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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/8/2026 has been entered.
Claims 1, 9, 12, 15 and 18-20 are presently amended.
Claims 2, 6, 13 and 17 are cancelled.
Claims 1, 3-5, 7-12, 14-16 and 18-20 are pending.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant’s arguments filed 6/8/2026 have been fully considered in view of further consideration of statutory law, Office policy, precedential common law, and the cited prior art as necessitated by the amendments to the claims, and are persuasive in-part for the reasons set forth below.
35 USC § 101 Rejections
First, Applicant argues that “Under Step 2A, Prong One, it is respectfully submitted that amended claim 1 does not recite a judicial exception. For example, it is submitted that amended claim 1 is not directed to a fundamental economic practice, a method of organizing human activity, or a mental process. Rather, for example, amended claim 1 recites a particular networked computing process for transforming heterogeneous weather observation records and parcel shipping activity records into linked parcel-weather records suitable for machine-learning processing and for updating operation of a parcel transportation management system. It is submitted that the claimed process relies on computer-implemented operations involving geospatial record association, database storage of linked records including distance error values, machine-learning model updating using defined performance parameters, and signal packet communication with a shipping entity computing device, for example” [Arguments, pages 18-19].
In response, Applicants arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the present claims recite a judicial exception. With regard to the machine learning and shipping entity computing device elements, Examiner observes that these elements do not alter the general thrust of the present invention. In particular, Examiner maintains that the present invention recites concepts relating to certain methods of organizing human activity. The claimed limitations describe steps for commercial or legal interactions, which includes agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations. Specifically, determining a probability of a particular parcel arriving at a particular location by a particular time sets forth sales activities.
With respect to the amended limitations and emphasized claim elements in the above-argument, Examiner observes the invention, when considered as a whole, remains primarily directed to commercial and sales activities, particularly relating to parcel delivery. Thus, Examiner respectfully maintains that the present invention recites a judicial exception, particularly an abstract idea. As such, Examiner remains unpersuaded.
Second, Applicant argues that … “amended claim 1 integrates any such alleged abstract idea into a practical application under Step 2A, Prong Two. For example, it is submitted that amended claim 1 does not merely recite a result of predicting parcel delivery or determining a risk, for example. Rather, for example, amended claim 1 recites a specific technological workflow by which a parcel transportation management system generates and stores linked parcel-weather records from misaligned time-dependent and location-dependent datasets. In particular, amended claim 1 recites identifying a nearest weather station having recorded weather condition parameters for a specified time period, iteratively expanding a specified geographical region and specified time period until such a nearest weather station is identified, and storing a linked parcel-weather record that associates the particular parcel shipping activity record with the weather condition record and includes a distance error value. These limitations define a particular computerized data association technique for improving the quality and usability of weather-linked parcel activity data.
It is further submitted that amended claim 1 also addresses the Examiner's stated concern that the claim merely recited use of a trained model without detailing how the model operates (e.g., see page 6 of the Final Office Action). It is noted that amended claim 1 now recites that the trained model comprises a random forest model, a decision-tree model, a neural-network model, or a combination thereof, trained using a training set of linked parcel-weather records including weather condition predictor parameters and parcel delay outcome parameters. It is further noted that amended claim 1 further recites that the monitored predictive performance parameters comprise ROC parameters, FPR parameters, FNR parameters, or a combination thereof, generated from application of the trained model to training data, test data, remainder data, or a combination thereof. Thus, it is submitted that amended claim 1 does not merely invoke machine learning as a generic, black-box tool. Rather, for example, amended claim 1 recites particular linked records used for training, types of model operations employed, predictor and outcome parameters used, and predictive-performance parameters used to update the trained model.
Claim 1, as amended, further addresses the Examiner's stated concern that the claim merely gathers and analyzes information and displays a result (e.g., see again page 6 of the Final Office Action). For example, amended claim 1 does not merely display probabilities to a user. Rather, for example, after receiving a second input indicating a selected transportation route, the parcel transportation management system automatically updates a parcel transportation schedule by storing an updated schedule record in a database and transmitting one or more schedule-update signal packets to a shipping entity computing device to affect transportation of the particular parcel in accordance with the updated schedule record. Thus, claim 1, as amended, applies the generated probability and updated weather condition parameters to update a machine-usable schedule record and communicate that update to another computing device in the parcel transportation management environment” [Arguments, pages 19-20].
In response, Applicants arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the present claims recite a judicial exception without significantly more.
First, with regard to the assertion that “amended claim 1 also addresses the Examiner's stated concern that the claim merely recited use of a trained model without detailing how the model operates”, Examiner respectfully disagrees and observes that the claims still recite a “trained” model which is trained outside of the scope of the present invention, rather than actively claiming a step for training the model.
Further, Examiner observes that claim 1 recites that the trained machine learning model “comprises a random forest model, a decision-tree model, a neural-network model, or a combination thereof” (Claim 1). Examiner respectfully maintains that the machine learning model itself is not recited at a level of specificity considered to demonstrate significantly more than the judicial exception. Thus, Examiner respectfully maintains that the aforementioned elements are considered to be mere instructions to apply an exception (see MPEP § 2106.05(f)), like the following patent ineligible examples:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not integrate the abstract idea into a practical application. Thus, Examiner respectfully maintains that the present claims recite a judicial exception without significantly more. As such, Examiner remains unpersuaded.
Third, Applicant argues that, “claim 1 recites significantly more than any alleged abstract idea. For example, claim 1, as amended, includes a particular sequence of technological operations, including, for example: obtaining weather and parcel records via a communications network; associating weather records with parcel activity records using iterative expansion of geographic and temporal search parameters; storing linked parcel-weather records including distance error values; training and updating a machine-learning model using linked parcel-weather records, weather condition predictor parameters, parcel delay outcome parameters, and monitored ROC, FPR, and/or FNR performance parameters; determining route-specific probabilities; receiving a selected route; and automatically updating and transmitting a schedule-update record to a shipping entity computing device. These limitations impose meaningful technological constraints on amended claim 1 and improve operation of the parcel transportation management system by providing a specific mechanism for generating machine-usable linked training records, updating model performance, and updating downstream transportation schedule records.
At pages 5-6 of the Final Office Action, for example, the Examiner contends that establishing communications links, expanding search parameters, using a trained model, and updating a parcel transportation schedule do not demonstrate an improvement to technology or a practical application. However, it is noted that amended claim now recites not merely expanding search parameters, but storing a linked parcel-weather record including a distance error value generated from the association process. Claim 1, as amended, now recites not merely use of a trained model, but a model trained using linked parcel-weather records including specified predictor and outcome parameters, and updated using defined predictive performance parameters. Amended claim 1 now recites not merely updating a schedule, but storing an updated schedule record and transmitting schedule update signal packets to a shipping entity computing device to affect transportation of the particular parcel in accordance with the updated schedule record. It is asserted that these additional limitations provide the type of practical application and technological implementation that the prior claim language was alleged to lack” [Arguments, pages 21-22].
In response, Applicants arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the present claims recite a judicial exception without significantly more for the same reasons as stated in response to the above-arguments.
In particular, Examiner observes that the claims recite the use of a trained model (trained outside of the scope of the invention) which comprises one or more of various listed models. Examiner disagrees with the assertion that “These limitations impose meaningful technological constraints on amended claim 1 and improve operation of the parcel transportation management system by providing a specific mechanism for generating machine-usable linked training records…” as alleged above, because the machine learning elements are not recited at a level of specificity to demonstrate either an improvement to the functioning of computers or to any particular technological field. As stated in response to the above argument and further in the rejection below, Examiner maintains that the processor, server, memory, user interface, computer device, machine learning operations and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following patent ineligible MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Further, the steps for updating a parcel transportation schedule are considered to amount to ‘gathering and analyzing information using conventional techniques and displaying the result’ (See 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48). Thus, Examiner respectfully disagrees and maintains that the present claims recite a judicial exception without significantly more. As such, Examiner remains unpersuaded.
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, 3-5, 7-12, 14-16 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Examiner formulates an abstract idea as follows:
Step 1: Claims 1, 3-5, 7-12, 14-16 and 18-20 are directed to statutory categories, namely a process (claims 1, 3-5 and 7-11), and a machine (claims 12, 14-16 and 18-20).
Step 2A, Prong 1: Claims 1 and 12 in part, recite the following abstract idea:
A method comprising: establishing a secure communications link between … to facilitate coordination of parcel transportation management responsive at least in part to a first input obtained… via the secure communications link, obtaining… and/or states representative of a plurality of weather condition records and a plurality of parcel shipping activity records via…; for individual parcel shipping activity records of the plurality of parcel shipping activity records, utilizing… associating at least one weather condition record of the plurality of weather condition records with a particular parcel shipping activity record at least in part by identifying a nearest weather station, with respect to a geographical location specified by the particular parcel shipping activity record, having one or more recorded weather condition parameters for a specified time period, including date, comprising: specifying the specified time period, including date, based at least in part on a particular time and date indicated by the particular parcel shipping activity record; specifying a geographical region based at least in part on the geographical location specified by the particular parcel shipping activity record; determining whether the plurality of weather condition records includes one or more recorded weather condition parameters generated by at least one weather station located within the specified geographical region for the specified time period; responsive at least in part to a determination that the plurality of weather condition records do not include one or more recorded weather condition parameters generated by at least one weather station located within the specified geographical region for the specified time period, iteratively expanding the specified geographical region and the specified time period until the nearest weather station having one or more recorded weather condition parameters for the specified time period is identified; and storing, in… a first linked parcel-weather record that associates the particular parcel shipping activity record with the at least one weather condition record of the plurality of weather condition records and that includes a distance error value indicative of a distance between the nearest weather station and the geographical location specified by the particular parcel shipping activity record; identifying… configured to correlate temporal weather features with parcel delay outcomes, one or more correlations between one or more parameters of the plurality of weather condition records and one or more parameters of the plurality of parcel shipping activity records based at least in part on, for the individual parcel shipping activity records of the plurality of parcel shipping activity records, associating the at least one weather condition record with the particular parcel shipping activity record, wherein … is updated based at least in part on monitored predictive performance parameters with respect to correlation of temporal weather features with parcel delay outcomes, wherein …trained using a training set of linked parcel- weather records including weather condition predictor parameters and parcel delay outcome parameters, and wherein the monitored predictive performance parameters comprise one or more receiver operating characteristic parameters, false positive rate parameters, or false negative rate parameters, or a combination thereof, generated from application of … to training data, test data, or remainder data, or a combination thereof; determining… one or more adverse effects on transportation of one or more parcels based at least in part on the identified one or more correlations between the one or more parameters of the plurality of weather condition records and the one or more parameters of the plurality of parcel shipping activity records; determining… a plurality of probabilities of a particular parcel arriving at a particular location by a particular time and/or date for a respective plurality of candidate transportation route based at least in part on the one or more determined adverse effects on the transportation of the one or more parcel; generating… content for display representative of the determined plurality of probabilities of the particular parcel arriving at the particular location by the particular time and/or date for the respective plurality candidate transportation routes; transmit the content for display… via the secure communications link; obtaining … via the secure communications link, a second input… indicating a selected transportation route of the plurality of candidate transportation routes; and automatically updating, … a parcel transportation schedule corresponding to the selected transportation route of the plurality of candidate transportation routes at least in part by storing an updated schedule record in… and transmitting one or more schedule update signal packets to… to affect transportation of the particular parcel in accordance with the updated schedule record based at least in part on an updated probability of the particular parcel arriving at the particular location by the particular time and/or date for the selected transportation route, wherein the updated probability is based at least in part on updated weather condition parameters [Claim 1],
…establish a secure communications link between… to facilitate coordination of parcel transportation management: responsive at least in part to a first input obtained from… via the secure communications link, obtain… and/or states representative of a plurality of weather condition records and a plurality of parcel shipping activity records; for individual parcel shipping activity records of the plurality of parcel shipping activity records, associate at least one weather condition record of the plurality of weather condition records with a particular parcel shipping activity record at least in part via identification of a nearest weather station, with respect to a geographical location specified by the particular parcel shipping activity record, having one or more recorded weather condition parameters for a specified time period, including date, wherein the at least one processor is to: specify the specified time period, including date, based at least in part on a particular time and date indicated by the particular parcel shipping activity record; specify a geographical region based at least in part on the geographical location specified by the particular parcel shipping activity record; determine whether the plurality of weather condition records includes one or more recorded weather condition parameters generated by at least one weather station located within the specified geographical region for the specified time period; responsive at least in part to a determination that the plurality of weather condition records do not include one or more recorded weather condition parameters generated by at least one weather station located within the specified geographical region for the specified time period, iteratively expand the specified geographical region and the specified time period until the nearest weather station having one or more recorded weather condition parameters for the specified time period is identified; and store, in… a first linked parcel-weather record that associates the particular parcel shipping activity record with the at least one weather condition record of the plurality of weather condition records and that includes a distance error value indicative of a distance between the nearest weather station and the geographical location specified by the particular parcel shipping activity record; identify… configured to correlate temporal weather features with parcel delay outcomes, one or more correlations between one or more parameters of the plurality of weather condition records and one or more parameters of the plurality of parcel shipping activity records, wherein… is updated based at least in part on monitored predictive performance parameters with respect to correlation of temporal weather features with parcel delay outcomes, wherein … trained using a training set of linked parcel-weather records including weather condition predictor parameters and parcel delay outcome parameters, and wherein the monitored predictive performance parameters comprise one or more receiver operating characteristic parameters, false positive rate parameters, or false negative rate parameters, or a combination thereof, generated from application of … to training data, test data, or remainder data, or a combination thereof; based at least in part on, for the individual parcel shipping activity records of the plurality of parcel shipping activity records, the associating the at least one weather condition record with the particular parcel shipping activity record; determine one or more adverse effects on transportation of one or more parcels based at least in part on the identified one or more correlations between the one or more parameters of the plurality of weather condition records and the one or more parameters of the plurality of parcel shipping activity records; determine a plurality of probabilities of a particular parcel arriving at a particular location by a particular time and/or date for a respective plurality of candidate transportation route based at least in part on the one or more determined adverse effects on the transportation of the one or more parcels; generate content for display representative of the determined plurality of probabilities of the particular parcel arriving at the particular location by the particular time and/or date for the respective plurality of candidate transportation routes; transmit the content for display… via the secure communications link; obtain … via the secure communications link, a second input… indicating a selected transportation route, of the plurality of candidate transportation routes; and automatically update … a parcel transportation schedule corresponding to the selected transportation route of the plurality of candidate transportation routes at least in part by storing an updated schedule record in …and transmitting one or more schedule update signal packets to … to affect transportation of the particular parcel in accordance with the updated schedule record based at least in part on an updated probability of the particular parcel arriving at the particular location by the particular time and/or date for the selected transportation route, wherein the updated probability is based at least in part on updated weather condition parameters [Claim 12].
These concepts are not meaningfully different than the following concepts identified by the 2019 Revised Patent Subject Matter Eligibility Guidance:
Concepts relating to certain methods of organizing human activity. The aforementioned limitations describe steps for commercial or legal interactions, which includes agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations. Specifically, determining a probability of a particular parcel arriving at a particular location by a particular time sets forth sales activities.
Further, aforementioned limitations describe fundamental economic principles or practices which includes hedging, insurance and mitigating risk. Specifically, determining a probability of a particular parcel arriving at a particular location by a particular time is considered to be mitigating the risk of unexpected delays. As such, claims 1, 3-5, 7-12, 14-16 and 18-20 recite abstract ideas.
The dependent claims recite limitations relative to the independent claims, including, for example:
…wherein the plurality of weather condition records comprises one or more historical weather condition records [Claim 3],
…wherein the determining the plurality of probabilities of the particular parcel arriving at the particular location by the particular time and/or date for the respective plurality of candidate transportation routes is further based, at least in part, on one or more forecasted weather condition record [Claim 4],
…wherein the determining the plurality of probabilities of the particular parcel arriving at the particular location by the particular time and/or date for the respective plurality of candidate transportation routes is further based, at least in part, on one or more shipping infrastructure characteristic parameters [Claim 5],
wherein the determining the one or more adverse effects on the transportation of the one or more parcels includes determining, at least in part via the one or more machine-learning operations, one or more particular parameters to indicate whether particular parcels of the one or more parcels were delayed beyond an expected time and/or date due at least in part to weather based, at least in part on at least a first parcel shipping activity record of the plurality of parcel shipping activity records and at least a first weather condition record of the plurality of weather condition records [Claim 7].
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims 1 and 12 only recite the following additional elements –
…a parcel transportation management system at a first network location and a user computing device at a second network location… from the user computing device… at one or more server computing devices of the parcel transportation management system, signals… a communications network; …at least one processor of the one or more server computing devices of the parcel transportation management system…; …a database of the parcel transportation management system…; …via one or more machine-learning operations executed by at least one processor of the one or more server computing devices of the parcel management system via a trained model… the trained model… the trained model comprises a random forest model, a decision-tree model, a neural- network model, or a combination thereof… the trained model…; …utilizing the at least one processor of the one or more server computing devices…; …utilizing the at least one processor of the one or more server computing devices…; …to the user computing device; …at the one or more server computing devices from the user computing device… from the user computing device…; …at the parcel transportation management system… the database of the parcel transportation management system… a shipping entity computing device… [Claim 1],
… An apparatus, comprising: at least one server computing device of a parcel transportation management system to include at least one processor, and at least one memory, wherein the at least one processor is configured to: …the parcel transportation management system at a first network location and a user computing device at a second network location… the user computing device… at the at least one server computing device of the parcel transportation management system, signals… via a communications network; …at least one processor of the one or more server computing devices…; …a database of the parcel transportation management system…; …via one or more machine-learning operations via a trained model... the trained model… the trained model comprises a random forest model, a decision-tree model, a neural- network model, or a combination thereof… the trained model…; …executed by at least one processor of the one or more server computing device …; …to the user computing device; …at the one or more server computing devices from the user computing device… from the user computing device…; …at the parcel transportation management system… the database of the parcel transportation management system… a shipping entity computing device… [Claim 12].
The processor, server, memory, user interface, computer device, machine learning operations and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following patent ineligible MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Further, the aforementioned elements are considered to be mere instructions to apply an exception (see MPEP § 2106.05(f)), like the following patent ineligible examples:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus cannot integrate the abstract idea into a practical application.
Step 2B: Claims 1 and 12 and their underlying limitations, steps, features and terms, considered both individually and as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
In particular, claims 1 and 12 only recite the following additional elements –
…a parcel transportation management system at a first network location and a user computing device at a second network location… from the user computing device… at one or more server computing devices of the parcel transportation management system, signals… a communications network; …at least one processor of the one or more server computing devices of the parcel transportation management system…; …a database of the parcel transportation management system…; …via one or more machine-learning operations executed by at least one processor of the one or more server computing devices of the parcel management system via a trained model… the trained model… the trained model comprises a random forest model, a decision-tree model, a neural- network model, or a combination thereof… the trained model…; …utilizing the at least one processor of the one or more server computing devices…; …utilizing the at least one processor of the one or more server computing devices…; …to the user computing device; …at the one or more server computing devices from the user computing device… from the user computing device…; …at the parcel transportation management system… the database of the parcel transportation management system… a shipping entity computing device… [Claim 1],
… An apparatus, comprising: at least one server computing device of a parcel transportation management system to include at least one processor, and at least one memory, wherein the at least one processor is configured to: …the parcel transportation management system at a first network location and a user computing device at a second network location… the user computing device… at the at least one server computing device of the parcel transportation management system, signals… via a communications network; …at least one processor of the one or more server computing devices…; …a database of the parcel transportation management system…; …via one or more machine-learning operations via a trained model... the trained model… the trained model comprises a random forest model, a decision-tree model, a neural- network model, or a combination thereof… the trained model…; …executed by at least one processor of the one or more server computing device …; …to the user computing device; …at the one or more server computing devices from the user computing device… from the user computing device…; …at the parcel transportation management system… the database of the parcel transportation management system… a shipping entity computing device… [Claim 12].
These additional elements do not amount to significantly more than the abstract idea for the reasons discussed in 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B. As such, both individually or in combination, these limitations do not add significantly more to the judicial exception.
The remaining dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims do not recite any new additional elements other than those mentioned in the independent claims, which amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). As such, these claims are not patent eligible.
Prior Art Considerations
Examiner conducted a thorough search of the body of available prior art (see attached documents regards PTO-892 Notice of Reference Cited and EAST Search History). Notably, Examiner discovered several patent literature documents that taught aspects of the invention, but no single disclosure taught “every element required by the claims under its broadest reasonable interpretation” [MPEP § 2131] to make a 35 USC § 102 rejection. Further, Examiner considered the individual elements of the recited claims taught across the prior art cited below, but did not find it obvious to combine such disclosures [MPEP § 2142] to make a 35 USC § 103 rejection.
In particular, Bateman, U.S. Publication No. 2017/0154347 discloses a method and system for generating delivery estimates which includes “retrieving historical delivery data from a plurality of shipping carriers; generating cross-carrier delivery features based on normalizing the historical delivery data; generating a cross-carrier delivery prediction model based on the cross-carrier delivery features; retrieving parcel data for the parcel based on a tracking number S140; generating parcel features based on normalizing the parcel data S150; determining a delivery estimate for the parcel based on processing the parcel features with the cross-carrier delivery prediction model S160; and responding to the delivery estimate S170” (Bateman, Abstract). While Bateman discloses a method for predicting transit time for a parcel associated with a user. See, for example, paragraph [0011] of Bateman, Bateman does not appear to disclose “identifying a nearest weather station a nearest weather station, with respect to a geographical location specified by the particular parcel shipping activity record, having one or more recorded weather condition parameters for a specified time period, including date” or “…iteratively expanding the specified geographical region and the specified time period until the nearest weather station having one or more recorded weather condition parameters for the specified time period is identified”, as discloses in the amended independent claims of the present invention.
Morey et al., U.S. Publication No. 2018/0336487 discloses a commercial scale sous-vide system and method which “ includes a conveyor (20) for carrying food products (FP) vacuum sealed in plastic food-grade pouch or container (222) through a chamber (40) heated with saturated steam. The conveyor is in the form of first and second spiral stacks (26) and (28). A control system controls the steam supply and the movement of the conveyor” (Morey, Abstract). While Morey discloses aspects of the present invention including weather parameters such as a dry bulb temperature and a dew point temperature parameter, Morey is silent with respect to “identifying a nearest weather station a nearest weather station, with respect to a geographical location specified by the particular parcel shipping activity record, having one or more recorded weather condition parameters for a specified time period, including date” or “…iteratively expanding the specified geographical region and the specified time period until the nearest weather station having one or more recorded weather condition parameters for the specified time period is identified”, as discloses in the amended independent claims of the present invention.
Stoman, U.S. Publication No. 2018/0053139 discloses systems, methods, and apparatuses for managing aerial drone parcel transfers wherein “an aerial drone parcel delivery/transfer management server (ADPTMS) is configured for facilitating flexible management of aerial drone parcel deliveries to or transfers between aerial drone landing pads (ADLPs). Each ADLP has a corresponding ADLP address that includes a unique ADLP identifier (e.g., a manufacturing serial number); current or most-recently known ADLP geolocation data (e.g., 2D or 3D geospatial coordinates); and possibly current or most-recently known ADLP elevation data. The ADPTMS can communicate with order management/fulfillment servers associated with online stores, which can communicate with aerial drone parcel delivery/transfer services for dispatching aerial drones to particular ADLP addresses as part of fulfilling online orders. An ADLP can present a machine readable code such as a quick response (QR) code thereon (e.g., on a landing mat) that can be captured by an aerial drone and processed to verify the ADLP's identity. An ADLP can output local RF guiding signals and/or local optical guiding signals (e.g., infrared signals) to aid aerial drone navigation to the ADLP” (Stoman, Abstract). While Stoman discloses some aspects of the present invention including a plurality of candidate transportation routes, Stoman does not appear to disclose “identifying a nearest weather station a nearest weather station, with respect to a geographical location specified by the particular parcel shipping activity record, having one or more recorded weather condition parameters for a specified time period, including date” or “…iteratively expanding the specified geographical region and the specified time period until the nearest weather station having one or more recorded weather condition parameters for the specified time period is identified”, as discloses in the amended independent claims of the present invention.
Altschule, U.S. Publication No. 2019/0004210 discloses a forensic weather system which “compares actual meteorological readings with data from multiple weather models. The data is compared and a forensic weather model is selected as the weather model that most closely matches the meteorological readings. The forensic weather model is then used to provide meteorological information pertaining to a weather event such as a hurricane, at a specific location such as a street address” (Altschule, Abstract). While Altschule discloses some aspects of the present invention including obtaining signals from weather stations, Altschule does not appear to disclose “identifying a nearest weather station a nearest weather station, with respect to a geographical location specified by the particular parcel shipping activity record, having one or more recorded weather condition parameters for a specified time period, including date” or “…iteratively expanding the specified geographical region and the specified time period until the nearest weather station having one or more recorded weather condition parameters for the specified time period is identified”, as discloses in the amended independent claims of the present invention.
Loppatto et al., U.S. Publication No. 2015/0363843 discloses dynamic provisioning of pick-up, delivery, transportation, and/or sortation options which includes “programmatically determining/identifying for determining a delivery time and/or cost for an item to be delivered and allowing customer selection of one of the delivery windows. One example embodiment may include a method comprising receiving customer location information/data indicative of a customer location, querying at least one of (a) a historical database, (b) a dynamic database, (c) predictive database, or (d) a combined database to determine a cost associated with each of the one or more time frames/periods and whether any pick-up, transportation, sortation, and/or delivery criteria associated with the cost, and providing the one or more time frames/periods and the cost associated with each of the one or more time frames” (Loppatto, Abstract). While Loppatto discloses some aspects of the present invention including the disclosure of signal packet communications over a network, Loppatto is silent with repscet to “identifying a nearest weather station a nearest weather station, with respect to a geographical location specified by the particular parcel shipping activity record, having one or more recorded weather condition parameters for a specified time period, including date” or “…iteratively expanding the specified geographical region and the specified time period until the nearest weather station having one or more recorded weather condition parameters for the specified time period is identified”, as discloses in the amended independent claims of the present invention.
For the above reasons, Examiner determined the currently pending claims novel and non-obvious given the current search. Amendment to the claims and further search in reaction to such amendment may yield the claims anticipated or obvious in future prosecution, determined at that time.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Anand et al., U.S. Publication No. 2019/0061939, discloses managing package deliveries by robotic vehicles.
Williams et al., U.S. Publication No. 2015/0046361, discloses methods and systems for managing shipped objects.
Satyanarayana Rao et al., U.S. Publication No. 2018/0121875 discloses delivery prediction automation and risk mitigation.
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/NICHOLAS D BOLEN/ Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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