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 .
Response to Arguments
Claim Rejections - 35 USC § 112:
Applicant’s arguments filed 1/15/2026 with respect to claims 3-7 and 12-16 have been fully considered and are persuasive. Amended claims 3-7 and 12-16 overcomes the rejection for being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. The rejection of claims 3-7 and 12-16 has been withdrawn.
Claim Rejections - 35 USC § 101:
Applicant's arguments filed 1/15/2026 have been fully considered but they are not persuasive.
The amendment to independent claims 1, 10 and 20 does not overcome the rejection. The additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular vehicle navigation or control problem, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the rejection is maintained.
Claim Rejections - 35 USC § 103:
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely the Natarajan et al. (US 20210118305 A1) reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is directed to a data alignment method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed
to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A data alignment method, applicable to a multi-device system in an environment, wherein the multi-device system comprises a host device and a client device, and the data alignment method comprises: by the client device,
establishing a client map of the environment by sensing the environment; by the client device,
transmitting image data to the host device; by the host device,
establishing a host map of the environment by sensing the environment; by the host device,
generating host-based spatial information of the client device in the host map of the environment established by the host device according to the image data,
wherein the host-based spatial information indicates orientation and position of the client device in the host map; by the host device,
transmitting the host-based spatial information to the client device; by the client device,
generating data alignment information according to a difference between the host-based spatial information and client-based spatial information of the client device in the client map of the environment established by the client device, wherein the client-based spatial information indicates orientation and position of the client device in the client map; and by the client device,
transforming the client-based spatial information from being in the client map into being in the host map using according to the data alignment information, to generate an aligned spatial information of the client device in the host map.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “transforming…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Additionally, the “transforming…” step is not too complex to be performed with the aid of pen and paper and indicates a mathematical relationship. Accordingly, the claim recites at least one abstract idea.
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as
follows (where the underlined portions are the “additional limitations” while the bolded portions
continue to represent the “abstract idea”:
establishing a client map of the environment by sensing the environment; by the client device,
transmitting image data to the host device; by the host device,
establishing a host map of the environment by sensing the environment; by the host device,
generating host-based spatial information of the client device in the host map of the environment established by the host device according to the image data,
wherein the host-based spatial information indicates orientation and position of the client device in the host map; by the host device,
transmitting the host-based spatial information to the client device; by the client device,
generating data alignment information according to a difference between the host-based spatial information and client-based spatial information of the client device in the client map of the environment established by the client device, wherein the client-based spatial information indicates orientation and position of the client device in the client map; and by the client device,
transforming the client-based spatial information from being in the client map into being in the host map using according to the data alignment information, to generate an aligned spatial information of the client device in the host map.
For the following reason(s), the examiner submits that the above identified additional
limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “establishing a client map…”, “transmitting image data..”, “establishing a host map…”, “generating host-based spatial information …”, “transmitting the host-based spatial information…”, “generating data alignment information …”, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the establishing and transmitting steps from external sources are recited at a high level of generality (i.e. as a general means of gathering for use in the transforming step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The generating steps are also recited at a high level of generality (i.e. as a general means of processing data and output for the transforming step), and amounts to mere post solution, which is a form of insignificant extra-solution activity. Lastly, the claim merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. The data alignment method is recited at a high level of generality and merely automates the adjusting step.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical
application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular vehicle navigation or control problem, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include
additional elements (considered both individually and as an ordered combination) that are sufficient
to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the adjusting… amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “establishing a client map…”, “transmitting image data…”, “establishing a host map…”, “generating host-based spatial information …”, “transmitting the host-based spatial information…”, “generating data alignment information …”; the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of “establishing a client map…”, “transmitting image data..”, “establishing a host map…”, “generating host-based spatial information …”, “transmitting the host-based spatial information…”, “generating data alignment information …” are well-understood, routine, and conventional activities, and the specification does not provide any indication that the computer is anything other than a conventional computer network component. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Same analysis applied to independent claims 10 and 20.
Dependent claims 2-9 and 11-19 do not recite any further limitations that cause the claim to
be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-9 and 11-19 are not patent eligible under the same rationale as provided for in the rejection of Claim 1. Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5, 10-11, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sugio et al. (US 20210233284 A1; hereinafter Sugio).
Regarding claim 1, Sugio teaches a data alignment method (see at least, [0669] alignment of the three-dimensional data and the three-dimensional map), applicable to a multi-device system in an environment, wherein the multi-device system comprises a host device and a client device (see at least, Fig 45, host device (*server) – Box 901; client device 902A), and the data alignment method comprises: by the client device, establishing a client map of the environment by sensing the environment (see at least, [0703] Client device 902 next creates three-dimensional data 1034 of the surrounding area of
client device 902 using sensor information 1033 obtained by sensors 1015 (S1004)); by the client device, transmitting image data to the host device (see at least, [0686] Data transmitter 1022 transmits sensor information 1037 to server 901…information obtained through sensors 1015, such as information obtained by…a luminance image obtained by a visible light camera, an infrared image
obtained by an infrared camera); by the host device, establishing a host map of the environment by sensing the environment (see at least, [0687] Server 901 transmits sensor information from client
device 902 and creates three-dimensional data based on the received sensor information); by the host device, generating host-based spatial information of the client device in the host map of the environment established by the host device according to the image data (see at least, [0708] Server
901 creates three-dimensional data 1134 of a vicinity of a position of client device 902 using sensor
information 1037 received from client device 902), wherein the host-based spatial information indicates orientation and position of the client device in the host map (see at least, [0665] Server 901 may also transmit the three-dimensional map suited to a position of client device 902 at fixed time intervals to client device 902); by the host device, transmitting the host-based spatial information to the client device; by the client device (see at least, [0665] Server 901 may also transmit the three-dimensional map managed by server 901 to client device 902), generating data alignment information according to a difference between the host-based spatial information and client-based spatial information of the client device in the client map of the environment established by the client device (see at least, [0670] Client device 902 may determine that there is a possibility of a change in the three-dimensional map of a surrounding area of client device 902 having occurred…when the error
during alignment of the three-dimensional data created by client device 902 based on the sensor information and the three-dimensional map obtained from server 901 is at least at the fixed level),
wherein the client-based spatial information indicates orientation and position of the client device in the client map (see at least, [0290] Client 1 then estimates the self-location information…includes three-dimensional position information, orientation, etc. of client 1); and by the client device, transforming the client-based spatial information from being in the client map into being in the host map using according to the data alignment information, to generate an aligned spatial information of the client device in the host map (see at least, [0670] Client device 902 may determine that there is a possibility of a change in the three-dimensional map of a surrounding area of client device 902 having occurred, and transmit this information and the sensor information to server 901, when the error during
alignment of the three-dimensional data created by client device 902 based on the sensor
information and the three-dimensional map obtained from server 901 is at least at the fixed level).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sugio to include the host-based spatial information indicates orientation of the client device in the host map in order to calibrate a sensor operation based on the data accuracy of the three-dimensional data.
Regarding claim 2, Sugio teaches the data alignment method of claim 1. Sugio further teaches comprising: by the host device, sending an alignment request to the client device to request for the image data (see at least, [0671] Server 901 sends a transmission request for the sensor information to client device 902; [0686] Data transmitter 1022 transmits sensor information 1037 to server 901… a luminance image obtained by a visible light camera, an infrared image obtained by an infrared camera)).
Regarding claim 5, Sugio teaches the data alignment method of claim 1. Sugio further teaches comprising: by the client device, determining if the host-based spatial information is different from the client-based spatial information, wherein the data alignment information is generated when the host-based spatial information is not different from the client-based spatial information due to a second difference between the client-based spatial information and the host-based spatial information smaller than a second threshold (see at least, [0772] The second threshold is greater than the above first threshold).
Regarding claim 10, Sugio teaches multi-device system, comprising: a host device (see at least, Fig 45, host device (*server)), configured to establish a host map of an environment sensing the environment (see at least, [0687] Server 901 transmits sensor information from client device 902 and creates three-dimensional data based on the received sensor information); and a client device (see at least, Fig 45, client device 902A), configured to: establish a client map of the environment by sensing the environment (see at least, [0703] Client device 902 next creates three-dimensional data 1034 of the surrounding area of client device 902 using sensor information 1033 obtained by sensors 1015
(S1004)); transmit image data to the host device when the host device and the client device are communicatively connected, wherein the host device is configured to generate host-based spatial information of the client device in the host map according to the image data (see at least, [0708] Server
901 creates three-dimensional data 1134 of a vicinity of a position of client device 902 using sensor
information 1037 received from client device 902), and is configured to transmit the host-based spatial information to the client device, wherein the host-based spatial information indicates orientation and position of the client device in the host map (see at least, [0665] Server 901 may also transmit the three-dimensional map suited to a position of client device 902 at fixed time intervals to
client device 902); generate data alignment information according to a difference between the host-based spatial information and client-based spatial information of the client device in the client map of the environment established by the client device (see at least, [0670] Client device 902 may determine that there is a possibility of a change in the three-dimensional map of a surrounding area of client
device 902 having occurred…when the error during alignment of the three-dimensional data created by client device 902 based on the sensor information and the three-dimensional map obtained from server 901 is at least at the fixed level), wherein the client-based spatial information indicates orientation and position of the client device in the client map (see at least, [0290] Client 1 then estimates the self-location information…includes three-dimensional position information, orientation, etc. of client 1); and transform the client-based spatial information from being in the client map into being in the host map using to the data alignment information, to generate an aligned spatial information of the client device in the host map (see at least, [0670] Client device 902 may determine that there is a possibility of a change in the three-dimensional map of a surrounding area of client
device 902 having occurred, and transmit this information and the sensor information to server 901, when the error during alignment of the three-dimensional data created by client device 902 based on the sensor information and the three-dimensional map obtained from server 901 is at least at the fixed level).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sugio to include the host-based spatial information indicates orientation of the client device in the host map in order to calibrate a sensor operation based on the data accuracy of the three-dimensional data.
Regarding claim 11, Sugio teaches the multi-device system of claim 10. Sugio further teaches wherein the host device is further configured to send an alignment request to the client device to request for the image data (see at least, [0671] Server 901 sends a transmission request for the sensor information to client device 902; [0686] Data transmitter 1022 transmits sensor information 1037 to server 901… a luminance image obtained by a visible light camera, an infrared image obtained by an infrared camera)).
Regarding claim 14, Sugio teaches the multi-device system of claim 10. Sugio further teaches wherein the client device is further configured to determine if the host-based spatial information is different from the client-based spatial information, wherein the data alignment information is generated when the host-based spatial information is not different from the client-based spatial information due to a second difference between the client-based spatial information and the host-based spatial information smaller than a second threshold (see at least, [0772] The second threshold is greater than the above first threshold).
Regarding claim 20, Sugio teaches a non-transitory computer readable storage medium with a computer program (see at least, [0164] an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be implemented) to execute (see at least, [1155] as a CPU or a processor reading out and executing the software program recorded in a recording medium ) a data alignment method (see at least, [0669] alignment of the three-dimensional data and the three-dimensional map), applicable to a multi-device system in an environment, wherein the multi-device system comprises a host device and a client device (see at least, Fig 45, host device (*server) – Box 901; client device 902A), and the data alignment method comprises: by the client device, establishing a client map of the environment by sensing the environment (see at least, [0703] Client device 902 next creates three-dimensional data 1034 of the surrounding area of
client device 902 using sensor information 1033 obtained by sensors 1015 (S1004)); by the client device, transmitting image data to the host device (see at least, [0686] Data transmitter 1022 transmits sensor information 1037 to server 901…information obtained through sensors 1015, such as information obtained by…a luminance image obtained by a visible light camera, an infrared image
obtained by an infrared camera); by the host device, establishing a host map of the environment by sensing the environment (see at least, [0687] Server 901 transmits sensor information from client
device 902 and creates three-dimensional data based on the received sensor information); by the host device, generating host-based spatial information of the client device in the host map of the environment established by the host device according to the image data (see at least, [0708] Server
901 creates three-dimensional data 1134 of a vicinity of a position of client device 902 using sensor
information 1037 received from client device 902), wherein the host-based spatial information indicates orientation and position of the client device in the host map (see at least, [0665] Server 901 may also transmit the three-dimensional map suited to a position of client device 902 at fixed time intervals to client device 902); by the host device, transmitting the host-based spatial information to the client device; by the client device (see at least, [0665] Server 901 may also transmit the three-dimensional map managed by server 901 to client device 902), generating data alignment information according to a difference between the host-based spatial information and client-based spatial information of the client device in the client map of the environment established by the client device (see at least, [0670] Client device 902 may determine that there is a possibility of a change in the three-dimensional map of a surrounding area of client device 902 having occurred…when the error
during alignment of the three-dimensional data created by client device 902 based on the sensor information and the three-dimensional map obtained from server 901 is at least at the fixed level),
wherein the client-based spatial information indicates orientation and position of the client device in the client map (see at least, [0290] Client 1 then estimates the self-location information…includes three-dimensional position information, orientation, etc. of client 1); and by the client device, transforming the client-based spatial information from being in the client map into being in the host map using according to the data alignment information, to generate an aligned spatial information of the client device in the host map (see at least, [0670] Client device 902 may determine that there is a possibility of a change in the three-dimensional map of a surrounding area of client device 902 having occurred, and transmit this information and the sensor information to server 901, when the error during
alignment of the three-dimensional data created by client device 902 based on the sensor
information and the three-dimensional map obtained from server 901 is at least at the fixed level).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sugio to include the host-based spatial information indicates orientation of the client device in the host map in order to calibrate a sensor operation based on the data accuracy of the three-dimensional data.
Claims 8, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sugio et al. (US 20210233284 A1; hereinafter Sugio) in view of Francis, Jr et al. (US 8874266 B1; hereinafter Francis, Jr.).
Regarding Claim 8, Sugio teaches the data alignment method of claim 1. Sugio does not explicitly teach by another client device of the multi-device system, transmitting another image data to the client device; by the client device, generating another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information; by the another client device, generating another data alignment information according to a difference between the another host-based spatial information and another client-based spatial information of the another client device in another client map of the environment established by the another client device; and by the another client device, adjusting the another client-based spatial information according to the another data alignment information, to generate another aligned spatial information of the another client device in the host map. However, Francis, Jr. teaches these limitations.
Francics, Jr. teaches comprising: by another client device of the multi-device system, transmitting another image data to the client device; by the client device, generating another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information (see at least, Col 9 lines 32-42, the second client device 34 can update the map created by the first client device 32 with additional information that the second client device 34 may have learned about the area…may include different types of sensors that the first client device 32 did not have…may be equipped with…a camera…at the time that it built the map); by the another client device, generating another data alignment information according to a difference between the another host-based spatial information and another client-based spatial information of the another client device in another client map of the environment established by the another client device; and by the another client device, adjusting the another client-based spatial information according to the another data alignment information, to generate another aligned spatial information of the another client device in the host map (see at least, Col 9 lines 32-44, the second client device 34 can update the map created by the first client device 32 with additional information that the second client device 34 may have learned about the area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sugio to include by another client device of the multi-device system, transmitting another image data to the client device; by the client device, generating another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information; by the another client device, generating another data alignment information according to a difference between the another host-based spatial information and another client-based spatial information of the another client device in another client map of the environment established by the another client device; and by the another client device, adjusting the another client-based spatial information according to the another data alignment information, to generate another aligned spatial information of the another client device in the host map as taught by Francis Jr. in order to facilitate collaborative work on a project or task from any number of user devices coupled to the cloud from any location (Francis, Jr., Col 1 lines 51-52).
Regarding Claim 17, Sugio teaches multi-device system of claim 10. Sugio does not explicitly teach wherein the multi-device system further comprises another client device; wherein the another client device is configured to: transmit another image data to the client device when the client device and the another client device are communicatively connected, wherein the client device is further configured to generate another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information; generate another data alignment information according to a difference between the another host-based spatial information and another client-based spatial information of the another client device in another client map of the environment established by the another client device; and adjust the another client-based spatial information according to the another data alignment information, to generate another aligned spatial information of the another client device in the host map. However, Francis, Jr. teaches these limitations.
Francis, Jr. teaches wherein the multi-device system further comprises another client device; wherein the another client device is configured to: transmit another image data to the client device when the client device and the another client device are communicatively connected, wherein the client device is further configured to generate another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information (see at least, Col 9 lines 32-42, the second client device 34 can update the map created by the first client device 32 with additional information that the second client device 34 may have learned about the area…may include different types of sensors that the first client device 32 did not have…may be equipped with…a camera…at the time that it built the map); wherein the multi-device system further comprises another client device; wherein the another client device is configured to: transmit another image data to the client device when the client device and the another client device are communicatively connected, wherein the client device is further configured to generate another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information (see at least, Col 9 lines 32-44, the second client device 34 can update the map created by the first client device 32 with additional information that the second client device 34 may have learned about the area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sugio to include by another client device of the multi-device system, transmitting another image data to the client device; by the client device, generating another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information; by the another client device, generating another data alignment information according to a difference between the another host-based spatial information and another client-based spatial information of the another client device in another client map of the environment established by the another client device; and by the another client device, adjusting the another client-based spatial information according to the another data alignment information, to generate another aligned spatial information of the another client device in the host map as taught by Francis Jr. in order to facilitate collaborative work on a project or task from any number of user devices coupled to the cloud from any location (Francis, Jr., Col 1 lines 51-52).
Regarding claim 19, the combination of Sugio and Francis, Jr. teaches the multi-device system of claim 17. Francis, Jr. further teaches wherein the client device is further configured to send another alignment request to the another client device to request for the another image data (see at least, Col 9 lines 32-44, the second client device 34 can update the map created by the first client device 32 with additional information that the second client device 34 may have learned about the area…may include different types of sensors that the first client device 32 did not have…may be equipped with…a camera).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sugio to include the client device is further configured to send another alignment request to the another client device to request for the another image data as taught by Francis Jr. in order to facilitate collaborative work on a project or task from any number of user devices coupled to the cloud from any location (Francis, Jr., Col 1 lines 51-52).
Claims 3-4, 6-7, 12-13 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Sugio et al. (US 20210233284 A1; hereinafter Sugio) in view of Liu et al. (US 20200082262 A1; hereafter Liu).
Regarding claim 3, Sugio teaches the data alignment method of claim 1. The combination does not explicitly teach by the client device, choosing at least two key frame images from a key frame set as the image data, wherein the key frame set is generated after the host device and the client device are communicatively connected, the at least two key frame images correspond to at least two first client pose data of the client device in the client map, and the at least two first client pose data are different from each other due to a first difference between the at least two first client pose data greater than a first threshold. However, Liu teaches these limitations.
Liu teaches further comprising: by the client device, choosing at least two key frame images from a key frame set as the image data (see at least, [0152] detection/collection logic 1601 may be used to collect and processing/training logic 1605 may be used to compute a second dataset…image pairs that are captured by a camera), wherein the key frame set is generated after the host device and the client device are communicatively connected, the at least two key frame images correspond to at least two first client pose data of the client device in the client map (see at least, [0026] an accurate camera pose may be estimated from matching an object frame with any found key frames), and the at least two first client pose data are different from each other due to a first difference between the at least two first client pose data greater than a first threshold (see at least, [0156] Since not all images can have enough matching key points corresponding to the found key frame; [0161] certain keyframes (selected by a predetermined criteria, such as when distance between the pose of an image and the last keyframe exceed certain threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Sugio to include by the client device, choosing at least two key frame images from a key frame set as the image data, wherein the key frame set is generated after the host device and the client device are communicatively connected, the at least two key frame images correspond to at least two first client pose data of the client device in the client map, and the at least two first client pose data are different from each other due to a first difference between the at least two first client pose data greater than a first threshold as taught by Liu in order to accuracy of localization is achieved using a couple of datasets that are collected using detection/collection logic (Liu, [0151).
Regarding claim 4, the combination of Sugio and Liu teaches the data alignment method of claim 3. Liu further teaches wherein generating the host-based spatial information of the client device in the host map according to the image data comprises: by the host device, calculating at least two second client pose data of the client device in the host map according to the at least two key frame images (see at least, [156] a CNN regression to find a matching key frame and use the visual odometry to compute an estimation the image pose).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Sugio to include generating the host-based spatial information of the client device in the host map according to the image data comprises: by the host device, calculating at least two second client pose data of the client device in the host map according to the at least two key frame images as taught by Liu in order to accuracy of localization is achieved using a couple of datasets that are collected using detection/collection logic (Liu, [0151).
Regarding claim 6, Sugio teaches the data alignment method of claim 5. Sugio does not explicitly teach comprising: by the client device, transmitting another at least two key frame images to the host device when the host-based spatial information is different from the client-based spatial information due to the second difference not smaller than the second threshold. However, Liu teaches this limitation.
Liu teaches by the client device, transmitting another at least two key frame images to the host device when the host-based spatial information is different from the client-based spatial information due to the second difference not smaller than the second threshold ([0156] Since not all images can have enough matching key points corresponding to the found key frame, the visual odometry may fail, in which case, the result of the original CNN regression result is accepted and forwarded on to be displayed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Sugio to include by the client device, transmitting another at least two key frame images to the host device when the host-based spatial information is different from the client-based spatial information due to the second difference not smaller than the second threshold as taught by Liu in order to accuracy of localization is achieved using a couple of datasets that are collected using detection/collection logic (Liu, [0151).
Regarding claim 7, Sugio teaches the data alignment method of claim 5. Sugio does not explicitly teach wherein determining if the host-based spatial information is different from the client-based spatial information comprises: by the client device, determining if a difference between a first transformation data and a second transformation data is smaller than an invalid threshold, wherein the first transformation data is configured to transform one first client pose data of the client device in the client map into another first client pose data of the client device in the client map, and the second transformation data is configured to transform one second client pose data of the client device in the host map into another second client pose data of the client device in the host map. However, Liu teaches this limitation.
Liu teaches wherein determining if the host-based spatial information is different from the client-based spatial information comprises: by the client device, determining if a difference between a first transformation data and a second transformation data is smaller than an invalid threshold (see at least, [0155] re-localization techniques…utilize image features of feature points (e.g., ORB, scale-invariant feature transform (SIFT), SURF, etc.) or neural code to find matching key frames and using visual odometry to accurately compute poses a camera…have a low recall rate for relying on the visual odometry, which is based on feature point detection and matching that often fails), wherein the first transformation data is configured to transform one first client pose data of the client device in the client map into another first client pose data of the client device in the client map, and the second transformation data is configured to transform one second client pose data of the client device in the host map into another second client pose data of the client device in the host map ([0174] At block 1923, the original frames of keyframes for keyframe image list 1921 of the first N nearest keyframes to current input image 1911 are selected from database 1941. At block 1925, feature point correspondences between current input image 1911 and the N nearest keyframes are computed using transformation matrix computing).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Sugio to include the first transformation data is configured to transform one first client pose data of the client device in the client map into another first client pose data of the client device in the client map, and the second transformation data is configured to transform one second client pose data of the client device in the host map into another second client pose data of the client device in the host map as taught by Liu in order to accuracy of localization is achieved using a couple of datasets that are collected using detection/collection logic (Liu, [0151).
Regarding claim 12, Sugio teaches the multi-device system of claim 10. Sugio not explicitly teach wherein the client device is further configured to choose at least two key frame images from a key frame set as the image data, wherein the key frame set is generated after the host device and the client device are communicatively connected, the at least two key frame images correspond to at least two first client pose data of the client device in the client map, and the at least two first client pose data are different from each other due to a first difference between the at least two first client pose data greater than a first threshold. However, Liu teaches these limitations.
Liu teaches wherein the client device is further configured to choose at least two key frame images from a key frame set as the image data (see at least, [0152] detection/collection logic 1601 may be used to collect and processing/training logic 1605 may be used to compute a second dataset…image pairs that are captured by a camera), wherein the key frame set is generated after the host device and the client device are communicatively connected, the at least two key frame images correspond to at least two first client pose data of the client device in the client map (see at least, [0026] an accurate camera pose may be estimated from matching an object frame with any found key frames), and the at least two first client pose data are different from each other due to a first difference between the at least two first client pose data greater than a first threshold (see at least, [0156] Since not all images can have enough matching key points corresponding to the found key frame; [0161] certain keyframes (selected by a predetermined criteria, such as when distance between the pose of an image and the last keyframe exceed certain threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of Sugio to include by the client device is further configured to choose at least two key frame images from a key frame set as the image data, wherein the key frame set is generated after the host device and the client device are communicatively connected, the at least two key frame images correspond to at least two first client pose data of the client device in the client map, and the at least two first client pose data are different from each due to a first difference between the at least two first client pose data greater than a first threshold other as taught by Liu in order to accuracy of localization is achieved using a couple of datasets that are collected using detection/collection logic (Liu, [0151).
Regarding claim 13, the combination of Sugio and Liu teaches the multi-device system of claim 12. Liu further teaches wherein an amount of feature points of each of the at least two key frame images is greater than an amount threshold (see at least, [0161] transforming input image to the pose of the image…three position parameters and three rotation parameters…and subsequently, certain keyframes (selected by a predetermined criteria, such as when distance between the pose of an image and the last keyframe exceed certain threshold) and put into a list and are processed to get their respective poses).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Sugio to include an amount of feature points of each of the at least two key frame images is greater than an amount threshold as taught by Liu in order to accuracy of localization is achieved using a couple of datasets that are collected using detection/collection logic (Liu, [0151).
Regarding claim 15, Sugio teaches the multi-device system of claim 14. Sugio does not explicitly teach wherein the client device is further configured to transmit another at least two key frame images to the host device when the host-based spatial information is different from the client-based spatial information due to the second difference not smaller than the second threshold. However, Liu teaches this limitation.
Liu teaches wherein the client device is further configured to transmit another at least two key frame images to the host device when the host-based spatial information is different from the client-based spatial information due to the second difference not smaller than the second threshold. (see at least, [0156] Since not all images can have enough matching key points corresponding to the found key
frame, the visual odometry may fail, in which case, the result of the original CNN regression result is accepted and forwarded on to be displayed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Sugio to include the client device is further configured to transmit another at least two key frame images to the host device when the host-based spatial information is different from the client-based spatial information due to the second difference not smaller than the second threshold as taught by Liu in order to accuracy of localization is achieved using a couple of datasets that are collected using detection/collection logic (Liu, [0151).
Regarding claim 16, Sugio teaches the multi-device system of claim 14. Sugio does not explicitly teach wherein the client device is configured to determine if a difference between a first transformation data and a second transformation data is smaller than an invalid threshold, wherein the first transformation data is configured to transform one first client pose data of the client device in the client map into another first client pose data of the client device in the client map, and the second transformation data is configured to transform one second client pose data of the client device in the host map into another second client pose data of the client device in the host map. However, Liu teaches this limitation.
Sugio does not explicitly teach wherein the client device is configured to determine if a difference between a first transformation data and a second transformation data is smaller than an invalid threshold (see at least, [0155] utilize image features of feature points (e.g., ORB, scale-invariant feature transform (SIFT), SURF, etc.) or neural code to find matching key frames and using visual odometry to accurately compute poses a camera…they all have a low recall rate for relying on the visual odometry, which is based on feature point detection and matching that often fails), wherein the first transformation data is configured to transform one first client pose data of the client device in the client map into another first client pose data of the client device in the client map, and the second transformation data is configured to transform one second client pose data of the client device in the host map into another second client pose data of the client device in the host map see at least, ([0174] At block 1923, the original frames of keyframes for keyframe image list 1921 of the first N nearest keyframes to current input image 1911 are selected from database 1941. At block 1925, feature point correspondences between current input image 1911 and the N nearest keyframes are computed using transformation matrix computing).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Sugio to include the client device is configured to determine if a difference between a first transformation data and a second transformation data is smaller than an invalid threshold, wherein the first transformation data is configured to transform one first client pose data of the client device in the client map into another first client pose data of the client device in the client map, and the second transformation data is configured to transform one second client pose data of the client device in the host map into another second client pose data of the client device in the host map as taught by Liu in order to accuracy of localization is achieved using a couple of datasets that are collected using detection/collection logic (Liu, [0151).
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sugio et al. (US 20210233284 A1; hereinafter Sugio) in view of Francis, Jr et al. (US 8874266 B1; hereinafter Francis, Jr.) in further view of Wang et al. (CN 114398455 A; hereinafter Wang).
Regarding claim 9, the combination of Sugio and Francis, Jr. teaches the data alignment method of claim 8. The combination does not explicitly teach wherein generating the another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information comprises: by the client device, calculating at least two client pose data of the another client device in the client map according to at least two key frame images of the another image data; and by the client device, adjusting the at least two client pose data according to the data alignment information, to generate the another host-based spatial information. However, Wang teaches these limitations.
Wang teaches wherein generating the another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information comprises: by the client device, calculating at least two client pose data of the another client device in the client map according to at least two key frame images of the another image data (see at least, [0006] the second related information comprises a plurality of second key frames of the second
map and the motion track of the second unmanned aerial vehicle); and by the client device, adjusting the at least two client pose data according to the data alignment information (see at least, [0007] determining the overlapping area between the first map and the second map), to generate the another host-based spatial information (see at least, [0009] fusing the first map and the second map, obtaining a global map comprising the first area and the second area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified combination of Sugio and Francis, Jr. to include generating the another host-based spatial information of the another client device in the host map according to the another image data and the data alignment information comprises: by the client device, calculating at least two client pose data of the another client device in the client map according to at least two key frame images of the another image data; and by the client device, adjusting the at least two client pose data according to the data alignment information, to generate the another host-based spatial information at taught by Wang in order to improves the precision of the global map (Wang, Abstract).
Regarding claim 18, combination of Sugio and Francis, Jr. teaches the multi-device system of claim 17. The combination does not explicitly teach wherein the client device is configured to: calculate at least two client pose data of the another client device in the client map according to at least two key frame images of the another image data; and adjust the at least two client pose data according to the data alignment information, to generate the another host-based spatial information. However, Wang teaches these limitations.
Wang teaches wherein the client device is configured to: calculate at least two client pose data of the another client device in the client map according to at least two key frame images of the another image data; (see at least, [0006] the second related information comprises a plurality of second key frames of the second map and the motion track of the second unmanned aerial vehicle) ; and adjust the at least two client pose data according to the data alignment information (see at least, [0007] determining the overlapping area between the first map and the second map), to generate the another host-based spatial information (see at least, [0009] fusing the first map and the second map, obtaining a global map comprising the first area and the second area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified combination of Sugio and Francis, Jr. to include the client device is configured to: calculate at least two client pose data of the another client device in the client map according to at least two key frame images of the another image data; and adjust the at least two client pose data according to the data alignment information, to generate the another host-based spatial information at taught by Wang in order to improves the precision of the global map (Wang, Abstract).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662
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