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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/15/2023 is in compliance with provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
The present application claims foreign priority based on European Patent Application No. EP20215790.5, filed on 12/18/2020.
A certified copy of European Patent Application No. EP20215790.5 in English has been received (on 06/15/2023), as required by 37 CFR 1.55.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 26 – 28 are rejected under 35 U.S.C. 101 because the dependent claims of the invention are directed to non-statutory subject matter. The claim(s) does/do not fall withing at least one of the four categories of patent eligible subject matter and are considered “signal per se”, explanation bellow:
Claim 26 recites, a data processing system, which defines a system but the specification does not define said system as including a processor and memory. It is recommended to positively recite the processor and the memory in the claim limitation.
Claim 27 recites, a computer program product comprising computer-executable instructions, which recites instructions to be carried out on a computer but the specification does not define said computer as including a processor and memory. It is recommended to positively recite the processor and the memory in the claim limitation.
Claim 28 recites, a computer readable storage medium, which defines a medium but the specification does not state the medium is non-transitory. It is recommended to positively recite that the medium is non-transitory in the claim limitation.
Claims 15 – 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following sections following the 2019 PEG guidelines for analyzing subject matter eligibility.
The analysis below of the claims’ subject matter eligibility follows the 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”)
and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial
Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined
whether the claim is directed to one of the four statutory categories of invention, 1.e., process,
machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the
statutory categories, the second step in the analysis is to determine whether the claim is directed
to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first
prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception
(e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If
it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis
proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the
claims integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical application, the
analysis proceeds to determining whether the claim is a patent-eligible application of the
exception (Step 2B). If an abstract idea is present in the claim, any element or combination of
elements in the claim must be sufficient to ensure that the claim integrates the judicial exception
into a practical application, or else amounts to significantly more than the abstract idea itself.
Claims 26 - 28 are 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 claims as a whole do not fall within any statutory category and thus is non-statutory, warranting a rejection for failure to claim statutory subject matter.
Claim 15
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
constructing a graph representation of the localities based on spatial relation between the respective localities; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
populating the constructed graph with traffic data characterizing the traffic in the respective localities over consecutive time periods; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
convolving, for a respective locality in the graph representation along a time dimension, the traffic data in the respective locality with the traffic data in its neighboring localities, thereby obtaining relation-based traffic data; (Mathematical Concepts: are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations)
encoding, for the respective localities, the relation-based traffic data into a fixed-length vector and decoding the fixed-length vector into predicted traffic data for future time periods for the respective localities; and (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
using a loss function, estimating a loss between the predicted traffic data and actual traffic data for the respective localities and updating based on the estimated loss the learning model (Mathematical Concepts: are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
by the convolution engine, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
by the encoder-decoder, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
thereby training the learning model to predict traffic data. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
by the convolution engine, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
by the encoder-decoder, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
thereby training the learning model to predict traffic data. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to a method of decision making to plan a route of travel this is nothing more than selecting input values that fit a certain criteria. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 16 incorporates the rejection of claim 15.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of
claim 15 above. Regarding the method steps recited in claim 15, these steps cover mental processes based on creating and modifying a graph based on a criteria.
Therefore, claim 16 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the encoding is performed for a selected time interval of time periods. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the encoding is performed for a selected time interval of time periods. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 17 incorporates the rejection of claim 15.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated.
populating the graph with static traffic data characterizing the respective localities; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
wherein the convolving is also performed for said static traffic data. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical application. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “adding traffic data to the graphs data points” of base claim 15) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., adding traffic data to the graphs data points method of base claim 15) cannot provide an inventive concept. The claim is not patent eligible.
Claim 18 incorporates the rejection of claim 15.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated.
wherein the neighboring localities comprise direct neighboring localities. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical application. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “establishing relations between data points” of base claim 15) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., establishing relations between data points method of base claim 15) cannot provide an inventive concept. The claim is not patent eligible.
Claim 19 incorporates the rejection of claim 18.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 18 are incorporated.
wherein the neighboring localities further comprise indirect neighboring localities. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical application. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “establishing relations between data points” of base claim 18) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., establishing relations between data points method of base claim 18) cannot provide an inventive concept. The claim is not patent eligible.
Claim 20 incorporates the rejection of claim 15.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated.
wherein the traffic data comprises traffic information matched to the respective localities and comprises at least a start time and time duration, a travelled distance, and an average speed. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical application. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “establishing relations between data points” of base claim 15) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., establishing relations between data points method of base claim 15) cannot provide an inventive concept. The claim is not patent eligible.
Claim 21 incorporates the rejection of claim 20.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 20 are incorporated.
wherein the traffic information associated with respective localities is aggregated over a period of time according to the time periods. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical application. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “establishing relations between data points” of base claim 20) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., establishing relations between data points method of base claim 20) cannot provide an inventive concept. The claim is not patent eligible.
Claim 22 incorporates the rejection of claim 20.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 20 are incorporated.
wherein the traffic information is processed to compensate for missing traffic data. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical application. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “establishing relations between data points” of base claim 20) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., establishing relations between data points method of base claim 20) cannot provide an inventive concept. The claim is not patent eligible.
Claim 23 incorporates the rejection of claim 20.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 20 are incorporated. Please see the analysis of
claim 20 above. Regarding the method steps recited in claim 20, these steps cover mental processes based on creating and modifying a graph based on a criteria.
Therefore, claim 23 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the traffic information is obtained from at least one of GPS tracking systems, traffic cameras, inductive-loops traffic detectors, and GSM networks. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the traffic information is obtained from at least one of GPS tracking systems, traffic cameras, inductive-loops traffic detectors, and GSM networks. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
Claim 24 incorporates the rejection of claim 15.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated.
wherein the graph is a directed graph representing a direction of traffic in a respective locality. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical application. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “establishing relations between data points” of base claim 15) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e., establishing relations between data points method of base claim 15) cannot provide an inventive concept. The claim is not patent eligible.
Claim 25 incorporates the rejection of claim 15.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible
matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of
claim 15 above. Regarding the method steps recited in claim 15, these steps cover mental processes based on creating and modifying a graph based on a criteria.
Therefore, claim 25 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the encoder-decoder is a Long Short-Term Memory encoder-decoder. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the encoder-decoder is a Long Short-Term Memory encoder-decoder. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 26 incorporates the rejection of claim 15.
Step 1: Although the claim has been determined to be ineligible subject matter under Step 1, if the claim was amended to become directed toward a statutory category, the claim would be analyzed as such:
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of
claim 15 above. Regarding the method steps recited in claim 15, these steps cover mental processes based on creating and modifying a graph based on a criteria.
Therefore, claim 26 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
A data processing system programmed for carrying out the method according to claim 15. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
A data processing system programmed for carrying out the method according to claim 15. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 27 incorporates the rejection of claim 15.
Step 1: Although the claim has been determined to be ineligible subject matter under Step 1, if the claim was amended to become directed toward a statutory category, the claim would be analyzed as such:
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of
claim 15 above. Regarding the method steps recited in claim 15, these steps cover mental processes based on creating and modifying a graph based on a criteria.
Therefore, claim 27 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
A computer program product comprising computer-executable instructions for causing at least one computer to perform the method according to claim 15 when the program is run on a computer. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
A computer program product comprising computer-executable instructions for causing at least one computer to perform the method according to claim 15 when the program is run on a computer. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
Claim 28 incorporates the rejection of claim 15.
Step 1: Although the claim has been determined to be ineligible subject matter under Step 1, if the claim was amended to become directed toward a statutory category, the claim would be analyzed as such:
Step 2A Prong 1: The judicial exceptions of claim 27 are incorporated. Please see the analysis of
claim 27 above. Regarding the method steps recited in claim 27, these steps cover mental processes based on creating and modifying a graph based on a criteria.
Therefore, claim 28 is directed to an abstract idea – mental processes (i.e., observation and
evaluation/judgment/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
A computer readable storage medium comprising the computer program product according to claim 27. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
A computer readable storage medium comprising the computer program product according to claim 27. (Field of use and technological environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP § 2106.05(h))
The courts have found that generally linking the use of the judicial exceptions to a
particular technological environment or field of use does not qualify as “significantly more”.
(See MPEP § 2106.05(I)(A))
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 15 – 19 and 25 – 28 are rejected under 35 U.S.C. 103 as being unpatentable over Geisberger (US 20140200807 A1) in view of Schmidtler (US 20200349430 A1)
Regarding claim 15, Geisberger teaches constructing a graph representation of the localities based on spatial relation between the respective localities; (See e.g. [0007], The present invention provides a computer-implemented route planning method, comprising: determining source and destination nodes [spatial relation between the respective localities] in a graph data structure based on a route planning query, executing an initial graph search on the graph data structure using graph costs based on real-time traffic data)(See e.g. [0036], Graph generation module 122 [constructing a graph representation] is configured to generate a graph data structure comprising nodes and arcs, based on map data obtained from a map database.)
populating the constructed graph with traffic data characterizing the traffic in the respective localities over consecutive time periods; (See e.g. [0007], executing an initial graph search on the graph data structure using graph costs based on real-time traffic data [traffic data], wherein the initial graph search starts at the source node and settles nodes [populating the constructed graph] until it stops, and computing one or more routes to the destination node from one or more of said settled nodes using precomputed data based on traffic prediction data, thereby to determine a route from the source node to the destination node via one of said settled nodes.) (See e.g. [0070], A route may be calculated from the start location to the destination location via the specified location(s) by carrying out a separate query for each consecutive pair of waypoints [over consecutive time periods] (e.g. from the source to the first via, from the first via to the second, etc).)
convolving, [by the convolution engine], for a respective locality in the graph representation along a time dimension, the traffic data in the respective locality with the traffic data in its neighboring localities, thereby obtaining relation-based traffic data; (See e.g. [0052], the route from source node 304 to destination node 305 thus comprises a path 306 [traffic data in its neighboring localities] computed by the Dijkstra search with live traffic and a path 307 computed using precomputed data with predicted traffic. The path 306 computed by the Dijkstra search extends from the source node to an intermediate node 308 at which the Dijkstra search stopped. The path computed using predicted traffic data extends from the intermediate node 308 [obtaining relation-based traffic data] to the destination node 305.) (See e.g. [0043], Initial graph search module 112 then executes 208 an initial query time graph search [graph representation along a time dimension] on the graph data structure starting at the source node.) (See e.g. [0003], Costs may be assigned to arcs of the graph to apply weights based on the travel time between locations [convolving, for a respective locality in the graph representation along a time dimension], and may take into account the distance between particular locations, the type of road, traffic conditions, and other factors which may affect travel time. Costs can alternatively or additionally be assigned to other parts of the graph, e.g: to nodes or subpaths.)
predicted traffic data (See e.g. [0041], the predicted traffic data is time independent--ie: the predictions do not change at different times. Those skilled in the art cognizant of the present disclosure will appreciate that in some embodiments, the predicted traffic data may alternatively be time-dependent, ie: the data may specify traffic predictions at different times of day and/or different days.)
Geisberger does not teach convolving, by the convolution engine, by the encoder-decoder, encoding, [for the respective localities, the relation-based traffic] data into a fixed-length vector and decoding the fixed-length vector into predicted [traffic] data [for future time periods for the respective localities]; and using a loss function, estimating a loss between the predicted [traffic] data and actual [traffic] data [for the respective localities] and updating based on the estimated loss the learning model thereby training the learning model to predict [traffic] data.
Schmidtler teaches convolving, by the convolution engine, (See e.g. [0045], Other example approaches include tweet2vec, which employs a character-level CNN-LSTM [convolving, by the convolution engine] hybrid encoder-decoder architecture)
by the encoder-decoder, encoding, [for the respective localities, the relation-based traffic] data into a fixed-length vector and decoding the fixed-length vector into predicted [traffic] data [for future time periods for the respective localities]; and (See e.g. [0087], Domain attribute feature extractor 400 uses machine learning encoders to encode domain attribute data that is represented as variable length text as a fixed length feature vector. [encoding, data into a fixed-length vector]) (See e.g. [0097], Each of these generated word encodings may be used as the initial state of the word decoder model to produce a sequence of characters, with the target of regenerating the original domain sequence (shown as the upper layer of the decoder component 580) from only the data contained from the code (i.e., the fixed length feature vector).) (See e.g. [0098], The word decoder model outputs may be provided to a categorical layer 586, such as a component that applies a softmax function to the vector [decoding the fixed-length vector] to predict [predicted data] the character.)
using a loss function, estimating a loss between the predicted [traffic] data and actual [traffic] data [for the respective localities] and updating based on the estimated loss the learning model thereby training the learning model to predict [traffic] data. (See e.g. [0114], the error of each output may be calculated by a loss function such as cross entropy loss or mean squared error between the observed domain profile vector [actual data] for the time and the predicted domain profile vector [predicted data]) (See e.g. [0117], an updated recurrent domain state is produced for time t.sub.i, t.sub.c−1, t.sub.c, which is sequentially updated and fed into a domain-level logistic layer (as illustrated by logistic layer components 606, 614 . . . 626)….For example, logistic layer components 606, 614, 624, 626 may determine the cross-entropy loss according to Eqn. 6 above or otherwise determine the error. [updating based on the estimated loss])
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger and Schmidtler before them, to include Schmidtler’s convolutional neural network and loss function which would allow Geisberger’s model to reduce computation cost and guiding model optimization. One would have been motivated to make such a combination in order to create a stronger prediction on the gathered data, as suggested by Schmidtler (US 20200349430 A1) (0108)
Regarding claim 16, Geisberger and Schmidtler teaches the method of claim 15.
Geisberger does not teach wherein the encoding is performed for a selected time interval of time periods.
Schmidtler teaches wherein the encoding is performed for a selected time interval of time periods. (See e.g. [0086], the domain profile database 372 may comprise one or more of domain activity history (including but not limited to one or more of hosted malicious content, new pages added in a given time interval [selected time interval], traffic volume, and http header information, for example))(See e.g. [0087], In some embodiments, domain attribute feature extractor 400 performs a method of encoding the information present in domain attribute sources (which, in some examples, are text strings) into a concatenated feature vector 450 for a time period (e.g., a feature vector that may be used by a profile prediction trainer (e.g., profile prediction trainer 230) and a domain reputation assignment (e.g., domain reputation assignment model 242) to predict malicious activity that may occur on the domain.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger and Schmidtler before them, to include Schmidtler’s convolutional neural network and loss function which would allow Geisberger’s model to reduce computation cost and guiding model optimization. One would have been motivated to make such a combination in order to create a stronger prediction on the gathered data, as suggested by Schmidtler (US 20200349430 A1) (0108)
Regarding claim 17, Geisberger and Schmidtler teaches the method of claim 15. Geisberger teaches populating the graph with static traffic data characterizing the respective localities; and [wherein the convolving is also performed for said static traffic data.] (See e.g. [0003], Costs may be assigned to arcs of the graph to apply weights based on the travel time between locations, and may take into account the distance between particular locations, the type of road [static traffic data], traffic conditions, and other factors which may affect travel time.)
Geisberger does not teach wherein the convolving is also performed for said static traffic data.
Schmidtler teaches wherein the convolving is also performed for said [static traffic] data. (See e.g. [0044], Other example approaches include tweet2vec, which employs a character-level CNN-LSTM [wherein the convolving is also performed] hybrid encoder-decoder architecture but is limited to sentiment categorization or semantic processing of natural language rather than domain names or related fields found from DNS or certificate queries.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger and Schmidtler before them, to include Schmidtler’s convolutional neural network and loss function which would allow Geisberger’s model to reduce computation cost and guiding model optimization. One would have been motivated to make such a combination in order to create a stronger prediction on the gathered data, as suggested by Schmidtler (US 20200349430 A1) (0108)
Regarding claim 18, Geisberger and Schmidtler teaches the method of claim 15. Geisberger teaches wherein the neighboring localities comprise direct neighboring localities. (See e.g. [Claim 1], determining source and destination nodes in a graph data structure based on a route planning query, wherein the graph data structure represents a road network; executing an initial graph search on the graph data structure using graph costs based on real-time traffic data, wherein the initial graph search starts at the source node and settles nodes [direct neighboring localities] until it stops;)
Regarding claim 19, Geisberger and Schmidtler teaches the method of claim 18. Geisberger teaches wherein the neighboring localities further comprise indirect neighboring localities. (See e.g. [0070], the route planning query may specify one or more "via" locations [indirect neighboring localities.] in addition to the start and destination locations (for example, a user may request a route from Zurich to Berlin via Frankfurt). A route may be calculated from the start location to the destination location via the specified location(s) by carrying out a separate query for each consecutive pair of waypoints (e.g. from the source to the first via, from the first via to the second, etc).)
Regarding claim 25, Geisberger and Schmidtler teaches the method of claim 15.
Geisberger does not teach wherein the encoder-decoder is a Long Short-Term Memory encoder-decoder.
Schmidtler teaches wherein the encoder-decoder is a Long Short-Term Memory encoder-decoder. (See e.g. [0044], Other example approaches include tweet2vec, which employs a character-level CNN-LSTM hybrid encoder-decoder architecture but is limited to sentiment categorization or semantic processing of natural language rather than domain names or related fields found from DNS or certificate queries.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger and Schmidtler before them, to include Schmidtler’s convolutional neural network and loss function which would allow Geisberger’s model to reduce computation cost and guiding model optimization. One would have been motivated to make such a combination in order to create a stronger prediction on the gathered data, as suggested by Schmidtler (US 20200349430 A1) (0108)
Regarding claim 26, Geisberger and Schmidtler teaches the method of claim 15. Geisberger teaches A data processing system programmed for carrying out the method according to claim 15. (See e.g. [0008], The present invention also provides a system, [data processing system] comprising: one or more communication modules for communication with one or more client devices, a precomputation module configured to generate a graph data structure based on map data, and a query processing module configured to determine source and destination nodes in the graph data structure based on a route planning query, wherein the query processing module comprises a first graph search module configured to execute an initial graph search on the graph data structure using graph costs based on real-time traffic data, wherein the initial graph search starts at the source node and settles nodes until it stops)
Regarding claim 27, Geisberger and Schmidtler teaches the method of claim 15. Geisberger teaches A computer program product comprising computer-executable instructions for causing at least one computer to perform the method according to claim 15 when the program is run on a computer. (See e.g. [0021], Examples of client devices 104 are personal computers, [program is run on a computer] digital assistants, personal digital assistants, cellular phones, mobile phones, smart phones or laptop computers.) (See e.g. [0022], In one embodiment, the modules are program code files stored on a storage device, loaded into memory and executed [computer-executable instructions], or can be provided from computer program products, for example computer executable instructions, that are stored in a tangible computer-readable storage medium such as RAM, a hard disk or optical or magnetic media.)
Regarding claim 28, Geisberger and Schmidtler teaches the computer program product of claim 27. Geisberger teaches A computer readable storage medium comprising the computer program product according to claim 27. (See e.g. [0022], In one embodiment, the modules are program code files stored on a storage device, loaded into memory and executed or can be provided from computer program products, for example computer executable instructions, that are stored in a tangible computer-readable storage medium [computer readable storage medium] such as RAM, a hard disk or optical or magnetic media.)
Claims 20, 21 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Geisberger (US 20140200807 A1) in view of Schmidtler (US 20200349430 A1) further in view of McGavran (US 20140278070 A1)
Regarding claim 20, Geisberger and Schmidtler teaches the method of claim 15. Geisberger teaches wherein the traffic data comprises traffic information matched to the respective localities and comprises at least a [start time and time duration, a travelled distance,] and an average speed. (See e.g. [0040], The predicted traffic data may be based on past traffic conditions and could for example be determined by time-averaging data relating to vehicle speeds over a suitably long period of time. [average speed]) (See e.g. [0047], It will be appreciated that in addition to taking into account real time traffic data, the graph costs for the initial graph search may also account for the distance between particular locations, the type of road, and other factors which may affect travel time. [wherein the traffic data comprises traffic information matched to the respective localities])
Geisberger and Schmidtler do not teach start time and time duration, a travelled distance
McGavran teaches start time and time duration, a travelled distance (See e.g. [0197], it generates the travel time expression as "to arrive at 5, you have to leave by 4" [start time] or "you need to leave by 4 if you want to arrive at 5.")(See e.g. [Claim 20], wherein the notification manager provides a notice that a journey to the predicted destination has a particular estimated duration. [time duration]) (See e.g. [0203], in some embodiments, the process identified (at 1410) the distance between the current location and predicted destination [travelled distance], and based on the identified distance value)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger, Schmidtler and McGavran before them, to include McGavran’s time and travel distance which would allow Geisberger and Schmidtler’s model to make stronger recommendations on routes. One would have been motivated to make such a combination in order to improve the accuracy of the data gathering and route recommendations , as suggested by McGavran (US 20140278070 A1) (0034)
Regarding claim 21, Geisberger, Schmidtler and McGavran teaches the method of claim 20.
Geisberger and Schmidtler do not teach wherein the traffic information associated with respective localities is aggregated over a period of time according to the time periods.
McGavran teaches wherein the traffic information associated with respective localities is aggregated over a period of time according to the time periods. (See e.g. [0057], the machine-learning/data mining engine 208 of some embodiments identifies a start time for entering the region and an end time for leaving the region [time periods]. As these regions are identified over several days [aggregated over a period of time] of raw data [traffic information], the machine-learning engine 208 computes the start and end times for a region based on the statistical averages over the sample of raw data that collectively define the region.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger, Schmidtler and McGavran before them, to include McGavran’s time and travel distance which would allow Geisberger and Schmidtler’s model to make stronger recommendations on routes. One would have been motivated to make such a combination in order to improve the accuracy of the data gathering and route recommendations , as suggested by McGavran (US 20140278070 A1) (0034)
Regarding claim 24, Geisberger and Schmidtler teaches the method of claim 15. Geisberger teaches wherein the graph is a directed graph [representing a direction of traffic in a respective locality.] (See e.g. [0048], In the embodiment shown in FIG. 3, the initial graph search comprises a Dijkstra search, and therefore settles nodes in non-decreasing order of the shortest path cost to define a search space 302 which expands outwardly from the source node 304 as the graph search progresses and more nodes are settled. [wherein the graph is a directed graph])
Geisberger and Schmidtler do not teach representing a direction of traffic in a respective locality.
McGavran teaches representing a direction of traffic in a respective locality. (See e.g. [0259], A navigation system provides directions or route information [direction of traffic in a respective locality], which may be displayed to a user.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger, Schmidtler and McGavran before them, to include McGavran’s time and travel distance which would allow Geisberger and Schmidtler’s model to make stronger recommendations on routes. One would have been motivated to make such a combination in order to improve the accuracy of the data gathering and route recommendations , as suggested by McGavran (US 20140278070 A1) (0034)
Claims 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Geisberger (US 20140200807 A1) in view of Schmidtler (US 20200349430 A1) further in view of McGavran (US 20140278070 A1) further in view of Wang (US 20200372322 A1)
Regarding claim 22, Geisberger, Schmidtler and McGavran teaches the method of claim 20.
Geisberger, Schmidtler and McGavran do not teach wherein the traffic information is processed to compensate for missing traffic data.
Wang teaches wherein the traffic information is processed to compensate for missing traffic data. (See e.g. [0071], Data processor 612 may iteratively smooth out the raw traffic data so that any missing traffic data points are temporally interpolated, and duplicated traffic data points are removed)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger, Schmidtler, McGavran and Wang before them, to include Wang’s compensations for missing data and traffic cameras which would allow Geisberger, Schmidtler and McGavran’s model to adjust for lacking data and different forms of data gathering. One would have been motivated to make such a combination in order to improve the accuracy of data gathering and allows for more stable predictions, as suggested by Wang (US 20200372322 A1) (0051)
Regarding claim 23, Geisberger, Schmidtler and McGavran teaches the method of claim 20. Geisberger teaches wherein the traffic information is obtained from at least one of GPS tracking systems, [traffic cameras], inductive-loops traffic detectors, and GSM networks. (See e.g. [0040], For example, a time-average may be taken of data relating to the speed of mobile devices (e.g: mobile phones) participating in a crowd-sourcing scheme [GSM networks] in which GPS [GPS tracking systems] or other location service information is anonymously shared, or a time-average may be taken of traffic flow sensor data from traffic flow sensors at highways [inductive-loops traffic detectors] or other routes.)
Geisberger, Schmidtler and McGavran do not teach traffic cameras
Wang teaches traffic cameras (See e.g. [0054], Historical traffic data 402 may be traffic observations collected by traffic sensors disposed along the link in a centralized way or on a plurality of vehicles (e.g. probe vehicles, GPS-enabled devices, traffic cameras [traffic cameras]) in a distributed way, and transmitted to the system 400 over a wired or wireless communication.)
Accordingly, it would have been obvious to a person having ordinary skill in the art
before the effective filing date of the claimed invention, having the teaching of Geisberger, Schmidtler, McGavran and Wang before them, to include Wang’s compensations for missing data and traffic cameras which would allow Geisberger, Schmidtler and McGavran’s model to adjust for lacking data and different forms of data gathering. One would have been motivated to make such a combination in order to improve the accuracy of data gathering and allows for more stable predictions, as suggested by Wang (US 20200372322 A1) (0051)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ALLMAN THOMPSON whose telephone number is (571)272-3671. The examiner can normally be reached Monday - Thursday, 6 a.m. - 3 p.m. ET..
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/K.A.T./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125