DETAILED ACTION
1. This office action is a response to communication submitted on 05/25/2023.
Information Disclosure Statement
2. The information disclosure statement(s) (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
3. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
4. 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and extra post solution without integrating it into a practical application Claims 1-9 are ineligible. The claim(s) recite(s) “accept one or multiple current elevator requests”;… accept a partial trajectory of a motion of a person”…” process the partial trajectory with a neural network trained to estimate a weighted combination of probability density functions “…”the weighted combination of probability density functions indicates an arrival time distribution “…; “generate a set of possible future requests “and “optimize a schedule of the bank of elevators to serve the one or multiple current elevator requests and the set of possible future request..”. This judicial exception is not integrated into a practical application because these data gathering is an extra post solution without integrating it into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea (organizing human activity and mental processes) without significantly more.
Regarding claims 1, 9 and 17 the claim(s) recite(s) “accept a partial trajectory of a motion of a person moving in an environment serviced by the bank of elevators”, “ obtain a probability of a future elevator request; process the partial trajectory with a neural network trained to estimate a weighted combination of probability density functions”, and “ generate a set of possible future requests jointly representing the probability of the future elevator request and the arrival time distribution; optimize a schedule of the bank of elevators to serve the one or multiple current elevator requests and the set of possible future requests”. The acquire of the data information as to accept obtain said data could be performed by mental processes, and generate a set of possible future requests and optimize a schedule is merely organizing human activity. However, these limitations constitute mental processes, which are recognized judicial exceptions. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); MPEP 2106.04.
The claim further recites additional elements, such as “a processor” and “accept one or multiple current elevator requests for service by the bank of elevators; accept a partial trajectory of a motion of a person moving in an environment serviced by the bank of elevators” to perform the abstract tasks; however, these additional elements are not sufficient to amount to significantly more than the judicial exception because:
The “processor” is recited at a high level of generality and serves as a generic computing device for implementing the abstract idea;
the “processor” is a well-known device for collecting image data, therefore, it is no more than using well-known generic hardware as a tool to collect data. The courts have held that utilizing well-known and conventional tool to perform abstract tasks do not supply “significantly more”.
The claims recites “accept one or multiple current elevator requests”;… accept a partial trajectory of a motion of a person”…” process the partial trajectory with a neural network trained to estimate a weighted combination of probability density functions “…”he weighted combination of probability density functions indicates an arrival time distribution “…; “generate a set of possible future requests “and “optimize a schedule of the bank of elevators to serve the one or multiple current elevator requests and the set of possible future request..”, These all are all mental steps as evident from the disclosure. The grouping of "mathematical concepts" in the 2019 PEG is not limited to formulas or equations, and in fact specifically includes "mathematical calculations" as an exemplar of a mathematical concept. 2019 PEG Section I, 84 Fed. Reg. at 52. Thus, limitation recites a concept that falls into the "mathematical concept"
Accordingly, the claim does not integrate the abstract idea into a practical application. See Enfish, LLC v. Microsoft Corp., 822 F. 3d 1327 (Fed. Cir. 2016) (improvement to computer architecture); Diehr, 450 U.S. 175 (1981) (transformation); MPEP 2106.05.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For purposes of office action, the claims will be interpreted as controlling the arrival of the elevators based on possibilities gathered by historic user traffic data and training functions from stored and new data gathering.
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9 and 17, recites “optimize a schedule of the bank of elevators to serve the one or multiple current elevator requests and the set of possible future requests”; and “control the bank of elevators according to the schedule”, however, it is unclear and vague what action or type of optimization is executed to the schedule so as to the bank of elevators and what type of control is performed. Control what of “the bank of elevators”.
For purposes of office action, the claims will be interpreted as controlling the arrival of the elevators based on possibilities gathered by historic user traffic data and training functions from stored and new data gathering.
Claim Rejections – 35 USC § 102
5. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
(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.
6. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) or 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al. (US 20220019948 A1) or in alternative rejected under 35 U.S.C. 102(a)(1) or 35 U.S.C. 102(a)(2) as being anticipated by YOSHIKAWA et al. (CN 1939830).
In regards to claims 1-20 and best understood based on rejection under 101 and 112 stated above, Zhang discloses and shows (Figs. 1a to 4) a control system for controlling motion of a bank of elevators (109), comprising:
at least one processor (101/137, Figs. 1a-1b); and
a memory (139) having instructions stored thereon that cause the at least one processor (par. 71) of the control system to:
accept one or multiple current elevator requests for service by the bank of elevators (pars. 21, 59, 69);
accept a partial trajectory (i.e. 113) of a motion of a person moving in an environment serviced by the bank of elevators (pars. 20-30, see Figs. 2);
obtain a probability of a future elevator request (see Fig. 2E, pars. 53-56, 60-65);
process the partial trajectory with a neural network (i.e. 103) trained to estimate a weighted combination of probability density functions, wherein the weighted combination of probability density functions indicates an arrival time distribution of the person arriving to the bank of elevators (109) by one of multiple paths in the environment (pars. 57, 60-64, 72, 137-145);
generate a set of possible future requests (i.e. 115/119) jointly representing the probability of the future elevator request and the arrival time distribution (pars. 51-63, see Figs. 3-4);
optimize a schedule (i.e. by 105/141) of the bank of elevators (109) to serve the one or multiple current elevator requests and the set of possible future requests (see Fig. 3, pars. 51-55, 65-72, 95, 105, 107, 115, 120); and
control the bank of elevators (109) according to the schedule (Fig. 4, pars. 63, 66, 73, 83, 86-87, 123, 129, 135, 181-183, 189)
In regards to claims 1-20 and best understood based on rejection under 101 and 112 stated above, YOSHIKAWA discloses and shows (Figs. 1-53) a control system for controlling motion of a bank of elevators (i.e. elevator car 32A, 32B, 32C of N control station operation), comprising:
at least one processor (1); and
a memory (i.e. 2) having instructions stored thereon that cause the at least one processor (pars. 34, 147, 204) of the control system to:
accept one or multiple current elevator requests for service by the bank of elevators (pars. 21, 59, 69, 147);
accept a partial trajectory of a motion of a person moving in an environment serviced by the bank of elevators (pars. 147-154, 170-175);
obtain a probability of a future elevator request (pars. 147, 168-172, 184-187, 200, 203, 217);
process the partial trajectory with a neural network trained to estimate a weighted combination of probability density functions, wherein the weighted combination of probability density functions indicates an arrival time distribution of the person arriving to the bank of elevators (32s) by one of multiple paths in the environment (i.e. setting weighting coefficient part 8, sets the weighting coefficient WTs according to the corresponding time point, pars. 34, 13, 27-34, 38-70, 91-204, see Figs. 46-47);
generate a set of possible future requests (i.e. call) jointly representing the probability of the future elevator request and the arrival time distribution (pars. 4, 29, 50, 74-89, 102, 108, 110-112, 126, 134-136, 142-147, 171, 200);
optimize a schedule of the bank of elevators (32s) to serve the one or multiple current elevator requests and the set of possible future requests (pars. 30-34, 59, 62, 66, 92-93, 96-98, 105, 107-109, 113, 127, 144, Figs. 19-22, “predicted arrival schedule chart”); and
control the bank of elevators (32s) according to the schedule (pars. 22-24, 43, 92, 108 i.e. judge whether to accept the elevator hall calling or calling of elevator cage, setting the driving time of one week for each elevator.)
Claims 2-8, 10-16 and 18-20 does not set other patentable weight other than claims 1, 9 and 17, and based on the interpretation given after rejection under 101 and 112. i.e. Zhang clearly shows controlling motion of elevators of a bank of elevators (109) uses a neural network trained (par. 19, i.e. Such a destination predictor is implemented based on deep learning-based approaches (Deep neural network (DNN)). The deep learning-based methods have an ability to be adapted to non-language applications, such as destination/trajectory prediction. Examples of the DNN include, but are not limited to, Recurrent Neural Networks (RNN), Long short-term memory (LSTM) based neural network, Bi-LSTM based neural network, transformer architecture based neural network) for an extended destination prediction of a person based on a partial trajectory of the person to produce a multinomial of the extended destination prediction…. configured to draw samples from the multinomial distribution determined for the tessellation element associated with the area serviced by the bank of elevators to produce multiple combinations of times of request, where a frequency of occurrence of a specific time instance in the multiple combinations of times of request is a probability of future request for the elevator service at the specific time interval having a value in the multinomial at an intersection of the specific time interval and the tessellation element associated with the area serviced by the bank of elevators. The actual time of the request is assumed to be the beginning or midpoint of the time interval of the multinomial distribution. The processor is further configured to combine each of the multiple combinations of times of request with the current request to produce multiple continuation sets; and determine the schedule of the bank of elevators to optimize a metric of performance for at least some passengers in combination of all of the continuation sets (see entire Description and Drawings).
YOSHIKAWA also discloses a system for supporting a reduction in average waiting time of an elevator group management system, and an evaluation on group management control performance. A forecasted trajectory of each elevator within a predetermined time from a current time point is found and displayed. Also, an evaluation value is calculated for a forecasted interval with respect to a target interval between respective elevators in a predetermined time from the evaluation value, and an elevator is allocated to a generated hall call based on the evaluation value such that the forecasted interval comes closer to the target interval. By displaying the forecasted trajectory of each elevator, it is possible to support the evaluation on the performance of the group management system through clarification of the reason for allocation. Also, the control performance of the group management system is improved, including a reduction in average waiting time, through allocation control close to an ideal one. .. judging whether to carry out the weighting coefficient optimal answer search (ST001). This step uses off-line simulation the weighting coefficient for searching processing step. For example, The terminal microprocessor and computer calculation processing device load condition such as day or night and is representing time information, determines whether or not to perform weighting coefficient optimal answer retrieval. if it is determined to search the best answer (ST002) then searching the weighting coefficients for extracting the human traffic patterns. as the retrieval method, it can use all values (all values in a certain range) to the weighting coefficient for searching method or branch defining method, steepest descent method, neural network search method, genetic operation search method. after performing the weighting coefficient optimal answer retrieval (including occasions of not searching the best answer and stopped halfway), or when not performing the search, input information from the input information memory part (1 picture 2) (ST003). after obtaining the input information, checking whether the elevator car allocation processing.
Related Prior Arts
7. The following related prior arts made of record are considered pertinent to applicant’s disclosure to further show the general state of the art and may be applied alone or in combination for rejection of the claims.
(JP 2664766 B2) discloses a hall call allocation control of the elevator predicts the occurrence of the hall call from the state of each elevator based on the demand data unique to the building, predicts the schedule for each car, and predicts the arrival time. Expressed as time, each control index on group management performance is modeled by an objective function based on the predicted arrival time, and a weight value for each control index… For each control index parameter P (k .sub.1p , k .sub.2p , k .sub.3p ,..., K .sub.mp ) determined for an arbitrary combination P, a signal in the positive direction is input to the neural network B10 together with the demand parameter in the current time zone. calculating the hall call response time accumulating sequence T .sub.p by the transmission process. (Step S2, S3).
KIM (KR 20180043928 A) discloses method for predicting elevator upcoming calls using a hybrid artificial intelligence learning technique, in which elevator calls occurring in the near future are predicted using an artificial intelligence technique, and the prediction is applied to a military management system, so military is efficiently controlled. To this end, in the method for predicting elevator upcoming calls using a hybrid artificial intelligence learning technique according to the present invention, the process of generating and storing learning data according to call information of an elevator system through the hybrid artificial intelligence includes: formalizing floor/time/call data from a database in which elevator call information is stored; starting the learning upon inputting the formalized floor/time/call data into an artificial neural network; filtering the inputted data by using a fuzzy logic to refine the call data which can be actually utilized; generating (predicted) time series data, which is a learned result, by outputting an artificial neural network value by calculating an error rate with respect to the call data refined through the filtering of the fuzzy logic; and storing the time series data, which is formalized while passing through the artificial neural network and the fuzzy logic, in a learning database.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE L CARRASQUILLO whose telephone number is (571)270-7879. The examiner can normally be reached on Monday to Friday (9am to 5pm).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Eduardo Colon-Santana can be reached on (571) 272-2060. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORGE L CARRASQUILLO/Primary Examiner Engineer, Art Unit 2846