Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/25/2026 has been entered.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification (MPEP 608.01, ¶6.31).
Claim Objections
Applicant amended claim 29 as suggested, and thus the objection is withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7, 9, 14-21, 23, 28-32, 34-36 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler (US20190368882A1) in view of Nordbruch (US20210086766A1) and Eriksson (US20200013281A1).
Regarding claim 1, Wheeler teaches;
A method performed by a processor of an autonomous driving system of a vehicle for using map data in performing an autonomous driving function (taught as a vehicle computing system, element 120, performing calculation and control related to autonomous vehicles, and communicate with an online HD map system, paragraph 0036), comprising:
accessing, from a map database accessible by the processor, map data regarding an object or feature in a vicinity of the vehicle (taught as a landmark map, paragraph 0050, which includes geographic features such as road infrastructure/markings, paragraph 0065, and is accessed by the vehicle computing system, paragraph 0066);
accessing, by the processor, confidence information associated with the map data regarding the object or feature in the vicinity of the vehicle (taught as the landmark map, which is accessed by the vehicle, including associated confidence values which measure a probability of a representation being accurate, paragraph 0066);
wherein the confidence information is based on one or more of additional vehicles detecting the object or feature (taught as accounting for number of verification records detecting or not detecting an object/feature, paragraph 0095); wherein the weight increases based on a number of additional vehicles detecting the object or feature (taught as using a number of total verification records reaching a threshold number of records, and summarizing data including a number of times a detected object is verified/not verified, paragraph 0095; while not explicitly creating weighted map data, such a procedure creates a step function/binary choice of whether or not to update based on the number of verification reports).
However, Wheeler does not explicitly teach; accessing safety information associated with the map data and including a safety level associated with the object or the feature,
detecting the safety level, applying a weight to the accessed map data based on the confidence information and the safety information to generate weighted map data, determining, based on the weighted map data, an autonomous driving level associated with the vicinity of the object or feature and based on the safety level; and
adjusting an autonomous driving mode of the vehicle based on the autonomous driving level.
Nordbruch teaches; accessing safety information associated with the map data and including a safety level associated with the object or the feature (taught as determining ASIL levels based on infrastructure data, paragraph 0138),
detecting the safety level (indicated in comparing the predetermined ASIL level to an ASIL level of the respective infrastructure to determine the use of the infrastructure for functions, paragraph 0142),
determining, based on the weighted map data, an autonomous driving level associated with the vicinity of the object or feature and based on the safety level (taught as determining allowed automotive safety integrity level based on the detected infrastructure, paragraph 0138, and adjusting use based on ASIL level of the infrastructure data, paragraph 0142); and
adjusting an autonomous driving mode of the vehicle based on the autonomous driving level (taught as, for example, terminating automatic control based on limits on the automatic control, paragraph 0058).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine the autonomous driving level restrictions such as what is taught in in the system taught by Wheeler in order to follow rules of the road and improve safety. Such a system for determining allowed/required autonomous driving levels, as taught by Nordbruch, helps fulfill specific safety requirements before using a vehicle function (paragraph 0048).
However, Nordbruch does not explicitly teach; applying a weight to the accessed map data based on the confidence information and the safety information.
Eriksson teaches; applying a weight to the accessed map data based on the confidence information and the safety information (taught as fusing map data with relative position data [corresponding to safety information], paragraph 0116, for example, weighting regions of overlap between map and cooperatively generated relative positioning data higher for ASIL considerations than non-overlap regions, paragraph 0083).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply weights to the accessed map data based on confidence and safety information as taught by Eriksson in the system taught by Wheeler as modified by Eriksson and Nordbruch in order to improve map matching and correct location of the vehicle, as suggested by Eriksson (paragraph 0116). Additionally, Eriksson teaches that using verified relative positioning data helps prevent false negatives and false positives (paragraph 0117).
Regarding claim 2, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection). However, Wheeler does not explicitly teach; wherein the safety level is an Automotive Safety Integrity Level (ASIL) autonomous driving level in the vicinity of the object or feature.
Nordbruch teaches; wherein the safety level is an Automotive Safety Integrity Level (ASIL) autonomous driving level in the vicinity of the object or feature (taught as detecting ASIL for infrastructure data, paragraph 0138).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine the autonomous driving level restrictions such as what is taught in in the system taught by Wheeler in order to follow rules of the road and improve safety. Such a system for determining allowed/required autonomous driving levels, as taught by Nordbruch, helps fulfill specific safety requirements before using a vehicle function (paragraph 0048).
Regarding claim 3, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection). Wheeler further teaches; wherein the confidence information comprises an indication related to accuracy of the map data regarding the object or feature (taught as evaluating a confidence value measuring a probability of a representation being accurate, paragraph 0066).
Regarding claim 4, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection). Wheeler further teaches; wherein the confidence information comprises an indication related to reliability of the map data regarding the object or feature (taught as determining indications of the number of times a represented object is verified/not verified, paragraph 0097).
Regarding claim 5, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection). Wheeler further teaches; wherein the confidence information comprises a statistical score indicative of a precision of the map data regarding the object or feature (taught as verification records, part of the map information, including statistical information regarding number of times verifying/not verifying, paragraph 0097).
Regarding claim 6, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection). Wheeler further teaches; wherein the confidence information comprises an age or freshness of the map data regarding the object or feature (taught as including information about age data of the HD map and sending alerts if data is outdated, paragraph 0140, with statistical analysis for a freshness constraint, paragraph 0163).
Regarding claim 7, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection). Wheeler further teaches; wherein accessing confidence information associated with the map data regarding the object or feature in the vicinity of the vehicle comprises:
obtaining the confidence information by the processor from the map database (taught as vehicle obtaining the confidence values from the local HD store, element 275, paragraph 0087), wherein information in the map database is obtained from one or more of system memory (taught as the local HD store, shown to be part of the vehicle computing system in Fig 2), a remote computing device (taught as the online HD map, deployed by networking to other machines, paragraph 0167), or another vehicle (taught as multiple vehicles contributing to the modification of the HD map, paragraph 0108).
Regarding claim 9, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection). However, Wheeler doesn’t explicitly teach; wherein using the confidence information in performing an autonomous or semi-autonomous driving action by the vehicle comprises:
applying, by the processor, a weight to the accessed map data regarding the object or feature based upon the confidence information; and
using weighted map data regarding the object or feature by the processor while performing a path planning, object avoidance or steering autonomous driving action.
Eriksson teaches; wherein using the confidence information in performing an autonomous or semi-autonomous driving action by the vehicle comprises:
using weighted map data regarding the object or feature by the processor while performing a path planning, object avoidance or steering autonomous driving action (taught as fusing map data with relative position data [corresponding to safety information], paragraph 0116, and activating automatic responses based on the data, paragraph 0022, including active responses such as movement that would affect other vehicles, and passive responses such as notifications, paragraph 0096;).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply weights to the accessed map data based on confidence and safety information as taught by Eriksson in the system taught by Wheeler as modified by Eriksson and Nordbruch in order to improve map matching and correct location of the vehicle, as suggested by Eriksson (paragraph 0116). Additionally, Eriksson teaches that using verified relative positioning data helps prevent false negatives and false positives (paragraph 0117).
Regarding claim 14, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection), further comprising:
obtaining, by the processor from vehicle sensors, sensor data regarding the object or feature in the vicinity of the vehicle (taught as recording vehicle sensor data related to the map, paragraph 0059, such as for performing matching verifications, paragraph 0093);
determining, by the processor, whether the obtained sensor data regarding the object or feature in the vicinity of the vehicle differs from the map data regarding the object or feature obtained from the map database by a threshold amount (taught as determining matches as being a difference between data being within a predetermined threshold, paragraph 0085); and
uploading, by the processor to a remote computing device, the obtained sensor data regarding the object or feature in the vicinity of the vehicle (taught as sharing raw sensor data to the online HD map system, paragraph 0124) along with confidence information based on one or more [examiner notes that only one of the features need be true to read on the limitations] of
a type of sensor used to detect or classify the object or feature,
a quality of perception of the object or features achieved by the sensor, or
an accuracy or precision of the sensor data (taught as determining a significance value, such that larger discrepancies [more inaccurate map data] having higher priority, paragraph 0123)
in response to determining that the obtained sensor data differs from the map data regarding the object or feature obtained from the map database by at least the threshold amount (taught as providing discrepancy data between the sensor and map data, paragraph 0124).
Regarding claim 23, it has been determined that no further limitations exist apart from those addressed in claim 9. Therefore, claim 23 is rejected under the same rationale as claim 9.
Regarding claim 29, Wheeler teaches;
A method performed by a computing device for including safety and confidence information within map data useful by autonomous and semiautonomous driving systems in vehicles (taught as a vehicle computing system, element 120, performing calculation and control related to autonomous vehicles, and communicate with an online HD map system, paragraph 0036), comprising:
receiving, by the computing device from a source, information regarding an object or feature for inclusion in a map database including a measure of confidence in the information regarding the object or feature (taught as a landmark map, paragraph 0050, which includes geographic features such as road infrastructure/markings, paragraph 0065, and is accessed by the vehicle computing system, paragraph 0066); wherein the information is based on one or more vehicles detecting the object or the feature (taught as accounting for number of verification records detecting or not detecting an object/feature, paragraph 0095);
using the received measure of confidence in the information regarding the object or feature to generate a weight associated with safety and confidence information regarding the object or feature suitable for use by vehicle autonomous and semi-autonomous driving systems in autonomous or semi-autonomous driving operations (taught as the landmark map, which is accessed by the vehicle, including associated confidence values which measure a probability of a representation being accurate in considerations for controlling the vehicle, paragraph 0066), wherein the safety information and confidence information comprises;
an indication related to accuracy of the map data regarding the object or feature (taught as evaluating a confidence value measuring a probability of a representation being accurate, paragraph 0066);
a statistical score indicative of a precision of the map data regarding the object or feature (taught as verification records, part of the map information, including statistical information regarding number of times verifying/not verifying, paragraph 0097);
an indication related to reliability of the map data regarding the object or feature (taught as determining indications of the number of times a represented object is verified/not verified, paragraph 0097) and
an age or freshness of the map data regarding the object or feature (taught as including information about age data of the HD map and sending alerts if data is outdated, paragraph 0140, with statistical analysis for a freshness constraint, paragraph 0163);
wherein the weight increases based on a number of additional vehicles detecting the object or feature (taught as using a number of total verification records reaching a threshold number of records, and summarizing data including a number of times a detected object is verified/not verified, paragraph 0095); and
storing weighted map data in a manner that enables access by vehicle autonomous and semi- autonomous driving systems (taught as vehicle obtaining the confidence values from the local HD store, element 275, paragraph 0087, and information being stored accessible to the vehicle by an online HD map store, shown in Fig 1).
However, Wheeler does not explicitly teach; applying a weight to the accessed map data based on the confidence information and the safety level to generate weighted map data, determining, based on the weighted map data, an autonomous driving level associated with the vicinity of the object or feature and based on the safety level; and
adjusting an autonomous driving mode of the vehicle based on the autonomous driving level.
Nordbruch teaches; a safety level in a vicinity of the object or feature (indicated in comparing the predetermined ASIL level to an ASIL level of the respective infrastructure to determine the use of the infrastructure for functions, paragraph 0142);
determining, based on the weighted map data, an autonomous driving level associated with the vicinity of the object or feature and based on the safety level (taught as determining allowed automotive safety integrity level based on the detected infrastructure, paragraph 0138 and adjusting use based on ASIL level of the infrastructure data, paragraph 0142); and
adjusting an autonomous driving mode of the vehicle based on the autonomous driving level (taught as, for example, terminating automatic control based on limits on the automatic control, paragraph 0058).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine the autonomous driving level restrictions such as what is taught in in the system taught by Wheeler in order to follow rules of the road and improve safety. Such a system for determining allowed/required autonomous driving levels, as taught by Nordbruch, helps fulfill specific safety requirements before using a vehicle function (paragraph 0048).
However, Nordbruch does not explicitly teach; applying a weight to the accessed map data based on the confidence information and the safety level to generate weighted map data.
Eriksson teaches; applying a weight to the accessed map data based on the confidence information and the safety level to generate weighted map data, (taught as fusing map data with relative position data [corresponding to safety information], paragraph 0116, for example, weighting regions of overlap between map and cooperatively generated relative positioning data higher for ASIL considerations than non-overlap regions, paragraph 0083).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply weights to the accessed map data based on confidence and safety information as taught by Eriksson in the system taught by Wheeler as modified by Eriksson and Nordbruch in order to improve map matching and correct location of the vehicle, as suggested by Eriksson (paragraph 0116). Additionally, Eriksson teaches that using verified relative positioning data helps prevent false negatives and false positives (paragraph 0117).
Regarding claim 30, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 29 (see claim 29 rejection). Wheeler further teaches; wherein receiving information regarding an object or feature for inclusion in a map database including a measure of confidence in the information regarding the object or feature comprises
receiving from one or more vehicles information including:
a location of the object or feature (taught as detecting mismatches between sensor and HD map data regarding location data, paragraph 0093);
a characteristic of the object or feature (taught as detecting mismatches regarding object ID, paragraph 0093, or other information such as classification errors, paragraph 0094); and
a measure of confidence in the information regarding either the location or the characteristic of the object or feature (taught as mismatch records for associated confidence values of a level, indicating a knowledge of the confidence value in the mismatch checking steps, paragraph 0096).
Regarding claim 31, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 29 (see claim 29 rejection). Wheeler further teaches; further comprising updating information regarding the object or feature in the map database based at least in part on the received measure of confidence in the received information regarding the object or feature confidence (taught as updating the online HD map system based on verification records, with consideration to the mismatch record confidence levels, paragraph 0105).
Regarding claim 32, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 29 (see claim 29 rejection). Wheeler further teaches; wherein storing the safety and confidence information regarding the object or feature comprises including the safety and confidence information as part of location and other information regarding the object or feature in the map database provided to vehicles for use in autonomous or semi-autonomous driving operations (taught as including spatial location considerations with the confidence score to be used for controlling the vehicle operations, paragraph 0066).
Regarding claim 34. Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 29 (see claim 29 rejection). Wheeler further teaches; wherein:
receiving information regarding an object or feature for inclusion in a map database comprises
receiving, from a plurality of sources, information regarding the object or feature along with measures of confidence in the information regarding the object or feature (taught as a landmark map, paragraph 0050, which includes geographic features such as road infrastructure/markings, paragraph 0065, and is accessed by the vehicle computing system, paragraph 0066, where the online HD map that encompasses the landmark map is constructed from the collection and verification from multiple sources in groups, paragraph 0102);
the method further comprises determining, from information received from the plurality of sources, one set of information regarding the object or feature and consolidated safety and confidence information for the determined set of information regarding the object or feature (taught as the landmark map, which is accessed by the vehicle, including associated confidence values which measure a probability of a representation being accurate in considerations for controlling the vehicle, paragraph 0066, where confidence values are based partially on the matching verification records in a group, paragraph 0105); and
storing safety and confidence information regarding the object or feature in a manner that enables access by vehicle autonomous and semi-autonomous driving systems for use in autonomous or semi-autonomous driving operations (taught as the landmark map, which is accessed by the vehicle, including associated confidence values which measure a probability of a representation being accurate in considerations for controlling the vehicle, paragraph 0066) comprises;
storing the consolidated safety and confidence information for the determined set of information regarding the object or feature in a manner that enables access by vehicle autonomous and semi-autonomous driving systems for use in autonomous or semi-autonomous driving operations (taught as the vehicle storing and obtaining the confidence values from the local HD store, element 275, paragraph 0087, which is used during autonomous driving, paragraph 0066).
Regarding claims 15-21, 28, 35-36 and 38 it has been determined that no further limitations exist apart from those addressed in claims 1-7, 14, 29-32 and 34. Therefore, claims 15-21, 28, 35-36 and 38 are rejected under the same rationale as claims 1-7, 14, 29-32 and 34 respectively.
Claim(s) 8, 22, 33, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler (US20190368882A1) as modified by Nordbruch (US20210086766A1) and Eriksson (US20200013281A1), and further in view of Yukun (JP2020067402A).
Regarding claim 8, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 1 (see claim 1 rejection). However, Wheeler does not explicitly teach; wherein accessing confidence information associated with the map data regarding the object or feature in the vicinity of the vehicle comprises:
obtaining the confidence information based on a location of the object or feature from a data structure accessible by the processor that is different from the map database.
Yukun teaches; wherein accessing confidence information associated with the map data regarding the object or feature in the vicinity of the vehicle comprises:
obtaining the confidence information based on a location of the object or feature from a data structure accessible by the processor that is different from the map database (taught as evaluating a reliability of measurements to obtain confidence information, paragraph 0033, which is distinct from a mapping database [in the cloud, paragraph 0032]).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to separate the storage of confidence information from map information as suggested by Yukun in the system taught by Wheeler, since it has been held that constructing a formerly integral structure in various elements involves only routine skill in the art. Nerwin v. Erlichman, 168 USPQ 177, 179. Such a feat helps promote redundancy.
Regarding claim 33, Wheeler as modified by Nordbruch and Eriksson teaches;
The method of claim 29 (see claim 29 rejection). However, Wheeler does not explicitly teach; wherein storing the safety and confidence information regarding the object or feature comprises:
storing the safety and confidence information in a database separate from the map database correlated with location information of the object or feature; and
providing the database to vehicles for use in autonomous or semi- autonomous driving operations.
Yukun teaches; wherein storing the safety and confidence information regarding the object or feature comprises:
storing the safety and confidence information in a database separate from the map database correlated with location information of the object or feature (taught as evaluating a reliability of measurements to obtain confidence information, paragraph 0033, which is distinct from a mapping database [in the cloud, paragraph 0032]); and
providing the database to vehicles for use in autonomous or semi- autonomous driving operations (taught as the methods provided being used for a vehicle to be driven autonomously, paragraph 0011, for sensor reliability, paragraph 0036).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to separate the storage of confidence information from map information as suggested by Yukun in the system taught by Wheeler, since it has been held that constructing a formerly integral structure in various elements involves only routine skill in the art. Nerwin v. Erlichman, 168 USPQ 177, 179. Such a feat helps promote redundancy.
Regarding claims 22 and 37, it has been determined that no further limitations exist apart from those addressed in claims 8 and 33. Therefore, claim 22 and 37 are rejected under the same rationale as claims 8 and 33 respectively.
Claim(s) 11 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler (US20190368882A1) as modified by Nordbruch (US20210086766A1) and Eriksson (US20200013281A1), and in further view of Kim (US20190258249A1).
Regarding claim 11, Wheeler as modified by Nordbruch, Eriksson, and Kim teaches;
The method of claim 1 (see claim 1 rejection). However, Wheeler does not explicitly teach; wherein adjusting the autonomous driving mode of the vehicle based on the autonomous driving level comprises:
adjusting the autonomous driving mode of the vehicle implemented by the processor to a driving mode compatible with the confidence information regarding the object or feature in the vicinity of the vehicle.
Kim teaches; wherein adjusting the autonomous driving mode of the vehicle based on the autonomous driving level comprises:
adjusting the autonomous driving mode of the vehicle implemented by the processor to a driving mode compatible with the confidence information regarding the object or feature in the vicinity of the vehicle (taught as, in response to receiving a reliability result, switching the driving mode of the vehicle based on the result, paragraph 0068, exemplified in Fig 4).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to switch driving modes as taught by Kim in the system taught by Wheeler in order to improve safety. Should a system encounter a situation where it cannot trust the sensor data, it would be safer to revert to manual control, as faulty data can result in dangerous behavior. Furthermore, Kim suggests that such considerations for switching the driving mode ensures the vehicle driving is efficiently controlled (paragraph 0021).
Regarding claims 24-25, it has been determined that no further limitations exist apart from those addressed in claims 10-11. Therefore, claims 24-25 are rejected under the same rationale as claims 10-11.
Claim(s) 12-13 and 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler (US20190368882A1) as modified by Nordbruch (US20210086766A1) and Eriksson (US20200013281A1), and Kim (US20190258249A1), and further in view of Yoo (US20190227545A1).
Regarding claim 12, Wheeler as modified by Nordbruch, Eriksson, and Kim teaches;
The method of claim 1 (see claim 1 rejection). However, Wheeler does not explicitly teach; further comprising:
notifying a driver of a need to participate in driving of the vehicle in response to determining that the confidence information regarding the object or feature in the vicinity of the vehicle does not support a fully autonomous driving mode; and
adjusting the autonomous driving mode of the vehicle implemented by the processor after notifying the driver.
Yoo teaches; notifying a driver of a need to participate in driving of the vehicle in response to determining that the confidence information regarding the object or feature in the vicinity of the vehicle does not support a fully autonomous driving mode (taught as outputting a notification message to confirm to change a driving mode, including consideration of map reliability, paragraph 0123); and
adjusting the autonomous driving mode of the vehicle implemented by the processor after notifying the driver (taught as receiving input to change the driving mode, paragraph 0124).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide a notification of a driving mode change as taught by Yoo in the system taught by Wheeler as modified by Kim in order to improve safety. Transitioning driving modes without informing the driver may cause inattentive or delayed responses, and could lead to accidents.
Regarding claim 13, Wheeler as modified by Nordbruch, Eriksson, Kim teaches;
The method of claim 1 (see claim 1 rejection). However, Wheeler does not explicitly teach; wherein: the confidence information regarding the object or feature comprises confidence information regarding objects and features within a defined area; and
adjusting the autonomous driving mode of the vehicle based on the autonomous driving level comprises adjusting the autonomous driving mode of the vehicle implemented by the processor to an autonomous driving mode consistent with the confidence information while the vehicle is in the defined area.
Yoo teaches; the confidence information regarding the object or feature comprises confidence information regarding objects and features within a defined area (taught as maps being considered in sections for determining reliability, paragraph 0051); and
adjusting the autonomous driving mode of the vehicle based on the autonomous driving level comprises adjusting the autonomous driving mode of the vehicle implemented by the processor to an autonomous driving mode consistent with the confidence information while the vehicle is in the defined area (taught as considering reliability within sections of the map, and adjusting driving behavior or modes based on the section reliability, paragraph 0051).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide a segment the map in reliability considerations as taught by Yoo in the system taught by Wheeler as modified by Kim in order to improve efficiency. By reducing the map to sections, one can avoid unnecessarily prolonging certain driving mode restrictions, and further better determine which map data needs updates.
Regarding claims 26-27, it has been determined that no further limitations exist apart from those addressed in claims 12-13. Therefore, claims 26-27 are rejected under the same rationale as claims 12-13.
Response to Arguments
Applicant argues on pages 14-15 of the remarks that the cited references fail to disclose “Accessing, from a map database accessible by the processor, map data regarding…information associated with the map data and including a safety level associated with the object or the feature”. For example, the applicant argues on page 15 that ASIL is not safety level, but rather a risk level.
The examiner respectfully disagrees. Claim 2 clearly states that the safety level is an ASIL, and thus treating it as such fits with the applicant’s disclosure.
Applicant further argues on page 15 that the infrastructure in Nordbruch does not amount to safety information associated with the map data as claimed.
The examiner respectfully disagrees. In Nordbruch, the system takes a consideration of the combination of corresponding function and infrastructure based on the ASIL levels to determine whether to allow the corresponding function (paragraph 0138). In essence, the feature, as currently recited, can be reasonably interpreted to be/include the environment/surroundings for the vehicle/infrastructure. The applicant’s argument regarding paragraph 0052 refers to one specific embodiment and ignores other embodiments which include ASIL (paragraph 0130, 0138). As claim 2 clearly states that the safety level is an ASIL, the resource lookup in Nordbruch would account for providing safety level information related to infrastructure data.
Applicant argues on pages 15-17 that the prior art fails to disclose/make obvious “applying a weight to the accessed map data based on the confidence information and the safety information, to generate weighted map data, wherein the weight increases based on a number of additional vehicles detecting the object or feature”, and that Erikkson forms classification based on the map data, not applying a weight to the accessed map data.
The examiner respectfully disagrees. A fusion of map data would take some amount of information from each included map data to form a final map. The effective binary step function whether or not to update is equivalent to applying/fusing map data with a mask.
Wheeler teaches that the number of verification records increases the weight [as in a weight of 0 if not verified sufficiently, or a weight of 1 if sufficiently verified] of the map/features (paragraph 0095). In other words, it increases [from 0x to 1x] based on getting a threshold number of verifications.
Eriksson then essentially teaches that, when there's enough agreement/overlap in map data, such information can be used to increase the ASIL classification (paragraph 0083), and thus associates the ASIL classification [safety level] with the fused map data.
In combination, both references address using sensor fusion/verification, and adjusting behavior based on the verification/overlap levels such that a further adjustment of the autonomous vehicle driving level is taken if a certain threshold is exceeded.
Additionally, Nordbruch teaches the use of ASIL levels based on the infrastructure data (paragraph 0138). This, in combination with Wheeler in Erikkson, would also create a finalized map, including safety levels and confidence information.
Applicant argues on pages 17 of the remarks that the dependent claims should be allowable based on the allowability of the independent claims.
In light of the above rejections and arguments regarding the independent claims, this argument is rendered moot.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20220057230A1, for further use of sensor quality information for confidence/map updates (such as claim 14)
US20220179423A1 for further use of reliability values in vehicle controls, including of features such as roadside units [infrastructure], (such as in independent claims 1, 15, 29, and 35)
US20200393829A1 for the use of geofences regarding autonomous driving level permissions based on map/feature considerations, (such as in independent claims 1, 15, 29, and 35)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL ANFINRUD whose telephone number is (571)270-3401. The examiner can normally be reached M-F 9:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached on (571)270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GABRIEL ANFINRUD/
Examiner, Art Unit 3662
/JELANI A SMITH/ Supervisory Patent Examiner, Art Unit 3662
Prosecution Insights