DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a Final Action on the merits in response to the communications filed on 03/17/2026.
Claims amended are 1, 8, and 15;
Claims 1, 5, 7 – 8, 12, 14 – 15, 18, and 20 – 28 are pending in this application.
Examiner’s Response to Remarks
Response to Rejections
Examiner’s Response to Rejections under 35 U.S.C. § 101.
Examiner’s Response Rejections under 35 U.S.C. § 103.
Examiner’s Response to Rejections Under 35 U.S.C. § 101.
Applicant argues Examiner’s characterization of claims 1, 5, 7-8, 12, 14-15, 18, and 20-28 under 35 U.S.C. § 101 as being directed to an abstract idea without significantly more is incorrect under the proper application of the Alice/Mayo framework.
Examiner respectfully disagrees. Applicant’s claims are directed to a statutory category. However Applicant’s claim 1 recites an abstract idea, as the claim recites certain methods of organizing human activity, where there are managing interactions between a human and a computer. For example, claim 1 recites loading heuristic data associated with a trip performed by a vehicle, the heuristic data comprising driver identifiers associated with one or more of a previous trip, a next trip, and an inspection report; identifying a plurality of driver identifiers near to the vehicle during the trip; generating vectors, identifying the plurality of driver identifiers comprises: analyzing position and time data; generating location match scores for each of the plurality of driver identifiers based on the time difference statistics; generating a set of binary comparisons; and these are all merely collecting and analyzing data for assignment of the binary classification; and this recites an abstract idea of certain methods of organizing human activity. Claim 1 also recites mathematical concepts, and particularly mathematical calculations as the claim is training a model, calculating time difference statistics between the mobile device pings and in-vehicle monitoring device pings; and generating scores.
Applicant’s claim 1 is not significantly more than the judicial exception. Applicant recites the additional elements of mobile device pings, wherein the in-vehicle monitoring data is generated by an in- vehicle monitoring device communicatively coupled to a control system of the vehicle via an onboard diagnostic port, the in-vehicle monitoring device configured to independently record and transmit vehicle position data, training a decision tree classifier, an in-vehicle monitoring device, non-transitory computer readable storage medium, and a computer processor, however these additional elements are merely used to collect data for analysis with the computer; and claim 1 as whole merely collects data, analyzes data, and trains a decision tree classifier for binary comparison of the strongest match for assignment of a driver identifier to an unassigned trip; and this is merely collecting, analyzing, and comparing data for driver identifier assignment.
In Ex parte Carmody, Appeal 2025-002843 (PTAB Dec. 31, 2025), PTAB determined training at least one of a plurality of modular plug-and-play tactic-specific models using machine learning with a second training dataset comprising labeled feature vectors integrates the judicial exception into a practical application reciting “modular approach to tactic recommendation (i.e., with aseparate model for each tactic) enables the model for each tactic to be updated and improved separately and independently from other tactic-specific models, and also enables models for new tactics to be easily incorporated into tactic recommendation model 475 (e.g., as a plug-and-play module).” Likewise, Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), recites an improvement to technology, where Enfish provides a self-referential database table and is contrasting to the standard relational database table; as the first the self-referential model can store all entity types in a single table, and second the self-referential model can define the table’s columns by rows in that same table, and thus providing an improvement. Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) recites claims to a method of training a machine learning model were directed to improvements in the machine learning technology itself and additionally included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values. However, in the instant claims, there is no improvement or integration of the judicial exception into a practical application; where claim 1 is merely collecting, analyzing, and comparing data for driver identifier assignment, and does not integrate the judicial exceptions into a practical application that uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Claims 8 and 15 are substantially similar and recite the same subject matter and abstract ideas as claim 1; and the dependent claims are inherit the same deficiencies as the respective independent claims. Accordingly, 35 U.S.C. § 101 rejection remains for all pending claims.
Examiner’s Response to Rejections under 35 U.S.C. § 103.
Applicant argues the cited prior art does not teach Applicant’s claims, and argues claims 1, 5, 7-8, 12, 14-15, 18, and 20-28 under 35 U.S.C. § 103 as being patentable over U.S. Pat. Pub. No. 2019/0005412 ("Matus") in view of Wang, Yan, et al. "Sensing vehicle dynamics for determining driver phone use" ("Wang"); the Office Action rejects claims 21, 24, and 27 under 35 U.S.C. § 103 over Matus, Wang and U.S. Pat. No. 9,764,742 ("Goldfarb"); the Office Action rejects claims 22, 25, and 28 under 35 U.S.C. § 103 over Matus, Wang, and U.S. Pat. Pub. No. 2021/0142222 ("Chang").
Examiner respectfully agrees. Although Applicant has amended claims 1, 8, and 15 and argues amendments, Examiner’s cited art fails to teach generating a set of vectors based on the plurality of driver identifiers and the set of binary comparisons, wherein each vector in the set of vectors is generated by augmenting a binary comparison vector. For these reasons Examiner has removed rejection under 35 U.S.C. § 103.
Claim Rejections – 35 U.S.C. §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, 5, 7 – 8, 12, 14 – 15, 18 and 20 – 28 are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 8, and 15 recite:
identifying a plurality of driver identifiers near to the vehicle during the trip, the plurality of driver identifiers wherein identifying the plurality of driver identifiers comprises: analyzing position and time data;
calculating time difference statistics;
and generating location match scores for each of the plurality of driver identifiers based on the time difference statistics;
generating a set of binary comparisons based on the heuristic data, wherein generating a set of binary comparisons comprises comparing a candidate driver identifier to the driver identifiers in the heuristic data and to a matching driver identifier in the plurality of driver identifiers;
generating a set of vectors based on the plurality of driver identifiers and the set of binary comparisons;
assigning a label to each vector in the set of vectors to generate a set of labeled vectors;
inputting new vectors associated with an unassigned trip;
and assigning a driver identifier to the unassigned trip based on the binary classifications.
The limitations of claim 1 under its broadest reasonable interpretation recites certain methods of organizing human activity managing interactions between a human and a computer as we have loading heuristic data associated with a trip; identifying a plurality of driver identifiers during the trip, the plurality of driver identifiers wherein identifying the plurality of driver identifiers comprises: analyzing position and time data; calculating time difference statistics; and generating location match scores for each of the plurality of driver identifiers based on the time difference statistics; assigning a label to each vector in the set of vectors to generate a set of labeled vectors; wherein the predictive model generates a binary classification for each vector indicating whether a corresponding driver identifier is associated with the trip; inputting new vectors associated with an unassigned trip into the trained predictive model to obtain binary classifications for candidate driver identifiers; and assigning a driver identifier to the unassigned trip based on the binary classifications; and these all involve certain activity such as following a set of instructions, between a person and a computer or between two computers. Accordingly, claim 1 recites certain methods of organizing human activity.
Applicant’s claim 1 recites mathematical concepts, and particularly calculations because the claim is training a predictive model, calculating time difference statistics between the mobile device pings and in-vehicle monitoring device pings; and generating location match scores where Applicant defines calculating time difference statistics as “calculating time difference statistics based on mobile ping and ELD ping data (Applicant Spec. ¶ 0053) and recites mathematical relationships as we have generating a set of binary comparisons based on the heuristic data, wherein generating a set of binary comparisons comprises comparing a candidate driver identifier to the driver identifiers in the heuristic data and to a matching driver identifier in the plurality of driver identifiers. Accordingly, the claim recites mathematical concepts.
The dependent claims encompass the same abstract ideas as well. For instance, claims 5, 12, and 18 are directed towards observing heuristic data driver identifiers associated with one or more trips, a next trip, and an inspection report; claims 7, 14, and 20 are directed towards observation of driver identifiers and evaluating a feature vector; claims 21, 24, and 27 are directed towards observing assigned driver identifier with the unassigned trip in a database, and observing a user interface to allow review of the assigned driver identifier; claims 22, 25, and 28 are directed towards evaluating training of the predictive model is segmenting a set of vectors into a training set and a validation set according to a predefined split position; and claims 23 and 26 are directed towards observing predictive models as a binary classification model, a decision tree model, a random forest model, or an extreme gradient boosting model; and thus, the dependent claims further limit the abstract concepts found in the independent claims.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of wherein the in-vehicle monitoring data is generated by an in- vehicle monitoring device communicatively coupled to a control system of the vehicle via an onboard diagnostic port, the in-vehicle monitoring device configured to independently record and transmit vehicle position data, decision tree classifier, mobile device pings, in-vehicle monitoring device pings, generating a set of vectors based on the plurality of driver identifiers and the set of binary comparisons, wherein each vector in the set of vectors is generated by augmenting a binary comparison vector for a respective candidate driver identifier with a location match vector and a vehicle; Claim 8 recites the additional elements of a computer processor, a vehicle, a non-transitory computer-readable storage medium, the mobile device pings, wherein the in-vehicle monitoring data is generated by an in- vehicle monitoring device communicatively coupled to a control system of the vehicle via an onboard diagnostic port, the in-vehicle monitoring device configured to independently record and transmit vehicle position data, generating a set of vectors based on the plurality of driver identifiers and the set of binary comparisons, wherein each vector in the set of vectors is generated by augmenting a binary comparison vector for a respective candidate driver identifier with a location match vector, training a decision tree classifier, and in-vehicle monitoring device pings; and claim 15 recites the additional elements of a device, training decision tree classifier, a processor, wherein the in-vehicle monitoring data is generated by an in- vehicle monitoring device communicatively coupled to a control system of the vehicle via an onboard diagnostic port, the in-vehicle monitoring device configured to independently record and transmit vehicle position data, generating a set of vectors based on the plurality of driver identifiers and the set of binary comparisons, wherein each vector in the set of vectors is generated by augmenting a binary comparison vector for a respective candidate driver identifier with a location match vector, a vehicle, training decision classifier tree the mobile device pings and in- vehicle monitoring device pings. The additional elements of a device, a computer processor a vehicle, wherein the in-vehicle monitoring data is generated by an in- vehicle monitoring device communicatively coupled to a control system of the vehicle via an onboard diagnostic port, the in-vehicle monitoring device configured to independently record and transmit vehicle position data, generating a set of vectors based on the plurality of driver identifiers and the set of binary comparisons, wherein each vector in the set of vectors is generated by augmenting a binary comparison vector for a respective candidate driver identifier with a location match vector, mobile device pings and in-vehicle monitoring device pings and a non-transitory computer-readable storage medium are considered generic computer components which are all generic computer components as per Applicant’s Specifications shown below:
“[0029] The central platform 210 can comprise a server-based application platform. In some embodiments, the central platform 210 can comprise a single computing device. In other embodiments, the central platform 210 can comprise multiple computing devices operating as a private network. In some embodiments, the central platform 210 can comprise a cloud platform and thus can comprise changing amounts of hardware and instances of software. The various components of central platform 210 described herein can be implemented in software, hardware, or a combination thereof, and the disclosure is not limited to a specific deployment option.”
[0098] In some embodiments, the CPU 602 may comprise a general-purpose CPU. The CPU 602 may comprise a single-core or multiple-core CPU. The CPU 602 may comprise a system-on- a-chip (SoC) or a similar embedded system. In some embodiments, a graphics processing unit (GPU) may be used in place of, or in combination with, a CPU 602.Memory 604 may comprise a memory system including a dynamic random-access memory (DRAM), static random-access memory (SRAM), Flash (e.g., NAND Flash), or combinations thereof. In one embodiment, the bus 614 may comprise a Peripheral Component Interconnect Express (PCIe) bus. In some embodiments, the bus 614 may comprise multiple busses instead of a single bus.
[0099]Memory 604 illustrates an example of a non-transitory computer storage media for the storage of information such as computer-readable instructions, data structures, program modules, or other data. Memory 604 can store a basic input/output system (BIOS) in read-only memory (ROM), such as ROM 608 for controlling the low-level operation of the device. The memory can also store an operating system in random-access memory (RAM) for controlling the operation of the device.”
and thus are not practically integrated nor significantly more.
The combination of these additional elements are no more than mere instructions to apply the exception using a generic computer component (e.g., a computer processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 5, 7, 12, 14, 18, and 20 – 28, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1, 5, 7 – 8, 12, 14 – 15, 18 and 20 – 28 are not patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
06/12/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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