DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the application filed on 06/12/2023. Claim(s) 12-21 are presently pending and are examined in this first action on the merits (FAOM).
Priority
Examiner acknowledges Applicant’s claim to priority based on Application DE10 2020 118 629.0 filed on 07/15/2020.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 12-21 are rejected under 35 U.S.C. 101, because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Independent claim 12 is directed toward a method, claim 20 is directed towards a device and claim 21 towards a non-transitory computer readable medium. Therefore, each of the independent claims 12, 20 and 21 along with the corresponding dependent claims 13-19 are directed to a statutory category of invention under Step 1.
Step 2A Prong 1
Under Step 2A, Prong 1, the claims are analyzed to determine whether one or more of the claims recites subject matter that falls within one of the following groups of abstract ideas: (1) mental processes, (2) certain methods of organizing human activity, and/or (3) mathematical concepts.
In this case, the independent claims 12, 20 and 21 are directed to an abstract idea without significantly more. Specifically, the claims, under their broadest reasonable interpretation cover certain mental processes and/or organizing human activity.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejections. Claim 1 recites:
A computer-implemented method for determining a validity of an estimated position of a vehicle, the method comprising:
receiving a digital map;
receiving the estimated position of the vehicle, wherein the estimated position is a position in the digital map or is assignable to a position in the digital map;
detecting a number of first features in the digital map, wherein the first features indicate at least one object adjacent to a roadway;
grouping the first features into a number of first feature groups such that all of the first features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area belong to a first feature group assigned to the respective area;
receiving sensor information about an environment of the vehicle;
detecting a number of second features in the sensor information, wherein the second features indicate at least one object adjacent to a roadway;
grouping the second features into a number of second feature groups such that all of the second features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area belong to a second feature group of the respective area;
rejecting first feature groups whose maximum lateral extent exceeds a first predetermined value and/or rejecting second feature groups whose maximum lateral extent exceeds a second predetermined value;
assigning at least some unrejected first feature groups to a respective unrejected second feature group;
and
determining the validity of the estimated position of the vehicle based on a comparison of positions of a number of the unrejected first feature groups with positions of the respective assigned second feature groups.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under the broadest reasonable interpretation, the claim covers performance of the limitations in the human mind.
For example,
“detecting a number of first features in the digital map” in context of this claim a person looking at a map and identifying number features on a map
“detecting a number of second features in the sensor information” in context of this claim a scanning the area surrounding a vehicle and identifying number features laterally and longitudinally spaced from the vehicle
“grouping the first features into a number of first feature groups” in context of this claim encompasses a person sorting and mentally organizing various features on a map into a set subset
“grouping the second features into a number of second feature groups” in context of this claim encompasses a person sorting and mentally organizing various features observed in the surrounding of a vehicle
“rejecting first feature groups … and/or rejecting second feature groups.” in the context of this claim encompasses a person mentally filtering the features that are outliers based on a perceived lateral spacing;
“assigning at least some unrejected first feature groups to a respective unrejected second feature group” in the context of this claim encompasses a person in the vehicle looking at a map and the environment and identifying features appear common between the first group of features marked on a map and a second group of features observed by the human eye (sensor).
“determining the validity of the estimated position of the vehicle ...” in the context is comparing the set of features shown on a map with features visible to the eye and determining ones true position. This is similar to a mental process employed when people try to identify where they are on a map
As explained above, independent claim 1 recites at least one abstract idea. The other independent claims 11 and 20 which are of similar scope to claim 1, likewise recite at least one abstract idea under Step 2A, Prong 1. 1.
Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, 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 such as 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”; see at least MPEP 2106.04(d).
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A computer-implemented method for determining a validity of an estimated position of a vehicle, the method comprising:
receiving a digital map;
receiving the estimated position of the vehicle, wherein the estimated position is a position in the digital map or is assignable to a position in the digital map;
detecting a number of first features in the digital map, wherein the first features indicate at least one object adjacent to a roadway;
grouping the first features into a number of first feature groups such that all of the first features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area belong to a first feature group assigned to the respective area;
receiving sensor information about an environment of the vehicle;
detecting a number of second features in the sensor information, wherein the second features indicate at least one object adjacent to a roadway;
grouping the second features into a number of second feature groups such that all of the second features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area belong to a second feature group of the respective area;
rejecting first feature groups whose maximum lateral extent exceeds a first predetermined value and/or rejecting second feature groups whose maximum lateral extent exceeds a second predetermined value;
assigning at least some unrejected first feature groups to a respective unrejected second feature group;
and
determining the validity of the estimated position of the vehicle based on a comparison of positions of a number of the unrejected first feature groups with positions of the respective assigned second feature groups.
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.
Regarding the additional limitation of “computer-implemented method” the examiner submits that this limitation of merely using a computer (processor) to perform the process is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of granularity, to the judicial exception. The claimed computer components (i.e. computer implemented, processor, memory, computer readable storage device, processing device, transport manager application, transport manager interface) are recited at a high-level of generality and are merely invoked as tools to perform an existing manual process. Simply implementing the abstract idea on a generic / general-purpose computer is not a practical application of the abstract idea. See MPEP 2106.04(d) and 2016.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea.
Regarding the additional limitation of “receiving a digital map” and “receiving the estimated position of the vehicle,” the examiner submits that this limitation is adding insignificant extra-solution activity to the judicial exception. In particular, the “receiving” step is recited at a high level of granularity and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Furthermore, the computer / processor and memory / computer readable storage device and processing device, user request graphical user interface (generic computer / general computer components) are only being used as a tool in the receiving / selecting, which is also not indicative of integration into a practical application. See MPEP 2106.04(d) and 2106.05(f). Note that there are no particular technical steps regarding receiving / selecting more than using computers as a tool to perform an otherwise manual process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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 limitations add nothing significant that is not already present when looking at the elements taken individually. 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.
Therefore, independent claims 12, 20 and 21 are directed to an abstract idea.
Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 12, 20 and 21 do 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 “a computer implemented method” 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. And as discussed above, the additional limitations of “an exchange of messages between the ego vehicle and the one or more surrounding vehicles” is recited at a high level of granularity and amounts to mere data gathering, the examiner submits that these limitation amounts to insignificant extra-solution activity.
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 limitations of “a computer” are well-understood, routine, and conventional activity because the background recites that the exchange of messages between the vehicles, and the specification does not provide any indication that the processor is anything other than a conventional computer within a vehicle. 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.
Because the claims fail to recite anything sufficient to amount to significantly more than the judicial exception, independent Claims 12, 20, and 21 are patent ineligible under 35 U.S.C. 101.
Dependent Claims 13 – 19 have been given the full two-part analysis, including analyzing the additional limitations, both individually and in combination. Dependent Claims 13 - 19 when analyzed both individually and in combination, are also patent ineligible under 35 U.S.C. § 101 based on same analysis as above. The additional limitations recited in the dependent claims fail to establish that the dependent claims are not directed to an abstract idea. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. Dependent claim(s) 13 - 19 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/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent Claims 13 - 19 are not patent eligible under the same rationale as provided for in the rejection of Claims 12, 20 and 21.
Therefore, claim(s) 12-21 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 12-15 and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Florian Ries et. al. US 20210215503 (“Ries”) in view of Marek Stess US 20190271551 A1 (“Stess”)
As per Claim 12, 20 and 21,
Ries discloses,
A computer-implemented method for determining a validity of an estimated position of a vehicle, the method comprising (see at least [0019] the device comprises, in particular, at least one processing unit, in particular for carrying out one or more or all of the above-mentioned methods)
receiving a digital map (see at least [0017] a map section of the digital map is downloaded from a server external to the vehicle, which is assigned to a current position of the vehicle determined, and [0058] the map section KA of the in particular high-resolution digital map 5 (HAD map) assigned to the current position GNSSP is downloaded from a server 7 external to the vehicle.
receiving the estimated position of the vehicle, wherein the estimated position is a position in the digital map or is assignable to a position in the digital map (see at least [0014] in the GNSS-based localization method, i.e., the localization method based on the at least one global navigation satellite system, a current position of the vehicle (GNSS position) is determined based on a satellite, and [0019] device for carrying out the method comprises in particular at least one receiver for signals from at least one global navigation satellite system
detecting a number of first features in the digital map, wherein the first features indicate at least one object adjacent to a roadway (see at least [0018] Landmark signatures are extracted from the digital map, and [0058] Landmark objects LMK and thus landmark signatures are extracted from the digital map 5)
receiving sensor information about an environment of the vehicle (see at least [0019] a vehicle sensor system, in particular comprising at least one sensor or a plurality of identical and/or different sensors, for detecting landmark signatures
detecting a number of second features in the sensor information, wherein the second features indicate at least one object adjacent to a roadway (see at least [0012] landmark signatures determined by sensors, [0018] the determined landmark signatures, a plurality of position hypotheses are generated representing the position of the vehicle, and [0058] With the vehicle sensor system 4, landmark signatures are recorded by sensors, i.e., landmark objects LMS are determined from sensor data SD of the vehicle sensor system 4)
rejecting first feature groups whose maximum lateral extent exceeds a first predetermined value and/or rejecting second feature groups whose maximum lateral extent exceeds a second predetermined value (see at least [0013] in particular is only evaluated as plausible if the two determined positions are close together, in particular if they are within a predetermined maximum distance from each other. Otherwise, the determined most probable position is rejected as implausible, and [0051] as a further method component VK2, the localization method LMG based on at least one global navigation satellite system is carried out, wherein it is checked whether a lateral error upper limit PL is small enough to exclude a competing route section FS, NFS, such that no incorrect localization on a neighboring route section FS, NFS is possible)
assigning at least some unrejected first feature groups to a respective unrejected second feature group (see at least [0123] A classification of the segments based on the determined landmark parameters is performed in a step S24 a)
and
determining the validity of the estimated position of the vehicle based on a comparison of positions of a number of the unrejected first feature groups with positions of the respective assigned second feature groups (see at least [0011] The solution according to the invention combines two completely independent, integral localization methods for ensuring a street-specific localization of the vehicle, [0013] the determined most probable position is compared with a satellite-based determined position, i.e., with a position determined by means of the localization method based on the at least one global navigation satellite system, and in particular is only evaluated as plausible if the two determined positions are close together, in particular if they are within a predetermined maximum distance from each other)
Ries does not discloses,
grouping the first features into a number of first feature groups such that all of the first features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area belong to a first feature group assigned to the respective area;
grouping the second features into a number of second feature groups such that all of the second features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area belong to a second feature group of the respective area;
Stess teaches,
grouping the first features into a number of first feature groups such that all of the first features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area belong to a first feature group assigned to the respective area; (see at least [0022] semantically interpretable elements of the street infrastructure are recorded as landmarks. Furthermore, these can be grouped by their geometry as well as according to the options for processing and recording, and [0036] the segmentation is performed by means of a Euclidean Cluster Extraction Algorithm, wherein data points for which the separation distance is less than a predetermined threshold value are each assigned to a segment)
grouping the second features into a number of second feature groups such that all of the second features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area belong to a second feature group of the respective area; (see at least [0022] semantically interpretable elements of the street infrastructure are recorded as landmarks. Furthermore, these can be grouped by their geometry as well as according to the options for processing and recording, and [0036] the segmentation is performed by means of a Euclidean Cluster Extraction Algorithm, wherein data points for which the separation distance is less than a predetermined threshold value are each assigned to a segment)
Thus, Ries discloses a method of localizing using two different methods where the two localization methods include at least one landmark-based localization method and one localization method based on at least one global navigation satellite system and Stess teaches segmentation method wherein data points for which the separation distance is less than a predetermined threshold value are each assigned to a segment.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ries with an object class being assigned to the segments based on the landmark parameters taught by Stess, with a reasonable expectation of success, wherein landmark parameters and an object class are assigned to each landmark observation, and the landmark observations are output (0009).
As per Claim 13,
Ries discloses,
wherein the validity of the estimated position is determined such that, based on an assumption that the estimated position was correct, positions of the number of the first feature groups are compared with positions of the respective assigned second feature groups (see at least [0013] the determined most probable position is compared with a satellite-based determined position, i.e., with a position determined by means of the localization method based on the at least one global navigation satellite system, and in particular is only evaluated as plausible if the two determined positions are close together, in particular if they are within a predetermined maximum distance from each other).
Ries does not discloses,
in a common coordinate system
Stess teaches,
in a common coordinate system (see at least [0042] this position can be determined together with the other landmark parameters within the framework of the principal component analysis. For instance, Cartesian coordinates can thereby be used or a position can be determined by means of distance and angle. The origin of the coordinate system can be assumed in particular at the mobile unit so that a position determination first takes place relative to it. In a further step, a transformation into another coordinate system, in particular a global coordinate system, can then take place).
Thus, Ries discloses a method of localizing using two different methods where the two localization methods include at least one landmark-based localization method and one localization method based on at least one global navigation satellite system and Stess teaches segmentation method wherein data points for which the separation distance is less than a predetermined threshold value are each assigned to a segment.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ries with an object class being assigned to the segments based on the landmark parameters taught by Stess, with a reasonable expectation of success, wherein landmark parameters and an object class are assigned to each landmark observation, and the landmark observations are output (0009).
As per Claim 14,
Ries discloses,
wherein the estimated position is confirmed if positions of all mutually assigned feature groups differ by less than a predetermined distance (see at least [0063] During the comparison VLM, all possible matches are taken into account, not only closest points, such that a plurality of position hypotheses PH are determined. Each position hypothesis PH has a spatial uncertainty based on measurement noise, map errors, and odometry uncertainties).
As per Claim 15,
Ries discloses,
wherein the estimated position is confirmed if positions of at least a defined proportion of the mutually assigned feature groups differ by less than a predetermined distance (see at least [0064] By analyzing all position hypotheses PH and filtering out all false information by means of the probabilistic analysis PA, a position hypothesis PHI with an integrity value is determined. For this purpose, the probabilistic analysis PA is carried out with the position hypotheses PH, a distribution model VII of misleading information and a distribution model VKI of correct information. This results in the position hypotheses PHI with the integrity value).
As per Claim 17,
Ries discloses,
wherein the method is carried out separately for features indicating objects on different sides of the roadway (see at least [0011] An equally integrated method creates a sufficiently confusion-proof signature from these landmarks on both sides, i.e., on the side of the map and on the side of the vehicle sensors and compares them with each other).
As per Claim 18,
Ries does not disclose,
wherein for the rejecting, the first features and/or the second features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area are additionally considered as part of the first or second feature groups which are assigned to respective longitudinally adjacent areas.
Stess teaches,
wherein for the rejecting, the first features and/or the second features located in a respective area next to the roadway defined with regard to a longitudinal extent of the respective area are additionally considered as part of the first or second feature groups which are assigned to respective longitudinally adjacent areas (see at least [0017] Data points from a certain number of data sets are saved as output data and, based on the output data, segments are determined by means of segmentation, wherein data points are assigned to each of the segments. For each of the determined segments, landmark parameters of the respective segment are determined by means of a principal component analysis. An object class is assigned to the segments based on the landmark parameters determined for each of them).
Thus, Ries discloses a method of localizing using two different methods where the two localization methods include at least one landmark-based localization method and one localization method based on at least one global navigation satellite system and Stess teaches segmentation method wherein data points for which the separation distance is less than a predetermined threshold value are each assigned to a segment.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ries with an object class being assigned to the segments based on the landmark parameters taught by Stess, with a reasonable expectation of success, wherein landmark parameters and an object class are assigned to each landmark observation, and the landmark observations are output (0009).
As per Claim 19,
Ries does not disclose,
wherein each of the digital map and the sensor information also provides height information about the features, and wherein features with heights which differ by at least a predetermined height difference are not assigned to a same feature group.
Stess teaches,
wherein each of the digital map and the sensor information also provides height information about the features, and wherein features with heights which differ by at least a predetermined height difference are not assigned to a same feature group (see at least [0023] For characterizing a landmark observation, a state vector is generated, wherein the data stored in this state vector differs depending on the type of landmark: The state vector s.sup.PO of a point-based landmark observation comprises a position p.sup.vrf in the vehicle coordinate system, an uncertainty Σ.sub.p and an absolute position p.sup.wgs in a global coordinate system. The points are thereby specified as a triple p=(x,y,z). Furthermore, a diameter and a height are defined, and a time stamp for the observation as well as an identification of the observing sensor are saved, and [0029] The characterization of the recorded landmarks is specified by the fields “Diameter,” “Height,” “Length” and “Width.”)
Thus, Ries discloses a method of localizing using two different methods where the two localization methods include at least one landmark-based localization method and one localization method based on at least one global navigation satellite system and Stess teaches segmentation method wherein data points for which the separation distance is less than a predetermined threshold value are each assigned to a segment.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ries with an object class being assigned to the segments based on the landmark parameters taught by Stess, with a reasonable expectation of success, wherein landmark parameters and an object class are assigned to each landmark observation, and the landmark observations are output (0009).
Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable over Ries in view of Stess as in Claim 12 and further in view of Timo Nachsetedt et. al. US 20230204364 A1 (“Nachsetedt”)
As per Claim 16,
Ries discloses,
wherein the sensor information is generated by a LiDAR sensor.
Ries does not disclose,
a LiDAR sensor.
Nachsetedt discloses,
wherein the sensor information is generated by a LiDAR sensor (see at least [0010] measurement data from a LiDAR sensor system, a radar sensor system and/or a camera sensor system are received, and static features are extracted from the measurement data received, and [0016] the loaded map section may be used for a feature-based localization, drawing on measurement data from a LiDAR sensor system, a radar sensor system and/or a camera sensor system)
Thus, Ries discloses a method of localizing using two different methods where the two localization methods include at least one landmark-based localization method and one localization method based on at least one global navigation satellite system and Nachsetedt teaches receiving measurement data from a LiDAR sensor system, a radar sensor system and/or a camera sensor system, and extracting static features are from the measurement data.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ries with ascertaining a starting position by comparing the static features extracted from the measurement data with features stored in the map section as taught by Nachsetedt, with a reasonable expectation of success, the loaded map section may be used for a feature-based localization, drawing on measurement data from a LiDAR sensor system, a radar sensor system and/or a camera sensor system(0016).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicants should take note of the prior art in the PTO-892.
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/A.P./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668