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 .
DETAILED CORRESPONDENCE
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 27, 2026 has been entered.
Status of Claims
Claims 1, 11, 12, 19, 22, 23 have been amended.
Claims 7, 10, 18, 21 have been cancelled.
No claims have been added.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
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 – 6, 8 – 9, 11 – 17, 19, 20, 22, 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
receive the telematics data;
determine for each of the plurality of vehicles, using the telematics data, when a vehicle maintenance event has occurred by identifying at least a binary vehicle status indicator change, time-logged telematics data that meets a predetermined condition, a vehicle service indicator, or a combination thereof;
determine, using the telematics data, a location of each of the plurality of vehicles at a time at which the vehicle maintenance event occurred by;
identifying a first location reported immediately before when the binary vehicle status indicator change occurred, the time-logged engine data meets the predetermined condition, and/or the vehicle service indicator has occurred, and a second location reported when, or immediately after, the binary vehicle status indicator change occurred, the time-logged engine data meets the predetermined condition, and/or the vehicle service indicator has occurred, and
determining the location of the vehicle maintenance event based on whether the first location and the second location are within a selected distance of each other, based on an average location of the first location and the second location, or a combination thereof; and
identify one or more service centers by:
applying to a plurality of maintenance event locations a clustering model, and
classifying one or more clusters of maintenance event locations as a service center:
identifying potential service-center zones that overlap a labeled service-center zone, and
inputting as data one or more vehicle maintenance features associated with the potential service-center zones that overlap with the labeled center zone
The invention is directed towards the abstract idea of collecting and comparing information and, based on a rule(s), identify options, in this case, collecting information and comparing it to known information to determine if a location is a service center, which corresponds to “Mental Processes” and “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed in the human mind and/or using pen and paper, e.g., a human can collect maintenance event information of a vehicle and the vehicle’s location, collect location information of service center, and compare the location information to determine if the event took place at a service center. This process can also be performed by simply having two humans (one located at the service center and the other at the service center or some remote location) speaking with one another.
The limitations of:
receive the telematics data;
determine for each of the plurality of vehicles, using the telematics data, when a vehicle maintenance event has occurred by identifying at least a binary vehicle status indicator change, time-logged telematics data that meets a predetermined condition, a vehicle service indicator, or a combination thereof;
determine, using the telematics data, a location of each of the plurality of vehicles at a time at which the vehicle maintenance event occurred by;
identifying a first location reported immediately before when the binary vehicle status indicator change occurred, the time-logged engine data meets the predetermined condition, and/or the vehicle service indicator has occurred, and a second location reported when, or immediately after, the binary vehicle status indicator change occurred, the time-logged engine data meets the predetermined condition, and/or the vehicle service indicator has occurred, and
determining the location of the vehicle maintenance event based on whether the first location and the second location are within a selected distance of each other, based on an average location of the first location and the second location, or a combination thereof; and
identify one or more service centers by:
applying to a plurality of maintenance event locations a clustering model, and
classifying one or more clusters of maintenance event locations as a service center:
identifying potential service-center zones that overlap a labeled service-center zone, and
inputting as data one or more vehicle maintenance features associated with the potential service-center zones that overlap with the labeled center zone
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium. That is, other than reciting a generic processor executing computer code stored on a computer medium nothing and a generic machine learning model in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium and a generic machine learning model in the context of this claim encompasses a human can collect maintenance event information of a vehicle and the vehicle’s location, collect location information of service center, and compare the location information to determine if the event took place at a service center. This process can also be performed by simply having two humans (one located at the service center and the other at the service center or some remote location) speaking with one another. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium and a generic machine learning model, then it falls within the “Mental Processes” and “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic processor executing computer code stored on a computer medium to communicate information, as well as performing operations that a human can perform in their mind or using pen and paper, i.e. comparing information and referring to a rule(s) to identify options. The generic processor executing computer code stored on a computer medium in the steps are recited at a high-level of generality (i.e., as a generic processor executing computer code stored on a computer medium can perform the insignificant extra solution steps of communicate information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium are merely being applied to perform the steps that can be performed in the human mind or using pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor executing computer code stored on a computer medium.
Although the claim recites “machine learning model,” the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses 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 more than a drafting effort designed to monopolize the exception.
Even training and applying a machine learning model is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a machine learning model is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards the collection and comparison information and, based on a rule(s), identifying options, in this case, collecting information and comparing it to known information to determine if a location is a service center. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. The Examiner asserts that the claimed invention fails to recite any iterative process being performed on the machine learning algorithm/model in order to demonstrate that the machine learning algorithm/model is being improved upon, i.e. a demonstration that would support an improvement upon machine learning technology. Referring to MPEP § 2106.05(f), the training is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims 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 a generic processor executing computer code stored on a computer medium t and a generic machine learning model o perform the steps of:
receive the telematics data;
determine for each of the plurality of vehicles, using the telematics data, when a vehicle maintenance event has occurred by identifying at least a binary vehicle status indicator change, time-logged telematics data that meets a predetermined condition, a vehicle service indicator, or a combination thereof;
determine, using the telematics data, a location of each of the plurality of vehicles at a time at which the vehicle maintenance event occurred by;
identifying a first location reported immediately before when the binary vehicle status indicator change occurred, the time-logged engine data meets the predetermined condition, and/or the vehicle service indicator has occurred, and a second location reported when, or immediately after, the binary vehicle status indicator change occurred, the time-logged engine data meets the predetermined condition, and/or the vehicle service indicator has occurred, and
determining the location of the vehicle maintenance event based on whether the first location and the second location are within a selected distance of each other, based on an average location of the first location and the second location, or a combination thereof; and
identify one or more service centers by:
applying to a plurality of maintenance event locations a clustering model, and
classifying one or more clusters of maintenance event locations as a service center:
identifying potential service-center zones that overlap a labeled service-center zone, and
inputting as data one or more vehicle maintenance features associated with the potential service-center zones that overlap with the labeled center zone,
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.
Additionally:
Claim 2 is directed towards descriptive subject matter and data collection, in this case, describing the vehicle status change indicator and collecting data that corresponds to the type of indicator.
Claims 3, 4, 5 are directed towards descriptive subject matter.
Claim 6 is directed towards descriptive subject matter and data collection, in this case, describing the vehicle status change indicator and collecting data at a particular point in time.
Claim 8 is directed towards collecting and comparing information and, based on a rule, identify options, as well as descriptive subject matter.
Claim 9 is directed towards extra-solution activities, as well as collecting and comparing information and, based on a rule, identify options, in this case, collecting information at a particular point in time based on a threshold.
Claim 11 is directed towards concepts already discussed above, but being performed multiple times, i.e. for multiple vehicles and their respective maintenance events and location.
The remaining claims are similar to what has already been discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for determining whether an event (maintenance event) occurred at a service provider (service center). Accordingly, the claims are not patent eligible.
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 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.
Claims 1 – 6, 8 – 9, 11 – 17, 19, 20, 22, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Colvin et al. (US PGPub 20120053778 A1) in view of Spruyt et al. (US PGPub 2019/0387365 A1).
In regards to claims 1, 12, 23, Colvin discloses (Claim 1) a system for identifying a service center, the system comprising; (Claim 12) a method for identifying a service center, the method comprising operating at least one processor to; (Claim 23) a non-transitory computer-readable medium having instructions stored thereon executable by at least one processor to implement a method for identifying a service center, the method comprising operating the at least one processor to:
In regards to:
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
(Fig. 2, 3, 4; ¶ 17, 45, 70 wherein a communication network comprising the network, a telematic unit, and a remote computing device for conveying operational data of a vehicle is disclosed and used by fleet owners operating multiple vehicles):
receive the telematics data (¶ 56, 70 wherein telematics data is received);
determine for each of the plurality of vehicles, using the telematics data, when a vehicle maintenance event has occurred by identifying at least a binary vehicle status indicator change, time-logged telematics data that meets a predetermined condition, a vehicle service indicator, or a combination thereof (¶ 18, 51, 54, 56, 62, 70, 86, 87 wherein a maintenance event is determined using the telematics data and includes, at least, time-logged telematics data that meets a predetermined condition, vehicle status indicator, and vehicle status indicator change);
determine, using the telematics data, a location of each of the plurality of vehicles at a time at which the vehicle maintenance event occurred by (¶ 17, 45, 54, 62, 86 wherein the telematics data is used to determine the location of a vehicle at which the maintenance event occurred):
identifying a first location reported immediately before when the binary vehicle status indicator change occurred, the time-logged engine data meets the predetermined condition, and/or the vehicle service indicator has occurred, and a second location reported when, or immediately after, the binary vehicle status indicator change occurred, the time-logged engine data meets the predetermined condition, and/or the vehicle service indicator has occurred, and
determining the location of the vehicle maintenance event based on whether the first location and the second location are within a selected distance of each other, based on an average location of the first location and the second location, or a combination thereof
(¶ 18, 62, 86, 87 wherein a service center is identified by analyzing the detected maintenance event(s) at vehicle startup to determine the proximate location of the events and determine if the events are occurring within proximity to a service center and classifying the event as occurring at a service center so that the event can be ignored, e.g., the vehicle is being driven around the service facility trying to replicant an intermittent. In other words, multiple location data is collected and analyzed during each vehicle startup and operation associated with detected maintenance events and the all of the location data are analyzed to determine their proximity with one another and determine whether the event should be ignored because the events/vehicle are in proximity to a repair facility.)
In regards to:
identify one or more service centers by:
applying to a plurality of maintenance event locations a clustering model, and
classifying one or more clusters of maintenance event locations as a service center […]:
(¶ 18, 62, 86, 87 wherein a service center is identified by analyzing the detected maintenance event(s) at vehicle startup to determine the proximate location of the events and determine if the events are occurring within proximity to a service center and classifying the event as occurring at a service center so that the event can be ignored, e.g., the vehicle is being driven around the service facility trying to replicant an intermittent fault):
[…], and
[…].
Colvin discloses a system and method that intelligently determines whether to ignore a maintenance event due to the system knowing the location of service center that is at or proximate to the maintenance event. Although Colvin discloses that the system and method intelligently determines the location of a service center, Colvin fails to explicitly disclose whether it would have been known to utilize and train a machine learning model to determine the location of a service provider based on sensor data of an asset.
To be more specific, Colvin fails to explicitly disclose:
classifying one or more clusters of maintenance event locations as a service center by applying to the one or more clusters of maintenance event locations a machine learning model, the machine learning model trained by:
identifying potential service-center zones that overlap a labeled service-center zone, and
inputting as training data one or more vehicle maintenance features associated with the potential service-center zones that overlap with the labeled center zone.
However, Spruyt teaches that it is well-known in the art to use machine learning to collect movement data of an event using sensors of an asset to predict or determine the location of the event. As a non-limiting, Spruyt teaches that machine learning can be used to collect sensor data associated with driving a vehicle, determine the vehicle’s location using GPS, and refer to external data sources to determine that the vehicle is located at a service provider. Spruyt teaches:
“The extraction includes iteratively deriving 1110 user events from the set of measurements, preferably using machine learning. The user events are data structures including at least time data (when did the event occur), user location data (where did the event occur), and user movement data (what occurred). The user movement data may include time-varying motion vectors and acceleration vectors, allowing the motion to be classified as, for example, resting or being idle, sitting, standing, walking, jogging/running, cycling, riding a motorcycle, driving a car, driving a tram or a train, flying aboard an aircraft, and the like, in known manners. The user event data may be augmented by data obtained from external data sources, such as current or predicted traffic information, current or predicted weather information, location-related information including points-of-interest, street names, road types, speed limits, shops and service providers, and the like (for example obtained via a web service).” (¶ 32)
(For support see also: ¶ 5, 7, 14, 32, 38, 39, 41, 42)
The Examiner asserts that Spruyt has not been provided to teach all possible types of events, i.e. maintenance events, or all types of service provider, i.e. service center, but to answer the question of whether it was known at the art, before the effective filing date of the invention, to use machine learning to collect and analyze information collected from sensors associated with an asset to determine the proximate location of the asset and the location of service providers for, at least, pattern recognition (¶ 10, 11, 12). The Examiner asserts that Colvin has been already been provided to establish that it is known to correlate the location of a vehicle (asset) and a maintenance event (user event/user movement) with location of a service provider, such as, a service center (service provider) to determine whether a maintenance event should be ignored. The sole difference between Colvin and the claimed invention is that despite Colvin using a system and method to intelligently make the correlation, Colvin fails to explicitly disclose whether machine learning can be used to make the correlation. Accordingly, Spruyt has been provided to teach that this is, indeed, known in the art and further teaches that such a system would overcome all the defects in employed human-generated data. That is to say, advancements in technology yields improved results.
Further, one of ordinary skill in the art of location tracking and determination would have found it obvious to update the location identification system and method for an event and a corresponding point of interest as disclosed by Colvin using modern technology, as taught in Spruyt, in order to gain the commonly understood benefits of such adaptation, such as improved analysis and results when compared to human-generated data.
Accommodating the prior arts more manual and antiquated process with modern electronics, in this case, using machine learning to correlate the location of an asset and event with a point of interest, e.g., correlating the location and event of a vehicle with a service provider, would have been obvious. As stated in Leapfrog, “applying modern electronics to older mechanical devices has been commonplace in recent years.”
In regards to claims 2, 13, the combination of Colvin and Spruyt discloses the system of claim 1 (the method of claim 12), wherein the at least one processor is operable to identify the binary vehicle status indicator change by identifying a change in vehicle warning lights, a change in diagnostic trouble codes (DTCs), or a combination thereof (Colvin – ¶ 18 wherein an anomaly is detected based on the occurrence of a DTC).
In regards to claims 3, 14, the combination of Colvin and Spruyt discloses the system of claim 1 (the method of claim 12), wherein the time-logged telematics data comprises engine oil quality data, battery data, fluid level data, or a combination thereof (Colvin – ¶ 4, 54 wherein a fault code can be associated with, at least, engine oil quality data and battery data).
In regards to claims 4, 15, the combination of Colvin and Spruyt discloses the system of claim 3 (the method of claim 14), wherein the predetermined condition comprises an increase in engine oil quality and/or a fluid level that is greater than a predetermined threshold (Colvin – ¶ 54 wherein the condition is associated with oil temperature data and this data is used to determine the occurrence of a fault, i.e. increase in oil temperature would degrade the oil after a certain temperature).
In regards to claims 5, 16, the combination of Colvin and Spruyt discloses the system of claim 1 (the method of claim 12), wherein the vehicle service indicator comprises a vehicle diagnosis indicator, a vehicle tow indicator, or a combination thereof (Colvin – ¶ 62 wherein the indicator is a DTC).
In regards to claims 6, 17, the combination of Colvin and Spruyt discloses the system of claim 1 (the method of claim 12), wherein the binary vehicle status indicator change, the time-logged telematics data meeting the predetermined condition occur, and/or the vehicle service indicator occur during an ignition cycle (Colvin – ¶ 18, 51, 54, 56, 62, 70, 86, 87 wherein a maintenance event is determined using the telematics data and includes, at least, time-logged telematics data that meets a predetermined condition, vehicle status indicator, and vehicle status indicator change and can occur during startup, i.e. during an ignition cycle).
In regards to claims 8, 19, the combination of Colvin and Spruyt discloses the system of claim 1 (the method of claim 12), wherein the at least one processor is further operable to determine the location of vehicle maintenance event by:
identifying an end cycle location of an immediately preceding ignition cycle and a starting cycle location of a current ignition cycle; and
determining the location of the vehicle maintenance event based on whether the first location, the second location, the end cycle location, and the starting cycle location are within a selected distance of each other, based on an average location of the first location, the second location, the end cycle location, and the starting cycle location, or a combination thereof
(Colvin – ¶ 18, 62, 86, 87 wherein a service center is identified by analyzing the detected maintenance event(s) at vehicle startup to determine the proximate location of the events and determine if the events are occurring within proximity to a service center and classifying the event as occurring at a service center so that the event can be ignored, e.g., the vehicle is being driven around the service facility trying to replicant an intermittent. In other words, multiple location data is collected and analyzed during each vehicle startup and operation associated with detected maintenance events and the all of the location data are analyzed to determine their proximity with one another and determine whether the event should be ignored because the events/vehicle are in proximity to a repair facility.).
In regards to claims 9, 20, the combination of Colvin and Spruyt discloses the system of claim 1 (the method of claim 12), wherein the at least one processor is operable to determine the location of the vehicle maintenance event based on an ignition cycle duration of the vehicle being greater than or equal to a predetermined time threshold (Colvin – ¶ 18, 62, 86, 87 wherein a service center is identified by analyzing the detected maintenance event(s) at vehicle startup to determine the proximate location of the events and determine if the events are occurring within proximity to a service center and classifying the event as occurring at a service center so that the event can be ignored, e.g., the vehicle is being driven around the service facility trying to replicant an intermittent. In other words, multiple location data is collected and analyzed during each vehicle startup and operation associated with detected maintenance events and the all of the location data are analyzed to determine their proximity with one another and determine whether the event should be ignored because the events/vehicle are in proximity to a repair facility).
In regards to claims 11, 22, the combination of Colvin and Spruyt discloses the system of claim 1 (the method of claim 12), wherein the one or more vehicle maintenance features comprise a number of distinct vehicles having one or more vehicle maintenance events occur at the maintenance event location, an average time spent by the vehicles at the maintenance event location, an average vehicle ignition cycle time of the vehicles at the maintenance event location, a number of distinct vehicle maintenance event types occurring at the maintenance event location, a number of vehicle maintenance events occurring within a selected time period at the maintenance event location, a percentage of time within which a vehicle maintenance event occurs at the maintenance event location, or a combination thereof (Colvin – ¶ 18, 45, 62, 86, 87 wherein a service center is identified by analyzing the detected maintenance event(s) at vehicle startup to determine the proximate location of the events and determine if the events are occurring within proximity to a service center and classifying the event as occurring at a service center so that the event can be ignored, e.g., the vehicle is being driven around the service facility trying to replicant an intermittent. In other words, multiple location data is collected and analyzed during each vehicle startup and operation associated with detected maintenance events and the all of the location data are analyzed to determine their proximity with one another and determine whether the event should be ignored because the events/vehicle are in proximity to a repair facility. Finally, the system is configured to monitor a plurality of vehicles at a time, i.e. fleet owners can utilize the system to manage their fleet).
Response to Arguments
Applicant's arguments filed 6/29/2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
With regards to the rejection under 35 USC 101, the rejection is maintained because the claimed invention is not improving upon technology or resolving an issue that arose in technology, let alone "improvement to the field of telematics technology." The Examiner asserts that generic telematic technology has been recited at a high level of generality and applied to the abstract idea to perform the extra-solution activity of transmitting data that they are designed to collect and transmit. There are absolutely no limitations directed towards improving telematic technology. The claimed invention recites that telematics data is received and used to determine location for the purpose to group the locations and determine specific information about corresponding locations, e.g., service provider locations and, more specifically, service center locations, and nothing more. The claimed invention is not directed or concerned with improving telematic technology. Collecting and using information provided by generic technology is not an improvement to the technology, but reciting generic technology at a high level of generality and applying it to the abstract idea.
Neither the specification nor claimed invention are directed towards utilizing the results of machine learning to improve technology or perform activities that cannot be practically performed by a human. The applicant goes as far as to admit that the invention can, indeed, be performed by a human (Page 11, last paragraph, “…may require manual confirmation”), but has applied generic technology/machine learning for the advantages that it provides, i.e. more efficient, faster, less prone to human error, and the like. As a result, the Examiner asserts that the claimed invention is, indeed, reciting generic technology and applying it to the abstract idea.
Rejection under 35 USC 102/103
In response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., whether the service center locations were previously known or not) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Specifically, what the applicant is arguing and what has been claimed do not match or, more specifically, the scope of the claimed invention is broader than what is being argued. Nowhere in the claimed invention is there any recitation that the service centers were not previously known. The claimed invention is also broad with respect to what the maintenance events are, i.e. they can be anything from the vehicle indicating that it has a dead battery to a technician at a service center connecting an OBD II to collect DTC's. The claimed invention is void of any recitation of what the specific maintenance events are, what information is being extracted from the maintenance events, and what is the link between maintenance events and service center locations.
Further, ¶ 52 of the specification provides an example where maintenance events are retrieved from a vehicle and that based on these events the system is able to determine that the events are occurring at a particular location, e.g., service center.
¶ 82 provides verbatim support for the claimed invention.
¶ 86, 87, 90, 91, 92, 93 discloses a wide range of maintenance events.
¶ 94 discloses that the maintenance event information can be used to provide a tow vehicle and identifying a change in the vehicle's location.
¶ 96 discloses "...a vehicle maintenance event occurring at a given location may indicate that that location could be a service center."
¶ 98 discloses that "vehicles being maintained at a service center may not experience a change in location immediately therebefore and thereafter."
¶ 99 discloses "by using multiple locations, further confirmation may be provided that a vehicle maintenance event occurred at a service center and is not a false positive due to noisy a data and/or a vehicle maintenance event that occurred at a location other than a service center (e.g., road-side assistance)."
¶ 105 discloses "Once any clusters of maintenance event locations are identified, one or more of 20 the clusters may be classified as a service center, or not. In some embodiments, one or more of the clusters of maintenance event locations may be classified as a service center by comparing (e.g., by operating one or more of the processors 112, 132, 152) the locations of each of the clusters to labelled service center locations. In such embodiments, if a cluster of maintenance event locations is found to overlap with a labelled service center location (e.g., an overlap, based 25 on area, of 25%, 50% 75%, or more, or less, or any percentage therebetween), that cluster may be classified as a service center. The labelled service center locations may be provided, for example, by map data provided by map information providers such as OpenStreetMaps (OSM), by users of the systems and methods of the present disclosure (i.e., user-submitted labels), etc."
¶ 113, 114, 115 provides similar disclosure to ¶ 105.
The Examiner asserts that none of the aforementioned paragraphs, which are directed to how the system determines what the maintenance events are and how they are used to determine the location of a service center, disclose whether the location of a service center was known or not. If anything, ¶ 105, 113, 114, 115 explicitly disclose that service center locations were already known and labelled as such.
As a result, the Examiner asserts that the claimed location is collecting maintenance events locations and comparing them to known locations of service centers to determine if both locations match and, if so, determine that the event likely occurred at a service center location. The applicant's arguments appear to imply that the system is completely unaware of service center locations, analyzing specific maintenance events, e.g., connecting an OBD-II reader to a vehicle to extract DTC codes, and based on the type of information the system is, somehow, able to determine that multiple readings of these events at the same location is indicative that the vehicle is likely to be located at a service center because a service center would be a location where such events occur. However, the claimed invention does not support or is limited to such an interpretation and the specification, as indicated above, provides no such support.
The Examiner asserts that the claimed invention is doing nothing more than determining the location(s) of where maintenance events are occurring, clustering those events, and, based on the locations, determine that they are likely occurring at a service center location, i.e. classifying the events as a service center. There is no determination, assumption, providing, or the like that the location of a service center was never known and that the grouping/clustering of the maintenance events is how the system is able to know that the events are at a service, again, without the use of any other information that indicate service center locations.
With that said, the fact that the service locations are known, filtered, and/or ignored does not change the fact that Colvin is still applying a clustering model that is based on the locations of service centers being proximate to a maintenance event location. Colvin discloses that multiple maintenance event locations are identified and clustered together and compared to the location of a service center in order to determine if the events occurred in the proximity of a service center, thereby allowing the system to ignore the event and correlate the event as occurring under the supervision/control of the service center, e.g., the vehicle is being driven around a service facility that is trying to replicate an intermittent fault. It further appears that the applicant’s arguments are also based on features that have not been claimed or argued and, consequently, based on the applicant’s arguments, the claimed invention, and Colvin, the Examiner asserts that there is no difference between what has been claimed and what is disclosed by Colvin.
Finally, The Examiner asserts that Spruyt has not been provided to teach all possible types of events, i.e. maintenance events, or all types of service provider, i.e. service center, but to answer the question of whether it was known at the art, before the effective filing date of the invention, to use machine learning to collect and analyze information collected from sensors associated with an asset to determine the proximate location of the asset and the location of service providers for, at least, pattern recognition (¶ 10, 11, 12). The Examiner asserts that Colvin has been already been provided to establish that it is known to correlate the location of a vehicle (asset) and a maintenance event (user event/user movement) with location of a service provider, such as, a service center (service provider) to determine whether a maintenance event should be ignored. The sole difference between Colvin and the claimed invention is that despite Colvin using a system and method to intelligently make the correlation, Colvin fails to explicitly disclose whether machine learning can be used to make the correlation. Accordingly, Spruyt has been provided to teach that this is, indeed, known in the art and further teaches that such a system would overcome all the defects in employed human-generated data. That is to say, advancements in technology yields improved results.
Further, one of ordinary skill in the art of location tracking and determination would have found it obvious to update the location identification system and method for an event and a corresponding point of interest as disclosed by Colvin using modern technology, as taught in Spruyt, in order to gain the commonly understood benefits of such adaptation, such as improved analysis and results when compared to human-generated data.
Accommodating the prior arts more manual and antiquated process with modern electronics, in this case, using machine learning to correlate the location of an asset and event with a point of interest, e.g., correlating the location and event of a vehicle with a service provider, would have been obvious. As stated in Leapfrog, “applying modern electronics to older mechanical devices has been commonplace in recent years.” (See also MPEP § 2143 F Known Work in One Field of Endeavor May Prompt Variations of It for Use in Either the Same Field or a Different One Based on Design Incentives or Other Market Forces if the Variations Are Predictable to One of Ordinary Skill in the Art)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Starkey et al. (US PGPub 2026/0112210 A1); Kumar et al. (EP 3662336 B1); Hellgren et al. (US Patent 12,646,017 B2); Liu et al. (US Patent 12,694,721 B2); Isaac (US PGPub 2026/0131798 A1) – which discloses the monitoring and management of vehicles to identify service-related issues and identifying available and/or nearby service centers to address service-related issues
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 8/17/2026
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