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 .
Claim Objections
Claim(s) 1, 4 and 10 is/are objected to because of the following informalities:
claim 1: “…a turn time of the container” should read “…a turn time parameter of the one or more containers”.
Claim 4/10: “the container” should read “the one or more containers”.
Appropriate correction is required.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1 is/are directed towards a method (i.e. a process), respectively. Thus, each of the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Claim(s) 1 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “obtaining shipment data associated with a shipment of one or more containers; generating, based on the shipment data, a turn time parameter indicative of a turn time of the container by applying a prediction model to the shipment data; and providing an output based on the turn time parameter.”.
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of “predicting a turn time parameter” which is a method of organizing a human activity, mental process and mathematical concepts. That is, the method allows for 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); concepts performed in the human mind and mathematical relationships/formula/equations/calculations.
This judicial exception is not integrated into a practical application. In particular, the claim only recites “an electronic device”. The additional limitation is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, the(se) additional element(s), alone or in combination, do(es) not integrate the abstract idea into a practical application because it/they do(es) not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does 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 additional element(s) is/are nothing more than mere instructions to apply the exception on a general computer.
Dependent claim(s) 2 is/are also directed to an abstract idea without significantly more because it/they further narrow(s) the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“machine learning techniques” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim(s) 7 is/are also directed to an abstract idea without significantly more because it/they further narrow(s) the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“turn time system of the electronic devices” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim(s) 3-6 and 8-10 is/are also directed to an abstract idea without significantly more because it/they further narrow(s) the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4, 10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Saraswathi Hathikal, “Prediction of ocean import shipment lead time using machine learning methods”, published by Springer Nature Switzerland AG 2020 on June 23, 2020, hereinafter “Sara”.
As per claim 1, Sara discloses a method, performed by an electronic device, for predicting turn time for a container, the method comprising:
obtaining shipment data associated with a shipment of one or more containers (page 7, “In this research, all the data points are obtained from a partner company, one of the leading New York-based logistic and supply chain company. In collaboration with global partners, the company provides the end-to-end logistics solutions and diverse customized services such as freight forwarding services including ocean import, ocean export, air import, and air export, customs and brokerage, pricing and quoting, consulting, customer service, and logistics…”);
generating, based on the shipment data, a turn time parameter indicative of a turn time of the container by applying a prediction model to the shipment data (page 4, “Accordingly, we define the two terminal conditions to consider both parties (freight forwarders and customers) interests as follows: 1. Shipment Lead Time—Empty Container Return (SLECR): Time between the customer’s first contact with overseas agent and the empty container return to the port…page 6, “With respect to the application, this research aims to predict ocean import shipment lead time to enhance shipment visibility and predictability considering interests of multiple stakeholders by predicting SLECR and SLDC…page 9-10, “SLECR and SLDC. Such terminal criteria can be calculated using the milestones in the shipment, and therefore, the variables in the dataset need to be calculated and preprocessed based on such milestones. The total shipment lead time for both terminal criteria is calculated by taking appropriate milestones into account. SLECR and SLDC are calculated as the summation of number of days between milestones. The milestones for SLECR and SLDC are shown in Figs. 4 and 5…. To predict the SLECR and SLDC, the entire dataset is divided into two sets, respectively: 1) SLECR dataset: the one with variables including the empty container return date (with or without the container pick-up date and delivery confirmation) and 2) SLDC dataset: the one with variables including container pickup date, delivery confirmation date, and empty container return date. The variable names and associated formula used in the dataset to calculate the time between milestones are shown in Tables 6 and 7.”); and
providing an output based on the turn time parameter (page 13, “The final list of features considered for the shipment lead time prediction (SLECR and SLDC) is shown the Table 8. Since there are abundant uncontrollable factors that affect the shipment lead time, this research treats the prediction of shipment lead time as a classification problem rather than a regression problem to account for the inherent uncertainties. Hence, the outcome of the experiments is a ran”).
As per claim 2, Sara discloses wherein applying the prediction model comprises applying, to the shipment data, one or more machine learning techniques and/or a forecasting technique (abstract, “Real data obtained from an industry partner are used for implementation, and multinomial logistic regression is identified as the best classifier with the highest accuracy in each binning method, which is followed by a decision tree method. Additionally, commonly used classifiers such as multinomial logistic regression, decision tree, K-nearest neighbors, and support vector machine perform better than Naïve Bayes when the categorical variables are binarized, and vice versa when the categorical variables are converted into ordinal values” page 7, “In the implementation step, machine learning methods such as multinomial logistic regression, decision tree, K-nearest neighbors, and support vector machine are employed. Finally, the results are discussed in the interpretation step.”)
As per claim 4, Sara discloses wherein the shipment data comprises one or more of: time extension data associated with a time extension of the container, transaction data associated with one or more transactions, container movement data associated with movement of the one or more containers, commodity data associated with one or more commodities, consignee data associated with one or more consignees, container data associated with the one or more containers, booking data associated with one or more shipment bookings, port data associated with one or more ports for shipment, turn time data, and historical shipment data (table 2, “Port of discharge”).
As per claim 10, Sara discloses wherein the turn time parameter comprises a consignee turn time parameter associated with a consignee of the container (page 4, “Accordingly, we define the two terminal conditions to consider both parties (freight forwarders and customers) interests as follows: 1. Shipment Lead Time—Empty Container Return (SLECR): Time between the customer’s first contact with overseas agent and the empty container return to the port…Most customers (either shipper or consignee) are concerned about the shipment lead time even before they sign a contract with a freight forwarder, because it has a great effect on their businesses... we focus on developing effective machine learning methods to predict the shipment lead time for both customers (either shippers or consignees) and freight forwarder using a variety of information…page 6, “With respect to the application, this research aims to predict ocean import shipment lead time to enhance shipment visibility and predictability considering interests of multiple stakeholders by predicting SLECR and SLDC…page 9-10, “SLECR and SLDC. Such terminal criteria can be calculated using the milestones in the shipment, and therefore, the variables in the dataset need to be calculated and preprocessed based on such milestones. The total shipment lead time for both terminal criteria is calculated by taking appropriate milestones into account. SLECR and SLDC are calculated as the summation of number of days between milestones. The milestones for SLECR and SLDC are shown in Figs. 4 and 5…. To predict the SLECR and SLDC, the entire dataset is divided into two sets, respectively: 1) SLECR dataset: the one with variables including the empty container return date (with or without the container pick-up date and delivery confirmation) and 2) SLDC dataset: the one with variables including container pickup date, delivery confirmation date, and empty container return date. The variable names and associated formula used in the dataset to calculate the time between milestones are shown in Tables 6 and 7.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sara, as disclosed in the rejection of claim 1, in view of Linda Li, “Forward cycle time distributions for returnable transport items”, published by Journal of Remanufacturing on September 17, 2021, Hereinafter “Li”.
As per claim 3, Sara discloses wherein the one or more machine learning techniques comprise one or more of: a linear regression model, a random forest model, a decision tree-based model, and an ensemble model (abstract, “Real data obtained from an industry partner are used for implementation, and multinomial logistic regression is identified as the best classifier with the highest accuracy in each binning method, which is followed by a decision tree method. Additionally, commonly used classifiers such as multinomial logistic regression, decision tree, K-nearest neighbors, and support vector machine perform better than Naïve Bayes when the categorical variables are binarized, and vice versa when the categorical variables are converted into ordinal values” page 7, “In the implementation step, machine learning methods such as multinomial logistic regression, decision tree, K-nearest neighbors, and support vector machine are employed. Finally, the results are discussed in the interpretation step.”)
However, Sara does not disclose but Li discloses wherein the forecasting technique comprises a time-series forecasting technique (page 128, “The primary focus of the literature review and this paper are forecasting of RTI returns, but we also note that forecasting of customer demand is an important input to inventory control models for RTI and production models for remanufacturing. Time series forecasting methods, including exponential smoothing [63] and autoregressive integrative moving average (ARIMA) methods [5] have traditionally been used for forecasting customer demand…page 133, “An adaptive exponential smoothing method that corrects for trend and seasonality [63] is used to forecast the mean and standard deviation of FCT based on the historical data”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Li in the teaching of Sara, in order to incorporate appropriate trend and seasonality (please see Li abstract).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sara, as disclosed in the rejection of claim 1, in view of Subramanian (US 2018/0075408) and Ionna Kourounioti, “Identification of container dwell time determinants using aggregate data”, published by International journal of transport economics in December 2017, hereinafter “Ionna”.
As per claim 5, Sara does not disclose but Subramanian discloses wherein the method comprises generating a commodity risk factor ([0086] In some implementations, adaptive logistics platform 230 may use an artificial intelligence model and/or a machine learning technique to reduce the risk of non-payment by a buyer or a seller. For example, adaptive logistics platform 230 may require a buyer or a seller to pay a demurrage and/or detention charge prior to a shipment of containers. Here, adaptive logistics platform 230 may use an artificial intelligence model and/or a machine learning technique (e.g., artificial neural networks, Bayesian statistics, linear and quadratic classifiers, learning automata, etc.) to predict, based on past transactions, the amount that the buyer or the seller will owe. In this case, a buyer or a seller may simply by refunded any overcharge amount at the conclusion of the transaction. As an example, adaptive logistics platform 230 may use an artificial neural networking model to determine that a buyer or a seller is likely to commit demurrage and/or detention over a holiday period (as well as a prediction on the exact amount the buyer or the seller is likely to owe).”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Subramanian in the teaching of Sara, in order to to reduce the risk of non-payment by a buyer or a seller (please see Subramanian paragraph 86).
However, Sara in view of Subramanian does not disclose but Ionna discloses wherein the method comprises generating, for at least one commodity, [a predictive parameter ] based on the shipment data, wherein [the predictive parameter] is associated with the at least one commodity and a port involved in the shipment (abstract, “Aggregate data were collected from the Terminal Operation Systems (TOS) of three container terminals; two in the Middle East and one in Asia. The Poisson regression models that were developed for each terminal revealed the factors affecting the Dwell Time (OT)..”, Information on the receiver of goods and the commodity was available and incorporated in the Poisson regression models resulting in higher R' and better model applicability”… The developed models can be used to predict the DT and consequently the day an import container is to be picked-up from the terminal.” Ionna generates a statistical prediction model result based in part on commodity information. Page 571, “Dwell Time (OT) is defined as "the total time a container spends in one or more terminal stacks (Ottjes et aL, 2007)." Container OT can be influenced by several factors, including gate operations, availability and efficiency of hinterland connections, customs regulations, and recipient characteristics. Consignees, namely, the receiver of the goods, can be identified as one of the key stakeholders who determine OT.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Ionna in the teaching of Sara and Subramanian, because if Information on the receiver of goods and the commodity was available and incorporated in the Poisson regression models result[ing] in higher R' and better model applicability (please see Ionna abstract).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sara in view of Subramanian (US 2018/0075408) and Ionna, as disclosed in the rejection of claim 5, in further view of Barry Cobb, “Estimating cycle time and return rate distributions for returnable transport items”, published by International Journal of Production Research in 2016, hereinafter “Cobb”.
As per claim 6, Sara in view of Subramanian and Ionna discloses wherein generating the commodity risk factor as in claim 5 rejection. Sara discloses shipment data (page 7-8, table 2).
Thus, Sara teaches the “shipment data” from which the claimed turn time analysis is performed.
However, Sara does not teach the particular requirement that the commodity risk factor be generated by calculating “one or more commodity distribution parameters” that are indicative of a distribution of delayed container return for the commodity. Sara discloses shipment lead time – empty container return, but does not disclose calculating a commodity conditioned distribution of delayed container returns. But Subramanian, Ionna and Cobb are relied upon to teach the missing limitation.
First, Subramanian teaches wherein generating the commodity risk factor comprises:
a delay of return of the container for the at least one commodity and generating the commodity risk factor (0017] As shown by reference number 110, an adaptive logistics platform may receive event information from one or more event reporting devices. For example, event information may include information associated with booking a cargo container, information associated with a container event (e.g., a container pickup, a container drop-off, a container status, etc.), exception events (e.g., port congestion delays, customs delays, processing delays by the shipping carrier, port employee strikes, weather, etc.), or the like. In some implementations, as shown, the event information may include a customer identifier, one or more container identifiers, information that identifies one or more container events, and/or information that identifies one or more exception events…[0022] As shown in FIG. 1C, and by reference number 140, the adaptive logistics platform may create a model using past baseline information and event information. For example, the adaptive logistics platform may create a model using artificial intelligence and/or machine learning techniques (e.g., artificial neural networks, Bayesian statistics, linear and quadratic classifiers, learning automata, etc.). In some implementations, the adaptive logistics platform may create a model that compares past baseline information and event information by considering customer identifiers (e.g., a unique identifier to differentiate between each buyer, seller, and/or freight forwarder), container identifiers (e.g., a unique container identifier to differentiate between containers), time information (e.g., a time and date for each shipping request, a frequency associated with a buyer or a seller shipping request, etc.), location information (e.g., GPS coordinates associated with a shipping vessel, GPS coordinates associated with a container, etc.), container events (e.g., damage to a container), and/or any other information that may be used to create a model. Here, the adaptive logistics platform may use the baseline information and event information to look for patterns and reoccurring trends. [0055] As shown in FIG. 4, process 400 may include maintaining baseline information associated with determining demurrage and/or detention data associated with shipping containers (block 410). For example, adaptive logistics platform 230 may receive baseline information. The baseline information may include information relating to a contract, such as a customer identifier (e.g., an identifier of a buyer of goods, an identifier of a seller of goods, etc.), location information (e.g., a location drop-off point, a location pickup point, etc.), time information (e.g., a time for drop-off, a time for pickup, a time period for determining demurrage and/or detention data, an exception period to credit a buyer or a seller if the buyer or the seller is not at fault for the shipping delay, etc.), charge information (e.g., daily or hourly demurrage and/or detention rates), booking information (e.g., a vessel to be used for the shipping transaction, a container or group of containers to be used for the shipping transaction, etc.), and/or other information that may be included in a contract between a shipper and a buyer or a seller. [0056] Adaptive logistics platform 230 may generate demurrage and/or detention data using the baseline information and the event information. For example, demurrage and/or detention data may include information identifying a customer responsible for demurrage and/or detention (e.g., using a customer identifier), information identifying a container associated with demurrage and/or detention (e.g., using a container identifier), information relating to the demurrage and/or detention amount owed (e.g., daily or hourly demurrage and/or detention rates, a total amount owed for demurrage and/or detention), information that identifies one or more container events that trigger demurrage and/or detention data to be determined (e.g., that trigger demurrage and/or detention data relating to charges), information that identifies one or more exception events that trigger an exception to demurrage and/or detention (e.g., an exception to demurrage and/or detention data), and/or any other information that may relate to demurrage and/or detention.” Subramanian teaches the predictive risk (i.e. a predicted likelihood associated with late empty container return) and it teaches that such predictions are generated from historical container/customer event information. It also defines the meaning of a delay of return of the container because it defines a delayed return relative to a return threshold which is the emptied container is not returned before the applicable interval expires. )(please see claim 5 rejection for combination rationale).
However, Subramanian does not teach calculating a statistical return time distribution for a commodity, nor does it teach generating the commodity risk factor based on such distribution parameters.
But Ionna discloses the commodity specific aspect of the above calculation (abstract, “Aggregate
data were collected from the Terminal Operation Systems (TOS) of three container terminals; two in the Middle East and one in Asia. The Poisson regression models that were developed for each terminal revealed the factors affecting the Dwell Time (OT). Terminal charging policies and customs inspection affect OT especially in ports that impose storage fees from the first day and adopt more efficient customs inspection methods. In addition, DT was found to depend on: 1) container's weight; 2) container status (full or empty); 3) billable line; 4) seasonality, and; 5) pick-up day of the week. Information on the receiver of goods and the commodity was available and incorporated in the Poisson regression models resulting in higher R' and better model applicability. The combined model of the three terminals explains the influence of the different monetary policies and the terminal~ on the OT. The developed models can be used to predict the DT and consequently the day an import container is to be picked-up from the terminal).
Thus, Ionna teaches that container timing should be analyzed for the commodity, not merely across all containers generically.
Accordingly, when Cobb’s return distribution calculation is applied in the combined system, Ionna teaches performing that analysis with commodity information incorporated into it. Therefore, Ionna supplies the commodity specific conditioning necessary to transform Cobb’s general container return distribution into the claimed “commodity distribution parameters for the at least one commodity”
With that said, Sara in view of Subramanian and Ionna do not disclose but Cobb discloses calculating….one or more…distribution parameters, wherein the one or more distribution parameters…are indicative of…a distribution of at least one of: return of the container (abstract, “A return rate distribution for returnable transport items (RTI) is estimated from radio frequency identification (RFID) data. The technique is dependent on estimating a probability density function for backward fill-to-fill cycle times for the returnable containers. The cycle time distribution yields an estimate of a time period where most containers will return to the manufacturer. To obtain return rate observations, the number of returns in a production lot is observed by tracking the fill and return of uniquely tagged containers over this time period, adjusting for containers with long cycle times. The process also gives an estimate of the percentage of RTI tagged in the population or fleet of containers. The effects of estimation errors due to a partial RFID-tagging of the fleet are examined, and satisfactory results can be obtained when not all containers are tagged. The use of the cycle time and return rate distributions for creating a forecast of container returns is illustrated” because Cobb calculates/estimates a return rate distribution and a probability density function for container cycle times. Those parameters are indicative of a distribution of a return of the container.).
So, when Cobb’s known distribution calculation is performed separately for the commodity information taught by Ionna above, the resulting distribution parameters become “commodity distribution parameters for the at least one commodity”.
Subramanian supplies the threshold that distinguishes timely return from “delay of return of the container”. Thus, the portion of Cobb’s commodity specific return time distribution corresponding to returns beyond Subramanian’s threshold is indicative of: “a distribution of…a delay of return of the container for the at least one commodity”.
Finally, Cob expressly teaches using the calculated cycle time and return rate distribution to forecast container returns. That teaching supports using those distribution parameters as predictive inputs to the already established Subramanian/Ionna commodity risk model. Therefore, the combination of Subramanian, Ionna and Cobb discloses “generating the commodity risk factor based on the one or more commodity distribution parameters”.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Cobb in the teaching of Sara and Subramanian and Ionna, in order to estimate a probability density function for backward fill-to-fill cycle times for the returnable containers (please see Cobb abstract).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sara, as disclosed in the rejection of claim 1, in view of Susairaju (US 2023/0032429).
As per claim 7, Sara discloses wherein the method comprises grouping consignees in a turn time system of the electronic device (page 4, “Shipment Lead Time—Empty Container Return (SLECR): Time between the customer’s first contact with overseas agent and the empty container return to the port” and proposes machine learning approaches to predict that shipment lead time…. In this research, we notice that freight forwarders also have the unique capacity to access the important information obtained from different stakeholders in a supply chain, and we focus on developing effective machine learning methods to predict the shipment lead time for both customers (either shippers or consignees) and freight forwarder using a variety of information.” Table 2, “Customer_id Customer identification number”. Sara discloses that its prediction is developed for consignees using information from the ocean import process and the database contains consignee/customer information).
However, Sara does not disclose “grouping consignees into two groups according to whether the consignees exists or do not exist in the turn time system. But, Sasairaju expressly teaches separating customers according to the availability of customer specific historical information. ([0036]… BTTC slots across similar type customers will help in determining the BTTC slots of unknown customers whose data is not present in the LCM database. This will act as the first gauge mechanism to dial out calls on unknown contacts. [0041]… the full model may only be used for those customers who have been dialed out at least 5 times in the past. This will look at customer's specific relationship history with the business among other features as described above in the Business Categorization section. The persona model is for new customers with insufficient personal historical information. One definition of new customer may be any customer with less than 5 customer interactions, as an example. The model may use, instead, the churn history of similar customers with longer history in combination with the customer's demographics and other features to predict the likelihood of churn for newer customers. “ the system teaches two distinct customer populations: customer having sufficient existing historical information for the full model and new/unknown customers lacking such historical information, including customer whose data is not present in the database”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Susairaju in the teaching of Sara, in order to to optimize the order of tasks that are carried out by integrated business systems (please see Susairaju abstract).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sara, as disclosed in the rejection of claim 1, in view of Subramanian (US 2018/0075408).
As per claim 8, Sara discloses generating a predicted time through empty container return for a consignee based on shipment data as shown in claim 1. However, Sara does not disclose but Subramanian discloses generating a risk factor for the receiving party’s container return behavior based on shipment data ([0012] In some implementations, such as in water transportation, demurrage and/or detention data may involve events occurring in or around ports. At the shipping port, a seller may have a fixed time period to load a shipping container and complete all paperwork necessary to leave the port on time. At the receiving port, a buyer may have a fixed time period to pick up a shipping container of goods, as well as a fixed time period to drop off an empty shipping container. Failure to satisfy these deadlines may result in demurrage and/or detention data (e.g., charge information) as indicated in the contract…. [0013] Demurrage data may be determined for a buyer if a container of goods is not picked up within a threshold interval (e.g., a contracted time period, such as a contractual pickup time period). After the buyer picks up the container, detention data may be determined when the buyer fails to return the emptied container before a threshold interval (e.g., a contractual drop-off time period). Similarly, demurrage data may be determined for a seller if an empty container of goods is not picked up within a threshold interval (e.g., a pickup time period)… [0017] As shown by reference number 110, an adaptive logistics platform may receive event information from one or more event reporting devices. For example, event information may include information associated with booking a cargo container, information associated with a container event (e.g., a container pickup, a container drop-off, a container status, etc.), exception events (e.g., port congestion delays, customs delays, processing delays by the shipping carrier, port employee strikes, weather, etc.), or the like. In some implementations, as shown, the event information may include a customer identifier, one or more container identifiers, information that identifies one or more container events, and/or information that identifies one or more exception events. [0086]… As an example, adaptive logistics platform 230 may use an artificial neural networking model to determine that a buyer or a seller is likely to commit demurrage and/or detention over a holiday period (as well as a prediction on the exact amount the buyer or the seller is likely to owe). Here, adaptive logistics platform 230 may require demurrage and/or detention payment in advance of shipping and refund the buyer or the seller if the containers are dropped off on time. In this way, adaptive logistics platform 230 is able to reduce the risk of non-payment by a buyer or a seller.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Subramanian in the teaching of Sara, in order to reduce the risk of non-payment by a buyer or a seller (please see Subramanian paragraph 86).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sara in view of Subramanian (US 2018/0075408) , as disclosed in the rejection of claim 8, in further view of Stashluk (US 2006/0149577) and Ionna.
As per claim 9, Sara disclose shipment data associated with the consignee and container return events (page 8, “The shipment details report provides the detailed shipment information such as booking reference number, carrier information, container details, port of origin, port of destination, and shipper and consignee information. The shipment dates reports provide a list of available container events for every file number created within the specified date range that is transmitted either via Electronic Data Interchange (EDI) from the carrier or entered manually by the company representatives into AS400. The variables in the shipment details and shipment dates reports are summarized in Tables 2 and 3.”).
However, Sara does not identify which containers associated with a consignee are delayed; calculate a proportion of delayed containers for the consignee; calculate a consignee distribution parameter; or generate the consignee risk factor based on such a parameter.
While Subramanian does not explicitly discloses “a proportion of delayed containers for the at least one consignee”, but it discloses the underlying data necessary to determine it. Subramanian discloses “[0097] In some implementations, adaptive logistics platform 230 may receive, in association with a departure port (e.g., port 1), information regarding one or more container events (e.g., events A and B). For example, adaptive logistics platform 230 may receive information regarding one or more container events (e.g., event A) if a seller fails to pick up one or more of the empty containers from the departure port within a threshold interval (e.g., a pickup time period). Additionally, or alternatively, adaptive logistics platform 230 may receive information regarding one or more container events (e.g., event B) if a seller fails to drop off one or more of the full containers to the departure port within a threshold interval (e.g., a drop-off period). As an example, the seller may drop off 500 of the 1,000 containers (e.g., to port 1 terminal 1, to port 1 terminal 2, to port 1 terminals 1 and 2, etc.), but be late to drop off the remaining 500 containers (e.g., to port 1 terminal 1, to port 1 terminal 2, to port 1 terminals 1 and 2, etc.). In this case, adaptive logistics platform 230 receives container event information regarding both the timely and the late container drop-offs, and may use baseline information regarding a free period and daily or hourly demurrage and/or detention rates to determine if a charge may be owed by the seller.”
Nevertheless, Satshluk expressly teaches reducing historical return events to per customer return analytics ([0049] Returns server 306 may also offer returns analysis services performed by a business intelligence framework 340. Business intelligence framework 340 may operate to perform online transaction processing to extract, transfer, and/or load information stored in relational database 308 or another data warehouse. The extraction, transfer, and loading of information may result in return analytics such as the percentage of customers returning items, the frequency of returns on a per-customer basis, the frequency of returns on a per-item basis, or other analytics useful to a remote retailer 304 in determining general or specific customer satisfaction and retention. Particular return analytics offerings that may be provided by business intelligence framework 340 are discussed in more detail below… [0077] On a customer-specific level, returns analytics may impact the remote retailer's management of the customer relationship. For example, the return history of customer 302 may be used in conjunction with the customer's purchase history to categorize customer 302 into one of a plurality of pre-defined categories. Specifically, chart 400 includes four types of customers categorized based on return rate and customer value. “)
When Stashluk’s per customer rate calculation is applied to Subramanian’s customer specific timely/late container return records, the calculation becomes: delayed containers associated with receiving customer/total containers associated with that receiving customer which is the claimed “a proportion of delayed containers for the at least one consignee”. This also teaches “calculating…one or more consignee distribution parameters for the at least one consignee” because the resulting historical delayed return proportion is calculated separately on a per-consignee/customer basis.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Stashluk in the teaching of Sara in view of Subramanian, in order to optimize the retailers business and evolve the relationship with the customer (please see Stashluk abstract).
However, Sara in view of Subramanian and Stashluk does not esxpressly associate this consignee parameter with the claim’s “at least one commodity”.
But, Ionna expressly teaches incorporating both the receiver and commodity into statistical container time modeling (abstract, “Aggregate data were collected from the Terminal Operation Systems (TOS) of three container terminals; two in the Middle East and one in Asia. The Poisson regression models that were developed for each terminal revealed the factors affecting the Dwell Time (OT)..”, Information on the receiver of goods and the commodity was available and incorporated in the Poisson regression models resulting in higher R' and better model applicability”… The developed models can be used to predict the DT and consequently the day an import container is to be picked-up from the terminal.” Ionna generates a statistical prediction model result based in part on commodity information. Page 571, “Dwell Time (OT) is defined as "the total time a container spends in one or more terminal stacks (Ottjes et aL, 2007)." Container OT can be influenced by several factors, including gate operations, availability and efficiency of hinterland connections, customs regulations, and recipient characteristics. Consignees, namely, the receiver of the goods, can be identified as one of the key stakeholders who determine OT.”).
Accordingly, in the combined system, Subramanian’s delayed container records and Stashluk’s per customer rate calculation would be maintained/calculated for the receiver/consignee in association with the commodity identified according to Ionna. This combination teaches “wherein the one or more consignee distribution parameters for the at least one commodity are indicative of, for the at least one commodity” without treating the commodity as the container itself. Rather, the commodity identifies the goods associated with the shipment whose container return behavior is being analyzed.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Ionna in the teaching of Sara, Subramanian and Stashluk, because if Information on the receiver of goods and the commodity was available and incorporated in the Poisson regression models result[ing] in higher R' and better model applicability (please see Ionna abstract).
Conclusion
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3629
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