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 .
Claim Status
Applicant's preliminary amendments filed on 8/8/24 have been entered.
Claim Objections
Claim 32 is objected to because of the following informalities: it is suggested to add the period “.” punctuation at the end of the claim sentence to fix a grammatical issue.
Claim 69 is objected to because of the following informalities: it is suggested to make the following amendment fix an editorial issue: in line 2: executed by one or --more-- processors.
Claim 95 is objected to because of the following informalities: it is suggested to make the following amendment fix an editorial issue: in line 2: executed by one or --more-- processors.
Claim 102 is objected to because of the following informalities: it is suggested to make the following amendment fix an editorial issue: in line 2: executed by one or --more-- processors.
Claims 70-94 and 96-101 are also objected to due to dependency.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 102 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 102 recites the limitation “the sensor” in line 7. There is insufficient antecedent basis for this limitation in the claim.
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 1-102 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process. This judicial exception is not integrated into a practical application because the combination of additional elements fails to integrate the judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the additional elements do not add significantly more (also known as an “inventive concept”) to the exception.
(See MPEP 2106 Patent Subject Matter Eligibility.)
Independent Claims:
Regarding independent claims 1, 27, 34-35, 61, 68-69, 95, and 102, independent claim 34 is generally representative of the other independent claim(s) 1, 27, 35, 61, 68-69, 95, and 102 with respect to the judicial exception while the claims may recite some variations of additional elements. The abstract limitations are shown in bold font (bold font) while the additional elements are shown in italicized font (italicized font).
Claim 34:
A method for tracking objects of interest in an environment, comprising: maintaining a collection of a plurality of tracked objects identified in the environment, the plurality of tracked objects each comprising: one or more trackable properties associated with an object of interest, and a tracked object location within the environment; acquiring, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identifying an object at a location within an observation of the plurality of observations, the object comprising a trackable property associated with the object of interest; transforming, based on the associated sensor pose, the location of the object within the observation to a predicted location within the environment; determining a correspondence between the object and each of the plurality of tracked objects based on a distance between the predicted location within the environment and the tracked object location within the environment; identifying the correspondence to a tracked object having the highest correspondence, and, based on the correspondence: merging the object with the tracked object, or registering the object as a new candidate object for maintaining in a collection of candidate objects.
Claim 1:
A method for tracking objects of interest in an environment, comprising: acquiring, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identifying an object at a location within an observation of the plurality of observations; transforming, based on the pose of the sensor associated with the observation, the location of the object within the observation to an object location within the environment, and identifying the object in a different observation of the plurality of observations based on a correspondence between a location of the object within the different observation and the object location in the environment.
Claim 27:
A method for tracking objects of interest in an environment, comprising: acquiring, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identifying a candidate object at a location within an observation of the plurality of observations based on a first trackable property associated with an object of interest; transforming, based on the associated sensor pose, the location of the candidate object within the observation to a candidate object location within the environment; identifying an object at a location within a different observation of the plurality of observations based on a second trackable property associated with the object of interest, and determining a correspondence between the location of the object within the different observation and the candidate object location within the environment and, based on the correspondence: merging the object with the candidate object, or disregarding the object as the candidate object and registering the object as a new candidate object.
Claim 34:
A method for tracking objects of interest in an environment, comprising: maintaining a collection of a plurality of tracked objects identified in the environment, the plurality of tracked objects each comprising: one or more trackable properties associated with an object of interest, and a tracked object location within the environment; acquiring, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identifying an object at a location within an observation of the plurality of observations, the object comprising a trackable property associated with the object of interest; transforming, based on the associated sensor pose, the location of the object within the observation to a predicted location within the environment; determining a correspondence between the object and each of the plurality of tracked objects based on a distance between the predicted location within the environment and the tracked object location within the environment; identifying the correspondence to a tracked object having the highest correspondence, and, based on the correspondence: merging the object with the tracked object, or registering the object as a new candidate object for maintaining in a collection of candidate objects.
Claim 35:
A system for tracking objects of interest in an environment, the system comprising: a sensor; one or more processors, and a memory storing instructions thereon that, when executed by the one or more processors, configure the system to: acquire, from the sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify an object at a location within an observation of the plurality of observations; transform, based on the pose of the sensor associated with the observation, the location of the object within the observation to an object location within the environment, and identify the object in a different observation of the plurality of observations based on a correspondence between a location of the object within the different observation and the object location in the environment.
Claim 61:
A system for tracking objects of interest in an environment, the system comprising: a sensor; one or more processors, and a memory storing instructions thereon that, when executed by the one or more processors, configure the system to: acquire, from the sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify a candidate object at a location within an observation of the plurality of observations based on a first trackable property associated with an object of interest; transform, based on the associated sensor pose, the location of the candidate object within the observation to a candidate object location within the environment; identify an object at a location within a different observation of the plurality of observations based on a second trackable property associated with the object of interest, and determine a correspondence between the location of the object within the different observation and the candidate object location within the environment and, based on the correspondence: merge the object with the candidate object, or disregard the object as the candidate object and register the object as a new candidate object.
Claim 68:
A system for tracking objects of interest in an environment, the system comprising: a sensor; one or more processors, and a memory storing instructions thereon that, when executed by the one or more processors, configure the system to: maintain a collection of a plurality of tracked objects identified in the environment, the plurality of tracked objects each comprising: one or more trackable properties associated with an object of interest, and a tracked object location within the environment; acquire, from the sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify an object at a location within an observation of the plurality of observations, the object comprising a trackable property associated with the object of interest; transform, based on the sensor pose associated with the observation, the location of the object within the observation to a predicted location within the environment; determine a correspondence between the object and each of the plurality of tracked objects based on a distance between the predicted location within the environment and the tracked object location within the environment; identify the correspondence to a tracked object having the highest correspondence, and, based on the correspondence: merge the object with the tracked object, or register the object as a new candidate object for maintaining in a collection of candidate objects.
Claim 69:
A non-transitory computer-readable storage medium having instructions stored thereon that when executed by one or processors, cause the one or more processors to: acquire, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify an object at a location within an observation of the plurality of observations; transform, based on the pose of the sensor associated with the observation, the location of the object within the observation to an object location within the environment, and identify the object in a different observation of the plurality of observations based on a correspondence between a location of the object within the different observation and the object location in the environment.
Claim 95:
A non-transitory computer-readable storage medium having instructions stored thereon that when executed by one or processors, cause the one or more processors to: acquire, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify a candidate object at a location within an observation of the plurality of observations based on a first trackable property associated with an object of interest; transform, based on the associated sensor pose, the location of the candidate object within the observation to a candidate object location within the environment; identify an object at a location within a different observation of the plurality of observations based on a second trackable property associated with the object of interest, and determine a correspondence between the location of the object within the different observation and the candidate object location within the environment and, based on the correspondence: merge the object with the candidate object, or disregard the object as the candidate object and register the object as a new candidate object.
Claim 102:
A non-transitory computer-readable storage medium having instructions stored thereon that when executed by one or processors, cause the one or more processors to: maintain a collection of a plurality of tracked objects identified in the environment, the plurality of tracked objects each comprising: one or more trackable properties associated with an object of interest, and a tracked object location within the environment; acquire, from the sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify an object at a location within an observation of the plurality of observations, the object comprising a trackable property associated with the object of interest; transform, based on the sensor pose associated with the observation, the location of the object within the observation to a predicted location within the environment; determine a correspondence between the object and each of the plurality of tracked objects based on a distance between the predicted location within the environment and the tracked object location within the environment; identify the correspondence to a tracked object having the highest correspondence, and, based on the correspondence: merge the object with the tracked object, or register the object as a new candidate object for maintaining in a collection of candidate objects.
Eligibility Step 1:
Claims 1-34 are method/process claims.
Claims 35-102 are apparatus/system/machine/manufacture claims.
Thus, the claims fall within a statutory category of invention, respectively.
Eligibility Step 2A Prong One:
The bolded limitations constitute judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation (BRI), the limitations can be “performed in the human mind, or by a human using a pen and paper” (MPEP 2106.04(a)(2)(III) Mental Processes).
The claims recite the limitations of maintaining a collection of a plurality of tracked objects; maintaining a collection of a plurality of tracked objects; transforming, based on the associated sensor pose, the location of the object within the observation to a predicted location within the environment; determining a correspondence between the object and each of the plurality of tracked objects based on a distance between the predicted location within the environment and the tracked object location within the environment; identifying the correspondence to a tracked object having the highest correspondence, and, based on the correspondence: merging the object with the tracked object, or registering the object as a new candidate object for maintaining in a collection of candidate objects. These limitations, as drafted, are processes that, under broadest reasonable interpretation (BRI), cover performance of the limitations in the mind but for the recitations of the “sensor”, “processor”, “memory”, and/or “non-transitory computer readable medium” (CRM) respectively (hereinafter referred to as the “structural elements” which is an example of an additional element) and the recitation of the “acquiring, from a sensor, a plurality of observations of the environment” (hereinafter referred to as “pre-solution activity” which is an example of an additional element). That is, other than reciting the additional elements that include structural elements and pre-solution activity, nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the structural elements and the pre-solution activity language, the claims encompass a person analyzing sensor data/information, identifying an object based on the data, transforming data from one format to another format, determining a correspondence/match between data, identifying/analyzing/interpreting the correspondence/match data, merging data, and registering/assigning data to another dataset. The mere nominal recitation of the structural elements and and pre-solution activity do not take the claim limitations out of the mental process grouping (MPEP 2106.04(a)(2)(111)(C)(3. Using a computer as a tool to perform a mental process.)).
Thus, the claims recite a mental process.
Eligibility Step 2A Prong Two:
The italicized limitations recite additional elements that do not integrate the recited judicial exception into a practical application.
The claims recite additional elements or steps of “sensor”, “processor”, “memory”, and/or “non-transitory computer readable medium” (CRM) respectively. The “sensor”, “processor”, “memory”, and/or “non-transitory computer readable medium” (CRM) respectively are each directed to generic computer and/or generic sensory/detection/measurement components and generally “apply” the otherwise mental judgements using generic or general-purpose computer components (for example, a generic computer/processor and/or memory) and/or using generic or general-purpose sensory/detection/measurement components (for example, a generic sensor). The computer component(s) and the sensory/detection/measurement components are recited at a high level of generality and they merely automate the evaluation/determination step and the data acquisition step, respectively (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
The claims recite additional elements or steps of “acquiring, from a sensor, a plurality of observations of the environment”. The “acquiring, from a sensor, a plurality of observations of the environment” limitation is recited at a high level of generality (for example, as a general means of gathering environmental/object data for use in the maintaining/transforming/determining/identifying/merging/registering steps) and amount to mere data gathering which is a form of insignificant extra-solution activity (pre-solution activity).
Additionally, there is no technical improvement to computing systems because the improvement described in applicant’s disclosure of reducing computation resources and providing improvements in real-time performance (see 18836968 instant application [00139]) is considered to be “Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer” (MPEP 2106.05(a)(I) FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). In this case, the claims appear to be directed to processing/computer using conventional, general-purpose computer and/or memory to provide the computing efficiencies without reciting, for example, any particular/special computer/memory to implement the technical solution so that the additional elements integrate the recited judicial exception into a practical application.
Additionally, there is no technical improvement to data quality because the improvement described in applicant’s disclosure of improving a semantic comprehension of an object (see 18836968 instant application [00183]), and improving an accuracy of an object's locations (see 18836968 instant application [00220]) is considered to be “Gathering and analyzing information using conventional techniques and displaying the result” (MPEP 2106.05(a)(II) TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48). In this case, the claims appear to be directed to gathering and analyzing data using conventional techniques to provide the semantic comprehension and the object location accuracy without reciting, for example, any particular algorithm steps/structures to implement the technical solution so that the additional elements integrate the recited judicial exception into a practical application.
Additionally, there is no claimed specific solution to a specific technical problem because the improvements described in applicant’s disclosure of providing reduction in computation resources and providing improvements in real-time performance, and improving a semantic comprehension of an object, and improving an accuracy of an object's locations are not recited in the claims, and if they were recited, then they would be considered to be providing “only a result-oriented solution and lacking details as to how the computer/memory performed the solution steps, which was equivalent to the words "apply it"” (MPEP 2106.05(f)(1) Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Thus, the claims are directed to the mental process.
Eligibility Step 2B:
As discussed with respect to Step 2A Prong Two, the additional elements in the claims relating to the computer, memory, and sensor component(s) amount to no more than mere instructions to apply the exception using generic or general-purpose computer, memory, and sensor component(s), and that analysis similarly applies in Step 2B.
Regarding the insignificant extra-solution activity/activities identified in Step 2A Prong Two, the “acquiring, from a sensor, a plurality of observations of the environment” limitation for receiving data was considered to be insignificant extra-solution activity/activities (in this case, pre-solution activity) in Step 2A. The claimed receipt of data as a pre-solution activity is a well‐understood, routine, and conventional (WURC) function when it is claimed in a generic manner (MPEP 2106.05(d)(II)(i); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 121 USPQ2d 1928 (Fed. Cir. 2017); “But receiving transmitted data over a network and displaying it to a user merely implicates purely conventional activities that are the "most basic functions of a computer." Alice, 134 S.Ct. at 2359.”).
Accordingly, when considered individually and in combination, the additional elements do not amount to significantly more than the abstract idea because they do not contribute an inventive concept beyond the abstract idea.
Thus, the claims are ineligible under 35 USC §101.
Dependent Claims:
In general, regarding dependent claims 2-26, 28-33, 36-60, 62-67, 70-94, and 96-101, the claims do not recite any further limitations that cause the claims to become patent eligible. Rather, the limitations of the dependent claims are directed towards additional aspects of the judicial exception (or further refine/characterize the judicial exception) and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application nor do they amount to significantly more than the abstract idea.
Specifically, claims 2-25, 28-33, 36-59, 62-67, 70-93, and 96-101recite limitations that are directed towards additional aspects of the judicial exception (and/or further refine/characterize the judicial exception).
Specifically, claims 26, 60, and 94 recite limitations that are directed towards additional aspects of the additional element(s) which is the sensor comprising a rangefinder (and/or further refine/characterize the additional element(s)).
Accordingly, the claims are not patent eligible under the same rationale as provided for in the rejection of independent claims 1, 27, 34-35, 61, 68-69, 95, and 102.
Thus, the claims are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-9, 13, 17, 21, 24-43, 47, 51, 58-77, 81, 85, 92-102 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Das US20210181758.
Regarding independent claims 1, 27, 34-35, 61, 68-69, 95, and 102, Das discloses, in Figures 1-6,
(The claim mapping for at least independent claim 1 is representative for the subsequent independent claims, and subsequent independent claims are mapped when new limitations are introduced in comparison to earlier, previously-recited and previously-mapped independent claims.)
1. A method (Das; Fig. 1-6) for tracking objects (Das; Fig. 1; multiple tracked objects identified by multiple rectangular region of interests ROI 130; Fig. 3 with environment representation 316 and lidar top-down ROI(s) 320 and occupancy map 318) of interest in an environment (Das; Fig. 1; the environment the encompasses the plurality of region of interests ROI 130), comprising: acquiring, from a sensor (Das; Fig. 1; [0024] lidar sensor of sensor set 104; lidar data 128), a plurality of observations of the environment (Das; Fig. 5 flowchart; [0020] previous track(s) and current track/time/frame; [0032] tracking component 114), each observation associated with a pose (Das; [0049] localization component 226 determines pose of the autonomous vehicle 202) of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment (Das; [0049] simultaneous localization and mapping SLAM; [0049] localization component 226 determines pose of the autonomous vehicle 202; “localization component 226 may provide, to the tracking component 232, a location and/or orientation of the vehicle 202 relative to the environment and/or sensor data associated therewith.”); identifying an object at a location within an observation of the plurality of observations (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332)); transforming, based on the pose of the sensor associated with the observation, the location of the object within the observation to an object location within the environment (Das; [0049] simultaneous localization and mapping SLAM; [0049] localization component 226 determines pose of the autonomous vehicle 202; “localization component 226 may provide, to the tracking component 232, a location and/or orientation of the vehicle 202 relative to the environment and/or sensor data associated therewith.”), and identifying the object in a different observation of the plurality of observations based on a correspondence between a location of the object within the different observation and the object location in the environment (Das; Fig. 5 flowchart; [0020] previous track(s) and current track/time/frame; [0032] tracking component 114) (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”).
27. A method (Das discloses the invention substantially the same as described above in reference to the above/preceding/earlier independent claim(s)) for tracking objects of interest in an environment, comprising: acquiring, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identifying a candidate object at a location within an observation of the plurality of observations based on a first trackable property (Das; Fig. 4; “first object detection associated with a first sensor type”) associated with an object of interest; transforming, based on the associated sensor pose, the location of the candidate object within the observation to a candidate object location within the environment; identifying an object at a location within a different observation of the plurality of observations based on a second trackable property (Das; Fig. 4; “second object detection associated with a second sensor type”) associated with the object of interest, and determining a correspondence between the location of the object within the different observation and the candidate object location within the environment (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”; Fig. 4 step 432 - determine an updated or new track associated with the object based at least in part on the estimated object detection) and, based on the correspondence: merging the object with the candidate object (Das; Fig. 4 step 432 - “determine an updated or new track associated with the object based at least in part on the estimated object detection”; Fig. 5 step 510 - “associate the estimated object detection with the track”), or disregarding the object as the candidate object and registering the object as a new candidate object (Das; Fig. 4 step 432 - “determine an updated or new track associated with the object based at least in part on the estimated object detection”; Fig. 5 step 512 - “generate a new track and/or provide the first object detection, the second object detection, and/or the estimated object detection to an alternate tracking component 512”).
34. A method (Das discloses the invention substantially the same as described above in reference to the above/preceding/earlier independent claim(s)) for tracking objects of interest in an environment, comprising: maintaining a collection of a plurality of tracked objects (Das; Fig. 1; multiple tracked objects identified by multiple rectangular region of interests ROI 130; Fig. 3 with environment representation 316 and lidar top-down ROI(s) 320 and occupancy map 318) identified in the environment, the plurality of tracked objects each comprising: one or more trackable properties (Das; Fig. 4; “first object detection associated with a first sensor type”) (Das; Fig. 4; “second object detection associated with a second sensor type”) associated with an object of interest, and a tracked object location within the environment (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332)); acquiring, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identifying an object at a location within an observation of the plurality of observations, the object comprising a trackable property associated with the object of interest (Das; Fig. 4; “first object detection associated with a first sensor type”) (Das; Fig. 4; “second object detection associated with a second sensor type”); transforming, based on the associated sensor pose, the location of the object within the observation to a predicted location within the environment (Das; Fig. 5 flowchart; [0020] previous track(s) and current track/time/frame; [0032] tracking component 114; [0030] “predicted object position” for a track); determining a correspondence (Das; Fig. 4 step 432 - “determine an updated or new track associated with the object based at least in part on the estimated object detection”; Fig. 5 step 510 - “associate the estimated object detection with the track”) between the object and each of the plurality of tracked objects based on a distance between the predicted location within the environment and the tracked object location within the environment (Das; Fig. 1; [0024] lidar sensor of sensor set 104; lidar data 128; [0037] using distance data for track matching); identifying the correspondence to a tracked object having the highest correspondence (Das; [0037] “Munkres match of the projected ROI 142 to the estimated ROI 130 satisfies a threshold”), and, based on the correspondence: merging the object with the tracked object, or registering the object as a new candidate object for maintaining in a collection of candidate objects (Das; Fig. 1; multiple tracked objects identified by multiple rectangular region of interests ROI 130).
35. A system (Das discloses the invention substantially the same as described above in reference to the above/preceding/earlier independent claim(s)) (Das; Fig. 1-6) for tracking objects of interest in an environment, the system comprising: a sensor; one or more processors (Das; [0046] processor(s) 218), and a memory (Das; [0047] non-transitory memory 220) storing instructions (Das; [0047] instructions) thereon that, when executed by the one or more processors, configure the system to: acquire, from the sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify an object at a location within an observation of the plurality of observations; transform, based on the pose of the sensor associated with the observation, the location of the object within the observation to an object location within the environment, and identify the object in a different observation of the plurality of observations based on a correspondence between a location of the object within the different observation and the object location in the environment.
61. A system (Das discloses the invention substantially the same as described above in reference to the above/preceding/earlier independent claim(s)) for tracking objects of interest in an environment, the system comprising: a sensor; one or more processors, and a memory storing instructions thereon that, when executed by the one or more processors, configure the system to: acquire, from the sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify a candidate object at a location within an observation of the plurality of observations based on a first trackable property associated with an object of interest; transform, based on the associated sensor pose, the location of the candidate object within the observation to a candidate object location within the environment; identify an object at a location within a different observation of the plurality of observations based on a second trackable property associated with the object of interest, and determine a correspondence between the location of the object within the different observation and the candidate object location within the environment and, based on the correspondence: merge the object with the candidate object, or disregard the object as the candidate object and register the object as a new candidate object.
68. A system (Das discloses the invention substantially the same as described above in reference to the above/preceding/earlier independent claim(s)) for tracking objects of interest in an environment, the system comprising: a sensor; one or more processors, and a memory storing instructions thereon that, when executed by the one or more processors, configure the system to: maintain a collection of a plurality of tracked objects identified in the environment (Das; Fig. 1; multiple tracked objects identified by multiple rectangular region of interests ROI 130; Fig. 3 with environment representation 316 and lidar top-down ROI(s) 320 and occupancy map 318), the plurality of tracked objects each comprising: one or more trackable properties associated with an object of interest, and a tracked object location within the environment; acquire, from the sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify an object at a location within an observation of the plurality of observations, the object comprising a trackable property associated with the object of interest; transform, based on the sensor pose associated with the observation, the location of the object within the observation to a predicted location within the environment (Das; Fig. 5 flowchart; [0020] previous track(s) and current track/time/frame; [0032] tracking component 114; [0030] “predicted object position” for a track); determine a correspondence between the object and each of the plurality of tracked objects based on a distance between the predicted location within the environment and the tracked object location within the environment; identify the correspondence to a tracked object having the highest correspondence, and, based on the correspondence: merge the object with the tracked object, or register the object as a new candidate object for maintaining in a collection of candidate objects.
69. A non-transitory computer-readable storage medium (Das discloses the invention substantially the same as described above in reference to the above/preceding/earlier independent claim(s)) (Das; [0047] non-transitory memory 220) having instructions (Das; [0047] instructions) stored thereon that when executed by one or processors (Das; [0046] processor(s) 218), cause the one or more processors to: acquire, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify an object at a location within an observation of the plurality of observations; transform, based on the pose of the sensor associated with the observation, the location of the object within the observation to an object location within the environment, and identify the object in a different observation of the plurality of observations based on a correspondence between a location of the object within the different observation and the object location in the environment.
95. A non-transitory computer-readable storage medium (Das discloses the invention substantially the same as described above in reference to the above/preceding/earlier independent claim(s)) having instructions stored thereon that when executed by one or processors, cause the one or more processors to: acquire, from a sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify a candidate object at a location within an observation of the plurality of observations based on a first trackable property associated with an object of interest; transform, based on the associated sensor pose, the location of the candidate object within the observation to a candidate object location within the environment; identify an object at a location within a different observation of the plurality of observations based on a second trackable property associated with the object of interest, and determine a correspondence between the location of the object within the different observation and the candidate object location within the environment and, based on the correspondence: merge the object with the candidate object, or disregard the object as the candidate object and register the object as a new candidate object.
102. A non-transitory computer-readable storage medium (Das discloses the invention substantially the same as described above in reference to the above/preceding/earlier independent claim(s)) having instructions stored thereon that when executed by one or processors, cause the one or more processors to: maintain a collection of a plurality of tracked objects identified in the environment, the plurality of tracked objects each comprising: one or more trackable properties associated with an object of interest, and a tracked object location within the environment; acquire, from the sensor, a plurality of observations of the environment, each observation associated with a pose of the sensor for use in transforming between location coordinates in the plurality of observations and location coordinates in the environment; identify an object at a location within an observation of the plurality of observations, the object comprising a trackable property associated with the object of interest; transform, based on the sensor pose associated with the observation, the location of the object within the observation to a predicted location within the environment; determine a correspondence between the object and each of the plurality of tracked objects based on a distance between the predicted location within the environment and the tracked object location within the environment; identify the correspondence to a tracked object having the highest correspondence, and, based on the correspondence: merge the object with the tracked object, or register the object as a new candidate object for maintaining in a collection of candidate objects.
Regarding claim 2, Das discloses The method according to claim 1, further comprising: identifying (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”; Fig. 4 step 432 - determine an updated or new track associated with the object based at least in part on the estimated object detection) the object in the observation based on a first trackable property associated with an object of interest (Das; Fig. 4; “first object detection associated with a first sensor type”), and identifying the object in the different observation based on a second trackable property associated with the object of interest (Das; Fig. 4; “second object detection associated with a second sensor type”).
Regarding claim 3, Das discloses The method according to claim 2, further comprising, associating each of the first trackable property (Das; Fig. 4; “first object detection associated with a first sensor type”) and the second trackable property (Das; Fig. 4; “second object detection associated with a second sensor type”) with the object location within the environment (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”; Fig. 4 step 432 - determine an updated or new track associated with the object based at least in part on the estimated object detection).
Regarding claim 4, Das discloses The method according to claim 2, wherein the first trackable property (Das; Fig. 4; “first object detection associated with a first sensor type”) and the second trackable property (Das; Fig. 4; “second object detection associated with a second sensor type”) comprise a same trackable property of the object of interest (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”; Fig. 4 step 432 - determine an updated or new track associated with the object based at least in part on the estimated object detection; [0021 and 0037] the same trackable property is distance-measurement for matching between previous tracks and current/present tracks).
Regarding claim 5, Das discloses The method according to claim 2, further comprising updating a semantic comprehension (Das; [0028] “semantic segmentation”; [0101] “semantic label”) of the object based on at least one of the first trackable property, the second trackable property, and the location of the object within the different observation (Das; Fig. 5 flowchart; [0020] previous track(s) and current track/time/frame; [0032] tracking component 114) (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”).
Regarding claim 6, Das discloses The method according to claim 5, further comprising determining a first observing perspective for the first tracked property (Das; Fig. 4; “first object detection associated with a first sensor type”) and a second observing perspective for the second tracked property (Das; Fig. 4; “second object detection associated with a second sensor type”) based on the sensor pose respectively associated with the observation and the different observation and the location of the object within the environment (Das; Fig. 5 flowchart; [0020] previous track(s) and current track/time/frame; [0032] tracking component 114).
Regarding claim 7, Das discloses The method according to claim 6, wherein updating the semantic comprehension (Das; [0028] “semantic segmentation”; [0101] “semantic label”) based on the first trackable property and/or the second trackable property is further based on determining a uniqueness of the first and second observing perspectives (Das; Fig. 4 step 432 - “determine an updated or new track associated with the object based at least in part on the estimated object detection”; Fig. 5 step 512 - “generate a new track and/or provide the first object detection, the second object detection, and/or the estimated object detection to an alternate tracking component 512”; generating a new track based on a uniqueness/difference between the tracked properties).
Regarding claim 8, Das discloses The method according to claim 2, further comprising maintaining a collection of objects identified in the environment, the collection of objects comprising candidate objects and tracked objects (Das; Fig. 1; multiple tracked objects identified by multiple rectangular region of interests ROI 130; Fig. 3 with environment representation 316 and lidar top-down ROI(s) 320 and occupancy map 318).
Regarding claim 9, Das discloses The method according to claim 8, further comprising identifying a tracked object in the collection of objects based on a tracked object correspondence (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”; Fig. 4 step 432 - determine an updated or new track associated with the object based at least in part on the estimated object detection).
Regarding claim 13, Das discloses The method according to claim 8, further comprising determining a tracked property correspondence between the object and the tracked object, the tracked property correspondence based on correspondence between each of the first and second tracked property (Das; Fig. 5 flowchart; [0020] previous track(s) and current track/time/frame; [0032] tracking component 114) (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”), and one or more tracked properties of the tracked object (Das; [0010] “heading of an object”).
Regarding claim 17, Das discloses The method according to claim 8, further comprising identifying a candidate object in the collection of objects based on a candidate object correspondence (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332)).
Regarding claim 21, Das discloses the invention substantially the same as described above in reference to claim 13.
Regarding claim 24, Das discloses The method according to claim 17, further comprising promoting the candidate object as a new tracked object in the collection of objects based on a promotion criteria (Das; Fig. 4 step 432 - “determine an updated or new track associated with the object based at least in part on the estimated object detection”; Fig. 5 step 512 - “generate a new track and/or provide the first object detection, the second object detection, and/or the estimated object detection to an alternate tracking component 512”).
Regarding claim 25, Das discloses The method according to claim 24, wherein the promotion criteria comprises a semantic comprehension (Das; [0028] “semantic segmentation”; [0101] “semantic label”) threshold criteria (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”; Fig. 4 step 432 - determine an updated or new track associated with the object based at least in part on the estimated object detection).
Regarding claim 26, Das discloses The method according to claim 1, wherein the sensor comprises a range finder for determining a sensor-object distance between the sensor and the object (Das; Fig. 1; [0024] lidar sensor of sensor set 104; lidar data 128).
Regarding claim 28, Das discloses The method according to claim 27, wherein merging comprises updating the candidate object location within the environment based on the location of the object in the different observation (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”; Fig. 4 step 432 - determine an updated or new track associated with the object based at least in part on the estimated object detection).
Regarding claim 29, Das discloses The method according to claim 27, wherein the first trackable property and the second trackable property comprise a same trackable property associated with the object of interest (Das; Fig. 3-5; Fig. 3 - estimated object detection 330/332; [0087] objection detection(s) 332 (examiner note: there is an editorial consistency/inconsistency error in the use of the reference numeral for the “object detection” in which Fig. 3 shows object detection 330 while specification para. [0087] shows objection detection(s) 332); Fig. 5 step 510 - “associate the estimated object detection with the track”; Fig. 4 step 432 - determine an updated or new track associated with the object based at least in part on the estimated object detection; [0021 and 0037] the same trackable property is distance-measurement for matching between previous tracks and current/present tracks).
Regarding claim 30, Das discloses The method according to claim 27, wherein the first trackable property and The method according to claim 27, wherein merging comprises updating a trackable property of the candidate object (Das; [0021 and 0037] the same trackable property is distance-measurement for matching between previous tracks and current/present tracks) based on the first trackable property (Das; Fig. 4; “first object detection associated with a first sensor type”) and the second trackable property (Das; Fig. 4; “second object detection associated with a second sensor type”).
Regarding claim 31, Das discloses The method according to claim 30, wherein merging comprises updating a semantic comprehension (Das; [0028] “semantic segmentation”; [0101] “semantic label”) of the candidate object based on the updating of the trackable property.
Regarding claim 32, Das discloses The method according to claim 31, wherein the semantic comprehension comprises a confidence measure of the candidate object comprising the trackable property (Das; [0020] “degree of association” based on “Munkres match score”).
Regarding claim 33, Das discloses The method according to claim 31, further comprising promoting the candidate object to a tracked object based on the semantic comprehension (Das; [0028] “semantic segmentation”; [0101] “semantic label”) exceeding a semantic comprehension criteria (Das; [0020] “degree of association” based on “Munkres match score”).
Regarding claim 36, Das discloses the invention substantially the same as described above in reference to claim 2.
Regarding claim 37, Das discloses the invention substantially the same as described above in reference to claim 3.
Regarding claim 38, Das discloses the invention substantially the same as described above in reference to claim 4.
Regarding claim 39, Das discloses the invention substantially the same as described above in reference to claim 5.
Regarding claim 40, Das discloses the invention substantially the same as described above in reference to claim 6.
Regarding claim 41, Das discloses the invention substantially the same as described above in reference to claim 7.
Regarding claim 42, Das discloses the invention substantially the same as described above in reference to claim 8.
Regarding claim 43, Das discloses the invention substantially the same as described above in reference to claim 9.
Regarding claim 47, Das discloses the invention substantially the same as described above in reference to claim 13.
Regarding claim 51, Das discloses the invention substantially the same as described above in reference to claim 17.
Regarding claim 58, Das discloses the invention substantially the same as described above in reference to claim 24.
Regarding claim 59, Das discloses the invention substantially the same as described above in reference to claim 25.
Regarding claim 60, Das discloses the invention substantially the same as described above in reference to claim 26.
Regarding claim 62, Das discloses the invention substantially the same as described above in reference to claim 28.
Regarding claim 63, Das discloses the invention substantially the same as described above in reference to claim 29.
Regarding claim 64, Das discloses the invention substantially the same as described above in reference to claim 30.
Regarding claim 65, Das discloses the invention substantially the same as described above in reference to claim 31.
Regarding claim 66, Das discloses the invention substantially the same as described above in reference to claim 32.
Regarding claim 67, Das discloses the invention substantially the same as described above in reference to claim 33.
Regarding claim 70, Das discloses the invention substantially the same as described above in reference to claim 2.
Regarding claim 71, Das discloses the invention substantially the same as described above in reference to claim 3.
Regarding claim 72, Das discloses the invention substantially the same as described above in reference to claim 4.
Regarding claim 73, Das discloses the invention substantially the same as described above in reference to claim 5.
Regarding claim 74, Das discloses the invention substantially the same as described above in reference to claim 6.
Regarding claim 75, Das discloses the invention substantially the same as described above in reference to claim 7.
Regarding claim 76, Das discloses the invention substantially the same as described above in reference to claim 8.
Regarding claim 77, Das discloses the invention substantially the same as described above in reference to claim 9.
Regarding claim 81, Das discloses the invention substantially the same as described above in reference to claim 13.
Regarding claim 85, Das discloses the invention substantially the same as described above in reference to claim 17.
Regarding claim 92, Das discloses the invention substantially the same as described above in reference to claim 24.
Regarding claim 93, Das discloses the invention substantially the same as described above in reference to claim 25.
Regarding claim 94, Das discloses the invention substantially the same as described above in reference to claim 26.
Regarding claim 96, Das discloses the invention substantially the same as described above in reference to claim 28.
Regarding claim 97, Das discloses the invention substantially the same as described above in reference to claim 29.
Regarding claim 98, Das discloses the invention substantially the same as described above in reference to claim 30.
Regarding claim 99, Das discloses the invention substantially the same as described above in reference to claim 31.
Regarding claim 100, Das discloses the invention substantially the same as described above in reference to claim 32.
Regarding claim 101, Das discloses the invention substantially the same as described above in reference to claim 33.
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.
Claim(s) 10-12, 14-16, 18-20, 22-23, 44-46, 48-50, 52-57, 78-80, 82-84, and 86-91 is/are rejected under 35 U.S.C. 103 as being unpatentable over Das in view of Glesinger US20230140125.
Regarding claim 10, Das discloses The method according to claim 9, further comprising determining the tracked object correspondence based on a tracked object distance between the object (Das; Fig. 1; [0024] lidar sensor of sensor set 104; lidar data 128; [0062] “depth estimate”).
Das is silent regarding further comprising determining the tracked object correspondence based on a tracked object distance between the object and each of the tracked objects.
Glesinger teaches a tracked object distance between the object and each of the tracked objects (Glesinger; [0067] “Calculations of distances between objects and/or users in the multi-dimensional space and clusters among objects and/or users” and “These calculations may be used by the adaptive system 100 in generating recommendations and/or in clustering elements of the multi-dimensional space.”).
It would have been obvious to one having ordinary skill at the effective filing date of the invention to modify the correspondence determination as taught by Das to include being based on a tracked object distance between the object and each of the tracked objects as taught by Glesinger for the purpose of providing “clustering elements of the multi-dimensional space” (Glesinger; [0067] “These calculations may be used by the adaptive system 100 in generating recommendations and/or in clustering elements of the multi-dimensional space.”).
Regarding claim 11, Modified Das teaches the invention substantially the same as described above, and The method according to claim 10, wherein the tracked object distance comprises at least one of a Euclidean distance, a Manhattan distance, or a Minkowski distance, between the object location within the environment and a tracked object location within the environment (Das; [0112] “Euclidean distance”).
Regarding claim 12, Modified Das teaches the invention substantially the same as described above, and The method according to claim 10, wherein the tracked object comprises the tracked object distance having a shortest distance to the object (Das; [0112] “determining a degree of association” is based on the shortest/nearest/closest distance in order to yield the highest degree of association for matching purposes).
Regarding claim 14, Modified Das teaches the invention substantially the same as described above, and The method according to claim 12, further comprising merging the object with the tracked object based on a merging criteria (Das; Fig. 4 step 432 - “determine an updated or new track associated with the object based at least in part on the estimated object detection”; Fig. 5 step 510 - “associate the estimated object detection with the track”).
Regarding claim 15, Modified Das teaches the invention substantially the same as described above, and The method according to claim 14, wherein the merging criteria ([0021] “degree of association” is based on a “threshold”)) comprises a maximum distance and wherein the shortest distance between the object and the tracked object is less than the maximum distance of the merging criteria (Das; [0112] “determining a degree of association” is based on the shortest/nearest/closest distance in order to yield the highest degree of association for matching purposes).
Regarding claim 16, Modified Das teaches the invention substantially the same as described above, and The method according to claim 14, wherein the object does not meet the merging criteria (MPEP 2111.04(II) Contingent Limitations), the method further comprising registering the object as a new candidate object in the collection of objects (Das; Fig. 4 step 432 - “determine an updated or new track associated with the object based at least in part on the estimated object detection”; Fig. 5 step 512 - “generate a new track and/or provide the first object detection, the second object detection, and/or the estimated object detection to an alternate tracking component 512”).
Regarding claim 18, Modified Das teaches the invention substantially the same as described above in reference to claim 10.
Regarding claim 19, Modified Das teaches the invention substantially the same as described above in reference to claim 11.
Regarding claim 20, Modified Das teaches the invention substantially the same as described above in reference to claim 12.
Regarding claim 22, Modified Das teaches the invention substantially the same as described above in reference to claim 14.
Regarding claim 23, Modified Das teaches the invention substantially the same as described above in reference to claim 15.
Regarding claim 44, Modified Das teaches the invention substantially the same as described above in reference to claim 10.
Regarding claim 45, Modified Das teaches the invention substantially the same as described above in reference to claim 11.
Regarding claim 46, Modified Das teaches the invention substantially the same as described above in reference to claim 12.
Regarding claim 48, Modified Das teaches the invention substantially the same as described above in reference to claim 14.
Regarding claim 49, Modified Das teaches the invention substantially the same as described above in reference to claim 15.
Regarding claim 50, Modified Das teaches the invention substantially the same as described above in reference to claim 16.
Regarding claim 52, Modified Das teaches the invention substantially the same as described above in reference to claim 18.
Regarding claim 53, Modified Das teaches the invention substantially the same as described above in reference to claim 19.
Regarding claim 54, Modified Das teaches the invention substantially the same as described above in reference to claim 20.
Regarding claim 55, Modified Das teaches the invention substantially the same as described above in reference to claim 21.
Regarding claim 56, Modified Das teaches the invention substantially the same as described above in reference to claim 22.
Regarding claim 57, Modified Das teaches the invention substantially the same as described above in reference to claim 23.
Regarding claim 78, Modified Das teaches the invention substantially the same as described above in reference to claim 10.
Regarding claim 79, Modified Das teaches the invention substantially the same as described above in reference to claim 11.
Regarding claim 80, Modified Das teaches the invention substantially the same as described above in reference to claim 12.
Regarding claim 82, Modified Das teaches the invention substantially the same as described above in reference to claim 14.
Regarding claim 83, Modified Das teaches the invention substantially the same as described above in reference to claim 15.
Regarding claim 84, Modified Das teaches the invention substantially the same as described above in reference to claim 16.
Regarding claim 86, Modified Das teaches the invention substantially the same as described above in reference to claim 18.
Regarding claim 87, Modified Das teaches the invention substantially the same as described above in reference to claim 19.
Regarding claim 88, Modified Das teaches the invention substantially the same as described above in reference to claim 20.
Regarding claim 89, Modified Das teaches the invention substantially the same as described above in reference to claim 21.
Regarding claim 90, Modified Das teaches the invention substantially the same as described above in reference to claim 22.
Regarding claim 91, Modified Das teaches the invention substantially the same as described above in reference to claim 23.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang US20190346271 teaches SLAM, multiple moving features/objects/vehicles/people, relative pose, and coordinate systems (camera/laser, IMU, world).
Gee US20150098614 teaches, in Fig. 3, estimating pose.
Yang US20190197196 teaches tracking pose of an object.
Tahir US20190249980 teaches mathematical transformations for coordinate systems.
Jasiobedzki US20140062772 teaches, in Fig. 3, a pose camera 16 for a laser rangefinder 22.
Doyle US8467992 teaches, in Fig. 8, identifying an origin coordinate system with an origin 806, SLAM, and a transformation matrix.
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/JONATHAN MALIKASIM/ Primary Examiner, Art Unit 3645 7/22/26
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