way to divide space distribution of different current land use types by having different layers for different levels of classifications based on using points of interest with main road and with entire road (see [2/4-3/4] and Fig. 1).
Regarding claim 19, modified Sarti in view of Choudhry teaches the method of claim 11,
Modified Sarti in view of Choudhry does not teach: further comprising operating the at least one processor to:
generate an initial polygon encompassing the commercial domicile;
identify, using map data, one or more road segments that intersect the polygon;
generate from the initial polygon a plurality of sub-polygons based on the one or more road segments that intersect the polygon; and
generating a domicile polygon based on one or more of the sub-polygons within which a vehicle stop point is located.
However, Chen teaches first a research area, which can be any determined area that is of interest, i.e. initial polygon of interest that includes commercial domicile. Then, Chen teaches main and entire road to map first and second class plot, which generates multiple polygons with roads as boundaries, and it is combined with point of interest in a hierarchical grading classification process to determine which polygons belongs to different classification, which includes industrial and warehouses, i.e. commercial domicile (see [pg 2/4-3/4] and Figs 2 and 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters of modified Sarti in view of Choudhry by incorporating teaching of Chen such that initial polygon encompassing a commercial domicile, which is an area or city or district or country that has cluster of stop pings from Sarti, are separated into multiple polygons with road segments as boundaries, where multiple polygons are combined and classified based on point of interests, which would include vehicle stop points to classify a work stop zone of industrial and warehouses, i.e. commercial domicile.
The motivation to have a polygon divided into multiple polygons, using road segments, to determine a domicile polygon through points of interest that includes industrial and warehouses is that, as indicated by Chen, this would allow for more efficient way to divide space distribution of different current land use types by having different layers for different levels of classifications based on using points of interest with main road and with entire road (see [2/4-3/4] and Fig. 1).
Regarding claim 20, modified Sarti in view of Choudhry and Chen teaches the method of claim 19, wherein the generating of the domicile polygon comprises operating the at least one processor to:
filter any sub-polygons within which a vehicle stop point is not located; and
merge any sub-polygons within which a vehicle stop point is located.
Chen teaches [pg 3/4] overlay and merging of results of point of interest assigned and layered in first and second land parcels where land, which are bounded by roads to form sub-polygons, are divided into different classifications that includes industrial and warehouse and commercial facilities. In [pg 2/4] it also indicates that different levels of classification results should be merged together and reunion. In the final classification results shown in Fig. 4, it can be seen that there are polygons and areas merged together that share the same classifications.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify a vehicle stop zone in which stop-wise feature of shape of a stop based on vehicle stop pings, which are clusters of modified Sarti in view of Choudhry and Chen by incorporating further teaching of Chen such that multiple polygons are combined and classified based on point of interests, which would include vehicle stop points to classify a work stop zone of industrial and warehouses, i.e. commercial domicile, and filtering of any sub-polygons within which a vehicle stop point is not located is already done by classification to indicate what is considered vehicle stop zone of commercial, industrial and warehouse classification and what is not, i.e. all other classification.
The motivation to have a polygon divided into multiple polygons, using road segments, to determine a domicile polygon through points of interest that includes industrial and warehouses is that, as indicated by Chen, this would allow for more efficient way to divide space distribution of different current land use types by having different layers for different levels of classifications based on using points of interest with main road and with entire road (see [2/4-3/4] and Fig. 1).
Conclusion
12. 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.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HYANG AHN whose telephone number is (571)272-4162. The examiner can normally be reached M-F 9-5.
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/H.A./Examiner, Art Unit 3661
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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