Nq
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 6-12 and 16-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,7-13 and 16-20 of U.S. Patent No. 12,300,105. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims include all of the limitations of the instant application claims, respectively. The patent claims also include additional limitations. Hence, the instant application claims are generic to the species of invention covered by the respective patent claims. As such, the instant application claims are anticipated by the patent claims and are therefore not patentably distinct therefrom. (See Eli Lilly and Co. v. Barr Laboratories Inc., 58 USPQ2D 1869, "a later genus claim limitation is anticipated by, and therefore not patentably distinct from, an earlier species claim", In re Goodman, 29 USPQ2d 2010, "Thus, the generic invention is 'anticipated' by the species of the patented invention" and the instant “application claims are generic to species of invention covered by the patent claim, and since without terminal disclaimer, extant species claims preclude issuance of generic application claims”).
For Example:
Instant Application
US Patent #12,300,105
1. A method comprising:
determining, by one or more processors based on observations of one or more vehicles parked at an edge identified as not parkable in a roadgraph,
whether a sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph,
wherein the edge comprises a distance between two graph nodes of the roadgraph and defines a drivable area in the roadgraph;
and responsive to determining that the sub-portion of the edge is parkable,
generating, by the one or more processors, map information associated with the edge.
1. A method comprising:
identifying, by one or more processors, from logged data, observations of one or more vehicles parked in an area identified as not parkable in a roadgraph;
determining, by the one or more processors based on the observations of the one or more vehicles parked in the area identified as not parkable in the roadgraph,
whether a sub-portion of an edge in the area identified as not parkable in the roadgraph corresponds to a parkable area irrespective of the sub-portion of the edge being in the area identified as not parkable in the roadgraph,
wherein the edge comprises a distance between two graph nodes of the roadgraph and defines a drivable area in the roadgraph;
and responsive to determining that the sub-portion of the edge in the area identified as not parkable in the roadgraph corresponds to the parkable area,
generating, by the one or more processors, map information associated with the area identified as not parkable.
6. The method of claim 1, further comprising determining, by the one or more processors based on the observations, a percentage of time that a vehicle is stopped at the edge.
7. The method of claim 1, further comprising, further analyzing the observations of the one or more vehicles parked in the area identified as not parkable in the roadgraph to determine a percentage of time that the parkable area was occupied by a vehicle.
7. The method of claim 1, further comprising determining, by the one or more processors based on the observations, respective likelihoods of availability of the edge for a plurality of different periods of time.
8. The method of claim 1, further comprising, further analyzing the observations of the one or more vehicles parked in the area identified as not parkable in the roadgraph to determine likelihoods of the parkable area being available during a plurality of different periods of time.
8. The method of claim 1, further comprising, training, by the one or more processors, a machine learned model, based on the observations, to provide a likelihood of availability of the edge at a future time.
9. The method of claim 1, further comprising, using the observations of the one or more vehicles parked in the area identified as not parkable in the roadgraph to train a machine learned model to provide a likelihood of the parkable area being occupied at some point in the future.
9. The method of claim 8, further comprising: determining, by the one or more processors based on the observations, a percentage of time that a vehicle is stopped at the edge; and training, by the one or more processors, the machine learned model based on the percentage of time.
10. The method of claim 9, further comprising further analyzing the observations of the one or more vehicles parked in the area identified as not parkable in the roadgraph to determine a percentage of time that the parkable area was occupied by a vehicle, and using the percentage of time to train the model.
10. The method of claim 9, further comprising providing, by the one or more processors, the machine learned model to an autonomous vehicle.
11. The method of claim 9, further comprising, providing the model to an autonomous vehicle to enable the autonomous vehicle to use the model to make driving decisions.
11. The method of claim 1, further comprising identifying, by the one or more processors based on the map information, one or more potential locations for a vehicle to stop and pick up or drop off passengers or goods.
12. The method of claim 1, further comprising, using the map information to identify potential locations for a vehicle to stop and pick up or drop off passengers or goods.
12. A system comprising one or more processors configured to:
determine, based on observations of one or more vehicles parked at an edge identified as not parkable in a roadgraph, whether a sub-portion of the edge is parkable area irrespective of the edge being identified as not parkable in the roadgraph,
wherein the edge comprises a distance between two graph nodes of the roadgraph and defines a drivable area in the roadgraph;
and responsive to a determination that the sub-portion of the edge is parkable,
generate map information associated with the edge.
13. A system comprising: memory storing logged data; and one or more processors configured to:
identify from the stored logged data, observations of one or more vehicles parked in an area identified as not parkable in a roadgraph;
determine, based on the observations of the one or more vehicles parked in the area identified as not parkable in the roadgraph, whether a sub-portion of an edge in the area identified as not parkable in the roadgraph corresponds to a parkable area irrespective of the sub-portion of the edge being in the area identified as not parkable in the roadgraph,
wherein the edge comprises a distance between two graph nodes of the roadgraph and defines a drivable area in the roadgraph;
and responsive to a determination that the sub-portion of the edge in the area identified as not parkable in the roadgraph corresponds to the parkable area,
generate map information associated with the area identified as not parkable.
16. The system of claim 12, wherein the one or more processors are further configured to determine, based on the observations, a percentage of time that a vehicle is stopped at the edge.
16. (currently amended) The system of claim 13, wherein the one or more processors are further configured to further analyze the observations of the one or more vehicles parked in the area identified as not parkable in the roadgraph to determine a percentage of time that the parkable area was occupied by a vehicle.
17. The system of claim 12, wherein the one or more processors are further configured to determine, based on the observations, respective likelihoods of availability of the edge for a plurality of different periods of time.
17. The system of claim 13, wherein the one or more processors are further configured to further analyze the observations of the one or more vehicles parked in the area identified as not parkable in the roadgraph to determine likelihoods of the parkable area being available during a plurality of different periods of time.
18. The system of claim 12, wherein the one or more processors are further configured to provide the map information to an autonomous vehicle.
18. The system of claim 13, wherein the one or more processors are further configured to provide the map information to an autonomous vehicle to enable the autonomous vehicle to use the map information to make driving decisions
19. The system of claim 12, wherein the one or more processors are further configured to train a machine learned model, based on the observations to provide a likelihood of availability of the edge at a future time.
19. The system of claim 13, wherein the one or more processors are further configured to use the observations to train a machine learned model to provide a likelihood of the parkable area being occupied at some point in the future.
20. The system of claim 12, wherein the one or more processors are further configured to identify, based on the map information, potential locations for a vehicle to stop and pick up or drop off passengers or goods.
20. The system of claim 13, wherein the one or more processors are further configured to use the map information to identify potential locations for a vehicle to stop and pick up or drop off passengers or goods.
Allowance of application claims would result in an unjustified time-wise extension of the monopoly granted for the invention defined by US Patent claims. Therefore, Obviousness-type double patenting is appropriate.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3,5,12,13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Shurkhovetskyy (US 20190073901 A1) in view of Shaffer (DE 102014206235 A1).
Claim 1. Shurkhovetskyy teaches a method comprising:
determining, by one or more processors based on observations of one or more vehicles
parked at an edge identified as not parkable in a roadgraph
([0032] Ascertaining vehicle 1,...includes surroundings sensors 14 for detecting possible parking spaces 22 in the surroundings of vehicle 1. [0036] Street section 200 also includes space 208 in which no parking is allowed. As ascertaining vehicle 228 drives down street section 200 in direction 206, ascertaining vehicle 228 detects, inter alia, the presence of vehicles 201-205 and space 208.
[0037] Also depicted in FIG. 2 is graph 250 that corresponds to the data collected by ascertaining vehicle 228 and includes distance data 223 and positional data 224.
[0049] The graph thus represents spaces in which parking is permitted as regions 401-405 and represents spaces in which parking is not permitted as regions 406-409.)
and wherein the edge comprises a distance between two graph nodes of the roadgraph and defines a drivable area in the roadgraph
([0036]-[0040] Distance data 223...corresponding ending edges and beginning edges 215-222 can be obtained over a period of time by vehicles traveling through street section 200. In this manner, each time a vehicle travels through a particular street section, a total number of ending edges 215, 217, 219, and 221 of vehicles 201-205 and beginning edges 216, 218, 220, and 222 of vehicles 201-205 are obtained.
[0049] The graph thus represents spaces in which parking is permitted as regions 401-405 and represents spaces in which parking is not permitted as regions 406-409. Plot 412 represents a total number of edges that are detected for the particular street section over a particular time period.
e.g. drivable area is where parking is permitted;
e.g. beginning and ending edges identified as not parkable in the road graph to corresponding parkable areas occupied by vehicles 201-205; Fig 4 shows nodes such as 401, 406, 402, etc. (showing boundaries 411/410)).
; and responsive to determining that a sub-portion of the edge is parkable, generating, by the
one or more processors, map information associated with the edge
([0049] The graph thus represents spaces in which parking is permitted as regions 401-405 and represents spaces in which parking is not permitted as regions 406-409. (e.g. regions are the sub portions)
[0019][0041] determine at least one boundary of at least one space in which parking is permitted based on the received data, the at least one space having at least one of a first boundary region and a second boundary region; generate a display representation of the boundaries of the at least one space in which parking is permitted; and provide the display representation on a display device...a map depicting spaces in which parking is permitted can be generated.).
Shurkhovetskyy further teaches the process of determining and identifying a plurality of sub-portions of edges based on sensed data (Fig. 4 and [0041] [0049]e.g. regions)and utilization of sensors ([0032])but does not specifically disclose whether a sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph.
However, Shaffer teaches process of determining whether a sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph
(Page 7– At higher pass-by speeds, the edges of the surrounding vehicles may not be accurately detected if the returned data points have skipped those edges... There might be some gaps that are too narrow and other available parking spaces could be rejected...
That's why the process closes 400 a radar for improving the calculation of the gap length by providing the missing information about the edges of the surrounding vehicles...
Therefore, the combined sensor may be a corrective measure for checking the distance to objects in a closed loop and for correcting incorrectly calculated distances from the ultrasonic sensor. For example, if a vehicle length above a certain threshold does not match the ultrasonic sensor, the parking space would not be offered. This would prevent gaps being offered that are too narrow and gaps not being offered where vehicle lengths are not measured correctly and the gap is large enough to park
e.g. a second radar is used to detect missing gaps (i.e. sub-portions of the edge) to find mistakes of a parkable area when first sensor determines as not parkable).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of determining a sub-portion as parkable as taught by Shaffer within the system of Shurkhovetskyy for the purpose of optimizing detection of parking spaces using two sensors in order to improve accuracy and parking efficiency for the vehicle.
Claim 2. Shurkhovetskyy and Shaffer teach the method of claim 1, wherein the edge is in an area identified as not parkable in the roadgraph
(Shurkhovetskyy [0049) parking is not permitted as regions 406-409. Plot 412 represents a total number of edges...Histogram 413 represents the number of detected ending edges for the particular street section ).
Claim 3. Shurkhovetskyy and Shaffer teach the method of claim 1. The prior art does not specifically teach updating, by the one or more processors, the roadgraph to identify the edge identified as not parkable, as parkable. However, Shurkhovetskyy further teaches the process of each vehicle with sensors providing data when passing a roadgraph ([0011][0012]); and Shaffer teaches a two sensor vehicle sensor system which provides enhancements to identify edge as not parkable as parkable (Page 7). Since Shurkhovetskyy has the ability to receive data for each passing vehicle, then one ordinarily skilled in the art would have found it obvious to incorporate an update to the roadgraph when Shaffer’s vehicle passes through the roadgraph in an effort to improve identification of parkable areas.
Claim 5. Shurkhovetskyy and Shaffer teach the method of claim 1, further comprising, responsive to determining that the sub-portion of the edge is parkable, determining, by the one or more processors based on the observations, whether a parkable arca corresponding to the edge is left of the edge, along the edge, or between the edge and a different edge of the roadgraph
(Shaffer Page 3 - As in 1 can represent four transceivers or sensors 114 , such as ultrasonic sensors, on the left and right side of the vehicle 102 positioned adjacent to the front and rear bumpers to provide a complete or nearly complete 360 ° detection area around the vehicle 102 provide (e.g. left edge can be detected by sensor)
Page 5/6 - Therefore, in the block 422 the object is judged and whether radar data is detected at the same position as the curb, and if so, the method further comprises determining the first object as a wall. Identified objects are judged to determine if they are farther away than the curb (Block 424 ). If that's the case 426 , then the object is in the block 428 rejected because the object is not an obstacle and is beyond the area where the vehicle is parked. Further objects are within the data 430 judged and if these objects are not the last to be judged 432 Objects are, the controller returns to block 422 back. However, if the object is the last to be judged 434 Object is, the possible parking space is accepted 418 , (e.g. between the edge and different edge are the wall and curb)).
Claim 12. Shurkhovetskyy teaches a system comprising one or more processors configured to:
determine, based on observations of one or more vehicles parked at an edge identified as not parkable in a roadgraph
([0032] Ascertaining vehicle 1,...includes surroundings sensors 14 for detecting possible parking spaces 22 in the surroundings of vehicle 1. [0036] Street section 200 also includes space 208 in which no parking is allowed. As ascertaining vehicle 228 drives down street section 200 in direction 206, ascertaining vehicle 228 detects, inter alia, the presence of vehicles 201-205 and space 208.
[0037] Also depicted in FIG. 2 is graph 250 that corresponds to the data collected by ascertaining vehicle 228 and includes distance data 223 and positional data 224.
[0049] The graph thus represents spaces in which parking is permitted as regions 401-405 and represents spaces in which parking is not permitted as regions 406-409.),
wherein the edge comprises a distance between two graph nodes of the roadgraph and defines a drivable area in the roadgraph
([0036]-[0040] Distance data 223...corresponding ending edges and beginning edges 215-222 can be obtained over a period of time by vehicles traveling through street section 200. In this manner, each time a vehicle travels through a particular street section, a total number of ending edges 215, 217, 219, and 221 of vehicles 201-205 and beginning edges 216, 218, 220, and 222 of vehicles 201-205 are obtained.
[0049] The graph thus represents spaces in which parking is permitted as regions 401-405 and represents spaces in which parking is not permitted as regions 406-409. Plot 412 represents a total number of edges that are detected for the particular street section over a particular time period.
e.g. drivable area is where parking is permitted;
e.g. beginning and ending edges identified as not parkable in the road graph to corresponding parkable areas occupied by vehicles 201-205; Fig 4 shows nodes such as 401, 406, 402, etc. (showing boundaries 411/410 which is interpretated as the distance between two graph nodes))
; and responsive to a determination that the sub-portion of the edge is parkable, generate map
information associated with the edge
([0049] The graph thus represents spaces in which parking is permitted as regions 401-405 and represents spaces in which parking is not permitted as regions 406-409. (e.g. regions are the sub portions)
[0019] determine at least one boundary of at least one space in which parking is permitted based on the received data, the at least one space having at least one of a first boundary region and a second boundary region; generate a display representation of the boundaries of the at least one space in which parking is permitted; and provide the display representation on a display device.
[0041] a map depicting spaces in which parking is permitted can be generated.).
Shurkhovetskyy further teaches the process of determining and identifying a plurality of sub-portions of edges based on sensed data (Fig. 4 and [0041] [0049]e.g. regions)and utilization of sensors ([0032])but does not specifically disclose whether a sub-portion of the edge is parkable area irrespective of the edge being identified as not parkable in the roadgraph
However, Shaffer teaches process of determining whether a sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph
(Page 7– At higher pass-by speeds, the edges of the surrounding vehicles may not be accurately detected if the returned data points have skipped those edges... There might be some gaps that are too narrow and other available parking spaces could be rejected...
That's why the process closes 400 a radar for improving the calculation of the gap length by providing the missing information about the edges of the surrounding vehicles...
Therefore, the combined sensor may be a corrective measure for checking the distance to objects in a closed loop and for correcting incorrectly calculated distances from the ultrasonic sensor. For example, if a vehicle length above a certain threshold does not match the ultrasonic sensor, the parking space would not be offered. This would prevent gaps being offered that are too narrow and gaps not being offered where vehicle lengths are not measured correctly and the gap is large enough to park
e.g. a second radar is used to detect missing gaps (i.e. sub-portions of the edge) to find mistakes of a parkable area when first sensor determines as not parkable).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of determining a sub-portion as parkable as taught by Shaffer within the system of Shurkhovetskyy for the purpose of optimizing detection of parking spaces using two sensors in order to improve accuracy and parking efficiency for the vehicle.
Claim 13. Shurkhovetskyy and Shaffer teach the system of claim 12. The prior art does not specifically teach wherein the one or more processors are further configured to, responsive to the determination that the sub-portion of the edge is parkable, update the roadgraph to identify the edge identified as not parkable, as parkable. However, Shurkhovetskyy further teaches the process of each vehicle with sensors providing data when passing a roadgraph ([0011][0012]); and Shaffer teaches a two sensor vehicle sensor system which provides enhancements to identify edge as not parkable as parkable (Page 7). Since Shurkhovetskyy has the ability to receive data for each passing vehicle, then one ordinarily skilled in the art would have found it obvious to incorporate an update to the roadgraph when Shaffer’s vehicle passes through the roadgraph in an effort to improve identification of parkable areas.
Claim 15. Shurkhovetskyy and Shaffer teach the system of claim 12, wherein the one or more processors are further configured to, responsive to the determination that the sub-portion of the edge is parkable, determine, based on the observations, whether a parkable area corresponding to the edge is left of the edge, along the edge, or between the edge and a different edge of the roadgraph
(Shaffer Page 3 - As in 1 can represent four transceivers or sensors 114 , such as ultrasonic sensors, on the left and right side of the vehicle 102 positioned adjacent to the front and rear bumpers to provide a complete or nearly complete 360 ° detection area around the vehicle 102 provide (e.g. left edge can be detected by sensor)
Page 5/6 - Therefore, in the block 422 the object is judged and whether radar data is detected at the same position as the curb, and if so, the method further comprises determining the first object as a wall. Identified objects are judged to determine if they are farther away than the curb (Block 424 ). If that's the case 426 , then the object is in the block 428 rejected because the object is not an obstacle and is beyond the area where the vehicle is parked. Further objects are within the data 430 judged and if these objects are not the last to be judged 432 Objects are, the controller returns to block 422 back. However, if the object is the last to be judged 434 Object is, the possible parking space is accepted 418 , (e.g. between the edge and different edge are the wall and curb)).
Claim(s) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shurkhovetskyy, Shaffer and further in view of Krekel (DE 102019127367 A1).
Claim 4. Shurkhovetskyy and Shaffer teach the method of claim 1, and further discloses the process of detecting edges to find available parking but does not specifically disclose determining, by the one or more processors based on the observations, whether a different sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph.
However, Krekel teaches determining, by the one or more processors based on the observations, whether a different sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph
(Page 12- In 7A is the potential parking lot 706 by the parking support control 124 was identified wrong. In response to receiving a correction from the vehicle operator 100 , is the parking assist control 124 configured to determine whether the correction corresponds to another potential parking space.
Page 13- 7B illustrates the example parking assistance scenario after the parking assistance control 124 received the
correction from the operator. As in 7B has illustrated the parking assistance control 124 based on correction
provided by the operator, a potential parking lot 708 and a potential parking lot 710 identified. When
identifying the potential parking spaces 708 , 710 shows the parking assistance control 124 the operator an
interface that identifies the potential parking spaces 708 , 710 represents.).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of determining whether a different sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph as taught by Krekel within the system of Shurkhovetskyy for the purpose of enhancing the system to provide alternative parking when a determined parking space is not suitable for the vehicle.
Claim 14. Shurkhovetskyy and Shaffer teach the system of claim 12, and further discloses the process of detecting edges to find available parking but does not specifically disclose wherein the one or more processors are further configured to determine, based on the observations, whether a different sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph.
However, Krekel teaches wherein the one or more processors are further configured to determine, based on the observations, whether a different sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph
(Page 12- In 7A is the potential parking lot 706 by the parking support control 124 was identified wrong. In response to receiving a correction from the vehicle operator 100 , is the parking assist control 124 configured to determine whether the correction corresponds to another potential parking space.
Page 13- 7B illustrates the example parking assistance scenario after the parking assistance control 124 received the
correction from the operator. As in 7B has illustrated the parking assistance control 124 based on correction
provided by the operator, a potential parking lot 708 and a potential parking lot 710 identified. When
identifying the potential parking spaces 708 , 710 shows the parking assistance control 124 the operator an
interface that identifies the potential parking spaces 708 , 710 represents.).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of determining whether a different sub-portion of the edge is parkable irrespective of the edge being identified as not parkable in the roadgraph as taught by Krekel within the system of Shurkhovetskyy for the purpose of enhancing the system to provide alternative parking when a determined parking space is not suitable for the vehicle.
Claim(s) 6, 7 , 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shurkhovetskyy and Shaffer and further in view of Korman (US 20200257909 A1).
Claim 6. Shurkhovetskyy and Shaffer teach the method of claim 1, and discloses the process of detecting available parking spaces from each passing vehicle (Shurkhovetskyy [0011][0012]) but does not specifically disclose determining, by the one or more processors based on the observations, a percentage of time that a vehicle is stopped at the edge
However, Korman teaches determining, by the one or more processors based on the observations, a percentage of time that a vehicle is stopped at the edge
([0181] The heat map represents the probability... The heat map may be calculated using analyzed data from the database 284 over a large time, which form a statistical measurement.
[0185] The processor 282 also communicates with the mutual data base 284, which stores additional data such as static information, such as a parking lot and street parking around a certain location is probably filled or highly occupied at a certain time based on an event nearby...parking lots filling up at certain times, such as business hours on work days.
[0254] Other indications of stopped vehicles, to determine whether a vehicle is stopped, and typically parked, include viewing data from multiple image frames taken close in time e.g., within a predetermined time, showing the vehicle in the same location, or from cameras in different camera equipped vehicles, close in time. ).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of analyzing the observations to determine a percentage of time that a vehicle is stopped at the edge as taught by Korman within the system of Shurkhovetskyy for the purpose of enhancing the system to determine a quantitative likelihood where occupancy is more prevalent in order to avoid the specific parking areas.
Claim 7. Shurkhovetskyy and Shaffer teach the method of claim 1, and further discloses the process of analyzing vehicle occupancy and a database storing information related available parking ([0019]) but does not specifically disclose determining, by the one or more processors based on the observations, respective likelihoods of availability of the edge for a plurality of different periods of time
However, Korman teaches determining by a processor respective likelihoods of availability of the edge for a plurality of different periods of time ([0225] The process moves to block 703, where a general heat map 720 for parking is created, for example, the heat map of FIG. 7b, which shows parking probabilities along various streets. In FIG. 7b and also Jo-2 and 7d-2, also heat maps, three probabilities, high, medium and low, are shown. [0031] Some exemplary AVPPL embodiment may build a statistical occupancy database. An exemplary statistical occupancy database may store statistics on vacancy of parking places over the time of a day/week/weekend, and so on. [0041] For example a vacant parking place that was located 1 minutes ago may be more bright than a vacant parking place that was located 5 minutes ago (the brightness may slowly fade away as time passes)).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of determining respective likelihoods of availability of the edge for a plurality of different periods of time as taught by Korman within the system of Shurkhovetskyy for the purpose of enhancing the system to forecast the best chance to obtain an available parking space based on historical observations.
Claim 16. Shurkhovetskyy and Shaffer teach the system of claim 12, and discloses the process of detecting available parking spaces from each passing vehicle (Shurkhovetskyy [0011][0012]) but does not specifically disclose wherein the one or more processors are further configured to determine, based on the observations, a percentage of time that a vehicle is stopped at the edge.
However, Korman teaches determining, by the one or more processors based on the observations, a percentage of time that a vehicle is stopped at the edge
([0181] The heat map represents the probability... The heat map may be calculated using analyzed data from the database 284 over a large time, which form a statistical measurement.
[0185] The processor 282 also communicates with the mutual data base 284, which stores additional data such as static information, such as a parking lot and street parking around a certain location is probably filled or highly occupied at a certain time based on an event nearby...parking lots filling up at certain times, such as business hours on work days.
[0254] Other indications of stopped vehicles, to determine whether a vehicle is stopped, and typically parked, include viewing data from multiple image frames taken close in time e.g., within a predetermined time, showing the vehicle in the same location, or from cameras in different camera equipped vehicles, close in time. ).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of one or more processors which are further configured to determine, based on the observations, a percentage of time that a vehicle is stopped at the edge as taught by Korman within the system of Shurkhovetskyy for the purpose of enhancing the system to determine a quantitative likelihood where occupancy is more prevalent in order to avoid the specific parking areas.
Claim 17. Shurkhovetskyy and Shaffer teach the system of claim 12, and further discloses the process of analyzing vehicle occupancy and a database storing information related available parking ([0019]) but does not specifically disclose wherein the one or more processors are further configured to determine, based on the observations, respective likelihoods of availability of the edge for a plurality of different periods of time.
However, Korman teaches wherein the one or more processors are further configured to determine, based on the observations, respective likelihoods of availability of the edge for a plurality of different periods of time
([0225] The process moves to block 703, where a general heat map 720 for parking is created, for example, the heat map of FIG. 7b, which shows parking probabilities along various streets. In FIG. 7b and also Jo-2 and 7d-2, also heat maps, three probabilities, high, medium and low, are shown. [0031] Some exemplary AVPPL embodiment may build a statistical occupancy database. An exemplary statistical occupancy database may store statistics on vacancy of parking places over the time of a day/week/weekend, and so on. [0041] For example a vacant parking place that was located 1 minutes ago may be more bright than a vacant parking place that was located 5 minutes ago (the brightness may slowly fade away as time passes)).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of determining respective likelihoods of availability of the edge for a plurality of different periods of time as taught by Korman within the system of Shurkhovetskyy for the purpose of enhancing the system to forecast the best chance to obtain an available parking space based on historical observations.
Claim(s) 8-10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shurkhovetskyy and Shaffer and further in view of Zhao (US 20180349792 A1).
Claim 8. Shurkhovetskyy and Shaffer teach the method of claim 1, and further discloses the process of predicting parking spaces ([0028]) but does not specifically disclose training, by the one or more processors, a machine learned model, based on the observations, to provide a likelihood of availability of the edge at a future time.
However, Zhao teaches the process of training, by the one or more processors, a machine learned model, based on the observations, to provide a likelihood of availability of the edge at a future time
([0006] using the observations to train a machine learned model to provide a likelihood of the parkable area being occupied at some point in the future
[0056] In one embodiment, when machine learning classification is used, the output the parking occupancy model can be a probability that given a set of features for a link of interest, the link is classified as either (1) having a particular parking occupancy pattern, and/or
(2) having parking available or parking unavailable to indicate parking occupancy at the link. In other words, the prediction is made based on a probability range spanning from 0% to 100% probability that a link has a particular parking occupancy pattern or has parking available (e.g., 0O)).
Therefore, it would have been obvious to one ordinarily skilled in the art in the art before the effective filing date of invention to use the process of training a machine learned model, based on the observations, to provide a likelihood of availability of the edge at a future time as taught by Zhao within the system of Shurkhovetskyy for the purpose of enhancing the system to create a prediction of parking based on a learned model.
Claim 9. Shurkhovetskyy, Shaffer and Zhao teach the method of claim 8, further comprising:
determining, by the one or more processors based on the observations, a percentage of time
that a vehicle is stopped at the edge; and training, by the one or more processors, the machine learned model based on the percentage of time
(Zhao [0056] In one embodiment, when machine learning classification is used, the output the parking occupancy model can be a probability that given a set of features for a link of interest, the link is classified as either (1) having a particular parking occupancy pattern, and/or (2) having parking available or parking unavailable to indicate parking occupancy at the link. In other words, the prediction is made based on a probability range spanning from 0% to 100% probability that a link has a particular parking occupancy pattern or has parking available (e.g., 0<classification probability<1).).
Claim 10. Shurkhovetskyy, Shaffer and Zhao teach the method of claim 9, further comprising providing, by the one or more processors, the machine learned model to an autonomous vehicle
(Zhao [0066] interfaced with an on-board navigation system of an autonomous vehicle
[0056] In one embodiment, when machine learning classification is used,...).
Claim 19. Shurkhovetskyy and Shaffer teach the system of claim 12, and further discloses the process of predicting parking spaces ([0028]) but does not specifically disclose wherein the one or more processors are further configured to train a machine learned model, based on the observations to provide a likelihood of availability of the edge at a future time.
However, Zhao teaches the process of training, by the one or more processors, a machine learned model, based on the observations, to provide a likelihood of availability of the edge at a future time
([0006] using the observations to train a machine learned model to provide a likelihood of the parkable area being occupied at some point in the future
[0056] In one embodiment, when machine learning classification is used, the output the parking occupancy model can be a probability that given a set of features for a link of interest, the link is classified as either (1) having a particular parking occupancy pattern, and/or
(2) having parking available or parking unavailable to indicate parking occupancy at the link. In other words, the prediction is made based on a probability range spanning from 0% to 100% probability that a link has a particular parking occupancy pattern or has parking available (e.g., 0O)).
Therefore, it would have been obvious to one ordinarily skilled in the art in the art before the effective filing date of invention to use the process of training a machine learned model, based on the observations, to provide a likelihood of availability of the edge at a future time as taught by Zhao within the system of Shurkhovetskyy for the purpose of enhancing the system to create a prediction of parking based on a learned model.
Claim(s) 11, 18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shurkhovetskyy and Shaffer and further in view of Dyer (US 20190066515 A1).
Claim 11. Shurkhovetskyy and Shaffer teach the method of claim 1, and discloses the ability to find available parking but do not specifically disclose identifying, by the one or more processors based on the map information, one or more potential locations for a vehicle to stop and pick up or drop off passengers or goods.
However, Dyer teaches identifying, by the one or more processors based on the map information, one or more potential locations for a vehicle to stop and pick up or drop off passengers or goods
([0019] the vehicle's computing devices may identify a list of possible places to stop within some distance of the passenger (once identified) and/or the pickup location, or in the case of a drop off, the drop off location.)).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of identifying one or more potential locations for a vehicle to stop and pick up or drop off passengers or goods as taught by Dyer within the system of Shurkhovetskyy for the purpose of providing an additional feature for temporary parking based on certain requirements of the driver.
Claim 18. Shurkhovetskyy and Shaffer teach the system of claim 12, wherein the one or more processors are further configured to provide the map information to an autonomous vehicle.
However, Dyer teaches wherein the one or more processors are further configured to provide the map information to an autonomous vehicle
([0019] the vehicle's computing devices may identify a list of possible places to stop within some distance of the passenger (once identified) and/or the pickup location, or in the case of a drop off, the drop off location.)).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of identifying one or more potential locations for a vehicle to stop and pick up or drop off passengers or goods as taught by Dyer within the system of Shurkhovetskyy for the purpose of providing an additional feature for temporary parking based on certain requirements of the driver.
Claim 20. Shurkhovetskyy and Shaffer teach the system of claim 12, and discloses the ability to find available parking but do not specifically disclose wherein the one or more processors are further configured to identify, based on the map information, potential locations for a vehicle to stop and pick up or drop off passengers or goods.
However, Dyer teaches wherein the one or more processors are further configured to identify, based on the map information, potential locations for a vehicle to stop and pick up or drop off passengers or goods
([0019] the vehicle's computing devices may identify a list of possible places to stop within some distance of the passenger (once identified) and/or the pickup location, or in the case of a drop off, the drop off location.)).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of identifying one or more potential locations for a vehicle to stop and pick up or drop off passengers or goods as taught by Dyer within the system of Shurkhovetskyy for the purpose of providing an additional feature for temporary parking based on certain requirements of the driver.
Conclusion
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/RUFUS C POINT/Primary Examiner, Art Unit 2689