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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR
1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 22, 2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 9 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Regarding claim 9, the original disclosure does not discuss determining the target vehicle and a status of the target vehicle according to the area object data and the corresponding object status data, by identifying the target vehicle from the area object data based on a vehicle identification obtained from the finding instruction and retrieving the status from the object status data. Therefore, this limitation is new matter.
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.
I. Claims 1-2, 4, 11-12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire US 20200132502 A1 in view of Gupta US 20230419200 A1 and Miron US 20230055538 A1.
Regarding Claim 1, Beaurepaire teaches a vehicle parking method applied to a computer device, comprising: obtaining sensing data in an area of a parking lot using sensing devices distributed in the area of the parking lot ([0032] As shown in FIG. 1, the system comprises a first vehicle 101 and at least one other vehicle 103a-103n (also collectively referred to as vehicles 103) that the vehicle 101 may encounter during a parking search in an area of interest 105. The vehicle 101 and/or vehicles 103 may be equipped with respective sensors 107a-107m (also collectively referred to as sensors 107) (e.g., camera sensors, proximity sensors, LiDAR, RADAR, etc.) for detecting the nearby presence (e.g., within a threshold distance) of other vehicles. Claim 1 determining a first detection and a second detection of a second vehicle by the first vehicle based on at least one sensor of the first vehicle during the search, wherein the second vehicle has not found parking at the first detection and at the second detection);
generating a real-time area map of the area of the parking lot based on the sensing data, comprising: obtaining preprocessed sensing data by preprocessing the sensing data, the real-time area map dynamically storing area object data and object status data corresponding to the area object data in the area of the parking lot, wherein the area object data comprises identifiers of a plurality of objects, the plurality of objects comprise vehicles ([0007] In addition, for various example embodiments of the invention, the following is applicable: a method comprising facilitating a processing of and/or
processing (1) data and/or (2) information and/or (3) at least one signal, the (1) data and/or (2) information and/or (3) at least one signal based, at least in part, on (or derived at least in part from) any one or any combination of methods (or processes) disclosed in this application as relevant to any embodiment of the invention. [0038] the parking initiation module 301 can collect and process
trajectory or probe data from the vehicle 101 to analyze for parking related behaviors (e.g., looping or circling over the same set of streets, slowing down, etc.). [0041] In one embodiment, the image data
generated by the camera sensor can then be processed using any image recognition or processing
technique known in the art. In other words, the vehicle detection module 303 determines each detection of the second or other vehicle 103 based on image recognition. As part of the detection process, the vehicle detection module 303 can also detect identifying characteristics of the second vehicle 103, so that the vehicle detection module 303 can correlate a first or initial detection with any subsequent or second detections of the same vehicle 103. For example, the vehicle detection module 303 can determine that it is detecting the second vehicle 103 a second time based on determining and tracking a make, a model, and/or any other identifying feature of the second vehicle 103 such as, but not limited to, a license plate or any other unique feature (e.g., a logo, a sticker, a marking, etc.). [0057] once the vehicle detection module 303 detects the second vehicle 103 based on a sensor 107 (e.g., a front facing camera) and processes the generated image data to determine one or more identifying features of the second vehicle 103 (e.g., a vehicle make, model, a license plate, etc.), then the path prediction module 305 can query one or more databases (e.g., the geographic database 115) based on the identifying features to determine whether the second vehicle 103 was recently parked. [0031] a system 100 of FIG. 1 introduces a capability to generate an optimized parking search route to find parking in a given area by deducing or inferring the paths driven by other vehicles that are detected nearby and that are also seeking parking spots in the same area. [0032] As shown in FIG. 1, the system comprises a first vehicle 101 and at least one other vehicle 103a-103n (also collectively referred to as vehicles 103) that the vehicle 101 may encounter during a parking search in an area of interest 105. The vehicle 101 and/or vehicles 103 may be equipped with respective sensors 107a-107m (also collectively referred to as sensors 107) (e.g., camera sensors, proximity sensors, LiDAR, RADAR, etc.) for detecting the nearby presence (e.g., within a threshold distance) of other vehicles. In one embodiment, the vehicle 101 is further equipped with a routing module 109 (e.g., a vehicle navigation system or equivalent) executing one or more applications 111 (e.g., a navigation or mapping application) capable or generating parking search routes according to the various embodiments described herein. In addition or alternatively, the system 100 can include a routing platform 113 (e.g., a server-side component) for performing all or a portion of the functions associated with generating a parking search route based on the driven paths of other vehicles (e.g., vehicles 103). [0051] the predicted routes driven by the second vehicle 103 is determined with respect to road link or segments represented in the geographic database 115. Accordingly, in step 503, the path prediction module 305, determines one or more road links associated with the predicted route by querying for or otherwise
map matching the predicted route to corresponding road links, nodes, etc. In other words, the path prediction module 305 can determine the one or more road links based on mapping or navigation information stored in a geographic database 115. In some embodiments, the path prediction module
305 can determine the one or more road links or predicted driven paths based on probe data associated with the first vehicle 101 and/or second vehicle 103. FIGS. 8B and 8C);
the object status data comprises locations of the plurality of objects and correspondences between the plurality of objects ([0062] as previously stated the sensors 107 may be any type of sensor. In certain embodiments, the sensors 103 may include, for example, a global positioning sensor (GPS) for gathering location data, a network detection sensor for detecting wireless signals or receivers for different short-range communications (e.g., Bluetooth, Wi-Fi, light fidelity (Li-Fi), near field communication (NFC) etc.), temporal information sensors, a camera/imaging sensor for gathering image data (e.g., front facing cameras, backwards facing cameras, or combination thereof for detecting a make, model, or license plate of a vehicle 103), velocity sensors, and the like. In another embodiment, the sensors 107 may include sensors (e.g., mounted along a perimeter of the vehicle 101) to detect the relative distance of the vehicle from lanes or roadways, the presence of other vehicles 103, pedestrians, animals, traffic lights, road features (e.g., curves) and any other objects, or a combination thereof. In one scenario, the sensors 107 may detect weather data, traffic information, or a combination thereof. In one example embodiment, the vehicle 101 may include GPS receivers to obtain geographic coordinates from satellites 125 for determining current or live location and time. Further, the location can be determined by a triangulation system such as A-GPS, Cell of Origin, or other location extrapolation technologies when cellular or network signals are available);
in response that a parking instruction of the target vehicle is received and the target vehicle enters the area of the parking lot, determining a vehicle location of a target vehicle on the real-time area map ([0038] In step 401, the parking initiation module 301 determines that a first vehicle (e.g., vehicle 101) has initiated a search for a parking space. It is contemplated that the parking initiation module 301 can use any means to determine when the vehicle 101 has started a parking search. For example, the parking initiation module 301 can receive a manual input by a user that the user is looking for a parking space. In another example, the start of a parking search can be determined based on a routing request by the user (e.g., a route to a point of interest (POI) or other destination with parking nearby). [0039] In addition or alternatively, the parking initiation module 301 can determine that a user or vehicle 101 has started searching for parking based on a combination of one or more vehicle related inputs (e.g., location, speed, direction, etc.). For example, the parking initiation module 301 can determine that the vehicle 101 is driving much slower than the known speed limit near the home or office of the user or passenger. In one instance, the parking initiation module 301 can determine that a user or vehicle 101 has started searching for parking based on a comparison of one or more temporal parameters (e.g., a day of time or day of the week), location information, and one or more entries in an application 111 (e.g., a doctor's appointment, grocery shopping, etc.). [0040] After determining that a parking search has been started, the vehicle detection module 303 can begin detecting and monitoring for other vehicles (e.g., vehicles 103) that are encountered by the vehicle 101. For example, in step 403, the vehicle detection module 303 determines a first detection and a second detection of a second vehicle 103 by the first vehicle 101 during the parking search initiated by the vehicle 101. In one embodiment, the vehicle detection module 303 determines the first detection and the second detection based on at least one sensor (e.g., a sensor 107) associated with or otherwise equipped on the first vehicle 101. The sensor 107, for instance, can include a camera sensor (e.g., a front facing camera, a backwards facing camera, etc.). In this case, each detection can be based on a line-of-sight detection as seen from the field of view of the camera sensor) and
determining a parking space location corresponding to a target parking space in the area of the parking lot on the real-time area map based on the area object data and the corresponding object status data ([0047] In step 407, the routing module 307 generates an optimized parking search route for the first vehicle 101 based on deprioritizing the predicted route taken by the second vehicle 103. By way of example, the predicted route is deprioritized by the routing module 307 so that the user or passenger of the first vehicle 101 can benefit from the paths covered by the second vehicle 103. For example, the routing module 307 can infer that because the second vehicle 103 is still driving around looking for a parking space (e.g., as evidenced by the second encounter) that there were no available parking
spaces on the route covered by the second vehicle 103 during the time t2−t1. Additionally, the routing module 307 can also infer that given the short period of time that elapsed between time t1 and time t2, that the parking situation on the route covered by the second vehicle 103 is likely still the same as it was when the second vehicle 103 was traveling the route. As a result, the routing module 307 can increase the probability of the first vehicle 101 finding a parking space by temporarily excluding the route of the second vehicle 103 from its search. In one embodiment, the routing module 307 can also generate an optimized parking search route that includes a nearby suitable destination [parking space location] that increases the user's or driver's chances to find a parking spot in the area rather a route based on the first vehicle 101 continuing to drive. By way of example, a suitable destination may include a destination where users often spend a short amount of time away from their vehicles (e.g., a gasoline station, a convenience store, etc.). [0048] In one embodiment, it is contemplated that as time passes, the routing module 307 may again begin prioritizing the route covered by the second vehicle 103 given the limited number of convenient options (e.g., alternative parking search routes) in the area. For example, if the first vehicle 101 keeps meeting or encountering the second vehicle 103 but then stops encountering the second vehicle 103, the routing module 307 can infer that the second vehicle 103 found a parking
space and that there may be more free spots in that vicinity. Consequently, in one embodiment, the routing module 307 can include the spot where the second vehicle 103 likely found a parking
space in the optimized parking search route);
generating a parking route of the target vehicle based on the vehicle location, the parking space location and the real-time area map ([0033] FIG. 2 is a diagram illustrating an example of
generating an optimized parking search route based on the driving paths of other vehicles. [0071] FIG. 9 is a diagram of the geographic database 115, according to one embodiment. In one embodiment, parking search route information and/or any other information used or generated by the system 100
with respect to generating an optimized parking search route based on one or more other vehicle driving paths can be stored, associated with, and/or linked to the geographic database 115 or data thereof. In one embodiment, the geographic or map database 115 includes geographic data 901 used for (or configured to be compiled to be used for) mapping and/or navigation-related services, such as for route information, service information, estimated time of arrival information, location sharing information, speed sharing information, and/or geospatial information sharing, according to exemplary embodiments. For example, the geographic database 115 includes node data records 903, road segment or link data records 905, POI data records 907, vehicle attributes data 909, other data records 911, and indexes 913, for example. More, fewer or different data records can be provided. In one embodiment, the other data records 911 include cartographic (“carto”) data records, routing data, and maneuver data. One or more portions, components, areas, layers, features, text, and/or symbols of the POI or event data can be stored in, linked to, and/or associated with one or more of these data records. For example, one or more portions of the POI, event data, or recorded route information can be matched with respective map or geographic records via position or GPS data associations (such as using known or future map matching or geo-coding techniques), for example. In one embodiment, the POI data records 907 may also include information on locations of traffic controls (e.g., stoplights, stop signs, crossings, etc.), driving restrictions (e.g., speed, direction of travel, etc.), or a combination thereof).
Beaurepaire does not explicitly teach, however Gupta teaches the plurality of objects comprise parking spaces ([0040] the first artificial intelligence network is inputted with sensor data describing the parking lot so that the first artificial intelligence network includes digital data that describes, from the pool of parking spaces within the parking lot, which of these parking spaces are presently available for provision to a connected vehicle. [0041] the parking lot agent 198 uses the lot sensor data 197 to determine which of the set of parking spaces 141 are currently occupiable by a vehicle such as the connected vehicle 123. [0102] The connected computing device 103 includes map data within the first personalization data 173 describing the parking lot 140 and the GPS location of each parking space 141
within the parking lot (e.g., the GPS location of each parking space 141 included in the set of parking
spaces included in the parking lot 140).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the objects comprising parking spaces as taught in Gupta with the mapping method of Beaurepaire because such a combination enables the system to determine “which of these parking spaces are presently available for provision to a connected vehicle” (Gupta [0040]).
Beaurepaire does not explicitly teach, however Gupta teaches obtaining a training sample set, which comprises multiple sets of data samples collected by sensing devices; obtaining a preset map construction model by training an initial map construction model using the training sample set ([0040] In some embodiments, the training data for the first artificial intelligence network and the second artificial intelligence network includes the first personalization data 173 and the second personalization data 174 depicted in FIG. 1. In some embodiments, the first artificial intelligence network is also trained with digital data that describes a map of its parking lot and the parking spaces within the parking lot. In some embodiments, the first artificial intelligence network is inputted with sensor data describing the parking lot so that the first artificial intelligence network includes digital data that describes, from the pool of parking spaces within the parking lot, which of these parking spaces are presently available for provision to a connected vehicle. [0065] In some embodiments, each of the entities train their artificial intelligence networks using both the first personalization data 173 and the second personalization data 174. Accordingly, the first personalization data 173 and the second personalization data 174 are included in the training data for the artificial intelligence networks. In this way the first artificial intelligence network and the second artificial intelligence network are both trained. In some embodiments, each of the artificial intelligence networks are configured to solve the function using the training data as well as other variables inputted to the artificial intelligence networks. The variables include, for example, the lot sensor data 197 and the vehicle sensor data 195, both of which may be updated on a real-time or periodic basis, shared with the other entity via V2X communication, and inputted anew to the artificial intelligence networks) (see rejection above for combination rationale);
Beaurepaire does not explicitly teach, however Gupta teaches generating a first control instruction based on the parking route, sending the parking route and the first control instruction to the target vehicle, and controlling the target vehicle to drive to the target parking space according to the parking route ([0054] When the distributed parking fulfillment system identifies a parking space
141 that satisfies the function, then the distributed parking fulfillment system provides one or more of a notification and routing instructions to the parking space 141 to the operator/driver of the connected
vehicle 123 and/or the vehicle control system 153 of the connected vehicle 123. The notification describes the parking space. The routing instructions describe how to get to the parking space from a present geographic location of the connected vehicle 123. The routing data includes digital data that describes a driving route to the parking space. The routing data may be provided to the vehicle
control system which then automatically drives the connected vehicle 123 to the parking space or displays the driving route on an electronic display of the connected vehicle 123. An example of the routing data according to some embodiments includes the routing data 176 depicted in FIG. 1. [0068] - [0088]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include in the vehicle parking method of Beaurepaire the process of generating a first control instruction based on the parking route, sending the parking route and the first control instruction to the target vehicle, and controlling the target vehicle to drive to the target parking space according to the parking route as taught by Gupta since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination is predictable. Such a combination would yield the predictable result of a vehicle parking method where a first control instruction is generated based on the parking route, the parking route and the first control instruction are sent to the target vehicle, and the target vehicle is controlled to drive to the target parking space according to the parking route.
Beaurepaire does not explicitly teach, however Miron teaches generating the real-time area map by analyzing the preprocessed sensing data using the preset map construction model ([0009] a computer-implemented method for providing training data for training of a data-driven depth completion model as a machine-learning model is provided, wherein the depth completion model is to be trained to generate dense depth maps from sensor acquired raw depth maps. [0029] provide multiple real raw depth map data items obtained from real-world depth sensor measurements of real-world scenes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the process of generating the real-time area map by analyzing the preprocessed sensing data using the preset map construction model as taught in Miron with the mapping method of Beaurepaire “for shape and location prediction of unknown objects” (Miron [0004]).
Regarding Claim 2, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 1, as discussed above. Beaurepaire does not explicitly teach, however Gupta teaches wherein determining the parking space location corresponding to the target parking space in the area of the parking lot on the real-time area map comprises: determining parking space status data of all parking spaces in the area of the parking lot based on the area object data and the corresponding object status data ([0043] Lot sensor data includes digital data that describes the sensor data measurements taken by the sensors that are included in the parking lot. The sensors include any sensor described herein. An example of a sensor includes one or more of the following: a camera; a lidar sensor; a range-finding
sensor; an infrared sensor; an infrared camera; a stereoscopic camera; a sonar device; a radar speed gun; a lidar speed gun; satellite-based imaging devices; and any another type of sensor that is capable of identifying the status of the parking spaces and measuring the status of the personalization factors of the operator of the parking lot. The lot sensor data includes digital data that describes the measurements recorded by any sensor described herein. An example of the lot sensor data according to some embodiments includes the lot sensor data 197 depicted in FIG. 1. [0191] The parking lot sensor set 226 includes any of the sensors described herein. The sensors of the parking lot sensor set 226 are mounted and operable to record lot sensor data 197. In some embodiments, the parking lot sensor set
226 includes one or more of the following sensors: a camera; a lidar sensor; a range-finding sensor; an infrared sensor; an infrared camera; a stereoscopic camera; a sonar device; a radar speed gun; a lidar speed gun; satellite-based imaging devices; a GPS sensor; and any another type of sensor that is capable of identifying the status of the parking spaces and measuring the status of the personalization factors of the operator of the parking lot. The lot sensor data 197 includes digital data that describes the measurements recorded by any sensor described herein);
determining a usage status of each of all parking spaces based on the parking space status
data; determining all idle parking spaces in the area of the parking lot based on the usage status of
each parking space ([0041] the first personalization data 173 includes digital data describing a map of the parking lot 140 and a set of parking spaces 141 within the parking lot. In some embodiments, the parking lot agent 198 uses the lot sensor data 197 to determine which of the set of parking spaces
141 are currently occupiable by a vehicle such as the connected vehicle 123. In some embodiments, the first analysis data 181 includes digital data describing, among other things, the outcome of this determination so that the first analysis data 181 describes which of the set of parking spaces 141 are currently occupiable by a vehicle such as the connected vehicle 123. [0042] a parking space is available for provision if it is not currently occupied by another vehicle and does not include any qualities that would make it undesirable or unoccupiable as a parking space (e.g., includes a shopping cart of other object within the parking space, is adjacent to another parking space in which the occupying vehicle has parked over the line and encroaches into the otherwise available parking space, includes a pothole, snow mound, or some other defect that would affect the desirability of the parking space or render the parking space not occupiable);
determining the target parking space from all idle parking spaces according to a preset strategy ([0038] In some embodiments, an artificial intelligence network includes digital data that describes an artificial learning network that is trained to solve the problem of identifying whether a parking lot includes a parking space that is (1) available and (2) whose provision to a particular connected vehicle would satisfy both (3) a threshold number of personalization factors for an operator of a parking lot and (4) a threshold number of personalization factors for an operator/driver of the connected vehicle. [0218] In some embodiments, the distributed parking fulfillment system provides a distributed parking fulfillment service that locates and notifies a connected vehicle about an available parking spaces using two different artificial intelligence networks that are trained with different
personalization factors (one is trained to satisfy the personalization factors of a driver of a connected vehicle whereas the other is trained to satisfy the personalization factors of an operator of a parking lot). The prior art does not include a distributed parking fulfillment system that provides a distributed parking fulfillment service that locates and notifies a connected vehicle about an available parking spaces
using two different artificial intelligence networks that are trained with different personalization
factors); and
determining the parking space location corresponding to the target parking space on the real-time area map based on the parking space status data ([0054] When the distributed parking fulfillment system identifies a parking space 141 that satisfies the function, then the distributed parking fulfillment system provides one or more of a notification and routing instructions to the parking space 141 to the operator/driver of the connected vehicle 123 and/or the vehicle control system 153 of the connected vehicle 123. The notification describes the parking space. The routing instructions describe how to get to the parking space from a present geographic location of the connected vehicle 123. The notification data includes digital data that describes the notification (e.g., a graphical display on an electronic display device and/or an audible noise from a speaker). An example of the notification data according to some embodiments includes the notification data 187 depicted in FIG. 1. The routing data includes digital data that describes a driving route to the parking space. The routing data may be provided to the vehicle control system which then automatically drives the connected vehicle 123 to the parking space or displays the driving route on an electronic display of the connected vehicle 123. An example of the routing data according to some embodiments includes the routing data 176 depicted in FIG. 1) (see claim 1 rejection, above, for combination rationale).
Regarding Claim 11, Beaurepaire teaches a storage device storing at least one instruction; and
at least one processor, when the at least one instruction is executed by the at least one processor, the at least one processor is caused to ([0004] an apparatus comprises at least one processor, and at least one memory including computer program code for one or more computer programs, the at least one memory and the computer program code configured to, with the at least one processor, cause, at least in part, the apparatus to determine that a first vehicle has initiated a search for a parking space):
obtain sensing data of an area of a parking lot using the sensing devices distributed in the
area of the parking lot ([0032] As shown in FIG. 1, the system comprises a first vehicle 101 and at least one other vehicle 103a-103n (also collectively referred to as vehicles 103) that the vehicle 101 may encounter during a parking search in an area of interest 105. The vehicle 101 and/or vehicles 103 may be equipped with respective sensors 107a-107m (also collectively referred to as sensors 107) (e.g., camera sensors, proximity sensors, LiDAR, RADAR, etc.) for detecting the nearby presence (e.g., within a threshold distance) of other vehicles. Claim 1 determining a first detection and a second detection of a second vehicle by the first vehicle based on at least one sensor of the first vehicle during the search, wherein the second vehicle has not found parking at the first detection and at the second detection);
generate a real-time area map of the area of the parking lot based on the sensing data, and comprising: obtaining preprocessed sensing data by preprocessing the sensing data; the real-time area map dynamically storing area object data and object status data corresponding to the area object data in the area of the parking lot, wherein the area object data comprises identifiers of a plurality of objects, the plurality of objects comprise vehicles ([0007] In addition, for various example embodiments of the invention, the following is applicable: a method comprising facilitating a processing of and/or
processing (1) data and/or (2) information and/or (3) at least one signal, the (1) data and/or (2) information and/or (3) at least one signal based, at least in part, on (or derived at least in part from) any one or any combination of methods (or processes) disclosed in this application as relevant to any embodiment of the invention. [0038] the parking initiation module 301 can collect and process
trajectory or probe data from the vehicle 101 to analyze for parking related behaviors (e.g., looping or circling over the same set of streets, slowing down, etc.). [0041] In one embodiment, the image
data generated by the camera sensor can then be processed using any image recognition or processing
technique known in the art. In other words, the vehicle detection module 303 determines each detection of the second or other vehicle 103 based on image recognition. As part of the detection process, the vehicle detection module 303 can also detect identifying characteristics of the second vehicle 103, so that the vehicle detection module 303 can correlate a first or initial detection with any subsequent or second detections of the same vehicle 103. For example, the vehicle detection module 303 can determine that it is detecting the second vehicle 103 a second time based on determining and tracking a make, a model, and/or any other identifying feature of the second vehicle 103 such as, but not limited to, a license plate or any other unique feature (e.g., a logo, a sticker, a marking, etc.). [0057] once the vehicle detection module 303 detects the second vehicle 103 based on a sensor 107 (e.g., a front facing camera) and processes the generated image data to determine one or more identifying features of the second vehicle 103 (e.g., a vehicle make, model, a license plate, etc.), then the path prediction module 305 can query one or more databases (e.g., the geographic database
115) based on the identifying features to determine whether the second vehicle 103 was recently parked. [0031] a system 100 of FIG. 1 introduces a capability to generate an optimized parking search route to find parking in a given area by deducing or inferring the paths driven by other vehicles that are detected nearby and that are also seeking parking spots in the same area. [0032] As shown in FIG. 1, the system comprises a first vehicle 101 and at least one other vehicle 103a-103n (also collectively referred to as vehicles 103) that the vehicle 101 may encounter during a parking search in an area of interest 105. The vehicle 101 and/or vehicles 103 may be equipped with respective sensors 107a-107m (also collectively referred to as sensors 107) (e.g., camera sensors, proximity sensors, LiDAR, RADAR, etc.) for detecting the nearby presence (e.g., within a threshold distance) of other vehicles. In one embodiment, the vehicle 101 is further equipped with a routing module 109 (e.g., a vehicle navigation system or equivalent) executing one or more applications 111 (e.g., a navigation or mapping application) capable or generating parking search routes according to the various embodiments described herein. In addition or alternatively, the system 100 can include a routing platform 113 (e.g., a server-side component) for performing all or a portion of the functions associated with generating a parking search route based on the driven paths of other vehicles (e.g., vehicles 103). [0051] the predicted routes driven by the second vehicle 103 is determined with respect to road link or segments represented in the geographic database 115. Accordingly, in step 503, the path prediction module 305, determines one or more road links associated with the predicted route by querying for or otherwise
map matching the predicted route to corresponding road links, nodes, etc. In other words, the path prediction module 305 can determine the one or more road links based on mapping or navigation information stored in a geographic database 115. In some embodiments, the path prediction module 305 can determine the one or more road links or predicted driven paths based on probe data associated with the first vehicle 101 and/or second vehicle 103. FIGS. 8B and 8C),
the object status data comprises locations of the plurality of objects and correspondences between the plurality of objects ([0062] as previously stated the sensors 107 may be any type of sensor. In certain embodiments, the sensors 103 may include, for example, a global positioning sensor (GPS) for gathering location data, a network detection sensor for detecting wireless signals or receivers for different short-range communications (e.g., Bluetooth, Wi-Fi, light fidelity (Li-Fi), near field communication (NFC) etc.), temporal information sensors, a camera/imaging sensor for gathering image data (e.g., front facing cameras, backwards facing cameras, or combination thereof for detecting a make, model, or license plate of a vehicle 103), velocity sensors, and the like. In another embodiment, the sensors 107 may include sensors (e.g., mounted along a perimeter of the vehicle 101) to detect the relative distance of the vehicle from lanes or roadways, the presence of other vehicles 103, pedestrians, animals, traffic lights, road features (e.g., curves) and any other objects, or a combination thereof. In one scenario, the sensors 107 may detect weather data, traffic information, or a combination thereof. In one example embodiment, the vehicle 101 may include GPS receivers to obtain geographic coordinates from satellites 125 for determining current or live location and time. Further, the location can be determined by a triangulation system such as A-GPS, Cell of Origin, or other location extrapolation technologies when cellular or network signals are available);
determine a vehicle location of a target vehicle on the real-time area map and determining a parking space location corresponding to a target parking space in the area of the parking lot on the real-time area map based on the area object data and the corresponding object status data, in response that a parking instruction of the target vehicle is received and the target vehicle enters the area of the parking lot ([0038] In step 401, the parking initiation module 301 determines that a first vehicle (e.g., vehicle 101) has initiated a search for a parking space. It is contemplated that the parking initiation module 301 can use any means to determine when the vehicle 101 has started a parking search. For example, the parking initiation module 301 can receive a manual input by a user that the user is looking for a parking space. In another example, the start of a parking search can be determined based on a routing request by the user (e.g., a route to a point of interest (POI) or other destination with parking nearby). [0039] In addition or alternatively, the parking initiation module 301 can determine that a user or vehicle 101 has started searching for parking based on a combination of one or more vehicle related inputs (e.g., location, speed, direction, etc.). For example, the parking initiation module 301 can determine that the vehicle 101 is driving much slower than the known speed limit near the home or office of the user or passenger. In one instance, the parking initiation module 301 can determine that a user or vehicle 101 has started searching for parking based on a comparison of one or more temporal parameters (e.g., a day of time or day of the week), location information, and one or more entries in an application 111 (e.g., a doctor's appointment, grocery shopping, etc.). [0040] After determining that a parking search has been started, the vehicle detection module 303 can begin detecting and monitoring for other vehicles (e.g., vehicles 103) that are encountered by the vehicle 101. For example, in step 403, the vehicle detection module 303 determines a first detection and a second detection of a second vehicle 103 by the first vehicle 101 during the parking search initiated by the vehicle 101. In one embodiment, the vehicle detection module 303 determines the first detection and the second detection based on at least one sensor (e.g., a sensor 107) associated with or otherwise equipped on the first vehicle 101. The sensor 107, for instance, can include a camera sensor (e.g., a front facing camera, a backwards facing camera, etc.). In this case, each detection can be based on a line-of-sight detection as seen from the field of view of the camera sensor. [0047] In step 407, the routing module 307 generates an optimized parking search route for the first vehicle 101 based on deprioritizing the predicted route taken by the second vehicle 103. By way of example, the predicted route is deprioritized by the routing module 307 so that the user or passenger of the first vehicle 101 can benefit from the paths covered by the second vehicle 103. For example, the routing module 307 can infer that because the second vehicle 103 is still driving around looking for a parking space (e.g., as evidenced by the second encounter) that there were no available parking spaces on the route covered by the second vehicle 103 during the time t2−t1. Additionally, the routing module 307 can also infer that given the short period of time that elapsed between time t1 and time t2, that the parking situation on the route covered by the second vehicle 103 is likely still the same as it was when the second vehicle 103 was traveling the route. As a result, the routing module 307 can increase the probability of the first vehicle 101 finding a parking space by temporarily excluding the route of the second vehicle 103 from its search. In one embodiment, the routing module 307 can also generate an optimized parking search route that includes a nearby suitable destination [parking space location] that increases the user's or driver's chances to find a parking spot in the area rather a route based on the first vehicle 101
continuing to drive. By way of example, a suitable destination may include a destination where users often spend a short amount of time away from their vehicles (e.g., a gasoline station, a convenience store, etc.). [0048] In one embodiment, it is contemplated that as time passes, the routing module 307
may again begin prioritizing the route covered by the second vehicle 103 given the limited number of convenient options (e.g., alternative parking search routes) in the area. For example, if the first vehicle 101 keeps meeting or encountering the second vehicle 103 but then stops encountering the second vehicle 103, the routing module 307 can infer that the second vehicle 103 found a parking space
and that there may be more free spots in that vicinity. Consequently, in one embodiment, the routing
module 307 can include the spot where the second vehicle 103 likely found a parking space in the optimized parking search route);
generate a parking route of the target vehicle based on the vehicle location, the parking space
location and the real-time area map map ([0033] FIG. 2 is a diagram illustrating an example of
generating an optimized parking search route based on the driving paths of other vehicles. [0071] FIG. 9 is a diagram of the geographic database 115, according to one embodiment. In one embodiment, parking search route information and/or any other information used or generated by the system 100
with respect to generating an optimized parking search route based on one or more other vehicle driving paths can be stored, associated with, and/or linked to the geographic database 115 or data thereof. In one embodiment, the geographic or map database 115 includes geographic data 901 used for (or configured to be compiled to be used for) mapping and/or navigation-related services, such as for route information, service information, estimated time of arrival information, location sharing information, speed sharing information, and/or geospatial information sharing, according to exemplary embodiments. For example, the geographic database 115 includes node data records 903, road segment or link data records 905, POI data records 907, vehicle attributes data 909, other data records 911, and indexes 913, for example. More, fewer or different data records can be provided. In one embodiment, the other data records 911 include cartographic (“carto”) data records, routing data, and maneuver data. One or more portions, components, areas, layers, features, text, and/or symbols of the POI or event data can be stored in, linked to, and/or associated with one or more of these data records. For example, one or more portions of the POI, event data, or recorded route information can be matched with respective map or geographic records via position or GPS data associations (such as using known or future map matching or geo-coding techniques), for example. In one embodiment, the POI data records 907 may also include information on locations of traffic controls (e.g., stoplights, stop signs, crossings, etc.), driving restrictions (e.g., speed, direction of travel, etc.), or a combination thereof).
Beaurepaire does not explicitly teach, however Gupta teaches the plurality of objects comprise parking spaces ([0040] the first artificial intelligence network is inputted with sensor data describing the parking lot so that the first artificial intelligence network includes digital data that describes, from the pool of parking spaces within the parking lot, which of these parking spaces are presently available for provision to a connected vehicle. [0041] the parking lot agent 198 uses the lot sensor data 197 to determine which of the set of parking spaces 141 are currently occupiable by a vehicle such as the connected vehicle 123. [0102] The connected computing device 103 includes map data within the first personalization data 173 describing the parking lot 140 and the GPS location of each parking space 141
within the parking lot (e.g., the GPS location of each parking space 141 included in the set of parking
spaces included in the parking lot 140).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the objects comprising parking spaces as taught in Gupta with the mapping method of Beaurepaire because such a combination enables the system to determine “which of these parking spaces are presently available for provision to a connected vehicle” (Gupta [0040]).
Bearepaire does not explicitly teach, however Gupta teaches obtaining a training sample set, which comprises multiple sets of data samples collected by sensing devices; obtaining a preset map construction model by training an initial map construction model using the training sample set ([0040] In some embodiments, the training data for the first artificial intelligence network and the second artificial intelligence network includes the first personalization data 173 and the second personalization data 174 depicted in FIG. 1. In some embodiments, the first artificial intelligence network is also trained with digital data that describes a map of its parking lot and the parking spaces within the parking lot. In some embodiments, the first artificial intelligence network is inputted with sensor data describing the parking lot so that the first artificial intelligence network includes digital data that describes, from the pool of parking spaces within the parking lot, which of these parking spaces are presently available for provision to a connected vehicle. [0065] In some embodiments, each of the entities train their artificial intelligence networks using both the first personalization data 173 and the second personalization data 174. Accordingly, the first personalization data 173 and the second personalization data 174 are included in the training data for the artificial intelligence networks. In this way the first artificial intelligence network and the second artificial intelligence network are both trained. In some embodiments, each of the artificial intelligence networks are configured to solve the function using the training data as well as other variables inputted to the artificial intelligence networks. The variables include, for example, the lot sensor data 197 and the vehicle sensor data 195, both of which may be updated on a real-time or periodic basis, shared with the other entity via V2X communication, and inputted anew to the artificial intelligence networks) (see rejection above for combination rationale).
Bearepaire does not explicitly teach, however Gupta teaches generate a first control
instruction based on the parking route, send the parking route and the first control instruction to the target vehicle, and control the target vehicle to drive to the target parking space according to the parking route ([0054] When the distributed parking fulfillment system identifies a parking space 141
that satisfies the function, then the distributed parking fulfillment system provides one or more of a notification and routing instructions to the parking space 141 to the operator/driver of the connected
vehicle 123 and/or the vehicle control system 153 of the connected vehicle 123. The notification describes the parking space. The routing instructions describe how to get to the parking space from a present geographic location of the connected vehicle 123. The routing data includes digital data that describes a driving route to the parking space. The routing data may be provided to the vehicle
control system which then automatically drives the connected vehicle 123 to the parking space or displays the driving route on an electronic display of the connected vehicle 123. An example of the routing data according to some embodiments includes the routing data 176 depicted in FIG. 1. [0068] - [0088]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include in the vehicle parking method of Beaurepaire the process of generating a first control instruction based on the parking route, sending the parking route and the first control instruction to the target vehicle, and controlling the target vehicle to drive to the target parking space according to the parking route as taught by Gupta since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination is predictable. Such a combination would yield the predictable result of a vehicle parking method where a first control instruction is generated based on the parking route, the parking route and the first control instruction are sent to the target vehicle, and the target vehicle is controlled to drive to the target parking space according to the parking route.
Bearepaire does not explicitly teach, however Miron teaches generating the real-time area map by analyzing the preprocessed sensing data using the preset map construction model ([0009] a computer-implemented method for providing training data for training of a data-driven depth completion model as a machine-learning model is provided, wherein the depth completion model is to be trained to generate dense depth maps from sensor acquired raw depth maps. [0029] provide multiple real raw depth map data items obtained from real-world depth sensor measurements of real-world scenes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the process of generating the real-time area map by analyzing the preprocessed sensing data using the preset map construction model as taught in Miron with the mapping method of Beaurepaire “for shape and location prediction of unknown objects” (Miron [0004]).
Regarding Claim 12, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 11, as discussed above. Beaurepaire does not explicitly teach, however Gupta teaches wherein the at least one processor determine the parking space location corresponding to the target parking space in the area of the parking lot on the real-time area map by: determining parking space status data of all parking spaces in the area of the parking lot based on the area object data and the corresponding object status data ([0043] Lot sensor data includes digital data that describes the sensor data measurements taken by the sensors that are included in the parking lot. The sensors include any sensor described herein. An example of a sensor includes one or more of the following: a camera; a lidar
sensor; a range-finding sensor; an infrared sensor; an infrared camera; a stereoscopic camera; a sonar device; a radar speed gun; a lidar speed gun; satellite-based imaging devices; and any another type of sensor that is capable of identifying the status of the parking spaces and measuring the status of the personalization factors of the operator of the parking lot. The lot sensor data includes digital data that describes the measurements recorded by any sensor described herein. An example of the lot sensor data according to some embodiments includes the lot sensor data 197 depicted in FIG. 1. [0191] The parking lot sensor set 226 includes any of the sensors described herein. The sensors of the parking lot sensor set 226 are mounted and operable to record lot sensor data 197. In some embodiments, the parking
lot sensor set 226 includes one or more of the following sensors: a camera; a lidar sensor; a range-finding sensor; an infrared sensor; an infrared camera; a stereoscopic camera; a sonar device; a radar speed gun; a lidar speed gun; satellite-based imaging devices; a GPS sensor; and any another type of sensor that is capable of identifying the status of the parking spaces and measuring the status of the personalization factors of the operator of the parking lot. The lot sensor data 197 includes digital data that describes the measurements recorded by any sensor described herein);
determining a usage status of each of all parking spaces based on the parking space status data; determining all idle parking spaces in the area of the parking lot based on the usage status of each parking space ([0041] the first personalization data 173 includes digital data describing a map of the parking lot 140 and a set of parking spaces 141 within the parking lot. In some embodiments, the parking lot agent 198 uses the lot sensor data 197 to determine which of the set of parking spaces
141 are currently occupiable by a vehicle such as the connected vehicle 123. In some embodiments, the first analysis data 181 includes digital data describing, among other things, the outcome of this determination so that the first analysis data 181 describes which of the set of parking spaces 141 are currently occupiable by a vehicle such as the connected vehicle 123. [0042] a parking space is available for provision if it is not currently occupied by another vehicle and does not include any qualities that would make it undesirable or unoccupiable as a parking space (e.g., includes a shopping cart of other object within the parking space, is adjacent to another parking space in which the occupying vehicle has parked over the line and encroaches into the otherwise available parking space, includes a pothole, snow mound, or some other defect that would affect the desirability of the parking space or render the parking space not occupiable);
determining the target parking space from the all idle parking spaces according to a preset strategy ([0038] In some embodiments, an artificial intelligence network includes digital data that describes an artificial learning network that is trained to solve the problem of identifying whether a parking lot includes a parking space that is (1) available and (2) whose provision to a particular connected vehicle would satisfy both (3) a threshold number of personalization factors for an operator of a parking lot and (4) a threshold number of personalization factors for an operator/driver of the connected vehicle. [0218] In some embodiments, the distributed parking fulfillment system provides a distributed parking fulfillment service that locates and notifies a connected vehicle about an available parking spaces using two different artificial intelligence networks that are trained with different
personalization factors (one is trained to satisfy the personalization factors of a driver of a connected vehicle whereas the other is trained to satisfy the personalization factors of an operator of a parking lot). The prior art does not include a distributed parking fulfillment system that provides a distributed parking fulfillment service that locates and notifies a connected vehicle about an available parking spaces using two different artificial intelligence networks that are trained with different personalization factors); and
determining the parking space location corresponding to the target parking space on the real-time area map based on the parking space status data ([0054] When the distributed parking fulfillment system identifies a parking space 141 that satisfies the function, then the distributed parking fulfillment system provides one or more of a notification and routing instructions to the parking space 141 to the operator/driver of the connected vehicle 123 and/or the vehicle control system 153 of the connected vehicle 123. The notification describes the parking space. The routing instructions describe how to get to the parking space from a present geographic location of the connected vehicle 123. The notification data includes digital data that describes the notification (e.g., a graphical display on an electronic display device and/or an audible noise from a speaker). An example of the notification data according to some embodiments includes the notification data 187 depicted in FIG. 1. The routing data includes digital data that describes a driving route to the parking space. The routing data may be provided to the vehicle control system which then automatically drives the connected vehicle 123 to the parking space or displays the driving route on an electronic display of the connected vehicle 123. An example of the routing data according to some embodiments includes the routing data 176 depicted in FIG. 1) (see claim 11 rejection, above, for combination rationale).
Regarding Claim 15, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 11, as discussed above. Beaurepaire further teaches wherein the at least one processor generates the parking route of the target vehicle based on the vehicle location, the parking space location and the real-time area map by: outputting the parking route of the target vehicle using a preset route planning model ([0036] As described above, in one embodiment, the routing module
109 (e.g., local to the vehicle 101) and/or the routing platform 113 (e.g., a server-side component) can perform the functions associated with generating an optimized parking search route based on paths driven by other vehicles according to the various embodiments described herein. FIG. 3 is a diagram of the components of the routing module 109/routing platform 113, according to one embodiment. By way of example, the routing module 109 and/or platform 113 include one or more components for
generating an optimized parking search route based on one or more other vehicle driving paths. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. In one embodiment, the routing module
109 and/or platform 113 include a parking initiation module 301, a vehicle detection module 303, a path prediction module 305, and a routing module 307 with connectivity to a geographic database 115. The above presented modules and components of the routing module 109 and/or platform 113 can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as separate entities in FIG. 1, it is contemplated that the routing module 109 and/or platform 113 may be implemented as a module of any of the components of the system 100. In another embodiment, the routing module 109, routing platform 113, and/or one or more of the modules 301-307 may be implemented as a cloud-based service, local service, native application, or combination thereof. The functions of the routing module 109, routing platform 113, and/or the modules 301-307 are discussed with respect to FIGS. 4-7 below)
based on the vehicle location, the parking space location and the real-time area map ([0033] FIG. 2 is a diagram illustrating an example of generating an optimized parking search route based on the driving paths of other vehicles. [0071] FIG. 9 is a diagram of the geographic database 115, according to one embodiment. In one embodiment, parking search route information and/or any other information used or generated by the system 100 with respect to generating an optimized parking search route based on one or more other vehicle driving paths can be stored, associated with, and/or linked to the geographic database 115 or data thereof. In one embodiment, the geographic or map
database 115 includes geographic data 901 used for (or configured to be compiled to be used for) mapping and/or navigation-related services, such as for route information, service information, estimated time of arrival information, location sharing information, speed sharing information, and/or geospatial information sharing, according to exemplary embodiments. For example, the geographic database 115 includes node data records 903, road segment or link data records 905, POI data records 907, vehicle attributes data 909, other data records 911, and indexes 913, for example. More, fewer or different data records can be provided. In one embodiment, the other data records 911 include cartographic (“carto”) data records, routing data, and maneuver data. One or more portions, components, areas, layers, features, text, and/or symbols of the POI or event data can be stored in, linked to, and/or associated with one or more of these data records. For example, one or more portions of the POI, event data, or recorded route information can be matched with respective map or geographic records via position or GPS data associations (such as using known or future map matching or geo-coding techniques), for example. In one embodiment, the POI data records 907 may also include information on locations of traffic controls (e.g., stoplights, stop signs, crossings, etc.), driving restrictions (e.g., speed, direction of travel, etc.), or a combination thereof).
II. Claims 3, 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire in view of Gupta, Miron, and Costello US 20230342126 A1.
Regarding Claim 3, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 1, as discussed above. Beaurepaire does not explicitly teach, however Costello teaches further comprising: deploying the sending devices in the parking lot based on a structure and a scope of the area of the parking lot ([0125] FIG. 5a depicts a graphical diagram 500 of an example physical installation to illustrate embodiments of the present disclosure. An organization selling security services to homeowners, commercial companies, and industrial installations may employ one or more embodiments of the present disclosure as a system provisioned on a combination of cloud-based and on-premises physical and virtual processing, storage, and sensor hardware. A new client purchases services to install and monitor camera and audio feeds at all ingress/egress points, all hallways within the premises, the parking lot, and the cafeteria. FIG. 5a shows the on-premises edge devices (e.g., audio-visual (AV) devices 501-510) and installation layout).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the process of deploying the sending devices in the parking lot based on a structure and a scope of the area of the parking lot as taught in Costello with the monitoring method of Beaurepaire to enable the system to perform “tracking vehicles in and out of the parking lot, delivery areas, and other vehicle access points” (Costello [0004]).
Regarding Claim 5, the combination of Beaurepaire, Gupta, Miron and Costello teaches the limitations of claim 3, as discussed above. Beaurepaire further teaches wherein generating the parking route of the target vehicle based on the vehicle location, the parking space location and the real-time area map comprises: outputting the parking route of the target vehicle using a preset route planning model ([0036] As described above, in one embodiment, the routing module 109 (e.g., local to the vehicle 101) and/or the routing platform 113 (e.g., a server-side component) can perform the functions associated with generating an optimized parking search route based on paths driven by other vehicles according to the various embodiments described herein. FIG. 3 is a diagram of the components of the routing module 109/routing platform 113, according to one embodiment. By way of example, the routing module 109 and/or platform 113 include one or more components for generating an optimized parking search route based on one or more other vehicle driving paths. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. In one embodiment, the routing module 109 and/or platform 113 include a parking initiation module 301, a vehicle detection module 303, a path prediction module 305, and a routing module 307 with connectivity to a geographic database 115. The above presented modules and components of the routing module 109 and/or platform 113 can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as separate entities in FIG. 1, it is contemplated that the routing module 109 and/or platform 113 may be implemented as a module of any of the components of the system 100. In another embodiment, the routing module 109, routing platform 113, and/or one or more of the modules 301-307 may be implemented as a cloud-based service, local service, native application, or combination thereof. The functions of the routing module 109, routing platform 113, and/or the modules 301-307 are discussed with respect to FIGS. 4-7 below)
based on the vehicle location, the parking space location and the real-time area map ([0033] FIG. 2 is a diagram illustrating an example of generating an optimized parking search route based on the driving paths of other vehicles. [0071] FIG. 9 is a diagram of the geographic database 115, according to one embodiment. In one embodiment, parking search route information and/or any other information used or generated by the system 100 with respect to generating an optimized parking search route based on one or more other vehicle driving paths can be stored, associated with, and/or linked to the geographic database 115 or data thereof. In one embodiment, the geographic or map
database 115 includes geographic data 901 used for (or configured to be compiled to be used for) mapping and/or navigation-related services, such as for route information, service information, estimated time of arrival information, location sharing information, speed sharing information, and/or geospatial information sharing, according to exemplary embodiments. For example, the geographic database 115 includes node data records 903, road segment or link data records 905, POI data records 907, vehicle attributes data 909, other data records 911, and indexes 913, for example. More, fewer or different data records can be provided. In one embodiment, the other data records 911 include cartographic (“carto”) data records, routing data, and maneuver data. One or more portions, components, areas, layers, features, text, and/or symbols of the POI or event data can be stored in, linked to, and/or associated with one or more of these data records. For example, one or more portions of the POI, event data, or recorded route information can be matched with respective map or geographic records via position or GPS data associations (such as using known or future map matching or geo-coding techniques), for example. In one embodiment, the POI data records 907 may also include information on locations of traffic controls (e.g., stoplights, stop signs, crossings, etc.), driving restrictions (e.g., speed, direction of travel, etc.), or a combination thereof).
Regarding Claim 13, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 11, as discussed above. Beaurepaire does not explicitly teach, however Costello teaches wherein the at least one processor is further caused to deploy the sending devices in the parking lot based on a structure and a scope of the area of the parking lot ([0125] FIG. 5a depicts a graphical diagram 500 of an example physical installation to illustrate embodiments of the present disclosure. An organization selling security services to homeowners, commercial companies, and industrial installations may employ one or more embodiments of the present disclosure as a system provisioned on a combination of cloud-based and on-premises physical and virtual processing, storage, and sensor hardware. A new client purchases services to install and monitor camera and audio feeds at all ingress/egress points, all hallways within the premises, the parking lot, and the cafeteria. FIG. 5a shows the on-premises edge devices (e.g., audio-visual (AV) devices 501-510) and installation layout).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the at least one processor further caused to deploy the sending devices in the parking lot based on a structure and a scope of the area of the parking lot as taught in Costello with the monitoring system of Beaurepaire to enable the system to perform “tracking vehicles in and out of the parking lot, delivery areas, and other vehicle access points” (Costello [0004]).
III. Claim 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire in view of Gupta, Miron, and Choi US 20220357167 A1.
Regarding Claim 6, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 5, as discussed above. Beaurepaire does not explicitly teach, however Choi teaches wherein before outputting the parking route of the target vehicle using the preset route planning model based on the vehicle location, the parking space location and the real-time area map, the vehicle parking method further comprises: obtaining model training samples, which comprise multiple sets of data, each set of data of the multiple sets of data comprising an initial location of a vehicle, a destination location of the vehicle, and a real-time regional map, the multiple sets of data being different from each other; and obtaining the preset route planning model by training an initial route planning model using the model training samples ([0003] At a high level, aspects described herein relate to a cloud-based platform that collects data, trains an inference model, uses the trained inference model to generate possible routes to a target location based on the current location of an autonomous vehicle, selects an optimal route from the possible routes, and generates computer-executable instructions that, when communicated to an autonomous vehicle from the cloud-based platform, automatically cause the autonomous vehicle to travel from the current location to the target location. Various related methods, including methods of use, among others, are also described. More specifically, various aspects herein provides for a cloud-based autonomous vehicle delivery route generation platform that ingest historical travel information from tracked movement of delivery vehicles and/or from delivery personnel. The platform can generate a highly-precise delivery route or trajectory from an initial dispatching location to a service location (e.g., package delivery or pick-up), which is provided to and executed by an autonomous vehicle for traversing “on-street” and/or “off-street” terrain, particularly for targeting the “last 10 feet” of a delivery or pickup task. [0028] At a high level, aspects herein provide a cloud-based platform that collects data, trains an inference model, uses the trained inference model to generate
possible routes to a target location based on the current location of an autonomous vehicle, selects an optimal route from the possible routes, and generates computer-executable instructions that, when communicated to an autonomous vehicle from the cloud-based platform, automatically cause the autonomous vehicle to travel from the current location to the target location. Generally, historical data is collected for prior travel, whether by vehicle, autonomous vehicle, or personnel, for example. The historical data may include prior travel for delivery or pick-up of items to any number of geographic locations that may be associated with a street address, a business address, an apartment building, and the like. The historical data can include, in some aspects, time-series data such as the combination of a latitude, a longitude, and a time when the latitude and longitude were recorded, for example.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the process of obtaining model training samples, which comprise multiple sets of data, each set of data of the multiple sets of data comprising an initial location of a vehicle, a destination location of the vehicle, and a real-time regional map, the multiple sets of data being different from each other; and obtaining the preset route planning model by training an initial route planning model using the model training samples as taught in Choi with the mapping method of Beaurepaire because such a combination enables the system “to generate possible routes to a target
location” (Choi [0003]).
Regarding Claim 16, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 15, as discussed above. Beaurepaire does not explicitly teach, however Choi teaches wherein before outputting the parking route of the target vehicle using the preset route planning model based on the vehicle location, the parking space location and the real-time area map, the at least one processor is further caused to: obtain model training samples, which comprise multiple sets of data, each set of data of the multiple sets of data comprising an initial location of a vehicle, a destination location of the vehicle, and a real-time regional map, the multiple sets of data being different from each other; and obtain the preset route planning model by training an initial route planning model using the model training samples ([0003] At a high level, aspects described herein relate to a cloud-based platform that collects data, trains an inference model, uses the trained inference model to
generate possible routes to a target location based on the current location of an autonomous vehicle, selects an optimal route from the possible routes, and generates computer-executable instructions that, when communicated to an autonomous vehicle from the cloud-based platform, automatically cause the autonomous vehicle to travel from the current location to the target location. Various related methods, including methods of use, among others, are also described. More specifically, various aspects herein provides for a cloud-based autonomous vehicle delivery route generation platform that ingest historical travel information from tracked movement of delivery vehicles and/or from delivery personnel. The platform can generate a highly-precise delivery route or trajectory from an initial dispatching location to a service location (e.g., package delivery or pick-up), which is provided to and executed by an autonomous vehicle for traversing “on-street” and/or “off-street” terrain, particularly for targeting the “last 10 feet” of a delivery or pickup task. [0028] At a high level, aspects herein provide a cloud-based platform that collects data, trains an inference model, uses the trained inference model to generate
possible routes to a target location based on the current location of an autonomous vehicle, selects an optimal route from the possible routes, and generates computer-executable instructions that, when communicated to an autonomous vehicle from the cloud-based platform, automatically cause the autonomous vehicle to travel from the current location to the target location. Generally, historical data is collected for prior travel, whether by vehicle, autonomous vehicle, or personnel, for example. The historical data may include prior travel for delivery or pick-up of items to any number of geographic locations that may be associated with a street address, a business address, an apartment building, and the like. The historical data can include, in some aspects, time-series data such as the combination of a latitude, a longitude, and a time when the latitude and longitude were recorded, for example.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the process of obtaining model training samples, which comprise multiple sets of data, each set of data of the multiple sets of data comprising an initial location of a vehicle, a destination location of the vehicle, and a real-time regional map, the multiple sets of data being different from each other; and obtaining the preset route planning model by training an initial route planning model using the model training samples as taught in Choi with the mapping method of Beaurepaire because such a combination enables the system “to generate possible routes to a target
location” (Choi [0003]).
IV. Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire in view of Gupta, Miron and Cai US 20170124874 A1.
Regarding Claim 7, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 1, as discussed above. Beaurepaire does not explicitly teach, however Cai teaches further comprises: in response that the target parking space is determined to be changed based on the area object data and the corresponding object status data, obtaining an updated parking space location corresponding to an updated target parking space ([0037] Parking advisor program 200 analyzes real-time camera data 124 for changes (e.g., vehicle parks in an available parking space, vehicle leaves an occupied space, etc.). Parking advisor program 200 updates the available parking spaces within parking database 126 as changes occur. Parking advisor program 200 evaluates assigned preferred parking spaces associated with client device 110 with the updates to parking database 126 with respect to preferences. In one embodiment, parking advisor program 200 determines a change to the preferred parking space assigned to client device 110 and assigns a new preferred parking space to client device 110 (e.g., higher ranked parking space becomes available, assigned parking space changes to unavailable).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include in the vehicle parking method of Beaurepaire the process of obtaining an updated parking space location corresponding to an updated target parking space as taught by Cai since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination is predictable. Such a combination would yield the predictable result of a vehicle parking method where an updated parking space location is obtained that corresponds to an updated target parking space.
Beaurepaire does not explicitly teach, however Cai teaches obtaining a current location of the target vehicle; and regenerating the parking route of the target vehicle based on the current location, the updated parking space location and the real-time area map ([0017] Navigation parking route 128
includes driving directions, visual aids (e.g., maps), and time estimates from the location associated with a vehicle and an available parking space. Navigation parking route 128 is the output of parking advisor program 200. Navigation parking route 128 updates in real-time based on changes parking advisor program 200 identifies within real-time camera data 124) (see rejection above for combination rationale).
Regarding Claim 8, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 1, as discussed above. Beaurepaire does not explicitly teach, however Cai teaches wherein after controlling the target vehicle to drive to the target parking space according to the parking route, the vehicle parking method further comprises: updating the area object data and the corresponding object status data in the area of the parking lot based on a corresponding relationship between the target vehicle and the target parking space (Claim 6. The method of claim 5, further comprising: responsive to determining the identified vehicle is parked, determining, by one or more computer processors, a location associated with the identified vehicle within the parking database; updating, by one or more computer processor, the historical data with the determined location associated with the identified vehicle; and updating, by one or more computer processors, the parking database to change a status of the determined location associated with the identified vehicle from available to unavailable) (see rejection above for combination rationale).
Regarding Claim 17, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 11, as discussed above. Beaurepaire does not explicitly teach, however Cai teaches wherein the at least one processor is further caused to: in response that the target parking space is determined to be changed based on the area object data and the corresponding object status data, obtain an updated parking space location corresponding to an updated target parking space ([0037] Parking advisor program 200 analyzes real-time camera data 124 for changes (e.g., vehicle parks in an available parking space, vehicle leaves an occupied space, etc.). Parking advisor program 200 updates the available parking spaces within parking database 126 as changes occur. Parking advisor program 200 evaluates assigned preferred parking spaces associated with client device 110 with the updates to parking database 126 with respect to preferences. In one embodiment, parking advisor program 200 determines a change to the preferred parking space assigned to client device 110 and assigns a new preferred
parking space to client device 110 (e.g., higher ranked parking space becomes available, assigned parking space changes to unavailable).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include in the vehicle parking method of Beaurepaire the process of obtaining an updated parking space location corresponding to an updated target parking space as taught by Cai since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination is predictable. Such a combination would yield the predictable result of a vehicle parking method where an updated parking space location is obtained that corresponds to an updated target parking space.
Beaurepaire does not explicitly teach, however Cai teaches obtain a current location of the target vehicle; and regenerate the parking route of the target vehicle based on the current location, the updated parking space location and the real-time area map ([0017] Navigation parking route 128
includes driving directions, visual aids (e.g., maps), and time estimates from the location associated with a vehicle and an available parking space. Navigation parking route 128 is the output of parking advisor program 200. Navigation parking route 128 updates in real-time based on changes parking advisor program 200 identifies within real-time camera data 124) (see rejection above for combination rationale).
Regarding Claim 18, the combination of Beaurepaire, Gupta, and Miron teaches the limitations of claim 11, as discussed above. Beaurepaire does not explicitly teach, however Cai teaches wherein after controlling the target vehicle to drive to the target parking space according to the parking route, the at least one processor is further caused to: update the area object data and the corresponding object status data in the area of the parking lot based on a corresponding relationship between the target vehicle and the target parking space (Claim 6. The method of claim 5, further comprising: responsive to determining the identified vehicle is parked, determining, by one or more computer processors, a location associated with the identified vehicle within the parking database; updating, by one or more computer processor, the historical data with the determined location associated with the identified vehicle; and updating, by one or more computer processors, the parking database to change a status of the determined location associated with the identified vehicle from available to unavailable) (see rejection above for combination rationale).
Novel & Non-Obvious Subject Matter
Claim 9 would be allowable if rewritten to overcome the 35 U.S.C. 112 rejections.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 9 would be allowable for disclosing determining the target vehicle and a status of the target vehicle according to the area object data and the corresponding object status data, by identifying the target vehicle from the area object data based on a vehicle identification obtained from the finding instruction and retrieving the status from the object status data. Beaurepaire teaches that the target vehicle 101 may include GPS receivers to obtain geographic coordinates from satellites 125 for determining current or live location and time. Further, the location can be determined by a triangulation system such as A-GPS, Cell of Origin, or other location extrapolation technologies when cellular or network signals are available (Beaurepaire [0062]).
Beaurepaire further teaches determining the area object data and the corresponding object status data. For example, Beaurepaire teaches that the routing module 109 in target vehicle 101 recognizes Object/Car 103 using its front camera (and/or any other equivalent sensor or process) (Beaurepaire [0034]). However, nothing in the prior art teaches determining the target vehicle and a status of the target vehicle according to the area object data and the corresponding object status data, by identifying the target vehicle from the area object data based on a vehicle identification obtained from the finding instruction and retrieving the status from the object status data.
Response to Arguments
Applicant’s arguments regarding the previous 35 U.S.C. 112 rejections have been fully considered and are persuasive. The previous 35 U.S.C. 112 rejections have been withdrawn.
Regarding the prior art rejections, Applicant argues that “Beaurepaire does not disclose any step of generating a map or any step regarding training a map construction model” (p. 12). The Examiner disagrees. As described more fully above, such features are taught by at least paragraph [0031] of Beaurepaire.
Applicant argues that “Beaurepaire is silent about preprocessing data collected by sensors,
using a preset map construction model, or training a map construction model with data collected
by sensing devices … Gupta does not disclose preprocessing sensing data, and does not disclose training a map construction model either” (pp. 12-13). As described more fully above, such features are taught by the combination of Beaurepaire and Miron.
Applicant argues that:
The Office Action indicates that Miron teaches obtaining a training sample set and training a generator model. However, Applicant submits that the generator model in Miron is not a model for generating a map. The generator model outputs pixel-wise depth values, not a map storing the identifiers of objects in the parking lot and correspondences between the objects. Moreover, Miron is directed to training a depth completion model for robotics applications, not a map construction model for parking lot management
(p. 13). As described more fully above, such features are taught by Gupta.
Conclusion
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/D.N.M./Examiner, Art Unit 3628
/GEORGE CHEN/Primary Examiner, Art Unit 3628
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