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
Claims 1-17 are currently pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/25/2025 and 04/29/2025 have been considered by the examiner and initialed copies of the IDS are hereby attached.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites multiple instances of the limitation "each subarea". There is insufficient antecedent basis for this limitation in the claim as two different kinds of subarea have been introduce, for example in “the area comprising a plurality of sub-areas” and in “comprising splitting the area into a plurality of subareas”. Furthermore, claim 9 recites “at least one sub-area” which is also indefinite due to the same rationale as claim 1.
The term “heavy-duty vehicle” in claims 2,11 and 17 is a relative term which renders the claim indefinite. The term “heavy-duty vehicle” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Simply providing a few examples of heavy-duty vehicles in the specification does not limit the claim language to those examples. As such the limitation is indefinite.
The term “short-range positioning sensor” in claims 2,11 and 17 is a relative term which renders the claim indefinite. The term “short-range positioning sensor” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 12 recites the limitation "the map". There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the sensor". There is insufficient antecedent basis for this limitation in the claim.
All dependent claims are also rejected under 35 U.S.C. 112(b) due to the dependency on a claim rejected under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7 and 9-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rohr et al. (US 20160349362 A1).
Regarding claim 1, Rohr discloses
A method of localizing a vehicle in an area of vehicle operation (see paragraph 0061, “The present invention relates in general to positional estimation and more particular to estimation of the position of an object. Frequently the object is a device, robot, or mobile device.”, further see paragraph 0070, “While the present invention is described by way of examples in which objects may be represented by vehicles or cellular telephones, an object is to be interpreted as an arbitrary entity that can implement the inventive concepts presented herein. For example, an object can be a robot, vehicle, aircraft, ship, bicycle, or other device or entity that moves in relation to another.”), the area comprising a plurality of sub-areas, at least two of the sub-area using different types of sensors for vehicle localization (see Fig. 9, further see paragraph 0118, “The object is assigned the task to move from point A 910 to point B 990. Historically several areas between the two points has been identified as experiences sensor failure. In this case objects traversing the lower area 920 have experienced failure of one type of positional sensor. The area on the upper portion of the page represents a similar region of failure 940 but one that is associated with a different type of positional sensor. Lastly the third area 930 immediately above the starting point represents degradation or failure of yet a third type of sensor.”), the method comprising:
receiving sensor data at the vehicle from a plurality of different types of sensors positioned in the area (see paragraph 0121, “One methodology of the Adaptive Positioning System of the present invention begins 1005 with receiving 1010 sensor data from each of a plurality of positional sensors. From the data the unimodal estimator determines 1015 an estimated position of an object for each sensor. Each of these positional estimations is maintained 1020 for each instantiation of a period of time. Environmental factors are identified 1030 and considered as the system correlates 1035 each determined position.”), at least two of the different types of sensors providing location data using different positioning coordinate systems wherein the different positioning coordinate systems are non-linear with respect to each other (see paragraph 0128, “According to one embodiment, the location of the transmitters is known/defined a priori and each transmitter is uniquely identified by a time-of-flight conversation. When the object is within a predetermined distance of a transmitter's predefined location, based on an estimation of the object's position from dead reckoning, the transmitter initiates a time-of-flight conversation with that transmitter. The conversation is time stamped and paired with the dead reckoning local frame position. When a threshold number of conversations have taken place a non-linear least-squares optimization is run to update the transformation of the robot's local frame of reference to a global frame of reference.”);
mapping the different positioning coordinate systems to a common coordinate system, comprising splitting the area into a plurality of subareas based on sensor coverage in each subarea and assigning a transform to be used for each subarea (see paragraph 0128, “Each time-of-flight conversation defines a ranging sphere (shell) where the object at the corresponding time stamp could have been. A distance is defined from the local frame position estimate to the ranging sphere is defined (distance from position to beacon minus the range estimate) for each time stamped range-position pair. A non-linear, “least-squares” optimization is then run to find the dead reckoning path through the global frame with the least distance-based cost.”);
automatically calibrating received sensor data associated with different positioning coordinate systems to form sensor data associated with a common positioning coordinate system in each subarea (see paragraph 0128, “Each time-of-flight conversation defines a ranging sphere (shell) where the object at the corresponding time stamp could have been. A distance is defined from the local frame position estimate to the ranging sphere is defined (distance from position to beacon minus the range estimate) for each time stamped range-position pair. A non-linear, “least-squares” optimization is then run to find the dead reckoning path through the global frame with the least distance-based cost.”, where this process is indeed “calibrating received sensor data associated with different positioning coordinate systems to form sensor data associated with a common positioning coordinate system in each subarea”); and
localizing the vehicle using the calibrated sensor data associated with the common positioning coordinate system in a subarea of interest in the area, provided by the different types of sensors to provide the sensor data associated with the common positioning coordinate system (see method of block diagram Fig. 13, localization module 1330, further see paragraphs 0143-0144, “FIG. 13 is a high level block diagram of a system for mobile localization of an object having an object positional frame of reference using sparse time-of-flight data and dead reckoning. The mobile localization system 1300 includes a dead reckoning module 1310, a time-of-flight module 1320 and a localization module 1330. Each module is communicatively coupled to each other to establish a positional location of an object.”).
Regarding claim 2, Rohr further discloses
The method of claim 1, wherein the vehicle is a heavy-duty vehicle (see paragraph 0070, “For example, an object can be a robot, vehicle, aircraft, ship, bicycle, or other device or entity that moves in relation to another.”).
Regarding claim 3, Rohr further discloses
The method of claim 1, wherein received sensor data from a sensor of the plurality of sensors is weighted based on the position of the sensor in the common coordinate system (see paragraph 0087, “One embodiment of the present invention uses each positional state with a fitness score as a particle and thereafter applies particle filters. An algorithm places a score as to the fitness of each sensor's ability to estimate the position of the object by way of a particle. A state of a particle represents the position of (x, y, z, roll, pitch, yaw) of the object…With the information about new states, each particle is assigned weights by a specific cost criterion for that sensor and only the fittest particles survive an iteration. This approach allows multimodal state estimation where (as an example) 80% of amassed particles will contribute to the most certain position of the object while others can be at a different position. Hence, the density of these particles governs the certainty of the state the robot is in using a particle filter approach.”).
Regarding claim 4, Rohr further discloses
The method of claim 1, wherein at least one of the sensors providing sensor data comprises a global positioning system, GPS, sensor and the received sensor data includes global positioning system data from the GPS sensor (see paragraph 0020, “Other features of an Adaptive Positioning System (APS) of the present invention synthesize one or more unimodal positioning systems by utilizing a variety of different, complementary methods and sensor types to estimate the multimodal position of the object as well as the health/performance of the various sensors providing that position. Examples of different sensor types include: 1) GPS/GNSS…”).
Regarding claim 5, Rohr further discloses
The method of claim 1, wherein at least one of the sensors is a short-range positioning sensor, wherein the received sensor data comprises sensor data indicative of a relative position of the vehicle to the sensor (see paragraph 0020, “Examples of different sensor types include: 1) GPS/GNSS; 2) dead-reckoning systems using distance-time-direction calculations from some combination of wheel encoders, inertial sensors, compasses, tilt sensors and similar dead-reckoning components; 3) optical, feature-based positioning using some combination of lasers, cameras, stereo-vision systems, multi-camera systems and multispectral/hyperspectral or IR/thermal imaging systems; 4) range-based positioning using some combination of peer-to-peer (P2P), active-ranging systems, such as P2P ultra-wideband radios, P2P ultra-low-power Bluetooth, P2P acoustic ranging, and various other P2P ranging schemes and sensors. 5) Radar-based positioning based on some combination of sensors such as Ultra-Wideband (UWB) radar or other forms of radar, and various other sensor types commonly used in position determination.”).
Regarding claim 6, Rohr further discloses
The method of claim 1, wherein the received sensor data which is transformed to the common coordinate system includes one or more of: roll data, pitch data, and yaw data and wherein localizing the vehicle determines a vehicle pose in the area (see paragraph 0113, “Sensor failure is identified 850 by comparing the fitness of particles remaining to those particles that have been removed. A particle is evaluated based a defined cost function that evaluates the fitness of a particle. This cost incurs from the deviation of the particle state from the “most fit” state of a particle in the current pool. These states are the pose of the vehicle (x, y, z, roll, pitch, yaw) and can also include sensor failure modes. For example, GPS will have a binary failure mode, fit or multi-path, thus if the unfit particle has predicted a state with GPD in multi-path while particles from most densely populated region (fit particles) do not match then that particle will have a lower probability of existence after this iteration.”).
Regarding claim 7, Rohr further discloses
The method of claim 1, wherein a plurality of transforms are performed on received sensor data, each transform being only applied for sensor data received from a particular sensor area of coverage (see paragraph 0128, “When the object is within a predetermined distance of a transmitter's predefined location, based on an estimation of the object's position from dead reckoning, the transmitter initiates a time-of-flight conversation with that transmitter. The conversation is time stamped and paired with the dead reckoning local frame position. When a threshold number of conversations have taken place a non-linear least-squares optimization is run to update the transformation of the robot's local frame of reference to a global frame of reference. Each time-of-flight conversation defines a ranging sphere (shell) where the object at the corresponding time stamp could have been. A distance is defined from the local frame position estimate to the ranging sphere is defined (distance from position to beacon minus the range estimate) for each time stamped range-position pair. A non-linear, “least-squares” optimization is then run to find the dead reckoning path through the global frame with the least distance-based cost.”).
Regarding claim 9, Rohr further discloses
The method of claim 1, wherein at least one sub-area has no line of sight to a global positioning system satellite (see paragraph 0067, “One aspect of the present invention is to enhance and optimize the ability to estimate an object's position by identifying weaknesses or failures of individual sensors and sensor systems while leveraging the position-determining capabilities of other sensor systems. For example, the limitations of GPS-derived positioning in urban areas or outdoor areas with similar line-of-sight limitations (e.g., mountainous areas, canyons, etc.) can be offset by range information from other sensors (e.g., video, radar, sonar, laser data, etc.).”, further see paragraph 0091, “For example, if it determined by the multimodal estimator 330 that a GPS sensor is experiencing degraded accuracy due to multipath or interference the multimodal estimator 330 can convey such information to the unimodal estimator that RF reception generally appears degraded. Accordingly, the unimodal estimator may devalue or degrade the positional estimation of UWB or other sensors that similar in operation to the GPS sensor. This data is then used to update a sensor's probability of failure or degraded operation (defined a sensor “heatmap”) from prior information for future position evaluations. Thus, each particle can use noisy sensor data to estimate its location using history from the sensor heatmap.”).
Regarding claim 10, Rohr further discloses
An apparatus configured to localize a vehicle in an area of vehicle operation (see paragraph 0061, “The present invention relates in general to positional estimation and more particular to estimation of the position of an object. Frequently the object is a device, robot, or mobile device.”, further see paragraph 0070, “While the present invention is described by way of examples in which objects may be represented by vehicles or cellular telephones, an object is to be interpreted as an arbitrary entity that can implement the inventive concepts presented herein. For example, an object can be a robot, vehicle, aircraft, ship, bicycle, or other device or entity that moves in relation to another.”), the apparatus comprising:
memory (see paragraph 0105, “These computer program instructions may also be stored in a non-transitory computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the flowchart block or blocks.”);
one or more processors or processing circuitry (see paragraph 0105, “These computer program instructions may also be stored in a non-transitory computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the flowchart block or blocks.”);
and computer program code, which, when loaded from the memory and executed by the one or more processors or processing circuitry, cause the apparatus to perform a method according to claim 1 (see paragraph 0105, “These computer program instructions may also be stored in a non-transitory computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed in the computer or on the other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.”).
Regarding claim 11, the same cited section and rationale as claim 2 is applied.
Regarding claim 12, Rohr further discloses
The apparatus of claim 10, wherein received sensor data from a sensor of the plurality of sensors is binary weighted based on the vehicle location and the coverage area of the sensor in the common coordinate system so that sensor data from a sensor is used when the vehicle is inside the defined coverage area for that sensor in the map (see paragraph 0113, “Sensor failure is identified 850 by comparing the fitness of particles remaining to those particles that have been removed. A particle is evaluated based a defined cost function that evaluates the fitness of a particle. This cost incurs from the deviation of the particle state from the “most fit” state of a particle in the current pool. These states are the pose of the vehicle (x, y, z, roll, pitch, yaw) and can also include sensor failure modes. For example, GPS will have a binary failure mode, fit or multi-path, thus if the unfit particle has predicted a state with GPD in multi-path while particles from most densely populated region (fit particles) do not match then that particle will have a lower probability of existence after this iteration.”).
Regarding claim 13, the same cited section and rationale as claims 4 and 5 is applied.
Regarding claim 14, the same cited section and rationale as claim 6 is applied.
Regarding claim 15, the same cited section and rationale as claim 6 is applied.
Regarding claim 16, the same cited section and rationale as claim 10 is applied.
Claim Rejections - 35 USC § 103
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.
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) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rohr et al. (US 20160349362 A1).
Regarding claim 8, Rohr discloses
The method of claim 1, wherein the method is performed at the vehicle, see paragraph 0070, “While the present invention is described by way of examples in which objects may be represented by vehicles or cellular telephones, an object is to be interpreted as an arbitrary entity that can implement the inventive concepts presented herein. For example, an object can be a robot, vehicle, aircraft, ship, bicycle, or other device or entity that moves in relation to another.”).
It would have been obvious to someone with ordinary skill in the art prior to the
effective filing date of the claimed invention to combine the features as disclosed by Rohr to yield the invention of claim 8 above. Rohr is considered analogous art to the claimed invention as it discloses vehicle localization using a plurality of different types of sensors. Rohr discloses in paragraph 0070, “object can be a robot, vehicle, aircraft, ship, bicycle, or other device or entity that moves in relation to another.”. Although Rohr does not specifically disclose that the vehicle is an autonomous vehicle, Rohr discloses in paragraph 0005, “Other applications that may be provided using positioning services include asset tracking services, asset monitoring and recovery services, fleet and resource management, personal-positioning services, autonomous vehicle guidance, conflict avoidance, and so on.”. Therefore, combination of these features as disclosed by Rohr would make it obvious with a reasonable expectation of success to utilize a the disclosed localization system within an autonomous vehicle for efficient vehicle guidance.
Regarding claim 17, the same cited section and rationale as claim 8 is applied.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
MOSKOWITZ et al. (US 20210063162 A1) discloses having a vehicle determine its position in two ways: one from onboard sensing relative to a road navigation model, and one from satellite signals. By comparing the two, the vehicle can estimate GNSS error and transmit that error to a server or other vehicles. The server can aggregate error reports from multiple vehicles and distribute correction information to improve positioning for other users [0005]-[0010], [0353]-[0361], [0384]-[0405].
Kumar et al. (US 20200233094 A1) discloses an invention that combines satellite signals with a stored 3D building model to detect NLOS conditions, estimate a modeled position from reflected signals, and then refine that position using vehicle motion and carrier-phase information. The controller starts with an approximate position, evaluates whether a signal is NLOS, and uses the building model to constrain candidate locations. It then tightens the estimate using heading, speed, and carrier-phase data before producing a final position [0002], [0051]-[0064].
Alvarez et al. (US 20200226790 A1) discloses and invention that uses a calibration target observed over time during relative motion to estimate sensor alignment from multiple views, then issues a calibration command based on that estimate. The system may also use a machine-readable symbol on the target to identify the pattern, confirm authenticity, and provide size, pose, or roadway-direction information. By combining repeated observations with target metadata, the system can reconstruct pose and derive calibration adjustments without stopping the vehicle or infrastructure. A separate embodiment compares new sensor data against stored reference data to detect abnormal sensors and trigger a mode change or calibration instruction..
Guo et al. (US 20190220678 A1) discloses combining images from multiple vehicles in the same road region, identifies objects related to a traffic event, and matches those objects across different viewpoints. It uses the matched image features to compute 3D sensor coordinates, then converts those coordinates into geolocation coordinates using each vehicle’s GPS position and camera parameters. Finally, it uses the object geolocations to draw and update the traffic situation’s coverage area on a map [0004]-[0009], [0060]-[0081].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAZRA N. WAHEED whose telephone number is (571)272-6713. The examiner can normally be reached M-F (8 AM - 4:30 PM).
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, Vladimir Magloire can be reached at (571)270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAZRA NUR WAHEED/Primary Examiner, Art Unit 3648
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