Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-19 are pending.
Claim Objections
Claims 19 is objected to because of the following informalities:
Claim 19 recites ‘A computer-readable storage medium…’. This specific language would typically warrant a signal per se rejection. However, the specification, on pages 14-15, recites “non-transitory” computer readable medium. Claim 20 should therefore explicitly recite “a non-transitory computer readable storage medium”. This appears to be a typographical error. For the purpose of compact prosecution, examiner will interpret the limitation as such.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-19 is directed to a series of steps, and therefore is a process.
Independent Claims
Step 2A Prong One
The limitation of Claim 1 recites:
A method, …, for handling shipment of a container, the method comprising:
obtaining historical data associated with container shipment;
determining, based on the historical data, a container usage pattern associated with the container;
generating, based on the container usage pattern, a time extension package associated with the container shipment characterized by package data, wherein the package data comprises a time extension parameter indicative of a time period for extending a return time of the container and a cost parameter associated with the time extension parameter;
predicting, for one or more time extension packages of a plurality of time extension packages, a selection parameter indicative of a likelihood of selection of the respective time extension package by … to the historical data and previous package data;
determining, based on the selection parameter for the one or more time extension packages, extension cost data associated with time extension of the container shipment; and
providing, based on the extension cost data, updated package data associated with the time extension package.
The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as determining time extension for extending time to return a booked container. The generic computer implementations (see below) do not change the character of the limitations. Accordingly, the claims recite an abstract idea.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
Claim 1:
Electronic device
Machine learning model
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h))
Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
Dependent Claims
Dependent claims 2-19 further narrow the same abstract ideas recited in Claims 1. Therefore, claims 2-19 are directed to an abstract idea for the reasons given above.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the dependent claims recite the following additional elements:
Claim 8
Training the machine learning model
Claim 9
Linear regression model
Claim 12
Monte carlo simulation
Claim 18
An electronic device comprising a memory, an interface and a processor
Claim 19
A non-transitory* computer readable storage medium storing one or more programs, the one or more programs comprising instructions
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
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.
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.
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 1-10 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian (US20180075408) and in further view of Otillar (US20180276573A1).
Claim 1: Subramanian teaches A method, performed by an electronic device, for handling shipment of a container, (Subramanian, Par. 0019 and Fig. 1: adaptive logistics platform) the method comprising:
obtaining historical data associated with container shipment; (Subramanian, Par. 0022 and 0017)
Subramanian, in par. 0022, teaches 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. … 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. (i.e. historical data)
Subramanian, in par. 0017, teaches 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. (i.e. container information)
determining, based on the historical data, a container usage pattern associated with the container; (Subramanian, Par. 0023)
Subramanian, Par. 0023, teaches the adaptive logistics platform may create a model that uses time information and location information to look for patterns and reoccurring trends.
generating, based on the container usage pattern, a time extension package associated with the container shipment characterized by package data, wherein the package data comprises a time extension parameter indicative of a time period for extending a return time of the container and a cost parameter associated with the time extension parameter; (Subramanian, par. 0023-0024)
Subramanian, Par. 0023, teaches he adaptive logistics platform may create a model that uses time information and location information to look for patterns and reoccurring trends. For example, the adaptive logistics platform may use a model to determine that port congestion is likely to occur during the end of the year (e.g., due to an increase in the sale of goods during the holiday period). In this case, the adaptive logistics platform may determine port congestion with a model that maps out the movement of all shipping customers over a sample size of five years. The adaptive logistics platform may create the map by recording GPS coordinates of each shipping vessel and/or each shipping container. By taking, for example, hourly GPS readings, the adaptive logistics platform model may show that a shipping vessel and/or shipping container is in motion or is at rest. The adaptive logistics platform may use this intelligence to come to conclusions regarding port congestion (e.g., a container that reaches a port and docks immediately may be indicative of no port congestion, while a container that sits motionless just outside of the dock may be indicative of port congestion). Here, the adaptive logistics platform model may determine that during the final two weeks of December, shipping vessels and/or shipping containers spend 30% more time motionless outside of a port (as compared to the average month). This information allows the adaptive logistics platform to make future predictions that optimize resources (both computing and personnel).
Subramanian, par. 0024, the adaptive logistics platform may establish predictions needed to optimize resources. For example, the adaptive logistics platform may use a model based on time information and location information to make predictions. As an example, the adaptive logistics platform may predict that port congestion is likely to occur during the final two weeks of December. In this case, adaptive logistics platform may use the prediction to make recommendations to the shipping carrier that may optimize resources. Here, the adaptive logistics platform may recommend that the shipping carrier hire additional workers during the holiday periods, or may recommend that the shipping carrier increase the rate of the demurrage and/or detention charge over the holiday to increase the chance that a buyer or seller will return the containers in a timely fashion. (i.e. time extension package)
predicting, for one or more time extension packages of a plurality of time extension packages, a likelihood of the demurrage and /or detention by applying a machine-learning model to the historical data and previous package data; (Subramanian, Par. 0085-0086)
Subramanian, in Par. 0085, teaches adaptive logistics platform 230 may use an artificial intelligence model and/or a machine learning technique to send demurrage and/or detention data to a buyer or a seller in advance of incurring any charges. For example, adaptive logistics platform 230 may use artificial intelligence or machine learning techniques (e.g., artificial neural networks, Bayesian statistics, linear and quadratic classifiers, learning automata, etc.) to determine whether certain exception events are more likely to occur at certain time periods. As an example, adaptive logistics platform 230 may use artificial intelligence or machine learning to determine that port congestion is more likely to occur over a holiday due to an increase in the sale of goods. In this case, a buyer or a seller may receive advance notice which may allow the buyer or the seller to take preventative measures to plan for the arrival and delivery of the container of goods.
Subramanian, in par. 0086 teaches 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).
determining, based on the selection parameter for the one or more time extension packages, extension cost data associated with time extension of the container shipment; and (Subramanian, par. 0084)
Subramanian, Par. 0084, teaches adaptive logistics platform 230 may use artificial intelligence or machine learning techniques (e.g., artificial neural networks, Bayesian statistics, linear and quadratic classifiers, learning automata, etc.) to determine a range of average demurrage and/or detention cost for a sample set of shipping carriers.
providing, based on the extension cost data, updated package data associated with the time extension package. (Subramanian, par. 0087)
Subramanian, Par. 0087, teaches process 400 may include providing charge information that identifies the demurrage and/or detention data (block 440). For example, adaptive logistics platform 230 may provide charge information. The charge information may include demurrage and/or detention charges, as well as any other charges associated with shipping one or more containers.
However, Subramanian does not explicitly teach but Otillar in par. 0084 teaches that a machine learning model can determine the likelihood of user selection of an extension.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the determination of a likelihood of in demurrage and/or detention of a container of Subramanian to include the likelihood of a user selection of the extension as taught by Otillar, in order to provide additional services/offers to a user (Otillar, abstract)
Claim 2: Subramanian and Otillar teach The method according to claim 1, wherein generating, based on the container usage pattern, the time extension package comprises generating, based on the container usage pattern, package data indicative of the time extension package. (Subramanian, Par. 0023: based on usage pattern)
Claim 3: Subramanian and Otillar teach The method according to claim 1, wherein generating, based on the container usage pattern, the time extension package comprises:
generating, based on the container usage pattern, the plurality of time extension packages, wherein each time extension package is characterized by corresponding package data. (Subramanian, Par. 0023-0024: various predictions based on usage pattern)
Claim 4: Subramanian and Otillar teach The method according to claim 1, wherein generating, based on the container usage pattern, the time extension package comprises:
generating, for each time extension package, based on the container usage pattern, the respective time extension parameter and the respective cost parameter associated with each time extension package. (Subramanian, Par. 0084: specific time and cost)
Claim 5: Subramanian and Otillar teach The method according to claim 1, wherein the time extension parameter comprises the time period. (Subramanian, Par. 0019: time interval)
Claim 6: Subramanian and Otillar teach The method according to claim 4, wherein generating for each time extension package, based on the container usage pattern, the respective time extension parameter and the respective cost parameter associated with each time extension package comprises:
determining a first cost parameter associated with a first time extension package of the plurality of the time extension packages, and determining, based on the first cost parameter and a difference parameter, a second cost parameter associated with a second time extension package of the plurality of the time extension packages by maintaining a difference parameter between the first and the second cost parameters. (Subramanian, Par. 0024: difference in rates for demurrage/detention because of holiday period)
Claim 7: Subramanian and Otillar teach The method according to claim 1, wherein predicting the selection parameter by applying the machine-learning model to the historical data and the previous package data comprises:
determining, for the one or more time extension packages, based on the historical data and the previous package data, a first change parameter indicative of change in the cost parameter of the package data and a second change parameter indicative of a corresponding change in the corresponding selection parameter. (Subramanian, Par. 0024: difference in rates for demurrage/detention because of holiday period)
Claim 8: Subramanian and Otillar teach The method according to claim 7, wherein predicting the selection parameter by applying the machine-learning model to the historical data and the previous package data comprises:
training the machine learning model based on the first change parameter and the second change parameter. (Subramanian, Par. 0022: model trained on various identifiers)
Claim 9: Subramanian and Otillar teach The method according to claim 1, wherein the machine learning model includes a linear regression model. (Subramanian, Par. 0022: linear regression)
Claim 10: Subramanian and Otillar teach The method according to claim 1, wherein predicting the selection parameter by applying the machine-learning model to the historical data and previous package data comprises:
determining, for the one or more time extension packages, a third change parameter indicative of change in the cost parameter of the package data with respect to a current fixed cost of a time extension package; and (Subramanian, Par. 0092)
predicting a fourth change parameter indicative of change in the selection parameter for each cost parameter of the package data using the trained machine-learning model. (Subramanian, Par. 0092)
Claim 15: Subramanian and Otillar teach The method according to claim 1, wherein determining, based on the historical data, a container usage pattern associated with the container comprises:
determining, based on the historical data, one or more container usage parameters; and (Subramanian, Par. 0023-0024: various predictions based on usage pattern)
determining, based on the one or more container usage parameters, a container usage pattern associated with the container. (Subramanian, Par. 0023-0024: various predictions based on usage pattern)
Claim 16: Subramanian and Otillar teach The method according to claim 1, wherein the one or more container usage parameters comprise one or more of:
a proportion parameter indicative of a proportion of users selecting a respective time extension package, an average delay for returning the container, an average turnaround time for returning the container, one or more rate parameters indicative of a rate for a respective container size and/or a country, one or more extension day for a respective container size and/or a country, a statistical rate parameter indicative of a rate for a respective container size and/or a country, and a statistical extension day for a respective container size and/or a country. (Subramanian, Par. 0084: average rate)
Claim 17: Subramanian and Otillar teach The method according to claim1, wherein providing, based on the extension cost data, updated package data comprises providing, based on a maximum extension cost of the extension cost data, the updated packaged data. (Subramanian, Par. 0084: outlier)
Claim 18: Subramanian and Otillar teach An electronic device comprising a memory, an interface and a processor configured to perform any the method of claim 1. (Subramanian, par. 0029 and 0046-0047: interface and memory and processor)
Claim 19: Subramanian and Otillar teach A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device cause the electronic device to perform the method of claim 1. (Subramanian, Par. 0048: non-transitory CRM)
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian (US20180075408) and in further view of Otillar (US20180276573A1) and in further view of Binns (US7249040B1)
Claim 11: Subramanian and Otillar teach The method according to claim 1, wherein determining , based on the selection parameter for each time extension package, the extension cost data associated with time extension of the container shipment comprises:
While Subramanian and Otillar teach the determination of packages based on the adaptive logistics platform in Subramanian par. 0083, it does not explicitly teach but Binns in Col. 20 lines 5-27 teaches:
performing a simulation of the extension cost data for the package data of each time extension package based on the selection parameter for each time extension package; and
generating, based on the simulated extension cost data, the extension cost data.
Binns in Col. 20 lines 5-27 teaches, An alternative approach uses Monte Carlo simulation for estimating disability pricing rather than a regression analysis for the discounted cost estimate. A probability density function is fit to the discounted cost data with stratification by morbidity category and demographic strata. The Monte Carlo simulation will calculate numerous random samples for a group using that group's morbidity categories and demographic stratification. The incidence rate for an individual will be 0 or 1, selected randomly in proportion to the morbidity incidence for that person's morbidity category, age and gender strata. If it is 1 then the discounted cost is randomly selected from the probability density function for that morbidity category and demographic strata and added to the group's sum of expected claims costs. When zero is selected, a zero duration or cost estimate is added to the sum of the other peoples' estimates for that group. The expected discounted costs are summed for all people in the group. This process is repeated with random selections numerous times providing a distribution of expected discounted cost for the group. This is the preferred embodiment for calculating the distribution of total discounted cost probability for a group or block of business.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning and AI models of Subramanian to include Monte Carlo simulation for cost estimates, as taught by Binns, in order to provide a breakdown of costs based on various factors. (Binns in Col. 20 lines 5-27)
Claim 12: Subramanian and Otillar and Binns teach The method according to claim 11, wherein the simulation is a Monte Carlo simulation configured to randomize the selection proportion of each time extension package. (Binns in Col. 20 lines 5-27: randomization)
See above rationale to combine.
Claim 13: Subramanian and Otillar and Binns teach The method according to claim 11, wherein performing the simulation comprises generating, for each time extension package, demurrage, and detention cost data indicative of cost of demurrage and detention of the container. (Subramanian, Par. 0083: determination of demurrage and detention)
Claim 14: Subramanian and Otillar and Binns teach The method according to claim 13, wherein the extension cost data comprises the demurrage and detention cost data. (Subramanian, Par. 0083: determination of demurrage and detention)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISMAIL A MANEJWALA whose telephone number is (571)272-8904. The examiner can normally be reached M-F 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISMAIL A MANEJWALA/Primary Examiner, Art Unit 3628
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