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
Claims 25-27, 31-32, 34-36, and 41-42 are pending in this application.
Claims 1-24, 28-30, 33, 37-40, and 43-44 are cancelled.
Claims 25-27, 31-32, 34-36, and 41-42 are amended.
Claims 25, 27, 31-32, 34, 36, and 41-42 are presented for examination.
Response to Amendments
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 25-27, 31-32, 34-36, and 41-42 are rejected under 35 U.S.C. 103 as being unpatentable over Artes et al. (US Publication 2021/0096579 A1) in view of Kimchi et al. (US Patent 11, 495, 030 B1).
Regarding claim 25, Artes teaches a method of operation by a map server, for reducing similarities between map regions in a localisation map used by a mobile device for determining a location of the mobile device within a physical space represented by the localisation map, the method comprising: determining that first and second map regions in the localisation map contain one or more similar features (Artes: Para. 9; identifying similar and/or identical areas within one or more maps), and wherein each of the first and second map regions represent a respective delimited space within the physical space represented by the localisation map (Artes: Para. 30; location-related information is e.g. the partitioning of an area of deployment into numerous rooms; designation of the rooms can also be stored in the map); ………. for determining a pose of the mobile device within the physical space, based on the mobile device sensing physical features and correlating the sensed physical features with the map features contained in the localisation map (Artes: Para. 8; self-localization of an autonomous mobile robot on at least one permanently stored map; the robot determines whether and/or where it is located on the at least one map); and transmitting the modified localisation map to the mobile device, for use in the localisation algorithm executed by the mobile device (Artes: Para. 14, 57, 124; determining the map that is needed to carry out the task; maps may be stored on an external device such as a computer; similar regions on the permanently stored map; determine the regions in which there are distinct differences; move to these regions in order to achieve a fast, distinct and reliable self-localization).
Artes doesn’t explicitly teach thereby reducing an extent to which the one or more similar map features are relied upon in a localisation algorithm executed by the mobile device
However, Artes is deemed to disclose an equivalent teaching. Artes teaches map data that is relevant for the navigation. The system places a value on features in the area for navigational purposes. Where not relevant objects can be ignored because they are not being used for navigational purposes (Artes: Para. 70, 82). Where there are similar regions on the permanently stored map that make a reliable self-localization difficult, the system will determine the region through distinct differences (Artes: Para. 124). Using distinct features of an area for navigation means similar features from multiple regions would need a reduction of weight.
It would have been obvious to one of ordinary skill in the art to determine a localization map of similar regions based on detection of distinct features and then use those distinct features to determine navigation because similar regions on the permanently stored map that make a reliable self-localization difficult, therefor they need to use distinct differences in order to achieve a fast, distinct and reliable self-localization. (Artes: Para. 124).
Artes doesn’t explicitly teach generating a modified localisation map by reducing an algorithmic weighting of the one or more similar map features.
However Kimchi, in the same field of endeavor, teaches generating a modified localisation map by reducing an algorithmic weighting of the one or more similar map features (Kimchi: Col. 2 Lines 60-64, Col. 3 Lines 22-33, Col. 5 Lines 9-27; one source of information (e.g., historical occupancy map, sensor, etc.) can indicate a presence of an object at a location while another source of information (e.g., historical occupancy map, sensor, etc.) can indicate that no such object exists at that same location; an objective function can be utilized to resolve the conflicting information by scaling the weights).
It would have been obvious to one of ordinary skill to have modify the map determination and self-localization of a robot (Artes: Para. 9) with the scaling weights of detected objects (Kimchi: Col. 3 Lines 22-33) with a reasonable expectation of success using weighted detected stationary objects to create a historical occupancy map used in vehicle location and navigation (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5).
Regarding claim 26, Artes teaches the method according to claim 25, wherein the localisation algorithm executed by the mobile device is a simultaneous localisation and mapping (SLAM) algorithm (Artes: Para. 34; the robot in the present example has a navigation module with which it can orientate itself in its environment; navigation module can operate, for example, using an obstacle avoidance strategy and/or a SLAM algorithm).
Regarding claim 27, Artes teaches the method according to claim 25, wherein the method further comprises selecting a portion of a larger localisation map as the localisation map, based on a current location of the mobile device within a larger physical space represented by the larger localization map (Artes: Para. 30; location-related information is e.g. the partitioning of an area of deployment into numerous rooms; designation of the rooms can also be stored in the map).
Regarding claim 31, Artes doesn’t explicitly teach wherein reducing the algorithm weighting comprises setting the algorithmic weighting to zero, such that the one or more similar map features are ignored in the localisation algorithm.
However Kimchi, in the same field of endeavor, teaches wherein reducing the algorithm weighting comprises setting the algorithmic weighting to zero, such that the one or more similar map features are ignored in the localisation algorithm (Kimchi: Col. 3 Lines 22-33; deciding whether to include (or remove) the presence of the object at that location in an occupancy map so that the constructed occupancy map aligns with the goals of the objective function).
It would have been obvious to one of ordinary skill to have modify the map determination and self-localization of a robot (Artes: Para. 9) with the scaling weights of detected objects (Kimchi: Col. 3 Lines 22-33) with a reasonable expectation of success using weighted detected stationary objects to create a historical occupancy map used in vehicle location and navigation (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5).
Regarding claim 32, Artes doesn’t explicitly teach wherein an extent of algorithmic weighting reduction is based on an extent of similarity between the one or more similar map features.
However Kimchi, in the same field of endeavor, teaches wherein an extent of algorithmic weighting reduction is based on an extent of similarity between the one or more similar map features (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5; occupancy map of environment which aerial vehicle may utilize to navigate environment; the objective function can determine weights and relative value based on the age of the occupancy map, the object type of the object in question; the object is expected to remain at a given location at a later point in time).
It would have been obvious to one of ordinary skill to have modify the map determination and self-localization of a robot (Artes: Para. 9) with the scaling weights of detected objects (Kimchi: Col. 3 Lines 22-33) with a reasonable expectation of success using weighted detected stationary objects to create a historical occupancy map used in vehicle location and navigation (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5).
Regarding claim 34, Artes teaches a map server comprising: (Artes: Para. 57; the maps may be stored on an external device such as a computer (e.g. home server) or cloud service (internet server)) a processor; and a memory storing instructions that, when executed by the processor to reduce similarities between map regions in a localisation map used by a mobile device for determining a location of the mobile device within a physical space represented by the localisation map, wherein execution of the stored instructions configures the processor to: (Artes: Para. 31; software responsible for the robot's behavior may be executed in the control module of the robot (using a corresponding processor and memory element) or be at least partially delegated to an external computer (e.g. personal computer, server, etc.)) determine that first and second map regions in the localisation map contain one or more similar map features (Artes: Para. 9; identifying similar and/or identical areas within one or more maps), and wherein each of the first and second map regions represents a respective delimited space within the physical space represented by the localisation map (Artes: Para. 30; location-related information is e.g. the partitioning of an area of deployment into numerous rooms; designation of the rooms can also be stored in the map); ……….. for determining a pose of the mobile device within the larger physical space, based on the mobile device sensing physical features and correlating the sensed physical features with the local localisation map (Artes: Para. 8; self-localization of an autonomous mobile robot on at least one permanently stored map; the robot determines whether and/or where it is located on the at least one map); and transmit the modified local localisation map to the mobile device, for use in the localisation algorithm executed by the local device (Artes: Para. 14, 57, 124; determining the map that is needed to carry out the task; maps may be stored on an external device such as a computer; similar regions on the permanently stored map; determine the regions in which there are distinct differences; move to these regions in order to achieve a fast, distinct and reliable self-localization).
Artes doesn’t explicitly teach thereby reducing an extent to which the one or more similar map features are relied upon in a localisation algorithm executed by the mobile device.
However, Artes is deemed to disclose an equivalent teaching. Artes teaches map data that is relevant for the navigation. The system places a value on features in the area for navigational purposes. Where not relevant objects can be ignored because they are not being used for navigational purposes (Artes: Para. 70, 82). Where there are similar regions on the permanently stored map that make a reliable self-localization difficult, the system will determine the region through distinct differences (Artes: Para. 124). Using distinct features of an area for navigation means similar features from multiple regions would need a reduction of weight.
It would have been obvious to one of ordinary skill in the art to determine a localization map of similar regions based on detection of distinct features and then use those distinct features to determine navigation because similar regions on the permanently stored map that make a reliable self-localization difficult, therefor they need to use distinct differences in order to achieve a fast, distinct and reliable self-localization. (Artes: Para. 124).
Artes doesn’t explicitly teach generate a modified localisation map by reducing an algorithmic weighting of one or more similar map features.
However Kimchi, in the same field of endeavor, teaches generate a modified localisation map by reducing an algorithmic weighting of one or more similar map features (Kimchi: Col. 2 Lines 60-64, Col. 3 Lines 22-33, Col. 5 Lines 9-27; one source of information (e.g., historical occupancy map, sensor, etc.) can indicate a presence of an object at a location while another source of information (e.g., historical occupancy map, sensor, etc.) can indicate that no such object exists at that same location; an objective function can be utilized to resolve the conflicting information by scaling the weights).
It would have been obvious to one of ordinary skill to have modify the map determination and self-localization of a robot (Artes: Para. 9) with the scaling weights of detected objects (Kimchi: Col. 3 Lines 22-33) with a reasonable expectation of success using weighted detected stationary objects to create a historical occupancy map used in vehicle location and navigation (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5).
Regarding claim 35, Artes teaches the map server according to claim 34, wherein the localisation algorithm executed by the mobile device is a simultaneous localisation and mapping (SLAM) algorithm (Artes: Para. 34; the robot in the present example has a navigation module with which it can orientate itself in its environment; navigation module can operate, for example, using an obstacle avoidance strategy and/or a SLAM algorithm).
Regarding claim 36, Artes teaches the map server according to claim 34, wherein the instructions further configure the processor to select a portion of a larger localisation map as the localisation map, based on a current location of the mobile device within a larger physical space represented by the larger localization map (Artes: Para. 30; location-related information is e.g. the partitioning of an area of deployment into numerous rooms; designation of the rooms can also be stored in the map).
Regarding claim 41, Artes doesn’t explicitly teach wherein the instructions configure the processor to reduce the algorithmic weighting to zero, such that the one or more similar features are ignored the localisation algorithm.
However Kimchi, in the same field of endeavor, teaches wherein the instructions configure the processor to reduce the algorithmic weighting to zero, such that the one or more similar features are ignored the localisation algorithm (Kimchi: Col. 3 Lines 22-33; deciding whether to include (or remove) the presence of the object at that location in an occupancy map so that the constructed occupancy map aligns with the goals of the objective function).
It would have been obvious to one of ordinary skill to have modify the map determination and self-localization of a robot (Artes: Para. 9) with the scaling weights of detected objects (Kimchi: Col. 3 Lines 22-33) with a reasonable expectation of success using weighted detected stationary objects to create a historical occupancy map used in vehicle location and navigation (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5).
Regarding claim 42, Artes doesn’t explicitly teach wherein an extent of algorithmic weighting reduction is based on an extent of similarity between the one or more similar map features.
However Kimchi, in the same field of endeavor, teaches wherein an extent of algorithmic weighting reduction is based on an extent of similarity between the one or more similar map features (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5; occupancy map of environment which aerial vehicle may utilize to navigate environment; the objective function can determine weights and relative value based on the age of the occupancy map, the object type of the object in question; the object is expected to remain at a given location at a later point in time).
It would have been obvious to one of ordinary skill to have modify the map determination and self-localization of a robot (Artes: Para. 9) with the scaling weights of detected objects (Kimchi: Col. 3 Lines 22-33) with a reasonable expectation of success using weighted detected stationary objects to create a historical occupancy map used in vehicle location and navigation (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5).
Response to Arguments
Applicant’s arguments, filed 13 October 2025, with respect to the rejection of claims 25-44 under 35 U.S.C. §103 have been considered, but are not persuasive.
Applicant’s attorney argues that the reweighting in Kimchi differs so markedly from the algorithmic weight reduction details in claims 25 and 34.
In response to the applicant’s argument above, the applicant’s specification does not include “algorithmic weighting” therefore the examiner is interpreting that term as a weight as described by the specification. The applicant’s specification includes adjusting the weight of a feature to zero, deactivating that feature (Specification: Para. 89). The weight can be computed based on the matching algorithm (Specification: Para. 90).
Kimchi teaches a vehicle detecting an object by a sensor and adjusting the weight higher as more sensors corroborate the object and adjusting the weight lower as more sensors do not detect the object at that location (Kimchi: Col. 2 Lines 60-64, Col. 5 Lines 9-27). Kimchi computes the weight of the object based on matching the object over multiple sensors. Kimchi teaches one source of information (e.g., historical occupancy map, sensor, etc.) can indicate a presence of an object at a location while another source of information (e.g., historical occupancy map, sensor, etc.) can indicate that no such object exists at that same location. Additionally, the system resolves the conflicting information by scaling the weights, parameters, values, thresholds, etc. accordingly to determine the confidence score(s) in deciding whether to include (or remove) the presence of the object at that location in an occupancy map so that the constructed occupancy map aligns with the goals of the objective function (Kimchi Col. 3 Lines 22-33). Kimchi creates a localization map by detected objects and reducing the weight of the object if other sensors do not detect the object. Kimchi’s adjusted weight is in aligned with the applicant’s adjusted weight described in the specification.
Applicant next argues that Kimchi would not have motivated the ordinary skilled person to modify Artes in the manner.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, both Artes and Kimchi teach control vehicles that rely on localization maps. Artes teaches self-localization of a robot by identifying identical areas within one or more maps (Artes: Para. 9). Kimchi constructs their occupancy map through collecting information about objects in their area. The system resolves the conflicting information by scaling the weights accordingly to determine the confidence scores in deciding whether to include or remove the presence of the object at that location in an occupancy map so that the constructed occupancy map aligns with the goals of the objective function (Kimchi: Col. 3 Lines 22-33). It would be obvious to one of ordinary skill in the art to modify Artes with the teaches of Kimchi to improve the robot’s localization maps using historically detected stationary objects, weighted based on current detection of that object, to create an occupancy map used in vehicle location and navigation (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5).
Applicant next argues that the prior arts do not teach “generating a modified localization map via reduction of the algorithmic weighting of map features that are similar between first and second regions of a localization map.”
In response to the applicant’s argument above, Kimchi teaches a vehicle detecting an object by a sensor and adjusting the weight higher as more sensors corroborate the object and adjusting the weight lower as more sensors do not detect the object at that location (Kimchi: Col. 2 Lines 60-64, Col. 5 Lines 9-27). Kimchi computes the weight of the object based on matching the object over multiple sensors. Kimchi teaches one source of information (e.g., historical occupancy map, sensor, etc.) can indicate a presence of an object at a location while another source of information (e.g., historical occupancy map, sensor, etc.) can indicate that no such object exists at that same location. Additionally, the system resolves the conflicting information by scaling the weights, parameters, values, thresholds, etc. accordingly to determine the confidence score(s) in deciding whether to include (or remove) the presence of the object at that location in an occupancy map so that the constructed occupancy map aligns with the goals of the objective function (Kimchi Col. 3 Lines 22-33). Kimchi creates a localization map by detected objects and reducing the weight of the object if other sensors do not detect the object. The prior art does teach generating a modified localization map by reducing an algorithmic weighting of the one or more similar map features.
Applicant next argues that Artes differs so profoundly from the similarity reductions at issue in claims 25 and 35.
In response to the applicant’s argument above, Artes teaches map data that is relevant for the navigation. The system places a value on features in the area for navigational purposes. Where not relevant objects can be ignored because they are not being used for navigational purposes (Artes: Para. 70, 82). Where there are similar regions on the permanently stored map that make a reliable self-localization difficult, the system will determine the region through distinct differences (Artes: Para. 124). It would have been obvious to one of ordinary skill in the art to determine a localization map of similar regions based on detection of distinct features and then use those distinct features to determine navigation. Using distinct features of an area for navigation means similar features from multiple regions would need a reduction of weight.
Applicant next argues that modification of Artes would not have been obvious, almost regard to the contents of Kimchi.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, both Artes and Kimchi teach control vehicles that rely on localization maps. Artes teaches self-localization of a robot by identifying identical areas within one or more maps (Artes: Para. 9). Kimchi constructs their occupancy map through collecting information about objects in their area. The system resolves the conflicting information by scaling the weights accordingly to determine the confidence scores in deciding whether to include or remove the presence of the object at that location in an occupancy map so that the constructed occupancy map aligns with the goals of the objective function (Kimchi: Col. 3 Lines 22-33). It would be obvious to one of ordinary skill in the art to modify Artes with the teaches of Kimchi to improve the robot’s localization maps using historically detected stationary objects, weighted based on current detection of that object, to create an occupancy map used in vehicle location and navigation (Kimchi: Col. 8 Lines 4-7, Col. 11 Line 54 - Col. 12 Line 5).
The applicant’s arguments have failed to point out the distinguishing characteristics of the amended claim language over the prior art. For the above reasons, Artes’ robot navigation through a localized map with Kimchi’s adjustment of weight reads on applicant’s supporting localization of a mobile device. The rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA E LINHARDT whose telephone number is (571)272-8325. The examiner can normally be reached on M-TR, M-F: 8am-4pm.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663