Status of Claims
Claims 1-13 were previously pending and subject to the non-final office action mailed 06/24/2025. In the Response, submitted 10/06/2025, claims 1 and 7 were amended. Therefore, claims 1-13 are currently pending and subject to the following final office action.
Response to Applicant’s Remarks
Applicant’s arguments and remarks filed on 10/06/2025, have been fully considered and each argument will be respectfully addressed in the following final office action.
Response to 35 U.S.C. § 101 Remarks
Applicant’s remarks filed on pages 10-13 of the Response concerning the 35 U.S.C. § 101 rejection of claims 1-13 have been fully considered but are found not persuasive and are moot in view of the amended rejection that may be found starting on page 6 of this final office action.
On page 11 of the Response the Applicant, the Applicant respectfully submits that “the claims do not recite an abstract idea […] when viewed as a whole, the claims recite a specific technological process for optimizing logistics supply chains through structured data integration and AI-driven analysis […] This is not merely “planning and facilitating business relations” or generic calculations, but a concrete solution to logistics data handling problems, enabling transparent demand optimization”.
The Examiner respectfully disagrees that the claims do not recite an abstract idea. The independent claims recite steps for collecting a plurality of datasets corresponding to commercial entities (i.e., a “capacity dataset” comprising “logistics partner capacity dataset, a logistics partner contract dataset, a logistics partner booking dataset, a logistics partner sub-contractor dataset, a logistics partner event dataset, a logistics partner financial dataset, and a logistics partner qualitative dataset” and a “demand dataset” corresponding to various shipper datasets), configuring a decision making algorithm based on the collected datasets, determining prediction data based on the decision making algorithm, and providing recommendations of a logistics supply chain to users based on the prediction data and decision making process. These limitations, as a whole, are directed towards planning/building a logistics supply chain which is clearly a commercial interaction involving the coordination of business relations. This is further evidenced by the Applicant’s specification at ¶ [0064] which indicates the “outcome of the predictions act 350 is provided to the users (e.g. suppliers, manufacturers, consumer product groups, distributors, logistics partners, and retailers) […] at any time to improve the supply chain service based on the predictive data”.
On page 11 of the Response, the Applicant submits “the sequential loading integrates disparate data sources for the deep learning neural network akin the McRo, Inc. v. Bandai Namco Games AM. Inc. […] where specific processes improved technology […] This enhances logistics functionality by ensuring comprehensive, ordered data input for accurate predictions”. The Examiner respectfully disagrees that the amended limitations directed towards sequentially loading capacity datasets and demand datasets, when viewed as a whole/ordered combination, provide an improvement to technology. As currently drafted, the recited steps for “sequentially loading” datasets merely recite the use of a computer in its ordinary capacity (e.g., to receive, store, or transmit data) to perform the abstract idea. See MPEP 2106.05 (f). Moreover, these claimed steps are considered to be additional elements involving retrieving/storing information in a memory, which the courts have found to be well-understood, routine, and conventional activities when recited as insignificant extra solution activity. See MPEP 2106.05(d)(II).
Furthermore, the claimed steps involving the use of artificial intelligence (“wherein the AI block implements a deep learning neural network trained on historical data from the capacity dataset, demand dataset, and conditions dataset to generate the predictive capacity algorithm, predictive demand algorithm, prescriptive booking algorithm, prescriptive performance algorithm, and prescriptive sustainability algorithm, and wherein the deep learning neural network adapts its weight based on feedback data to finetune the decision making process”) are recited at a high level of generality and do not reflect any type of improvement to the technology itself. Thus, the additional elements involving the use of artificial intelligence and “sequentially loading” datasets still merely serve as generic computer components/instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
On page 12 of the Response, the Applicant submits the “claims improve logistics technology by specifying sequential data loading from Figures 3-4, which structures input for the AI block […] this feeds the deep learning neural network to generate predictive/prescriptive algorithms, adapting weights via feedback, and transmitting recommendations”; “This addresses inefficient data handling in logistics, pivoting to demand-driving models […] it improves system functioning through structured data processing”.
The Examiner respectfully disagrees that the amended independent claims recite additional elements that reflect an improvement to technology and integrate the abstract idea into a practical application. As noted further above, the claimed steps involving the use of artificial intelligence are recited at a high level of generality and do not reflect any type of improvement to the technology itself. The independent claims describe “determining, by the logistics communication flow system, a prediction data based on the configured decision making algorithm using an artificial intelligence (AI) block”, “using the AI block comprises generating a predictive capacity algorithm […] and prescriptive sustainability algorithm”, and “wherein the deep learning neural network adapts its weight based on feedback data to finetune the decision making process”. However, the independent claims do not provide further technical detail regarding how the AI block technically performs these claimed functions beyond generally asserting “using” the AI block to arrive at the claimed results. Accordingly, merely “using” artificial intelligence and deep neural networks to arrive at a result, without further technical detail or structure, is not considered to be a technical improvement to the technical field of artificial intelligence.
Furthermore, the recited steps for “sequentially loading” datasets merely recite the use of a computer in its ordinary capacity (e.g., to receive, store, or transmit data) to perform the abstract idea. Thus, the additional elements involving the use of artificial intelligence and “sequentially loading” datasets, when considered as a whole/ordered combination, still merely serve as generic computer components/instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
On pages 12-13 of the Response, the Applicant submits the “claims amount to significantly more, with the ordered combination – including sequential loading from Figs. 3-4 – not well-understood, routine or conventional (WURC). Traditional systems lack this integration for demand optimization”. The Examiner respectfully disagrees that the additional elements of the claim, when considered as a whole/ordered combination, provide significantly more than the abstract idea. As noted further above, the recited steps for “sequentially loading” datasets merely recite the use of a computer in its ordinary capacity (e.g., to receive, store, or transmit data) to perform the abstract idea. See MPEP 2106.05 (f). Furthermore, the claimed steps involving sequentially loading datasets are considered to be additional elements that involve retrieving/storing information in a memory that amount to no more than mere data gathering/outputting, which is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, the claim steps for retrieving/storing information in a memory fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II).
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 1-13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
First of all, claims must be directed to one or more of the following statutory categories: a process, a machine, a manufacture, or a composition of matter. Claims 1-6 are directed to a process (“a method”), and claims 7-13 are directed to a machine (“a system”). Thus, claims 1-13 satisfy Step One because they are all within one of the four statutory categories of eligible subject matter. Claims 1-13, however, are directed to an abstract idea without significantly more.
Regarding independent claim 1, the specific limitations that recite an abstract idea are:
Receiving […] a capacity dataset, a demand dataset, and a conditions dataset, wherein the capacity dataset comprises a logistics partner capacity dataset, a logistics partner contract dataset, a logistics partner booking dataset, a logistics partner sub-contractor dataset, a logistics partner event dataset, a logistics partner financial dataset and a logistics partner qualitative dataset;
Configuring […] a decision making algorithm based on the capacity dataset, the demand dataset, and the conditions dataset;
Determining […] a prediction data based on the configured decision making algorithm […], wherein the prediction data corresponds to an on-time prediction, an in-budget prediction, a loss prediction, a contract conversion prediction, a demand volatility prediction, a sustainability prediction and a happiness prediction, wherein determining the prediction data based on the configured decision making algorithm […] comprises generating a predictive capacity algorithm, a predictive demand algorithm, a prescriptive booking algorithm, a prescriptive performance algorithm, and a prescriptive sustainability algorithm […];
Wherein receiving the capacity dataset comprises: […] the logistics partner capacity dataset, the logistics partner contract dataset, the logistics partner booking dataset, the logistics partner sub-contractor dataset, the logistics partner event dataset, the logistics partner financial dataset, and the logistics partner qualitative dataset, and
Wherein receiving the demand dataset comprises […] a shipper demand dataset, a shipper contract dataset, a shipper booking dataset, a shipper sub-contractor dataset, a shipper event dataset, a shipper financial dataset, and a shipper qualitative dataset;
Generating […] a feedback by using each of the predictive capacity algorithm, the predictive demand algorithm, the prescriptive booking algorithm, the prescriptive performance algorithm, and the prescriptive sustainability algorithm;
finetuning […] a decision making process based on the generated feedback; and
recommending […] a logistics supply chain to at least one user based on the determined prediction data and the decision making process, wherein the recommending includes transmitting a configured logistics supply chain recommendation to a user […] to optimize asset utilization and shipment flow.
Therefore, claims 1 and 2-6, by virtue of dependence, recite certain methods of organizing human activity. In particular, the limitations of claim 1 identified above, as a whole, recite concepts of planning and facilitating business relations based on a plurality of datasets corresponding to commercial entities, which is the abstract idea of commercial interactions. See MPEP 2106.04(a)(2)(II). This is further evidenced in the Applicant’s specification at ¶ [0004] and ¶ [0064]. Furthermore, the limitations directed towards generating and using the plurality of algorithms recite concepts of mathematical calculations, which is the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include a “logistics communication flow system”, “AI block”, “user interface”, “user terminal”, and steps for “sequentially loading” the claimed datasets corresponding to the capacity dataset and demand dataset. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
Claim 1 does introduce more specific technology, artificial intelligence, but again, this is merely being used as a generic tool to implement the abstract idea above. The steps involving the use of artificial intelligence (“wherein the AI block implements a deep learning neural network trained on historical data from the capacity dataset, demand dataset, and conditions dataset to generate the predictive capacity algorithm, predictive demand algorithm, prescriptive booking algorithm, prescriptive performance algorithm, and prescriptive sustainability algorithm, and wherein the deep learning neural network adapts its weight based on feedback data to finetune the decision making process”) are recited at a high level of generality and do not reflect any type of improvement to the technology itself. The additional elements involving the use of artificial intelligence therefore still merely serve as generic computer components/instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
Furthermore, the claim recites additional elements involving steps for transmitting information over a network (“transmitting a configured logistics supply chain recommendation to a user interface of a user terminal over a network“) and retrieving/storing information in a memory (“wherein receiving the capacity dataset comprises: sequentially loading the […] dataset[s]” and “wherein receiving the demand dataset comprises sequentially loading […] dataset[s]”). These additional elements fail to integrate the claim into a practical application because the steps for transmitting information over a network and retrieving/storing information from in a memory amount to no more than mere data gathering/outputting, which is insignificant extra-solution activity. See MPEP 2106.05(g).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for transmitting information over a network fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network and retrieving/storing information in a memory to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve transmitting information over a network and retrieving/storing information in a memory, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 1 is not patent eligible.
Regarding independent claim 7, the specific limitations that recite an abstract idea are:
Receive a capacity dataset, a demand dataset, and a conditions dataset, wherein the capacity dataset comprises a logistics partner capacity dataset, a logistics partner contract dataset, a logistics partner booking dataset, a logistics partner sub-contractor dataset, a logistics partner event dataset, a logistics partner financial dataset and a logistics partner qualitative dataset;
Configure a decision making algorithm based on the capacity dataset, the demand dataset, and the conditions dataset;
Determine a prediction data based on the configured decision making algorithm […], wherein the prediction data corresponds to an on-time prediction, an in-budget prediction, a loss prediction, a contract conversion prediction, a demand volatility prediction, a sustainability prediction and a happiness prediction, wherein determining the prediction data based on the configured decision making algorithm […] comprises generating a predictive capacity algorithm, a predictive demand algorithm, a prescriptive booking algorithm, a prescriptive performance algorithm, and a prescriptive sustainability algorithm […];
Wherein receiving the capacity dataset comprises: […] the logistics partner capacity dataset, the logistics partner contract dataset, the logistics partner booking dataset, the logistics partner sub-contractor dataset, the logistics partner event dataset, the logistics partner financial dataset, and the logistics partner qualitative dataset, and
Wherein receiving the demand dataset comprises […] a shipper demand dataset, a shipper contract dataset, a shipper booking dataset, a shipper sub-contractor dataset, a shipper event dataset, a shipper financial dataset, and a shipper qualitative dataset;
Generate a feedback by using each of the predictive capacity algorithm, the predictive demand algorithm, the prescriptive booking algorithm, the prescriptive performance algorithm, and the prescriptive sustainability algorithm;
finetune a decision making process based on the generated feedback; and
recommend a logistics supply chain to at least one user based on the determined prediction data and the decision making process, wherein the recommendation includes transmitting a configured logistics supply chain recommendation to a user […] to optimize asset utilization and shipment flow.
Therefore, claims 7 and 8-13, by virtue of dependence, recite certain methods of organizing human activity. In particular, the limitations of claim 7 identified above, as a whole, recite concepts of planning and facilitating business relations based on a plurality of datasets corresponding to commercial entities, which is the abstract idea of commercial interactions. See MPEP 2106.04(a)(2)(II). This is further evidenced in the Applicant’s specification at ¶ [0004] and ¶ [0064]. Furthermore, the limitations directed towards generating and using a plurality of algorithms recite concepts of mathematical calculations, which is the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include a “logistics communication flow system”, “processor”, “memory”, “demand and capacity maximizer module, coupled with the processor and the memory”, “AI block”, “user interface”, “user terminal”, and steps for “sequentially loading” the claimed datasets corresponding to the capacity dataset and demand dataset. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
Claim 7 does introduce more specific technology, artificial intelligence, but again, this is merely being used as a generic tool to implement the abstract idea above. The steps involving the use of artificial intelligence (“wherein the AI block implements a deep learning neural network trained on historical data from the capacity dataset, demand dataset, and conditions dataset to generate the predictive capacity algorithm, predictive demand algorithm, prescriptive booking algorithm, prescriptive performance algorithm, and prescriptive sustainability algorithm, and wherein the deep learning neural network adapts its weight based on feedback data to finetune the decision making process”) are recited at a high level of generality and do not reflect any type of improvement to the technology itself. The additional elements involving the use of artificial intelligence therefore still merely serve as generic computer components/instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
Furthermore, the claim recites additional elements involving steps for transmitting information over a network (“transmitting a configured logistics supply chain recommendation to a user interface of a user terminal over a network“) and retrieving/storing information in a memory (“wherein receiving the capacity dataset comprises: sequentially loading the […] dataset[s]” and “wherein receiving the demand dataset comprises sequentially loading […] dataset[s]”). These additional elements fail to integrate the claim into a practical application because the steps for transmitting information over a network amount to no more than mere data gathering/outputting, which is insignificant extra-solution activity. See MPEP 2106.05(g).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for transmitting information over a network and retrieving/storing information in a memory fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve transmitting information over a network and retrieving/storing information in a memory, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 7 is not patent eligible.
Claim 2 recites steps for learning feedback data and modifying prediction data, and thus further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 3 further defines the information utilized to perform the abstract idea, and further defines the outputs of the claimed algorithms. Thus, the limitations of claim 3 merely further describe the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 4 further defines the information included in a demand dataset and thus further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 5 further defines the information included in a conditions dataset and thus further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 6 further defines the information included in the feedback data and thus further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claims 1-2 from which the claim depends.
Claim 8 recites steps for learning feedback data and modifying prediction data, and thus further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 9 further defines the information utilized to perform the abstract idea, and further defines the outputs of the claimed algorithms. Thus, the limitations of claim 3 merely further describe the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 10 further defines the information included in a demand dataset and thus further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 11 further defines the information included in a conditions dataset and thus further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 12 further defines the information included in the feedback data and thus further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claims 1-2 from which the claim depends.
Claim 13 recites the same abstract idea as claim 7, by virtue of dependence, and is rejected for substantially the same reasons. The claim further introduces the additional elements of a “cloud-based platform”.
The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JORGE G DEL TORO-ORTEGA/Examiner, Art Unit 3628 /JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628