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 communication is in response to application No. 18/044,364, filed on 03/07/2023. Claims 1-26 have been cancelled. Claims 27-46 are currently pending and have been examined. Claims 27-46 have been rejected as follows.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 03/07/2023 and 01/28/2025 has been acknowledged.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: passenger compartment 234 in Fig. 3, introduced in specification pg. 21, paragraph 6. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: On pg. 15, the brief description of Figures 4 and 5 appear to be switched. The brief description states that Figure 4 illustrates the invention when the angle between the control surface and a surface of the vehicle is substantially 0 degrees, however the drawing portrays the control surface to be more than 0 degrees. Similarly, the reverse is true for Figure 5..
Appropriate correction is required.
Claim Objections
Claim 28, 39, 29, 44, and 46 are objected to because of the following informalities:
In claim 28, “A drag reduction system according to claim 27” should be “The drag reduction system according to claim 27”
In claim 39, “characteristic” should be specified as the characteristic of the sensor data so as to not be confused with the optimization characteristic.
In claim 29, “wherein at least two vectors are generated” should be “wherein the at least two vectors are generated”, as the at least two vectors were introduced in claim 28. Therefore, there is insufficient antecedent basis.
In claim 44, “The drag reduction system according to claim 1” should most likely be “The drag reduction system according to claim 27”, and claim 1 has been cancelled
In claim 46, “at least one pressure sensor positioned so as to sense fluid pressure at, or near, a rear end surface of the vehicle” should be “at least one pressure sensor positioned so as to sense fluid pressure at, or near, the rear end surface of the vehicle”, as the rear end surface of the vehicle was introduced earlier in the claim. Therefore, there is insufficient antecedent basis.
Appropriate correction is required.
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.
Claim(s) 27-38 and 40-46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sandgren (US 20180178859) in view of Thede (Thede, Scott. An introduction to genetic algorithms. (October 2004). Journal of Computing Sciences in Colleges. Volume 20 Issue 1. Pages 115-123.).
Regarding claim 27, Sandgren teaches a drag reduction system for a vehicle (Fig. 3, trucking rig 110), comprising:
at least one control surface movable relative to at least one surface of the vehicle (Fig. 3, Eddy disrupter elements 122); at least one actuator for adjusting a position of the at least one control surface relative to the at least one surface of the vehicle (par. 38, “Movement of eddy disrupter element 22 may be actuated”); at least one pressure sensor (par. 46 Fig. 3, sensor 112) positioned so as to sense fluid pressure at, or near, a rear end surface of the vehicle (par. 17, “the fluid pressure is measured at the rear surface of the vehicle body”); and a processor (Fig. 1, processor 18) comprising an optimisation algorithm (par. 57, “the design parameters to be adjusted from run to run via an optimization algorithm”), programmed to:
receive sensor data from the at least one pressure sensor; compare a characteristic of the sensor data to a threshold condition (par. 42, “the measured fluid pressure may then be compared with a threshold pressure value”);
upon the threshold condition being satisfied: operate the at least one actuator so to adjust a position of the at least one control surface relative to the at least one surface of the vehicle (par. 15, “actuating movement of the eddy disrupter element to the transverse position relative to the plane of the vehicle if the threshold condition is satisfied”)
or, upon the threshold condition not being satisfied: receive new sensor data from the at least one pressure sensor and compare a characteristic of the new sensor data with the threshold condition (par. 43, “If the threshold condition is not satisfied, then system 10 continues to receive measured data but no action”).
Sandgren fails to teach randomly generate a first generation of control surface adjustment parameters, wherein each one of the control surface adjustment parameters is generated within a pre-defined range; upon the threshold condition being satisfied: operate the at least one actuator so to adjust a position of the at least one control surface relative to the at least one surface of the vehicle based on at least two control surface adjustment parameters of the first generation; compare an optimisation characteristic of each of the at least two control surface adjustment parameters of the first generation to determine at least one preferred adjustment parameter; and populate a second generation of control surface adjustment parameters based on the at least one preferred adjustment parameter.
However, Thede teaches randomly generate a first generation of control surface adjustment parameters, wherein each one of the control surface adjustment parameters is generated within a pre-defined range (pg. 116 Fig. 1, step 1, “Create a population of random candidate solutions named pop”);
upon the threshold condition being satisfied: operate the at least one actuator so to adjust a position of the at least one control surface relative to the at least one surface of the vehicle based on at least two control surface adjustment parameters of the first generation (pg. 116 Fig. 1, step 2 is repeated, leading to a more ‘fit’ population by building upon the previous generation);
compare an optimisation characteristic of each of the at least two control surface adjustment parameters of the first generation to determine at least one preferred adjustment parameter (pg. 116 Fig. 1, step 2a, individuals are selected that are more ‘fit’; pg. 115, “The fitness of an individual is a measure of how “good” the solution represented by the individual is”);
and populate a second generation of control surface adjustment parameters based on the at least one preferred adjustment parameter (pg. 116 Fig. 1, step d).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sandgren to incorporate the teachings of Thede. Sandgren describes testing the system, and states “After the drag force was measured, results from earlier tests required both the prototype software control loop and the design parameters to be adjusted from run to run via an optimization algorithm” (Sandgren par. 57). This led to an improved system (see Sandgren Fig. 8). Although the optimization algorithm used is not specified, a genetic algorithm is a well-known algorithm used to find the optimal solution to a problem (Thede pg. 115, abstract), and are useful in cases where the problem cannot be solved in more traditional ways (Thede par. 123, Summary). One of ordinary skill in the art would be able to recognize Sandgren’s optimization algorithm could have been a genetic algorithm. The optimization algorithm described in the instant claim does not differ from a standard genetic algorithm. Given Sandgren’s drag reduction system and descriptions of design testing, and the basics of a genetic algorithm, one could easily devise a system for reducing drag that is optimized using a genetic algorithm.
Regarding claim 28, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren fails to teach the processor comprising the optimisation algorithm is further programmed to generate at least two vectors based on at least two of the control surface adjustment parameters of the first generation; and wherein: the at least one actuator is operated so to adjust a position of the at least one control surface relative to the at least one surface of the vehicle according to, in turn, each of the at least two vectors; an optimisation characteristic of each of the at least two vectors is compared to determine at least one preferred one of the at least two vectors; and the second generation of control surface adjustment parameters is populated with the at least one preferred one of the at least two vectors.
However, Thede teaches the processor comprising the optimisation algorithm is further programmed to generate at least two vectors based on at least two of the control surface adjustment parameters of the first generation (pg. 116 Fig. 1, step 2a "Select two individuals at random from pop so that individuals which are more fit are more likely to be selected"); and wherein: the at least one actuator is operated so to adjust a position of the at least one control surface relative to the at least one surface of the vehicle according to, in turn, each of the at least two vectors (par. 57, “the design parameters to be adjusted from run to run via an optimization algorithm”); an optimisation characteristic of each of the at least two vectors is compared to determine at least one preferred one of the at least two vectors; and the second generation of control surface adjustment parameters is populated with the at least one preferred one of the at least two vectors (pg. 116 Fig. 1, step 2a, individuals are selected that are more ‘fit’; pg. 115, “The fitness of an individual is a measure of how “good” the solution represented by the individual is”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sandgren in view of Thede to further incorporate the teachings of Thede. Sandgren describes testing the system, and states “After the drag force was measured, results from earlier tests required both the prototype software control loop and the design parameters to be adjusted from run to run via an optimization algorithm” (Sandgren par. 57). This led to an improved system (see Sandgren Fig. 8). Although the optimization algorithm used is not specified, a genetic algorithm is a well-known algorithm used to find the optimal solution to a problem (Thede pg. 115, abstract), and are useful in cases where the problem cannot be solved in more traditional ways (Thede par. 123, Summary). One of ordinary skill in the art would be able to recognize Sandgren’s optimization algorithm could have been a genetic algorithm. The optimization algorithm described in the instant claim does not differ from a standard genetic algorithm. Given Sandgren’s drag reduction system and descriptions of design testing, and the basics of a genetic algorithm, one could easily devise a system for reducing drag that is optimized using a genetic algorithm.
Regarding claim 29, the combination of Sandgren in view of Thede teaches the drag reduction system of claim 28. Sandgren fails to teach at least two vectors are generated based on, in turn, each one of the first generation of parameters.
However, Thede teaches at least two vectors are generated based on, in turn, each one of the first generation of parameters (pg. 116 Fig. 1, step 2b “Cross-over the two individuals to produce two new individuals”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sandgren in view of Thede to further incorporate the teachings of Thede. Sandgren describes testing the system, and states “After the drag force was measured, results from earlier tests required both the prototype software control loop and the design parameters to be adjusted from run to run via an optimization algorithm” (Sandgren par. 57). This led to an improved system (see Sandgren Fig. 8). Although the optimization algorithm used is not specified, a genetic algorithm is a well-known algorithm used to find the optimal solution to a problem (Thede pg. 115, abstract), and are useful in cases where the problem cannot be solved in more traditional ways (Thede par. 123, Summary). One of ordinary skill in the art would be able to recognize Sandgren’s optimization algorithm could have been a genetic algorithm. The optimization algorithm described in the instant claim does not differ from a standard genetic algorithm. Given Sandgren’s drag reduction system and descriptions of design testing, and the basics of a genetic algorithm, one could easily devise a system for reducing drag that is optimized using a genetic algorithm.
Regarding claim 30, the combination of Sandgren in view of Thede teaches the drag reduction system of claim 28. Sandgren fails to teach at least one of the at least two vectors is generated using at least one of: a crossover operation; a combination operation; and a mutation operation, optionally wherein the at least one of the at least two vectors is generated by the crossover operation, wherein the crossover operation is performed on a target vector of a number of randomly selected ones of the first generation parameters and a different one of the first generation parameters (see pg. 116 Fig. 1, step 2).
Regarding claim 31, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches the at least one actuator is configured to adjust a position of the at least one control surface relative to the at least one surface of the vehicle between a first position (see Fig. 5), in which the at least one control surface is substantially parallel to the at least one surface of the vehicle, and a second position (see Fig. 6) in which at least a portion of the at least one control surface extends outwardly from the at least one surface of the vehicle (par. 19, “the step of actuating movement of the eddy disrupter element further comprises moving the eddy disrupter from a substantially non-transverse position to the transverse position”).
Regarding claim 32, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches the at least one control surface comprises an elongate control surface, and preferably wherein the elongate control surface is one of: a panel; a tubular control surface; a flap; a wing (see Fig. 3, elements 122).
Regarding claim 33, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren fails to teach the optimisation algorithm is a differential genetic optimisation algorithm.
However, Thede teaches the optimisation algorithm is a differential genetic optimisation algorithm (abstract, genetic algorithm).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sandgren in view of Thede to further incorporate the teachings of Thede. Sandgren describes testing the system, and states “After the drag force was measured, results from earlier tests required both the prototype software control loop and the design parameters to be adjusted from run to run via an optimization algorithm” (Sandgren par. 57). This led to an improved system (see Sandgren Fig. 8). Although the optimization algorithm used is not specified, a genetic algorithm is a well-known algorithm used to find the optimal solution to a problem (Thede pg. 115, abstract), and are useful in cases where the problem cannot be solved in more traditional ways (Thede par. 123, Summary). One of ordinary skill in the art would be able to recognize Sandgren’s optimization algorithm could have been a genetic algorithm. The optimization algorithm described in the instant claim does not differ from a standard genetic algorithm. Given Sandgren’s drag reduction system and descriptions of design testing, and the basics of a genetic algorithm, one could easily devise a system for reducing drag that is optimized using a genetic algorithm.
Regarding claim 34, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches the at least one control surface is pivotably coupled to the at least one surface of the vehicle (see Fig. 3, elements 122), optionally wherein the at least one actuator adjusts a position of the at least one pivotably coupled control surface from a first position (see Fig. 5), in which the angle between the at least one control surface and the at least one surface of the vehicle is substantially 0 degrees, and a second position (see Fig. 6), in which an angle between the at least one control surface and the at least one surface of the vehicle is between about 2 degrees and about 90 degrees (par. 19, “the step of actuating movement of the eddy disrupter element further comprises moving the eddy disrupter from a substantially non-transverse position to the transverse position”).
Regarding claim 35, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches the at least one actuator comprises at least one of: a motor; a linear solenoid; a rotary solenoid; a linear actuator; a rotary actuator (par. 25, “The vehicle may also include a motor that moves the at least one panel”).
Regarding claim 36, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren fails to teach the processor is programmed to repeat for a plurality of generations, wherein each subsequent generation of parameters is based on an immediately preceding generation of parameters.
However, Thede teaches the processor is programmed to repeat for a plurality of generations, wherein each subsequent generation of parameters is based on an immediately preceding generation of parameters (pg. 116 Fig. 1, step 2: the algorithm is repeated, using cross-over and mutations of the previous population to make the new population).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sandgren in view of Thede to further incorporate the teachings of Thede. Sandgren describes testing the system, and states “After the drag force was measured, results from earlier tests required both the prototype software control loop and the design parameters to be adjusted from run to run via an optimization algorithm” (Sandgren par. 57). This led to an improved system (see Sandgren Fig. 8). Although the optimization algorithm used is not specified, a genetic algorithm is a well-known algorithm used to find the optimal solution to a problem (Thede pg. 115, abstract), and are useful in cases where the problem cannot be solved in more traditional ways (Thede par. 123, Summary). One of ordinary skill in the art would be able to recognize Sandgren’s optimization algorithm could have been a genetic algorithm. The optimization algorithm described in the instant claim does not differ from a standard genetic algorithm. Given Sandgren’s drag reduction system and descriptions of design testing, and the basics of a genetic algorithm, one could easily devise a system for reducing drag that is optimized using a genetic algorithm.
Regarding claim 37, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 28. Sandgren fails to teach the processor is programmed to repeat for a plurality of generations, wherein each subsequent generation of parameters is based on an immediately preceding generation of parameters, optionally wherein each subsequent generation is populated with a plurality of preferred vectors, wherein each of the plurality of preferred vectors is based on at least one control surface adjustment parameter of the immediately preceding generation of parameters, further optionally wherein at least two vectors are generated based on, in turn, each control surface adjustment parameter of the immediately preceding generation of parameters.
However, Thede teaches the processor is programmed to repeat for a plurality of generations, wherein each subsequent generation of parameters is based on an immediately preceding generation of parameters, optionally wherein each subsequent generation is populated with a plurality of preferred vectors, wherein each of the plurality of preferred vectors is based on at least one control surface adjustment parameter of the immediately preceding generation of parameters, further optionally wherein at least two vectors are generated based on, in turn, each control surface adjustment parameter of the immediately preceding generation of parameters (pg. 116 Fig. 1, step 2: the algorithm is repeated, using cross-over and mutations of the previous population to make the new population).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sandgren in view of Thede to further incorporate the teachings of Thede. Sandgren describes testing the system, and states “After the drag force was measured, results from earlier tests required both the prototype software control loop and the design parameters to be adjusted from run to run via an optimization algorithm” (Sandgren par. 57). This led to an improved system (see Sandgren Fig. 8). Although the optimization algorithm used is not specified, a genetic algorithm is a well-known algorithm used to find the optimal solution to a problem (Thede pg. 115, abstract), and are useful in cases where the problem cannot be solved in more traditional ways (Thede par. 123, Summary). One of ordinary skill in the art would be able to recognize Sandgren’s optimization algorithm could have been a genetic algorithm. The optimization algorithm described in the instant claim does not differ from a standard genetic algorithm. Given Sandgren’s drag reduction system and descriptions of design testing, and the basics of a genetic algorithm, one could easily devise a system for reducing drag that is optimized using a genetic algorithm.
Regarding claim 38, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches each generation comprises at least 3 control surface adjustment parameters and/or wherein the threshold condition is related to aerodynamic drag resulting from the vehicle moving through a fluid, in particular air (par. 7, “the threshold condition is associated with drag resulting from the vehicle body movement through the fluid”).
Regarding claim 40, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches each control surface adjustment parameter comprises at least one of: a delay between the threshold condition being satisfied and an adjustment of the position of the control surface; a frequency of an adjustment of the position of the control surface; a wave form of the motion of the control surface; the threshold condition; an adjustment speed of each adjustment; a number of adjustments (par. 44, “the timing, including the overall time and time lapse between the threshold being reached and actuation of eddy disrupter 22, the movement waveform, the uniformity, pattern, extent and magnitude of the movement, among other things, may all be determined by a program stored in memory 20 and control system 14”—these are the design variables tested in Sandgren).
Regarding claim 41, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches the optimisation characteristic comprises at least one of: a fuel economy measure; a pressure measure at, or near, a rear surface of the vehicle (par. 59, “Additional variables measured include pressure readings and fuel mileage increases”).
Although Sandgren does not explicitly teach the optimization characteristic comprises a fuel economy measure or a pressure measure, they state, “Much effort has been put into aerodynamic design on vehicles to reduce drag to increase fuel efficiencies. Drag, especially at speeds above 30 km/hour, becomes increasingly important in fuel efficiency and is still a major concern for vehicle manufactures” (par. 4), and, “The system and method of the invention are transformational in their beneficial impact on the trucking industry and the reduction in U.S. fuel consumption” (par. 61). As they state the importance of fuel efficiency, and they recorded fuel consumption and pressure, one of ordinary skill of the art could conclude that the testing they performed optimized fuel consumption. Doing so would lead to a system that decreased fuel consumption due to drag.
Regarding claim 42, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches a plurality of pressure sensors, wherein each one of the plurality of pressure sensors is positioned so as to sense fluid pressure at, or near, the rear end surface of the vehicle (claim 1, “a plurality of sensors mounted to the vehicle body and configured to measure a fluid pressure adjacent to the vehicle body, the plurality of sensors including a first sensor mounted near the rear end of the vehicle body”).
Regarding claim 43, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches a plurality of pressure sensors, wherein at least one of the plurality of pressure sensors is positioned to sense fluid pressure at, or near, a side surface and/or a top surface of the vehicle (claim 1, “a plurality of sensors mounted to the vehicle body and configured to measure a fluid pressure adjacent to the vehicle body, the plurality of sensors including a first sensor mounted near the rear end of the vehicle body and a second sensor mounted near the front end of the vehicle body”; Fig. 4, sensors 212).
Regarding claim 44, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Sandgren further teaches the processor is configured to receive data relating to vehicle operation from the vehicle (par. 44, “the processor is configured for receiving data relating to vehicle operation, efficiency or movement to facilitate the comparative analysis”).
Regarding claim 45, Sandgren teaches a drag reduction method for a vehicle comprising the steps of:
receiving sensor data from at least one pressure sensor; comparing a characteristic of the sensor data to a threshold condition; (par. 42, “the measured fluid pressure may then be compared with a threshold pressure value”)
upon the threshold condition being satisfied: operating at least one actuator so to adjust a position of at least one control surface relative to at least one surface of the vehicle (par. 15, “actuating movement of the eddy disrupter element to the transverse position relative to the plane of the vehicle if the threshold condition is satisfied”)
or, upon the threshold condition not being satisfied: receiving new sensor data from the at least one pressure sensor and compare a characteristic of the new sensor data with the threshold condition (par. 43, “If the threshold condition is not satisfied, then system 10 continues to receive measured data but no action”).
Sandgren fails to teach randomly generating a first generation of control surface adjustment parameters, wherein each one of the control surface adjustment parameters is generated within a pre- defined range; upon the threshold condition being satisfied: operating at least one actuator so to adjust a position of at least one control surface relative to at least one surface of the vehicle based on at least two control surface adjustment parameters of the first generation; comparing an optimisation characteristic of each of the at least two control surface adjustment parameters of the first generation to determine at least one preferred adjustment parameter; and populating a second generation of control surface adjustment parameters based on the at least one preferred adjustment parameter.
However, Thede teaches randomly generating a first generation of control surface adjustment parameters, wherein each one of the control surface adjustment parameters is generated within a pre-defined range (pg. 116 Fig. 1, step 1, “Create a population of random candidate solutions named pop”);
upon the threshold condition being satisfied: operating at least one actuator so to adjust a position of at least one control surface relative to at least one surface of the vehicle based on at least two control surface adjustment parameters of the first generation (pg. 116 Fig. 1, step 2 is repeated, leading to a more ‘fit’ population by building upon the previous generation);
comparing an optimisation characteristic of each of the at least two control surface adjustment parameters of the first generation to determine at least one preferred adjustment parameter (pg. 116 Fig. 1, step 2a, individuals are selected that are more ‘fit’; pg. 115, “The fitness of an individual is a measure of how “good” the solution represented by the individual is”);
and populating a second generation of control surface adjustment parameters based on the at least one preferred adjustment parameter (pg. 116 Fig. 1, step d).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sandgren to incorporate the teachings of Thede. Sandgren describes testing the system, and states “After the drag force was measured, results from earlier tests required both the prototype software control loop and the design parameters to be adjusted from run to run via an optimization algorithm” (Sandgren par. 57). This led to an improved system (see Sandgren Fig. 8). Although the optimization algorithm used is not specified, a genetic algorithm is a well-known algorithm used to find the optimal solution to a problem (Thede pg. 115, abstract), and are useful in cases where the problem cannot be solved in more traditional ways (Thede par. 123, Summary). One of ordinary skill in the art would be able to recognize Sandgren’s optimization algorithm could have been a genetic algorithm. The optimization algorithm described in the instant claim does not differ from a standard genetic algorithm. Given Sandgren’s drag reduction system and descriptions of design testing, and the basics of a genetic algorithm, one could easily devise a system for reducing drag that is optimized using a genetic algorithm.
Regarding claim 46, Sandgren teaches a vehicle comprising: a vehicle body comprising a front end surface and an opposite rear end surface, a top surface, and first and second side surfaces extending between the front end surface and the opposite rear end surface (Fig. 3, trucking rig 110);
and a drag reduction system for a vehicle comprising: at least one control surface movable relative to at least one surface of the vehicle (Fig. 3, Eddy disrupter elements 122); at least one actuator for adjusting a position of the at least one control surface relative to the at least one surface of the vehicle (par. 38, “Movement of eddy disrupter element 22 may be actuated”); at least one pressure sensor (par. 46 Fig. 3, sensor 112) positioned so as to sense fluid pressure at, or near, a rear end surface of the vehicle (par. 17, “the fluid pressure is measured at the rear surface of the vehicle body”); and a processor (Fig. 1, processor 18) comprising an optimisation algorithm (par. 57, “the design parameters to be adjusted from run to run via an optimization algorithm”), programmed to:
receive sensor data from the at least one pressure sensor; compare a characteristic of the sensor data to a threshold condition (par. 42, “the measured fluid pressure may then be compared with a threshold pressure value”);
upon the threshold condition being satisfied: operate the at least one actuator so to adjust a position of the at least one control surface relative to the at least one surface of the vehicle (par. 15, “actuating movement of the eddy disrupter element to the transverse position relative to the plane of the vehicle if the threshold condition is satisfied”)
or, upon the threshold condition not being satisfied: receive new sensor data from the at least one pressure sensor and compare a characteristic of the new sensor data with the threshold condition (par. 43, “If the threshold condition is not satisfied, then system 10 continues to receive measured data but no action”).
Sandgren fails to teach randomly generate a first generation of control surface adjustment parameters, wherein each one of the control surface adjustment parameters is generated within a pre-defined range; upon the threshold condition being satisfied: operate the at least one actuator so to adjust a position of the at least one control surface relative to the at least one surface of the vehicle based on at least two control surface adjustment parameters of the first generation; compare an optimisation characteristic of each of the at least two control surface adjustment parameters of the first generation to determine at least one preferred adjustment parameter; and populate a second generation of control surface adjustment parameters based on the at least one preferred adjustment parameter.
However, Thede teaches randomly generate a first generation of control surface adjustment parameters, wherein each one of the control surface adjustment parameters is generated within a pre-defined range (pg. 116 Fig. 1, step 1, “Create a population of random candidate solutions named pop”);
upon the threshold condition being satisfied: operate the at least one actuator so to adjust a position of the at least one control surface relative to the at least one surface of the vehicle based on at least two control surface adjustment parameters of the first generation (pg. 116 Fig. 1, step 2 is repeated, leading to a more ‘fit’ population by building upon the previous generation);
compare an optimisation characteristic of each of the at least two control surface adjustment parameters of the first generation to determine at least one preferred adjustment parameter (pg. 116 Fig. 1, step 2a, individuals are selected that are more ‘fit’; pg. 115, “The fitness of an individual is a measure of how “good” the solution represented by the individual is”);
and populate a second generation of control surface adjustment parameters based on the at least one preferred adjustment parameter (pg. 116 Fig. 1, step d).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sandgren to incorporate the teachings of Thede. Sandgren describes testing the system, and states “After the drag force was measured, results from earlier tests required both the prototype software control loop and the design parameters to be adjusted from run to run via an optimization algorithm” (Sandgren par. 57). This led to an improved system (see Sandgren Fig. 8). Although the optimization algorithm used is not specified, a genetic algorithm is a well-known algorithm used to find the optimal solution to a problem (Thede pg. 115, abstract), and are useful in cases where the problem cannot be solved in more traditional ways (Thede par. 123, Summary). One of ordinary skill in the art would be able to recognize Sandgren’s optimization algorithm could have been a genetic algorithm. The optimization algorithm described in the instant claim does not differ from a standard genetic algorithm. Given Sandgren’s drag reduction system and descriptions of design testing, and the basics of a genetic algorithm, one could easily devise a system for reducing drag that is optimized using a genetic algorithm.
Claim(s) 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sandgren in view of Thede as applied above, and further in view of Höcker (US 20200363243).
Regarding claim 39, the combination of Sandgren in view of Thede teaches the drag reduction system according to claim 27. Both Sandgren and Thede fail to explicitly teach the characteristic comprises at least one of: a tone frequency and a phase.
However, Höcker teaches the characteristic comprises at least one of: a tone frequency and a phase (par. 23, “The pressure sensors 116, 117 together form a vortex detector, wherein a fluctuation of the difference between their sensor signals is evaluated by an operating and evaluation circuit 120 in order to determine a vortex frequency and thus the flow rate”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sandgren in view of Thede to incorporate the teachings of Höcker. Sandgren states that the pressure sensors are used to detect eddies (Sandgren par. 27, “These sensors sense pressure changes on the moving body that are related to drag or may be direct measurements of drag, instantaneous fuel consumption or some other relevant factor. Upon indication of a threshold change in pressure indicating the eddies are forming on at least one surface of the moving bodies, eddy disruptor elements are engaged to disrupt eddies on the surface of the moving body and to appropriately alter the sensed pressure change so as to reduce drag”). As Höcker’s sensors perform the same function, one of ordinary skill in the art could conclude that Sandgren’s sensors either are the same sensors as Höcker’s, or could be replaced with Höcker’s with a reasonable expectation it would have the same results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MINATO LEE HORNER whose telephone number is (571)272-5425. The examiner can normally be reached M-F 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.L.H./
Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665