DETAILED ACTION
This final rejection is responsive to the claims filed on 01/12/2026. Claims 1-20 are pending. Claims 1, 14, and 18 are independent claims.
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 .
Response to Arguments
Applicant's arguments filed 01/12/2026 regarding the rejection of the independent claims under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant asserts on Page 9 of the remarks that the claims are not directed to a mental process, and therefore do not trigger Step 2A, Prong 1 of the 101 analysis. Applicant argues that the human mind cannot receive a digital point cloud consisting a digital point cloud or perform feature detection analysis, for example, on millions of data points. Examiner notes that the limitations of Claim 1 do not specify the form or size of the LiDAR scan, but simply state that a LiDAR scan is received, and that feature detection analysis is performed on ‘scenery depicted in the LiDAR scan’. A plain reading of this limitation suggests that the feature detection analysis can be performed on an image or visual representation of the LiDAR scan, which is an action that the human mind performs naturally. Paragraphs [0060] – [0063] establish a broadest reasonable interpretation (BRI) supporting this interpretation of the claim language. [0062] states that the LiDAR scan can be replaced with an image scan, and that “the camera can create stereogram(s) with an illusion of depth therein by means of stereopsis for binocular vision, e.g., a pair of stereo images to be viewed using a stereoscope.” This establishes that the BRI of the claim in light of the specification can encompass a human viewing the scan, and therefore that the analysis step could be performed by the human mind.
Applicant asserts that on Page 10 of the remarks that the claims integrate the idea into a practical application. Applicant argues that by using the context of a scene to pre-load specific feature recognition parameters, the processing of a LiDAR scan can be optimized and the efficiency of the computer doing the processing can be increased. The examiner notes here that Claim 1, for example, contains no limitations relating to computer hardware, an image processing pipeline, or increased computational efficiency due to pre-loading information relevant to said computer’s image processing pipeline, and that the argument therefore does not apply to Claim 1 as it is currently written.
Applicant’s arguments with respect to the rejections of Claims 1 and 10 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-9 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
101 Analysis – Step 1
Claim 1 is directed toward a method receiving a LiDAR scan of a location, determining a context or point of interest of the location, selecting a featuring recognition parameter based on the context where the feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan, and initiating the feature detection analysis of the scan to identify a feature, an attribute of a feature, or a combination thereof in the scenery. Therefore, Claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
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.
Independent Claim 1 includes limitations that recite an abstract idea and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method comprising:
receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device;
determining a context of the location, a point of interest (POI) associated with the location, or a combination thereof;
selecting a feature recognition parameter based on the context, wherein the feature recognition parameter performs a feature detection analysis of a scenery depicted in the LiDAR scan;
and initiating the feature detection analysis of the LiDAR scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery.
The examiner submits that the foregoing bolded limitation constitutes a ‘mental process’ because under its broadest reasonable interpretation, the claim covers the performance of the limitation in the human mind. For example, ‘receiving’, ‘determining’, ‘selecting’, and ‘initiating’ in the context of this claim encompasses a person looking at collected data representing a scene and forming a judgement about the contents of that scene.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II 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 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 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.’ In the present case, therefore, since there are no additional limitations beyond the above-noted abstract idea above, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent Claim 1 does not include addition elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more that 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, there are no additional limitations that amount to significantly more.
Dependent claims 3-9 and 11-13 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claims are directed towards 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.
Claim 2 uses the limitation of “… wherein the context includes a location type, a POI type, or a combination thereof queried from a geographic database”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 3 uses the limitation of “… retrieving one or more historical LiDAR scan results of the location…” which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 4 uses the limitation of “… detecting a change at the location the POI, or a combination thereof based on comparing the feature, the attribute of the feature, or a combination thereof identified in the LiDAR scan to the one or more historical LiDAR scan results,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 5 uses the limitation of “…updating a data record of a geographic database representing the location, the POI, or a combination thereof…,” which amounts to selecting a particular data source or type of data to be manipulated and is a form of insignificant extra-solution activity.
Claim 6 uses the limitation of “…wherein the feature recognition parameter includes a feature detector to be used for the analysis,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 7 uses the limitation of “…wherein the feature recognition parameter includes one or more expected features, an expected number of the one or more expected features, an expected arrangement of the one or more expected features, expected spatial dimensions of the one or more expected features, or a combination thereof associated with the location, the POI, or a combination thereof,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 8 uses the limitation of “determining one or more feature detection probabilities for a feature detector based one or more expected features, an expected number of the one or more expected features, an expected arrangement of the one or more expected features, expected spatial dimensions of the one or more expected features, or a combination thereof, wherein the feature, the attribute of the feature, or a combination thereof is identified in the LiDAR scan based on the one or more feature detection probabilities,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 9 uses the limitation of “storing the feature, the attribute of the feature, or a combination thereof as metadata describing the location, the POI, or a combination thereof,” which amounts to selecting a particular data source or type of data to be manipulated and is a form of insignificant extra-solution activity.
Claim 11 uses the limitation of “… wherein the feature includes furniture, objects, decorations, or a combination thereof associated with the location, the POI, or a combination thereof, and wherein the attribute of the feature includes a number, a spatial arrangement, a dimension, or a combination of the feature,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 12 uses the limitation of “… wherein the feature includes people at the location, the POI, or a combination thereof, and wherein the attribute of the feature includes a number of the people,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 13 uses the limitation of “… determining an occupancy of the location, the POI, or a combination thereof based on the number of the people,” which amounts to data gathering and is a form of insignificant extra-solution activity.
The examiner notes that the limitation of Claim 10 amounts to a practical extension beyond the judicial exception, and that the rejections under 35 U.S.C. 101 would likely be overcome if this or equivalent language were incorporated into independent Claim 1.
Regarding Claim 14,
101 Analysis – Step 1
Claim 14 is directed toward an apparatus containing a processor and memory containing computer code which has the ability to receive a LiDAR scan of a location, determine a context or point of interest of the location, select a feature recognition parameter based on the context where the feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan, and initiate the feature detection analysis of the scan to identify a feature, an attribute of a feature, or a combination thereof in the scenery. Therefore, Claim 14 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
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.
Independent Claim 14 includes limitations that recite an abstract idea and will be used as a representative claim for the remainder of the 101 rejection. Claim 14 recites:
An apparatus comprising:
at least one processor; and
at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, within the at least one processor, cause the apparatus to perform at least the following,
receive a scan of a location captured using a sensor of a portable device;
determine a context of the location, a point of interest (POI) associated with the location, or a combination thereof;
select a feature recognition parameter based on the context, wherein the feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan; and
initiate the feature detection analysis of the scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery.
The examiner submits that the foregoing bolded limitation constitutes a ‘mental process’ because under its broadest reasonable interpretation, the claim covers the performance of the limitation in the human mind. For example, ‘receive’, ‘determine’, ‘select’, and ‘initiate’ in the context of this claim encompasses a person looking at collected data representing a scene and forming a judgement about the contents of that scene.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II 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 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 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.’ In the present case, therefore, since there are no additional limitations beyond the above-noted abstract idea above, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent Claim 14 does not include addition elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more that 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, there are no additional limitations that amount to significantly more.
Dependent claims 15-17 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claims are directed towards 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.
Dependent Claim 15 uses the limitation of “…wherein the scan is a LiDAR scan, an image scan, or a combination thereof,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Dependent Claim 16 uses the limitation of “…wherein the context includes a location type, a POI type, or a combination thereof queried from a geographic database,” which amounts to selecting a particular data source or type of data to be manipulated and is a form of insignificant extra-solution activity.
Dependent Claim 17 uses the limitation of “… wherein the apparatus is further caused to:
retrieve one or more historical scan results of the location, wherein the one or more historical scan results indicate one or more historical features previously detected at the location or POI, an historical number of the one or more historical features, a historical arrangement of the one or more historical features, historical spatial dimensions of the one or more historical features, or a combination thereof, and wherein the feature recognition parameter is further based on the one or more historical scan results,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Regarding Claim 18,
101 Analysis – Step 1
Claim 18 is directed toward A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which when processed cause an apparatus to perform: receiving a LiDAR scan of a location, determining a context or point of interest of the location, selecting a feature recognition parameter based on the context where the feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan, and initiating the feature detection analysis of the scan to identify a feature, an attribute of a feature, or a combination thereof in the scenery. Therefore, Claim 18 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
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.
Independent Claim 18 includes limitations that recite an abstract idea and will be used as a representative claim for the remainder of the 101 rejection. Claim 18 recites:
A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:
receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device;
determining a context of the location, a point of interest (POI) associated with the location, or a combination thereof;
selecting a feature recognition parameter based on the context, wherein the feature recognition parameter performs a feature detection analysis of a scenery depicted in the LiDAR scan; and
initiating the feature detection analysis of the LiDAR scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery.
The examiner submits that the foregoing bolded limitation constitutes a ‘mental process’ because under its broadest reasonable interpretation, the claim covers the performance of the limitation in the human mind. For example, ‘receiving’, ‘determining’, ‘selecting’, and ‘initiating’ in the context of this claim encompasses a person looking at collected data representing a scene and forming a judgement about the contents of that scene.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II 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 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 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.’ In the present case, therefore, since there are no additional limitations beyond the above-noted abstract idea above, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent Claim 18 does not include addition elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more that 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, there are no additional limitations that amount to significantly more.
Dependent claims 19 and 20 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claims are directed towards 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.
Dependent Claim 19 uses the limitation of “…wherein the context includes a location type, a POI type, or a combination thereof queried from a geographic database,” which amounts to selecting a particular data source or type of data to be manipulated and is a form of insignificant extra-solution activity.
Dependent Claim 20 uses the limitation of “… retrieving one or more historical LiDAR scan results of the location, wherein the one or more historical LiDAR scan results indicate one or more historical features previously detected at the location or POI, an historical number of the one or more historical features, a historical arrangement of the one or more historical features, historical spatial dimensions of the one or more historical features, or a combination thereof, and wherein the feature recognition parameter is further based on the one or more historical LiDAR scan results,” which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim Rejections - 35 USC § 102
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)(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.
Claims 1-9 and 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shestak (EP 3543907).
Regarding Claim 1, Shestak discloses a method comprising:
receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device ([0063]; [0086]);
determining a context of the location, a point of interest (POI) associated with the location, or a combination thereof ([0107]: “The geographic database 101 can include data about the POIs and their respective locations in the POI data records 807. The geographic database 101 can also include data about places, such as cities, towns, or other communities, and other geographic features, such as bodies of water, mountain ranges, etc.”; [0108]: “the feature detection data records 809 can be associated with one or more of the node records 803, road segment records 805, and/or POI data records 807 to support localization or visual odometry based on the features stored therein and the corresponding estimated quality of the features.”) ;
selecting a feature recognition parameter based on the context ([0108]: “In one embodiment, the geographic database 101 can provide the tile-based feature detection data records 809 to dynamic adaptation of the in-vehicle feature detector 105.”), wherein the feature recognition parameter performs a feature detection analysis of a scenery depicted in the LiDAR scan ([0076]: “the adaptation is referred to as occurring “dynamically” because the change in model or model weights is automatically triggered by changes in location and/or operational condition of the vehicle 103, in-vehicle feature detector 105, and/or computer vision system 111.”);
and initiating the feature detection analysis of the LiDAR scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery ([0079]: “the in-vehicle feature detector 105 can detect road features (e.g., lane lines, signs, etc.”).
Regarding Claim 2, which depends from rejected Claim 1, Shestak further discloses wherein the context includes a location type, a POI type, or a combination thereof queried from a geographic database ([0097]).
Regarding Claim 3, which depends from rejected Claim 1, Shestak discloses further comprising: retrieving one or more historical LiDAR scan results of the location ([0091]: “For example, the geographic database 101 can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths.),
wherein the one or more historical LiDAR scan results indicate one or more historical features previously detected at the location or POI, an historical number of the one or more historical features, a historical arrangement of the one or more historical features, historical spatial dimensions of the one or more historical features, or a combination thereof ([0091]: “For example, the geographic database 101 can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths.”; [0063]: “In each of the map tiles 501, respective data collection vehicles 503a-503d (also collectively referred to as data collections vehicles 503 collect sensor data (e.g., imagery data, radar data, LiDAR data) as they travel within each respective map tile 501. The mapping platform 113 then collects the sensor data from each set of data collection vehicles 503 for each map tile 501 respectively as training data sets 505a-505b”), and
wherein the feature recognition parameter is further based on the one or more historical LiDAR scan results ([0108]: “In one embodiment, the geographic database 101 can provide the tile-based feature detection data records 809 to dynamic adaptation of the in-vehicle feature detector 105.”).
Regarding Claim 4, which depends from rejected Claim 3, Shestak further discloses detecting a change at the location, the POI, or a combination thereof based on comparing the feature, the attribute of the feature, or a combination thereof identified in the LiDAR scan to the one or more historical LiDAR scan results ([0058]: “First, real-time sensing of the environment provides information about potential obstacles, the behavior of others on the road, and safe, drivable areas… Even in a situation where the world is completely mapped in high resolution, exceptions will occur in which a vehicle 103 might need to drive off the road 20 to avoid a collision, or where a road's geometry or other map attributes like direction of travel have changed.”).
Regarding Claim 5, which depends from rejected Claim 4, Shestak further discloses updating a data record of a geographic database representing the location, the POI, or a combination thereof based on the identified feature, the identified attribute, the detected change, or a combination thereof ([0091]: “For example, the geographic database 101 can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number 30 lanes and their widths. In one embodiment, the HD mapping data (e.g., HD data records 811) capture and store details such as the slope and curvature of the road, lane markings, roadside objects such as sign posts, including what the signage denotes.”)
Regarding Claim 6, which depends from rejected Claim 1, Shestak further discloses wherein the feature recognition parameter includes a feature detector to be used for the analysis ([0011]).
Regarding Claim 7, which depends from rejected Claim 1, Shestak further discloses wherein the feature recognition parameter includes one or more expected features, an expected number of the one or more expected features, an expected arrangement of the one or more expected features ([0044]; [0091]), expected spatial dimensions of the one or more expected features ([0092]), or a combination thereof associated with the location, the POI, or a combination thereof.
Regarding Claim 8, which depends from rejected Claim 7, Shestak further discloses determining one or more feature detection probabilities for a feature detector based one or more expected features, an expected number of the one or more expected features, an expected arrangement of the one or more expected features, expected spatial dimensions of the one or more expected features, or a combination thereof, wherein the feature, the attribute of the feature, or a combination thereof is identified in the LiDAR scan based on the one or more feature detection probabilities.
Regarding Claim 9, which depends from rejected Claim 1, Shestak discloses further comprising: storing the feature, the attribute of the feature, or a combination thereof as metadata describing the location, the POI, or a combination thereof ([0108]: “the records 809 can also be associated with or used to classify the characteristics or metadata of the corresponding records 803, 805, and/or 807.”; Thus metadata are stored with, e.g., the POI data records 807.)
Regarding Claim 14, Shestak discloses an apparatus comprising:
at least one processor ([0004]);
and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, within the at least one processor, cause the apparatus to perform at least the following ([0004]),
receive a scan of a location captured using a sensor of a portable device ([0063]; [0086]);
determine a context of the location, a point of interest (POI) associated with the location, or a combination thereof ([0107]: “The geographic database 101 can include data about the POIs and their respective locations in the POI data records 807. The geographic database 101 can also include data about places, such as cities, towns, or other communities, and other geographic features, such as bodies of water, mountain ranges, etc.”; [0108]: “the feature detection data records 809 can be associated with one or more of the node records 803, road segment records 805, and/or POI data records 807 to support localization or visual odometry based on the features stored therein and the corresponding estimated quality of the features.”);
select a feature recognition parameter based on the context ([0108]: “In one embodiment, the geographic database 101 can provide the tile-based feature detection data records 809 to dynamic adaptation of the in-vehicle feature detector 105.”), wherein the feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan ([0076]: “the adaptation is referred to as occurring “dynamically” because the change in model or model weights is automatically triggered by changes in location and/or operational condition of the vehicle 103, in-vehicle feature detector 105, and/or computer vision system 111.”); and
initiate the feature detection analysis of the scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery ([0108]: “In one embodiment, the geographic database 101 can provide the tile-based feature detection data records 809 to dynamic adaptation of the in-vehicle feature detector 105.”).
Regarding Claim 15, which depends from rejected Claim 14, Shestak further discloses wherein the scan is a LiDAR scan, an image scan, or a combination thereof ([0063]; [0086]).
Regarding Claim 16, which depends from rejected Claim 14, Shestak further discloses wherein the context includes a location type, a POI type, or a combination thereof queried from a geographic database ([0097]).
Regarding Claim 17, which depends from rejected Claim 14, Shestak further discloses wherein the apparatus is further caused to:
retrieve one or more historical scan results of the location ([0091]: “For example, the geographic database 101 can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths.), wherein the one or more historical scan results indicate one or more historical features previously detected at the location or POI, an historical number of the one or more historical features, a historical arrangement of the one or more historical features, historical spatial dimensions of the one or more historical features, or a combination thereof ([0091]: “For example, the geographic database 101 can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths.”; [0063]: “In each of the map tiles 501, respective data collection vehicles 503a-503d (also collectively referred to as data collections vehicles 503 collect sensor data (e.g., imagery data, radar data, LiDAR data) as they travel within each respective map tile 501. The mapping platform 113 then collects the sensor data from each set of data collection vehicles 503 for each map tile 501 respectively as training data sets 505a-505b”), and
wherein the feature recognition parameter is further based on the one or more historical scan results ([0108]: “In one embodiment, the geographic database 101 can provide the tile-based feature detection data records 809 to dynamic adaptation of the in-vehicle feature detector 105.”).
Regarding Claim 18, Shestak discloses a non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform ([0005]):
receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device ([0063]; [0086]);
determining a context of the location, a point of interest (POI) associated with the location, or a combination thereof ([0107]: “The geographic database 101 can include data about the POIs and their respective locations in the POI data records 807. The geographic database 101 can also include data about places, such as cities, towns, or other communities, and other geographic features, such as bodies of water, mountain ranges, etc.”; [0108]: “the feature detection data records 809 can be associated with one or more of the node records 803, road segment records 805, and/or POI data records 807 to support localization or visual odometry based on the features stored therein and the corresponding estimated quality of the features.”);
selecting a feature recognition parameter based on the context ([0108]: “In one embodiment, the geographic database 101 can provide the tile-based feature detection data records 809 to dynamic adaptation of the in-vehicle feature detector 105.”), wherein the feature recognition parameter performs a feature detection analysis of a scenery depicted in the LiDAR scan ([0091]: “For example, the geographic database 101 can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths.”; [0063]: “In each of the map tiles 501, respective data collection vehicles 503a-503d (also collectively referred to as data collections vehicles 503 collect sensor data (e.g., imagery data, radar data, LiDAR data) as they travel within each respective map tile 501. The mapping platform 113 then collects the sensor data from each set of data collection vehicles 503 for each map tile 501 respectively as training data sets 505a-505b”);
and initiating the feature detection analysis of the LiDAR scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery ([0108]: “In one embodiment, the geographic database 101 can provide the tile-based feature detection data records 809 to dynamic adaptation of the in-vehicle feature detector 105.”).
Regarding Claim 19, which depends from rejected Claim 18, Shestak further discloses wherein the context includes a location type, a POI type, or a combination thereof queried from a geographic database ([0097]).
Regarding Claim 20, which depends from rejected Claim 18, Shestak further discloses wherein the apparatus is caused to further perform:
retrieving one or more historical LiDAR scan results of the location ([0091]: “For example, the geographic database 101 can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths.),
wherein the one or more historical LiDAR scan results indicate one or more historical features previously detected at the location or POI, an historical number of the one or more historical features, a historical arrangement of the one or more historical features, historical spatial dimensions of the one or more historical features, or a combination thereof ([0091]: “For example, the geographic database 101 can be based on Light Detection and Ranging (LiDAR) or equivalent technology to collect billions of 3D points and model road surfaces and other map features down to the number lanes and their widths.”; [0063]: “In each of the map tiles 501, respective data collection vehicles 503a-503d (also collectively referred to as data collections vehicles 503 collect sensor data (e.g., imagery data, radar data, LiDAR data) as they travel within each respective map tile 501. The mapping platform 113 then collects the sensor data from each set of data collection vehicles 503 for each map tile 501 respectively as training data sets 505a-505b”), and
wherein the feature recognition parameter is further based on the one or more historical LiDAR scan results ([0108]: “In one embodiment, the geographic database 101 can provide the tile-based feature detection data records 809 to dynamic adaptation of the in-vehicle feature detector 105.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shestak in view of (Frei US 2023/0306539 A1).
Regarding Claim 8, which depends from rejected Claim 7, Shestak suggests but does not explicitly teach ([0066]) and Frei further teaches determining one or more feature detection probabilities for a feature detector based one or more expected features, an expected number of the one or more expected features, an expected arrangement of the one or more expected features, expected spatial dimensions of the one or more expected features, or a combination thereof, wherein the feature, the attribute of the feature, or a combination thereof is identified in the LiDAR scan ([0022]) based on the one or more feature detection probabilities ([0025]: “A segmentation model can utilize one or more image segmentation techniques and/or algorithms, such as region-based segmentation that separates the media content into different regions based on threshold values, an edge detection segmentation that utilizes discontinuous local features of the media content to detect edges and hence define a boundary of an item, clustering segmentation that divides pixels of the media content into different clusters (e.g., K-means clustering or the like), each cluster corresponding to a particular area, machine/deep-learning-based segmentation that perform segmentation to determine that estimates probabilities that each point/pixel of the media content belongs to a class”; The probabilities are assigned to pre-existing classes developed during a training phase, these are reasonably taken here to constitute a set of expected features in the image.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shestak with the further teaching of Frei to determine feature detection probabilities for the set of expected features in the scan. Frei notes in [0004] that it can be expensive and potentially even dangerous for human operators to manually assess the presence and condition of items in a property scene, and in [0005] states that “automated computer vision systems and methods for property scene understanding from digital images, videos, media content and/or sensor information which address the foregoing” would be desirable. The set of probabilities corresponding to the expected features are the deliverables which are used to automate the work of identifying objects in the scene.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Shestak in view of Kim (US 2012/0123988 A1).
Regarding Claim 10, which depends from rejected Claim 9, Shestak teaches that the method further comprises providing a user interface (Figure 1, 110; [0072]).
Shestak suggests but does not explicitly teach and Kim does teach that the user interface is for a location or POI search based on the metadata ([0071]: “The interface providing unit 160 may provide a response to a query that is requested by at least one application,”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Kim to allow for user queries into the method of Shestak. User queries are well-known in the art, and are essential for properly interacting with map databases as the point of a map is to search for a feature, object, or destination. Such capacity makes the overall method more versatile and useful to the end user.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Shestak in view of Curtis (US 2022/0121852 A1).
Regarding Claim 11, which depends from rejected Claim 1, Shestak does not teach and Curtis does teach wherein the feature includes furniture, objects, decorations, or a combination thereof associated with the location, the POI, or a combination thereof, and wherein the attribute of the feature includes a number, a spatial arrangement, a dimension, or a combination of the feature ([0050]: “In an embodiment of the system and method, 2D image recognition and 3D point cloud scanning are synergistically combined to identify and count the number of objects in a display.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shestak with the teaching of Curtis to identify objects and count them. Curtis notes in [0016] that a method of three-dimensional object counting when implemented, for example, in a store or warehouse generate actionable information for why a particular display or shelf resulted in sales and generates an improved selection, quantity, and configuration of objects in a space such as a store or warehouse. Such data and insights can be useful and profitable in a commercial setting.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Shestak in view of Budge (US 2011/0176000 A1).
Regarding Claim 12, which depends from rejected Claim 1, Shestak does not teach and Budge does teach wherein the feature includes people at the location, the POI, or a combination thereof, and wherein the attribute of the feature includes a number of the people (Budge describes a system that counts ‘people as the enter and exit a portal’ using a LiDAR camera; [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shestak to incorporate the teaching of Budge to count the number of people in a given location. Budge notes in [0003] that in the context of public transportation that “it is imperative for a transit system to track statistics about their ridership in order to plan public transportation routes.”
Regarding Claim 13, which depends from rejected Claim 12, Shestak does not teach and Budge does teach determining an occupancy of the location, the POI, or a combination thereof based on the number of the people ([0095]: “There are Nt people on the vehicle or area of interest at this time.”; The method of Budge is therefore configured to keep track of the number of people on a vehicle or in an area at any given time, which necessarily means that the occupancy of the vehicle is known).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Shestak and further in view of Budge with the further teach of Budge to determine the occupancy of a location. In the context of transportation, Budge notes in [0003] that “this information will, for example, allow a transit system to track usage of vehicles and stops,” which is useful for route planning and fleet deployments. Budge further notes in [0095] that occupancy tracking can be applied more broadly to any area of interest.
Conclusion
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/B.W.C./Examiner, Art Unit 3645
/ISAM A ALSOMIRI/Supervisory Patent Examiner, Art Unit 3645
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