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 .
Introduction
The following is a non-final Office action in response to Applicant’s submission filed on 6/20/2025. Currently claims 1-20 are pending and claims 1, 10, and 19 are independent. This application is a continuation of 18/211,404 filed 6/19/2023
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/20/2025 appears to be in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more. With respect to claims 1-20, following the guidance contained within MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea?
In accordance with these steps, the Examiner finds the following:
Step 1: Claim 1 and its dependent claims (claims 2-9) are directed to a statutory category, namely a system/machine. Claim 10 and its dependent claims (claims 11-18) are directed to a statutory category, namely a method. Claim 19 and its dependent claims (claims 20) are directed to a statutory category, namely an article of manufacture.
Step 2A (Prong 1): Claims 1, 10, and 19 which are substantially similar claims to one another, are directed to the abstract idea of “Mental processes”, or more particularly, “Concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (See MPEP 2106).” In this application that refers to using a computer system to manage and analyze routes and lines for buses. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function that a bus dispatcher performs for a public transit system. The abstract elements of claims 1, 10, and 19, recite in part “Receive request…Receive data…Retrieve historical data…Optimize route assignment…Determine available vehicles…Update GUI…Receive route group…Unassign route…Reassign route…”. Dependent claims 2-9, 11-18, 20 add to the abstract idea the following limitations which recite in part “Display each route… Display each route…Color code route…Display with time…Select group…Optimize assignment…Retain data…Optimize assignment based on input…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 10, and 19.
Step 2A (Prong 2): Independent claims 1, 10, and 19, which are substantially similar claims to one another, do not contain additional elements that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “User device…Server device…Processors…Memory…Artificial intelligence engine…Vehicle device…Interface…Graphical representation…Non-transitory computer readable medium…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Additionally, dependent 2-9, 11-18, 20 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis.
Step 2B: Independent claims 1, 10, and 19, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “User device…Server device…Processors…Memory…Artificial intelligence engine…Vehicle device…Interface…Graphical representation…Non-transitory computer readable medium…”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (manage and analyze routes and lines for buses) on a general purpose computer (See MPEP 2106.05(f)). This is exemplified in the Applicant’s specification in [0028] – “hardware components may include a combination of Central Processing Units ("CPUs"), buses, volatile and non-volatile memory devices, storage units, non-transitory computer-readable storage media, data processors, processing devices, control devices transmitters, receivers, antennas, transceivers, input devices, output devices, network interface devices, and other types of components that are apparent to those skilled in the art.”
Additionally, dependent claims 2-9, 11-18, 20 do not include any additional elements to conduct a further 2B analysis.
Accordingly, whether taken individually or as an ordered combination claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more.
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.
Claims 1-3, 5-12, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Moran et al. (US 20070024440 A1) in view of LaPlante et al. (CA 3035241 A1)
Regarding claims 1, 10, and 19, Moran discloses a system, comprising: a server device comprising: one or more processors, and a memory storing instructions that, when executed by the one or more processors (Moran ¶18 - According to an embodiment of the present invention, with reference to FIGS. 1-12, a school bus tracking and notification system 20 utilizes the infrastructure of an existing cellular communications network 22 (e.g., mobile phone network) for tracking school buses 24. Each school bus 24 is provided with a mobile station 26 (e.g., mobile phone) and a unique bus identifier ("ID") 28. A tracking application system 30 is operably connected to the cellular network 22, and has a database portion 32 that stores the bus ID's 28 and the buses' mobile stations' respective communications identifiers 34, e.g., mobile station telephone numbers or other identifiers), cause the one or more processors to perform steps comprising: receiving a request for a route to be reassigned from a first vehicle among a fleet of vehicles, wherein each one of a plurality of routes is serviced by the fleet of vehicles, and wherein the plurality of routes, including the route to be reassigned from the first vehicle, are scheduled and repeating routes (Moran ¶38 - Alternatively, to avoid having to physically "reshuffle" bus mobile stations, the tracking application system 30 may be used for reassigning bus routes using the reassignment module 82 and route entries 72. As shown in FIG. 8); receiving real time data from vehicle devices associated with one or more vehicles in the fleet of vehicles (Moran ¶18 - According to an embodiment of the present invention, with reference to FIGS. 1-12, a school bus tracking and notification system 20 utilizes the infrastructure of an existing cellular communications network 22 (e.g., mobile phone network) for tracking school buses 24. Each school bus 24 is provided with a mobile station 26 (e.g., mobile phone) and a unique bus identifier ("ID") 28. A tracking application system 30 is operably connected to the cellular network 22, and has a database portion 32 that stores the bus ID's 28 and the buses' mobile stations' respective communications identifiers 34, e.g., mobile station telephone numbers or other identifiers); updating a graphical user interface to display a graphical representation of one or more available route groups wherein the graphical representation includes the route to be reassigned added to each one of the one or more available route groups and identified with a visual cue (Moran Fig. 8, 9,13); receiving, by the server device, a selected route group among the one or more available route groups, wherein the selected route group is serviced by a second vehicle among the one or more other vehicles within the fleet of vehicles to be reassigned (Moran Fig. 8, 9); unassigning the route to be reassigned from the first vehicle; and reassigning the route to be reassigned to the second vehicle which services the selected route group (Moran Fig. 8 - Moran ¶38 - Alternatively, to avoid having to physically "reshuffle" bus mobile stations, the tracking application system 30 may be used for reassigning bus routes using the reassignment module 82 and route entries 72. As shown in FIG. 8).
Moran lacks an artificial intelligence engine; retrieving historical data previously received from the vehicle devices servicing the plurality of routes in past, wherein the artificial intelligence engine is iteratively trained with the historical data by learning from the historical data associated with servicing the plurality of past routes; optimizing, by the artificial intelligence engine trained with historical data, assignment of the route to one or more other vehicles within the fleet based on learning from the historical data, optimizing including: determining, by the artificial intelligence engine trained with historical data, based on comparing the real time data and the historical data with machine learning from previous assignments of the route, the one or more other vehicles within the fleet of vehicles which are available to service the route to be reassigned relative to the previous assignments of the route; and as determined by the artificial intelligence engine an advisability indicator as determined by the artificial intelligence engine.
LaPlante, from the same field of endeavor, teaches an artificial intelligence engine (LaPlante ¶88 - In some embodiments, the RORE 114 may optionally comprise a machine learning module (MLM) 150. This MLM 150 provides a more advanced analytic capability to the RORE 114 and is operable to utilize a plurality of machine learning algorithms (including deep-learning algorithms) to analyze, in real-time or after each run completion, the plurality of data acquired by the RTME 110 and to generate more advanced optimization strategies and recommendations); retrieving historical data previously received from the vehicle devices servicing the plurality of routes in past, wherein the artificial intelligence engine is iteratively trained with the historical data by learning from the historical data associated with servicing the plurality of past routes(LaPlante ¶90 - The MLM 150, having access to the large amount of data accumulated by the RTME 110, it is operable, once trained, to detect trends and autonomously discover optimization strategies that are too complex for other computer techniques or even human users. In some embodiments, the MLM 150 may be pre-trained on a previously obtained data set, or it may be configured to be in a "learning mode" during some initial trial period wherein authorized users may provide feedback to the system without using its optimization strategies – LaPlante ¶84 -In some embodiments, the RORE 114 may iterate through multiple redistribution trials, retaining only the new set of route maps deemed the most efficient); optimizing, by the artificial intelligence engine trained with historical data, assignment of the route to one or more other vehicles within the fleet based on learning from the historical data, optimizing including: determining, by the artificial intelligence engine trained with historical data, based on comparing the real time data and the historical data with machine learning from previous assignments of the route, the one or more other vehicles within the fleet of vehicles which are available to service the route to be reassigned relative to the previous assignments of the route (LaPlante ¶88 - In some embodiments, the RORE 114 may optionally comprise a machine learning module (MLM) 150. This MLM 150 provides a more advanced analytic capability to the RORE 114 and is operable to utilize a plurality of machine learning algorithms (including deep-learning algorithms) to analyze, in real-time or after each run completion, the plurality of data acquired by the RTME 110 and to generate more advanced optimization strategies and recommendations); and as determined by the artificial intelligence engine an advisability indicator as determined by the artificial intelligence engine (LaPlante ¶91 - In some embodiments, once RORE 114 (including MLM 150) has determined that one or more optimization strategies may be used, it may create a suggestion).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the school bus management methodology/system of Moran by including the bus route management techniques of LaPlante because LaPlante discloses “providing for real-time routing optimizations to promote increased reliability, driver productivity and/or fuel-efficient driving (LaPlante ¶59)”. Additionally, Moran further details a “wireless systems for tracking and scheduling moving objects (Moran ¶1)” so it would be obvious to consider including the additional bus route management techniques that LaPlante discloses because it would help optimize route management techniques disclosed within Moran.
Regarding claims 2, 11, 20, Moran in view of LaPlante discloses a timeline view interface provided to a user device by the server device, the timeline view interface displaying each route in the one or more available route groups for the route to be reassigned (Moran Fig. 9 – Moran ¶13 - FIG. 9 is a schematic diagram of a graphic interface version of the reassignment module).
Regarding claims 3, 12, Moran in view of LaPlante discloses a timeline view interface provided to the user device by the server device, the timeline view interface displaying each route in the one or more available route groups (Moran Fig. 9 – Moran ¶13 - FIG. 9 is a schematic diagram of a graphic interface version of the reassignment module).
Regarding claims 5, 14, Moran in view of LaPlante discloses a graphical representation of each route in the one or more available route groups is displayed with a start time and stop time of each route in the one or more available route groups ( Moran Fig. 9 – Moran ¶39 - view information 108 relating to the routes, e.g., a schedule 110 and route map 112, by clicking on route links 114).
Regarding claims 6, 15, Moran in view of LaPlante discloses he graphical representation of each route in the one or more available route groups is selectable to be identified as the route to be reassigned (Moran Fig. 9 – Moran ¶13 - FIG. 9 is a schematic diagram of a graphic interface version of the reassignment module - Moran ¶39 - The reassignment module 82 could also be provided with a route scheduler 98, accessible from an active link 100, whereby schools could add routes 102, delete routes 104, modify routes 106, or view information 108 relating to the routes, e.g., a schedule 110 and route map 112, by clicking on route links 114).
Regarding claims 7, 16, Moran in view of LaPlante discloses a system, comprising: a server device comprising: one or more processors, and a memory storing instructions that, when executed by the one or more processors (Moran ¶18 - According to an embodiment of the present invention, with reference to FIGS. 1-12, a school bus tracking and notification system 20 utilizes the infrastructure of an existing cellular communications network 22 (e.g., mobile phone network) for tracking school buses 24. Each school bus 24 is provided with a mobile station 26 (e.g., mobile phone) and a unique bus identifier ("ID") 28. A tracking application system 30 is operably connected to the cellular network 22, and has a database portion 32 that stores the bus ID's 28 and the buses' mobile stations' respective communications identifiers 34, e.g., mobile station telephone numbers or other identifiers).
LaPlante further teaches the assignment of the route to one or more other vehicles within the fleet is optimized based on at least one of travel distance, minimum aggregate vehicle emissions, or minimum total driver hours (Laplante ¶29 - In one embodiment, the digital data processor redistributes said affected pick-up locations based at least in part on at least one of a geographical proximity of said distinct route to said affected pick-up locations, an available transport capacity of said distinct vehicle or a temporal availability of said distinct vehicle as determined from said timing of said designated route associated therewith).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the school bus management methodology/system of Moran by including the bus route management techniques of LaPlante because LaPlante discloses “providing for real-time routing optimizations to promote increased reliability, driver productivity and/or fuel-efficient driving (LaPlante ¶59)”. Additionally, Moran further details a “wireless systems for tracking and scheduling moving objects (Moran ¶1)” so it would be obvious to consider including the additional bus route management techniques that LaPlante discloses because it would help optimize route management techniques disclosed within Moran.
Regarding claims 8, 17, Moran in view of LaPlante discloses a system, comprising: a server device comprising: one or more processors, and a memory storing instructions that, when executed by the one or more processors (Moran ¶18 - According to an embodiment of the present invention, with reference to FIGS. 1-12, a school bus tracking and notification system 20 utilizes the infrastructure of an existing cellular communications network 22 (e.g., mobile phone network) for tracking school buses 24. Each school bus 24 is provided with a mobile station 26 (e.g., mobile phone) and a unique bus identifier ("ID") 28. A tracking application system 30 is operably connected to the cellular network 22, and has a database portion 32 that stores the bus ID's 28 and the buses' mobile stations' respective communications identifiers 34, e.g., mobile station telephone numbers or other identifiers).
LaPlante further teaches historical data received from the vehicle devices is retained for a limited period when the data pertains to temporary conditions that were identified (Laplante ¶90 - The MLM 150, having access to the large amount of data accumulated by the RTME 110, it is operable, once trained, to detect trends and autonomously discover optimization strategies that are too complex for other computer techniques or even human users. In some embodiments, the MLM 150 may be pre-trained on a previously obtained data set, or it may be configured to be in a "learning mode" during some initial trial period wherein authorized users may provide feedback to the system without using its optimization strategies).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the school bus management methodology/system of Moran by including the bus route management techniques of LaPlante because LaPlante discloses “providing for real-time routing optimizations to promote increased reliability, driver productivity and/or fuel-efficient driving (LaPlante ¶59)”. Additionally, Moran further details a “wireless systems for tracking and scheduling moving objects (Moran ¶1)” so it would be obvious to consider including the additional bus route management techniques that LaPlante discloses because it would help optimize route management techniques disclosed within Moran.
Regarding claims 9, 18, Moran in view of LaPlante discloses a system, comprising: a server device comprising: one or more processors, and a memory storing instructions that, when executed by the one or more processors (Moran ¶18 - According to an embodiment of the present invention, with reference to FIGS. 1-12, a school bus tracking and notification system 20 utilizes the infrastructure of an existing cellular communications network 22 (e.g., mobile phone network) for tracking school buses 24. Each school bus 24 is provided with a mobile station 26 (e.g., mobile phone) and a unique bus identifier ("ID") 28. A tracking application system 30 is operably connected to the cellular network 22, and has a database portion 32 that stores the bus ID's 28 and the buses' mobile stations' respective communications identifiers 34, e.g., mobile station telephone numbers or other identifiers).
LaPlante further teaches the assignment of the route to one or more other vehicles within the fleet is optimized based driver input (Laplante ¶80 - For instance, the driver may be unable to continue driving the vehicle safely, for medical reasons (concerning the driver or one of the students) or similar. A disruptive event may have happened outside or inside the vehicle, such as a crash, a violent attack or similar. Other types of events may include, but are not limited to, traffic congestion, a traffic accident, severe weather conditions (e.g., snow storm, etc.), or similar. The time, location and nature for these events may be entered by an authorized user (e.g., bus driver)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the school bus management methodology/system of Moran by including the bus route management techniques of LaPlante because LaPlante discloses “providing for real-time routing optimizations to promote increased reliability, driver productivity and/or fuel-efficient driving (LaPlante ¶59)”. Additionally, Moran further details a “wireless systems for tracking and scheduling moving objects (Moran ¶1)” so it would be obvious to consider including the additional bus route management techniques that LaPlante discloses because it would help optimize route management techniques disclosed within Moran.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Moran et al. (US 20070024440 A1) in view of LaPlante et al. (CA 3035241 A1) further in view of Hodge et al. (CN-107810386-A)
Regarding claims 4 and 13, Moran in view of LaPlante discloses a system, comprising: a server device comprising: one or more processors, and a memory storing instructions that, when executed by the one or more processors (Moran ¶18 - According to an embodiment of the present invention, with reference to FIGS. 1-12, a school bus tracking and notification system 20 utilizes the infrastructure of an existing cellular communications network 22 (e.g., mobile phone network) for tracking school buses 24. Each school bus 24 is provided with a mobile station 26 (e.g., mobile phone) and a unique bus identifier ("ID") 28. A tracking application system 30 is operably connected to the cellular network 22, and has a database portion 32 that stores the bus ID's 28 and the buses' mobile stations' respective communications identifiers 34, e.g., mobile station telephone numbers or other identifiers).
Moran in view of LaPlante lacks each route in the one or more available route groups in the timeline view interface is color coded to identify the route and each route in the one of the one or more available route groups is color coded in the timeline view interface.
Hodge, from the same field of endeavor, teaches each route in the one or more available route groups in the timeline view interface is color coded to identify the route and each route in the one of the one or more available route groups is color coded in the timeline view interface (Hodge Figs 20-26).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the school bus management methodology/system of Moran by including the bus route mapping techniques of Hodge because Hodge discloses “The mapping application also provides the second map browsing mode for emphasizing the map area in the second set of bus-related features (Hodge ABS)”. Additionally, Moran further details a “wireless systems for tracking and scheduling moving objects (Moran ¶1)” so it would be obvious to consider including the additional bus route mapping techniques that Hodge discloses because it would help visualize the route management techniques disclosed within Moran.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wang (US 20210248704 A1)
Li (CN 108681824 A)
Fryer et al. (US 9140567 B2)
Racah et al. (US 9562785 B1)
Sun et al. (US-20230245044-A1)
and
Y. Wang, et al. “Reassignment Algorithm of the Ride-Sourcing Market Based on Reinforcement Learning,” IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2023.3274636 [online], [retrieved on 2023-09-06]. Retrieved from the Internet https://ieeexplore.ieee.org/document/10128792?source=IQplus
and
J. Wang, et al. “Routing school bus for better student learning,” 2017 25th International Conference on Geoinformatics, Buffalo, NY, USA, 2017, pp. 1-7, doi: 10.1109/GEOINFORMATICS.2017.8090947 [online], [retrieved on 2023-09-06]. Retrieved from the Internet <https://ieeexplore.ieee.org/document/8090947?source=IQplus>
These pieces of prior art are identified because they disclose methods of adjusting routes for buses and general bus route management.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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