DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1, 3-7, 9-11, 13-17, and 19-23 are currently pending. Claims 1, 6, 9-11, 16, 19, and 20 were amended in the reply filed March 11, 2026. Claims 21-23 were added.
Response to Arguments
101:
Applicant's arguments filed with respect to the rejection made under 35 U.S.C. § 101 have been fully considered but they are not persuasive. Applicant first argues that the claims are not directed to an abstract idea under Step 2A, Prong 1. Specifically, that “the claims recite a specific data-processing architecture that uses trained machine learning models to reconstructs raw journey data into structured routing segments (inbound, outbound, and empty) and applies a multi-model machine learning pipeline” (Remarks p. 11). Examiner respectfully disagrees. Examiner clarifies that the questions of whether a claim recites and abstract idea under Step 2A – Prong 1 (see MPEP 2106.04(II)(A)(1)) is different that the question of whether a claim is directed to an abstract idea under Step 2A – Prong 2 (see MPEP 2106.04(II)(A)(2)). The recitation of an abstract idea is not negated by the presence of additional elements. As such, the amended claims do recite an abstract idea. The additional elements that make up the “data-processing architecture” and “multi-model machine learning pipeline” are analyzed in Step 2A, Prong 2.
Applicant next argues that any abstract idea is integrated into a practical application under Step 2A, Prong 2. Specifically, that “the claims recite a specific multi-model training architecture that use distinctly trained machine learning models to generate forecast parameters, and distinctly trained adjustment models to adjust the forecast parameters, to identify an optimal route with reduced empty segments” (Remarks p. 12). Examiner respectfully disagrees. The claimed invention uses parameters specific to the abstract idea and training inputs and selects the best machine learning model to perform the abstract idea. This is not an improvement to the training process of the machine learning models or any other technology. Rather, it is an improvement to the abstract idea of optimizing a carrier route because the models are merely being trained and used as tools to output a better route. An improvement in the abstract idea itself is not an improvement in technology (see MPEP 2106.05(a)(II)).
Furthermore, the alleged improvement to technology is not supported by Applicant’s specification (see MPEP 2106.05(a) – “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement”). Examiner notes that paragraphs [0002-0004] of Applicant’s specification describe improvements to the abstract idea (e.g., “adapt to real-time changes or future disruptions” or reducing “inefficiencies, such as underutilized capacity, missed delivery windows, and increased operating costs”), not to any underlying technology or technical field. As such, the amended claims do not provide an improvement to the functioning of a computer or to any other technology or technical field. Rather, they only improve the recited abstract idea.
Lastly, Applicant argues that the claims provide an inventive concept under Step 2B. Specifically, that “the claims recite ‘specific limitation[s] other than what is well-understood, routine, conventional activity in the field,’ and ‘add[] unconventional steps that confine the claim to a particular useful application,’ as the prior art fails to teach or suggest the claimed subject matter” (Remarks p. 13). Examiner respectfully disagrees for the reasons above and further below in the 101 rejection. Examiner notes that “lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements” (see MPEP 2106.05 (I)). Examiner also notes that whether or not the claims include well-understood, routine, conventional activities is only one of multiple considerations when determining whether additional elements amount to an inventive concept (see 2106.05 (I)(A)).
Accordingly, the rejection is maintained.
103:
Applicant's amendments overcome the rejection made under 35 U.S.C. § 103 and it is withdrawn.
Claim Objections
Claims 1, 3-7, 9-11, 13-17, and 19-23 are objected to because of the following informalities:
Claim 1 recites, “and execute the selected machine learning model using the plurality of segments to generate forecast parameters identifying an optimal route associated with the transportation carrier from a plurality of candidate routes” (emphasis added). It appears that this limitation contains a typographical/grammatical error where there is an extra “and” preceding the limitation. For the purposes of examination, the claim is interpreted to read, “execute the selected machine learning model using the plurality of segments to generate forecast parameters identifying an optimal route associated with the transportation carrier from a plurality of candidate routes” (emphasis added). Appropriate correction is required.
Claims 1, 11, and 20 recite, “and execute the selected machine learning model using the plurality of segments to generate forecast parameters identifying an optimal route associated with the transportation carrier from a plurality of candidate routes” and “wherein the adjusted forecast parameters identify an adjusted optimal route that differs from the optimal route” (emphasis added). It appears that this limitation contains a typographical/grammatical error. For the purposes of examination, the claims are interpreted to read, “execute the selected machine learning model using the plurality of segments to generate forecast parameters for identifying an optimal route associated with the transportation carrier from a plurality of candidate routes” and “wherein the adjusted forecast parameters are used to identify an adjusted optimal route that differs from the optimal route” (emphasis added). Examiner notes that this interpretation is consistent with paragraph [0095] of Applicant’s specification, which states that the forecast parameters are used to “generate optimal routes”. Appropriate correction is required.
Claims 3-7, 9, 10, 13-17, 19, and 21-23 are also rejected by virtue of dependency.
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, 3-7, 9-11, 13-17, and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent Claims
MPEP 2106 Step 2A- Prong 1:
Independent claims 1, 11, and 20 recite, storing historical data associated with a transportation carrier, wherein the historical data characterizes previous routes with inbound segments, outbound segments, and empty segments;
receive journey data associated with the transportation carrier;
parse and extract, from the journey data, a plurality of segments including: an inbound segment, an outbound segment, and an empty segment;
train a plurality of models based on the historical data to learn to minimize the empty segments;
identify a selected model from the plurality of models based on one or more performance metrics over a first predetermined period of time;
execute the selected model using the plurality of segments to generate forecast parameters identifying an optimal route associated with the transportation carrier from a plurality of candidate routes, wherein the optimal route has a least number of empty segments among the plurality of candidate routes
train, in parallel, a plurality of adjustment models configured to incorporate one or more adjustment factors;
train, in parallel, a plurality of adjustment models configured to incorporate one or more adjustment factors;
identify a selected adjustment model from the plurality of adjustment models;
and execute the selected adjustment model based on the forecast parameters to generate adjusted forecast parameters associated with the transportation carrier during a second predetermined period of time, wherein the adjusted forecast parameters identify an adjusted optimal route that differs from the optimal route; and
transmit the adjusted optimal route for display.
The limitations above are processes that under broadest reasonable interpretation cover “certain methods of organizing human activity” (including sales activities or behaviors, or business relations). Specifically, optimizing a carrier route is establishing business relationships and performing sales activities/behaviors. Examiner particularly notes paragraph [0002] of Applicant’s specification, which references transporting goods between distribution centers and point of sale locations.
Furthermore, Examiner notes that, per Fig. 6 of Applicant’s specification, an “adjustment model” includes models such as ARIMA models and Regression models, which are mathematical/statistical models. As such, under the broadest reasonable interpretation, the addition of a plurality of adjustment models merely narrows the abstract idea and is not treated as an additional element under Step 2A- Prong 2.
MPEP 2106 Step 2A- Prong 2:
The judicial exceptions are not integrated into a practical application. Claims 1, 11, and 20 as a whole amount to: merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, or “apply it”; or generally linking the use of the judicial exception to a particular technological environment or field of use.
Independent claims 1, 11, and 20 recite the following additional elements to perform the above recited steps: a database (claims 1, 11, and 20), a computing device (claims 1 and 20), at least one processor (claims 1 and 20), a plurality of machine learning models (claims 1, 11, and 20), and a non-transitory computer readable medium (claim 20). These additional elements are generic computer components performing generic computer functions at a high level of generality, and are recited at a high level of generality. These additional elements amount to no more than mere instructions to apply the exception using a generic computer component.
Individually and as a whole, these additional elements do not integrate the judicial exceptions into a practical application because the claims do not: improve the functioning of the computer itself or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment to transform the judicial exception into patent-eligible subject matter; amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer.
MPEP 2106 Step 2B:
Independent claims 1, 11, and 20 do not include additional elements that are sufficient to amount to significantly more (also known as an “inventive concept”) than the judicial exception. As discussed above, the additional elements are generic computer components performing generic computer functions at a high level of generality. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Alone or in combination, the additional elements do not contribute significantly more than the judicial exception and as a result, the claims are ineligible.
Dependent Claims
Dependent claims 3-7, 9, 10, 13-17, 19, and 21-23 recite additional details that merely narrow the previously recited abstract idea limitations, without adding any additional elements for analysis. Thus, claims 3-7, 9, 10, 13-17, 19, and 21-23 are also ineligible for the reasons stated above with respect to independent claims 1, 11, and 20.
Allowable over Prior Art
Available prior art, alone or in combination, fails to teach “train a plurality of machine learning models based on the historical data to learn to minimize the empty segments” and “and execute the selected adjustment model based on the forecast parameters to generate adjusted forecast parameters associated with the transportation carrier during a second predetermined period of time” in combination with the additional limitations recited in independent claims 1, 11, and 20 (and incorporated into claims 3-7, 9, 10, 13-17, 19, and 21-23 by dependency). Examiner notes that there is a 101 rejection of the claims as well as objections.
The following are the closest prior art:
U.S. Patent Publication No. 2020/0116508 to Dashti et al. (Dashti) teaches, selecting a machine learning model to optimize carrier routes taking into account holiday and seasonal variations. However, Dashti does not teach, optimizing the routes to minimize empty segments or the use of adjustment models.
U.S. Patent Publication No. 2016/0104111 to Jones et al. (Jones) teaches, minimizing empty segments (i.e. “deadheading”) on a route using machine learning. However, Jones does not teach a plurality of machine learning models or the use of adjustment models.
U.S. Patent Publication No. 2020/0134641 to Morgan et al. (Morgan) teaches, the selection and use of supplemental models (i.e., adjustment models) to accommodate for changes in demand of an item caused by seasonality, holidays, or other variations in demand. However, Morgan does not teach identifying an optimal route.
U.S. Patent Publication No. 2007/0221791 to Voelk et al. (Voelk) teaches, minimizing empty segments (i.e. “deadheading”) on a route. However, Voelk does not teach machine learning models or the use of adjustment models.
NPL “How AI Algorithms Revolutionize Route Optimization in Logistics” to RTS Labs (RTS Labs) teaches, using machine learning to minimize empty miles in transportation logistics.
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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/S.S.W./Examiner, Art Unit 3628
/RUPANGINI SINGH/Primary Examiner, Art Unit 3628
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