The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 06/23/2026, the following is a Final Office Action for Application No. 19044828.
Status of Claims
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 05/01/2026 and 06/17/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 19044828 filed 02/04/2025 is a Continuation of 18501608, filed 11/03/2023, now U.S. Patent # 12217199 and having 1 RCE-type filing therein.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment. As per the DP rejection, Applicant will hold the rejection in abeyance until claims are determined to be allowable.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The train, processor and/or memory medium is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, train, processor and/or memory medium to inter alia perform the function of generating the optimized operating schedule includes layering together a first time-space network and a second time-space network is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of generating the optimized operating schedule includes layering together a first time-space network and a second time-space network which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: train, processor and/or memory medium. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, train, processor and/or memory medium to inter alia perform the function of generating the optimized operating schedule includes layering together a first time-space network and a second time-space network is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 12217199 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because: referring to MPEP 804 II.B.2. Anticipation Analysis, “The claim under examination is not patentably distinct from the reference claim(s) if the claim under examination is anticipated by the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 1052, 29 USPQ2d 2010, 2015-16 (Fed. Cir. 1993). This type of nonstatutory double patenting situation arises when the claim being examined is, for example, generic to a species or sub-genus claimed in a conflicting patent or application, i.e., the entire scope of the reference claim falls within the scope of the examined claim.” Here, claim under examination is anticipated by the reference claim(s) since the claims recite substantially similar limitations. Particularly, the entire scope of independent claims of Patent US 12217199 B1 falls within the scope of independent claims of the present application.
The following is a mapping of the claims of the Patent against the claims of the present application:
Patent US 12217199 B1
Present Application
Identified differences and rationale as to why that does not amount to a patentable difference.
1. A method of optimizing utilization of resources in a hub, comprising: representing a first flow of units through the hub as a consolidation stream including a plurality of consolidation stages, wherein units flowing through the consolidation stream are consolidated into one or more departing trains based, at least in part, on a destination of each of the units flowing through the consolidation stream; representing a second flow of units through the hub as a deconsolidation stream including a plurality of deconsolidation stages, wherein units flowing through the deconsolidation stream are deconsolidated from one or more arriving trains based, at least in part, on a destination of each of the units flowing through the deconsolidation stream; obtaining first data associated with a first set of units flowing through the consolidation stream over a planning horizon timeframe, wherein the first data includes: a dwell time associated with each unit in the first set of units indicating a duration of time that each unit dwells within the hub, and a prediction of a volume of units in the first set of units using a first prediction model; obtaining second data associated with a second set of units flowing through the deconsolidation stream over the planning horizon timeframe, wherein the second data includes: a volume of units in the second set of units, and a prediction of a dwell time associated with each unit in the second set of units indicating a duration of time that each unit dwells within the hub using a second prediction model; generating, using a dual-stream optimization model, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of thehub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe; andgenerating a signal indicating one or more actions to be performed based on the optimized operating schedule.
1. A method of optimizing utilization of resources in a hub, comprising: obtaining first data associated with a first set of units flowing through a consolidation stream over a planning horizon timeframe, wherein units flowing through the consolidation stream are consolidated into one or more departing trains and the first data includes a prediction of a volume of units in the first set of units using a first prediction model; obtaining second data associated with a second set of units flowing through a deconsolidation stream over the planning horizon timeframe, wherein units flowing through the deconsolidation stream are deconsolidated from one or more arriving trains and the second data includes a prediction of a dwell time associated with each unit in the second set of units using a second prediction model; generating, using a dual-stream optimization model, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe; and sending a control signal to at least one physical resource of the hub to cause the at least one physical resource to physically reposition from a first location to a second location to enable the optimized operating schedule.
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The present application does not include the following from the parent: wherein representing the first flow of units through the hub as a consolidation stream includes generating a first time-space network based on the consolidation stream over a planning horizon timeframe. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
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2. The method of Claim 1, wherein the optimized operating schedule configured to optimize the at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe is configured to maximize the throughput of units processed through the hub over the planning horizon timeframe.
2. The method of claim 1, wherein the optimized operating schedule configured to optimize the at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe is configured to maximize the throughput of units processed through the hub over the planning horizon timeframe.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
3. The method of Claim 1, further comprising: generating a first time-space network based on the consolidation stream, wherein each node of the first time-space network represents a respective stage of the plurality of consolidation stages, and an edge between two nodes of the first time-space network represents a capacity between the two nodes of the first time-space network.
3. The method of claim 1, further comprising: generating a first time-space network based on the consolidation stream, wherein each node of the first time-space network represents a respective stage of a plurality of consolidation stages, and an edge between two nodes of the first time-space network represents a capacity between the two nodes of the first time-space network.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
4. The method of Claim 3, further comprising: generating a second time-space network based on the deconsolidation stream, wherein each node of the second time-space network represents a respective stage of the plurality of deconsolidation stages, and an edge between two nodes of the second time-space network represents a capacity between the two nodes of the second time-space network.
4. The method of claim 3, further comprising: generating a second time-space network based on the deconsolidation stream, wherein each node of the second time-space network represents a respective stage of a plurality of deconsolidation stages, and an edge between two nodes of the second time-space network represents a capacity between the two nodes of the second time-space network.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
5. The method of Claim 1, wherein generating the optimized operating schedule includes:allocating the at least one resource of the hub optimally between the consolidation and deconsolidation streams; and identifying one or more changes for implementing the optimized operating scheduled.
5. The method of claim 1, wherein generating the optimized operating schedule includes: allocating the at least one resource of the hub optimally between the consolidation and deconsolidation streams; and identifying one or more changes for implementing the optimized operating scheduled.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
6. The method of Claim 5, wherein the signal indicating the one or more actions to be performed based on the optimized operating schedule includes an indication to implement the one or more changes.
6. The method of claim 5, wherein the signal indicating the one or more actions to be performed based on the optimized operating schedule includes an indication to implement the one or more changes.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
7. The method of Claim 5, wherein the one or more changes include one or more of:an indication to perform a change to a train schedule to achieve the optimized operating schedule; and an indication that additional resources are required to be added at particular time increment of the planning horizon timeframe to achieve the optimized operating schedule.
7. The method of claim 5, wherein the one or more changes include one or more of an indication to perform a change to a train schedule to achieve the optimized operating schedule; and an indication that additional resources are required to be added at particular time increment of the planning horizon timeframe to achieve the optimized operating schedule.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
8. The method of Claim 1, wherein the optimized operating schedule includes one or more of:an indication of a number of units processed through each stage of the plurality of consolidation stage at each time increment of the planning horizon timeframe; and an indication of a number of units processed through each stage of the plurality of deconsolidation stage at each time increment of the planning horizon timeframe.
8. The method of claim 1, wherein the optimized operating schedule includes one or more of: an indication of a number of units processed through each stage of a plurality of consolidation stage at each time increment of the planning horizon timeframe; and an indication of a number of units processed through each stage of a plurality of deconsolidation stage at each time increment of the planning horizon timeframe.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
9. The method of Claim 1, wherein the at least one resource of the hub includes one or more of:one or more resources for which the consolidation stream and the deconsolidation stream compete during at least one consolidation stage of the plurality of consolidation stages and at least one deconsolidation stage of the plurality of deconsolidation stages; and one or more resources for which the consolidation stream and the deconsolidation stream complement each other during the at least one consolidation stage of the plurality of consolidation stages and the at least one deconsolidation stage of the plurality of deconsolidation stages.
9. The method of claim 1, wherein the at least one resource of the hub includes one or more of:one or more resources for which the consolidation stream and the deconsolidation stream compete during at least one consolidation stage of a plurality of consolidation stages and at least one deconsolidation stage of a plurality of deconsolidation stages; and one or more resources for which the consolidation stream and the deconsolidation stream complement each other during the at least one consolidation stage of the plurality of consolidation stages and the at least one deconsolidation stage of the plurality of deconsolidation stages.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
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10. The method of Claim 1, wherein a first set of consolidation stages of the plurality of consolidation stages supply resources during the consolidation stream, wherein a second set of consolidation stages of the plurality of consolidation stages consume resources during the consolidation stream, wherein a first set of deconsolidation stages of the plurality of consolidation stages supply resources during the deconsolidation stream, and wherein a second set of deconsolidation stages of the plurality of consolidation stages consume resources during the deconsolidation stream.
10. The method of claim 1, wherein a first set of consolidation stages of a plurality of consolidation stages supply resources during the consolidation stream, wherein a second set of consolidation stages of the plurality of consolidation stages consume resources during the consolidation stream, wherein a first set of deconsolidation stages of the plurality of consolidation stages supply resources during the deconsolidation stream, and wherein a second set of deconsolidation stages of the plurality of consolidation stages consume resources during the deconsolidation stream.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
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11. A system configured to optimize utilization of resources in a hub, comprising: at least one processor; and a memory operably coupled to the at least one processor and storing processor-readable code that, when executed by the at least one processor, is configured to perform operations including:representing a first flow of units through the hub as a consolidation stream including a plurality of consolidation stages, wherein units flowing through the consolidation stream are consolidated into one or more departing trains based, at least in part, on a destination of each of the units flowing through the consolidation stream;representing a second flow of units through the hub as a deconsolidation stream including a plurality of deconsolidation stages, wherein units flowing through the deconsolidation stream are deconsolidated from one or more arriving trains based, at least in part, on a destination of each of the units flowing through the deconsolidation stream; obtaining first data associated with a first set of units flowing through the consolidation stream over a planning horizon timeframe, wherein the first data includes: a dwell time associated with each unit in the first set of units indicating a duration of time that each unit dwells within the hub, and a prediction of a volume of units in the first set of units using a first prediction model; obtaining second data associated with a second set of units flowing through the deconsolidation stream over the planning horizon timeframe, wherein the second data includes: a volume of units in the second set of units, and a prediction of a dwell time associated with each unit in the second set of units indicating a duration of time that each unit dwells within the hub using a second prediction model; generating, using a dual-stream optimization model, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe; and generating a signal indicating one or more actions to be performed based on the optimized operating schedule.
11. A system configured to optimize utilization of resources in a hub, comprising: at least one processor; and a memory operably coupled to the at least one processor and storing processor-readable code that, when executed by the at least one processor, is configured to perform operations including:obtaining first data associated with a first set of units flowing through a consolidation stream over a planning horizon timeframe, wherein units flowing through the consolidation stream are consolidated into one or more departing trains and the first data includes a prediction of a volume of units in the first set of units using a first prediction model;obtaining second data associated with a second set of units flowing through a deconsolidation stream over the planning horizon timeframe, wherein units flowing through the deconsolidation stream are deconsolidated from one or more arriving trains and the second data includes a prediction of a dwell time associated with each unit in the second set of units using a second prediction model;generating, using a dual-stream optimizationmodel, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe; and sending a control signal to at least one physical resource of the hub to cause the at least one physical resource to physically reposition from a first location to a second location to enable the optimized operating schedule.
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The present application does not include the following from the parent: wherein representing the first flow of units through the hub as a consolidation stream includes generating a first time-space network based on the consolidation stream over a planning horizon timeframe. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
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12. The system of Claim 11, wherein the optimized operating schedule configured to optimize the at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe is configured to maximize the throughput of units processed through the hub over the planning horizon timeframe.
12. The system of claim 11, wherein the optimized operating schedule configured to optimize the at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe is configured to maximize the throughput of units processed through the hub over the planning horizon timeframe.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
13. The system of Claim 11, wherein the operations further comprise: generating a first time-space network based on the consolidation stream, wherein each node of the first time-space network represents a respective stage of the plurality of consolidation stages, and an edge between two nodes of the first time-space network represents a capacity between the two nodes of the first time-space network.
l3. The system of claim 1 1, wherein the operations further comprise: generating a first time-space network based on the consolidation stream, wherein each node of the first time-space network represents a respective stage of a plurality of consolidation stages, and an edge between two nodes of the first time-space network represents a capacity between the two nodes of the first time-space network.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
14. The system of Claim 13, wherein the operations further comprise: generating a second time-space network based on the deconsolidation stream, wherein each node of the second time-space network represents a respective stage of the plurality of deconsolidation stages, and an edge between two nodes of the second time-space network represents a capacity between the two nodes of the second time-space network.
14. The system of claim l 3, wherein the operations further comprise: generating a second time-space network based on the deconsolidation stream, wherein each node of the second time-space network represents a respective stage of a plurality ofdeconsolidation stages, and an edge between two nodes of the second time-space network represents a capacity between the two nodes of the second time-space network.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
15. The system of Claim 11, wherein generating the optimized operating schedule includes: allocating the at least one resource of the hub optimally between the consolidation and deconsolidation streams; and identifying one or more changes for implementing the optimized operating scheduled.
15. The system of claim 11, wherein generating the optimized operating schedule includes: allocating the at least one resource of the hub optimally between the consolidation and deconsolidation streams; and identifying one or more changes for implementing the optimized operating scheduled.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
16. The system of Claim 15, wherein the signal indicating the one or more actions to be performed based on the optimized operating schedule includes an indication to implement the one or more changes.
16. The system of claim 15, wherein the signal indicating the one or more actions to be performed based on the optimized operating schedule includes an indication to implement the one or more changes.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
17. The system of Claim 5, wherein the one or more changes include one or more of:an indication to perform a change to a train schedule to achieve the optimized operating schedule; and an indication that additional resources are required to be added at particular time increment of the planning horizon timeframe to achieve the optimized operating schedule.
17. The system of claim 5, wherein the one or more changes include one or more of: an indication to perform a change to a train schedule to achieve the optimized operating schedule; and an indication that additional resources are required to be added at particular time increment of the planning horizon timeframe to achieve the optimized operating schedule.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
18. The system of Claim 11, wherein the optimized operating schedule includes one or more of:an indication of a number of units processed through each stage of the plurality of consolidation stage at each time increment of the planning horizon timeframe; and an indication of a number of units processed through each stage of the plurality of deconsolidation stage at each time increment of the planning horizon timeframe.
18. The system of claim 11, wherein the optimized operating schedule includes one ormore of:an indication of a number of units processed through each stage of a plurality of consolidation stage at each time increment of the planning horizon timeframe; andan indication of a number of units processed through each stage of a plurality of deconsolidation stage at each time increment of the planning horizon timeframe.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
19. The system of Claim 11, wherein the at least one resource of the hub includes one or more of:one or more resources for which the consolidation stream and the deconsolidation stream compete during at least one consolidation stage of the plurality of consolidation stages and at least one deconsolidation stage of the plurality of deconsolidation stages; and one or more resources for which the consolidation stream and the deconsolidation stream complement each other during the at least one consolidation stage of the plurality ofconsolidation stages and the at least one deconsolidation stage of the plurality of deconsolidation stages.
19. The system of claim 11, wherein the at least one resource of the hub includes one or more of:one or more resources for which the consolidation stream and the deconsolidation stream compete during at least one consolidation stage of a plurality of consolidation stages and at least one deconsolidation stage of a plurality of deconsolidation stages; and one or more resources for whichthe consolidation stream and the deconsolidation stream complement each other during the at least one consolidation stage of the plurality of consolidation stages and the at least one deconsolidation stage of the plurality of deconsolidation stages.
The present limitation is substantially equivalent to the parent limitation. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
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20. A computer-based tool for optimizing utilization of resources in a hub, the computer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations comprising: representing a first flow of units through the hub as a consolidation stream including a plurality of consolidation stages, wherein units flowing through the consolidation stream are consolidated into one or more departing trains based, at least in part, on a destination of each of the units flowing through the consolidation stream; representing a second flow of units through the hub as a deconsolidation stream including a plurality of deconsolidation stages, wherein units flowing through the deconsolidation stream are deconsolidated from one or more arriving trains based, at least in part, on a destination of each of the units flowing through the deconsolidation stream; obtaining first data associated with a first set of units flowing through the consolidation stream over a planning horizon timeframe, wherein the first data includes: a dwell time associated with each unit in the first set of units indicating a duration of time that each unit dwells within the hub, and a prediction of a volume of units in the first set of units using a first prediction model; obtaining second data associated with a second set of units flowing through the deconsolidation stream over the planning horizon timeframe, wherein the second data includes: a volume of units in the second set of units, and a prediction of a dwell time associated with eachunit in the second set of units indicating a duration of time that each unit dwells within the hub using a second prediction model;generating, using a dual-stream optimization model, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe; and generating a signal indicating one or more actions to be performed based on the optimized operating schedule.
20. A computer-based tool for optimizing utilization of resources in a hub, thecomputer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations comprising: obtaining first data associated with a first set of units flowing through a consolidation stream over a planning horizon timeframe, wherein units flowing through the consolidation stream are consolidated into one or more departing trains and the first data includes a prediction of a volume of units in the first set of units using a first prediction model;obtaining second data associated with a second set of units flowing through a deconsolidation stream over the planning horizon timeframe, wherein units flowing through the deconsolidation stream are deconsolidated from one ormore arriving trains and the second dataincludes a prediction of a dwell time associated with each unit in the second set of units using a second prediction model;generating, using a dual-stream optimization model, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizontimeframe; andsending a control signal to at least one physical resource of the hub to cause the at least one physical resource to physically reposition from a first location to a second location to enable the optimized operating schedule.
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The present application does not include the following from the parent: wherein representing the first flow of units through the hub as a consolidation stream includes generating a first time-space network based on the consolidation stream over a planning horizon timeframe. This is not a patentable difference because the present claim is anticipated by the patented claim since the patent claim is a species of the present claim.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 as directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of process or machine or manufacture. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
1. A method of optimizing utilization of resources in a hub, comprising: obtaining first data associated with a first set of units flowing through a consolidation stream over a planning horizon timeframe, wherein units flowing through the consolidation stream are consolidated into one or more departing trains and the first data includes a prediction of a volume of units in the first set of units using a first prediction model; obtaining second data associated with a second set of units flowing through a deconsolidation stream over the planning horizon timeframe, wherein units flowing through the deconsolidation stream are deconsolidated from one or more arriving trains and the second data includes a prediction of a dwell time associated with each unit in the second set of units using a second prediction model; generating, using a dual-stream optimization model, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe; and sending a control signal to at least one physical resource of the hub to cause the at least one physical resource to physically reposition from a first location to a second location to enable the optimized operating schedule.
[or]
11. A system configured to optimize utilization of resources in a hub, comprising: at least one processor; and a memory operably coupled to the at least one processor and storing processor-readable code that, when executed by the at least one processor, is configured to perform operations including:obtaining first data associated with a first set of units flowing through a consolidation stream over a planning horizon timeframe, wherein units flowing through the consolidation stream are consolidated into one or more departing trains and the first data includes a prediction of a volume of units in the first set of units using a first prediction model;obtaining second data associated with a second set of units flowing through a deconsolidation stream over the planning horizon timeframe, wherein units flowing through the deconsolidation stream are deconsolidated from one or more arriving trains and the second data includes a prediction of a dwell time associated with each unit in the second set of units using a second prediction model;generating, using a dual-stream optimizationmodel, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe; and sending a control signal to at least one physical resource of the hub to cause the at least one physical resource to physically reposition from a first location to a second location to enable the optimized operating schedule.
[or]
20. A computer-based tool for optimizing utilization of resources in a hub, thecomputer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations comprising: obtaining first data associated with a first set of units flowing through a consolidation stream over a planning horizon timeframe, wherein units flowing through the consolidation stream are consolidated into one or more departing trains and the first data includes a prediction of a volume of units in the first set of units using a first prediction model;obtaining second data associated with a second set of units flowing through a deconsolidation stream over the planning horizon timeframe, wherein units flowing through the deconsolidation stream are deconsolidated from one ormore arriving trains and the second dataincludes a prediction of a dwell time associated with each unit in the second set of units using a second prediction model;generating, using a dual-stream optimization model, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizontimeframe; andsending a control signal to at least one physical resource of the hub to cause the at least one physical resource to physically reposition from a first location to a second location to enable the optimized operating schedule.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The train, processor and/or memory medium is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic train, processor and/or memory medium limitation is no more than mere instructions to apply the exception using a generic computer component. Further, generating a signal by a train, processor and/or memory medium is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: train, processor and memory medium. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, generating a signal by a train, processor and/or memory medium is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0109 wherein “performed with a general-purpose processor”. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 of this title, 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rieppi (US 20140236957 A1) hereinafter referred to as Rieppi in view of Hill et al. (CA 3153593 A1) hereinafter referred to as Hill in further view of Brulin et al. (US 20250036826 A1) hereinafter referred to as Brulin.
Rieppi teaches:
Claim 1. A method of optimizing utilization of resources in a hub, comprising:
obtaining first data associated with a first set of units flowing through a consolidation stream over a planning horizon timeframe, wherein units flowing through the consolidation stream are consolidated into one or more departing trains and the first data includes a prediction of a volume of units in the first set of units using a first prediction model (¶0069 Referring to step 1107, the block list and associated track assignment data is consolidated into a final track list. The final track list may include a list of blocks and their classifications. For example, the first track in the track list may be a jumbo track containing a jumbo block. The last track in the track list may be a small track, containing two small blocks and two cars of overflow volume from the jumbo block. Using the car footage parameter specified in step 1103, the amount of square footage needed to accommodate all the blocks in each track may be determined);
obtaining second data associated with a second set of units flowing through a deconsolidation stream over the planning horizon timeframe, wherein units flowing through the deconsolidation stream are deconsolidated from one or more arriving trains and the second data includes a prediction of a dwell time associated with each unit in the second set of units using a second prediction model (¶0056 In one aspect of the invention, traffic data at subcomponents within the rail terminal may be modeled. For example, rail terminals may model the dwell times of every subcomponent of a hump yard, such as a receiving yard, classification bowl and forwarding yard. Each core hump yard component performs a specific function. Receiving yards are designed to support the arrival and staging of inbound trains. Classification bowls primarily support the processing of inbound traffic into homogeneous outbound blocks (groups of cars with the same outbound classification). Forwarding yards support the building, staging and forwarding of outbound trains. In this way, rail terminals may track terminal capacity demand separately at each hump yard subcomponent (i.e., receiving yard, classification bowl and forwarding yard);
generating, using a dual-stream optimization model, based on the first data and the second data, an optimized operating schedule configured to optimize at least one resource of the hub for the consolidation stream and the deconsolidation stream over the planning horizon timeframe, wherein generating the optimized operating schedule includes layering together a first time-space network generated over the planning horizon timeframe based on the consolidation stream and a second time-space network generated over the planning horizon timeframe based on the deconsolidation stream to align nodes of the first time-space network to nodes of the second time-space network at each time increment of the planning horizon timeframe such that a status of a resource of the hub affects both the consolidation stream and the deconsolidation stream (¶0063 Referring to step 1103, switching parameters, i.e., railcar handling parameters, are defined for assigning blocks to tracks. Switching parameters, similarly to business rules, allow rail terminals to perform sensitivity analyses to determine an optimal number of classification tracks and track lengths required to meet the rail terminal's capacity needs. ¶0064 Changes to the above listed parameters will, for a given list of blocks and associated block sizes, result in variations in the minimum number of classification tracks and track sizes needed. For example, a reduction in the volume threshold for large blocks would likely cause more blocks to be defined as "jumbo" blocks, which in turn, would cause more blocks to be distributed across multiple tracks, and therefore, raise the minimum number of tracks required to stage the railcar blocks. Accordingly, the specification parameters allow rail terminals to perform sensitivity analyses and optimize the minimum number of tracks and track sizes required to stage railcars at a rail terminal); and
sending a control signal to at least one physical resource of the hub to cause the at least one physical resource to physically reposition from a first location to a second location to enable the optimized operating schedule (¶0049 In one aspect of the invention, all of the rail terminal management indicators listed above may be converted into dynamic capacity demand estimates by applying a fluidity factor to the inventory statistics. ¶0050 In another aspect of the invention, graphical displays of the rail terminal management indicators described above may be generated. For example, a chart showing evolution of hourly railcar inventory may be generated, as illustrated in FIG. 9, or a chart showing evolution of hourly traffic diversity. ¶0053 Thus, by applying business rules, rail terminals may perform sensitivity analyses by simulating changes in actual or planned dwells for a given type of connection. Business rules may change according to the structure of the input dataset. Steps 1000-1003 in FIG. 10 illustrate the steps of applying the business rules to the traffic record dataset. Referring to step 1000, the traffic records to update are identified. Referring to step 1001, the arrival and departure times of the traffic record timestamps are updated with new values according to the business rules).
Although not explicitly taught by Rieppi, Hill teaches in the analogous art of systems for operation of railway systems:
using a dual-stream optimization model (Page 17 As will be discussed, the model 55 (Figure 6) is stored in an electronic data source in the form of database 42. The model 55 defines locations in the network 21 allowing passing of trains such as sidings, and double tracks. The model also contains information as to paths for journeys of each of the trains, for example journeys for them to carry out haulage assignments. Railway system 20 also includes a scheduling machine 33 that is in communication with the data communication system 29 for receiving the state data. As will be discussed in more detail shortly, the scheduling machine 33 includes one or more processors 35 and an electronic memory 47 in communication with the processors 35. The electronic memory contains instructions for the processors 35 to effect a number of tasks as follows: access the model 55 of the railway network 21 stored in the electronic data source 42; apply the state data xt1,..,xtn to the model 44 to determine, at each of the respective times of the state data, controls associated with each trains' path for each of the trains la,...,1n.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems for operation of railway systems of Hill with the system for terminal capacity management of Rieppi for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Rieppi ¶0003 teaches that there is a need for simulation tools that can model traffic and congestion patterns of rail terminals still in their planning or development stages, on the basis of high-level historical or forecast data;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Rieppi Abstract teaches determining rail terminal capacity needs, and Hill Abstract teaches a scheduling machine that accesses a model of the railway network that is stored in an electronic data source; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Rieppi at least the above cited paragraphs, and Hill at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the systems for operation of railway systems of Hill with the system for terminal capacity management of Rieppi. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Rieppi in view of Hill, Brulin teaches in the analogous art of system design process for multi-layered transport networks:
wherein generating the optimized operating schedule includes layering together a first time-space network generated over the planning horizon timeframe based on the consolidation stream and a second time-space network generated over the planning horizon timeframe based on the deconsolidation stream to align nodes of the first time-space network to nodes of the second time-space network at each time increment of the planning horizon timeframe such that a status of a resource of the hub affects both the consolidation stream and the deconsolidation stream (¶0045 A transit point is a node at which the passenger or object arrives via a first transportation layer of the transport network, switches to a second transportation layer of the transport network, and leaves via the second transportation layer of the transport network. Edges, sometimes referred as connections of the transport network connect the individual nodes of the transport network. The edges may represent streets, train tracks, navigable water channels or air corridors. A transport network design defines the topological connectivity and spatial structure of the transport network, including their spatial positions in the region and the associated transport capacity. The parameter flow capacity of an edge may describe a transport capacity of the edge or of the transport connection. ¶0080 In a step S13 following steps S11 and S12, the multimodal network design process S1 continues with evaluating the velocities on each edge of the transport system based on the information in the acquired current state information of the transport system. Step S13 includes evaluating velocity information of moving physical objects or users (passengers) moving along edges of a graph including plural layers representing the multimodal transport network based on at least the obtained current state of the transport network. In particular, the velocities on each edge are evaluated for all existing layers of the transport network. In particular, in step S13, velocities of objects, in particular vehicles, moving persons or transported goods on the edges of the transport network included in the current state information are compared with past information, in particular past state information obtained in step S12. Evaluating velocities includes determining (measuring) current velocities and predicting (estimating) future velocities of the objects or moving persons. ¶0098 The defined multimodal communicability measure may not guarantee communicability between those nodes in transport networks that have capacity constraints or a potential failure of edges between nodes. The optimization loop S10 of the multimodal network design process S1 may employ a more general communicability measure that considers all available paths of the transport network but decreases contributions of longer paths instead. The proposed multi-layered communicability of the present disclosure utilizes information about the independence of the separate layers of the transport network. It increases the contribution of paths from other layers of the transport network compared to a shortest path modal chain. The multimodal communicability measure identifies modal bottlenecks in the multimodal transport network and designs the new transport layer to find the optimal network layout that improves multimodal communicability, transportation efficiency, and robustness of the full transport network by reducing the effects of edge failures on the overall transportation process. ¶0099 The optimization loop includes evaluating the multimodal communicability measure and comparing the evaluated multimodal communicability measure (optimization parameter) with a convergence criterion in step S16. In step S17 that follows step S16 in case the convergence criterion is not yet met, active edges (e.g. active corridors) in the transport layers are adapted and a new cycle of the optimization loop is started with the processing to step S16 again.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system design process for multi-layered transport networks of Brulin with the system for terminal capacity management of Rieppi in view of Hill for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Rieppi ¶0003 teaches that there is a need for simulation tools that can model traffic and congestion patterns of rail terminals still in their planning or development stages, on the basis of high-level historical or forecast data;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Rieppi Abstract teaches determining rail terminal capacity needs, and Hill Abstract teaches a scheduling machine that accesses a model of the railway network that is stored in an electronic data source and Brulin Abstract teaches a system for designing a multimodal transport network, the method comprising steps of generating an optimized transport network design by optimizing a multimodal communicability measure by adapting active edges of at least one of the layers of the transport network; and outputting the optimized transport network design and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Rieppi in view of Hill at least the above cited paragraphs, and Brulin at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the system design process for multi-layered transport networks of Brulin with the system for terminal capacity management of Rieppi in view of Hill. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Rieppi teaches:
Claim 2. The method of claim 1, wherein the optimized operating schedule configured to optimize the at least one resource of the hub for the consolidation stream and the deconsolidationstream over the planning horizon timeframe is configured to maximize the throughput of units processed through the hub over the planning horizon timeframe (¶0012 In one aspect of the invention, the number of different outbound classifications present within a facility within a specified time interval may be determined. Other values may be determined, including an average hourly railcar inventory, an average daily maximum hourly railcar inventory, a minimum hourly railcar inventory, a maximum hourly railcar inventory, and a range between the minimum hourly railcar inventory and the maximum hourly railcar inventory. In one embodiment of the invention, a graphical display of terminal capacity and these other values may be generated over a specified time interval. ¶0063 Referring to step 1103, switching parameters, i.e., railcar handling parameters, are defined for assigning blocks to tracks. Switching parameters, similarly to business rules, allow rail terminals to perform sensitivity analyses to determine an optimal number of classification tracks and track lengths required to meet the rail terminal's capacity needs).
Although not explicitly taught by Rieppi, Hill teaches in the analogous art of systems for operation of railway systems:
Claim 3. The method of claim 1, further comprising: generating a first time-space network based on the consolidation stream, wherein each node of the first time-space network represents a respective stage of a plurality of consolidation stages, and an edge between two nodes of the first time-space network represents a capacity between the two nodes of the first time-space network (Figs. 21-23 and Page 19 Figure 9A depicts a corresponding to railway network 75 which comprises a node 85 with two slots 85a, 85b. Node 85 interconnects double edges 85-el and 85-e2. The movement of trains is modelled to occur in stages. A stage is the movement a node to the next node. Nodes are connected by edges, which can be single or edge represents a single line and at any given time only one train can transit over such an edge. A double edge models a double track, which allows the transit of two trains at the same time, as segment. Nodes in the double edges. As shown, nodes are also characterized by a number of slots indicating how present on the node at the same time. Locations where passing can occur (e.g., stations) can be modelled as nodes with multiple slots and are shown as double assume that all nodes have an infinite number of slots.) valid stage, and the siding is mapped into two separate nodes n3, n4. The same siding can be represented as a node with two slots, i.e. node n3 as shown in Figure 10c. The graph in Figure 10d simplification of that of Figure 10c in which nodes /22 and /25 are removed, nodes might be necessary when, for instance, trains are longer than a single any given time. Nodes in a model of a network represent the completion of processes rather This is mapped into the graph on Figure 10b as Ti transiting on the edge at node ni maps to the event that the train has completed transiting on its being on the same (double slotted) node /23 although their physical location It will be realised that the visual representation of nodes and edges nodes from the current location of the train to its destination and need not ordered list of nodes (and edges) that the train needs to occupy as it (node or edge) at the same time if it is for example a single capacity edge).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems for operation of railway systems of Hill with the system for terminal capacity management of Rieppi for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Rieppi ¶0003 teaches that there is a need for simulation tools that can model traffic and congestion patterns of rail terminals still in their planning or development stages, on the basis of high-level historical or forecast data;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Rieppi Abstract teaches determining rail terminal capacity needs, and Hill Abstract teaches a scheduling machine that accesses a model of the railway network that is stored in an electronic data source; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Rieppi at least the above cited paragraphs, and Hill at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the systems for operation of railway systems of Hill with the system for terminal capacity management of Rieppi. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Rieppi, Hill teaches in the analogous art of systems for operation of railway systems:
Claim 4. The method of claim 3, further comprising: generating a second time-space network based on the deconsolidation stream, wherein each node of the second time-space network represents a respective stage of a plurality of deconsolidation stages, and an edge between two nodes of the second time-space network represents a capacity between the two nodes of the second time-space network (Figs. 21-23 and Page 19 Figure 9A depicts a corresponding to railway network 75 which comprises a node 85 with two slots 85a, 85b. Node 85 interconnects double edges 85-el and 85-e2. The movement of trains is modelled to occur in stages. A stage is the movement a node to the next node. Nodes are connected by edges, which can be single or edge represents a single line and at any given time only one train can transit over such an edge. A double edge models a double track, which allows the transit of two trains at the same time, as segment. Nodes in the double edges. As shown, nodes are also characterized by a number of slots indicating how present on the node at the same time. Locations where passing can occur (e.g., stations) can be modelled as nodes with multiple slots and are shown as double assume that all nodes have an infinite number of slots.) valid stage, and the siding is mapped into two separate nodes n3, n4. The same siding can be represented as a node with two slots, i.e. node n3 as shown in Figure 10c. The graph in Figure 10d simplification of that of Figure 10c in which nodes /22 and /25 are removed, nodes might be necessary when, for instance, trains are longer than a single any given time. Nodes in a model of a network represent the completion of processes rather This is mapped into the graph on Figure 10b as Ti transiting on the edge at node ni maps to the event that the train has completed transiting on its being on the same (double slotted) node /23 although their physical location It will be realised that the visual representation of nodes and edges nodes from the current location of the train to its destination and need not ordered list of nodes (and edges) that the train needs to occupy as it (node or edge) at the same time if it is for example a single capacity edge).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems for operation of railway systems of Hill with the system for terminal capacity management of Rieppi for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Rieppi ¶0003 teaches that there is a need for simulation tools that can model traffic and congestion patterns of rail terminals still in their planning or development stages, on the basis of high-level historical or forecast data;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Rieppi Abstract teaches determining rail terminal capacity needs, and Hill Abstract teaches a scheduling machine that accesses a model of the railway network that is stored in an electronic data source; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Rieppi at least the above cited paragraphs, and Hill at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the systems for operation of railway systems of Hill with the system for terminal capacity management of Rieppi. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Rieppi teaches:
Claim 5. The method of claim 1, wherein generating the optimized operating schedule includes: allocating the at least one resource of the hub optimally between the consolidation and deconsolidation streams; and identifying one or more changes for implementing the optimized operating scheduled (¶0055 a rail terminal may vary the projected railcar lengths to study the impact of different sized railcars on overall terminal spatial requirements. As another example, terminals may perform a sensitivity analysis of the fluidity factor, which is a scaling factor that reflects additional capacity required to ensure fluidity of operations at most times, and varies according to the specific type or types of traffic handled by a given terminal. Thus, rail terminals may analyze how target fluidity operations impact dwell times. ¶0052 Referring to step 111 in FIG. 1, the traffic record dataset may be modified according to one or more business rules. Business rules allow rail terminals perform sensitivity analyses that model the differences in existing rail terminal traffic flow with projected or target traffic data. For example, business rules can be used to model the change in efficiency of rail terminal traffic flow caused by changing the average dwell time of that particular terminal and potentially in respect of a specific traffic type handled by that terminal. More specifically, if a particular group of traffic typically arrives at a rail terminal at 2:00 pm, and departs to a customer location at 8:00 pm, for a dwell period of 6 hours, the rail terminal may model the change in efficiency by decreasing the dwell time to 4 hours, or in other words, by departing at 6:00 pm, rather than 8:00 pm).
Rieppi teaches:
Claim 6. The method of claim 5, wherein the signal indicating the one or more actions to be performed based on the optimized operating schedule includes an indication to implement the one or more changes (¶0055 a rail terminal may vary the projected railcar lengths to study the impact of different sized railcars on overall terminal spatial requirements. As another example, terminals may perform a sensitivity analysis of the fluidity factor, which is a scaling factor that reflects additional capacity required to ensure fluidity of operations at most times, and varies according to the specific type or types of traffic handled by a given terminal. Thus, rail terminals may analyze how target fluidity operations impact dwell times. ¶0052 Referring to step 111 in FIG. 1, the traffic record dataset may be modified according to one or more business rules. Business rules allow rail terminals perform sensitivity analyses that model the differences in existing rail terminal traffic flow with projected or target traffic data. For example, business rules can be used to model the change in efficiency of rail terminal traffic flow caused by changing the average dwell time of that particular terminal and potentially in respect of a specific traffic type handled by that terminal. More specifically, if a particular group of traffic typically arrives at a rail terminal at 2:00 pm, and departs to a customer location at 8:00 pm, for a dwell period of 6 hours, the rail terminal may model the change in efficiency by decreasing the dwell time to 4 hours, or in other words, by departing at 6:00 pm, rather than 8:00 pm).
Rieppi teaches:
Claim 7. The method of claim 5, wherein the one or more changes include one or more of an indication to perform a change to a train schedule to achieve the optimized operating schedule; and an indication that additional resources are required to be added at particular time increment of the planning horizon timeframe to achieve the optimized operating schedule (¶0055 a rail terminal may vary the projected railcar lengths to study the impact of different sized railcars on overall terminal spatial requirements. As another example, terminals may perform a sensitivity analysis of the fluidity factor, which is a scaling factor that reflects additional capacity required to ensure fluidity of operations at most times, and varies according to the specific type or types of traffic handled by a given terminal. Thus, rail terminals may analyze how target fluidity operations impact dwell times).
Rieppi teaches:
Claim 8. The method of claim 1, wherein the optimized operating schedule includes one or more of: an indication of a number of units processed through each stage of a plurality of consolidation stage at each time increment of the planning horizon timeframe; and an indication of a number of units processed through each stage of a plurality of deconsolidation stage at each time increment of the planning horizon timeframe (¶0042 Referring to step 104 in FIG. 1, dwell occupancy values are then assigned to each occupancy index. Each occupancy index is given a value associated with a matching traffic record interval value. That is, the traffic information (e.g., railcar volume) associated with each traffic record in the dataset may be programmatically assigned to one or more hourly buckets across one or more days within the simulation timeframe. First, each occupancy interval is matched with the corresponding traffic interval. ¶0044 Referring to step 105 in FIG. 1, inventory capacity demand is then analyzed based on dwell occupancy values. For example, hourly inventory capacity demand may be calculated by cross-tabulating the railcar volumes in each occupancy index, as illustrated in step 106).
Rieppi teaches:
Claim 9. The method of claim 1, wherein the at least one resource of the hub includes one or more of:one or more resources for which the consolidation stream and the deconsolidation stream compete during at least one consolidation stage of a plurality of consolidation stages and at least one deconsolidation stage of a plurality of deconsolidation stages; and one or more resources for which the consolidation stream and the deconsolidation stream complement each other during the at least one consolidation stage of the plurality of consolidation stages and the at least one deconsolidation stage of the plurality of deconsolidation stages (¶0059 Steps 1100-107 in FIG. 11 illustrate the process of creating a list of tracks and railcars assigned to each track. This list of tracks represents the minimum number of tracks needed to stage every block arriving at a rail terminal. Using other parameters, such as the car footage per railcar, the total amount of physical space needed to accommodate every track may be determined. Significantly, by using the occupancy indexing process described in FIGS. 1-10, a rail terminal may create this track list without performing detailed simulations that require virtual representations of tracks. In this way, the rail terminal may determine the number and size of tracks needed to accommodate groups of railcars in a timely and efficient manner, consuming as few resources as possible ¶0052 Referring to step 111 in FIG. 1, the traffic record dataset may be modified according to one or more business rules. Business rules allow rail terminals perform sensitivity analyses that model the differences in existing rail terminal traffic flow with projected or target traffic data. For example, business rules can be used to model the change in efficiency of rail terminal traffic flow caused by changing the average dwell time of that particular terminal and potentially in respect of a specific traffic type handled by that terminal. More specifically, if a particular group of traffic typically arrives at a rail terminal at 2:00 pm, and departs to a customer location at 8:00 pm, for a dwell period of 6 hours, the rail terminal may model the change in efficiency by decreasing the dwell time to 4 hours, or in other words, by departing at 6:00 pm, rather than 8:00 pm).
Rieppi teaches:
Claim 10. The method of claim 1, wherein a first set of consolidation stages of a plurality of consolidation stages supply resources during the consolidation stream, wherein a second set of consolidation stages of the plurality of consolidation stages consume resources during the consolidation stream, wherein a first set of deconsolidation stages of the plurality of consolidation stages supply resources during the deconsolidation stream, and wherein a second set of deconsolidation stages of the plurality of consolidation stages consume resources during the deconsolidation stream (¶0059 Steps 1100-107 in FIG. 11 illustrate the process of creating a list of tracks and railcars assigned to each track. This list of tracks represents the minimum number of tracks needed to stage every block arriving at a rail terminal. Using other parameters, such as the car footage per railcar, the total amount of physical space needed to accommodate every track may be determined. Significantly, by using the occupancy indexing process described in FIGS. 1-10, a rail terminal may create this track list without performing detailed simulations that require virtual representations of tracks. In this way, the rail terminal may determine the number and size of tracks needed to accommodate groups of railcars in a timely and efficient manner, consuming as few resources as possible).
As per claims 11-19 and 20, the system and computer-based tool tracks the method of claims 1-9 and 1, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-9 and 1 are applied to claims 11-19 and 20, respectively. Rieppi discloses that the embodiment may be found as a system and computer-based tool (Fig. 1 and ¶0045 The use of a computer processor to create hourly interval indexes, retrieve dwell occupancy values and calculate inventory capacity demand allows rail terminals to analyze terminal capacity needs quickly and efficiently).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURTIS GILLS whose telephone number is (571)270-3315. The examiner can normally be reached on M-F 8-5 PM.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624
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