DETAILED ACTION
The present application is being examined under the pre-AIA first to invent provisions.
This final office action is in response to the amendment filed 7 August 2026.
Claims 1-5, 7-15, and 17-20 are pending. Claims 1 and 12 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 20 August 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-5, 7-15, and 17-20 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-11 are directed toward an apparatus (machine). Claims 12-20 are directed toward a method (process). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
The claim recites in part:
identify a first time point when a traffic speed decreases to reach a congestion state in the first time and a second time point when a traffic volume reaches a saturation state in the second time; identify a correlation between the first time point and the second time point (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to determine a correlation of the traffic speed decrease and traffic volume to perform a judgement (predict) to identify a congestion time point)
predict the congestion time point using at least a portion of the first output data and the second output data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an evaluation using first data (traffic speed data) and second data (traffic volume data) to predict a congestion time)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
a first deep learning device configured to output data using traffic speed data during a first time
a second deep learning device configured to output data using traffic volume data during a second time
The claimed “first deep learning device” and “second deep learning device” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Additionally, “output data using traffic speed data during a first time” and “output data using traffic volume data during a second time” amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
The claim recites the additional element:
a congestion time point prediction model
The “congestion time point prediction model” is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
a first deep learning device configured to output data using traffic speed data during a first time
a second deep learning device configured to output data using traffic volume data during a second time
The claimed “first deep learning device” and “second deep learning device” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Additionally, “output data using traffic speed data during a first time” and “output data using traffic volume data during a second time” amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
The claim recites the additional element:
a congestion time point prediction model
The “congestion time point prediction model” is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
obtain input data determined by performing concatenate calculation of the first output data and the second output data and to predict the congestion time point using the input data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an observation to obtain the first and second output data and performing evaluation to predict congestion time point using the data)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements considered under Step 2B.
Claim 3:
With respect to claim 3, the claim depends upon claim 2. The analysis of claim 2 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
predict a traffic speed up to a specified time in the future using the input data and to predict the congestion time point using the predicted traffic speed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an judgement (prediction) of traffic speed up to a specified time in the future using input data and a judgment (prediction) of a congestion time point)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements considered under Step 2B.
Claim 4:
With respect to claim 4, the claim depends upon claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 3.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
wherein the first time and the second time correspond to a same past time
The first time and second time recited in this limitation reference the “output data using traffic speed data during a first time” and “output data using traffic volume data during a second time” of claim 1, and amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
wherein the first time and the second time correspond to a same past time
The first time and second time recited in this limitation reference the “output data using traffic speed data during a first time” and “output data using traffic volume data during a second time” of claim 1, and amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 5:
With respect to claim 5, the claim depends upon claim 3. The analysis of claim 3 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 3.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
wherein the congestion time point prediction model is configured to update a weight included in the congestion time point prediction model so that a mean squared error is reduced, using the predicted traffic speed
The model is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
wherein the congestion time point prediction model is configured to update a weight included in the congestion time point prediction model so that a mean squared error is reduced, using the predicted traffic speed
The model is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 7:
With respect to claim 7, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
identify traffic volume on a forward road and traffic volume on a corresponding road, wherein the traffic volume on the forward road and the traffic volume on the corresponding road are included in the second output data, and to predict the congestion time point by further using whether each of the identified traffic volume on the forward road and the identified traffic volume on the corresponding road is saturated or is increased or decreased (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to perform a judgement (predict) to identify a congestion time point based on observed data from a forward road and a corresponding road)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements considered under Step 2B.
Claim 8:
With respect to claim 8, the claim depends upon claim 7. The analysis of claim 7 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
identify a first traffic speed at a first time point when a traffic speed decreases to reach a congestion state in the first time and configured to predict that congestion will not occur, when a current traffic speed is a same as the first traffic speed and when current traffic volume on the corresponding road does not reach a saturation state (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to perform a judgement (predict) to identify that a congestion time point will not occur based on observed data from a forward road and a corresponding road)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements considered under Step 2B.
Claim 9:
With respect to claim 9, the claim depends upon claim 7. The analysis of claim 7 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites in part:
identify first traffic volume at a second time point when the traffic volume on the corresponding road reaches a saturation state in the second time and configured to predict that congestion will be resolved, when it is identified that the current traffic value is a same as the first traffic volume and will decrease in the future (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to perform a judgement (predict) to identify a congestion time point will be resolved based on observed data from a forward road and a corresponding road)
Step 2A, Prong 2:
The claim does not recite any additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim does not recite any additional elements considered under Step 2B.
Claim 10:
With respect to claim 10, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 3.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
wherein the congestion time point prediction model is configured to divide the first output data and the second output data into a train set and a test set and configured to perform cross validation, using the train set and the test set
The model is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
wherein the congestion time point prediction model is configured to divide the first output data and the second output data into a train set and a test set and configured to perform cross validation, using the train set and the test set
The model is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 11:
With respect to claim 11, the claim depends upon claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
The claim is directed toward the same abstract idea identified with respect to claim 3.
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
wherein the congestion time point prediction model is configured to determine accuracy by use of at least a portion of the train set as a validation set and configured to perform an early stopping function in an epoch identified as having accuracy of a predetermined value or more the predetermined value
The model is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
wherein the congestion time point prediction model is configured to determine accuracy by use of at least a portion of the train set as a validation set and configured to perform an early stopping function in an epoch identified as having accuracy of a predetermined value or more the predetermined value
The model is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claims 12-15 and 17-20:
With respect to claims 12-15 and 17-20, the claims recite the limitations substantially similar to those in claims 1-3, 5, and 7-10, respectively. The analysis of claims 1-3, 5, and 7-10 is incorporated herein by reference.
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 1, 7-9, 12, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mitra et al. (US 2021/0201669, published 1 July 2021, hereafter Mitra) and further in view of Karri et al. (US 2022/0391712, published 8 December 2022, hereafter Karri) and further in view of Kamarianakis et al. (US 2014/0309977, published 16 October 2014, hereafter Kamarianakis).
As per independent claim 1, Mitra discloses an apparatus of predicting a congestion time point, the apparatus comprising:
a first learning device configured to output first output data using traffic speed data during a first time (Figure 1, item 111; paragraph 0027: Here, a first learning device (machine learning model) is trained with historical traffic data to predict the speed of each vehicle passing through the path and signal a time associated with each of the intersection points along the path)
output data using traffic volume data during a second time (Figure 1, item 105; paragraph 0024: Here, a plurality of sensors are used to monitor and report the number of vehicles entering and exiting the path. Each path may consist of multiple distance segments (Figure 1, item 102) each associated with one or more sensors (Figure 1, 105; paragraph 0024). As a vehicle navigates the path (Figure 1, item 101) it may be determined that the vehicle enters a distance segment, exits the distance segment, and enters a second distance segment along the path; each of these actions is associated with a time and is used to monitor and report the number of vehicles on a distance segment and the larger path)
a congestion time point prediction model configured to:
identify a first time point when a traffic speed decreases to reach a congestion state in a first time (Figure 1, item 111; paragraph 0027: Here, a first deep learning device (machine learning model) is trained with historical traffic data to predict the speed of each vehicle passing through the path and signal a time associated with each of the intersection points along the path)
predict the congestion time point using at least a portion of the first output data and the second output data (Figure 1, item 107; paragraphs 0032-0033: Here, a congestion is detected along the path by the congestion management system based upon the received traffic data including current location of each of the plurality of vehicles, weather conditions across the path, information of any incidents reported on the path, current speed of the plurality of vehicles, current signal condition and signal timing, number of vehicles entering and crossing the path, and number of vehicles waiting at the traffic signal (paragraph 0029). Based upon these factors, an optimal speed and optimal signal time are determined and communicated in an effort to alleviate the congestion. This includes determining analyzing the predicted speed and predicted time values to identify if a congestion time point will occur at an intersection, and based upon the prediction determining an optimal speed for each of the vehicles and an optimal signal time for each of the one or more predetermined intersection points (paragraphs 0031 and 0039-0040))
Mitra fails to specifically disclose a first deep learning device and a second deep learning device configured to output second output data.
However, Karri, which is analogous to the claimed invention because it is directed toward using a machine learning model to predict congestion, discloses a first deep learning device (Figure 5, item 510; paragraphs 0019 and 0067: Here, a first artificial intelligence model is disclosed. This model may be a deep learning model (paragraph 0019)) and a second deep learning device configured to output second output data (paragraph 0070: Here, an artificial intelligence model (second deep learning device (paragraph 0019)) is configured to output contextual data representing traffic conditions including changes in traffic volume). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Karri with Mitra, with a reasonable expectation of success, as it would have allowed with replacing the sensors of Mitra with model prediction of Karri. This would have allowed for predicting future traffic conditions/volume (Karri: paragraph 0070).
Mitra fails to specifically disclose a traffic speed in a congestion state in the first time and a second time point when a traffic volume reaches a saturation state in the second time, to identify a correlation between the first time point and the second time point, and to predict the congestion time point using the identified correlation.
However, Kamarianakis, which is analogous to the claimed invention because it is directed toward measuring traffic data, discloses a traffic speed in a congestion state in the first time and a second time point when a traffic volume reaches a saturation state in the second time, to identify a correlation between the first time point and the second time point, and to predict the congestion time point using the identified correlation (paragraphs 0028 and 0036: Here, a time-series data is identified that corresponds to traffic speed on a road segment. Additionally, alternative time-series data corresponding to traffic volume is obtained. The time-series data and alternate-time series data correspond to the same set of times). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kamarianakis with Mitra-Karri, with a reasonable expectation of success, as it would have allowed for identifying relationships between traffic speed data and traffic volume data (Kamarianakis: paragraph 0036).
As per dependent claim 7, Mitra, Karri, and Kamarianakis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Mitra disclose wherein the congestion time point prediction model is configured to identify traffic volume on a forward road and traffic volume on a corresponding road (paragraph 0027: Here, traffic volume on the current road (path) (Figure 1, item 101) and corresponding roads (intersections) are identified. Specifically, the volume of traffic on the corresponding roads is the “plurality of vehicles 103 passing through the path 101”), wherein the traffic volume on the forward road and the traffic volume on the corresponding road are included in the second output data, and to predict the congestion time point by further using whether each of the identified traffic volume on the forward road and the identified traffic volume on the corresponding road is saturated or is increased or decreased (Figure 1, item 107; paragraphs 0032-0033: Here, a congestion is detected along the path based on sensors indicating the number of vehicles entering/exiting the path and a trained traffic model. Additionally, the trained traffic model may be trained to include the vehicles passing through the path from corresponding roads (intersections). Finally, a predicted optimal speed and optimal signal time for the intersections are determined and communicated in an effort to alleviate the congestion).
As per dependent claim 8, Mitra, Karri, and Kamarianakis disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein. Mitra discloses wherein the congestion time point prediction model is configured to identify a first traffic speed at a first time point when a traffic speed decreases to reach a congestion state in the first time and configured to predict that congestion will not occur, when a current traffic speed is a same as the first traffic speed and when current traffic volume on the corresponding road does not reach saturation (Figure 1, item 107; paragraphs 0032-0033: Here, a congestion is detected along the path based on sensors indicating the number of vehicles entering/exiting the path and a trained traffic model. Additionally, the trained traffic model may be trained to include the vehicles passing through the path from corresponding roads (intersections). Finally, a predicted optimal speed and optimal signal time for the intersections are determined and communicated in an effort to alleviate the congestion. This optimal speed causes a congestion state to not occur based upon the traffic speed and the level of saturation).
As per dependent claim 9, Mitra, Karri, and Kamarianakis disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein. Mitra discloses wherein the congestion time point prediction model is configured to identify first traffic volume at a second time point when the traffic volume on the corresponding road reaches a saturation state in the second time and configured to predict that congestion will be resolved, when it is identified that current traffic volume is a same as the first traffic volume and will decrease in the future (paragraphs 0032-0033: Here, the congestion management system predicts a future congestion time and provides a notification of optimal speed and optimal signal times to alleviate (resolve) the predicted congestion. This is based both upon predicted speeds and the number of vehicles entering and exiting the road (path) (paragraphs 0025 and 0043)).
With respect to claims 12 and 17-19 the claim recites the limitations substantially similar to those in claims 1 and 7-9, respectively. Claims 12 and 17-19 are rejected under similar rationale.
Claims 2-3 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Mitra, Karri, and Kamarianakis and further in view of Ma et al. (US 2022/0092697, filed 24 September 2020, hereafter Ma).
As per dependent claim 2, Mitra, Karri, and Kamarianakis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Mitra discloses wherein the congestion time point prediction model is configured to obtain input data determined by performing evaluation of the first output data and the second output data and to predict the congestion time point using the input data (Figure 1; paragraphs 0024, 0027, and 0032-0033: Here, a congestion is detected along the path and an optimal speed and optimal signal time are determined. This is based upon the obtained predicted speed data (paragraph 0027) and the obtained sensor data indicating traffic volume (paragraph 0024)).
Mitra fails to specifically disclose performing concatenation calculation.
However, Ma, which is analogous to the claimed invention because it is directed toward predicting information using a calculated concatenation vector, discloses performing a concatenation calculation (paragraph 0077: Here, a concatenated vector is calculated for use in generating a prediction).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Ma with Mitra-Karri- Kamarianakis, with a reasonable expectation of success, as it would have allowed for generating a prediction using a concatenated vector representing the data (Ma: paragraph 0077).
As per dependent claim 3, Mitra, Karri, Kamarianakis, and Ma disclose the limitations similar to those in claim 2, and the same rejection is incorporated herein. Mitra further discloses wherein the congestion time point prediction model is configured to predict a traffic speed up to a specified time in the future using the input data and to predict the congestion time point using the predicted traffic speed (Figure 1, item 107; paragraphs 0032-0033: Here, a congestion is detected along the path. This congestion inherently occurs at a specified time in the future. Further, a predicted optimal speed and optimal signal time are determined and communicated in an effort to alleviate the congestion).
As per dependent claim 4, Mitra, Karri, Kamarianakis, and Ma disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Mitra fails to specifically disclose wherein the first time and the second time correspond to a same past time. However, Kamarianakis, which is analogous to the claimed invention because it is directed toward measuring traffic data, discloses wherein the first time and the second time correspond to a same past time (paragraphs 0028 and 0036: Here, a time-series data is identified that corresponds to traffic speed on a road segment. Additionally, alternative time-series data corresponding to traffic volume is obtained. The time-series data and alternate-time series data correspond to the same set of times). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kamarianakis with Mitra-Karri- Kamarianakis-Ma, with a reasonable expectation of success, as it would have allowed for identifying relationships between traffic speed data and traffic volume data (Kamarianakis: paragraph 0036).
With respect to claims 13-14 the claim recites the limitations substantially similar to those in claims 2-3, respectively. Claims 13-14 are rejected under similar rationale.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mitra, Karri, Kamarianakis, and Ma and further in view of Mamo et al. (US 2021/0182704, published 17 June 2021, hereafter Mamo).
As per dependent claim 5, Mitra, Karri, Kamarianakis, and Ma disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Mitra discloses a congestion time point prediction using the predicted traffic speed (Figure 1). However, Mitra fails to specifically disclose wherein the model is configured to update a weight included in the prediction model so that a mean square error is reduced.
However, Mamo, which is analogous to the claimed invention because it is directed toward traffic flow data, discloses wherein the model is configured to update a weight included in the prediction model so that a mean square error is reduced (paragraph 0051: here, the model training engine trains the model using training data and generates predictions. The model iteratively updates the weights until it correctly predicts data above a threshold and/or minimizes a mean squarer error loss). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Mamo with Mitra-Karri-Ma, with a reasonable expectation of success, as it would have allowed for improving the machine learning model (Mamo: paragraph 0051).
With respect to claim 15 the claim recites the limitations substantially similar to those in claim 5. Claim 15 is rejected under similar rationale.
Claims 10-11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mitra, Karri, and Kamarianakis and further in view of Al-Saleh et al. (US 2021/0302606, published 30 September 2021, hereafter Al-Saleh).
As per dependent claim 10, Mitra, Karri, and Kamarianakis disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Mitra fails to specifically disclose wherein the model is configured to divide the first output data and the second output data into a train set and a test set configured to perform cross validation, using the train set and the test set.
However, Al-Saleh, which is analogous to the claimed invention because it is directed toward training a machine learning model, discloses wherein the model is configured to divide the first output data and the second output data into a train set and a test set configured to perform cross validation, using the train set and the test set (paragraphs 0051-0052: Here, a data set is divided into multiple subsets including a training subset and a testing subset). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Al-Saleh with Mitra-Karri-Kamarianakis, with a reasonable expectation of success, as it would have allowed for iteratively training a machine learning model and testing the trained machine learning model (Al-Saleh: paragraphs 0051-0052).
As per dependent claim 11, Mitra, Karri, Kamarianakis, and Al-Saleh disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Al-Saleh further discloses wherein the model is configured to determine accuracy by use of at least a portion of the train set as a validation set and configured to perform an early stopping function in an epoch identified as having accuracy of a predetermined value or more the predetermined value (paragraphs 0051-0052: Here, training may be iteratively performed using epoch until the model achieves a predetermined level of accuracy). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Al-Saleh with Mitra-Karri-Kamarianakis-Al-Saleh, with a reasonable expectation of success, as it would have allowed for better training of the model which may lead to better predictions for updating the model (Al-Saleh: paragraph 0052).
With respect to claim 20 the claim recites the limitations substantially similar to those in claim 10. Claim 20 is rejected under similar rationale.
Response to Arguments
Applicant's arguments filed 7 August 2026 with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant argues that the amendment provides improvements in the technical field of determining the battery life of a vehicle and therefore, the claim is a practical application (pages 7-8). The examiner respectfully disagrees.
As an initial matter, the claim is silent with regard to “determining the battery life of a vehicle (page 8)” and instead is directed toward “predicting a congestion time point (claim 1, line 1).” Further, contrary to the argument, the “identify a first time point when a traffic speed decreases to reach a congestion state in the first time and a second time point when a traffic volume reaches a saturation state in the second state; identify a correlation between the first time point and the second time point (claim 1, lines 8-11)” is part of the mental process (abstract idea). Therefore, here, they do not serve to show integration into a practical application or “significantly more” than the judicial exception itself.
Therefore, there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of identifying/predicting a congestion state based upon speed decreases and traffic volume rather than to an improvement on the functioning of a computer or to any other technology. See MPEP 2106.05(a). Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application. For these reasons, this argument is not persuasive.
Applicant's arguments filed 7 August 2026 with respect to the rejection of claims under 35 USC 103 have been fully considered but they are not persuasive.
The applicant argues that “Mitra, Karri, Ma, Kamarianakis, Mamo, Al-Salah, taken individually or combined, fail to teach or suggest inventive features of the presently claimed invention including identifying a first time point when a traffic speed decreases to reach a congestion state in the first time and a second time point when a traffic volume reaches a saturation state in the second time and identifying a correlation between the first time point and the second time point (pages 8-9).” The examiner respectfully disagrees.
To support this position, the applicant argues that neither Mitra nor Karri teach or suggest “identifying a specific time-lag correlation between the time point and when traffic volume reaches a saturation state and the subsequent time point when traffic speed actually decreases to a congestion point (page 9).” The examiner agrees. However, neither Mitra nor Karri are relied upon for this disclosure. Instead, Kamarianakis, which is analogous to the claimed invention because it is directed toward measuring traffic data, discloses a traffic speed in a congestion state in the first time and a second time point when a traffic volume reaches a saturation state in the second time, to identify a correlation between the first time point and the second time point, and to predict the congestion time point using the identified correlation (paragraphs 0028 and 0036: Here, a time-series data is identified that corresponds to traffic speed on a road segment. Additionally, alternative time-series data corresponding to traffic volume is obtained. The time-series data and alternate-time series data correspond to the same set of times). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kamarianakis with Mitra-Karri, with a reasonable expectation of success, as it would have allowed for identifying relationships between traffic speed data and traffic volume data (Kamarianakis: paragraph 0036). For this reason, this argument is not persuasive.
The applicant further argues that Ma, Kamarianakis, Mamo, Al-Saleh, fail to account for these deficiencies of Mitra and Karri… because the examiner cites only Mitra and Karri, to support such features (page 11).” The examiner respectfully disagrees. Specifically, the amended limitations appear to disclose limitations from cancelled claim 6. The Office Action mailed 7 May 2026 provides a mapping between the Kamarianakis and the limitations on page 28. Specifically, “Kamarianakis… discloses a traffic speed in a congestion state in the first time and a second time point when a traffic volume reaches a saturation state in the second time, to identify a correlation between the first time point and the second time point, and to predict the congestion time point using the identified correlation (paragraphs 0028 and 0036: Here, a time-series data is identified that corresponds to traffic speed on a road segment. Additionally, alternative time-series data corresponding to traffic volume is obtained. The time-series data and alternate-time series data correspond to the same set of times).” For this reason, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ashida et al. (US 2018/0211523): Discloses predicting traffic congestion based upon speed (paragraph 0143) and volume (paragraph 0161)
Baik (US 2020/0135018): Discloses predicting traffic congestion based on volume (paragraph 0027) and speed (paragraph 0033)
Hhoshi et al. (US 2023/0143238): Discloses a prediction model based on traffic value and speed (paragraph 0037)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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