DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner Notes
2. The Examiner has cited particular paragraphs or columns and line numbers in the references applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested of the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure (see MPEP §2163.06). Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) of the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. SEE MPEP 2141.02 [R-07.2015] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851 (1984). See also MPEP §2123.
Claim Objections
3. Claim 1, and 19-20 objected to because of the following informalities:
“the probability data” should read “the received probability data”
“the set” should read “the set of one or more paths”
“the plurality of path segments” should read “the plurality of the path segments”
“wherein the determined extent of at least one of the paths of the set depends at least in part upon the received probability data” should read “wherein the determined extent of the path of the set of one or more paths depends at least in part upon the received probability data”
“the respective determined one or more path extents” should read “the respective determined path extent”
4. Claim 2 objected to because of the following informalities:
“the respective determined path extents” should read “the respective determined path extent”.
5. Claim 2-3, and 16-17 objected to because of the following informalities:
“the set of one or more paths” should read “the determined set of one or more paths”.
6. Appropriate correction is required.
Claim Rejections - 35 USC § 112
7. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
8. Claim 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
9. Claim 1 recites the limitation “the respective path segment”. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “a respective path segment”.
10. Claim(s) 2-18 rejected by virtue of its/their dependency on claim 1.
11. Claim 4, 7, 10, 12, and 18 recites the limitation “the set of paths”. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “the set of one or more paths”.
12. Claim 5-8, 11-14, and 18 recites the limitation “the vehicle”. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “the subject vehicle”.
13. Claim 7, and 12 recites the limitation “the addition or the removal of one path segment”. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “addition or removal of one path segment”.
14. Claim 10 recites the limitation “the maximum path-distance threshold”. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “a maximum path-distance threshold”.
15. Claim 19 recites the limitation “the respective path segment”. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “a respective path segment”.
16. Claim 20 recites the limitation “the respective path segment”. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “a respective path segment”.
Claim Rejections - 35 USC § 101
17. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
18. Claim(s) 1-20
is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
19. The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
20. Claim(s) 1-18 is/are directed to a method (i.e. a process).
Therefore, claim(s) 1-18 is/are within at least one of the four statutory categories.
21. Claim(s) 19 is/are directed to a system (i.e. an apparatus).
Therefore, claim(s) 19 is/are within at least one of the four statutory categories.
22. Claim(s) 20 is/are directed to a non-transitory storage medium (i.e. an article of manufacture).
Therefore, claim(s) 20 is/are within at least one of the four statutory categories.
Representative Claim
23. Claim 1 will be used as a representative claim for the remainder of the 101 rejection. The recited abstract ideas are emphasized below [with the category of abstract idea in brackets] and they are analyzed in 101 Analysis – Step 2A, Prong I section. The additional limitations beyond the abstract ideas are underlined below [with a description of the additional limitations in brackets]) and they are analyzed in 101 Analysis – Step 2A, Prong II section. Claim 1 recites:
A method for sending map-related data to a client apparatus, wherein the map- related data is extracted from digital map data representative of a network of navigable path segments within a geographic area, the method comprising:
receiving probability data for a subject vehicle at a location, wherein, for each of a plurality of the path segments of the digital map data, the probability data represents a respective probability of the subject vehicle traversing the respective path segment [pre-solution activity (data gathering), 2106.05(g) using generic sensors];
determining a set of one or more paths, each path comprising one or more of the plurality of path segments and having a respective determined extent, wherein the determined extent of at least one of the paths of the set depends at least in part upon the received probability data [mental process/step];
extracting data, associated with the determined set of one or more paths, from the digital map data in accordance with the respective determined one or more path extents [insignificant extra-solution activity, 2106.05(g)]; and
sending the extracted data to the client apparatus [insignificant post-solution activity (outputting results of the mental process) 2106.05(g)].
101 Analysis – Step 2A, Prong I
24. Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c).
25. Independent claim(s) 1, and 19-20 include(s) limitations that recite an abstract idea. As mentioned above, claim 1 will be used as a representative claim for the remainder of the 101 rejection.
26. The Examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers steps that could be carried out in the human mind. For example, “determining a set of one or more paths , …” step(s) encompass(es) a user making observation, evaluation or judgement about determining a path for a vehicle from a current location to a destination, could all be carried out in one’s mind. The same user looking at the data collected, could form a simple judgement and conclude whether the determined path is a suitable path for the vehicle based on the collected data. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
27. Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
28. For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
29. Regarding the additional limitations of “receiving probability data for a subject vehicle at a location, ...” “extracting data, associated with the determined set of one or more path, …” and “sending the extracted data to the client apparatus” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (vehicle controller or navigation system of a vehicle) to perform the process. In particular, the “receiving probability data for a subject vehicle at a location, ...” step is recited at a high level of generality (i.e. as a general means of gathering path information for use in the determining step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The “sending the extracted data to the client apparatus” step is also recited at a high level of generality (i.e. as a general means of transmitting the determination result from the determining step), and amounts to mere post solution activity of outputting the results of the mental process, which is a form of insignificant extra-solution activity. Lastly, the “extracting data, associated with the determined set of one or more paths, …” is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere insignificant extra-solution activity.
30. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
31. Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of” using a vehicle controller to perform the evaluating… amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above, with respect to integration of the abstract idea into a practical application, the additional limitations of “receiving probability data for a subject vehicle at a location, ...” “extracting data, associated with the determined set of one or more path, …” and “sending the extracted data to the client apparatus” the examiner submits that these limitations are insignificant extra-solution activities.
32. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitation(s) of “receiving probability data for a subject vehicle at a location, ...” and “sending the extracted data to the client apparatus” is/are well-understood, routine, and conventional activities. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
33. As established above claim 1 is representative of all independent claims and therefore claim(s) 19-20 is/are rejected for the same reason.
34. Dependent claim(s) 2-18 does/do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-18 are not patent eligible under the same rationale as provided for in the rejection of 1.
35. Therefore, claim(s) 1-20 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
36. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
37. Claim(s) 1-3, 8, 10, and 15-20
is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Manoliu et al. (US-20170328720-A1).
In regard to claim 1
, Manoliu discloses a method for sending map-related data to a client apparatus, wherein the map-related data is extracted from digital map data representative of a network of navigable path segments within a geographic area (See at least [0008 & 0017-0018 & 0102]: a method [i.e., a method] of generating a horizon for use in an ADAS of a vehicle. The generated horizon extends from a current position of a vehicle to a predetermined distance ahead of the current position defining a boundary of the horizon. The ADAS horizon extends to the predetermined distance from the current vehicle position along the or each predicted path. A path is representative of a trajectory that may be taken by a vehicle through the road network. A path is defined by at least a portion of one or more road segments of a digital map [i.e., wherein the map-related data is extracted from digital map data]. The digital map comprises a plurality of segments representative of road segments of a road network [i.e., a network of navigable path segments within a geographic area]. The method comprises storing data indicative of the or each path and/or providing, and transmitting such data over a vehicle bus to one or more ADAS applications of the vehicle [i.e., sending map-related data to a client apparatus] for use by the one or more applications), the method comprising:
receiving probability data for a subject vehicle at a location, wherein, for each of a plurality of the path segments of the digital map data, the probability data represents a respective probability of the subject vehicle traversing the respective path segment (See at least [0127-0129]: at various points in a road network there will be nodes, i.e. decision points at which the vehicle has a choice of possible outgoing paths. At a given time, a vehicle [i.e., a subject vehicle] has a current position defined on a particular road segment [i.e., at a location]. At the first junction the method is used to determine the relative probability that each of the possible outgoing paths may be taken [i.e., the probability data represents a respective probability of the subject vehicle traversing the respective path segment]. This enables a determination to be made as to which of the plurality of outgoing paths forms the continuation of the main path, if this is not already known (e.g. from a pre-calculated route), and which form alternative paths. A determination is also made as to the relative likelihood that one of these alternative paths may be taken. In order to determine this, a probability is determined for each outgoing path [i.e., for each of a plurality of the path segments of the digital map data] at a decision point indicative of the likelihood that the path will be chosen in preference of all other possible outgoing paths. Examiner notes, determining the probability encompasses receiving the probability);
determining a set of one or more paths, each path comprising one or more of the plurality of path segments and having a respective determined extent, wherein the determined extent of at least one of the paths of the set depends at least in part upon the received probability data (See at least [0015-0018 & 0030 & 0102]: the horizon comprises data indicative of the relative probability that each of a plurality of paths may be taken by the vehicle at a decision point. The step of generating the horizon using the stored digital location-based data, vehicle data and/or driver data, preferably comprises using the data to determine data indicative of the relative probability that each of a plurality of possible outgoing paths associated with a decision point will be taken by the vehicle in the immediate future [i.e., wherein the determined extent of at least one of the paths of the set depends at least in part upon the received probability data, Examiner notes, as mentioned above, for generating the horizon, the relative probability must be determined which means the horizon or the extent of the at least one of the paths depends upon the relative probability]. The generated horizon extends from a current position of a vehicle to a predetermined distance [i.e., having a respective determined extent] ahead of the current position defining a boundary of the horizon. A “path” comprises at least a portion of one or more road segments. A path is representative of a trajectory that is taken by a vehicle through the road network. A path is a path defined by at least a portion of one or more road segments [i.e., each path comprising one or more of the plurality of path segments] of a digital map. The digital map comprises a plurality of segments representative of road segments of a road network. The step of generating the horizon comprises determining one or more predicted paths, e.g. one or both of a most probable path and an alternative path [i.e., determining a set of one or more paths], the method comprises storing data indicative of the or each path and/or providing, e.g. transmitting such data over a vehicle bus to one or more ADAS applications of the vehicle for use by the one or more applications);
extracting data, associated with the determined set of one or more paths, from the digital map data in accordance with the respective determined one or more path extents (See at least [0124]: in order to provide ADAS functions, the ADAS applications require information about the road ahead, and its attributes, e.g. gradient, curvature, speed limit, etc. The ADAS horizon provides this information [i.e., extracting data, Examiner notes, to provide the information to the ADAS applications, the information is necessarily extracted] in respect of the one or more predicted paths up to a given distance ahead of the current position [i.e., associated with the determined set of one or more paths, from the digital map data in accordance with the respective determined one or more path extents]); and
sending the extracted data to the client apparatus (See at least Fig. 1, and [0124]: the ADAS horizon data [i.e., the extracted data] that is transmitted over the vehicle bus 5 to the ADAS applications [i.e., the client apparatus] contains at least attribute data of the most probable path).
In regard to claim 2
, Manoliu discloses the method of claim 1, wherein the set of one or more paths comprises a plurality of paths, and wherein the method comprises extracting, from the digital map data, data associated with each of the plurality of paths in accordance with the respective determined path extents (See at least [0036 & 0123-0124]: the decision point is any decision point defining a plurality of possible outgoing paths [i.e., wherein the set of one or more paths comprises a plurality of paths] for which it is desired to determine a relative probability associated with taking each of the possible paths. The ADAS horizon is based on digital map data. In order to provide ADAS functions, the ADAS applications require information about the road ahead, and its attributes, e.g. gradient, curvature, speed limit, etc. The ADAS horizon provides this information in respect of the one or more predicted paths up to a given distance ahead of the current position [i.e., extracting, from the digital map data, data associated with each of the plurality of paths in accordance with the respective determined path extents]).
In regard to claim 3
, Manoliu discloses the method of claim 2, wherein the extracted data contains only digital map data associated with the paths of the set of one or more paths (See at least Fig. 1, and [0123-0124]: the ADAS horizon is an electronic horizon comprising data indicative of one or more predicted paths ahead of a current position of a vehicle that it may be expected the vehicle will travel along. The ADAS horizon is based on digital map data. The ADAS horizon data [i.e., the extracted data] that is transmitted over the vehicle bus 5 to the ADAS applications contains at least attribute data of the most probable path up to a certain, often predetermined, extent ahead of the vehicle [i.e., only digital map data associated with the paths of the set of one or more paths]).
In regard to claim 8
, Manoliu discloses the method of claim 1, comprising determining the extent of at least one path of the set of paths at least partly based on a maximum path-distance threshold (See at least [0017]: the generated horizon extend from a current position of a vehicle to a predetermined distance [i.e., maximum path-distance threshold] ahead of the current position defining a boundary of the horizon. The ADAS horizon extends to the predetermined distance from the current vehicle position along the or each predicted path. A given distance ahead of a current position refers to a distance in the current direction of travel. The extent of the horizon is chosen as desired for a given application. The horizon extends to a given radius corresponding to the distance in the direction of travel, e.g. through a 180 degree angle in the forward direction of travel. Examiner notes, according a relevant paragraph of Applicant’s specification on page 7, “the extent of at least one path of the set of paths (e.g. of a most probable path of the set of paths), and optionally of all of the paths, may be determined at least partly based on a maximum path-distance threshold. The maximum path-distance threshold may be predetermined, e.g. being fixed, or it may be calculated dynamically, e.g. based on a speed of the vehicle.” As such, maximum path-distance threshold was interpreted under its broadest reasonable interpretation consistent with the Applicant' s specification and the knowledge of one of ordinary skill in the art as a predetermined distance for setting the horizon).
In regard to claim 10
, Manoliu discloses the method of claim 1, comprising determining the maximum path-distance threshold at least partly in dependence upon a current speed of the vehicle (See at least [0028]: generation of the horizon is based upon absolute values of attributes associated with the or each road segment. The generation of the horizon is alternatively or additionally based on a comparison of an attribute or attributes of a road segment to the corresponding attribute or attributes of one or more other road segments. Thus, the relative values of an attribute or attributes is considered. The relative values of attributes of any of the types, such as road class, gradient, angle, speed limit, speed profile [i.e., at least partly in dependence upon a current speed of the vehicle] etc. is considered. The method comprises comparing the data indicative of an attribute or attributes of one road segment with the corresponding attribute data of another road segment or segment in generating the horizon [i.e., determining the maximum path-distance threshold]).
In regard to claim 15
, Manoliu discloses the method of claim 1, wherein the subject vehicle carries or comprises the client apparatus, and wherein sending the extracted data to the client apparatus comprises transmitting the extracted data to the subject vehicle from a remote server over a data network (See at least [0083 & 0122 & 0166]: the positional data is received at a central controller, such as a server system [i.e., a remote server]. The server carries out the steps of using the positional data to determine a relative probability that each of a plurality of paths will be taken, or to determine the probability matrix. The ADAS system 1 includes a horizon generating subsystem 3, which communicates horizon data over a Controller Area Network (CAN) bus 5 to a plurality of ADAS applications 7, 9 and 11 [i.e., wherein the subject vehicle carries or comprises the client apparatus]. The data transmitted regarding the main path [i.e., the extracted data] includes any of the following data about attributes of the road segment or segments making up the determined portion of the main path: speed limit, recommended speed limit where no legal speed limit is associated with the road segment, functional road class, form of way, gradient, curvature, etc.).
In regard to claim 16
, Manoliu discloses the method of claim 1, wherein the extracted data comprises attribute data for one or more road segments of the set of one or more paths (See at least [0166]: The data transmitted regarding the main path [i.e., the extracted data] includes any of the following data about attributes of the road segment or segments making up the determined portion of the main path: speed limit, recommended speed limit where no legal speed limit is associated with the road segment [i.e., attribute data for one or more road segments of the set of one or more paths], functional road class, form of way, gradient, curvature, etc.).
In regard to claim 17
, Manoliu discloses the method of claim 1, wherein the extracted data comprises speed-limit data for the determined extent of each path of the set of one or more paths (See at least [0166]: The data transmitted regarding the main path [i.e., the extracted data] includes any of the following data about attributes of the road segment or segments making up the determined portion of the main path: speed limit, recommended speed limit where no legal speed limit is associated with the road segment [i.e., speed-limit data for the determined extent of each path of the set of one or more paths], functional road class, form of way, gradient, curvature, etc.).
In regard to claim 18
, Manoliu discloses the method of claim 1, comprising receiving probe data from the subject vehicle over a network, determining the set of paths at least partly in dependence upon the received probe data, and transmitting the extracted data to the vehicle (See at least [0070-0074]: the speed profile data is based on data relating to the position of a plurality of devices associated with vehicles with respect to time. Such data is referred to as vehicle “probe data”, and any references to vehicle “probe data” herein, should be understood to refer to such positional data [i.e., probe data]. As the devices are associated with respective vehicles, the position of the devices are considered to correspond to the position of the vehicles. The method extends to the steps of obtaining such positional data [i.e., receiving probe data from the subject vehicle over a network] and/or using the data to determine the speed profile data and associating the data with respective road segment(s) to which it relates. The speed profile data associated with a road segment is thus indicative of an average speed of travel for traversing the road segment during one or more, and preferably a plurality of, time periods. The relative probability that each outgoing path from the decision point will be taken is preferably determined using the average speed for the time period appropriate for the time at which the horizon is generated [i.e., determining the set of paths at least partly in dependence upon the received probe data]).
In regard to claim 19
, Manoliu discloses a system, comprising (See at least [0011]: a system for generating a horizon for use in an ADAS of a vehicle):
at least one processor configured to send map-related data to a client apparatus, wherein the map-related data is extracted from digital map data representative of a network of navigable path segments within a geographic area, and wherein the sending includes (See at least [0008 & 0014 & 0017-0018 & 0102]: a method of generating a horizon for use in an ADAS of a vehicle. The means for carrying out any of the steps of the method comprises a set of one or more processors [i.e., at least one processor] configured, e.g. programmed, for doing so. The generated horizon extends from a current position of a vehicle to a predetermined distance ahead of the current position defining a boundary of the horizon. The ADAS horizon extends to the predetermined distance from the current vehicle position along the or each predicted path. A path is representative of a trajectory that may be taken by a vehicle through the road network. A path is defined by at least a portion of one or more road segments of a digital map [i.e., wherein the map-related data is extracted from digital map data]. The digital map comprises a plurality of segments representative of road segments of a road network [i.e., network of navigable path segments within a geographic area]. The method comprises storing data indicative of the or each path and/or providing, and transmitting such data over a vehicle bus to one or more ADAS applications of the vehicle [i.e., send map-related data to a client apparatus] for use by the one or more applications):
receiving probability data for a subject vehicle at a location, wherein, for each of a plurality of the path segments of the digital map data, the probability data represents a respective probability of the subject vehicle traversing the respective path segment (See at least [0127-0129]: at various points in a road network there will be nodes, i.e. decision points at which the vehicle has a choice of possible outgoing paths. At a given time, a vehicle [i.e., a subject vehicle] has a current position defined on a particular road segment [i.e., at a location]. At the first junction the method is used to determine the relative probability that each of the possible outgoing paths may be taken [i.e., the probability data represents a respective probability of the subject vehicle traversing the respective path segment]. This enables a determination to be made as to which of the plurality of outgoing paths forms the continuation of the main path, if this is not already known (e.g. from a pre-calculated route), and which form alternative paths. A determination is also made as to the relative likelihood that one of these alternative paths may be taken. In order to determine this, a probability is determined for each outgoing path [i.e., for each of a plurality of the path segments of the digital map data] at a decision point indicative of the likelihood that the path will be chosen in preference of all other possible outgoing paths. Examiner notes, determining the probability encompasses receiving the probability);
determining a set of one or more paths, each path comprising one or more of the plurality of path segments and having a respective determined extent, wherein the determined extent of at least one of the paths of the set depends at least in part upon the received probability data (See at least [0015-0018 & 0030 & 0102]: the horizon comprises data indicative of the relative probability that each of a plurality of paths may be taken by the vehicle at a decision point. The step of generating the horizon using the stored digital location-based data, vehicle data and/or driver data, preferably comprises using the data to determine data indicative of the relative probability that each of a plurality of possible outgoing paths associated with a decision point will be taken by the vehicle in the immediate future [i.e., wherein the determined extent of at least one of the paths of the set depends at least in part upon the received probability data, Examiner notes, as mentioned above, for generating the horizon, the relative probability must be determined which means the horizon or the extent of the at least one of the paths depends upon the relative probability]. The generated horizon extends from a current position of a vehicle to a predetermined distance [i.e., having a respective determined extent] ahead of the current position defining a boundary of the horizon. A “path” comprises at least a portion of one or more road segments. A path is representative of a trajectory that is taken by a vehicle through the road network. A path is a path defined by at least a portion of one or more road segments [i.e., each path comprising one or more of the plurality of path segments] of a digital map. The digital map comprises a plurality of segments representative of road segments of a road network. The step of generating the horizon comprises determining one or more predicted paths, e.g. one or both of a most probable path and an alternative path [i.e., determining a set of one or more paths], the method comprises storing data indicative of the or each path and/or providing, e.g. transmitting such data over a vehicle bus to one or more ADAS applications of the vehicle for use by the one or more applications);
extracting data, associated with the determined set of one or more paths, from the digital map data in accordance with the respective determined one or more path extents (See at least [0124]: in order to provide ADAS functions, the ADAS applications require information about the road ahead, and its attributes, e.g. gradient, curvature, speed limit, etc. The ADAS horizon provides this information [i.e., extracting data, Examiner notes, to provide the information to the ADAS applications, the information is necessarily extracted] in respect of the one or more predicted paths up to a given distance ahead of the current position [i.e., associated with the determined set of one or more paths, from the digital map data in accordance with the respective determined one or more path extents]); and
sending the extracted data to the client apparatus (See at least Fig. 1, and [0124]: the ADAS horizon data [i.e., the extracted data] that is transmitted over the vehicle bus 5 to the ADAS applications [i.e., the client apparatus] contains at least attribute data of the most probable path).
In regard to claim 20
, Manoliu discloses a non-transitory computer-readable storage medium storing instructions which, when executed at least one processor, cause the at least one processor to perform a method for sending map-related data to a client apparatus, wherein the map-related data is extracted from digital map data representative of a network of navigable path segments within a geographic area, the method comprising (See at least [0008 & 0014 & 0017-0018 & 0102 & 0106]: a method of generating a horizon for use in an ADAS of a vehicle. The means for carrying out any of the steps of the method comprises a set of one or more processors [i.e., at least one processor] configured, e.g. programmed, for doing so. The system further comprises data storage means, such as computer memory, for storing the data indicative of the generated horizon, and/or the data used in determining the horizon, i.e. the vehicle data, driver data and/or digital location-based data. The generated horizon extends from a current position of a vehicle to a predetermined distance ahead of the current position defining a boundary of the horizon. The ADAS horizon extends to the predetermined distance from the current vehicle position along the or each predicted path. A path is representative of a trajectory that may be taken by a vehicle through the road network. A path is defined by at least a portion of one or more road segments of a digital map [i.e., wherein the map-related data is extracted from digital map data]. The digital map comprises a plurality of segments representative of road segments of a road network [i.e., network of navigable path segments within a geographic area]. The method comprises storing data indicative of the or each path and/or providing, and transmitting such data over a vehicle bus to one or more ADAS applications of the vehicle [i.e., sending map-related data to a client apparatus] for use by the one or more applications. Computer readable instructions adapted to carry out any or all of the method described herein when executed on suitable data processing means. The invention also extends to a computer software carrier comprising such software. Such a software carrier could be a physical (or non-transitory) storage medium [i.e., a non-transitory computer-readable storage medium storing instructions]):
receiving probability data for a subject vehicle at a location, wherein, for each of a plurality of the path segments of the digital map data, the probability data represents a respective probability of the subject vehicle traversing the respective path segment (See at least [0127-0129]: at various points in a road network there will be nodes, i.e. decision points at which the vehicle has a choice of possible outgoing paths. At a given time, a vehicle [i.e., a subject vehicle] has a current position defined on a particular road segment [i.e., at a location]. At the first junction the method is used to determine the relative probability that each of the possible outgoing paths may be taken [i.e., the probability data represents a respective probability of the subject vehicle traversing the respective path segment]. This enables a determination to be made as to which of the plurality of outgoing paths forms the continuation of the main path, if this is not already known (e.g. from a pre-calculated route), and which form alternative paths. A determination is also made as to the relative likelihood that one of these alternative paths may be taken. In order to determine this, a probability is determined for each outgoing path [i.e., for each of a plurality of the path segments of the digital map data] at a decision point indicative of the likelihood that the path will be chosen in preference of all other possible outgoing paths. Examiner notes, determining the probability encompasses receiving the probability);
determining a set of one or more paths, each path comprising one or more of the plurality of path segments and having a respective determined extent, wherein the determined extent of at least one of the paths of the set depends at least in part upon the received probability data (See at least [0015-0018 & 0030 & 0102]: the horizon comprises data indicative of the relative probability that each of a plurality of paths may be taken by the vehicle at a decision point. The step of generating the horizon using the stored digital location-based data, vehicle data and/or driver data, preferably comprises using the data to determine data indicative of the relative probability that each of a plurality of possible outgoing paths associated with a decision point will be taken by the vehicle in the immediate future [i.e., wherein the determined extent of at least one of the paths of the set depends at least in part upon the received probability data, Examiner notes, as mentioned above, for generating the horizon, the relative probability must be determined which means the horizon or the extent of the at least one of the paths depends upon the relative probability]. The generated horizon extends from a current position of a vehicle to a predetermined distance [i.e., having a respective determined extent] ahead of the current position defining a boundary of the horizon. A “path” comprises at least a portion of one or more road segments. A path is representative of a trajectory that is taken by a vehicle through the road network. A path is a path defined by at least a portion of one or more road segments [i.e., each path comprising one or more of the plurality of path segments] of a digital map. The digital map comprises a plurality of segments representative of road segments of a road network. The step of generating the horizon comprises determining one or more predicted paths, e.g. one or both of a most probable path and an alternative path [i.e., determining a set of one or more paths], the method comprises storing data indicative of the or each path and/or providing, e.g. transmitting such data over a vehicle bus to one or more ADAS applications of the vehicle for use by the one or more applications);
extracting data, associated with the determined set of one or more paths, from the digital map data in accordance with the respective determined one or more path extents (See at least [0124]: in order to provide ADAS functions, the ADAS applications require information about the road ahead, and its attributes, e.g. gradient, curvature, speed limit, etc. The ADAS horizon provides this information [i.e., extracting data, Examiner notes, to provide the information to the ADAS applications, the information is necessarily extracted] in respect of the one or more predicted paths up to a given distance ahead of the current position [i.e., associated with the determined set of one or more paths, from the digital map data in accordance with the respective determined one or more path extents]); and
sending the extracted data to the client apparatus (See at least Fig. 1, and [0124]: the ADAS horizon data [i.e., the extracted data] that is transmitted over the vehicle bus 5 to the ADAS applications [i.e., the client apparatus] contains at least attribute data of the most probable path).
Claim Rejections - 35 USC § 103
38. 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.
39. Claim(s) 4
is/are rejected under 35 U.S.C. 103 as being unpatentable over Manoliu et al. (US-20170328720-A1) in view of Kim et al. (US-20210129862-A1).
In regard to claim 4
, Manoliu discloses the method of claim 1, accordingly the rejection of claim 1 is incorporated.
Manoliu is silent on comprising determining the extent of at least one path of the set of paths at least partly based on a minimum path-probability threshold.
However, Kim teaches the path providing device 800 provides map data within a range from a location of the vehicle 100 to the horizon. The horizon path data includes data indicating a relative probability to select one road at a decision point (e.g., fork, intersection, crossroads, etc.). Relative probability is calculated based on a time taken to arrive at a final destination. For example, if a shorter time is taken to arrive at the final destination by selecting a first road than selecting a second road at a decision point, the probability to select the first road is calculated higher than the probability to select the second road. The processor 830 generates field-of-view (autonomous driving visibility) information for autonomous driving in which sensing information is merged with an optimal path to transmit it to at least one of electrical parts provided in a server or a vehicle (S1350). The field-of-view information for autonomous driving refers to electronic horizon information (or electronic horizon data) [i.e., the extent of at least one path of the set of paths]. When no destination is set in the vehicle 100, the processor 830 calculates a main path (most preferred path, MPP) having the highest possibility that the vehicle 100 may drive, and generate field-of-view for autonomous driving to guide the main path (MPP) in units of lanes. In this case, the field-of-view information for autonomous driving further includes sub path information on sub paths branched from the most preferred path (MPP) for the vehicle 100 to be movable at a higher probability than a predetermined reference [i.e., a minimum path-probability threshold] (See at least Figs. 1-14, and [0250 & 0419-0424]). Examiner notes, as mentioned above, the field-of-view information or electronic horizon data includes the sub paths that the probability of taking those sub paths is higher than a predetermined reference. As such, Kim teaches determining the extent of at least one path of the set of paths at least partly based on a minimum path-probability threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Manoliu, by incorporating the teachings of Kim, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – path providing devices, such that the horizon is determined based on the paths that have a probability higher than a predetermined reference.
The motivation to modify is that, as acknowledged by Kim, optimizing user's convenience and safety while driving a vehicle (See at least [0009]) which one of ordinary skill would have recognized allows the user to take the most efficient path based on the defined criteria.
40. Claim(s) 5
is/are rejected under 35 U.S.C. 103 as being unpatentable over Manoliu et al. (US-20170328720-A1) in view of Kim et al. (US-20210129862-A1) and further in view of White et al. (US-20230258462-A1).
In regard to claim 5
, Manoliu, as modified by Kim, teaches the method of claim 4, accordingly the rejection of claim 4 is incorporated.
Manoliu, as modified by Kim, is silent on comprising iteratively adding path segments to a path until the probability of the vehicle traversing the path crosses below the minimum path-probability threshold, and then ceasing to add any further path segment to the path.
However, White teaches process 2100 for determining an optimal path from a source node to a target node based on constraint regions. Process 2100 includes receiving (at 2102) information associated with a node map, including costs and constraints associated with links between nodes of the node map. Process 2100 also includes identifying (at 2104) a source node and a target node for which an optimal path is to be identified. Process 2100 includes performing (at 2106) a search for the optimal path to the target node, including iteratively exploring links based on link costs and constraint regions [i.e., iteratively adding path segments to a path]. Process 2100 includes determining (at 2108) that a path to the target node has been discovered via the search. If any potential paths to the target node, with a lower cost and/or fewer constraints (e.g., turns into constraint regions and/or other constraint costs , are possible (at 2110—YES), then TCNS 1951 continues (at 2106) the search. If, on the other hand, no potential paths to the target node are possible [i.e., until the probability of the vehicle traversing the path crosses below the minimum path-probability threshold, and then ceasing to add any further path segment to the path], with a lower cost and/or fewer constraints (at 2110—NO), then TCNS 1951 selects (at 2112) the lowest cost and/or least restrictive path from the source node to the target node as the optimal path (See at least Fig. 21, and [0083-0087]). Examiner notes, as mentioned above, when no potential paths to the target node are possible, then the probability of using the path that includes the node with no potential paths becomes zero, as the node cannot be used for navigating the vehicle from the source node to the target node. That means, adding any further path segments to the path will be ceased. As such, White teaches iteratively adding path segments to a path until the probability of the vehicle traversing the path crosses below the minimum path-probability threshold, and then ceasing to add any further path segment to the path.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Manoliu, as modified by Kim, by incorporating the teachings of White, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – path planning, such that an optimal path from a source node to a target node is found by iteratively exploring path segments or links between the nodes and adding the potential path segments and ceasing the process of adding path segments or links when a potential path from a node to the target is not probable.
The motivation to modify is that, as acknowledged by White, saving time and processing resources (See at least [0001]) which one of ordinary skill would have recognized allows the vehicle to reach its destination in a timely manner.
41. Claim(s) 6-7
is/are rejected under 35 U.S.C. 103 as being unpatentable over Manoliu et al. (US-20170328720-A1) in view of Serbanescu (US-20120209507-A1).
In regard to claim 6
, Manoliu discloses the method of claim 1, accordingly the rejection of claim 1 is incorporated.
Manoliu is silent on comprising determining the extent of each of the set of paths at least partly based on a minimum horizon-probability threshold.
However, Serbanescu teaches in step 408, the processor 210 compares the probabilities assigned to the paths and if the probabilities are below a predetermined threshold [i.e., a minimum horizon-probability threshold], the path is discarded (See at least Figs. 4-5 and [0099]). Examiner notes, Applicant has not provided a concise definition for the minimum horizon-probability threshold. Accordingly, the minimum horizon-probability threshold was interpreted under its broadest reasonable interpretation consistent with the Applicant' s specification and the knowledge of one of ordinary skill in the art as a minimum threshold that the probabilities assigned to the paths must meet. That is, when the probability assigned to the paths is below the threshold, then the paths will be discarded. Accordingly, Serbanescu teaches determining the extent of each of the set of paths at least partly based on a minimum horizon-probability threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Manoliu, by incorporating the teachings of Serbanescu, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – path planning, such that when the probability assigned to the paths is below a predetermined threshold, the paths are discarded.
The motivation to modify is that, as acknowledged by Serbanesc, to provide a reliable means for enabling users to navigate from one position to another (See at least [0019]) which one of ordinary skill would have recognized allows the user to reach their destination.
In regard to claim 7
, Manoliu, as modified by Serbanescu, teaches the method of claim 6, comprising determining the extents such that the probability of the vehicle traversing any path of the set for the respective extent of the path is such that the addition or the removal of one path segment from the set of paths would cause the probability to cross the minimum horizon-probability threshold.
Further, Serbanescu teaches estimating the current location further comprises expanding each path and recalculating the probability that the navigation device travelled that path for changes in distance. As with changes in orientation, the change in distance at which point the expansion of the paths and recalculation of the probability is carried out is a change in distance above a predetermined distance threshold. In step 408, the processor 210 compares the probabilities assigned to the paths and if the probabilities are below a predetermined threshold the path is discarded [i.e., the addition or the removal of one path segment from the set of paths would cause the probability to cross the minimum horizon-probability threshold] (See at least Figs. 4-5, and [0030 & 0099]). Examiner notes, Applicant has not provided a concise definition for the minimum horizon-probability threshold. Accordingly, the minimum horizon-probability threshold was interpreted under its broadest reasonable interpretation consistent with the Applicant' s specification and the knowledge of one of ordinary skill in the art as a minimum threshold that the probabilities assigned to the paths must meet. That is, when the probabilities assigned to the paths are below the threshold, they will be discarded. Accordingly, Serbanescu teaches determining the extents such that the probability of the vehicle traversing any path of the set for the respective extent of the path is such that the addition or the removal of one path segment from the set of paths would cause the probability to cross the minimum horizon-probability threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Manoliu, as modified by Serbanescu, by further incorporating the teachings of Serbanescu, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – path planning, such that when adding a path segment makes the probability assigned to the paths to fall below a predetermined threshold, then the path segment is discarded.
The motivation to do so is the same as acknowledged by Serbanescu in regard to claim 6.
42. Claim(s) 9
is/are rejected under 35 U.S.C. 103 as being unpatentable over Manoliu et al. (US-20170328720-A1) in view of Alesiani (US-20170219353-A1).
In regard to claim 9
, Manoliu discloses the method of claim 8, accordingly the rejection of claim 8 is incorporated.
Manoliu is silent on comprising iteratively adding path segments to a path until the extent of the path crosses above the maximum path-distance threshold, and then ceasing to add any further path segment to the path.
However, Alesiani teaches a method for determining a path of an object for moving from a starting node representing a starting state via one or more nodes to an end state using a local growing tree search with bounded approximated optimal cost and iteration solution [i.e., iteratively]. In a first step S1 a plurality of child nodes to a parent node is determined, wherein the starting node representing the starting state is the parent node when step S1 is performed for the first time, and wherein the child nodes are within a certain distance [i.e., the maximum path-distance threshold] to the parent node. In a second step S2 it is checked whether the transitions from the parent node to each of the child nodes are free of obstacles, and if not the corresponding child node and this partial path from the parent node to this child node is excluded. In a third step S3 a cost value for each of said partial paths is computed. In a forth step S4 said computed cost value is added to a cost value from the starting node to the parent node [i.e., adding path segments to a path]. In a fifth step S5 an estimated or expected cost value for the partial paths from each of the child nodes to an end node representing an end state. In a sixth step S6 the lowest overall cost value from the determined cost values according to steps d)-f) is determined and the child node as new parent node associated with the determined lowest cost is selected. In a seventh step S7 the steps S1-S6 are performed repeatedly until at least one termination condition is fulfilled including the termination condition if a selected child node is within a pregiven distance to the end node (See at least Fig. 11, and [0012 & 0046 & 0089]). Examiner notes, as mentioned above, the child nodes are required to be within a certain distance from the parent node. That is, when the distance between the parent node and the child node or the extent of the path crosses above a certain distance, which is the maximum path-distance threshold, then the process of adding any further path segment to the path is ceased. Accordingly, Alesiani teaches iteratively adding path segments to a path until the extent of the path crosses above the maximum path-distance threshold, and then ceasing to add any further path segment to the path.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Manoliu, by incorporating the teachings of Alesiani, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – path planning, such that the distance between the end node and the child node is within a predetermined distance and when the distance crossed above the predetermining distance, the operation of adding more segments is ceased.
The motivation to modify is that, as acknowledged by Alesiani, to identify candidate paths based on the preferential path being associated with the lowest estimated economic cost (See at least [0010]) which one of ordinary skill would have recognized allows the operation cost of the vehicle to be lower and more economical.
43. Claim(s) 11-12
is/are rejected under 35 U.S.C. 103 as being unpatentable over Manoliu et al. (US-20170328720-A1) in view of Alesiani (US-20170219353-A1) and further in view of Serbanescu (US-20120209507A1).
In regard to claim 11
, Manoliu discloses the method of claim 1, accordingly the rejection of claim 1 is incorporated.
Manoliu is silent on comprising determining the membership and extents of the set of paths by applying a maximum path-distance threshold for each path and a minimum horizon-probability threshold for the set of paths.
However, Alesiani teaches a method for determining a path of an object for moving from a starting node representing a starting state via one or more nodes to an end state using a local growing tree search with bounded approximated optimal cost and iteration solution. In a first step S1 a plurality of child nodes to a parent node is determined, wherein the starting node representing the starting state is the parent node when step S1 is performed for the first time, and wherein the child nodes are within a certain distance [i.e., a maximum path-distance threshold] to the parent node. In a second step S2 it is checked whether the transitions from the parent node to each of the child nodes are free of obstacles, and if not the corresponding child node and this partial path from the parent node to this child node is excluded. In a third step S3 a cost value for each of said partial paths is computed. In a forth step S4 said computed cost value is added to a cost value from the starting node to the parent node [i.e., determining the membership and extents of the set of paths]. In a fifth step S5 an estimated or expected cost value for the partial paths from each of the child nodes to an end node representing an end state. In a sixth step S6 the lowest overall cost value from the determined cost values according to steps d)-f) is determined and the child node as new parent node associated with the determined lowest cost is selected. In a seventh step S7 the steps S1-S6 are performed repeatedly until at least one termination condition is fulfilled including the termination condition if a selected child node is within a pregiven distance to the end node (See at least Fig. 11, and [0012 & 0046 & 0089]).
Serbanescu teaches estimating the current location further comprises expanding each path and recalculating the probability that the navigation device travelled that path for changes in distance. As with changes in orientation, the change in distance at which point the expansion of the paths and recalculation of the probability is carried out is a change in distance above a predetermined distance threshold. In step 408, the processor 210 compares the probabilities assigned to the paths and if the probabilities are below a predetermined threshold [i.e., a minimum horizon-probability threshold for the set of paths], the path is discarded (See at least Figs. 4-5, and [0030 & 0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Manoliu, by incorporating the teachings of Alesiani and Serbanescu, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor –path planning, such that the distance between the end node and the child node is within a predetermined distance and when the distance crossed above the predetermining distance, the operation of adding more segments is ceased and the expansion of the paths and recalculation of the probability is carried out when a change in distance is above a predetermined distance threshold and when the probability assigned to the paths is below a predetermined threshold, the paths are discarded and when adding a path segment makes the probability assigned to the paths to fall below a predetermined threshold, then the path segment is discarded.
The motivation to do so is the same as acknowledged by Alesiani in regard to claim 9.
The motivation to do so is the same as acknowledged by Serbanescu in regard to claim 6.
In regard to claim 12
, Manoliu, as modified by Alesiani and Serbanescu, teaches the method of claim 11, comprising extending each path of the set of paths using an iterative extension process until the path has an extent such that the addition or the removal of one path segment would cause the extent of the resulting path to cross the maximum path-distance threshold, and adding paths to the set of paths until the probability of the vehicle traversing any path of the set for the respective determined extent is such that the addition or the removal of one path segment from the set of paths would cause the probability to cross the minimum horizon-probability threshold.
Further, Alesiani teaches a method for determining a path of an object for moving from a starting node representing a starting state via one or more nodes to an end state using a local growing tree search with bounded approximated optimal cost and iteration solution [i.e., using an iterative extension process]. In a first step S1 a plurality of child nodes to a parent node is determined, wherein the starting node representing the starting state is the parent node when step S1 is performed for the first time, and wherein the child nodes are within a certain distance [i.e., the maximum path-distance threshold] to the parent node. In a second step S2 it is checked whether the transitions from the parent node to each of the child nodes are free of obstacles, and if not the corresponding child node and this partial path from the parent node to this child node is excluded. In a third step S3 a cost value for each of said partial paths is computed. In a forth step S4 said computed cost value is added to a cost value from the starting node to the parent node [i.e., adding path segments to a path until the extent of the path]. In a fifth step S5 an estimated or expected cost value for the partial paths from each of the child nodes to an end node representing an end state. In a sixth step S6 the lowest overall cost value from the determined cost values according to steps d)-f) is determined and the child node as new parent node associated with the determined lowest cost is selected. In a seventh step S7 the steps S1-S6 are performed repeatedly until at least one termination condition is fulfilled including the termination condition if a selected child node is within a pregiven distance to the end node (See at least Fig. 11, and [0012 & 0046 & 0089]). Examiner notes, as mentioned above, the child nodes required to be within a certain distance from the parent node. That is, when the distance between the parent node and the child node or the extent of the path crosses above a certain distance, which is the maximum path-distance threshold, then the process of adding any further path segment to the path is ceased. Accordingly, Alesiani teaches extending each path of the set of paths using an iterative extension process until the path has an extent such that the addition or the removal of one path segment would cause the extent of the resulting path to cross the maximum path-distance threshold.
Further, Serbanescu teaches estimating the current location further comprises expanding each path and recalculating the probability that the navigation device travelled that path for changes in distance. As with changes in orientation, the change in distance at which point the expansion of the paths and recalculation of the probability is carried out is a change in distance above a predetermined distance threshold. In step 408, the processor 210 compares the probabilities assigned to the paths and if the probabilities are below a predetermined threshold [i.e., the respective determined extent is such that the addition or the removal of one path segment from the set of paths would cause the probability to cross the minimum horizon-probability threshold], the path is discarded (See at least Figs. 4-5, and [0030 & 0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Manoliu, as modified by Alesiani and Serbanescu, by further incorporating the teachings of Alesiani and Serbanescu with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – path planning, such that the distance between the end node and the child node is within a predetermined distance and when the distance crossed above the predetermining distance, the operation of adding more segments is ceased and the expansion of the paths and recalculation of the probability is carried out when a change in distance is above a predetermined distance threshold and when the probability assigned to the paths is below a predetermined threshold, the paths are discarded and when adding a path segment makes the probability assigned to the paths to fall below a predetermined threshold, then the path segment is discarded.
The motivation to do so is the same as acknowledged by Alesiani in regard to claim 9.
The motivation to do so is the same as acknowledged by Serbanescu in regard to claim 6.
44. Claim(s) 13
is/are rejected under 35 U.S.C. 103 as being unpatentable over Manoliu et al. (US-20170328720-A1) in view of Dotan et al. (US-20060025923-A1).
In regard to claim 13
, Manoliu discloses the method of claim 1, accordingly the rejection of claim 1 is incorporated.
While Manoliu discloses it is desirable to reduce the amount of horizon data transmitted over the CAN bus. For this reason only attribute data for the most probable path is transmitted over the vehicle bus ([0156]), Manoliu does not explicitly disclose wherein the extents of each of the set of paths are determined at least partly based on a maximum data-volume threshold.
However, Dotan teaches a maximum data size [i.e., a maximum data-volume threshold] for each map segment [i.e., the extents of each of the set of paths are determined] is set, and further roads are added to the map segment if it has not reached this maximum size (See at least [0103]). Examiner notes, the horizon for the map segment is the edge of the map segment. As such, Dotan teaches the extents of each of the set of paths are determined at least partly based on a maximum data-volume threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Manoliu, by incorporating the teachings of Dotan, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor –navigation systems, such that a maximum data size is applied to the map segments.
The motivation to modify is that, as acknowledged by Dotan, to limit the amount of map data that is transmitted over the air from the server to the user's client device (See at least [0009]) which one of ordinary skill would have recognized allows the vehicle to be able to fetch the map data, especially when the vehicle is located in an area with slow data transfer rate.
Conclusion
45. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mubarek et al. (US-20210241614-A1) teaches a system capable of automatically verifying a road closure (e.g., a reported/detected closure) using multiple possible vehicle paths.
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/P.J.M./Examiner, Art Unit 3661
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
