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 Claims
Claims 1-20 are currently pending and have been examined.
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.
Claim(s) 1-20 is/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.
Claim(s) 1 and 11 recite(s) a system and series of steps for coordinating package delivery, which under broadest reasonable interpretation, is analogous to concepts performed in the human mind, such as observation, evaluation, judgment, opinion. These concepts are grouped as mental processes.
The limitation(s) of, ‘generate a set of provisioning data structures…’; ‘generate a set of routes of provisioning tasks’; ‘generate pairings of provisioning data structures and routes of provisioning tasks’, as drafted, recite a process that, under broadest reasonable interpretation, is/are certain methods of organizing human activity. Accordingly, the claim(s) recite(s) an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) the additional element(s) of ‘a first machine-learning model’, ‘a second machine-learning model’, ‘a third machine-learning model’, ‘a fourth machine-learning model’. These additional elements are recited at a high-level of generality such that in conjunction with the abstract limitations, they amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim(s) 2-10 and 12-20 further recite(s) the system and series of steps for coordinating package delivery, which under broadest reasonable interpretation, is analogous to concepts performed in the human mind, such as observation, evaluation, judgment, and opinion. These concepts are grouped as mental processes. Accordingly, the claim(s) recite(s) an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite no additional elements that use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Thus, the claims are directed to an abstract idea.
As analyzed above, the limitations as an ordered combination, are merely linking the use of the judicial exception to a particular technological environment or field of use. In addition, the claims do not improve functionality of a computer or improve any other technology. Thus, claims 1-20 are ineligible as the claims do not recite additional elements which result in significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 9-12, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malyack (U.S. Patent App. Pub. No. 20190156253), in view of Ryan (U.S. Patent App. Pub. No. 20200250617).
In regards to claim 1, Malyack teaches: A system comprising: a first machine-learning model executed using as input predicted package data to generate a set of provisioning data structures each comprising a predicted region, a predicted duration, and a value (Malyack: ¶8, ¶24, ¶57, ¶78, ¶131 disclose inputting predicted package data into a machine learning model to generate data structures comprising delivery destinations, delivery durations, and other delivery associated values).
Although Malyack teaches that the machine learning model may utilize a plurality of sub-models (Malyack: ¶101), the reference does not explicitly state wherein the sub-models generate delivery task routes and generate pairing of package data and delivery task routes.
However, Malyack and Ryan together teach a second machine-learning model executed using as input actual package data to generate a set of routes of provisioning tasks (Malyack: ¶101, ¶133-134 disclose utilizing sub-models to process actual package data; Ryan: ¶7-8, ¶23, ¶25-27 disclose utilizing published shipment requests to generate package delivery routes); and a third machine-learning model executed using as input the set of provisioning data structures generated by the first machine-learning model and the set of routes of provisioning tasks generated by the second machine-learning model to generate pairings of provisioning data structures and routes of provisioning tasks (Malyack: ¶101, ¶133-134 disclose utilizing sub-models to process actual package data; Ryan: ¶6, ¶27-29 disclose matching shipment requests, delivery routes, and delivery drivers).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the route generation and matching process, as taught by Ryan, into the system and method of Malyack. One of ordinary skill in the art would have been motivated to make this modification in order to “ensure that as many deliveries as possible are completed, and finding of the best driver based on the criteria” (Ryan: ¶28).
In regards to claim 2, Malyack and Ryan teach the system of claim 1. Ryan further teaches wherein the system provides the routes of provisioning tasks of the pairings of provisioning data structures and routes of provisioning tasks to one or more provisioning agents based on the one or more provisioning agents selecting the corresponding provisioning data structures for execution (Ryan: ¶6, ¶21-22, ¶27-29 disclose matching shipment requests, delivery routes, and delivery drivers based on a plurality of criteria).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the route generation and matching process, as taught by Ryan, into the system and method of Malyack. One of ordinary skill in the art would have been motivated to make this modification in order to “ensure that as many deliveries as possible are completed, and finding of the best driver based on the criteria” (Ryan: ¶28).
In regards to claim 9, Malyack and Ryan teach the system of claim 1. Malyack further teaches a fourth machine-learning model to generate the predicted package data (Malyack: ¶8, ¶24, ¶57, ¶115-116 disclose utilizing machine learning models to generate predicted package data).
In regards to claim 10, Malyack and Ryan teach the system of claim 1. Malyack further teaches wherein the first machine-learning model is executed using as input the predicted package data and provisioning agent information to generate the set of provisioning data structures (Malyack: ¶8, ¶24, ¶34, ¶57, ¶78, ¶131 disclose inputting predicted package data and shipping service provider data into a machine learning model to generate data structures comprising delivery destinations, delivery durations, and other delivery associated values).
In regards to claim 11, the claim recites the same or similar limitations as those addressed above in claim 1 and therefore is rejected for the same reasons set forth above for claim 1. Additionally, the rationale to combine the prior art set forth above for claim 1 applies to the rejection of claim 11.
In regards to claim 12, Malyack and Ryan teach the method of claim 11. Additionally, the claim recites the same or similar limitations as those addressed above in claim 2 and therefore is rejected for the same reasons set forth above for claim 2. Furthermore, the rationale to combine the prior art set forth above for claim 2 applies to the rejection of claim 12.
In regards to claim 19, Malyack and Ryan teach the method of claim 11. Additionally, the claim recites the same or similar limitations as those addressed above in claim 9 and therefore is rejected for the same reasons set forth above for claim 9.
In regards to claim 20, Malyack and Ryan teach the method of claim 11. Additionally, the claim recites the same or similar limitations as those addressed above in claim 10 and therefore is rejected for the same reasons set forth above for claim 10.
Novelty/Non-Obviousness
The subject matter of claims 3-8 and 13-18 is not taught by the cited prior art and is considered novel. However, claims 3-8 and 13-18 remain rejected under 35 U.S.C. 101, as described above.
The closest prior art of record are Malyack (U.S. Patent App. Pub. No. 20190156253), Ryan (U.S. Patent App. Pub. No. 20200250617), Beckmann (U.S. Patent App. Pub. No. 20190020578), Yamaguchi (U.S. Patent App. Pub. No. 20210090007), Fu (U.S. Patent App. Pub. No. 20210133677), and Ceniti (U.S. Patent App. Pub. No. 20230186226).
The cited prior art, taken either individually or in combination with other prior art of record, fails to teach or suggest wherein the first machine-learning model generates the set of provisioning data structures in a first time interval, the second machine-learning model generates the set of routes of provisioning tasks during a second time interval, and the third machine-learning model generates the pairings of provisioning data structures and routes of provisioning tasks during the second time interval.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Wayne S Murray whose telephone number is (571)272-4306. The examiner can normally be reached M-F 8am-5pm ET.
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/Wayne S. Murray/Examiner, Art Unit 3628
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