DETAILED ACTION
This final rejection is responsive to the amendment filed on November 11, 2025. Claims 14-17 are pending, claims 9-13 are canceled. Claim 14 is independent.
Claim rejection of claim 17 under 35 USC §101 is maintained. However, claim rejections of claims 14-17 under 35 USC §101 for being directed to an abstract idea are withdrawn. See sections Claim Rejections – 35 USC §101 and Response to Arguments below.
Claim rejections under 35 USC §103 are updated in light of applicant’s amendments. See sections Claim Rejections – 35 USC §103 and Response to Arguments below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim recites a computer program product comprising a computer program to carry out the computer-implemented method of claim 14. The broadest reasonable interpretation of this would include software, which does not fall under one of the statutory categories for patent eligibility (Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407), see MPEP 2106.03.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Baumann et al. (DE102013204241), hereinafter Baumann, in view of Chang et al. (CN110136438A), hereinafter Chang, in view of Hobohm et al. (US20200327803), hereinafter Hobohm.
Regarding claim 14, Baumann teaches:
A computer-implemented method for determining future switching behavior of a system unit, the system unit being a light signaling unit, the method comprising: (Baumann, paragraph 0001: “The invention relates on the one hand to a method for determining at least one expected switching time of at least one signal group of at least one light signal system and on the other hand to a corresponding device for determining at least one expected switching time of at least one signal group of at least one light signal system.” – a method of determining at least one expected switching time of at least one light signal system is analogous to the method of determining future switching behavior of a light signaling unit.)
ascertaining at least one random sample of a behavior of a sensor unit by sampling from the probability distribution; and (Baumann, paragraph 0063: “The pattern probability MW of a signal program pattern SM of the subset TM is representative, for example, of the respective pattern frequency H of the signal program pattern SM within the subset TM of signal program patterns SM, wherein the sum of all pattern probabilities MW results in one.” – The subset of the pattern probability of a signal program pattern is analogous to a random sample from the probability distribution of the behavior of the sensor unit.)
determining the future switching behavior of the system unit (Baumann, paragraph 0065: “For this purpose, the signal program patterns SM have, for example, pattern switching times. To determine the expected switching time UP, the pattern switching times of the respective signal program patterns SM of the subset TM are weighted with the respective pattern probability MW of the respective signal program patterns SM, for example. The expected switching time UP is representative of the sum of these weighted pattern switching times, for example.” – The expected switching time is analogous to determining the future switching behavior.) and/or at least one associated statistical value with the aid of the trained system model based on the ascertained random sample. (Baumann, paragraph 0066: “In addition to the expected switching time UP of the signal group, a quality value can be determined which is characteristic of an expected accuracy of the expected switching time UP. For this purpose, for example, the standard deviation of the pattern switching times of the respective signal program patterns SM of the subset TM can be determined.” – The quantity value being determined is analogous to the statistical value based on the ascertained random sample.)
Baumann does not explicitly teach:
receiving a configured system model, the system model being a machine learning model for determining a switching behavior of the system unit and the system model being configured by a computer-implemented method, wherein the machine learning model is a rule-based approach selected from the group consisting of a neural network and decision tree, and wherein the method includes the following:
providing at least one training data set with a plurality of known input elements of the system unit, in each case for a specific point in time or time period; wherein
the plurality of known input elements of the system unit includes at least one sensor data set of a sensor unit;
configuring the system model by a machine learning method using the at least one training data set;
receiving a configured sensor model, the sensor model being a machine learning model for determining a behavior of a sensor unit of the light signaling unit and the sensor model being configured by a computer-implemented method which includes the following:
providing at least one training data set with a plurality of known input elements of a sensor unit, in each case for a specific point in time or time period; wherein
the plurality of known input elements of the sensor unit includes at least one sensor data set for a specific point in time;
configuring the sensor model by a machine learning method using the at least one training data set; wherein
the configured sensor model is a probability distribution regarding how the sensor unit will behave in the specific time period;
However, Chang teaches:
receiving a configured system model, the system model being a machine learning model for determining a switching behavior of the system unit and the system model being configured by a computer-implemented method, wherein the machine learning model is a rule-based approach selected from the group consisting of a neural network and decision tree, and wherein the method includes the following: (Chang, page 2, paragraph 10: “Preferably, the method further includes: inputting the predicted time information into a preset traffic light prediction model for prediction, and obtaining the predicted traffic light state information, the method further includes:” and paragraph 13: “The multi-dimensional vector information is input into a convolutional neural network for training, and the preset traffic light prediction model is obtained.” – The convolutional neural network for training is analogous to the rule-based approach consisting of a neural network.)
providing at least one training data set with a plurality of known input elements of the system unit, in each case for a specific point in time or time period; wherein (Chang, page 2, paragraph 11: “Obtain historical time information and corresponding historical traffic light status information;” – The historical time information and corresponding historical traffic light status information is analogous to the training data set with a plurality of known input elements.)
the plurality of known input elements of the system unit includes at least one sensor data set of a sensor unit; (Chang, page 5, paragraph 3: “It should be noted that a data acquisition sensor, such as an infrared sensor, is provided on the navigation device of the currently traveling vehicle, and the vehicle speed information on the reference road in the reference road information can be collected by the infrared sensor, thereby realizing real-time acquisition of the current The driving condition of the road, wherein the vehicle driving state information is the number of vehicles of the current road, distance information of the front and rear vehicles, and the like, thereby obtaining a congestion state of the current road.” – The driving state information is included in the historical time information; this information being taken from a data acquisition sensor is analogous to the known input data including at least one sensor data of a sensor unit.)
configuring the system model by a machine learning method using the at least one training data set; (Chang, page 2, paragraphs 12-13: “Generating the multi-dimensional vector information by using the historical time information and the corresponding historical traffic light state information; The multi-dimensional vector information is input into a convolutional neural network for training, and the preset traffic light prediction model is obtained.”- The multi-dimensional vector information is created using the historical time information and the corresponding historical traffic light state information and is therefore analogous to the training data set. Using this to train a convolutional neural network is analogous to the model being provided as output.)
Chang is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning and traffic control. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Baumann, which already teaches a method for determining future switching behavior but does not explicitly teach a system model for determining the switching behavior of a system unit, to include the teachings of Chang which does teach a system model for determining the switching behavior of a system unit so that “the status of the traffic light are comprehensively processed to obtain an optimal driving route suitable for the user, thereby improving the optimal road selection.” (Chang, page 3, paragraph 12)
Baumann and Chang do not explicitly teach:
receiving a configured sensor model, the sensor model being a machine learning model for determining a behavior of a sensor unit of the light signaling unit and the sensor model being configured by a computer-implemented method which includes the following:
providing at least one training data set with a plurality of known input elements of a sensor unit, in each case for a specific point in time or time period; wherein
the plurality of known input elements of the sensor unit includes at least one sensor data set for a specific point in time;
configuring the sensor model by a machine learning method using the at least one training data set; wherein
the configured sensor model is a probability distribution regarding how the sensor unit will behave in the specific time period;
However, Hobohm teaches:
receiving a configured sensor model, the sensor model being a machine learning model for determining a behavior of a sensor unit of the light signaling unit and the sensor model being configured by a computer-implemented method which includes the following: (Hobohm, paragraph 0074: “The light signal system 303 is provided with traffic data 305. The traffic data 305 comprises detector data from three detectors, which is represented symbolically by three arrows with the reference characters 307, 309 and 311.” And paragraph 0075: “Based upon the traffic data 305, a control device (not shown) of the light signal system 303 determines respectively a signal image for three signal groups 313, 315 and 317. For this purpose, the control device by way of example uses an algorithm.”)
providing at least one training data set with a plurality of known input elements of a sensor unit, in each case for a specific point in time or time period; wherein (Hobohm, paragraph 0081: “The artificial intelligence in this case performs better when it detects various switching behavior and detector data of the light signal system 303 over an ever-increasing period of time, thus in other words is usually trained using data over multiple months”)
the plurality of known input elements of the sensor unit includes at least one sensor data set for a specific point in time; (Hobohm, paragraph 0074: “The traffic data 305 comprises detector data from three detectors, which is represented symbolically by three arrows with the reference characters 307, 309 and 311. Detector data is in particular not simply loop data from induction loops that are embedded in a road and detect motor vehicles but by way of example also pedestrian scanners and also arrival information of buses and trains, so-called public transportation telegrams.”)
configuring the sensor model by a machine learning method using the at least one training data set; wherein (Hobohm, paragraph 0063: “In accordance with a further embodiment, it is provided that an artificial intelligence template or rather the artificial intelligence is trained both based upon the data that the traffic infrastructure element itself uses in order to determine a future signaling as well as based upon the signaling that is determined and signaled by means of the traffic infrastructure element itself, wherein the trained artificial intelligence template is used as the artificial intelligence or rather the artificial intelligence is updated based upon the training.”)
the configured sensor model is a probability distribution regarding how the sensor unit will behave in the specific time period; (Hobohm, paragraph 0050: “The procedures of the artificial intelligence are characterized inter alia by virtue of the fact that using mathematical means they enable the ability to learn and the ability to handle uncertainty and probabilistic information.” And paragraph 0060-0061: “According to one embodiment, it is provided that the predictive model is embodied so as to predict the future signaling of the traffic infrastructure element based upon traffic data, wherein the traffic data is transmitted to the road user via the communications network. In accordance with a further embodiment, it is provided that the predictive model is embodied so as to predict the future signaling of the traffic infrastructure element based upon signaling data that describes a prevailing signaling of the traffic infrastructure element and/or based upon switching time data that describes a prevailing switching time of the traffic infrastructure element, wherein the signaling data and/or the switching time data is transmitted to the road user via the communications network.” – the artificial intelligence handling uncertainty and probabilistic information indicates a probability distribution with the predictive model being based on the input data above, this indicates that the sensor model is a probability distribution regarding how the sensor unit will behave in the specific time period.)
Hobohm is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning and traffic control. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Baumann and Chang, which already teaches a method for determining future switching behavior using a system model but does not explicitly teach creating and using a sensor model, to include the teachings of Hobohm which does teach creating and using a sensor model such that “light signal systems that are controlled as a function of traffic since such light signal systems adapt their green times and red times to a prevailing traffic situation in a flexible manner.” (Hobohm, paragraph 0003)
Regarding claim 15, Baumann, Chang, and Hobohm teach the computer-implemented method of claim 14, as cited above.
Baumann further teaches:
wherein the statistical value is selected from the group consisting of median, mean, and variance. (Baumann, paragraph 0066: “In addition to the expected switching time UP of the signal group, a quality value can be determined which is characteristic of an expected accuracy of the expected switching time UP. For this purpose, for example, the standard deviation of the pattern switching times of the respective signal program patterns SM of the subset TM can be determined. The quality value is representative of the determined standard deviation, for example.” – The standard deviation being an example of the quality value indicates that it is possible for the quality value to be the mean or variance as both are required in the calculation of the standard deviation.)
Regarding claim 17, Baumann, Chang, and Hobohm teach the computer-implemented method of claim 14, as cited above.
Baumann further teaches:
A computer program product comprising a computer program with program code for carrying out the computer-implemented method according to claim 14 when the computer program is executed on a program-controlled device. (Baumann, paragraph 0052: “FIG. 1 shows a flow diagram of a program for determining an expected switching time UP of a signal group of a traffic signal system. The program is preferably stored in a data and program memory of a control device SV. FIG. 1 shows a flow diagram of a program for determining an expected switching time UP of a signal group of a traffic signal system. The program is preferably stored in a data and program memory of a control device SV.”)
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Baumann in view of Chang in view of Hobohm in view of Hahn et al. (US20180157975), hereinafter Hahn.
Regarding claim 16, Baumann, Chang, and Hobohm teach the computer-implemented method of claim 14, as cited above.
Baumann, Chang, and Hobohm do not explicitly teach:
carrying out a step selected from the group consisting of:
outputting a future switching behavior of the system unit and/or at least one associated statistical value on a display unit;
storing the future switching behavior of the system unit and/or at least one associated statistical value in a storage unit; and
communicating the future switching behavior of the system unit and/or at least one associated statistical value to a computing unit.
However, Hahn teaches:
carrying out a step selected from the group consisting of: outputting a future switching behavior of the system unit and/or at least one associated statistical value on a display unit; (Hahn, paragraph 0122: “The signal pattern data corresponding to the forecast signal pattern can be made available for example to motor vehicle manufacturers or motor vehicle suppliers, who then send this data to their motor vehicles, so that this can then be displayed for each vehicle, which can for example be sold or purchased respectively as a convenience function.”)
storing the future switching behavior of the system unit and/or at least one associated statistical value in a storage unit; and (Hahn, paragraph 0121: “This thus means that, in accordance with one form of embodiment, the traffic data and the signal pattern data of the light signal system are sent to a Cloud infrastructure, wherein then the artificial intelligence, which is part of this Cloud infrastructure, is trained in accordance with the statements made above and forecasts a future signal pattern in accordance with the information given here.” – The cloud infrastructure being analogous to a storage unit, where the forecasts are given to the cloud infrastructure is analogous to storing the behavior.)
communicating the future switching behavior of the system unit and/or at least one associated statistical value to a computing unit. (Hahn, paragraph 0047: “In one form of embodiment the method further comprises sending of the signal pattern data corresponding to the forecast signal pattern to a network address over a communication network.”)
Hahn is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Baumann, Chang, and Hobohm, which already teaches the method for determining future switching behavior but does not explicitly teach outputting, storing, or communicating the method, to include the teachings of Hahn which does teach outputting, storing, or communicating the method in order to provide the method to vehicles, cities, and communities. (Hahn, paragraphs 0119 and 0122)
Response to Arguments
Applicant’s arguments on pages 5-7 of Applicant’s Remarks with respect to claim rejections under 35 USC §101 regarding the claims being directed toward an abstract idea have been fully considered and are persuasive. In particular, that the claims as a whole are directed to the practical application of traffic control or safety systems. The rejection under 35 USC §101 of claims 14-16 has been withdrawn. However, claim 17 is directed toward non-statutory subject matter and, therefore, the rejection is maintained. See section Claim Rejections – 35 USC §101 above.
Applicant's arguments regarding claim rejections under 35 USC §103 have been fully considered but they are not persuasive. First, examiner notes that the bulleted listing of how the amended claims differ from Baumann does not fully capture the claim language regarding the first bullet, the claim recites that the machine learning model can be a decision tree OR a neural network, not solely a decision tree. Examiner also notes that applicant is arguing that Baumann does not teach the bulleted features, however, examiner did not rely upon Baumann to teach these features.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The combination of Baumann, Chang, and Hobohm teaches the claimed invention as Baumann teaches the method for determining the switching behavior of the light signal while using the sensor data. Baumann does not teach a separate system model and sensor model, however, Chang teaches the sensor model which is a neural network (one of the choices of the rule-based model presented in the claim language), Baumann and Chang do not teach a separate sensor model, however, this is taught by Hobohm. Since Baumann is already using the sensor data to determine the switching time, incorporating a separate model to present the predicted sensor data adaptable and flexible switching time (Hobohm, paragraph 0003).
Previously, Zhao was relied upon to teach the sensor model, however, the amendment made to specify the sensor unit is “of the light signaling unit” changed the scope of the claim and, therefore, a new rejection was made using Hobohm to teach those limitations.
Applicant has presented arguments for how the prior art does not individually teach all the features of the claimed invention but has failed to present arguments for why the combination, as a whole, does not teach the claimed invention.
Therefore, claims 14-17 are rejected under 35 USC §103. See section Claim Rejections – 35 USC §103 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huo et al. (Tensor-based Cooperative Control for Large Scale Multi-intersection Traffic Signal Using Deep Reinforcement Learning and Imitation Learning)
Wei et al. (A Survey on Traffic Signal Control Methods)
Horvitz et al. (US 20060106530)
Wang et al. (US 20200372322)
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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/J.C.M./Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144