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
The action is responsive to the Application filed on 01/09/2024
Claims 1-20 are pending in the case. Claims 1, 8 and 15 are independent.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over YOU et al. (Pub No.: 20220205425 A1), hereinafter referred to as YOU in view of Gaither et al. (Pub No.: 20170082092 A1), hereinafter referred to as Gaither.
With respect to claim 1, YOU disclose:
A computer-implemented method comprising: measuring, by a set of sensors, a wind speed as it passes through a funnel system (In paragraph [0035], YOU disclose that the wind-condition measuring sensor 10 may be provided at a plurality of places spaced apart from the reference position where the foregoing wind turbine 40 is positioned. The wind-condition measuring sensor 10 may, for example, include an anemometer, a pitot tube, or the like widely used for measuring wind direction. )
Modulating, by the funnel system, the wind speed of wind that passes through the funnel system to an array of wind turbines, wherein modulating the wind speed comprises (In paragraph [0034], YOU disclose the configuration of a wind turbine system that includes sensors, a controller, and actuators for monitoring and controlling turbine operation. The controller 41 is configured to control the wind turbine based on the control variable. In paragraph [0038], YOU disclose that the controller 41 is based on predicted wind-condition data after the learning of the control algorithm learner 30 and controlling the wind turbine.)
Proactively adjusting, by a computing system, the funnel system to maintain a predetermined wind speed that is being fed to the array of wind turbines through the funnel system by employing machine learning models and control algorithms, using sensor data, vehicle to everything communication, and predictive analysis of wind patterns and road conditions (In paragraphs [0037-0041], YOU disclose an AI-based predictive wind control system that uses sensors-collected time-series wind data to predict future wind conditions and automatically adjust turbine operation through controllers (control algorithm learner 30) and actuators 42.)
With respect to claim 1, YOU does not explicitly disclose:
Adjusting, by a motor set, a cross-sectional area of an entry portion and an exit portion of the funnel system based on the measured wind speed
However, it is known by Gaither to disclose:
Adjusting, by a motor set, a cross-sectional area of an entry portion and an exit portion of the funnel system based on the measured wind speed (In paragraph [0039], Gaither disclose the motor or the generator 104 may include an M-G having the capability of actively rotating the wind turbine 106 to enhance cooling. In paragraph [0041], Gaither discloses the inlet/outlet portion, the inlet portion receives incoming airflow from outside the vehicle. The outlet portion directs airflow towards portions of the braking system. An inlet with a first cross-sectional area and an outlet with a second cross-sectional based on airflow. In paragraph [0043], Gaither disclose one or more wind turbines 206 having blades configured to be rotated by inlet airflow 214a.)
YOU and Gaither are analogous pieces of art because both references concern wind-turbine systems using wind conditions and a method of controlling a wind turbine. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify YOU by using an artificial intelligence (AI) model for predicting wind conditions to maximize the efficiency of power production as taught by YOU, while enhancing the aerodynamics of the vehicle as taught by Gaither. The motivation for doing so would have been to maximize the efficiency of power production depending on wind speeds and wind direction (See [0030] of YOU.)
Regarding claim 2, YOU in view of Gaither disclose the elements of claim 1. In addition, Gaither disclose:
The computer-implemented method of claim 1, wherein the array of wind turbines are embedded into a body of an autonomous vehicle, wherein the array of wind turbines generate power when exposed to wind and their operational range is defined by specific cut-in and cut-out wind speeds, wherein each turbine in the array of wind turbines is constructed out of lightweight composite material and strategically positioned to reduce aerodynamic drag while maximizing wind exposure (In paragraph [0031], Gaither discloses that the wind turbines can be positioned in an air duct extending from the front bumper to an area proximal to one or more of the vehicle's wheels. The air channels and the wind turbines can be advantageously designed to significantly improve vehicle aerodynamics by reducing aerodynamic drag components.)
Regarding claim 3, YOU in view of Gaither disclose the elements of claim 1. In addition, Gaither disclose:
The computer-implemented method of claim 1, wherein the funnel system is located in front of the array of wind turbines, and wherein the funnel system comprises a motor system (In paragraph [0034], Gaither discloses that the motor or the generator 104 may include an M-G having the capability of actively rotating the wind turbine.)
Regarding claim 4, YOU in view of Gaither disclose the elements of claim 1. In addition, Gaither disclose:
The computer-implemented method of claim 1, wherein adjusting the cross-sectional area comprises: utilizing Bernoulli's theorem to calculate a size or shape of the cross-sectional area of the funnel system (In paragraph [0051], Gather discloses Bernoulli's principle being used to explain why air speeds up as it moves through the air duct and around the wind turbines.)
Regarding claim 4, YOU in view of Gaither disclose the elements of claim 1. In addition, Gaither disclose:
The computer-implemented method of claim 1, wherein adjusting the cross-sectional area comprises: utilizing Bernoulli's theorem to calculate a size or shape of the cross-sectional area of the funnel system (In paragraph [0051], Gather discloses Bernoulli's principle being used to explain why air speeds up as it moves through the air duct and around the wind turbines.)
Executing a motor system to adjust the cross-sectional area based on the calculated size or shape (In paragraph [0050], Gaither discloses the design and orientation of the wind blades and how the blade shape and angle affect both power generation and airflow through the vehicle duct.)
Regarding claim 5, YOU in view of Gaither disclose the elements of claim 1. In addition, Gaither disclose:
The computer-implemented method of claim 1, wherein the set of sensors comprise anemometers, wherein the anemometers are located at the entry portion and exit portion of the funnel system (In paragraph [0035], YOU disclose the wind-condition measuring sensor 10 may, for example, include an anemometer, a pitot tube, or the like widely used for measuring a wind direction.)
Regarding claim 6, YOU in view of Gaither disclose the elements of claim 1. In addition, YOU disclose:
The computer-implemented method of claim 1, wherein the computing system is embedded and integrated into the vehicle and is designed to process sensor data in real-time (In paragraph [0056], YOU disclose the predicted wind-condition data generator is configured to predict future wind conditions based on the wind conditions varying in real time by making the AI learn)
Regarding claim 7, YOU in view of Gaither disclose the elements of claim 1. In addition, YOU disclose:
The computer-implemented method of claim 1, further comprising: collecting, by the sensor set, data associated with the wind speed and the road conditions to provide real-time data to the computing system (In paragraph [0036], YOU disclose the predicted wind-condition data generator 20 is configured to receive data from the plurality of wind-condition measuring sensors 10 and generate predicted wind-condition data.)
Utilizing the collected data to predict wind patterns and road conditions in real-time by employing machine learning models, control algorithms, vehicle-to-everything (V2X) communication, and predictive analysis (In paragraph [0036-0037], YOU disclose the control algorithm learner 30 is configured to generate the control algorithm of the wind turbine 40 based on the predicted wind-condition data generated from the foregoing predicted wind-condition data generator.)
With respect to claim 8, YOU disclose:
Program instructions to measure, by a set of sensors, a wind speed as it passes through a funnel system (In paragraph [0035], YOU disclose that the wind-condition measuring sensor 10 may be provided at a plurality of places spaced apart from the reference position where the foregoing wind turbine 40 is positioned. The wind-condition measuring sensor 10 may, for example, include an anemometer, a pitot tube, or the like widely used for measuring wind direction. )
Program instructions to modulate, by the funnel system, the wind speed of wind that passes through the funnel system to an array of wind turbines, wherein modulating the wind speed comprises (In paragraph [0034], YOU disclose the configuration of a wind turbine system that includes sensors, a controller, and actuators for monitoring and controlling turbine operation. The controller 41 is configured to control the wind turbine based on the control variable. In paragraph [0038], YOU disclose that the controller 41 is based on predicted wind-condition data after the learning of the control algorithm learner 30 and controlling the wind turbine.)
Program instructions to proactively adjust, by a computing system, the funnel system to maintain a predetermined wind speed that is being fed to the array of wind turbines through the funnel system by employing machine learning models and control algorithms, using sensor data, vehicle to everything communication, and predictive analysis of wind patterns and road conditions (In paragraphs [0037-0041], YOU disclose an AI-based predictive wind control system that uses sensors-collected time-series wind data to predict future wind conditions and automatically adjust turbine operation through controllers (control algorithm learner 30) and actuators 42.)
With respect to claim 8, YOU does not explicitly disclose:
A computer system comprising: one or more computer processors
One or more computer readable storage devices
Program instructions to adjust, by a motor set, a cross-sectional area of an entry portion and an exit portion of the funnel system based on the measured wind speed
However, it is known by Gaither to disclose:
A computer system comprising: one or more computer processors (In paragraph [0039], Gaither disclose the ECU 102 may alternatively be a separate electronic control unit having one or more processors directed to managing primarily or solely the operation of the at least one wind turbine.)
One or more computer readable storage devices (In paragraph [0039], Gaither disclose the ECU 102 may be connected to a memory that includes codes or instructions (such as look-up tables) for operations of the ECU.)
Program instructions to adjust, by a motor set, a cross-sectional area of an entry portion and an exit portion of the funnel system based on the measured wind speed (In paragraph [0031], Gaither discloses that the wind turbines can be positioned in an air duct extending from the front bumper to an area proximal to one or more of the vehicle's wheels. The air channels and the wind turbines can be advantageously designed to significantly improve vehicle aerodynamics by reducing aerodynamic drag components. In paragraph [0039], Gaither disclose the motor or the generator 104 may include an M-G having the capability of actively rotating the wind turbine 106 to enhance cooling. In paragraph [0041], Gaither discloses the inlet/outlet portion, the inlet portion receives incoming airflow from outside the vehicle. The outlet portion directs airflow towards portions of the braking system. An inlet with a first cross-sectional area and an outlet with a second cross-sectional based on airflow. In paragraph [0043], Gaither disclose one or more wind turbines 206 having blades configured to be rotated by inlet airflow 214a.)
YOU and Gaither are analogous pieces of art because both references concern wind-turbine systems using wind conditions and a method of controlling a wind turbine. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify YOU by using an artificial intelligence (AI) model for predicting wind conditions to maximize the efficiency of power production as taught by YOU, while enhancing the aerodynamics of the vehicle as taught by Gaither. The motivation for doing so would have been to maximize the efficiency of power production depending on wind speeds and wind direction (See [0030] of YOU.)
Regarding claim 9, YOU in view of Gaither disclose the elements of claim 8. In addition, Gaither disclose:
The computing system of claim 8, wherein the array of wind turbines are embedded into a body of an autonomous vehicle, wherein the array of wind turbines generate power when exposed to wind and their operational range is defined by specific cut-in and cut-out wind speeds, wherein each turbine in the array of wind turbines is constructed out of lightweight composite material and strategically positioned to reduce aerodynamic drag while maximizing wind exposure (In paragraph [0031], Gaither discloses that the wind turbines can be positioned in an air duct extending from the front bumper to an area proximal to one or more of the vehicle's wheels. The air channels and the wind turbines can be advantageously designed to significantly improve vehicle aerodynamics by reducing aerodynamic drag components.)
Regarding claim 10, YOU in view of Gaither disclose the elements of claim 8. In addition, Gaither disclose:
The computing system of claim 8, wherein the funnel system is located in front of the array of wind turbines, and wherein the funnel system comprises a motor system (In paragraph [0034], Gaither discloses that the motor or the generator 104 may include an M-G having the capability of actively rotating the wind turbine.)
Regarding claim 11, YOU in view of Gaither disclose the elements of claim 8. In addition, Gaither disclose:
The computing system of claim 8, wherein adjusting the cross-sectional area comprises: program instructions to utilize Bernoulli's theorem to calculate a size or shape of the cross-sectional area of the funnel system to maintain the wind speed within an operational range of the turbines to maximize power generation (In paragraph [0051], Gather discloses Bernoulli's principle being used to explain why air speeds up as it moves through the air duct and around the wind turbines.)
Program instructions to execute a motor system to adjust the cross-sectional area based on the calculated size or shape of the cross-sectional area of the funnel system (In paragraph [0050], Gaither discloses the design and orientation of the wind blades and how the blade shape and angle affect both power generation and airflow through the vehicle duct.)
Regarding claim 12, YOU in view of Gaither disclose the elements of claim 8. In addition, YOU disclose:
The computing system of claim 8, wherein the set of sensors comprise anemometers, wherein the anemometers are located at the entry portion and exit portion of the funnel system (In paragraph [0035], YOU disclose the wind-condition measuring sensor 10 may, for example, include an anemometer, a pitot tube, or the like widely used for measuring a wind direction.)
Regarding claim 13, YOU in view of Gaither disclose the elements of claim 8. In addition, YOU disclose:
The computing system of claim 8, wherein the computing system is embedded and integrated into the vehicle and is designed to process sensor data in real-time (In paragraph [0056], YOU disclose the predicted wind-condition data generator is configured to predict future wind conditions based on the wind conditions varying in real time by making the AI learn.)
Regarding claim 14, YOU in view of Gaither disclose the elements of claim 8. In addition, YOU disclose:
The computing system of claim 8, further comprising: program instructions to collect, by the sensor set, data associated with the wind speed and the road conditions to provide real-time data to the computing system (In paragraph [0036], YOU disclose the predicted wind-condition data generator 20 is configured to receive data from the plurality of wind-condition measuring sensors 10 and generate predicted wind-condition data.)
Program instructions to utilize the collected data to predict wind patterns and road conditions in real-time by employing machine learning models, control algorithms, vehicle-to-everything (V2X) communication, and predictive analysis (In paragraph [0036-0037], YOU disclose the control algorithm learner 30 is configured to generate the control algorithm of the wind turbine 40 based on the predicted wind-condition data generated from the foregoing predicted wind-condition data generator.)
With respect to claim 15, YOU disclose:
Program instructions to measure, by a set of sensors, a wind speed as it passes through a funnel system (In paragraph [0035], YOU disclose that the wind-condition measuring sensor 10 may be provided at a plurality of places spaced apart from the reference position where the foregoing wind turbine 40 is positioned. The wind-condition measuring sensor 10 may, for example, include an anemometer, a pitot tube, or the like widely used for measuring wind direction. )
Program instructions to modulate, by the funnel system, the wind speed of wind that passes through the funnel system to an array of wind turbines, wherein modulating the wind speed comprises (In paragraph [0034], YOU disclose the configuration of a wind turbine system that includes sensors, a controller, and actuators for monitoring and controlling turbine operation. The controller 41 is configured to control the wind turbine based on the control variable. In paragraph [0038], YOU disclose that the controller 41 is based on predicted wind-condition data after the learning of the control algorithm learner 30 and controlling the wind turbine.)
Program instructions to proactively adjust, by a computing system, the funnel system to maintain a predetermined wind speed that is being fed to the array of wind turbines through the funnel system by employing machine learning models and control algorithms, using sensor data, vehicle to everything communication, and predictive analysis of wind patterns and road conditions (In paragraphs [0037-0041], YOU disclose an AI-based predictive wind control system that uses sensors-collected time-series wind data to predict future wind conditions and automatically adjust turbine operation through controllers (control algorithm learner 30) and actuators 42.)
With respect to claim 15, YOU does not explicitly disclose:
A computer program product comprising: one or more computer readable storage devices and program instructions stored on the one or more computer readable storage devices, the stored program instructions comprising
Program instructions to adjust, by a motor set, a cross-sectional area of an entry portion and an exit portion of the funnel system based on the measured wind speed
However, it is known by Gaither to disclose:
A computer program product comprising: one or more computer readable storage devices and program instructions stored on the one or more computer readable storage devices, the stored program instructions comprising (In paragraph [0039], Gaither disclose the ECU 102 may be connected to a memory that includes codes or instructions (such as look-up tables) for operations of the ECU.)
Program instructions to adjust, by a motor set, a cross-sectional area of an entry portion and an exit portion of the funnel system based on the measured wind speed (In paragraph [0031], Gaither discloses that the wind turbines can be positioned in an air duct extending from the front bumper to an area proximal to one or more of the vehicle's wheels. The air channels and the wind turbines can be advantageously designed to significantly improve vehicle aerodynamics by reducing aerodynamic drag components. In paragraph [0039], Gaither disclose the motor or the generator 104 may include an M-G having the capability of actively rotating the wind turbine 106 to enhance cooling. In paragraph [0041], Gaither discloses the inlet/outlet portion, the inlet portion receives incoming airflow from outside the vehicle. The outlet portion directs airflow towards portions of the braking system. An inlet with a first cross-sectional area and an outlet with a second cross-sectional based on airflow.)
YOU and Gaither are analogous pieces of art because both references concern wind-turbine systems using wind conditions and a method of controlling a wind turbine. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify YOU by using an artificial intelligence (AI) model for predicting wind conditions to maximize the efficiency of power production as taught by YOU, while enhancing the aerodynamics of the vehicle as taught by Gaither. The motivation for doing so would have been to maximize the efficiency of power production depending on wind speeds and wind direction (See [0030] of YOU.)
Regarding claim 16, YOU in view of Gaither disclose the elements of claim 15. In addition, Gaither disclose:
The computer product of claim 15, wherein the array of wind turbines are embedded into a body of an autonomous vehicle, wherein the array of wind turbines generate power when exposed to wind and their operational range is defined by specific cut-in and cut-out wind speeds, wherein each turbine in the array of wind turbines is constructed out of lightweight composite material and strategically positioned to reduce aerodynamic drag while maximizing wind exposure (In paragraph [0031], Gaither discloses that the wind turbines can be positioned in an air duct extending from the front bumper to an area proximal to one or more of the vehicle's wheels. The air channels and the wind turbines can be advantageously designed to significantly improve vehicle aerodynamics by reducing aerodynamic drag components.)
Regarding claim 17, YOU in view of Gaither disclose the elements of claim 15. In addition, Gaither disclose:
The computer product of claim 15, wherein adjusting the cross-sectional area comprises: program instructions to utilize Bernoulli's theorem to calculate a size or shape of the cross-sectional area of the funnel system to maintain the wind speed within an operational range of the turbines to maximize power generation (In paragraph [0051], Gather discloses Bernoulli's principle being used to explain why air speeds up as it moves through the air duct and around the wind turbines.)
Program instructions to execute a motor system to adjust the cross-sectional area based on the calculated size or shape of the cross-sectional area of the funnel system (In paragraph [0050], Gaither discloses the design and orientation of the wind blades and how the blade shape and angle affect both power generation and airflow through the vehicle duct.)
Regarding claim 18, YOU in view of Gaither disclose the elements of claim 15. In addition, YOU disclose:
The computer product of claim 15, wherein the set of sensors comprise anemometers, wherein the anemometers are located at the entry portion and exit portion of the funnel system (In paragraph [0035], YOU disclose the wind-condition measuring sensor 10 may, for example, include an anemometer, a pitot tube, or the like widely used for measuring a wind direction.)
Regarding claim 19, YOU in view of Gaither disclose the elements of claim 15. In addition, YOU disclose:
The computer product of claim 15, wherein the computing system is embedded and integrated into the vehicle and is designed to process sensor data in real-time (In paragraph [0056], YOU disclose the predicted wind-condition data generator is configured to predict future wind conditions based on the wind conditions varying in real time by making the AI learn.)
Regarding claim 20, YOU in view of Gaither disclose the elements of claim 15. In addition, YOU disclose:
The computer product of claim 15, further comprising: program instructions to collect, by the sensor set, data associated with the wind speed and the road conditions to provide real-time data to the computing system (In paragraph [0036], YOU disclose the predicted wind-condition data generator 20 is configured to receive data from the plurality of wind-condition measuring sensors 10 and generate predicted wind-condition data.)
Program instructions to utilize the collected data to predict wind patterns and road conditions in real-time by employing machine learning models, control algorithms, vehicle-to-everything (V2X) communication, and predictive analysis (In paragraph [0036-0037], YOU disclose the control algorithm learner 30 is configured to generate the control algorithm of the wind turbine 40 based on the predicted wind-condition data generated from the foregoing predicted wind-condition data generator.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela D Reyes can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights