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 ACTION
This action is in response to communications filed on 2/27/2026. No other claims have been amended, added, or canceled. Accordingly, claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1- 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1- 20 are rejected under 35 U.S.C. 103 as being unpatentable over WO2015147723A1 (hereinafter 723) in view of Khasis (US 2018/0158020 A1).
As per claim 1, 723 discloses: a method for operating a vehicle, comprising:
receiving, at a computing circuitry comprising at least a processor and memory, an indication of a route for transporting a cargo (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1; journey time for a given route, freight vehicle);
receiving, at the computing circuitry, an indication of a required time of arrival at a destination of the route (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1; journey time for a given route, desired arrival time); and
controlling, by the computing circuitry, the vehicle based on the speed profile and the relaxation coefficient (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1; making it possible to drive the vehicle at the calculated speed).
723 further discloses a degree of importance of reaching a desired destination by the desired arrival time, this method further requires acquiring statical information about average journey times for section of the itinerary to the destination and the use of a density function which describes the spread of deviations from the average journey time (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1; degree of importance, desired arrival time, statistical information and density function, transport costs).
However, 723 does not appear to explicitly disclose the use of predictive analysis of transport conditions using a machine learning scheme trained on data for the route.
Nevertheless, the use of predictive analysis of transport conditions using a machine learning scheme trained on data for the route, was well known in the art, prior to the effective filing date of the given invention as is evident by the disclosure of Khasis (see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary; using AI and/or machine learning processes to predict distribution chain performance metrics, modeling shipping methods to yield predictions to destination, predictive model of shipping, with historical actual shipment data and optimization logic).
A person of ordinary skill in the art, prior to the effective filing date would have been motivated to combine Khasis’s machine learning processes with those of 723’s in or to form a more efficient and manageable system (i.e., by allowing for better visibility for in transit inventory and better handling of asset compatibility using optimization logic—see Khasis at least Abstract, Background and Summary and par. 7, 81-83).
Both 723 and Khasis disclose claim 2: wherein the relaxation coefficient is dependent on a combination of a delay risk for the route and a quantified penalty factor for deviation from a required time of arrival range at the destination (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 3: wherein the speed profile is adapted for a plurality of segments of the route, and the relaxation coefficient dynamically adjusts the vehicle’s adherence to the speed profile for ensuring optimal balance between at least two of punctuality, operational cost, and environmental considerations (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 4: wherein the speed profile is determined based on at least one of a desired maximum speed for the vehicle or a maximum legal speed for the vehicle at a section of the route (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 5: wherein the method is performed by an electronic control unit on-board the vehicle comprising the computing circuitry (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 6: wherein the method is performed by a cloud server comprising the computing circuitry, the cloud server being network connected to an electronic control unit of the vehicle (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 7: wherein controlling the vehicle based on the speed profile and the relaxation coefficient further comprises adjusting acceleration, deceleration, and braking patterns to optimize fuel efficiency or energy consumption (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 8: wherein the relaxation coefficient is dynamically updated during the route based on real-time transport conditions obtained from at least one external data source (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 9: wherein the predictive analysis of transport conditions includes weather conditions, traffic congestion, or road hazards, and the machine learning scheme is trained on data representing historical instances of these conditions for the route (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 10: wherein the relaxation coefficient is adjusted based on a predefined priority level assigned to the cargo (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 11: further comprising determining, by the computing circuitry, a permissible range of deviation from the speed profile for each segment of the route, based on the relaxation coefficient and the delay risk (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 12: wherein operational parameters for the vehicle are monitored, and the relaxation coefficient is adjusted based on real-time fuel consumption or battery level to extend operational range for the vehicle (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 13: A system for controlling a vehicle, the system comprising a computing circuitry comprising at least a processor and memory configured to:
receive an indication of a route for transporting a cargo (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1; journey time for a given route, freight vehicle);
receive an indication of a required time of arrival at a destination of the route (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1; journey time for a given route, desired arrival time);
determine a relaxation coefficient for a speed profile for traveling the route, wherein the relaxation coefficient is derived from predictive analysis of transport conditions using a machine learning scheme trained on data for the route (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1; degree of importance, desired arrival time, statistical information and density function, transport costs and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary); and
control the vehicle based on the speed profile and the relaxation coefficient (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1; making it possible to drive the vehicle at the calculated speed).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 14: wherein the relaxation coefficient is dependent on a combination of a delay risk for the route and a quantified penalty factor for deviation from a required time of arrival range at the destination (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 15: wherein the speed profile is adapted for a plurality of segments of the route, and the relaxation coefficient dynamically adjusts the vehicle’s adherence to the speed profile for ensuring optimal balance between at least two of punctuality, operational cost, and environmental considerations (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 16: wherein the speed profile is determined based on at least one of a desired maximum speed for the vehicle or a maximum legal speed for the vehicle at a section of the route (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 17: wherein the computing circuitry is comprised with an electronic control unit on-board the vehicle (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 18: wherein the computing circuitry is comprised with a cloud server, the cloud server being network connected to an electronic control unit of the vehicle (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 19: wherein controlling the vehicle based on the speed profile and the relaxation coefficient further comprises adjusting acceleration, deceleration, and braking patterns to optimize fuel efficiency or energy consumption (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Both 723 and Khasis disclose claim 20: wherein the predictive analysis of transport conditions includes weather conditions, traffic congestion, or road hazards, and the machine learning scheme is trained on data representing historical instances of these conditions for the route (see 723 at least fig. 1- 9 and Abstract, Summary & claim 1 and see Khasis at least fig. 1- 17, in particular fig. 3, 8, 11 & 15-17 and Abstract and Summary).
Motivation to combine 723 with Khasis, in the instant claim, is the same as that in claim 1 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MACEEH ANWARI whose telephone number is 571-272-7591. The examiner can normally be reached on Monday-Friday 7:30-5:00 PM ES.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached on 571-272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MACEEH ANWARI/Primary Examiner, Art Unit 3663
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