DETAILED ACTION
1. This communication is in response to the Application No. 18/136,285 filed on April 18, 2023 in which Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. 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
3. The information disclosure statements submitted on 07/18/2023 and 08/20/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent Claims 1, 8, and 15 recite the terms “dray input” and “dray output” – however, it is not clear, nor defined by the claim language, what comprises such a “dray input” and “dray output” and one of ordinary skill in the art would not readily understand the metes and bounds of the invention given these terms, in context of the claim limitations. Examiner notes that the term “dray” or “drayage”, within the art of cargo/shipping, refers to short-distance transportation of shipping containers, usually by a truck – this is similarly supported by Applicant’s specification Par. [0071]. However, it is not clear what comprises a “dray input” and “dray output” where each of the location nodes may include a dray input/output (i.e., does the dray input correspond to the contents of the actual shipment that has arrived at a destination and the dray output corresponds to the contents of the actual shipment that has departed to a destination or vice versa, does the dray input correspond to the destination/location the shipment is arriving to and the dray output corresponds to the destination/location that the shipment is departing to or vice versa, does the dray input/output correspond to different data points/features that represent the dray itself, etc.) As such, Claims 1-20 are rendered indefinite. For the purposes of examination, Examiner interprets the term “dray input” as a starting point for a drayage load (i.e., ocean port, intermodal facility, port of entry, etc.) and “dray output” as an ending point for a drayage load (i.e., rail ramps, warehouses, etc.) – this is also disclosed by the Nickerson reference (US PG-PUB 20190236165), per the 35 U.S.C. 103 rejection below.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the “computer-readable medium” encompasses signals per se. Applicant’s specification Par. [0053] describes the “computer-readable medium”, but does not explicitly limit the term “computer-readable medium” to being only non-transitory or excluding transitory signals per se. Further, the only recitation of the term “non-transitory” appears in Par. [0113] of Applicant’s specification and simply refers to an example of how code may be embodied on a “non-transitory machine-readable medium” – but again, this does not limit the term “computer-readable medium”, as recited in Claims 15-20, to being only non-transitory or excluding transitory signals per se. A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. See MPEP 2106.03(II). It is suggested that Claims 15-20 be amended to recite a “non-transitory computer-readable medium” to overcome this rejection.
8. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a method type claim. Therefore, Claims 1-7 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
[…] predict target outcomes corresponding to a cargo shipment (mental process – other than reciting “using machine learning”, predicting target outcomes corresponding to a cargo shipment may be performed manually by a user observing/analyzing a plurality of features related to the cargo shipment (such as origin, destination, and shipment events) and accordingly using judgement/evaluation to cast a prediction regarding target outcomes (such as net transit time, net transit cost, net emissions) based on said analysis)
constructing […] a plurality of location nodes, wherein each of the location nodes corresponds to a respective location type, wherein each of the location nodes corresponds to a respective real-world location, and wherein each of the location nodes includes a mode input, a dray input, a mode output, and a dray output (mental process – other than reciting “via one or more processors”, constructing a plurality of location nodes may be performed manually by a user constructing a plurality of nodes of a graph with the aid of pen and paper, where each node corresponds to a respective real-world location, location type, and includes a mode input, dray input, mode output, and dray output. For example, a user may construct the plurality of location nodes as illustrated by Applicant’s Figure 4C, with the aid of pen and paper)
adding […] the plurality of location nodes to the graph (mental process – other than reciting “via one or more processors”, adding the plurality of location nodes to the graph may be performed manually by a user observing/analyzing the plurality of location nodes and accordingly using judgement/evaluation to add the plurality of location nodes to the graph with the aid of pen and paper – See Applicant’s Figures 4C & 4D for examples)
processing […] the shipment events using the graph to determine one or more target outcomes corresponding to the cargo shipment (mental process – other than reciting “via one or more processors”, processing the shipment events using the graph to determine one or more target outcomes may be performed manually by a user observing/analyzing the shipment events and graph and accordingly using judgement/evaluation to process the shipment events using the graph (by tracking the event through the graph, with the aid of pen and paper) which may enable the user to determine one or more target outcomes (i.e., net transit time per instant claim 4) corresponding to the cargo shipment)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a computer-implemented method of using machine learning to construct and train a graph […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
[…] via one or more processors […] (recited at a high-level of generality (i.e., as a generic one or more processors configured to perform the specific operations of claim 1) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
receiving, via one or more processors, an origin input parameter, a destination input parameter, and one or more shipment events corresponding to the cargo shipment (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a computer-implemented method of using machine learning to construct and train a graph […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
[…] via one or more processors […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
receiving, via one or more processors, an origin input parameter, a destination input parameter, and one or more shipment events corresponding to the cargo shipment (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-7. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2 & Step 2B:
wherein each respective location type is selected from the group consisting of (i) seaport, (ii) railyard and (iii) airport (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the location types does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 3:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 3 depends on.
Step 2A Prong 2 & Step 2B:
wherein the one or more shipment events include at least one of (i) a transit event, (ii) a dwell event or (iii) a dray event (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the types of shipment events does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 4:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 4 depends on.
Step 2A Prong 2 & Step 2B:
wherein the target outcomes include at least one of (i) a net transit time of the cargo shipment, (ii) a net transit cost of the cargo shipment, (iii) a net emissions measure of the cargo shipment (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the target outcomes does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 5:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2 & Step 2B:
training, via one or more processors, a machine learning model using historical data to predict information related to at least one segment of the cargo shipment (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 - see MPEP 2106.05(f) – Examiner' s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 6 depends on.
dynamically generating a node representing an origin of the trucking segment (mental process – generating a node representing an origin of the trucking segment may be performed manually by a user observing/analyzing the trucking segment and accordingly using judgement/evaluation to generate a node representing an origin of the trucking segment – see for example, Applicant’s Figures 4C & 4D)
adding the dynamically-generated node to the graph (mental process – adding the generated node to the graph may be performed manually by a user observing/analyzing the generated node and accordingly using judgement/evaluation to add the node to the graph – see for example, Applicant’s Figures 4C & 4D)
selecting one or more candidate nodes (mental process – selecting one or more candidate nodes may be performed manually by a user observing/analyzing the set of nodes and accordingly using judgement/evaluation to select one or more candidate nodes to connect the generated node to, based on said analysis)
dynamically connecting the dynamically-generated node to each of the one or more candidate nodes (mental process – connecting the generated node to each of the one or more candidate nodes may be performed manually by a user connecting the nodes of the graph, with the aid of pen and paper – see for example, Applicant’s Figures 4C & 4D)
Step 2A Prong 2 & Step 2B:
wherein the cargo shipment includes at least one trucking segment (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the cargo shipment includes at least one trucking segment does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 7 depends on.
determining the one or more target outcomes based on one or more optimization metrics and/or one or more optimization constraints (mental process – determining the one or more target outcomes based on one or more optimization metrics/constraints may be performed manually by a user observing/analyzing the one or more optimization metrics/constraints and accordingly using judgement/evaluation to determine one or more target outcomes (such as net transit time, net transit cost, net emissions, etc.) based on said analysis)
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Independent Claim 8 recites substantially the same limitations as Claim 1, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 8 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 9-14. The additional limitations of the dependent claims are addressed below.
Claim 9 recites substantially the same limitations as Claim 2, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 10 recites substantially the same limitations as Claim 3, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 11 recites substantially the same limitations as Claim 4, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 12 recites substantially the same limitations as Claim 5, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 13 recites substantially the same limitations as Claim 6, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 14 recites substantially the same limitations as Claim 7, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Independent Claim 15 recites substantially the same limitations as Claim 1, in the form of a computer-readable medium, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 15 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 16-20. The additional limitations of the dependent claims are addressed below.
Claim 16 recites substantially the same limitations as Claim 2, in the form of a computer-readable medium, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 17 recites substantially the same limitations as Claim 3, in the form of a computer-readable medium, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 18 recites substantially the same limitations as Claim 5, in the form of a computer-readable medium, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 19 recites substantially the same limitations as Claim 6, in the form of a computer-readable medium, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 7, in the form of a computer-readable medium, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim Rejections - 35 USC § 103
9. 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.
10. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Balster et al. (hereinafter Balster) (“An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning”), in view of Nickerson et al. (hereinafter Nickerson) (US PG-PUB 20190236165).
Regarding Claim 1, Balster teaches a computer-implemented method of using machine learning to construct and train a graph to predict target outcomes corresponding to a cargo shipment (Balster, Pg. 1, “The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail.”, therefore, methods of using machine learning to construct a graph (See Balster Figures 7 which depicts an intermodal freight transport network/IFTN & Balster Pg. 7 Section 4.3 which describes how the machine learning model & graph may be trained) to predict target outcomes (estimated time of arrival/ETA) corresponding to a cargo shipment), the method comprising:
constructing, via one or more processors (Balster discloses computer-implemented methods including training a machine learning model, See Balster Pg. 7 Section 4.3. However, Nickerson is introduced below for the explicit recitation of one or more processors), a plurality of location nodes, wherein each of the location nodes corresponds to a respective location type (Balster, Pg. 4, Figure 1 which depicts one or more location/logistic nodes that are constructed (including Inland terminals 1 & 2, Port of Hamburg, Marshaling yard, etc.), wherein each of the location nodes corresponds to a respective location type (Inland terminals, seaports, etc.). The different types of location nodes are also disclosed by Figure 2 on Pg. 4. Further, it is mentioned on Pg. 3 Section 2 that intermodal freight transport networks (IFTNs) usually comprise different means of transport, such as truck (road), train (rail), plane (air), or ship (river, sea)),
wherein each of the location nodes corresponds to a respective real-world location (Balster, Pg. 3, “Based on the insights from the interviews, two main relations were identified for developing the heuristic solution. Two inland terminals, one in the south and one in the east of Germany, as well as the port of Hamburg in the north, were chosen (Fig. 1). These two terminals are an essential part of important transport routes connecting the Eastern respectively Southern European hinterland with the major ports in Northern Germany, Belgium and the Netherlands.”, thus, each of the location nodes corresponds to a respective real-world location – also shown by Pg. 4 Figure 1), and
wherein each of the location nodes includes a mode input (Balster, Pg. 3, “IFTNs usually consist of several distinct transport legs using multiple means of transportation that can include transport via truck (road), train (rail), plane (air) or ship (river, sea). The different natures of these modes of transport necessitate the existence of processing facilities within the IFTN. During a shift from road to rail transport, for example, the containers have to be transferred from trucks to trains.”, therefore, each location node would include a mode input, comprising a mode of transportation used to transfer the shipment into the location node – this is similarly supported by Figure 7 on Pg. 9 which depicts how each processing node has a mode input (for example, the transport arrow from the starting point to transition 1, or the transport arrow from transition 1 to transition 2, etc.), a dray input (See introduction of Nickerson reference below for teaching of a dray input), a mode output (Balster, Pg. 3, “IFTNs usually consist of several distinct transport legs using multiple means of transportation that can include transport via truck (road), train (rail), plane (air) or ship (river, sea). The different natures of these modes of transport necessitate the existence of processing facilities within the IFTN. During a shift from road to rail transport, for example, the containers have to be transferred from trucks to trains.”, therefore, each location node would include a mode input, comprising a mode of transportation used to transfer the shipment out of the location node – this is similarly supported by Figure 7 on Pg. 9 which depicts how each processing node has a mode output (for example, the transport arrow from transition 2 to transition 3, or the transport arrow from transition 3 to the destination, etc.), and a dray output (See introduction of Nickerson reference below for teaching of a dray output);
adding, via one or more processors, the plurality of location nodes to the graph (Balster, Pg. 3, “s. Consequently, the IFTN consists of transportation between nodes that can be conducted by either trains or trucks and of transfer and reallocation processes at the terminal nodes. In addition to terminal nodes, the IFTN includes starting nodes and destination nodes.”, therefore, the plurality of location nodes (for example, as illustrated by Figure 1) may be added to the intermodal freight transport network (IFTN) graph, as shown by exemplary Figure 7 on Pg. 9);
receiving, via one or more processors, an origin input parameter, a destination input parameter, and one or more shipment events corresponding to the cargo shipment (Balster, Pg. 3, “To reduce the complexity and to make specific and accurate predictions for each actor, the overall ETA prediction was divided into subproblems covering the individual legs of the intermodal transport chain, and an appropriate ML method was identified for each leg. […] As the data allow for the identification of single containers and for assignment to trucks and wagons on a train, all predictions can be transferred to subsequent legs of the transport chain, thus acting as inputs for the following predictions. Therefore, all the individual predictions can be combined into an overall ETA prediction that covers the entire intermodal transport chain from the origin to the final destination.”, therefore, an origin input, destination input, and one or more shipment events may be received); and
processing, via one or more processors, the shipment events using the graph to determine one or more target outcomes corresponding to the cargo shipment (Balster, Pgs. 9-10, Figures 7-9 which depict multiple IFTN’s which may be used to process different shipment events to determine one or more target outcomes (estimated time of arrival & travel and arrival times for the different transport legs) corresponding to the cargo shipment).
Balster does not explicitly disclose:
one or more processors
However, Nickerson teaches:
one or more processors (Nickerson, Par. [0183], “the instructions, when executed by one or more processing devices (for example, processor 552), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 564, the expansion memory 574, or memory on the processor 552).”, therefore, one or more processors are disclosed to perform computer-implemented methods)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of using machine learning to construct and train a graph to predict target outcomes corresponding to a cargo shipment, as disclosed by Balster to include one or more processors and memory to store instructions, as disclosed by Nickerson. One of ordinary skill in the art would have been motivated to make this modification to enable the efficient implementation of the computer-implemented method, such that the method may be implemented by a memory for storing computer readable instructions and be coupled to one or more processors capable of receiving/transmitting data and instructions (Nickerson, Par. [0187], “ These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.”)
Balster does not explicitly disclose:
a dray input, a dray output
However, Nickerson teaches:
a dray input (Balster, Par. [0161], “The starting point for a drayage load is typically an ocean port, an intermodal facility, or a port of entry, and is likely the same each day for a given driver.”, therefore, the dray input comprises a starting point for a drayage load), a dray output (Balster, Par. [0161], “Ending points are typically relatively nearby rail ramps, warehouses, or plants.”, thus, the dray output comprises an ending point for a drayage load)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer-implemented method of using machine learning to construct and train a graph to predict target outcomes corresponding to a cargo shipment, as disclosed by Balster in view of Nickerson to include a dray input and dray output, as disclosed by Nickerson. One of ordinary skill in the art would have been motivated to make this modification to enable the use of a dray input and dray output, which may provide valuable routing information regarding intermodal transportation/cargo shipment, hence improving accuracy of determining target outcomes, such as estimated time of arrival and/or transit time (Nickerson, Par. [0140], “The drayage business generally involves moving containers relatively short distances starting from ocean ports, intermodal facility, or port of entry and ending at relatively nearby rail ramps, warehouses, or plants. The driver may have one or several regular routes. Drayage drivers are principally identified by the starting points of their trips. For example, the companies of the drivers can be identified by stops at company facilities where vehicles may be stored.”).
Regarding Claim 2, Balster in view of Nickerson teaches the computer-implemented method of claim 1, wherein each respective location type is selected from the group consisting of (i) seaport, (ii) railyard and (iii) airport (Balster, Pg. 3, “IFTNs usually consist of several distinct transport legs using multiple means of transportation that can include transport via truck (road), train (rail), plane (air) or ship (river, sea). The different natures of these modes of transport necessitate the existence of processing facilities within the IFTN.”, thus, the location types may comprise a seaport, railyard, and airport).
Regarding Claim 3, Balster in view of Nickerson teaches the computer-implemented method of claim 1, wherein the one or more shipment events include at least one of (i) a transit event, (ii) a dwell event or (iii) a dray event (Balster, Pg. 7, “Artificial intelligence in the form of ML was employed to produce algorithms to draw insights from historical data sets without having to determine the individual relationships of the underlying system. Since data on actual transport times, processing times and connections reached are available, supervised learning can be used, which makes it possible to forecast future transports accurately on the basis of already realized transports. Thus, ETA prediction can be performed without modeling every detail of the complex intermodal transport network.”, thus, the shipment events include at least one of a transit/transport event).
Regarding Claim 4, Balster in view of Nickerson teaches the computer-implemented method of claim 1, wherein the target outcomes include at least one of (i) a net transit time of the cargo shipment, (ii) a net transit cost of the cargo shipment, (iii) a net emissions measure of the cargo shipment (Balster, Pg. 7, “The prediction model that estimates the transport time on road between the shipper and the inland terminal uses linear regression trees. The lead-time regression in the inland terminal is based on random forests, with which the connecting train is then determined. Random forest and gradient boosting are used to predict the transport time for all rail sections between the individual operating points along the transport route from the inland terminal to the sea terminal. To predict the connecting train in the marshaling yard ordinal forests are used.”, therefore, the target outcome includes at least one of a net transit/transport time of the cargo shipment).
Regarding Claim 5, Balster in view of Nickerson teaches the computer-implemented method of claim 1, wherein processing the events using the graph to determine one or more target outcomes corresponding to the cargo shipment includes:
training, via one or more processors, a machine learning model using historical data to predict information related to at least one segment of the cargo shipment (Balster, Pg. 1, “For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data.”, thus, a machine learning model is trained using historical data to predict information related to at least one segment/transport leg of the cargo shipment).
Regarding Claim 6, Balster in view of Nickerson teaches the computer-implement method of claim 1,
wherein the cargo shipment includes at least one trucking segment (Balster, Pg. 6, “Different results are generated for each transport leg. By being able to assign an intermodal container to a truck or wagon and train, all predictions can be transferred to other reference objects. Travel and arrival times are determined for transport by both truck and train. In the logistics nodes, that is, the inland terminal and marshaling yard, the lead times of containers and departure delays of trains are predicted.”, thus, the cargo shipment may include at least one trucking segment/transport leg), and
wherein processing, via one or more processors, the events using the graph to determine one or more target outcomes corresponding to the cargo shipment includes:
dynamically generating a node representing an origin of the trucking segment; adding the dynamically-generated node to the graph (Balster, Figure 7, which depicts how a node representing an origin (starting point) of a respective segment may be generated and added to the IFTN graph);
selecting one or more candidate nodes; and dynamically connecting the dynamically-generated node to each of the one or more candidate nodes (Balster, Figure 7, which depicts how one or more candidate nodes (transition 1, 2, 3, and destination) may be selected and dynamically connected to the origin/starting point. In the case of a direct-connection, exemplary Figure 8 depicts the selection/direct-connection of the nodes. In the case where the shipment/container misses one of the trains in a transition 1/2/3, exemplary Figure 9 depicts the selection/first-in first-out connection case of the nodes).
Regarding Claim 7, Balster in view of Nickerson teaches the computer-implemented method of claim 1, wherein processing, via one or more processors, the events using the graph to determine one or more target outcomes corresponding to the cargo shipment includes:
determining the one or more target outcomes based on one or more optimization metrics and/or one or more optimization constraints (Balster, Pg. 1, “The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail.”, therefore, the one or more target outcomes (ETA predictions) may be based on one or more optimization metrics (optimizing processing times at individual logistic nodes and transport times)).
Regarding Claim 8, Balster in view of Nickerson teaches a computing system for using machine learning to construct and train a graph to predict target outcomes corresponding to a cargo shipment (Balster, Pg. 1, “The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail.”, therefore, methods of using machine learning to construct a graph (See Balster Figures 7 which depicts an intermodal freight transport network/IFTN & Balster Pg. 7 Section 4.3 which describes how the machine learning model & graph may be trained) to predict target outcomes (estimated time of arrival/ETA) corresponding to a cargo shipment), comprising: one or more processors; and one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors (Nickerson, Par. [0183], “the instructions, when executed by one or more processing devices (for example, processor 552), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 564, the expansion memory 574, or memory on the processor 552).”, therefore, one or more processors and one or more memories are disclosed to perform computer-implemented methods), cause the computing system to: […]
The rest of the claim language in Claim 8 recites substantially the same limitations as Claim 1, in the form of a system, therefore it is rejected under the same rationale.
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Claim 9 recites substantially the same limitations as Claim 2 in the form of a system, therefore it is rejected under the same rationale.
Claim 10 recites substantially the same limitations as Claim 3 in the form of a system, therefore it is rejected under the same rationale.
Claim 11 recites substantially the same limitations as Claim 4 in the form of a system, therefore it is rejected under the same rationale.
Claim 12 recites substantially the same limitations as Claim 5 in the form of a system, therefore it is rejected under the same rationale.
Claim 13 recites substantially the same limitations as Claim 6 in the form of a system, therefore it is rejected under the same rationale.
Claim 14 recites substantially the same limitations as Claim 7 in the form of a system, therefore it is rejected under the same rationale.
Regarding Claim 15, Balster in view of Nickerson teaches a computer-readable medium having stored thereon a set of instructions that, when executed by one or more processors (Nickerson, Par. [0183], “the instructions, when executed by one or more processing devices (for example, processor 552), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 564, the expansion memory 574, or memory on the processor 552).”, therefore, a computer-readable medium having instructions to be executed by one or more processors is disclosed), cause a computer to: […]
The rest of the claim language in Claim 15 recites substantially the same limitations as Claim 1, in the form of a computer-readable medium, therefore it is rejected under the same rationale.
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Claim 16 recites substantially the same limitations as Claim 2 in the form of a computer-readable medium, therefore it is rejected under the same rationale.
Claim 17 recites substantially the same limitations as Claim 3 in the form of a computer-readable medium, therefore it is rejected under the same rationale.
Claim 18 recites substantially the same limitations as Claim 5 in the form of a computer-readable medium, therefore it is rejected under the same rationale.
Claim 19 recites substantially the same limitations as Claim 6 in the form of a computer-readable medium, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 7 in the form of a computer-readable medium, therefore it is rejected under the same rationale.
Conclusion
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123