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
This action is responsive to Applicant’s claims filed 03/20/2024.
Claims 1-11 and 22-30 are currently pending and have been examined here.
Claims 12-21 and 31-41 have been canceled.
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 1-11 and 22-30 are rejected under 35 U.S.C. § 101. The claims are drawn to ineligible patent subject matter, because the claims are directed to a recited judicial exception to patentability (an abstract idea), without claiming something significantly more than the judicial exception itself.
Claims are ineligible for patent protection if they are drawn to subject matter which is not within one of the four statutory categories, or, if the subject matter claimed does fall into one of the four statutory categories, the claims are ineligible if they recite a judicial exception, are directed to that judicial exception, and do not recite additional elements which amount to significantly more than the judicial exception itself. Alice Corp. v. CLS Bank Int'l, 375 U.S. ___ (2014). Accordingly, claims are first analyzed to determine whether they fall into one of the four statutory categories of patent eligible subject matter. Then, if the claims fall within one of the four statutory categories, it must be determined whether the claims are directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea). In determining whether a claim is directed to a judicial exception, the claim is first analyzed to determine whether the claim recites a judicial exception. If the claim does not recite one of these exceptions, the claim is directed to patent eligible subject matter under 35 U.S.C. 101. If the claim recites one of these exceptions, the claim is then analyzed to determine whether the claim recites additional elements that integrate the exception into a practical application of that exception. Claims which integrate the exception into a practical application of that exception are directed to patent eligible subject matter under 35 U.S.C. 101. If the claim fails to integrate the exception into a practical application of that exception, the claim is directed to an abstract idea. Finally, if the claims are directed to a judicial exception to patentability, the claims are then analyzed determine whether the claims are directed to patent eligible subject matter by reciting meaningful limitations which transform the judicial exception into something significantly more than the judicial exception itself. If they do not, the claims are not directed towards eligible subject matter under 35 U.S.C. § 101.
Regarding independent claims 1 and 22 the claims are directed to one of the four statutory categories (a machine, a process, and an article of manufacture, respectively.) The claimed invention of independent claims 1 and 22 is directed to a judicial exception to patentability, an abstract idea. The claims include limitations which recite elements which can be properly characterized under at least one of the following groupings of subject matter recognized as abstract ideas by MPEP 2106.04(a):
Mathematical Concepts: mathematical relationships, mathematical formulas or equations, and mathematical calculations;
Certain methods of organizing human activity: fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes: concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
Claims 1 and 22, as a whole, recite the following limitations:
a) determining a set of models, wherein each model is associated with a sliding training window; (claim 1; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine a set of models wherein each is associated with a sliding training window; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
b) determining an actual transit time for each package in a set of packages delivered within an evaluation period; (claim 1; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine an actual transit time for each package in this manner; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
c) for each model in the set of models, determining a predicted transit time for each package in the set of packages based on historic time-in-transit data selected based on the respective sliding training window; (claim 1; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine a predicted transit time for each package for each model in this manner; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
d) selecting a model from the set of models based the respective predicted transit time and actual transit time for each package in the set of packages; (claim 1; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could select a model based on these factors; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
and e) using the selected model, predicting a future transit time for the package, wherein the package is associated with a shipment created within a prediction period. (claim 1; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could predict a future transit time for a package using the selected model; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
a) determine actual transit times for a set of packages; (claim 22; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine actual transit times for a set of packages; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
b) train a set of models using supervised learning, wherein each model is trained using a different set of historic transit data; (claim 22; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could train a set of models using supervised learning in this manner; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services; further still, the broadest reasonable interpretation of this limitation recites mathematical concepts since training a model using supervised learning encompasses the use of mathematical formulas and operations to perform the training (predicting outcomes using mathematical operations, comparing outcomes to known solution sets using an error function, etc.))
c) for each model: determine predicted transit times for the set of packages using the model; (claim 22; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine predicted transit times for a set of packages using each model; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
and determine individual evaluation metrics for each of a set of time periods based on the predicted and actual transit times for the set of packages; (claim 22; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine individual evaluation metrics in this manner; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
d) select a model from the set of models based on the individual evaluation metrics for each trained model; (claim 22; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could select a model based on evaluation metrics; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
and e) predict a transit time for the target package using the selected model; (claim 22; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could predict a transit time using a selected model; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
. . . returns the predicted transit time for the target package. (claim 22; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could return a precited transit time for target package; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services)
The above elements, as a whole, recite mental processes since, but for the requirement to implement the steps on a set of generic computer components, the above elements could be practiced by a human using their min, pen and paper, and simple observation, evaluation, and judgment. Furthermore, the above elements, as a whole, recite certain methods of organizing human activity since they recite the business processes for determining transit times for packages, which comprises a business relation and commercial sales activity.
Moving forward, the above recited abstract idea is not integrated into a practical application.
The added limitations do not represent an integration of the abstract idea into a practical application because:
the claims represent mere instructions to implement an abstract idea on a computer, and merely use a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
the claims merely add insignificant extra-solution activity to the judicial exception (activity which can be characterized as incidental to the primary purpose or product that is merely a nominal or tangential addition to the claim). See MPEP 2106.05(g) and/or
the claims represent mere general linking of the use of the judicial exception to a particular technological environment or field of use. See MPEP 2016.05(h)
Beyond those limitations which recite the abstract idea, the following limitations are added:
A system, comprising: (claim 22; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
an interface configured to receive a request for a target package; (claim 22; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
and a processing system configured to: (claim 22; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
The claims, as a whole, are directed to the abstract idea(s) which they recite. The claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Therefore, because the claims recite a judicial exception (an abstract idea) and do not integrate the judicial exception into a practical application, the claims, as a whole, are directed to the judicial exception.
Turning to the final prong of the test (Step 2B), independent claims 1 and 22 do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because there are no meaningful limitations which transform the exception into a patent eligible application.
As outlined above, the claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
Furthermore, no specific limitations are added which represent something other than what is well-understood, routine, and conventional activity in the field. See MPEP 2106.05(d). Besides performing the abstract idea itself, the generic computer components only serve to perform the court-recognized well-understood computer functions of receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory. See MPEP 2106.05(d). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation. The specification details any combination of a generic computer system program to perform the method. Generically recited computer elements do not add a meaningful limitation to the abstract idea because they would be routine in any computer implementation and because the Alice decision noted that generic structures that merely apply the abstract ideas are not significantly more than the abstract ideas. Therefore, independent claims 1 and 22 are rejected under 35 U.S.C. §101 as being directed to ineligible subject matter.
Claims 2-11 and 23-30, recite the same abstract idea as their respective independent claims.
The following additional features are added in the dependent claims:
Claim 2:
wherein selecting a model comprises: for each model in the set of models, determining an evaluation metric based on the respective predicted transit time and actual transit time for each package in the set of packages;
selecting a model from the set of models based the respective evaluation metric for each of the set of models.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine an evaluation metric and select a model based on the evaluation metric; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services.
Claim 3:
further comprising repeating a)-e) for a successive prediction period.
The broadest reasonable interpretation of this limitation merely requires the repetition of the abstract idea steps above and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 4:
wherein the evaluation period is redetermined for the prediction period.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could redetermine an evaluation period for a prediction period; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services.
Claim 5:
wherein the sliding training window comprises a set of dates relative to a date associated with a package, wherein at least two packages in the set of packages are associated with different dates, and the sliding training window encompasses a first set of dates for the first package and slides to encompass a second set of dates for the second package.
The broadest reasonable interpretation of this limitation merely alters the sliding training window used in the abstract idea above and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 6:
wherein the date associated with a package is based on the respective shipment creation date.
The broadest reasonable interpretation of this limitation merely alters the dates used in the abstract idea above and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 7:
wherein the set of dates comprises nonconsecutive dates.
The broadest reasonable interpretation of this limitation merely alters the dates used in the abstract idea above and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 8:
wherein determining the predicted transit time comprises determining a minimum transit time for a predetermined percentile of packages in the historic time-in-transit transit data to be delivered.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine a minimum transit time for a predetermined percentile of packages to be delivered; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services.
Claim 9:
receiving, from a client computing system, a shipment request for the target package;
generating a response to the shipment request, wherein the response comprises the future transit time for delivering the target package;
and providing the response to the client computing system.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could receive a shipment request, generate a response comprising the future transit time, and provide it to a customer; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services. Regarding the use of a client computing system, the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use
Claim 10:
wherein the historic time-in- transit data is associated with at least one of: a shipping carrier, a shipping carrier service, or a shipping lane.
The broadest reasonable interpretation of this limitation merely alters the transit data used in the abstract idea above and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 11:
wherein the historic time-in- transit data comprises bi-directional transit times.
The broadest reasonable interpretation of this limitation merely alters the transit data used in the abstract idea above and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 23:
wherein each of the set of packages is associated with an evaluation period, wherein each of the set of time periods is within the evaluation period.
The broadest reasonable interpretation of this limitation merely alters the time periods used in the abstract idea above and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 24:
wherein each of the set of packages is delivered within the evaluation period.
The broadest reasonable interpretation of this limitation merely alters the evaluation period used in the abstract idea above and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 25:
wherein the processing system is further configured to: for each model, aggregate the individual evaluation metrics to determine an overall evaluation metric, wherein selecting a model from the set of models based on the individual evaluation metrics comprises selecting the model based on the overall evaluation metric for each trained model.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could aggregate evaluation metrics to determine an aggregated metric and select a model based on such; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services.
Claim 26:
wherein each model is associated with a sliding training window, wherein training the set of models comprises training a set of model instances of each model, wherein each model instance is associated with a different reference date, wherein the set of historic transit data used to train the model instance is selected based on the sliding training window and the associated reference date.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could train a model using a sliding training window in this manner; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services; further still, the broadest reasonable interpretation of this limitation recites mathematical concepts since training a model using supervised learning encompasses the use of mathematical formulas and operations to perform the training (predicting outcomes using mathematical operations, comparing outcomes to known solution sets using an error function, etc.).
Claim 27:
wherein predicting the transit time for the target package using the selected model comprises training a new model instance for the selected model, wherein the new model instance is associated with a reference date for the target package, wherein the transit time for the target package is predicted using the new model instance.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could train a new model instance for a selected model in this manner and use it to predict a transit time; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services; further still, the broadest reasonable interpretation of this limitation recites mathematical concepts since training a model using supervised learning encompasses the use of mathematical formulas and operations to perform the training (predicting outcomes using mathematical operations, comparing outcomes to known solution sets using an error function, etc.).
Claim 28:
wherein the reference date for the target package is determined based on a shipment creation date for the target package.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could determine a reference date based on a shipment creation date; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services.
Claim 29:
wherein, for each model instance of a model, the sliding training window associated with the model slides with the reference date for the respective model instance.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could slide a training window in this manner; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services.
Claim 30:
wherein the interface is further configured to return the predicted transit time for the target package, wherein the predicted transit time for the target package is used to select a shipping carrier service.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could return a predicted transit time and use it to select a shipment carrier service; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial shipment entities would perform this step in predicting shipment times for shipment services.
The above limitations do not represent a practical application of the recited abstract idea. The claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Therefore, because the claims recite a judicial exception (an abstract idea) and do not integrate the judicial exception into a practical application, the claims are also directed to the judicial exception.
Furthermore, the added limitations do not direct the claim to significantly more than the abstract idea. No specific limitations are added which represent something other than what is well-understood, routine, and conventional activity in the field. See MPEP 2106.05(d). Accordingly, none of the dependent claims 2-11 and 23-30, individually, or as an ordered combination, are directed to patent eligible subject matter under 35 U.S.C. 101.
Please see MPEP §2106.05(d)(II) for a discussion of elements that the Courts have recognized as well-understood, routine, conventional, activity in particular fields.
Please see MPEP §2106 for examination guidelines regarding patent subject matter eligibility.
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 (i.e., changing from AIA to pre-AIA ) 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, 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 1-2 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (U.S. PG Pub. No. 20200342398; hereinafter "Aggarwal") in view of Ting, Pei-Ya, et al. ("Freeway travel time prediction using deep hybrid model–taking Sun Yat-Sen freeway as an example." IEEE Transactions on Vehicular Technology 69.8 (2020): 8257-8266; hereinafter "Ting").
As per claim 1, Aggarwal teaches:
A method, comprising:
Aggarwal teaches a system and method for providing delivery time predictions. (Aggarwal: abstract)
With respect to the following limitation:
a) determining a set of models, wherein each model is associated with a sliding training window;
Aggarwal teaches that training data may be gathered regarding actual delivery times for previous sets of packages, and that various machine learning models may be trained using this information. (Aggarwal; paragraph [0051, 53-58], see paragraph [0026] of Aggarwal outlining a number of different supervised machine learning techniques) Aggarwal, however, does not appear to explicitly teach the use of a sliding training window for the models.
Ting, however, teaches that a sliding training window may be used to train a machine learning model which predicts travel time. (Ting: "3) Time-Level Sliding Window Sequence", Fig. 2) Ting teaches combining the above elements with the teachings of Aggarwal for the benefit of providing a system wherein the training data contains more historical features, which enhances the prediction effect of the time series GRU model and XGBoost. (Tin: "V. Conclusion and Future Work") Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Ting with the teachings of Aggarwal to achieve the aforementioned benefits.
Aggarwal in view of Ting further teaches:
b) determining an actual transit time for each package in a set of packages delivered within an evaluation period;
Aggarwal teaches that training data may be gathered regarding actual delivery times for previous sets of packages, and that various machine learning models may be trained using this information. (Aggarwal; paragraph [0051, 53-58]) Aggarwal further teaches that the training data may be separated by evaluation periods comprising months in which the deliveries were made. (Aggarwal: paragraph [0057])
c) for each model in the set of models, determining a predicted transit time for each package in the set of packages based on historic time-in-transit data selected based on the respective sliding training window;
Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42, 60]) Aggarwal further teaches that the evaluation metrics may comprise a mean square error or a root mean square error (an aggregated evaluation metric) of predictions which fall between an EDD min and an EDD max (an evaluation sub-period). (Aggarwal: paragraphs [0060-64])
d) selecting a model from the set of models based the respective predicted transit time and actual transit time for each package in the set of packages;
Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42])
and e) using the selected model, predicting a future transit time for the package, wherein the package is associated with a shipment created within a prediction period.
Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42, 60])
As per claim 2, Aggarwal in view of Ting teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein selecting a model comprises: for each model in the set of models, determining an evaluation metric based on the respective predicted transit time and actual transit time for each package in the set of packages;
Aggarwal further teaches that the evaluation metric may comprise a percentage of properly evaluated delivery predictions and therefore teaches an aggregated individual evaluation metric. (Aggarwal: paragraph [0041, 60-64]) Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42, 60]) Aggarwal further teaches that the evaluation metrics may comprise a mean square error or a root mean square error (an aggregated evaluation metric) of predictions which fall between an EDD min and an EDD max (an evaluation sub-period). (Aggarwal: paragraphs [0060-64])
selecting a model from the set of models based the respective evaluation metric for each of the set of models.
Aggarwal further teaches that the evaluation metric may comprise a percentage of properly evaluated delivery predictions and therefore teaches an aggregated individual evaluation metric. (Aggarwal: paragraph [0041, 60-64]) Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42, 60]) Aggarwal further teaches that the evaluation metrics may comprise a mean square error or a root mean square error (an aggregated evaluation metric) of predictions which fall between an EDD min and an EDD max (an evaluation sub-period). (Aggarwal: paragraphs [0060-64])
As per claim 8, Aggarwal in view of Ting teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein determining the predicted transit time comprises determining a minimum transit time for a predetermined percentile of packages in the historic time-in-transit transit data to be delivered.
Aggarwal further teaches estimated minimum delivery dates predicted using the system. (Aggarwal: paragraph [0019-20]) Aggarwal further teaches a predetermined percent (80% of the data) used to make the prediction. (Aggarwal: paragraph [0057])
As per claim 9, Aggarwal in view of Ting teaches all of the limitations of claim 1, as outlined above, and further teaches:
receiving, from a client computing system, a shipment request for the target package;
Aggarwal teaches that the system may receive an order for a package from the user via an interface. (Aggarwal: paragraph [0025], Fig. 2)
generating a response to the shipment request, wherein the response comprises the future transit time for delivering the target package;
Aggarwal teaches that the system may then output the delivery estimate to the user. (Aggarwal: paragraph [0020, 25, 27], Fig. 1)
and providing the response to the client computing system.
Aggarwal teaches that the system may then output the delivery estimate to the user. (Aggarwal: paragraph [0020, 25, 27], Fig. 1)
As per claim 10, Aggarwal in view of Ting teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the historic time-in- transit data is associated with at least one of: a shipping carrier, a shipping carrier service, or a shipping lane.
Aggarwal further teaches that the data use may comprise information from a given carrier. (Aggarwal: paragraph [0034-35])
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal in view of Ting further in view of Clem et al. (U.S. PG Pub. No. 20180365634; hereinafter "Clem").
As per claim 3, Aggarwal in view of Ting teaches all of the limitations of claim 1, as outlined above. With respect to the following limitation:
further comprising repeating a)-e) for a successive prediction period.
Aggarwal teaches that training data may be gathered regarding actual delivery times for previous sets of packages, and that various machine learning models may be trained using this information. (Aggarwal; paragraph [0051, 53-58], see paragraph [0026] of Aggarwal outlining a number of different supervised machine learning techniques) Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42]) In teaching that the process is repeated for an initial delivery estimate (a first period in which the request was received), and then again for each leg, Aggarwal in view of Cote teaches that the steps may be performed for packages associated with requests within a prediction period of a set of prediction periods.
Clem, however, teaches a prediction model used to predict arrival times of containers at locations, wherein the scanning of a container at an interim location may trigger the recalculation of the estimated time of arrival of the container at the next location, and therefore teaches repeating the calculation steps of Aggarwal for a successive prediction period. (Clem: paragraphs [0017-21, 25, 31-32], Fig. 2) Clem teaches combining the above elements with the teachings of Aggarwal in view of Cote for the benefit of facilitating model-based tracking-related prediction for shipped containers. (Clem: paragraph [0003]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Clem with the teachings of Aggarwal to achieve the aforementioned benefits.
As per claim 4, Aggarwal in view of Ting teaches all of the limitations of claim 1, as outlined above. With respect to the following limitation:
wherein the evaluation period is redetermined for the prediction period.
Aggarwal teaches that training data may be gathered regarding actual delivery times for previous sets of packages, and that various machine learning models may be trained using this information. (Aggarwal; paragraph [0051, 53-58], see paragraph [0026] of Aggarwal outlining a number of different supervised machine learning techniques) Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42])
Clem, however, teaches a prediction model used to predict arrival times of containers at locations, wherein the scanning of a container at an interim location may trigger the recalculation of the estimated time of arrival of the container at the next location, and therefore teaches redetermining the evaluation period of Aggarwal for the prediction period. (Clem: paragraphs [0017-21, 25, 31-32], Fig. 2) Clem teaches combining the above elements with the teachings of Aggarwal in view of Cote for the benefit of facilitating model-based tracking-related prediction for shipped containers. (Clem: paragraph [0003]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Clem with the teachings of Aggarwal to achieve the aforementioned benefits.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal in view of Ting further in view of Wang, Kevin Sunlin (WIPO Patent Document No. WO 2018/058072 A1; hereinafter "Wang").
As per claim 11, Aggarwal in view of Ting teaches all of the limitations of claim 1, as outlined above, but does not appear to explicitly teach:
wherein the historic time-in- transit data comprises bi-directional transit times.
Wang, however, teaches that estimated travel times may comprise the amount of time it takes a driver to leave from an origin, perform a delivery, and return to a given location (a bidirectional transit time). (Wang: paragraphs [0110, 124, 135]) It can be seen that each element is taught by either Aggarwal in view of Ting, or by Wang. Modifying Aggarwal in view of Ting such that the transit time comprises a bi-directional travel time does not affect the normal functioning of the elements of the claim which are taught by Aggarwal in view of Ting. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Wang with the teachings of Aggarwal in view of Ting, since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Claims 22-25 are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal et al. (U.S. PG Pub. No. 20200342398; hereinafter "Aggarwal") in view of Cote et al. (U.S. PG Pub. No. 20210374632; hereinafter "Cote").
As per claim 22, Aggarwal teaches:
A system, comprising:
Aggarwal teaches a system and method for providing delivery time predictions. (Aggarwal: abstract)
an interface configured to receive a request for a target package;
Aggarwal teaches that the system may receive an order for a package from the user via an interface. (Aggarwal: paragraph [0025], Fig. 2)
and a processing system configured to:
Aggarwal teaches a processing system in the form of a n optimization delivery range provider 110. (Aggarwal: paragraph [0025, 73-82], Figs. 2, 9)
a) determine actual transit times for a set of packages;
Aggarwal teaches that training data may be gathered regarding actual delivery times for previous sets of packages, and that various machine learning models may be trained using this information. (Aggarwal; paragraph [0051, 53-58])
With respect to the following limitation:
b) train a set of models using supervised learning, wherein each model is trained using a different set of historic transit data;
Aggarwal teaches that training data may be gathered regarding actual delivery times for previous sets of packages, and that various machine learning models may be trained using this information. (Aggarwal; paragraph [0051, 53-58], see paragraph [0026] of Aggarwal outlining a number of different supervised machine learning techniques) Aggarwal, however, does not appear to explicitly teach the use of separate training data for each model.
Cote, however, teaches that differing machine learning models in the field of supply chain forecasting may be trained using different training data sets. (Cote: paragraph [0046]) Cote teaches combining the above elements with the teachings of Aggarwal for the benefit of accounting for differing dynamics in different delivery regions. Id. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Cote with the teachings of Aggarwal to achieve the aforementioned benefits.
Furthermore, it can be seen that each element is taught by either Aggarwal or by Cote. Training the separate machine learning models taught by Aggarwal with separate training sets, as taught by Cote, does not affect the normal functioning of the elements of the claim which are taught by Aggarwal. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Cote with the teachings of Aggarwal, since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Aggarwal in view of Cote further teaches:
c) for each model: determine predicted transit times for the set of packages using the model;
Aggarwal teaches that training data may be gathered regarding actual delivery times for previous sets of packages, and that various machine learning models may be trained using this information. (Aggarwal; paragraph [0051, 53-59]) Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42, 60])
and determine individual evaluation metrics for each of a set of time periods based on the predicted and actual transit times for the set of packages;
Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42])
d) select a model from the set of models based on the individual evaluation metrics for each trained model;
Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42])
and e) predict a transit time for the target package using the selected model;
Aggarwal teaches that the models may be tested and the accuracy of the model may be determined using a set of evaluation metrics, wherein the model may only be selected if it passes this evaluation. (Aggarwal: paragraphs [0040-42, 60])
wherein the interface returns the predicted transit time for the target package.
Aggarwal teaches that the system may then output the delivery estimate to the user. (Aggarwal: paragraph [0027])
As per claim 23, Aggarwal in view of Cote teaches all of the limitations of claim 22, as outlined above, and further teaches:
wherein each of the set of packages is associated with an evaluation period, wherein each of the set of time periods is within the evaluation period.
Aggarwal further teaches that the training data may be separated by evaluation periods comprising months in which the deliveries were made. (Aggarwal: paragraph [0057])
As per claim 24, Aggarwal in view of Cote teaches all of the limitations of claim 22, as outlined above, and further teaches:
wherein each of the set of packages is delivered within the evaluation period.
Aggarwal further teaches that the training data may be separated by evaluation periods comprising months in which the deliveries were made. (Aggarwal: paragraph [0057])
As per claim 25, Aggarwal in view of Cote teaches all of the limitations of claim 22, as outlined above, and further teaches:
wherein the processing system is further configured to: for each model, aggregate the individual evaluation metrics to determine an overall evaluation metric, wherein selecting a model from the set of models based on the individual evaluation metrics comprises selecting the model based on the overall evaluation metric for each trained model.
Aggarwal further teaches that the evaluation metric may comprise a percentage of properly evaluated delivery predictions and therefore teaches an aggregated individual evaluation metric. (Aggarwal: paragraph [0041, 60-64])
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal in view of Cote further in view of Batemen et al. (U.S. PG Pub. No. 20170154347; hereinafter "Batemen").
As per claim 30, Aggarwal in view of Cote teaches all of the limitations of claim 22, as outlined above, but does not appear to explicitly teach:
wherein the interface is further configured to return the predicted transit time for the target package, wherein the predicted transit time for the target package is used to select a shipping carrier service.
Batemen, however, teaches that a machine learned delivery time estimate may be used to determine the proper service level of the package to be delivered. (Batemen: paragraphs [0036, 38, 53, 58-60]) Batemen teaches combining the above elements with the teachings of Aggarwal in view of Cote for the benefit of improving delivery of a parcel by leveraging delivery estimates. (Batemen: paragraph [0058]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Batemen with the teachings of Aggarwal in view of Cote to achieve the aforementioned benefits.
Novelty/Non-obviousness
Regarding claim 5, the prior art does not appear to teach, in the context of the systems and methods recited for determining transit times, and in ordered combination with the other elements of the claim, that the sliding training window may comprise a set of dates relative to a date associated with a package, wherein at least two packages in the set of packages are associated with different dates, and the sliding training window encompasses a first set of dates for the first package and a second set of dates for the second.
Regarding claim 26, the prior art does not appear to teach, in the context of the systems and methods recited for determining transit times, and in ordered combination with the other elements of the claim, that each model may be associated with a sliding training window, wherein a set of instances of each model may be trained wherein each model instance is associated with a different reference date, wherein the selected training data is based on the sliding training window and the associated reference date.
Conclusion
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/EMMETT K. WALSH/Primary Examiner, Art Unit 3628