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 .
Claim Objections
Claims 1, 9, and 15 are objected to because of the following informalities: “at least captured image” should recite “at least one captured image” for proper grammar. Appropriate correction is required.
Claims 2, 10, and 16 are objected to because of the following informalities: “the captured image” should recite “the at least one captured image” or “a first image of the at least one captured image” for proper antecedent basis. 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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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) 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), 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) are: “logic device” in claims 1, 5, 9, 13, 15, and 19.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f), they 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 these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 9-12, and 15-18 are rejected are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without integration into a practical application or recitation of significantly more.
In the analysis below, the method of independent claim 15 is considered representative of independent claims 1 and 9 since all of the independent claims recite identical steps despite being directed to different statutory matter (independent claims 1 and 9 have same statutory category of apparatus; however, they only differ slightly with claim 1 reciting a cockpit assembly for a micromobility transit vehicle and claim 9 reciting a micromobility transit vehicle itself). Furthermore, independent claims 1, 9, and 15 are directed to one of the four statutory categories of eligible subject matter (apparatuses for independent claim 1 and 9 and a process for independent claim 15); thus, the claims pass Step 1 of the Subject Matter Eligibility Test (See flowchart in MPEP 2106).
Step 2A, prong 1 analysis:
Independent claim 15 is directed to attaching a cockpit housing to the micromobility transit vehicle, placing a camera in the cockpit housing, the camera is configured to capture an image in front of the micromobility transit vehicle, the cockpit housing is coupled to a handlebar of the micromobility transit vehicle, determining a condition of the camera for the micromobility transit vehicle based on at least captured image, and providing a notification to perform an action with respect to the camera based on the condition of the camera.
Each of the above steps can be performed mentally. In particular, a human who wants to rent and drive an electric scooter (e-scooter) in a busy city that needs to be able to look at map directions on their smartphone while operating the e-scooter, acquires known phone holders (excellent options range from universal clamp-style mounts to dedicated locking systems) for an electric scooter that mount to the handlebar to handle high vibrations to protect the smartphone's camera which is used to take images in front of them as they drive without holding the smartphone; they take images with the smartphone attached to the handlebar facing forward and based on the images taken, they determine that their smartphone camera has blur, dust on the lenses, underexposure/overexposure, discoloration, etc.; they notify their smartphone provider or cellphone repair shop following use of the e-scooter that their smartphone camera is broken and needs servicing/maintenance; therefore, this process can all be done mentally.
As such, the description in independent claims 1, 9, and 15 is an abstract idea – namely, a mental process. Accordingly, the analysis under prong one of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Additional elements:
The additional element recited in independent claims 1, 9, and 15 is a logic device.
Step 2A, prong 2 analysis:
The above-identified additional elements do not integrate the judicial exception into a practical application. The logic device in a camera is akin to a generic computer doing image processing accomplished by human vision.
Each of the other additional elements (logic device) amounts to merely using different devices as tools to perform the claimed mental process. Implementing an abstract idea on a computer or using known generic devices does not integrate a judicial exception into a practical application (See MPEP 2106.05(f)).
Moreover, the additional elements of the claims do not recite an improvement in the functioning of a computer or other technology or technical field, the claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the claims do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)). Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Step 2B:
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Each of the other additional elements (logic device) are generic computer features which perform generic computer functions that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
For all of the foregoing reasons, independent claims 1, 9, and 15 do not recite eligible subject matter under 35 USC 101.
Claims 2, 10, and 16 recites comparing an image quality of the captured image to a reference image quality; determining whether the image quality of the captured image has degraded to a threshold level lower than the reference image quality; and in response to determining that the image quality of the captured image has degraded to the threshold level lower than the reference image quality, determining that the camera for the micromobility transit vehicle needs servicing or maintenance. The human riding the e-scooter compares images taken by their smartphone camera attached to the handlebar to previous images taken their smartphone camera and sees if the issues while driving have affected the camera, such as debris hitting the lens of the camera; they determine that something has broken their camera while driving and then finds a repair shop after the e-scooter ride to fix the smartphone camera; therefore, this process can all be done mentally.
Claim 3, 11, and 17 recite wherein the notification to perform the action is provided to a management system managing a fleet of micromobility transit vehicles including the micromobility transit vehicle. After the e-scooter ride, the person contacts the owner of the rented e-scooter (fleet company owner such as Lime) to tell them that their smartphone camera was damaged during the ride when attached to the handlebar of their e-scooter; therefore, this process can all be done mentally.
Claims 4, 12, and 18 recite wherein the notification to perform the action comprises an indication that the camera for the micromobility transit vehicle needs servicing or maintenance. The human riding the e-scooter determines that something has broken their camera while driving and then finds a repair shop after the e-scooter ride to fix the smartphone camera; therefore, this process can all be done mentally.
Therefore, dependent claims 2-4, 10-12, and 16-18 recite the same abstract idea of a mental process which can be performed in the mind with the aid of pen and paper, and are therefore also rejected under 35 U.S.C. 101.
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.
Claims 1-2, 4-5, 9-10, 12-13, 15-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No.: 2020/0124430 (Bradlow et al.) (hereinafter Bradlow), in view of U.S. Patent Application Publication No.: 2022/0245792 (Gao et al.) (hereinafter Gao).
Regarding claim 1, Bradlow teaches a cockpit assembly for a micromobility transit vehicle, the cockpit assembly comprising: (Bradlow, para. [0050]; para. [0092]; FIG. 1A; FIG. 3: “FIG. 1A depicts a PMV embodied as a battery-powered, electric scooter 100. A forehead 102 of the scooter 100 includes mechanisms to brake or throttle the scooter 100. The forehead 102 includes a dashboard for a user to interface with the electronic components of the scooter 100. The scooter 100 includes a light strip 104 to notify users information about the scooter 100. The user can be a rider or individual that is proximate to the scooter 100 such as a nearby pedestrian. The scooter 100 has a front light 106 configured to illuminate the forward path of a corridor on which the scooter 100 is traveling. The scooter 100 includes a central console 108 that houses hardware and/or software that controls the operations of the scooter 100.”; “FIG. 3 depicts a schematic diagram of a PMV 300 embodied as an electric scooter traveling on a travel surface 302. The PMV 300 has a body 304 that is coupled to one or more wheels 306-1 and 306-2. The PMV 300 has a steering system including a steering column 308 and a handle or wheel 310. The body 304 may include a shell or housing 312 that partially or completely surrounds an electrical system … The electrical distribution system 320 may also connect to other electrical components such as one or more sensor(s) 322, microprocessors 324, and wireless communication devices 326.”;
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a camera configured to capture an image in front of the micromobility transit vehicle (Bradlow, para. [0093]; para. [0107]; para. [0161]; para. [0164]; FIG. 11; FIG. 12A-12B; FIG. 17A-17B: “The sensor(s) 322 may perform a wide range of functions including, but not limited to … cameras,”; “In some embodiments, the sensors include one or more cameras disposed in a housing of a PMV. The cameras may collectively form part of a vision sensing system. The cameras may be capable of capturing image data for environmental sensing”; “FIG. 11 depicts an image of a sidewalk as captured by an onboard camera sensor disposed on a PMV”; “In some embodiments, certain visual information may trigger the collection of data to determine if the travel surface is a sidewalk. FIG. 12A depicts an image captured by a camera mounted to an electric scooter 1210 as the scooter enters a brick crosswalk 1220 … FIG. 12B depicts an image captured by the camera mounted to the electric scooter 1210 before the scooter travels across an accessibility pad 1240 at a sidewalk ramp 1230.”;
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as shown in FIG. 11-12B of Bradlow, the camera is mounted to the electric scooter (e-scooter) and takes images of the ground that is front of the e-scooter; although the camera is pointed downwards angle toward the ground, the space shown in the images is still in front of the e-scooter; FIG. 17A-17B show additional front-facing images of the camera attached the e-scooter); and
a cockpit housing coupled to a handlebar of the micromobility transit vehicle, wherein at least a portion of the camera is disposed within the cockpit housing (Bradlow, para. [0096]; para. [0035]; para. [0160]; FIG. 18; FIG. 10A-10B; para. [0094]: “In some embodiments, the sensor(s) 322 are secured to the housing 312 of a PMV 300. The sensor(s) 322 can be secured by a mechanical device such as a clamp or bracket. A sensor may be directly secured to a body 304 component such as a frame using adhesives (e.g. double-sided adhesive 1800 on a rider-facing structural interface as shown in FIG. 18), screws, bolts, or other attachments that hold the sensor position. In some instances, a sensor is partially exposed (e.g. a camera with a view window on the housing 312 to the outside environment). In some embodiments, the camera is fully sheltered or hermetically sealed in the housing 312 to prevent damage from exposure to moisture, dust, oils, and other environmental contaminants. A sensor may be secured along a non-vertical or non-horizontal axis.”; “FIG. 18 depicts a user-facing dashboard with an adhesive used to secure a mobile device on an electric scooter housing”; “FIG. 10A depicts a mobile device 1020 with a built-in camera secured inside a housing 1010 of an electric scooter. FIG. 10B depicts an aperture 1030 of the housing 1010 that permits the built-in camera of the mobile device 1020 to capture visual data. The mobile device 1020 is secured at a position such that its camera is angled downward toward the travel surface of the electric scooter. The downward angle of the camera is determined to prevent the incidental collection of identifying information from pedestrians that might be in the vicinity of the electric scooter during operation.”; “
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“In some embodiments, a sensor is secured or coupled to a location of particular advantage on the PMV. For example, a sensor may be secured or coupled to a body segment such as a frame, steering column, or handle bar.”; FIG. 10 shows the cockpit housing where the mobile device is inserted to use its camera; FIG. 18 shows a top-down view from a person’s POV looking down riding the e-scooter where the cockpit housing is attached to the handlebar);
wherein the camera comprises a logic device that is configured to: (Bradlow, para. [0092]: “FIG. 3 depicts a schematic diagram of a PMV 300 embodied as an electric scooter traveling on a travel surface 302. The PMV 300 has a body 304 that is coupled to one or more wheels 306-1 and 306-2. The PMV 300 has a steering system including a steering column 308 and a handle or wheel 310 … The electrical motor 314 may be connected with wiring 318 to an electrical distribution system 320. The electrical distribution system 320 may also connect to other electrical components such as one or more sensor(s) 322, microprocessors 324, and wireless communication devices 326.”)
determine a condition of the micromobility transit vehicle based on at least captured image (Bradlow, para. [0113]-[0014]; FIG. 4: “FIGS. 4 and 5 are flow diagrams that illustrate methods for generating a probability that a PMV is traveling on a given surface type. For example, a PMV equipped with a suitable sensor system can generate sensor data used to generate a probably for location mapping of types of travel pathways. In FIG. 4, during a PMV trip 400, the PMV can simultaneously collect location data 402 and sensor data 404 that is pertinent to characterizing a travel surface. The location data 402 may be obtained from a GPS system or other similar system. The sensor data 404 may be collected from output of accelerometers, cameras, gyroscopes, or other devices. During the PMV trip 400 or after the trip, the location data 402 and the sensor data 404 are exported to a map algorithm 406 that may include a data analysis algorithm 408. The data analysis algorithm 408 may include a neural network or other training algorithm. The data analysis algorithm 408 may utilize the sensor data 404 to determine the travel surface type as a function of location. Travel surface type information from the data analysis algorithm 408 and the location data 402 may be exported into a mapping algorithm 410 that creates a spatial layout of travel corridors and their surface compositions. The resulting map can be updated at routine intervals or as changes are determined by the data analysis algorithm 408.”;
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provide a notification to perform an action based on the condition of the micromobility transit vehicle (Bradlow, para. [0120]-[0122]; FIG. 6: “The map may be updated via wireless transmission to the PMV. The updated map may then be utilized to control or influence PMV usage during a PMV trip 606. For example, as a rider travels using a PMV, location data 606 may be plotted on the uploaded map. The location data 608 may be acquired from any method available, such as GPS. The location data 608 and the map may be fed into an analysis algorithm 610 that determines corrections, modifications, or actions are necessary based upon the travel corridor being utilized by the PMV … The analysis algorithm may make a determination about the travel corridor or travel surface type during PMV usage. The analysis algorithm 610 may output a command or action 612 that controls, influences, alters, maintains, restricts, or stops PMV travel based upon a determination of the PMV travel surface type. The command or action 612 for a PMV may include bypassing user control to limit the travel speed of the PMV, stop the PMV, issue an alert that suggests a route change, activate a warning alert signal, activate a warning sound, signal, or other alert to notify pedestrians of the PMV, displaying a message on a screen or interface (e.g. sending a message by SMS or other means of push notification), or issuing a warning alert or fine for violating an ordinance or a term of a user agreement … A command or action may occur if the analysis algorithm determines that a PMV has been traveling on a particular surface for a particular length of time or a particular number of times. For example, a warning message may issue if the PMV has been determined to be traveling on a sidewalk for at least 15 seconds. A command or action may occur if the analysis algorithm predicts that a PMV has been traveling on a particular surface at a particular confidence level. For example, a display device of the PMV may issue a warning message to move off a sidewalk if the analysis algorithm is 75% confident.”;
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Bradlow fails to teach
determine a condition of the camera for the vehicle based on at least captured image; and provide a notification to perform an action with respect to the camera based on the condition of the camera.
Gao teaches
determine a condition of the camera for the vehicle based on at least captured image (Gao, para. [0095]; para. [105]; para. [0067]: “The determination module 420 may determine a detection result of the image using the image quality detection model. For example, the determination module 420 may determine whether a specific image includes a quality anomaly using the image quality detection model. Exemplary quality anomalies of an image may include a blocking anomaly, a blur anomaly, an angle anomaly, a color cast anomaly, a fill light anomaly, etc … The blur anomaly of the image may refer to that an ambiguity of the image exceeds a first threshold or a definition of the image is less than a second threshold. The color cast anomaly may refer to that a difference between the color of the image and the actual color of subjects the image recording exceeds a threshold. The fill light anomaly of an image may refer to that the brightness of the image is less than a threshold.”; “The determination module 412 may determine data related to the training and/or construction of the image quality detection model. In some embodiments, the determination module 412 may determine one or more specific cameras whose lamps are in breakdown based on the plurality of candidate images. At least a portion of the plurality of training samples may be obtained from the one or more specific cameras. In some embodiments, the determination module 412 may determine a target threshold of the image quality detection model. For example, the determination module 412 may determine the target threshold based on a plurality of samples, each of at least a portion of the plurality of samples having a reference label indicating that each of the at least a portion of the plurality of samples having a color cast anomaly. In some embodiments, the determination module 412 may determine one or more target template images. The one or more target template images may present the one or more spots with different characteristics in different target template images. As used herein, different characteristics (e.g., size, shape, etc.) of two spots in two different target template images may refer to that a similarity degree between the characteristics of the two spots is less than a similarity threshold (e.g., 0.9, 0.8, etc.).”; “For example, the processing device 112 may obtain image data from a camera installed in the vehicle 150”); and
provide a notification to perform an action with respect to the camera based on the condition of the camera (Gao, para. [0120]-[0123]: “In 508, in response to a determination that the detection result includes a quality anomaly of the image, the processing device 112 (e.g., the generation module 430) may generate a strategy in response to the quality anomaly. The strategy may include generating an alert for reminding a related personnel (e.g., a driver, a passenger, an engineer, a repair personnel, etc.) associated with the camera that the image is anomalous, informing a related personnel (e.g., a driver, a passenger, an engineer, a repair personnel, etc.) associated with the camera to examine and/or repair the camera, generating a suggestion for removing the quality anomaly in the image, etc. In some embodiments, the processing device 112 may generate a signal including the strategy and detection result and transmit the signal to a terminal (e.g., a mobile terminal) associated with the related personnel. The signal may be also configured to direct the terminal to display the strategy and/or the detection result to the related personnel. The processing device 112 may determine the strategy based on the quality anomaly. For example, in response to a determination that the detection result includes an angle anomaly in the image collected by the camera, the processing device 112 may generate the strategy including a suggestion for removing the angle anomaly, which caused the quality anomaly of the image. Further, if the processing device 112 determines that the detection result includes the angle anomaly caused by the shooting angle of the camera installed inside a vehicle, the processing device 112 may suggest the driver or other related personnel to adjust the orientation of the lens of the camera. If the processing device 112 determines that the detection result includes the blur anomaly in the image collected by a camera installed inside a vehicle, the processing device 112 may suggest the driver or other related personnel to clean the lens of the camera and/or the windshield of the vehicle, or check whether the protective film of the lens of the camera has been removed. If the processing device 112 determines that the detection result includes the blur anomaly in the image collected by a camera installed inside a vehicle, the processing device 112 may suggest the driver or other related personnel to check whether the lens of the camera is covered by a filter film. If the detection result includes a fill light anomaly, the processing device 112 may suggest a maintenance personnel and/or a transportation department to schedule maintenance for the camera. In some embodiments, during an on-demand service, in response to a determination that a blocking anomaly, a color cast, or a blur anomaly is detected, the processing device 112 may transmit an alert signal to a third party requesting the third party to intervene.”).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the logic device configured to determine a condition of the micromobility transit vehicle based on at least captured image, and provide a notification to perform an action based on the condition of the micromobility transit vehicle, as taught by Bradlow, to be configured to determine a condition of the camera for the vehicle based on at least captured image, and provide a notification to perform an action with respect to the camera based on the condition of the camera, as taught by Gao.
The suggestion/motivation for doing so would have been “cameras are widely mounted on a vehicle for video surveillance … various anomalies may happen to the camera, causing the camera not to be capable of capturing a qualified image, thus providing useful information; therefore, it is desirable to provide systems and methods for efficiently detecting image quality and/or the state of the camera” (Gao, para. [0003]).
Bradlow, in view of Gao, teaches determine a condition of the camera for the micromobility transit vehicle based on at least captured image; and provide a notification to perform an action with respect to the camera based on the condition of the camera (Bradlow, para. [0113]-[0014]; FIG. 4; para. [0120]-[0122]; FIG. 6; Gao, para. [0095]; para. [105]; para. [0067]; para. [0120]-[0123]; Bradlow teaches taking images with a front-facing camera attached to a housing connected to the handlebar and analyzing those images depicting the environment in front of the e-scooter to determine the surface type (ex: street, sidewalk, bike lane, trail, etc.) using a trained machine learning model, and then prompting a response via a display or audio to the driver on the e-scooter to move to a more appropriate surface for the e-scooter (micromobility transit vehicle); Gao teaches using cameras in cars to take images and using an image quality model trained with different image quality thresholds to make determinations of the status of the camera and whether maintenance, servicing, cleaning, etc. need to be done to improve the image quality of the camera taking images and displaying the status and feedback to the driver of the vehicle; Bradlow, modified by Gao, teaches taking the concept of checking image quality of camera images and providing feedback to the user of a regular vehicles (cars, etc.) (Gao), and applies it to micromobility transit vehicles such as e-scooters that presently have systems of taking images with a camera and providing feedback to the user based off those images (Bradlow)).
Therefore, it would have been obvious to combine Bradlow, with Gao, to obtain the invention as specified in claim 1.
Regarding claim 2, Bradlow, in view of Gao teaches the cockpit assembly of Claim 1, wherein determining the condition of the camera for the micromobility transit vehicle comprises: comparing an image quality of the captured image to a reference image quality; determining whether the image quality of the captured image has degraded to a threshold level lower than the reference image quality (Gao, para. [0095]; para. [105]; para. [0067]; see rejection of claim 1 above; Gao teaches image quality detection model that compares the qualities in the camera image with reference image qualities such as blocking anomaly, blur anomaly, angle anomaly, color cast anomaly, fill light anomaly, etc. and the model has set reference values/threshold values for all the different anomalies that are compared to the camera image quality to determine if the image is degraded enough relative to threshold to determine the camera image as low quality that needs correcting; the image quality detection model is trained on candidate images/samples to determine a distinct thresholds of image quality) and
in response to determining that the image quality of the captured image has degraded to the threshold level lower than the reference image quality, determining that the camera for the micromobility transit vehicle needs servicing or maintenance (Gao, para. [0120]-[0123]; see rejection of claim 1 above; when the trained image quality detection model determines that the camera image has lower quality compared to the model’s threshold for different types of image quality characteristics (ex: image qualities such as blocking anomaly, blur anomaly, angle anomaly, color cast anomaly, fill light anomaly, etc.), then a display alert is generated to warn the user of the micromobility vehicle to correct the problem with the camera; “If the detection result includes a fill light anomaly, the processing device 112 may suggest a maintenance personnel and/or a transportation department to schedule maintenance for the camera.”).
Regarding claim 4, Bradlow, in view of Gao, teaches the cockpit assembly of Claim 1, wherein the notification to perform the action comprises an indication that the camera for the micromobility transit vehicle needs servicing or maintenance (Gao, para. [0120]-[0123]; see rejection of claim 1 above; when the trained image quality detection model determines that the camera image has lower quality compared to the model’s threshold for different types of image quality characteristics (ex: image qualities such as blocking anomaly, blur anomaly, angle anomaly, color cast anomaly, fill light anomaly, etc.), then a display alert is generated to warn the user of the micromobility vehicle to correct the problem with the camera; “If the detection result includes a fill light anomaly, the processing device 112 may suggest a maintenance personnel and/or a transportation department to schedule maintenance for the camera.”).
Regarding claim 5, Bradlow, in view of Gao, teaches the cockpit assembly of Claim 1, wherein: the cockpit housing comprises a stress sensor coupled to the camera and configured to measure vibration experienced by the camera; and the logic device is further configured to track a number of stress cycles for the camera based on the vibration experienced by the camera and provide the notification to perform the action further based on the number of stress cycles (Bradlow, para. [0142]-[0144]; FIG. 7; TABLE 1: “FIG. 7 depicts an example of vibration sensor data collected when a PMV is traveling over two adjacent areas with different surface roughness characteristics. As illustrated, the PMV can travel on a bike lane 702 travel corridor that has an asphalt surface or travel on an adjacent sidewalk 704 travel corridor that is composed of sections of concrete blocks. The bike lane 702 has a substantially smooth asphalt surface with some defects (e.g., cracks) that can transverse or substantially aligned with an axis z-z in the direction in which the PMV travels. The sidewalk 704 has proximal and distal ramps relative to axis z′-z′ and regular seams between concrete blocks with some defects. The graph 706 shows measured values output by PMV-mounted sensor as the PMV travels along the axis z-z in bike lane 702. The sensor may include an accelerometer, gyroscope, or other sensor. The graph 708 shows measured values output by the PMV-mounted sensor as the PMV travels along axis z′-z′ on sidewalk 704. In this example, the graph 706 shows more random roughness variations due to the nature of the asphalt roughness, as compared to the more regular roughness due to the regular seams between the concrete blocks … the overlaying of GPS data with the data in graphs 706 and 708 may create two unique surface fingerprints for that street that allow distinction between PMV usage in the bike lane and PMV usage on the sidewalk. Sensor data may include surface roughness data, energy consumption data, visual data, or sound data. A surface type may be determined via an ML method using sensor data. Surface type determinations may be associated with a surface fingerprint to generate a map of travel corridors in a region, area, or zone where PMV travel may occur. A map of travel corridors that contains surface fingerprint data may be utilized to alter, influence, or control PMV usage within a particular travel corridor as described elsewhere in the specification. A travel map containing surface fingerprint information may include a digital map that further comprises various layers of digital information such as surface roughness information, hazard information, and traffic or pedestrian density information.”;
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“For example, the sensitivity of vibration data collection at a maximum PMV travel speed of 20 mph could vary depending on the sampling frequency. At sampling frequencies of 10, 50, or 100 Hz, data collection could occur approximately every 0.9, 0.18, or 0.09 meters (m), respectively. At lower data sampling frequencies, the roughness pattern associated with the sidewalk would be largely missed, while higher frequency data collection captures information on a length scale comparable to the roughness pattern.”;
as shown in FIG. 7, vibration data Is taken by a stress sensor (combination of accelerometer that measures linear acceleration and gyroscope that measures angular velocity, or rotation) which is located with the other sensors including the camera in the cockpit housing attached to the handlebar (see rejection of claim 1 above); therefore, the sensors are coupled together and the stress/vibrational sensors and tracks the stress cycles while collecting images of in front of the micromobility transit vehicle pointed at the ground to determine the surface type; as discussed in the rejection of claim 1 above, a machine learning model determines a surface type from the images and the vibrational data/stress cycles when the vehicle is operating on a surface that is inappropriate, such as a sidewalk which has a high number of stress cycles due to the sidewalk stamps/control joints/sidewalk fossils that cause the e-scooter to have a bumpy and dangerous ride; the camera won’t be able to take high quality images and therefore the process of Gao determining camera image quality will determine together with the vibration patterns that the camera may be damaged by severe bumps in ride of the e-scooter).
Regarding claim 9, Bradlow teaches a micromobility transit vehicle comprising: a handlebar (Bradlow, FIG. 1A below shows an e-scooter with a handlebar:
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With regards to the remaining limitations of independent claim 9, and dependent claims 10 and 12-13 they recite the functions of the first apparatuses (cockpit for a micromobility transit vehicle) of claims 1-2 and 4-5, respectively, as second apparatuses (a micromobility transit vehicle). Thus, the analyses in rejecting 1-2 and 4-5 are equally applicable to the remaining limitations of independent claim 9 and depend claims 10 and 12-13, respectively.
With regards to claims 15-16 and 18-19, they recite the functions of the apparatus of claims 1-2 and 4-5, respectively, as processes. Thus, the analyses in rejecting claims 1-2 and 4-5 are equally applicable to claims 15-16 and 18-19, respectively.
Claims 3, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bradlow, in view of Gao, and in further view of U.S. Patent Application Publication No.: 2019/0248439 (Wang).
Regarding claim 3, Bradlow, in view of Gao, teaches the cockpit assembly of Claim 1, wherein the notification to perform the action is provided to a management system (Bradlow, para. [0066]: “For example, a PMV is controlled or limited to prevent dangerous situations arising from PMV operations near vehicles or pedestrians. The disclosed embodiments are particularly useful for purposes, including, but not limited to: control PMV to reduce or inhibit usage in prohibited areas; alter user behavior to operate the PMV more safely in particular situations; alter user behavior to incentivize safe conduct and careful stewardship of PMV usage; track PMV usage in areas through heat mapping; provide PMV usage information to municipal or other local governments; facilitate enforcement of local ordinances related to PMV usage; notify local governments and agencies of infrastructure needs; notify local governments regarding hazards or obstacles in travel corridors”).
Bradlow, in view of Gao, fails to teach
a management system managing a fleet of micromobility transit vehicles including the micromobility transit vehicle.
Wang teaches
a management system managing a fleet of micromobility transit vehicles including the micromobility transit vehicle (Wang, para. [0133]: “The tracked values provide the monitoring of location, movements, status and behavior of one electric scooter 100 or a fleet of electric scooters 100. This is achieved through a combination of a GPS and GNSS receiver and the MCU 162 usually comprising a GSM GPRS modem or SMS (Short Messages Service) sender installed at the MCU 162 of the electric scooter 100, communicating with the rider. The data is turned into information by management reporting tools in conjunction with the LED tactile display 148 on computerized mapping software. A plurality of electric scooters 100 can be managed remotely by having the telematics unit 182 installed on more than one electric scooter 100. An API (Application Programming Interface), or program is developed, installed and operating at the MCU 162 to integrate the data from each electric scooter 100 into a remote database controlled by a remote server. The remote communication between the electric scooter 100 and the remote server provides a single point of control for the monitoring of the status of the electric scooters 100. The API also provides a remote enablement or disablement of the electric scooter 100. In other words, the API can lock or unlock the electric scooter 100.”).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the management system, as taught by Bradlow, in view of Gao, to be managing a fleet of micromobility transit vehicles including the micromobility transit vehicle, as taught by Wang.
The suggestion/motivation for doing so would have been that “the motorized scooter periodically communicates with a base station to report its location by using its identification; an operator of the motorized scooter is kept informed about motorized scooter continuously and automatically” (Wang, para. [0016]); this provides the benefit of being able to track all different e-scooters from a single remote location.
Bradlow, in view of Gao, and in view of Wang, teaches wherein the notification to perform the action is provided to a management system managing a fleet of micromobility transit vehicles including the micromobility transit vehicle (Bradlow, para. [0066]; Wang, para. [0133]; Bradlow, in view of Gao, teaches notifying local/municipal governments with the e-scooter data including information on if the camera installed on the handlebar cockpit housing of the e-scooter (micromobility transit vehicle) needs servicing and/or maintenance; Wang teaches communication with a central fleet control of all e-scooters in a fleet so the company owning the e-scooters can be up-to-date on how the e-scooters are used; when Wang, in view of Gao, is modified by Wang, if the images taken by the camera on the e-scooter are not high enough quality, then the notification is not just sent to the user of the e-scooter, but also to the remote central fleet control location; perhaps, in response to notification of the issue to the fleet command, the e-scooter is deactivated until maintenance/servicing of the e-scooter’s front-facing camera is conducted).
Therefore, it would have been obvious to combine Bradlow and Gao, with Wang, to obtain the invention as specified in claim 3.
With regards to claim 11, it recites the functions of the first apparatus (cockpit for a micromobility transit vehicle) of claim 3, as a second apparatus (a micromobility transit vehicle). Thus, the analysis in rejecting 3 is equally applicable to claim 11.
With regards to claim 17, it recites the functions of the apparatus of claim 3 as a process. Thus, the analyses in rejecting claim 3 is equally applicable to claim 17.
Claims 6-8, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bradlow, in view of Gao, and in further view of U.S. Patent Application Publication No.: 2021/0155153 (Wendt).
Regarding claim 6, Bradlow, in view of Gao, teaches the cockpit assembly of Claim 1, wherein: the cockpit housing comprises a headlight assembly that is configured to illuminate a field of view in front of the micromobility transit vehicle (Bradlow, para. [0050]: “The scooter 100 has a front light 106 configured to illuminate the forward path of a corridor on which the scooter 100 is traveling”; see front light 106 in FIG. 1A above in the rejection of claim 1 above; it is connected to the long vertical portion of the scooter that is coupled to the cockpit housing attached to the handlebar; Examiner uses broadest reasonable interpretation that the “cockpit housing” includes the headlight as well shown in FIG. 1A;
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Bradlow, in view of Gao, fails to teach
the headlight assembly is synced with an operation of the camera to dynamically adjust an illumination and provide the camera with an optimal amount of light.
Wendt teaches
the headlight assembly is synced with an operation of the camera to dynamically adjust an illumination and provide the camera with an optimal amount of light (Wendt, para. [0017]; para. [0026]; FIG. 1: “The lighting system may be used in daylight as well as darkness and may employ various combinations of intensities of light and light effects to call visual attention to the scooter and rider. In addition to being manually actuated, the lighting system of the present disclosure may be configured to automatically illuminate or change light effects in response to road or environmental conditions. The device can include a variety of information gathering aspects such as sensors that detect lighting condition, sensors that detect certain sounds such as emergency sounds, cameras or other optical devices that can detect markings on roadways or changes in roadways, and receivers that can receive information from roadway infrastructure or other vehicles. The information that may be received by the device can include existing or future roadway infrastructure such as autonomous traffic infrastructure or adaptive traffic control systems which transmit or broadcast traffic information to vehicles and vehicle controllers. The information may include speed limits, traffic control signals, hazard warnings, etc.”; “The scooter 10 may also include a head light 26 and/or a tail light 28 to further increase the visibility of the scooter during both daylight and nighttime hours.”;
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the headlight assembly, as taught by Bradlow, in view of Gao, to be synced with an operation of the camera to dynamically adjust an illumination and provide the camera with an optimal amount of light, as taught by Wendt.
The suggestion/motivation to do so would have been to “address[es] the need for improved visibility of the rider on the scooter by other vehicles and drivers in traffic to make its presence known by employing a multi-faceted lighting system; the lighting system may be used in daylight as well as darkness and may employ various combinations of intensities of light and light effects to call visual attention to the scooter and rider” (Wendt, para. [0017]).
Therefore, it would have been obvious to combine Bradlow and Gao, with Wendt, to obtain the invention as specified in claim 6.
Regarding claim 7, Bradlow, in view of Gao, and in view of Wendt, teaches the cockpit assembly of Claim 6, wherein the camera is configured to capture the image in the field of view illuminated by the headlight assembly (Bradlow, para. [0050]: “The scooter 100 has a front light 106 configured to illuminate the forward path of a corridor on which the scooter 100 is traveling.”; see FIG. 1A in the rejection of claim 6 above showing the cockpit housing and FIG. 11-12B in the rejection of claim 1 above showing the camera pointing forward and downward to take images of the path in front of the e-scooter).
Regarding claim 8, Bradlow, in view of Gao, and in view of Wendt, teaches the cockpit assembly of Claim 6, wherein the camera is aligned with the illumination provided by the headlight assembly such that the camera has sufficient light to capture images (Wendt, para. [0017]; para. [0026]; FIG. 1; see rejection of claim 6 above; the front headlamp automatically adjusts depending on the road conditions, how dark the environment is etc.; if the camera is taking images of the road to use autonomous image processing to recognize road signs or obstacles, the lamp will brighten automatically if the images taken by the camera do not recognize any road signs or obstacles because it is too dark).
With regards to claim 14, it recites the functions of the first apparatus (cockpit for a micromobility transit vehicle) of claim 6, as a second apparatus (a micromobility transit vehicle). Thus, the analysis in rejecting 6 is equally applicable to claim 14.
With regards to claim 20, it recites the functions of the apparatus of claim 6 as a process. Thus, the analyses in rejecting claim 6 is equally applicable to claim 20.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ADAM SHARIFF whose telephone number is 571-272-9741. The examiner can normally be reached M-F 8:30-5PM.
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/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
Prosecution Insights