DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
Claims 1-20 filed on 5 DEC 2024 are currently pending and have been examined.
Priority
The pending application 18/969,672, filed on 5 DEC 2024, claims priority from provisional application 63/606,422, filed on 5 DEC 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7 MAR 2025 has been considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0003], line 11, “leas” should be “leads”
In paragraph [0048], lines 9 and 11, “264a-b” should be “364a-b”
Appropriate correction is required.
Claim Objections
Claims 10 and 13-14 objected to because of the following informalities:
In claim 10, line 8, “a vehicle” should be “the vehicle”
In claim 13, line 3, “operable o” should be “operable to”
In claim 14, line 2, “data descriptive includes capturing data descriptive of” should be “includes capturing data descriptive of”
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
ground-oriented RADAR devices in claim 4
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hoffmann et al. (US 10,247,816 B1) in view of Seo et al. (US 10,442,439 B1) and Weber et al. (US 2022/0340110 A1).
Regarding claim 1, Hoffmann et al. discloses:
[Note: what is not explicitly taught by Hoffmann et al. has been struck-through]
A ground speed computation system (Hoffmann et al. computer system 100, Fig. 1) for a vehicle (Hoffmann et al. vehicle 200, Fig. 2), comprising:
a plurality of ground-oriented RADAR devices (Hoffmann et al. “sensors 208, 212 can be orthogonally mounted and can be radar sensors.” – Col. 6, lines 60-61; Fig. 2);
at least one Inertial Measurement Unit (IMU) device (Hoffmann et al. inertial measurement unit IMU 132, Fig. 1);
a communication device (Hoffmann et al. “The components of the system 100 may communicate over any number of networks, including telecommunication networks and wireless networks.” – Col. 5, lines 42-46);
an electronic processing device (Hoffmann et al. slip and velocity processing module 104, Fig. 1) in communication with the plurality of ground-oriented RADAR devices (Hoffmann et al. “The slip and velocity of the road vehicle can be determined using the information collected by one or more sensors (e.g., lateral and longitudinal sensors 136, 138) placed on the road vehicle.” – Col. 4, line 65 – Col. 5, line 2; where the lateral and longitudinal sensors 136, 138 of Fig. 1 correspond to sensors 208, 212 of Fig. 2), the at least one IMU device (Hoffmann et al. “In addition to the one or more orthogonally placed sensors, an Inertia Measuring Unit (IMU) can be collectively used with the orthogonally placed sensors to independently extrapolate velocity before or while the orthogonal sensors are being used to determine velocity and slip angles and independently or in supplement may also be used for noise reduction.” – Col. 3, lines 15-21), and the communication device (Hoffmann et al. “The components of the system 100 may communicate over any number of networks, including telecommunication networks and wireless networks. Further, it should be noted that the system 100 may include any number of additional or fewer components, including components used to communicate between the components shown.” – Col. 5, lines 42-49); and
a non-transitory memory (Hoffmann et al. main memory 116, Fig. 1) storing (Hoffmann et al. “slip and velocity processing module 104 executing one or more sequences of one or more instructions contained in memory 116.” – Col. 4, lines 50-53) that when executed by the electronic processing device result in:
receiving, from the plurality of ground-oriented RADAR devices, data descriptive of at least one ground surface in proximity to the vehicle (Hoffmann et al. “The sensors 136, 138 can also be used to detect road surface conditions.” – Col. 5, lines 2-3);
receiving, from the at least one IMU device, data descriptive of a movement of the vehicle (Hoffmann et al. “In addition to the one or more orthogonally placed sensors, an Inertia Measuring Unit (IMU) can be collectively used with the orthogonally placed sensors to independently extrapolate velocity before or while the orthogonal sensors are being used to determine velocity and slip angles and independently or in supplement may also be used for noise reduction.” – Col. 3, lines 15-21; where the slip and velocity processing module receives the data from the IMU in order to reduce noise);
computing, (Hoffmann et al. “sensors 208, 212 can be radar sensors emitting radio waves toward the surface of the road and receiving the reflected radio frequency waves to determine the properties of the ground surface, such as ice on the road and potholes in the road, etc.” – Col. 6, line 65 – Col. 7, line 2) and (ii) the data descriptive of the movement of the vehicle, to determine an estimated ground speed of the vehicle (Hoffmann et al. “The implementation can include these two or more sensors 208 and 212 that are orthogonally mounted on the underside of the vehicle 200 in order to obtain longitudinal and lateral velocity vectors (218, 220) from which the velocity of the vehicle 221 and the slip angle can be determined.” – Col. 6, lines 31-37); and
outputting, by the communication device, an indication of the estimated ground speed of the vehicle (Hoffmann et al. “The computed vehicle velocity and/or slip angle may be provided to at least the computing system of Fig. 1, and then used in road vehicle stability and control functions.” – Col. 12, lines 41-43).
Seo et al. discloses:
A ground speed computation system for a vehicle (Seo et al. “The transport system 102 can be a road vehicle, a motorcycle, a truck, a recreational vehicle, or any other type of transport system that can benefit from the use of an ADAS system.” – Col. 3, lines 35-38), comprising:
a ground-oriented RADAR device (Seo et al. radar 112b, Figs. 1-2);
at least one Inertial Measurement Unit (IMU) device (Seo et al. inertial measurement units IMUs 108, Figs. 1-2; “As another example, the IMU 108 located on the transport system 102 can provide acceleration and rotational attributes that can help provide a better characterization of the driving conditions for better road coefficient approximation.” – Col. 6, lines 8-12);
a communication device (Seo et al. “Note that more or less data sources, including but not limited to, IMUs 108, temperature sensors 106, GNSS systems 110, image processing, computer vision, and in-system networking, can be collectively used with the onboard sensors 112a-112c for predicting the road friction coefficients.” – Col. 6, lines 15-20);
an electronic processing device (Seo et al. electronic system 400, Fig. 4) in communication with the ground-oriented RADAR device, the at least one IMU device, and the communication device (Seo et al. “Note that more or less data sources, including but not limited to, IMUs 108, temperature sensors 106, GNSS systems 110, image processing, computer vision, and in-system networking, can be collectively used with the onboard sensors 112a-112c for predicting the road friction coefficients.” – Col. 6, lines 15-20); and
a non-transitory memory storing (i) a machine learning (ML) calculation model and (ii) instructions that when executed by the electronic processing device result in:
receiving, from the plurality of ground-oriented RADAR devices, data descriptive of at least one ground surface in proximity to the vehicle (Seo et al. “The onboard system sensors can be sensors such as LIDAR sensors, RADAR sensors, cameras and the like, located on a system like a vehicle that can be used to capture the road surface characteristics.” – Col. 3, lines 13-16);
receiving, from the at least one IMU device, data descriptive of a movement of the vehicle (Seo et al. inertial measurement units IMUs 108, Figs. 1-2; “As another example, the IMU 108 located on the transport system 102 can provide acceleration and rotational attributes that can help provide a better characterization of the driving conditions for better road coefficient approximation.” – Col. 6, lines 8-12);
computing, by the ML calculation model and utilizing (i) the data descriptive of the at least one ground surface in proximity to the vehicle and (ii) the data descriptive of the movement of the vehicle (Seo et al. “In one embodiment, the mechanical components 202a-202d can be used for determining the estimated road friction coefficients {circumflex over (μ)}(t, x) for specific times and locations, while the onboard sensors 112a-112c are used for the road surface characteristics F(t). Using both data sets (e.g., road friction coefficients {circumflex over (μ)}(t, x) and road surface characteristics F(t)), a training data set can be determined which leads to the learned regression function {circumflex over (ƒ)}(μ(t, x), F(t, x)) for estimating real-time road friction coefficients.” – Col. 6, lines 48-57)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Seo et al. into the invention of Hoffmann et al. Both Hoffmann et al. and Seo et al. are considered analogous arts to the claimed invention as they both disclose systems comprising a plurality of sensors for detecting the ground surface and the velocity of a vehicle. Hoffmann et al. discloses the limitations of claim 1 outlined above. However, Hoffmann et al. fails to explicitly disclose the use of machine learning (ML). This feature is disclosed by Seo et al. where “The regression function can be learned using a machine learning technique using regression analysis and pattern recognition such that a relationship is estimated between variables and friction coefficient (e.g., estimated friction coefficients and road surface characteristics.” (Seo et al. Col. 8, lines 34-38). The combination of Hoffmann et al. and Seo et al. would be obvious with a reasonable expectation of success to provide a closer estimation of the real-time road friction coefficients during operation of the vehicle (Seo et al. Col. 6, lines 40-45).
Weber et al. discloses:
a non-transitory memory (Weber et al. machine-readable memory medium - ¶ [0009]; non-transitory machine-readable medium – claim 9) storing (i) a machine learning (ML) ground speed calculation model (Weber et al. “The present invention thus provides in one example embodiment to use for the purpose of ABS control an estimation of the groundspeed... The estimation may take place, for example, with the aid of a Luenberger observer, a Kalman filter, a particle filter, a neural network, or similar devices.” - ¶ [0015]) and (ii) instructions that when executed by the electronic processing device result in:
the data descriptive of the movement of the vehicle (Weber et al. “Signals 70 and 72 as well as wheel circumferential signal 68 are input into a system 80 that uses these to compute wheel slip 82.” - ¶ [0033]), to determine an estimated ground speed of the vehicle (Weber et al. “The present invention thus provides in one example embodiment to use for the purpose of ABS control an estimation of the groundspeed... The estimation may take place, for example, with the aid of a Luenberger observer, a Kalman filter, a particle filter, a neural network, or similar devices.” - ¶ [0015])
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Weber et al. into the invention of Hoffmann et al. as modified above to yield the invention of claim 1 above. Hoffmann et al., Seo et al. and Weber et al. are considered analogous arts to the claimed invention as they disclose systems comprising a plurality of sensors for detecting the velocity and wheel slip of a vehicle. Hoffmann et al. as modified above discloses the limitations outlined above. However, Hoffmann et al. as modified above fails to explicitly disclose using machine learning to calculate the vehicle velocity. This feature is disclosed by Weber et al. where “The present invention thus provides in one example embodiment to use for the purpose of ABS control an estimation of the groundspeed... The estimation may take place, for example, with the aid of a Luenberger observer, a Kalman filter, a particle filter, a neural network, or similar devices.” (Weber et al. ¶ [0015]). The combination of Hoffmann et al., Seo et al. and Weber et al. would be obvious with a reasonable expectation of success to provide a closer estimation of the real-time road friction coefficients during operation of the vehicle (Seo et al. Col. 6, lines 40-45) and “jointly estimate variable positions and speeds in a precise manner.” (Weber et al. ¶ [0017]).
Regarding claim 2, Hoffmann et al. as modified above discloses:
The system according to claim 1, wherein the plurality of RADAR devices are mounted relative to the vehicle (Hoffmann et al. “The implementation can include these two or more sensors 208 and 212 that are orthogonally mounted on the underside of the vehicle 200 in order to obtain longitudinal and lateral velocity vectors (218, 220) from which the velocity of the vehicle 221 and the slip angle can be determined.” – Col. 6, lines 31-37) .
Regarding claim 3, Hoffmann et al. as modified above discloses:
The system according to claim 2, including a first RADAR device of the plurality of ground-oriented RADAR devices oriented in a first direction relative to the vehicle and a second RADAR device of the plurality of ground-oriented RADAR devices oriented in a second direction relative to the vehicle different from the first direction (Hoffmann et al. “Orthogonal placement of sensors 208, 212 can include placement of the sensors 208, 212 in a perpendicular manner on the underside of the vehicle 200 such that a right (90°) angle exists between the sensors 208,212. In some instances, the sensors 208,212 may not be orthogonally placed and instead placed angled from each other (e.g., at a 45° angle).” – Col. 7, lines 5-11).
Regarding claim 4, Hoffmann et al. as modified above discloses:
The system according to claim 1, wherein the plurality of ground-oriented RADAR devices are configured to receive data descriptive of at least one of a ground surface The sensors 136, 138 can also be used to detect road surface conditions.” – Col. 5, lines 2-3), a ground feature, a static object or a moving object (Hoffmann et al. “In addition, the radar sensors may be multi-purposed such that in addition to being used for determining slip angle and vehicle velocity, the radar sensors may be used for the detection of other vehicles or objects.” – Col. 7, lines 2-5).
Regarding claim 5, Hoffmann et al. as modified above discloses:
The system according to claim 1, wherein the at least one IMU device comprises one or more Micro-Electro-Mechanical Systems (MEMS) gyroscope and/or Fiber Optic Gyroscope (FOG) devices, magnetometers, and/or accelerometers operable to detect, measure, and/or sense one or more vehicle movement, state, and/or orientation parameters (Hoffmann et al. “Further, additional sensors (e.g., yaw, wheel speed, gyroscopic, ultrasonic sensors) may be placed on the underside of the vehicle to create a triad of sensors which can provide further information for the velocity measurement, relative to a two sensor system.” – Col. 3, lines 25-29).
Regarding claim 6, Hoffmann et al. as modified above discloses:
[Note: what is not explicitly taught by Hoffmann et al. has been struck-through]
The system according to claim 5, including at least a first IMU device (Hoffmann et al. IMU 132, Fig. 1)
Seo et al. discloses:
including at least a first IMU device and a second IMU device (Seo et al. “The mechanical components 202a-202d can provide speed, velocity, and acceleration characteristics that can be used in conjunction with the extension system 114 and onboard sensors 112a-112c for better characterization of the learned regression function {circumflex over (ƒ)}(μ(t, x), F(t, x))” – Col. 6, lines 30-35).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Seo et al. into the invention of Hoffmann et al. as modified above to yield the invention of claim 6 above. Both Hoffmann et al. and Seo et al. are considered analogous arts to the claimed invention as they both disclose systems comprising a plurality of sensors for detecting the ground surface and the velocity of a vehicle. Hoffmann et al. discloses the invention of claim 1. However, Hoffmann et al. fails to explicitly disclose a second IMU device. This feature is disclosed by Seo et al. where “The mechanical components 202a-202d can provide speed, velocity, and acceleration characteristics that can be used in conjunction with the extension system 114 and onboard sensors 112a-112c for better characterization of the learned regression function {circumflex over (ƒ)}(μ(t, x), F(t, x))” (Seo et al. Col. 6, lines 30-35). The combination of Hoffmann et al. and Seo et al. would be obvious with a reasonable expectation of success to provide a closer estimation of the real-time road friction coefficients during operation of the vehicle (Seo et al. Col. 6, lines 40-45).
Further, it would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to include a second IMU device, since it has been held that mere duplication of essential working parts of a device involves only routine skill in the art. St. Regis Paper Co. v. Bemis Co., 193 USPQ 8.
Regarding claim 7, Hoffmann et al. as modified above discloses:
The system according to claim 1, further including one or more secondary sensors coupled relative to the vehicle, the one or more secondary sensors configured to collect additional data descriptive of a ground surface (Hoffmann et al. “Alternatively, a second set of ultrasonic sensors and corresponding hardware/software can be placed on the underside of the vehicle 200 or in a position with a sufficient view of the travel surface. This second set of sensors can also be orthogonally placed with a horizontal or partially horizontal orientation and used for slip angle 206 and velocity measurements as described herein.” – Col. 9, lines 17-23) or ground feature.
Regarding claim 8, Hoffmann et al. as modified above discloses:
The system according to claim 7, wherein the one or more secondary sensors include at least one of Light Detection and Ranging (LiDAR), LAser Detection and Ranging (LADAR), SOund Navigation And Ranging (SONAR), Infrared Radiation (IR), RF, ultrasound (Hoffmann et al. “Alternatively, a second set of ultrasonic sensors and corresponding hardware/software can be placed on the underside of the vehicle 200 or in a position with a sufficient view of the travel surface. This second set of sensors can also be orthogonally placed with a horizontal or partially horizontal orientation and used for slip angle 206 and velocity measurements as described herein.” – Col. 9, lines 17-23), structured light, and/or imaging device.
Regarding claim 9, Hoffmann et al. as modified above discloses:
The system according to claim 1, including one or more location devices coupled to the vehicle (Hoffmann et al. GPS 134, Fig. 1; “Additionally or alternatively, external positioning and velocity measurements such as those provided by a Global Positioning System (GPS) unit can also be collectively used with the sensors for error correction and location synchronization.” – Col. 3, lines 21-25).
Regarding claim 10, Hoffmann et al. discloses:
[Note: what is not explicitly taught by Hoffmann et al. has been struck-through]
A method, comprising:
receiving, from a plurality of ground-oriented RADAR devices (Hoffmann et al. “sensors 208, 212 can be orthogonally mounted and can be radar sensors.” – Col. 6, lines 60-61; Fig. 2), data descriptive of at least one ground surface (Hoffmann et al. “The sensors 136, 138 can also be used to detect road surface conditions.” – Col. 5, lines 2-3) in proximity to a vehicle (Hoffmann et al. vehicle 200, Fig. 2);
receiving, from at least one Inertial Measurement Unit (IMU) device (Hoffmann et al. inertial measurement unit IMU 132, Fig. 1), data descriptive of movement of the vehicle (Hoffmann et al. “In addition to the one or more orthogonally placed sensors, an Inertia Measuring Unit (IMU) can be collectively used with the orthogonally placed sensors to independently extrapolate velocity before or while the orthogonal sensors are being used to determine velocity and slip angles and independently or in supplement may also be used for noise reduction.” – Col. 3, lines 15-21; where the slip and velocity processing module receives the data from the IMU in order to reduce noise);
computing, (Hoffmann et al. “sensors 208, 212 can be radar sensors emitting radio waves toward the surface of the road and receiving the reflected radio frequency waves to determine the properties of the ground surface, such as ice on the road and potholes in the road, etc.” – Col. 6, line 65 – Col. 7, line 2) and (ii) the data descriptive of the movement of the vehicle, to determine an estimated ground speed of a vehicle (Hoffmann et al. “The implementation can include these two or more sensors 208 and 212 that are orthogonally mounted on the underside of the vehicle 200 in order to obtain longitudinal and lateral velocity vectors (218, 220) from which the velocity of the vehicle 221 and the slip angle can be determined.” – Col. 6, lines 31-37); and
outputting, by a communication device, an indication of the estimated ground speed of the vehicle (Hoffmann et al. “The computed vehicle velocity and/or slip angle may be provided to at least the computing system of Fig. 1, and then used in road vehicle stability and control functions.” – Col. 12, lines 41-43);
wherein the method is performed by at least one processor (Hoffmann et al. slip and velocity processing module 104, Fig. 1) coupled to memory (Hoffmann et al. main memory 116, Fig. 1).
Seo et al. discloses:
receiving, from at least one Inertial Measurement Unit (IMU) device (Seo et al. inertial measurement units IMUs 108, Figs. 1-2; “As another example, the IMU 108 located on the transport system 102 can provide acceleration and rotational attributes that can help provide a better characterization of the driving conditions for better road coefficient approximation.” – Col. 6, lines 8-12), data descriptive of movement of the vehicle (Seo et al. inertial measurement units IMUs 108, Figs. 1-2; “As another example, the IMU 108 located on the transport system 102 can provide acceleration and rotational attributes that can help provide a better characterization of the driving conditions for better road coefficient approximation.” – Col. 6, lines 8-12);
computing, with a Machine Learning (ML) calculation model and utilizing (i) the data descriptive of the at least one ground surface in proximity to the vehicle and (ii) the data descriptive of the movement of the vehicle, to determine an estimated ground speed of a vehicle (Seo et al. “In one embodiment, the mechanical components 202a-202d can be used for determining the estimated road friction coefficients {circumflex over (μ)}(t, x) for specific times and locations, while the onboard sensors 112a-112c are used for the road surface characteristics F(t). Using both data sets (e.g., road friction coefficients {circumflex over (μ)}(t, x) and road surface characteristics F(t)), a training data set can be determined which leads to the learned regression function {circumflex over (ƒ)}(μ(t, x), F(t, x)) for estimating real-time road friction coefficients.” – Col. 6, lines 48-57).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Seo et al. into the invention of Hoffmann et al. Both Hoffmann et al. and Seo et al. are considered analogous arts to the claimed invention as they both disclose systems comprising a plurality of sensors for detecting the ground surface and the velocity of a vehicle. Hoffmann et al. discloses the limitations of claim 1 outlined above. However, Hoffmann et al. fails to explicitly disclose the use of machine learning (ML). This feature is disclosed by Seo et al. where “The regression function can be learned using a machine learning technique using regression analysis and pattern recognition such that a relationship is estimated between variables and friction coefficient (e.g., estimated friction coefficients and road surface characteristics.” (Seo et al. Col. 8, lines 34-38). The combination of Hoffmann et al. and Seo et al. would be obvious with a reasonable expectation of success to provide a closer estimation of the real-time road friction coefficients during operation of the vehicle (Seo et al. Col. 6, lines 40-45).
Weber et al. discloses:
receiving, from at least one Inertial Measurement Unit (IMU) device (Weber et al. “The groundspeed may be furthermore estimated in terms of value and direction based on the signals from three acceleration sensors and three rotational speed sensors.” - ¶ [0014]), data descriptive of movement of the vehicle;
computing, with a Machine Learning (ML) ground speed calculation model (Weber et al. “The present invention thus provides in one example embodiment to use for the purpose of ABS control an estimation of the groundspeed... The estimation may take place, for example, with the aid of a Luenberger observer, a Kalman filter, a particle filter, a neural network, or similar devices.” - ¶ [0015]) the data descriptive of the movement of the vehicle (Weber et al. “Signals 70 and 72 as well as wheel circumferential signal 68 are input into a system 80 that uses these to compute wheel slip 82.” - ¶ [0033]), to determine an estimated ground speed of a vehicle (Weber et al. “The present invention thus provides in one example embodiment to use for the purpose of ABS control an estimation of the groundspeed... The estimation may take place, for example, with the aid of a Luenberger observer, a Kalman filter, a particle filter, a neural network, or similar devices.” - ¶ [0015]);
wherein the method is performed by at least one processor coupled to memory (Weber et al. machine-readable memory medium - ¶ [0009]; non-transitory machine-readable medium – claim 9).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Weber et al. into the invention of Hoffmann et al. as modified above to yield the invention of claim 10 above. Hoffmann et al., Seo et al. and Weber et al. are considered analogous arts to the claimed invention as they disclose systems comprising a plurality of sensors for detecting the velocity and wheel slip of a vehicle. Hoffmann et al. as modified above discloses the limitations outlined above. However, Hoffmann et al. as modified above fails to explicitly disclose using machine learning to calculate the vehicle velocity. This feature is disclosed by Weber et al. where “The present invention thus provides in one example embodiment to use for the purpose of ABS control an estimation of the groundspeed... The estimation may take place, for example, with the aid of a Luenberger observer, a Kalman filter, a particle filter, a neural network, or similar devices.” (Weber et al. ¶ [0015]). The combination of Hoffmann et al., Seo et al. and Weber et al. would be obvious with a reasonable expectation of success to provide a closer estimation of the real-time road friction coefficients during operation of the vehicle (Seo et al. Col. 6, lines 40-45) and “jointly estimate variable positions and speeds in a precise manner.” (Weber et al. ¶ [0017]).
Regarding claim 11, Hoffmann et al. as modified above discloses:
The method according to claim 10 further including transmitting with the plurality of ground-oriented RADAR devices one or more signals toward the at least one ground surface (Hoffmann et al. “sensors 208, 212 can be radar sensors emitting radio waves toward the surface of the road and receiving the reflected radio frequency waves to determine the properties of the ground surface, such as ice on the road and potholes in the road, etc.” – Col. 6, line 65 – Col. 7, line 2).
Regarding claim 12, the same cited section and rationale as corresponding claim 3 is applied.
Regarding claim 13, the same cited section and rationale as corresponding claim 5 is applied.
Regarding claim 14, the same cited section and rationale as corresponding claim 4 is applied.
Regarding claim 15, the same cited section and rationale as corresponding claim 5 is applied.
Regarding claim 16, the same cited section and rationale as corresponding claim 6 is applied.
Regarding claim 17, the same cited section and rationale as corresponding claim 7 is applied.
Regarding claim 18, the same cited section and rationale as corresponding claim 8 is applied.
Regarding claim 19, Hoffmann et al. as modified above discloses:
The method according to claim 10, including, obtaining data descriptive of location with one or more location devices coupled to the vehicle, location data associated with the vehicle (Hoffmann et al. GPS 134, Fig. 1; “Additionally or alternatively, external positioning and velocity measurements such as those provided by a Global Positioning System (GPS) unit can also be collectively used with the sensors for error correction and location synchronization.” – Col. 3, lines 21-25).
Regarding claim 20, Hoffmann et al. as modified above discloses:
The method according to claim 19 wherein the one or more location devices include one of a Global Positioning System (GPS) device (Hoffmann et al. GPS 134, Fig. 1; “Additionally or alternatively, external positioning and velocity measurements such as those provided by a Global Positioning System (GPS) unit can also be collectively used with the sensors for error correction and location synchronization.” – Col. 3, lines 21-25), wireless signal triangulation device, or atomic clock.
Conclusion
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NAOMI M. WOLFORD
Examiner
Art Unit 3648
/N.M.W./Examiner, Art Unit 3648
2 AUG 2026
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
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