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 Amendment
1. Claims 1-7, 9-17, and 19-20 are currently pending.
2. Claims 8 and 18 are canceled.
3. Claims 1-7, 9-17, 19-20 are currently amended.
4. The 112(a) and 112(b) rejections to Claims 1-10 have been overcome.
5. The 101 rejection to Claim 11 has been overcome.
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
9. Claims 1-7, 9-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu (US 20220196425 A1), in view of Suzuki (US 20220196416 A1), in view of Sudhakaran (US 20210302956 A1), and in further view of Li (CN 113885402 A).
10. Regarding Claim 1, Xu teaches a latency-based robot driving map generation apparatus, comprising (Xu: [0052] Route controller 121):
A communication module configured to measure network latency (Xu: [0041] Communication interface 102):
One or more non-transitory computer-readable media comprising an instruction; and one or more processors configured to perform an operation by executing the instruction, wherein the operation comprises (Xu: [0150] and [0153] Processor 801 and Memory 804):
Dividing a driving space into a plurality of grids, measuring network latency for each of the plurality of grids through the communication module (Xu: [0052] and [0066] Note that the scores for one or more path segments that reflect wireless performance is equivalent to dividing a space into a plurality of grids.),
Assigning a latency level to each grid based on the measured network latency to generate a network map with an assigned latency level for each grid (Xu: [0052] and [0064] Map 300),
And determining a driving path using the updated network map and the calculated success rate (Xu: [0060] and [0116] Note that the vehicle route generating a route with driver assistance features and based on network performance data is equivalent to determining a driving path using the calculated success rate and the updated network map.).
Xu fails to explicitly teach a success rate calculator configured to calculate a success rate by repeated driving on a path in a grid assigned a specific latency level in the network map. However, Xu teaches in [0115] that historical wireless performance data is used to determine a revised route to avoid the wireless performance failing to be above a level.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to calculate a success rate by repeated driving on a path in a grid assigned a specific latency level in the network map as similarly shown in Xu's [0115] use of historical performance data to avoid path segments at particular times. This provides the benefit of ensuring the vehicle will not fail to enable the driver assistance, which requires a threshold level of network performance.
Additionally, Suzuki teaches a success rate calculator configured to calculate a success rate by repeated driving on a path in a grid assigned a specific latency level in the network map (Suzuki: [0041] Note that Fig. 3 is a grid map of a path that identifies areas associated with ubiquitous events stored in database 204 with a solid circle. Each ubiquitous event is equivalent to a grid with a calculated success rate by repeated driving on the path.).
Xu and Suzuki are considered to be analogous to the claim invention because they are in the same field of autonomous vehicle navigation maps. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu to incorporate the teachings of Suzuki to calculate a success rate by repeated driving on a path in a grid because it provides the benefit of determining areas in a map that reduce the performance of an autonomous vehicle. Determining these areas provides the additional benefit of generating a route to a destination that avoids the reduced performance areas (ubiquitous area). Xu and Suzuki both generate a map that identify areas where the performance of the autonomous vehicle operation is reduced.
Xu and Suzuki fail to explicitly teach detecting an abnormality of a router, in response to detecting the abnormality of the router, allocating additional latency measurement time for grids located within a predetermined radius from the abnormal router... and updating the network map based on the refined latency mapping.
However, in the same field of endeavor, Sudhakaran teaches detecting an abnormality of a router (Sudhakaran: [0013] Note that determining the communication performance suffers from inconsistencies is equivalent to detecting an abnormality of a router.),
In response to detecting the abnormality of the router, allocating additional latency measurement time for grids located within a predetermined radius from the abnormal router (Sudhakaran: [0028] and [0038] Note that Equation 1 calculates the QoS score based at least in part on the latency. This is equivalent to allocating additional measurement time for grids. Also, under the broadest reasonable interpretation, allocating additional latency measurement time for grids located within a predetermined radius from the abnormal router is interpreted as updating the network latency map based on the changes to the network over time.),
And updating the network map based on the refined latency mapping (Sudhakaran: [0038] “The central controller may implement an aggregation/QoS mapping processing block 214 to map the aggregated QoS metrics received from each of the AMRs 102 within the environment 100 to the shared environment model 250 on a per cell basis, which may be updated as new data is received…”).
Xu, Suzuki, and Sudhakaran are considered to be analogous to the claim invention because they are in the same field of autonomous vehicle navigation maps. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu and Suzuki to incorporate the teachings of Sudhakaran to allocate additional latency measurement time for grids located within a predetermined radius from a detected abnormal router and to update the network map because it provides the benefit of calculating network efficient paths for the vehicles in the network. This provides the additional benefit of improving the overall efficiency of the navigation tasks in the environment as explicitly explain in [0027] of Sudhakaran.
Xu, Suzuki, and Sudhakaran fail to explicitly teach reducing an operating speed of a robot while performing the additional latency measurement for the grids located within the predetermined radius so as to refine latency mapping for the grids.
However, in the same field of endeavor, Li teaches reducing an operating speed of a robot while performing the additional latency measurement for the grids located within the predetermined radius so as to refine latency mapping for the grids (Li: [Page 5, Lines 6-9], [Page 6, Lines, 19-21], and [Page 7, Lines 37-40] "Exemplarily, when the communication quality of the remote control vehicle is poor, the maximum speed of the remote control vehicle is reduced…”).
Xu, Suzuki, Sudhakaran, and Li are considered to be analogous to the claim invention because they are in the same field of autonomous vehicle navigation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu, Suzuki, and Sudhakaran to incorporate the teachings of Li to reduce the operating speed of the robot when an abnormality is detected in a specific router because it provides the benefit of improving the safety of the remote-controlled vehicle based on the network signal.
11. Regarding Claim 2, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 1.
Xu fails to explicitly teach determining the latency level, as a first level when the network latency is equal to or greater than a first criterion, a second level when the network latency is less than the first criterion and equal to or greater than a second criterion, and a third level when the network latency is less than the second criterion and equal to or greater than a third criterion, and the first to third criteria vary depending on a service operation status of a driving robot. However, Xu teaches in [0053] that the scores are determined based on a plurality of threshold criterion. In the specific example, the network speed is used. Xu explains that the score can be based on the network performance that includes the latency. Note that the first criterion is equivalent to the score of a 1, the second criterion is equivalent to the score greater than 1 (e.g., 3), and the third criterion is equivalent to a score greater than the second criterion (e.g., 4).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective date to determine a first, second, and third level based on the network latency as similarly shown in Xu's [0053] use of the network speed at different thresholds to score each segment. This provides the benefit of calculating a score for the segment based on multiple network performance parameters to determine a route that satisfies the network performance thresholds.
12. Regarding Claim 3, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 2, and further, Xu teaches the driving path determiner excluding from the driving path any grid assigned the first latency level (Xu: [0091], [0112], and [0116] Note that excluding the segments with a congested route is equivalent to excluding from the driving path any grid assigns the first latency level because Xu avoid the segments with the reduction in network performance.).
13. Regarding Claim 4, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 3, and further, Xu teaches accumulating a statistical value through repeated driving operations for grids assigned the second level or the third level and calculating the success rates for each grid based on the accumulated statistical value (Xu: [0115] Note that the historical performance data is equivalent to the accumulated statistical data through repeated driving operations. Also, note that the predicted performance data for the path segments at specific times is equivalent to calculating the success rates for each grid because a route that fails to enable the preferred driving assistance features is considered unsuccessful.).
14. Regarding Claim 5, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 1, and further, Xu teaches setting a driving path selection priority proportional to the calculated success rates for each grid on the network map and determining the driving path based on the driving path selection priority (Xu: [0093] and [0115] Note that the increased wireless performance to enable driving assistance features is equivalent to having a higher driving path selection priority.).
15. Regarding Claim 6, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 5, and further, Xu teaches updating the network map and a driving path preset on the network map by reflecting the driving path selection priorities for each grid calculated in real time (Xu: [0093] and [0116] Note that the first route is equivalent to driving path preset.).
16. Regarding Claim 7, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 6, and further, Suzuki teaches excluding from the driving path any grid with a success rate of 50% or less (Suzuki: [0041] and [0061] It would have been well within the skill level of one ordinary skill in the art to exclude a grid with a success rate of 50% or less absent a showing to the contrary. The Applicant has not disclosed anything that solves any stated problem or is for any particular purpose, and it appears that the invention would perform equally as well by excluding a grid with any success rate to determine the driving path. Suzuki teaches that ubiquitous events are likely override would be repeated many times at the time/location. Under the broadest reasonable interpretation of the claims, this is equivalent to a grid with a success rate of 50% or less because the grid with the likelihood of override is considered unsuccessful (a successful route does not involve overriding the vehicle control).).
17. Regarding Claim 9, Xu, Suzuki, Sudhakaran, and Li remain as applied above in Claim 5, and further, Xu teaches reducing the driving path selection priority set for grids within the certain radius from the location of the specific router to a certain level (Xu: [0093] and [0116] Note that Xu determines the driving path priority based on bandwidth in this specific example. Additionally, the other network performance data (including latency) may be used as explained in [0052].).
18. Regarding Claim 10, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 3, and further, Suzuki teaches assigning the first latency level to the grids within a certain radius of the shutdown router’s location (Xu: [0053] Note that the wireless performance data being unknown or available is equivalent to the specific router shutting down and the grid being assigned a first latency level.).
19. Regarding Claim 11, Xu teaches a latency-based robot driving map generation method, the method being performed by a computing device comprising a processor and a memory, the method comprising (Xu: [0052] and [0150] Route controller 121 and Processor 801):
Dividing a driving space into a plurality of grids, and measuring network latency for each of the plurality of grids (Xu: [0052] and [0066] Note that the scores for one or more path segments that reflect wireless performance is equivalent to dividing a space into a plurality of grids.),
Assigning a latency level to each grid based on the measured network latency to generate a network map with an assigned latency level for each grid (Xu: [0052] and [0064] Map 300),
And determining a driving path using the updated network map and the calculated success rate (Xu: [0060] and [0116] Note that the vehicle route generating a route with driver assistance features and based on network performance data is equivalent to determining a driving path using the calculated success rate and the updated network map.).
Xu fails to explicitly teach a success rate calculator configured to calculate a success rate by repeated driving on a path in a grid assigned a specific latency level in the network map. However, Xu teaches in [0115] that historical wireless performance data is used to determine a revised route to avoid the wireless performance failing to be above a level.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to calculate a success rate by repeated driving on a path in a grid assigned a specific latency level in the network map as similarly shown in Xu's [0115] use of historical performance data to avoid path segments at particular times. This provides the benefit of ensuring the vehicle will not fail to enable the driver assistance, which requires a threshold level of network performance.
Additionally, Suzuki teaches a success rate calculator configured to calculate a success rate by repeated driving on a path in a grid assigned a specific latency level in the network map (Suzuki: [0041] Note that Fig. 3 is a grid map of a path that identifies areas associated with ubiquitous events stored in database 204 with a solid circle. Each ubiquitous event is equivalent to a grid with a calculated success rate by repeated driving on the path.).
Xu and Suzuki are considered to be analogous to the claim invention because they are in the same field of autonomous vehicle navigation maps. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu to incorporate the teachings of Suzuki to calculate a success rate by repeated driving on a path in a grid because it provides the benefit of determining areas in a map that reduce the performance of an autonomous vehicle. Determining these areas provides the additional benefit of generating a route to a destination that avoids the reduced performance areas (ubiquitous area). Xu and Suzuki both generate a map that identify areas where the performance of the autonomous vehicle operation is reduced.
Xu and Suzuki fail to explicitly teach detecting an abnormality of a router, in response to detecting the abnormality of the router, allocating additional latency measurement time for grids located within a predetermined radius from the abnormal router… and updating the network map based on the refined latency mapping,
However, in the same field of endeavor, Sudhakaran teaches detecting an abnormality of a router (Sudhakaran: [0013] Note that determining the communication performance suffers from inconsistencies is equivalent to detecting an abnormality of a router.),
In response to detecting the abnormality of the router, allocating additional latency measurement time for grids located within a predetermined radius from the abnormal router (Sudhakaran: [0028] and [0038] Note that Equation 1 calculates the QoS score based at least in part on the latency. This is equivalent to allocating additional measurement time for grids. Also, under the broadest reasonable interpretation, allocating additional latency measurement time for grids located within a predetermined radius from the abnormal router is interpreted as updating the network latency map based on the changes to the network over time.),
And updating the network map based on the refined latency mapping (Sudhakaran: [0038] “The central controller may implement an aggregation/QoS mapping processing block 214 to map the aggregated QoS metrics received from each of the AMRs 102 within the environment 100 to the shared environment model 250 on a per cell basis, which may be updated as new data is received…”).
Xu, Suzuki, and Sudhakaran are considered to be analogous to the claim invention because they are in the same field of autonomous vehicle navigation maps. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu and Suzuki to incorporate the teachings of Sudhakaran to allocate additional latency measurement time for grids located within a predetermined radius from a detected abnormal router and to update the network map because it provides the benefit of calculating network efficient paths for the vehicles in the network. This provides the additional benefit of improving the overall efficiency of the navigation tasks in the environment as explicitly explain in [0027] of Sudhakaran.
Xu, Suzuki, and Sudhakaran fail to explicitly teach reducing an operating speed of a robot while performing the additional latency measurement for the grids located within the predetermined radius so as to refine latency mapping for the grids.
However, in the same field of endeavor, Li teaches reducing an operating speed of a robot while performing the additional latency measurement for the grids located within the predetermined radius so as to refine latency mapping for the grids (Li: [Page 5, Lines 6-9], [Page 6, Lines, 19-21], and [Page 7, Lines 37-40] "Exemplarily, when the communication quality of the remote control vehicle is poor, the maximum speed of the remote control vehicle is reduced…”).
Xu, Suzuki, Sudhakaran, and Li are considered to be analogous to the claim invention because they are in the same field of autonomous vehicle navigation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu, Suzuki, and Sudhakaran to incorporate the teachings of Li to reduce the operating speed of the robot when an abnormality is detected in a specific router because it provides the benefit of improving the safety of the remote-controlled vehicle based on the network signal.
20. Regarding Claim 12 Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 11, and further, Xu teaches receiving an initial network map for the space through a control center and measuring the network latency for each grid on the initial network map using the network map generator (Xu: [0045], [0046], and [0059] Note that the platform server is equivalent to the control center).
21. Regarding Claim 13, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 11.
Xu fails to explicitly teach determining the latency level as a first level when the network latency is equal to or greater than a first criterion, a second level when the network latency is less than the first criterion and equal to or greater than a second criterion, and a third level when the network latency is less than the second criterion and equal to or greater than a third criterion, and the first to third criteria vary depending on a service operation status of a driving robot. However, Xu teaches in [0053] that the scores are determined based on a plurality of threshold criterion. In the specific example, the network speed is used. Xu explains that the score can be based on the network performance that includes the latency. Note that the first criterion is equivalent to the score of a 1, the second criterion is equivalent to the score greater than 1 (e.g., 3), and the third criterion is equivalent to a score greater than the second criterion (e.g., 4).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective date to determine a first, second, and third level based on the network latency as similarly shown in Xu's [0053] use of the network speed at different thresholds to score each segment. This provides the benefit of calculating a score for the segment based on multiple network performance parameters to determine a route that satisfies the network performance thresholds.
22. Regarding Claim 14, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 13, and further, Xu teaches accumulating statistical value through repeated driving operations for grids assigned the second or third latency level and calculate the success rates for each grid based on the accumulated statistical value (Xu: [0115] Note that the historical performance data is equivalent to the accumulated statistical data through repeated driving operations. Also, note that the predicted performance data for the path segments at specific times is equivalent to calculating the success rates for each grid because a route that fails to enable the preferred driving assistance features is considered unsuccessful.).
23. Regarding Claim 15, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 14, and further, Xu teaches excluding any grid assigned the first latency level from the driving path (Xu: [0091], [0112], and [0116] Note that excluding the segments with a congested route is equivalent to excluding from the driving path any grid assigns the first latency level because Xu avoid the segments with the reduction in network performance.).
24. Regarding Claim 16, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 11, and further, Xu teaches updating the network map and a driving path preset on the network map in real time by reflecting the driving path selection priorities for each grid proportional to the success rate (Xu: [0115] Note that the historical performance data is equivalent to the accumulated statistical data through repeated driving operations. Also, note that the predicted performance data for the path segments at specific times is equivalent to calculating the success rates for each grid because a route that fails to enable the preferred driving assistance features is considered unsuccessful.).
25. Regarding Claim 17, Xu, Suzuki, Sudhakaran, and Li remains as applied above in Claim 16, and further, Suzuki teaches excluding from the driving path any specific grids with a success rate of 50% or less (Suzuki: [0041] and [0061] It would have been well within the skill level of one ordinary skill in the art to exclude a grid with a success rate of 50% or less absent a showing to the contrary. The Applicant has not disclosed anything that solves any stated problem or is for any particular purpose, and it appears that the invention would perform equally as well by excluding a grid with any success rate to determine the driving path. Suzuki teaches that ubiquitous events are likely override would be repeated many times at the time/location. Under the broadest reasonable interpretation of the claims, this is equivalent to a grid with a success rate of 50% or less because the grid with the likelihood of override is considered unsuccessful (a successful route does not involve overriding the vehicle control).).
26. Regarding Claim 19, Xu, Suzuki, Sudhakaran, and Li remain as applied above in Claim 11, and further, Xu teaches reducing the driving path selection priority set for grids within the certain radius from the location of a specific router to a certain level (Xu: [0093] and [0116] Note that Xu determines the driving path priority based on bandwidth in this specific example. Additionally, the other network performance data (including latency) may be used as explained in [0052].).
27. Regarding Claim 20, Xu and Suzuki remains as applied above in Claim 15, and further, Suzuki teaches when a specific router shuts down, the generating of the network map further includes using the network map generator to: determine the first latency level to the grids within a certain radius of the shutdown router's location (Xu: [0053] Note that the wireless performance data being unknown or available is equivalent to the specific router shutting down and the grid being assigned a first latency level.).
Response to Arguments
28. Applicant’s arguments with respect to Claims 1-7, 9-17, and 19-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Sudhakaran (US 20210302956 A1) has been applied to teach the amended subject matter of allocating additional latency measurement time for grid located within a predetermined radius from a detected abnormal router in the rejection above as cited in at least paragraphs [0013] and [0038]. Sudhakaran teaches to dynamically update latency maps as new data is received due to inconsistencies in a changing environment for the wireless communication performance.
29. Xu (US 20220196425 A1), in view of Suzuki (US 20220196416 A1), in view of Sudhakaran (US 20210302956 A1), and in further view of Li (CN 113885402 A) teaches all aspects of the invention. The rejection is modified according to the newly amended language but still maintained with the current prior art of record.
30. Claims 1-7, 9-17, and 19-20 remain rejected under their respective grounds and rational as cited above, and as stated in the prior office action which is incorporated herein. Also, although not specifically argued, all remaining claims remain rejected under their respective grounds, rationales, and applicable prior art for these reasons cited above, and those mentioned in the prior office action which is incorporated herein.
Conclusion
31. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gogna (US 20210311471 A1)
Ko (US 20110267952 A1)
32. 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.
33. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL T SILVA whose telephone number is (571)272-6506. The examiner can normally be reached Mon-Tues: 7AM - 4:30PM ET; Wed-Thurs: 7AM-6PM ET; Fri: OFF.
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/MICHAEL T SILVA/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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