DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the claims filed on 13 January 2026.
Claims 1 and 12 have been amended.
Claims 8 and 19 have been cancelled.
Claims 1-7, 9-18, and 20-21 are currently pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 13 January 2026 has been entered.
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 1-7, 9-18, and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-7 and 9-11 is/are drawn to method (i.e., a process), claims 12-18 and 20-21 is/are drawn to a system (i.e., a machine). As such, claims 1-7, 9-18, and 20-21 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Representative Claim 1: A method, comprising:
receiving a pre-shipment notification from a customer indicating that a load will be collected from a source location and delivered to a destination location;
determining a collection date to collect the load based on a status of contents of the load;
identifying a first carrier and a service level for the load based on a requested delivery date;
prior to collection of the load, determining a predicted delivery date for the load considering friction within a transit configuration for the load using the first carrier, wherein the friction is associated with at least one intrinsic constraint of the first carrier or at least one extrinsic constraint, wherein the predicted delivery date is determined by:
identifying patterns correlating to a transit reconfiguration within carrier data,
structured data, and unstructured data based on historic instances of intrinsic constraints and extrinsic constraints, identify a root cause of the transit reconfiguration;
determining, for the transit configuration, a modification probability of the transit reconfiguration during transit based on the identified patterns and considering real-time events within the structured data and the unstructured data; and
predicting the predicted delivery date for the load based on the modification probability; and
in response to determining that the predicted delivery date is after the requested delivery date, identifying at least one corrective modification to the transit configuration for the load.
As noted by the claim limitations above, the independent claimed invention is directed to predicting delivery delays. This is considered to be an abstract idea because it is about tracking a load which is managing a personal interaction between people, which falls within the category of “certain methods of organizing human activity.”
See MPEP 2106.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of a trained machine learning model, wherein the trained machine learning model is trained on a training dataset by updating one or more parameters. This/these additional elements individually or in combination do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (see MPEP 2106.05(h)). Accordingly, these additional element(s) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: Claim 1 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) do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (see MPEP 2106.05(h)), which does not render a claim as being significantly more than the judicial exception. Accordingly, claim 1 is ineligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claim 1 is not eligible subject matter under 35 USC 101.
Examiner recommends positively reciting the active step of training into the scope of the claims. At present the claims are merely using a trained model.
Dependent claim(s) 2-7 and 9-11 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 1. Therefor claim(s) 2-7 and 9-11 are ineligible.
Representative Claim 12: A tracking management system for predicting delays, comprising:
receive a pre-shipment notification from a customer indicating that a load will be collected from a source location and delivered to a destination location;
determine a collection date to collect the load based on a status of contents of the load;
identify a first carrier and a service level for the load based on a requested delivery date;
prior to collection of the load, determine a predicted delivery date for the load considering friction within a transit configuration for the load using the first carrier, wherein the friction is associated with at least one intrinsic constraint of the first carrier and at least one extrinsic constraint, wherein the predicted delivery date is determined by:
identifying patterns correlating to a transit reconfiguration within carrier data, structured data, and unstructured data based on historic instances of intrinsic constraints and extrinsic constraints, identify a root cause of the transit reconfiguration;
determining, for the transit configuration, a modification probability of the transit reconfiguration during transit based on the identified patterns and considering real-time events within the structured data and the unstructured data; and
predicting the predicted delivery date for the load based on the modification probability; and
in response to determining that the predicted delivery date is after the requested delivery date, identify at least one corrective modification to the transit configuration for the load.
As noted by the claim limitations above, the independent claimed invention is directed to predicting delivery delays. This is considered to be an abstract idea because it is about tracking a load which is managing a personal interaction between people, which falls within the category of “certain methods of organizing human activity.”
See MPEP 2106.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: This judicial exception is not integrated into a practical application. In particular, claim 12 recites the following additional element(s): a storage configured to store instructions, and a processor configured to execute the instructions, a trained machine learning model, trained on a training dataset by updating one or more parameters. This/these additional elements individually or in combination do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (see MPEP 2106.05(h)). Accordingly, these additional element(s) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 12 is directed to an abstract idea.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: Claim 12 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) do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning) (see MPEP 2106.05(h)), which does not render a claim as being significantly more than the judicial exception. Accordingly, claim 12 is ineligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claim 12 is not eligible subject matter under 35 USC 101.
Examiner recommends positively reciting the active step of training into the scope of the claims. At present the claims are merely using a trained model.
Dependent claim(s) 13-18 and 20-21 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 12. Therefor claim(s) 13-18 and 20-21 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.
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) 1-7, 9-18, and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhry (US 20170255903 A1) in view of Neumann (US 11055655 B1) in further view of Williams (US 20150046361 A1).
Regarding claim 1, Chowdhry teaches a method, comprising:
receiving a pre-shipment notification from a customer (Paragraph [0071] “At 802, details associated with a product (or an order) may be received. The details may be received when a customer or sales person is viewing the product online prior to placing an order.”)
determining a collection date to collect the load based on a status of contents of the load; (Paragraph [0069] “At 716, […] when the commitment engine 102 receives a message from one of the sales channel 110 indicating a potential order for a product, the commitment engine 102 may perform one or more of 702, 704, 706, 708, 710, 712, or 714 to determine, approximately in real time, the ESD [estimated shipping date] for the product.”; Paragraph [0067] “At 712, […] if the order data includes a request for customization, the customization adder 424(1) of FIG. 4 may be added to the lead time determination and the ESD for the product.”; step 712 and 716 of Fig. 7)
identifying a first carrier and a service level for the load based on a requested delivery date; (Paragraph [0031] “The logistics data 138 may thus enable the commitment engine 102 to select a carrier and a delivery mode that provides the fastest delivery at the lowest cost taking into account the type of delivery that the customer has requested.”; Examiner notes paragraph [0081] explains delivery type includes “overnight delivery”, which is selected/requested by the customer.)
prior to collection of the load, determining, using a trained machine learning model, a predicted delivery date for the load considering friction within a transit configuration for the load using the first carrier, wherein the friction is associated with at least one intrinsic constraint of the first carrier or at least one extrinsic constraint, (Paragraph [0076] “when the commitment engine 102 receives a message from one of the sales channel 110 indicating a potential order for a product, the commitment engine 102 may perform one or more of 802, 804, 806, 808, or 810 to determine, approximately in real time, the EDD [estimated delivery date] for the product.”; Paragraph [0073] “The LLT [logistics lead time] 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”; Paragraph [0023] “The system may use machine learning to identify those components that typical (e.g., a high percentage of the time) cause delays, learn what the typical delay time comprises, and automatically count down based on the delay time.”) wherein the predicted delivery date is determined by:
(Paragraph [0023] “The system may use machine learning to identify those components that typical (e.g., a high percentage of the time) cause delays”)
Chowdhry does not teach:
receiving a pre-shipment notification from a customer indicating that a load will be collected from a source location and delivered to a destination location;
identifying patterns correlating to a transit reconfiguration within carrier data, structured data, and unstructured data based on historic instances of intrinsic constraints and extrinsic constraints wherein the trained machine learning model is trained on a training dataset by updating one or more parameters to identify the root cause of the transit reconfiguration; and
in response to determining that the predicted delivery date is after the requested delivery date, identifying, using the trained machine learning model, at least one corrective modification to the transit configuration for the load.
However, Neumann teaches:
receiving a pre-shipment notification from a customer indicating that a load will be collected from a source location and delivered to a destination location; (Col. 13, ll. 10-15 “user 116a makes a request for an alimentary combination 112 from an alimentary provider”; Col. 4, ll. 1-15 “a common column between two tables of database 108 may include an identifier of a first alimentary provider, for instance as defined below; as a result, a query may be able to retrieve all rows from any table pertaining to a given first alimentary provider.”; Col. 4, ll. 45-55 “alimentary providers may include any combination of one or more of the following: restaurants, bars, cafes, or other vendor of food or beverages, such as a hotel […] a user 116 may order a pepperoni pizza from a pizza restaurant. User 116 may select to have the pizza delivered to any location, such as but not limited to, the user's residence, the user's workplace, or the like.”; el. 112 of Fig. 1A of Neumann; Examiner notes the systems receives a request containing an order [alimentary combination], a source [alimentary provider], and a destination [delivery location]. This information can be received by the system. Col. 4, ll. 1-15 shows that there can by multiple providers to choose from, but the system receives information about a specific provider as requested by the customer.); and
in response to determining that the predicted delivery date is after the requested delivery date, identifying, using the trained machine learning model, at least one corrective modification to the transit configuration for the load. (Col. 12, ll. 29-50 “FIG. 1A, computer device 104 is configured to generate a modified physical transfer path 140 […] The modified physical transfer 140 may be selected, for example, if trouble state 116 may delay the delivery of an alimentary combination to user 116 by an amount of time that exceeds a delivery time threshold value.”; Col 2, ll. 13-21 “Machine-learning processes are used to determine the trouble state, the trouble state cause, and the trouble state owner.”; el. 140 of Fig. 1A of Neumann)
This step of Neumann is applicable to the method of Chowdhry as they both share characteristics and capabilities, namely, they are directed to determining a delay in shipping. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of Chowdhry to incorporate receiving a pre-shipment notification from a customer and identifying at least one corrective modification to the transit configuration for the load. as taught by Neumann. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in view of Neumann in order to generate an update to help a delivery (see Col. 1, ll. 5-10 of Neumann).
Neumann further teaches:
the trained machine learning model is trained on a training dataset by updating one or more parameters to identify the root cause of the transit reconfiguration; (Col 15, ll. 39-56 “Additionally, computing a trouble state cause may include training a machine-learning process using trouble state cause training data correlating delayed delivery notification to a trouble state cause. Computing a trouble cause may include outputting the trouble state cause as a function of the delayed delivery notification and the machine-learning process.” of Neumann)
This step of Neumann is applicable to the method of Chowdhry as they both share characteristics and capabilities, namely, they are directed to determining a delay in shipping. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the machine learning model of Chowdhry to the training as taught by Neumann. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in view of Neumann in order to generate an update to help a delivery (see Col. 1, ll. 5-10 of Neumann).
Chowdhry in view of Neumann does not teach:
wherein the predicted delivery date is determined by:
identifying patterns correlating to a transit reconfiguration within carrier data, structured data, and unstructured data based on historic instances of intrinsic constraints and extrinsic constraints, wherein the trained machine learning model is trained on a training dataset by updating one or more parameters to identify a root cause of the transit reconfiguration;
determining, for the transit configuration, a modification probability of the transit reconfiguration during transit based on the identified patterns and considering real-time events within the structured data and the unstructured data; and
predicting the predicted delivery date for the load based on the modification probability.
However, Williams teaches:
wherein the predicted delivery date is determined by:
identifying patterns correlating to a transit reconfiguration within carrier data, structured data, and unstructured data based on historic instances of intrinsic constraints and extrinsic constraints; (Paragraph [0087] “Predictive analytics module 217 may perform continuous background processing of some or all data received from sensor device 210, third party systems 150, customer device 110, and host carrier terminal 230, as well as host carrier data 221, customer data 222, journey data 224, third party data 226, and analytics data 228. Predictive analytics module 217 may determine aspects for a current journey, or for a new journey being created by customer 112, by analyzing historically collected data to detect trends and patterns. Aspects may include, […] probability that the package will become off pace (e.g., delayed) or off track (e.g., lost), […] Probabilities of delay may be determined by analyzing, for example, occurrences and patterns of flight or trip cancellations by different carriers, weather patterns at certain times of the year, traffic due to holidays, or average times for passing through different ports of customs agencies.”)
determining, for the transit configuration, a modification probability of the transit reconfiguration during transit based on the identified patterns and considering real-time events within the structured data and the unstructured data; (Paragraph [0083] “In step 640, server 210 evaluates alternative options to determine success and failure probabilities. Predictive analytics module 217 may use stored trends, patterns, and data models to calculate success and failure probabilities. […] For example, predictive analytics module 217 may analyze data regarding an alternative route to determine the probability of time delays, to mitigate a delay-related alert condition.”; Paragraph [0087] “Predictive analytics module 217 may perform continuous background processing of some or all data received from sensor device 210, […] Predictive analytics module 217 may determine aspects for a current journey, or for a new journey being created by customer 112, by analyzing historically collected data to detect trends and patterns.”) and
predicting the predicted delivery date for the load based on the modification probability. (Paragraph [0083] “In, 640 […] predictive analytics module 217 may analyze data regarding an alternative route to determine the probability of time delays,”; Paragraph [0084] “In step 650, server 210 generates one or more recommendations based on the evaluations. The recommendation may highlight one or more of the alternative options with the highest probability for mitigating the negative effects of the identified alert condition”; steps 640 and 650 of Fig. 6)
This technique of Williams is applicable to the method of Chowdhry as they both share characteristics and capabilities, namely, they are directed to determining a delay for a shipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the delivery date of Chowdhry to be determined by identifying patterns, determining a modification probability, and predicting the predicted delivery data as taught by Williams. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in order to mitigate losses (see paragraph [0002] of Williams).
Regarding claim 2, Chowdhry in view of Neumann in further view of Williams teaches the method of claim 1.
Chowdhry further teaches wherein determining the predicted delivery date comprises:
identifying (Paragraph [0021] “the system may automatically determine in approximately real-time (e.g., based on the components the customer is selecting to configure the product) whether the product is available for direct shipment to the customer or whether to route the order to a stocking location.”; Paragraph [0031] “The logistics data 138 may include […] logistics information received from carriers […] costs associated with the various modes of delivery (e.g., same day delivery, next day delivery, N day delivery, lowest cost delivery, etc.) for each carrier. […] historical logistics information associated with fulfillment centers. For example, in some cases, a first fulfillment center may be closer (e.g., distance-wise) to a particular region […] The logistics data 138 may thus enable the commitment engine 102 to select […] a delivery mode that provides the fastest delivery at the lowest cost taking into account the type of delivery that the customer has requested.”)
Chowdry does not teach:
identifying a route of the transit configuration based on properties of the load, the first carrier, the service level, the source location, and the destination location.
However, Neumann teaches:
identifying a route of the transit configuration based on properties of the load, the first carrier, the service level, the source location, and the destination location. (Col. 5, lines 20-32 “computing device may generate an initial physical transfer path 120 as a function of the physical distance between the alimentary provider and user 116. […] an initial physical transfer path 120 for an alimentary combination that includes highly perishable items may be different than an initial delivery path 120 of non-perishable alimentary combinations to the same user.”; Col. 6, ll. 7-38 “an estimated delivery time projected for a physical path may be determined based on […] historical transfer party performance data. In some embodiments, if the projected physical path delivery time is later than a predicted and/or requested time for delivery, then the route may be assigned a lower score.” Col. 12, ll. 13-30 “Perishable items may receive a higher priority over non-perishable items. […] computing device 104 may schedule delivery of the alimentary combination that may include perishable items ahead of the alimentary combination that includes non-perishable items.”)
The motivation for making this modification to the teachings of Chowdhry is the same as that set forth above, in the rejection of claim 1.
Chowdry in view of Neumann does not teach:
determining a likelihood of a delay to the load based on at least one of intrinsic constraints or extrinsic constraints associated with the transit configuration.
However, Williams teaches:
determining a likelihood of a delay to the load based on at least one of intrinsic constraints or extrinsic constraints associated with the transit configuration. (Paragraph [0087] “Predictive analytics module 217 may perform continuous background processing of […] journey data 224, […] Predictive analytics module 217 may determine aspects for a current journey […] by analyzing historically collected data to detect trends and patterns. Aspects may include, […] probability that the package will become off pace (e.g., delayed)” of Williams)
This step of Williams is applicable to the method of Chowdhry in view of Neumann as they both share characteristics and capabilities, namely, they are directed to determining a delay for a shipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of Chowdhry to incorporate determining a likelihood of a delay to the load as taught by Williams. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in order to modify the shipment journey and mitigate losses (see paragraph [0002] of Williams).
Regarding claim 3, Chowdhry in view of Neumann in further view of Williams teaches the method of claim 2.
Chowdhry further teaches:
identifying a first service related to a first extrinsic constraint corresponding to the route and the transit method; (Paragraph [0073] “The LLT [logistics lead time] 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”)
retrieving the first extrinsic constraint from the first service, wherein the first service is provided information related to the route (Paragraph [0036] “When determining lead times (e.g., the lead time 138), the commitment engine 102 may automatically take weather information into consideration based on weather data provided by a weather database 144. […] if a BTO product is to be shipped from a first country to a second country, the commitment engine 102 may determine the most likely route that the shipment will use and determine the weather along the likely route when determining the lead time.”) and
obtaining first information related to a first delay (Paragraph [0073] “At 806 […] The LLT 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”; step 806 of Fig. 8)
Chowdhry in view of Neumann does not teach:
retrieving the first extrinsic constraint from the first service, wherein the first service is provided information related to the route and the transit method;
obtaining first information related to a first delay probability of the load based on the first extrinsic constraint.
However, Williams teaches:
retrieving the first extrinsic constraint from the first service, wherein the first service is provided information related to the route and the transit method; (Paragraph [0044] “Journey data 224 may include data related to journeys in-progress for customer 112's shipments such as, for example, journey itinerary/schedule, time-stamped GPS data, […] transportation method”; Paragraph [0062] “In step 420, server 210 may receive data regarding the journey and package.”; Paragraph [0064] “In step 450, server 210 analyzes some or all received data […] to detect abnormalities,”; Paragraph [0065] “In step 460, server 210 determines whether one or more alert conditions have been identified. Alert conditions may include, […] flight delays due to weather, holiday high volume congestion, or delays in customs ports/terminals.”; Paragraph [0068] “the alert condition […] are presented to customer 112 in step 480.”; Fig. 4 of Williams)
obtaining first information related to a first delay probability of the load based on the first extrinsic constraint. (Paragraph [0087] “Predictive analytics module 217 may perform continuous background processing of […] journey data 224, […] Predictive analytics module 217 may determine aspects for a current journey […] by analyzing historically collected data to detect trends and patterns. Aspects may include, […] probability that the package will become off pace (e.g., delayed)” of Williams)
The motivation for making this modification to the teachings of Chowdhry in view of Neumann is the same as that set forth above, in the rejection of claim 2.
Regarding claim 4, Chowdhry in view of Neumann, in further view of Williams teaches the method of claim 3.
Chowdhry further teaches:
wherein the first information is obtained before consignment of the load. (Paragraph [0076] “At 812 […] when the commitment engine 102 receives a message from one of the sales channel 110 indicating a potential order for a product, the commitment engine 102 may perform one or more of […] 806, […] to determine, approximately in real time, the EDD for the product.”; Paragraph [0073] “At 806 […] The LLT 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”; step 812 and 806 of Fig. 8)
Regarding claim 5, Chowdhry in view of Neuman, in further view of Williams teaches the method of claim 3.
Chowdhry further teaches:
wherein the first information is obtained after consignment of the load. (Paragraph [0076] “the commitment engine 102 may perform one or more of […] 806, […] to determine, approximately in real time, the EDD for the product. […] After the order has been placed, the commitment engine may automatically update the EDD (e.g., due to an unplanned event) and notify the customer of the updated EDD.”; Paragraph [0073] “At 806 […] The LLT 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”)
Regarding claim 6, Chowdhry in view of Neumann, in further view of Williams teaches the method of claim 2. Chowdhry in view of Neumann does not teach:
obtaining a delay probability of the load based on intrinsic constraints associated with the first carrier and the transit method.
However, Williams teaches:
obtaining a delay probability of the load based on intrinsic constraints associated with the first carrier and the transit method. (Paragraph [0087] “Predictive analytics module 217 may determine aspects […] for a new journey being created by customer 112, […] Aspects may include, […] probability that the package will become off pace (e.g., delayed) […] Probabilities of delay may be determined by analyzing, for example, occurrences and patterns of flight or trip cancellations by different carriers” of Williams)
The motivation for making this modification to the teachings of Chowdhry in view of Neumann is the same as that set forth above, in the rejection of claim 2.
Regarding claim 7, Chowdhry in view of Neumann, in further view of Williams teaches the method of claim 6.
Chowdhry further teaches:
retrieving the intrinsic constraints based on a public interface or a private interface of the first carrier. (Paragraph [0031] “logistics information received from carriers (e.g., USPS®, UPS®, FedEx®, DHL®, and the like) as to how long shipments are taking between different locations, whether any major events (e.g., inclement weather, transport vehicle malfunction, etc.) have occurred to cause delays in shipments in particular routes, etc.”; Paragraph [0087] “The computing device 1000 may also include one or more communication interfaces 1006 for exchanging data”)
Regarding claim 9, Chowdhry in view of Neumann in further view of Williams teaches the method of claim 1.
Chowdhry further teaches:
obtaining (Paragraph [0022] “The systems and techniques may enable planned events and unplanned events to be taken into account when determining delivery times.”; Paragraph [0076] “After the order has been placed, the commitment engine may automatically update the EDD [estimated delivery date] (e.g., due to an unplanned event)”)
Chowdhry does not teach:
after deploying the load based on the transit configuration, monitoring transit of the load based on the first carrier;
obtaining an in-transit delay probability based on monitoring the transit of the load; and
providing a notification based on a change in the in-transit delay probability.
However, Williams teaches:
after deploying the load based on the transit configuration, monitoring transit of the load based on the first carrier; (Paragraph [0061] “The shipment journey may begin in step 420, once the host carrier has taken possession of the package and host carrier system 130 has transmitted appropriate data regarding the shipment to any necessary third parties such as partner carriers and customs agencies.”; Paragraph [0062] “In step 420, […] Package scan data may be received in real-time from third party systems 150 such as partner carriers or from host carrier terminal 230.” of Williams)
obtaining an in-transit delay probability based on monitoring the transit of the load; (Paragraph [0061] “The shipment journey may begin in step 420,”; Paragraph [0083] “predictive analytics module 217 may analyze data regarding an alternative route to determine the probability of time delays, to mitigate a delay-related alert condition.”) and
providing a notification based on a change in the in-transit delay probability. (Paragraph [0068] “Once server 210 has determined alternative options, the alert condition and alternative options are presented to customer 112 in step 480.”)
This step of Williams is applicable to the method of Chowdhry in view of Neumann as they both share characteristics and capabilities, namely, they are directed to determining a delay for a shipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the method of Chowdhry to incorporate monitoring transit of the load based on the first carrier, obtaining an in-transit delay probability and providing a notification based on a change in the in-transit delay probability as taught by Williams. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in order to modify the shipment journey and mitigate losses (see paragraph [0002] of Williams).
Regarding claim 10, Chowdhry in view of Neumann in further view of Williams teaches the method of claim 1.
Chowdhry does not teach:
in response to determining that the load will be delayed, identifying the at least one corrective modification to the transit configuration for the load; and
providing a notification including the at least one corrective modification.
However, Neumann teaches:
in response to determining that the load will be delayed, identifying the at least one corrective modification to the transit configuration for the load; (Col. 12, ll. 29-50 “FIG. 1A, computer device 104 is configured to generate a modified physical transfer path 140 […] The modified physical transfer 140 may be selected, for example, if trouble state 116 may delay the delivery of an alimentary combination to user 116 by an amount of time that exceeds a delivery time threshold value.”; el. 140 of Fig. 1A) and
providing a notification including the at least one corrective modification. (Col. 14, ll. 5-11 “computing device 104 is configured to initiate a user alert communication as a function of the modified physical transfer route 140.” of Neumann)
The motivation for making this modification to the teachings of Chowdhry is the same as that set forth above, in the rejection of claim 1.
Regarding claim 11, Chowdhry in view of Neumann in further view of Williams teaches the method of claim 1.
Chowdhry further teaches:
wherein determining the predicted delivery date further comprises determine a predicted delivery time, (Paragraph [0022] “The systems and techniques may enable planned events and unplanned events to be taken into account when determining delivery times.”)
Chowdhry does not teach:
wherein determining the predicted delivery date further comprises determine a predicted delivery time, and wherein the requested delivery date includes a requested delivery time.
However, Neumann teaches:
wherein determining the predicted delivery date further comprises determine a predicted delivery time, and wherein the requested delivery date includes a requested delivery time. (Col. 6, ll. 30-35 “if the projected physical path delivery time is later than a predicted and/or requested time for delivery”)
The motivation for making this modification to the teachings of Chowdhry is the same as that set forth above, in the rejection of claim 1.
Claim 12:
Claim(s) 12 is/are directed to a tracking management system. Claim(s) 12 recite limitations parallel in nature as those addressed above for claim(s) 1 which are directed towards a method. Claim(s) 12 is/are therefore rejected for the same reasons as set above for claim(s) 1. Claim 12 further recites “a storage configured to store instructions” (Paragraph [0086] “computer storage media (e.g., memory storage devices) for storing instructions” of Chowdhry) and “a processor configured to execute the instructions” (Paragraph [0085] “processor 1002” of Chowdhry).
Regarding claim 13, Chowdhry in view of Neumann in further view of Williams teaches the method of claim 12.
Chowdhry further teaches wherein determining the predicted delivery date comprises:
identify (Paragraph [0021] “the system may automatically determine in approximately real-time (e.g., based on the components the customer is selecting to configure the product) whether the product is available for direct shipment to the customer or whether to route the order to a stocking location.”; Paragraph [0031] “The logistics data 138 may include […] logistics information received from carriers […] costs associated with the various modes of delivery (e.g., same day delivery, next day delivery, N day delivery, lowest cost delivery, etc.) for each carrier. […] historical logistics information associated with fulfillment centers. For example, in some cases, a first fulfillment center may be closer (e.g., distance-wise) to a particular region […] The logistics data 138 may thus enable the commitment engine 102 to select […] a delivery mode that provides the fastest delivery at the lowest cost taking into account the type of delivery that the customer has requested.”)
Chowdry does not teach:
identify a route of the transit configuration based on properties of the load, the first carrier, the service level, the source location, and the destination location.
However, Neumann teaches:
identify a route of the transit configuration based on properties of the load, the first carrier, the service level, the source location, and the destination location. (Col. 5, lines 20-32 “computing device may generate an initial physical transfer path 120 as a function of the physical distance between the alimentary provider and user 116. […] an initial physical transfer path 120 for an alimentary combination that includes highly perishable items may be different than an initial delivery path 120 of non-perishable alimentary combinations to the same user.”; Col. 6, ll. 7-38 “an estimated delivery time projected for a physical path may be determined based on […] historical transfer party performance data. In some embodiments, if the projected physical path delivery time is later than a predicted and/or requested time for delivery, then the route may be assigned a lower score.” Col. 12, ll. 13-30 “Perishable items may receive a higher priority over non-perishable items. […] computing device 104 may schedule delivery of the alimentary combination that may include perishable items ahead of the alimentary combination that includes non-perishable items.”)
The motivation for making this modification to the teachings of Chowdhry is the same as that set forth above, in the rejection of claim 1.
Chowdry in view of Neumann does not teach:
determine a likelihood of a delay to the load based on at least one of intrinsic constraints or extrinsic constraints associated with the transit configuration.
However, Williams teaches:
determine a likelihood of a delay to the load based on at least one of intrinsic constraints or extrinsic constraints associated with the transit configuration. (Paragraph [0087] “Predictive analytics module 217 may perform continuous background processing of […] journey data 224, […] Predictive analytics module 217 may determine aspects for a current journey […] by analyzing historically collected data to detect trends and patterns. Aspects may include, […] probability that the package will become off pace (e.g., delayed)” of Williams)
This operation of Williams is applicable to the system of Chowdhry in view of Neumann as they both share characteristics and capabilities, namely, they are directed to determining a delay for a shipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Chowdhry to incorporate determining a likelihood of a delay to the load as taught by Williams. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in order to modify the shipment journey and mitigate losses (see paragraph [0002] of Williams).
Regarding claim 14, Chowdhry in view of Neumann in further view of Williams teaches the method of claim 13.
Chowdhry further teaches:
identify a first service related to a first extrinsic constraint corresponding to the route and the transit method; (Paragraph [0073] “The LLT [logistics lead time] 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”)
retrieve the first extrinsic constraint from the first service, wherein the first service is provided information related to the route (Paragraph [0036] “When determining lead times (e.g., the lead time 138), the commitment engine 102 may automatically take weather information into consideration based on weather data provided by a weather database 144. […] if a BTO product is to be shipped from a first country to a second country, the commitment engine 102 may determine the most likely route that the shipment will use and determine the weather along the likely route when determining the lead time.”) and
obtain first information related to a first delay (Paragraph [0073] “At 806 […] The LLT 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”; step 806 of Fig. 8)
Chowdhry in view of Neumann does not teach:
retrieve the first extrinsic constraint from the first service, wherein the first service is provided information related to the route and the transit method;
obtain first information related to a first delay probability of the load based on the first extrinsic constraint.
However, Williams teaches:
retrieve the first extrinsic constraint from the first service, wherein the first service is provided information related to the route and the transit method; (Paragraph [0044] “Journey data 224 may include data related to journeys in-progress for customer 112's shipments such as, for example, journey itinerary/schedule, time-stamped GPS data, […] transportation method”; Paragraph [0062] “In step 420, server 210 may receive data regarding the journey and package.”; Paragraph [0064] “In step 450, server 210 analyzes some or all received data […] to detect abnormalities,”; Paragraph [0065] “In step 460, server 210 determines whether one or more alert conditions have been identified. Alert conditions may include, […] flight delays due to weather, holiday high volume congestion, or delays in customs ports/terminals.”; Paragraph [0068] “the alert condition […] are presented to customer 112 in step 480.”; Fig. 4 of Williams)
obtain first information related to a first delay probability of the load based on the first extrinsic constraint. (Paragraph [0087] “Predictive analytics module 217 may perform continuous background processing of […] journey data 224, […] Predictive analytics module 217 may determine aspects for a current journey […] by analyzing historically collected data to detect trends and patterns. Aspects may include, […] probability that the package will become off pace (e.g., delayed)” of Williams)
The motivation for making this modification to the teachings of Chowdhry in view of Neumann is the same as that set forth above, in the rejection of claim 13.
Regarding claim 15, Chowdhry in view of Neumann, in further view of Williams teaches the method of claim 14.
Chowdhry further teaches:
wherein the first information is obtained before consignment of the load. (Paragraph [0076] “At 812 […] when the commitment engine 102 receives a message from one of the sales channel 110 indicating a potential order for a product, the commitment engine 102 may perform one or more of […] 806, […] to determine, approximately in real time, the EDD for the product.”; Paragraph [0073] “At 806 […] The LLT 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”; step 812 and 806 of Fig. 8)
Regarding claim 16, Chowdhry in view of Neuman, in further view of Williams teaches the method of claim 14.
Chowdhry further teaches:
wherein the first information is obtained after consignment of the load. (Paragraph [0076] “the commitment engine 102 may perform one or more of […] 806, […] to determine, approximately in real time, the EDD for the product. […] After the order has been placed, the commitment engine may automatically update the EDD (e.g., due to an unplanned event) and notify the customer of the updated EDD.”; Paragraph [0073] “At 806 […] The LLT 408 may be determined based on the logistics data 138 as well as current and predicted weather information provided by the weather database 144.”)
Regarding claim 17, Chowdhry in view of Neumann, in further view of Williams teaches the method of claim 2. Chowdhry in view of Neumann does not teach:
obtain a delay probability of the load based on intrinsic constraints associated with the first carrier and the transit method.
However, Williams teaches:
obtain a delay probability of the load based on intrinsic constraints associated with the first carrier and the transit method. (Paragraph [0087] “Predictive analytics module 217 may determine aspects […] for a new journey being created by customer 112, […] Aspects may include, […] probability that the package will become off pace (e.g., delayed) […] Probabilities of delay may be determined by analyzing, for example, occurrences and patterns of flight or trip cancellations by different carriers” of Williams)
The motivation for making this modification to the teachings of Chowdhry in view of Neumann is the same as that set forth above, in the rejection of claim 13.
Regarding claim 18, Chowdhry in view of Neumann, in further view of Williams teaches the method of claim 17.
Chowdhry further teaches:
retrieve the intrinsic constraints based on a public interface or a private interface of the first carrier. (Paragraph [0031] “logistics information received from carriers (e.g., USPS®, UPS®, FedEx®, DHL®, and the like) as to how long shipments are taking between different locations, whether any major events (e.g., inclement weather, transport vehicle malfunction, etc.) have occurred to cause delays in shipments in particular routes, etc.”; Paragraph [0087] “The computing device 1000 may also include one or more communication interfaces 1006 for exchanging data”)
Regarding claim 20, Chowdhry in view of Neumann in further view of Williams teaches the method of claim 12.
Chowdhry further teaches:
obtain (Paragraph [0022] “The systems and techniques may enable planned events and unplanned events to be taken into account when determining delivery times.”; Paragraph [0076] “After the order has been placed, the commitment engine may automatically update the EDD [estimated delivery date] (e.g., due to an unplanned event)”)
Chowdhry does not teach:
after deploying the load based on the transit configuration, monitor transit of the load based on the first carrier;
obtain an in-transit delay probability based on monitoring the transit of the load; and
provide a notification based on a change in the in-transit delay probability.
However, Williams teaches:
after deploying the load based on the transit configuration, monitor transit of the load based on the first carrier; (Paragraph [0061] “The shipment journey may begin in step 420, once the host carrier has taken possession of the package and host carrier system 130 has transmitted appropriate data regarding the shipment to any necessary third parties such as partner carriers and customs agencies.”; Paragraph [0062] “In step 420, […] Package scan data may be received in real-time from third party systems 150 such as partner carriers or from host carrier terminal 230.” of Williams)
obtain an in-transit delay probability based on monitoring the transit of the load; (Paragraph [0061] “The shipment journey may begin in step 420,”; Paragraph [0083] “predictive analytics module 217 may analyze data regarding an alternative route to determine the probability of time delays, to mitigate a delay-related alert condition.”) and
provide a notification based on a change in the in-transit delay probability. (Paragraph [0068] “Once server 210 has determined alternative options, the alert condition and alternative options are presented to customer 112 in step 480.”)
This operation of Williams is applicable to the system of Chowdhry in view of Neumann as they both share characteristics and capabilities, namely, they are directed to determining a delay for a shipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Chowdhry to incorporate after deploying the load based on the transit configuration, monitor transit of the load based on the first carrier, obtaining an in-transit delay probability and providing a notification based on a change in the in-transit delay probability as taught by Williams. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in order to modify the shipment journey and mitigate losses (see paragraph [0002] of Williams).
Regarding claim 21, Chowdhry in view of Neumann in further view of Williams teaches the tracking management system of claim 12.
Chowdhry does not teach:
in response to determining that the load will be delayed, identify the at least one corrective modification to the transit configuration for the load.
However, Neumann teaches:
in response to determining that the load will be delayed, identify the at least one corrective modification to the transit configuration for the load. (Col. 12, ll. 29-50 “FIG. 1A, computer device 104 is configured to generate a modified physical transfer path 140 […] The modified physical transfer 140 may be selected, for example, if trouble state 116 may delay the delivery of an alimentary combination to user 116 by an amount of time that exceeds a delivery time threshold value.”; el. 140 of Fig. 1A)
The motivation for making this modification to the teachings of Chowdhry is the same as that set forth above, in the rejection of claim 12.
Chowdhry in view of Neumann does not teach:
provide information to the first carrier to modify the transit configuration based on the at least one corrective modification. (Paragraph [0025] “Once a selection of action to be taken is received, the host carrier may take the appropriate actions, including, if necessary, notifying the involved carrier(s) and third parties to modify the journey.” of Williams)
This operation of Williams is applicable to the system of Chowdhry in view of Neumann as they both share characteristics and capabilities, namely, they are directed to determining a delay for a shipment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Chowdhry to incorporate providing information to the first carrier to modify the transit configuration based on the at least one corrective modification as taught by Williams. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in order to modify the shipment journey and mitigate losses (see paragraph [0002] of Williams).
Response to Arguments
Applicant's arguments, see Page(s) 9-13, filed 13 January 2026, with respect to the 35 USC § 101 rejection(s) of claim(s) 1-21 have been fully considered but they are not persuasive. Applicant argues 1) the claims are not directed to an abstract idea, 2) the claims and integrated into a practical application. The Examiner respectfully disagrees.
Regarding argument 1, that the claims do not recite an abstract idea, the Examiner respectfully disagrees. The Applicant argues that the claims are not directed to delivering a load, but rather methods of predicting and identifying potential delivery delays and identifying at least one corrective modification to the transit configuration for the load. However, assuming arguendo that the claims are not directed to tracking a delivery of a load, predicting and identifying potential delivery delays and identifying at least one corrective modification to the transit configuration for the load is an abstract idea as well. The applicant argues that the steps occur prior to a load ever being picked up by a carrier, but the steps that occur prior to a load being picked up by the carrier is still considered a method of organizing human activity. MPEP 2106.04(a)(2)(II) states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
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, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The predicting and identifying of potential delivery delays are for delivery delays of a load and the corrective modification is to the transit of a load. The movement of a load can be performed by people, and the tracking of the load as claimed can occur by people moving a load and updating another person with the movement of the load. The Applicant argues that they are claiming a computer-implemented method but no where in claim 1 recites that a computer is implementing the method. There is a trained machine learning model being used but there is no computer implementing the steps. Furthermore, even if a computer was implementing the method, like in claim 12, MPEP 2106.04(a)(2)II. recites:
Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping.
A single person performing a delivery and updating a computer with the progress of the delivery would still be a method of organizing human activity. Therefore, the Examiner maintains that the claims recite a method of organizing human activity/abstract idea.
Regarding argument 2, that the additional elements integrated the claims into a practical application, the Examiner respectfully disagrees. The claims as amended recite no active training step for the machine learning model. As claimed, the method/system could simply call upon a trained machine learning model. Therefore the training of the machine learning model is outside the scope of the invention. The claims are merely applying a trained machine learning model to an abstract idea to limit the scope of the claims to machine learning (see MPEP 2106.05(h)). The Examiner recommends positively reciting the training into the scope of the invention using an active training step being performed by a computer as opposed to merely using a machine learning model that is potentially pre-trained on a training dataset by a different method or system outside of the invention. The current recitation of “wherein the trained machine learning model is trained on a training dataset by updating one or more parameters” merely describes the machine learning model that is being used and is not an active training step. Therefore, the Examiner maintains the 101 rejections of claims 1-21.
Applicant's arguments, see Page(s) 13-14, filed 13 January 2026, with respect to the 35 USC § 103 rejection(s) of claim(s) 1-21 have been fully considered but are not persuasive. Applicant argues the cited prior art does not teach “the trained machine learning model is trained on a training dataset by updating one or more parameters to identify a root cause of the transit reconfiguration”. The Examiner respectfully disagrees. Paragraph [0023] of Chowdhry teaches:
A rules engine may determine lead-times for individual components (e.g., used in a BTO product). For example, the system may determine lead-times and availability for individual components (e.g., memory, hard drive, processor, etc.) used to build a product. Based on the lead-times and availability, the system may compute shipping dates and delivery dates. If the system determine that a component has become delayed at a manufacturer then the system may automatically (e.g., without human interaction) add the delay to the shipping dates and delivery dates. The system may use machine learning to identify those components that typical (e.g., a high percentage of the time) cause delays, learn what the typical delay time comprises, and automatically count down based on the delay time. For example, the system may, using machine learning, determine that a particular hard drive manufacturer is delayed by two days under a certain set of conditions, e.g., an order for a particular hard drive exceeds a threshold amount. In this example, the system may determine when the set of conditions have been satisfied, automatically add a two day delay, and count down the delay to take into account when the particular hard drive will become available.
Delays are a cause of transit configurations having to be modified. Therefore, Chowdhry paragraph [0023] teaches a machine learning model that identifies a root cause of the transit reconfiguration. Chowdhry is silent to training a machine learning model on a training dataset by updating one or more parameters. However, Neumann teaches training a machine learning model on a training dataset by updating one or more parameters to identify a root cause of the transit reconfiguration. For example, Col 15, ll. 39-56 of Neumann recites:
Still referring to FIG. 4, at step 415, computing device may identify a trouble state as a function of the request for the alimentary combination. This may be implemented, without limitation, as described in FIGS. 1A-3. Identifying a trouble state may include determining a physical transfer route for the alimentary combination. Identifying a trouble state may include generating a predicted time of delivery as a function of the physical transfer route. Additionally, a trouble state may include computing a trouble state cause. Computing a trouble state cause includes receiving a delayed delivery notification. Additionally, computing a trouble state cause may include training a machine-learning process using trouble state cause training data correlating delayed delivery notification to a trouble state cause. Computing a trouble cause may include outputting the trouble state cause as a function of the delayed delivery notification and the machine-learning process. This may be implemented, without limitation, as described in FIGS. 1A-3.
Col. 15, ll. 39-56 of Neumann explains that a machine learning model can be trained to determine a trouble state cause (i.e. cause for delay). This machine learning model is trained using trouble state cause training data. Therefore, Chowdhry’s machine learning model can be modified to be trained using a training dataset by updating one or more parameters as taught by Neumann. This step of Neumann is applicable to the method of Chowdhry as they both share characteristics and capabilities, namely, they are directed to determining a delay in shipping. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the machine learning model of Chowdhry to the training as taught by Neumann. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Chowdhry in view of Neumann in order to generate an update to help a delivery (see Col. 1, ll. 5-10 of Neumann). Therefore, the Examiner maintains that Chowdhry in view of Neumann teaches the amended limitations.
With regards to claim 12 the applicant argues these claims are allowable due to their similarities to claim 1. As stated in the arguments above, the Examiner is maintaining the rejections for claim 1. Therefore, claims 12 remain rejected. With regards to claims 2-7, 9-18 and 20-21 the applicant argues these claims are allowable due to their dependence on claims 1 and 12. As stated in the arguments above, the Examiner is maintaining the rejections for claims 1 and 12. Therefore, claims 2-7, 9-18 and 20-21 remain rejected. Claims 8 and 19 have been cancelled.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIELLE ELIZABETH ZEVITZ whose telephone number is (703)756-1070. The examiner can normally be reached Mo-Th 10am-6pm.
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/DANIELLE ELIZABETH ZEVITZ/Examiner, Art Unit 3628 /GEORGE CHEN/Primary Examiner, Art Unit 3628