DETAILED ACTION
This communication is in response to applicant’s reply filed on September 10, 2025.
Claims 1 and 18 have been amended.
Claims 4, 14 and 15 have been cancelled
Claims 1-3, 5-12 and 16-20 are currently pending
Claim Rejections - 35 USC § 101
Applicant’s amendment has overcome the 101-rejection raised in the previous action; therefore, the 101 rejection is hereby withdrawn.
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 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. (FP 7.08.aia)
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Siikonen U.S Patent No. 6,345,697.
As to claim 1, Siikonen teaches a method for elevator call allocations in an elevator group of an elevator system, the method comprising:
applying statistical traffic forecasts modelling future passenger arrivals in the elevator system (refer to abstract and col. 3, lines 25-37; wherein based on statistical data and/or statistical forecasts, virtual passenger traffic is generated and used in a simulation that creates events in the virtual passenger traffic, on the basis of which an elevator-specific cost is computed for each call to be allocated);
receiving an indication of at least one elevator call (col. 3, lines, 42-49);
generating, for a fixed parameter, a set of scenarios based on the statistical traffic forecasts (col. 3, lines 3--37; wherein simulation of future persons are generated based on several factors including traffic data and repeated for different decision alternatives);
determining a quality attribute for each candidate allocation policy of a set of candidate allocation policies by simulating, for each candidate allocation policy, the set of scenarios according to the candidate allocation policy in a current elevator call allocation situation in the elevator system (refer to col. 3, lines 42-49, wherein each time a new call is registered, simulation is immediately performed for different elevators and the call is allocated to the one that can serve it at minimum costs);
selecting a candidate allocation policy based on the quality attributes associated with the candidate allocation policies (refer to col. 3, lines 42-49; wherein the call is allocated to an elevator that can serve it at minimum costs); and
allocating the at least one elevator call to at least one elevator in the elevator group according to the selected candidate allocation policy (refer to col. 3, lines 42-49; wherein the call is allocated to an elevator from the group that can service the call at minimum costs);
wherein the quality attribute for each candidate allocation policy comprises an average value, an average of a non-linear utility function, a combination of mean and variance of the intermediate quality factor, a combination of means and variances of the intermediate quality factor, or a percentile of the intermediate quality factors ((see FIG. 1 decision tree; FIG. 4-5 simulation loop, Abstract; col. 1, lines 20-26 and col. 3, lines 42–49).
As to claim 2, Siikonen teaches the method according to claim 1, further comprising: determining, based on the simulating, for each candidate allocation policy, an intermediate quality factor for each scenario of the set of scenarios in the current elevator call allocation situation in the elevator system, wherein determining the quality attribute comprises determining the quality attribute for each candidate allocation policy of the set of candidate allocation policies based on the intermediate quality factors associated with each candidate allocation policy (refer to col. 3, lines 42-49, wherein each time a new call is registered, simulation is immediately performed for different elevators and the call is allocated to the one that can serve it at minimum costs).
As to claim 3, Siikonen teaches the method according to claim 2, wherein the intermediate quality factor comprises at least one of an average waiting time, a sum of waiting times, an average time to destination, a sum of times to destination, energy consumption, a waiting time and time to destination of each passenger, and proportion of long waiting times (refer to col. 1, lines 20-26).
As to claim 5, Siikonen teaches the method according to claim 1, wherein the step of generating, for the fixed parameter, the set of scenarios comprises generating the set of scenarios by randomly sampling the statistical traffic forecasts (refer to col. 4, lines 16-31).
As to claim 6, Siikonen teaches the method according to claim 1, wherein a passenger arrival comprises an arrival timestamp, an origin floor, a destination floor and a passenger batch size (refer to col. 3, lines 25-37).
As to claim 7, Siikonen teaches the method according to claim 1, wherein the candidate allocation policy comprises at least one of the following: allocation of calls from a specific floor at a specific time interval to a specific elevator; change of the candidate allocation policy as a function of time; allocation of calls to elevators depending on the order in which the calls arrive; and change of an elevator associated to a floor (refer to col. 3, lines 11-24).
As to claim 8, Siikonen teaches the method according to claim 1, wherein the elevator system is a destination control system applying immediate call allocation (refer to col. 3, lines 3-8).
As to claim 9, Siikonen teaches the method according to claim 1, wherein the fixed parameter comprises a fixed period of time or a fixed number of passenger arrivals (refer to col. 1, lines 20-26).
As to claim 10, Siikonen teaches an apparatus for elevator call allocations in an elevator group of an elevator system, the apparatus comprising means configured to perform the method of claim 1 (refer to abstract).
As to claim 11, Siikonen teaches an elevator system comprising the apparatus according to claim 10 (refer to abstract).
As to claim 12, Siikonen teaches a computer program embodied on a non-transitory computer readable medium and comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of claim 1 (refer to col. 2, lines 50-67).
As to claim 13, Siikonen teaches the non-transitory computer-readable medium comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of claim 1 (refer to col. 2, lines 50-67).
As to claim 16, Siikonen teaches the method according to claim 2, wherein the step of generating, for the fixed parameter, the set of scenarios comprises generating the set of scenarios by randomly sampling the statistical traffic forecasts (refer to col. 4, lines 16-31).
As to claim 17, Siikonen teaches the method according to claim 3, wherein the step of generating, for the fixed parameter, the set of scenarios comprises generating the set of scenarios by randomly sampling the statistical traffic forecasts (refer to col. 4, lines 16-31).
As to claim 18, Siikonen teaches the method according to claim 1, wherein the step of generating, for the fixed parameter, the set of scenarios comprises generating the set of scenarios by randomly sampling the statistical traffic forecasts (refer to col. 4, lines 16-31).
As to claim 19, Siikonen teaches the method according to claim 2, wherein a passenger arrival comprises an arrival timestamp, an origin floor, a destination floor and a passenger batch size (refer to col. 3, lines 25-37).
As to claim 20, Siikonen teaches the method according to claim 3, wherein a passenger arrival comprises an arrival timestamp, an origin floor, a destination floor and a passenger batch size (refer to col. 3, lines 25-37).
Response to Arguments
Applicant’s argument with respect to the 102 rejection and specifically the newly added limitation (previous claim 4) has been considered but is not persuasive. Under the broadest reasonable interpretation, Siikonen discloses a “quality attribute” via the computed cost (J_L) and expressly selects the “lowest cost on an average” (Abstract; col. 3, lines 42–49; FIGS. 4–5), which reads on the claim’s “average value” option. Siikonen also teaches that simulated calls can be allocated by using known control principles, such as collective control or an ACA algorithm. These are candidate allocation policies. Siikonen further simulates future operation for different decision alternatives (see FIG. 1 decision tree; FIG. 5 simulation loop), producing policy performance measures (costs) that serve as quality attributes for selection. Accordingly, the rejection is maintained.
Examiner Note: It is noted that claim 1 recites “the intermediate quality factor” which was not previously introduced in claim 1 and as such lacks antecedent basis. The examiner previously mistook the limitation of claim 4 as being dependent on claim 2. However, now a correction would be required to promote clarity of the record. The examiner suggests re-writing claim 2 into the independent claim for clarity and continuity.
THIS ACTION IS MADE FINAL. 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAWKI SAIF ISMAIL whose telephone number is (571)272-3985. The examiner can normally be reached M-F 8a.m.-4:30p.m..
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/SHAWKI S ISMAIL/Supervisory Patent Examiner, Art Unit 2837