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
Due to communications filed 7/15/26, the following is a final office action. Claims 1-20 are amended. Claims 1-20 are pending in this application and are rejected as follows.
Claim Rejections - 35 USC § 101
Claims 1-20 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to a
judicial exception without significantly more.
Under Step 2A, Prong One, claims 1 and 10 recite mathematical concepts, including determining a demand forecast, determining probabilities, calculating a marginal class value, calculating an adjusted class value as a weighted combination, and deriving and applying a threshold based on a joint expected value. In addition, the claims further use these calculations to manage transportation inventory and revenue/booking decisions, which constitutes a method of organizing human activity. determining constrained fare classes,
Under Step 2A, Prong Two, claims 1 and 10 do not integrate the judicial exception into a
practical application. The additional elements, including one or more processors, a bookings table, generating and transmitting an inventory-control instruction, and a downstream inventory or revenue-management system, do not integrate the judicial exception into a practical application. These elements merely apply the recited mathematical calculations to a transportation inventory/revenue-management context and do not recite a specific improvement to computer functionality or another technology. Accordingly, the claim is directed to an abstract idea.
Under Step 2B, claims 1 and 10 does not include significantly more than the judicial exception.
The additional elements, considered individually and as an ordered combination, amount to no more than generic computer implementation and conventional data processing, storage, calculation, and transmission functions. The limitations, individually and in combination, therefore do not provide significantly more than the judicial exception itself, and are directed to an abstract idea.
Dependent claims 2-9 and 11-20 are also directed to same grouping of certain methods of
organizing human activity. The additional elements of the method of claims 2-19; transportation resource units, seats of claim 2; transportation resource units of claim 6; method of claims 11-20; bookings table, external forecasting system, external revenue-management system of claim 16; one or more processors, bookings table of claim 17; seats of claim 18; one or more processors, bookings table of claim 20 are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-5, 7-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cleaz-Savoyen “Airline Revenue Management Methods for Less Restricted Fare Structures”.
As per claim 1, Cleaz-Savoyen discloses:
accessing, by one or more processors, a bookings table representing bookings for transportation resource units on a scheduled transportation service, the bookings being grouped into a plurality of inventory classes ordered by value, (Page 40: Fully Unrestricted/ Undifferentiated Fare Products: Table 5 and Table 6 are two examples of fare products, with either 8 (single market case - Table 5) or 4 fare classes (Network D case, in the so-called 10 NEM - Table 6) in a fully unrestricted environment, without Advance Purchase requirements or Restrictions. With respect to the restricted environment, the fare ratios have been compressed (and the base fare is reduced by $10), restrictions have disappeared and all fare products are available during the entire booking period (e.g. no more advance purchase requirement) unless closed by the Revenue Management system);
determining, by the one or more processors, a demand forecast for the scheduled transportation service using the bookings table; (Page 18 “The demand forecasts are then fed into a seat allocation optimizer to determine the Booking Limits to be applied to each booking class, using serial nesting described in Chapter 3. T”; Pg 31 “The goal of the revenue management optimization is to compute the booking limits for the different classes of each flight given the demand forecasts for each itinerary”);
determining, by the one or more processors, a buy-down percentage for an inventory class in the plurality of inventory classes, the buy-down percentage representing a proportion of price-oriented demand relative to total demand for the inventory class, wherein the price-oriented demand is identified using restated booking information that accounts for interdependence of demand between inventory classes, (Pg. 54 The principle of the fare adjustment methods proposed by the Fare Adjustment methods is, instead of optimizing the quantities "ODFare - Displacement Cost", to
optimize the quantities "Marginal Revenue - Displacement Cost". Given the specific demand curve used in PODS (negative exponential), it also means (by hazard) to subtract to the quantity "ODF - Displacement Cost" another amount, so-called Price Elasticity Cost (PEcost) which, in a sense, accounts for the risk of buy-down. The effect of this method is to decrease the displacement-adjusted fares of the unrestricted fare structures and send the corresponding fares to lower buckets. The
PEcost, which is not applied to the restricted fare structures, will allow decoupling the fares which previously were in the same buckets and allow managing both structures more independently, as shown on the Figure 28; Page 64-65: “Consider the case when both network carriers are using EMSRb. When the LCC enters the 10 markets using EMSRb and the incumbents match restrictions and
advance purchase requirements, the revenues of Airline 1 decrease by 8% on average, from $1,235,000 down to $1,135,000. This illustrates and somewhat quantifies the spiral-down effect in this particular case. Looking at the local loads in the 10 markets, we notice that bookings actually dropped in Y, B and M classes, whereas Q bookings increased by 140% (buy-down phenomenon due to the removal of the fences between fare products - restrictions and advance purchase requirements). If we compare with the exact same case except that Airline 1 uses DAVN, we notice that the advantage brought by DAVN over EMSRb is still the same in terms of percent as it was without LCC: revenues increase by 1.54%. In the next sections, DAVN will generally be the reference for the RM methods);
determining, by the one or more processors, a marginal class value for the inventory class based at least in part on: (i) an average class value multiplied by a probability that a transportation resource unit is purchased in the inventory class, and (ii) a loss value multiplied by a probability that the transportation resource unit is purchased in a higher inventory class, wherein the loss value represents a difference in revenue per booking between the inventory class and a parent inventory class, (Page 18 “The nested seat allocation problem has been solved by Littlewood'" for two classes, and then extended to n classes by Belobaba" 4 . It is based on the notion of Expected Marginal Seat Revenue (known as the EMSRb's method, which can be defined as "the average fare of the booking class under consideration multiplied by the probability that demand will materialize for this incremental seat"),
and uses heuristic decision rules for nested booking classes. It has become the most commonly used SIC model among airlines' RMSs. The use of fare class mix models allowed an increase in revenue by 2 to 4%14 with respect to judgmental methods”);
calculating, by the one or more processors, an adjusted class value for the inventory class as a weighted combination of the marginal class value and the average class value using the buy- down percentage, (Page 65: “Consider the case when both network carriers are using EMSRb. When the LCC enters the 10 markets using EMSRb and the incumbents match restrictions and advance purchase requirements, the revenues of Airline 1 decrease by 8% on average, from $1,235,000 down to $1,135,000. This illustrates and somewhat quantifies the spiral-down effect in this particular case. Looking at the local loads in the 10 markets, we notice that bookings actually dropped in Y, B and M classes, whereas Q bookings increased by 140% (buy-down phenomenon due to the removal
of the fences between fare products - restrictions and advance purchase requirements”);
comparing, by the one or more processors, the adjusted class value to a threshold value derived from a joint expected value for the inventory class, (Page 31: “Threshold Algorithms
The Threshold algorithms are the first step in managing booking limits. A threshold (between 0% and 100%) is associated with each fare class and as soon as the ratio of "total bookings over capacity" reaches one of the thresholds, the corresponding class is closed down. In PODS, two different threshold methods are implemented: Fixed Threshold (FT) for which the threshold values are specified (typically 1.00, 0.85, 0.60, 0.40 for the 4-fare-class case) in the input file and remain the same for
the entire booking period, and Adaptive (or Accordion) Threshold (AT), for which a load factor target is set in the input file - generally at 80% - and the threshold values are computed and optimized at each time frame according to the actual bookings recorded until this point”);
generating, by the one or more processors, an inventory-control instruction based on the comparing, and transmitting the inventory-control instruction to a downstream inventory or revenue- management system, (Page 31: “Fare Class Yield Management (FCYM) It is a leg-based RM Method, generally used with Pick-Up Forecasting and Probabilistic Detruncation. The optimizer deals with a nested inventory, based on the Expected Marginal Seat Revenue (EMSRb"), the revenue we can expect from the next seat available for a given class (= OD Fare * probability to sell it)”);
wherein the inventory-control instruction causes the downstream system to adjust at least one inventory control parameter selected from the group consisting of: class availability, protected inventory quantity, and class value used by the downstream system for booking decisions, (Page 32: “The booking limit for fare class i represents the total number of bookings at which the fare class is closed down, to protect seats for higher fare classes (<i). For instance, in the case described above, if BL(M) bookings have been recorded, only B and Y fares will be available for the next requests).
As per claim 4, this claim merely recites a formula used to implement the method of claim 1, and thus, this claim is rejected for the same reasons as disclosed above with respect to claim 1.
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s rejection to incorporate the formula of claim 5 with the motivation of using the formula to guide the processor to perform the algorithm for adjusting at least one inventory control parameter as shown in claim 1.
As per claim 5, this claim merely recites a formula used to implement the method of claim 1, and thus, this claim is rejected for the same reasons as disclosed above with respect to claim 1.
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s rejection to incorporate the formula of claim 5 with the motivation of using the formula to guide the processor to perform the algorithm for adjusting at least one inventory control parameter as shown in claim 1.
As per claim 7, Cleaz-Savoyen discloses:
wherein the threshold value is a joint expected marginal resource value associated with the candidate inventory class and one or more higher inventory classes, (“Threshold Algorithms: The Threshold algorithms are the first step in managing booking limits. A threshold (between 0% and 100%) is associated with each fare class and as soon as the ratio of "total bookings over capacity" reaches one of the thresholds, the corresponding class is closed down. In PODS, two different threshold methods are implemented: Fixed Threshold (FT) for which the threshold values are specified (typically 1.00, 0.85, 0.60, 0.40 for the 4-fare-class case) in the input file and remain the same for the entire booking period, and Adaptive (or Accordion) Threshold (AT), for which a load factor target is set in the input file - generally at 80% - and the threshold values are computed and optimized at each time frame according to the actual bookings recorded until this point).
As per claim 8, Cleaz-Savoyen discloses:
wherein the restated booking information accounts for both product-oriented demand, representing bookings by passengers who purchase in a higher inventory class than the lowest available, and the price-oriented demand, representing bookings by passengers who buy down to the lowest available inventory class, (Page 64-65: “Consider the case when both network carriers are using EMSRb. When the LCC enters the 10 markets using EMSRb and the incumbents match restrictions and
advance purchase requirements, the revenues of Airline 1 decrease by 8% on average, from $1,235,000 down to $1,135,000. This illustrates and somewhat quantifies the spiral-down effect in this particular case. Looking at the local loads in the 10 markets, we notice that bookings actually dropped in Y, B and M classes, whereas Q bookings increased by 140% (buy-down phenomenon due to the removal
of the fences between fare products - restrictions and advance purchase requirements). If we compare with the exact same case except that Airline 1 uses DAVN, we notice that the advantage brought by DAVN over EMSRb is still the same in terms of percent as it was without LCC: revenues increase by 1.54%. In the next sections, DAVN will generally be the reference for the RM methods).
As per claim 9, Cleaz-Savoyen discloses:
wherein, responsive to the inventory-control instruction, the downstream system revises at least one of: a demand forecast, an optimization input, a protected inventory allocation, or a publicly available class price for the candidate inventory class, (Page 18 “The demand forecasts are then fed into a seat allocation optimizer to determine the Booking Limits to be applied to each booking class, using serial nesting described in Chapter 3. T”; Pg 31 “The goal of the revenue management optimization is to compute the booking limits for the different classes of each flight given the demand forecasts for each itinerary”).
As per claim 10, this claim recites limitations similar to those of independent claim 1, and is therefore rejected for similar reasons.
As per claim 18, Cleaz-Savoyen discloses:
wherein the plurality of inventory classes comprise fare classes for seats on a flight of the scheduled transportation service, (page 31, Table One: “Example of Fare Class Structure in the Process of Optimizing the Seat Inventory”).
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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cleaz-Savoyen, and further in view of Walker (US 20050177402).
As per claim 2, Cleaz-Savoyen does not disclose:
further comprising receiving, by the one or more processors, the seat bookings in the additional fare class,
However, (Walker (US 20050177402) discloses in ([0090] In step 1525, the RMS 200 determines
whether the expected bookings on one or more of the actual flights exceeds the actual bookings for
those flights. The RMS 200, in step 1530 of FIG. 15b, selects one of the actual flights as the flight on
which to place the unspecified-time ticket holder. In step 1535, the RMS 200 updates the forecasted
demand analysis database 230 and seat allocation database245, accordingly. In particular, the RMS 200
accesses the forecasted demand analysis database 230 and modifies the record for the actual flight by
incrementing the "Actual Quantity Booked" by "1". The RMS 200 also accesses the seat allocation
database 245 and modifies the record for: (1) the actual flight by incrementing the "Total Inventory
Booked" by "1" and decrementing the "Total Seats Remaining" by "1"; and (2) the special fare listing by
decrementing the "Total Inventory Booked" by "1"). It would have been obvious to one of ordinary skill
in the art at the time the invention was filed to include the above limitations as taught by Walker '402 in
the systems of Kohavi, since the claimed invention is merely a combination of old elements, and in the
combination each element merely would have performed the same function as it did separately, and
one of ordinary skill in the art would have recognized that the results of the combination were
predictable.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Walker '402 in the systems of Cleaz-Savoyen, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 3, 14, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cleaz-Savoyen, and further in view of Kohavi (US 7136821).
As per claim 3, Cleaz-Savoyen does not disclose:
the seat bookings in the respective fare classes wherein the restated booking information used to identify the price-oriented demand is generated by an unobscuring process that determines, for one or more inventory classes, demand that is obscured due to availability of lower-value inventory classes, and restates historical booking information to account for the obscured demand,
However, (Kohavi (US 7136821) discloses: Description Paragraph - DETX (18): The ARMS will monitor the actual demand with each fare class relative to the forecasted demand to dynamically reevaluate the inventory allocated to both the actual flights and the airline special fare listing. In accordance with the actual demand, the ARMS 104 will allocate additional inventory, lower the fare/class, reduce or eliminate the inventory or increase the fare/class).).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Kohavi in the systems of Cleaz-Savoyen, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 14, Cleaz-Savoyen, does not disclose:
However, Kohavi (US 7136821) discloses: wherein the decrementing comprises reducing the previously remapped count for the selected lowest inventory class by a lesser of: (i) the previously remapped count for the selected lowest inventory class, and (ii) the remaining cancellation quantity.
Kohavi: Description Paragraph - DETX (18): The ARMS will monitor the actual demand with each fare class relative to the forecasted demand to dynamically reevaluate the inventory allocated to both the actual flights and the airline special fare listing. In accordance with the actual demand, the ARMS 104 will allocate additional inventory, lower the fare/class, reduce or eliminate the inventory or increase the fare/class).).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Kohavi in the systems of Cleaz-Savoyen, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 19, Cleaz-Savoyen does not disclose the following:
wherein the decrementing reduces the previously remapped count in a first inventory class of the plurality of inventory classes and subsequently reduces the previously remapped count in a second inventory class of the plurality of inventory classes that is higher in value than the first inventory class, responsive to the remaining cancellation quantity exceeding the previously remapped count in the first inventory class.
However, (Kohavi (US 7136821) discloses: Description Paragraph - DETX (18): The ARMS will monitor the actual demand with each fare class relative to the forecasted demand to dynamically reevaluate the inventory allocated to both the actual flights and the airline special fare listing. In accordance with the actual demand, the ARMS 104 will allocate additional inventory, lower the fare/class, reduce or eliminate the inventory or increase the fare/class).).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Kohavi in the systems of Cleaz-Savoyen, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 20, Cleaz-Savoyen does not disclose:
further comprising providing, by the one or more processors, the updated remapped bookings table to a demand model that accounts for priority-class bookings in a cumulative demand forecast, wherein the demand model uses the updated remapped bookings table to reduce displacement of higher-value bookings by priority-class bookings in the demand forecast.
However, (Kohavi (US 7136821) discloses: Description Paragraph - DETX (18): The ARMS will monitor the actual demand with each fare class relative to the forecasted demand to dynamically reevaluate the inventory allocated to both the actual flights and the airline special fare listing. In accordance with the actual demand, the ARMS 104 will allocate additional inventory, lower the fare/class, reduce or eliminate the inventory or increase the fare/class).).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Kohavi in the systems of Cleaz-Savoyen, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 6, 11 12, 13, 15, 16, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cleaz-Savoyen, and further in view of Campbell (US 5,918,209 A).
As per claim 6, Cleaz-Savoyen does not disclose:
wherein the inventory-control instruction causes the downstream system to adjust at least one of: (i) a number of protected transportation resource units allocated to the candidate inventory class, (ii) a class availability status for the candidate inventory class, or (iii) a class value used by the downstream system to determine whether to accept or reject a booking in the candidate inventory class.
However, Campbell et al discloses in col. 21, lines 21-44: "FIG. 18 is a flow diagram of a method for
generically determining marginal values for use in a client-specific perishable resource management
system. Data is loaded using the client-specific load() function (block 210). As described hereinabove,
the load() function is defined in the client-specific layer 192 as a derived class member for a particular
client. The loaded data is mapped into the problem space for the industry-specific layer 191 and generic
layer 190 (block 211). This mapping is accomplished through inheritance whereby the parent and
grandparent classes that is, the industry-specific layer 191 derived abstract classes and generic layer 190
abstract class, instantiate client-specific objects to industry-specific and finally generic objects. Generic
marginal values are then determined using the generic solve() function, such as shown in the perishable
RMP class 194 (block 212) as further described herein below in FIG. 19. The generic marginal values are
mapped back into the client-specific problem space, that is, to the particular client-specific layer 192
class (block 213). Finally, the marginal values as mapped from generic marginal values to client-specific
marginal values are stored using the store() function contained in the client-specific layer 192 class
(block 214)."
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Campbell et al in the systems of Cleaz-Savoyen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, Cleaz-Savoyen does not disclose:
wherein the seat bookings occurred during a first time period
However, Campbell discloses: (Col. 19, lines 5-12: FIG. 14B shows, by way of example, a demand point
list 140 for the ORD-JFK flight leg departure 31. The first entry in this list, entry 141a, corresponds to the
first demand record 39 in the demand records list 38 for the flight path departure 10 ORD-JFK-LHR-CDG
as shown in FIG. 3. The first demand point encountered in the associated set of demand records 39
corresponds to the booking class price 40 of $500).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Campbell in the system of Cleaz-Savoyen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have
recognized that the results of the combination were predictable.
As per claim 12, Cleaz-Savoyen does not disclose:
wherein the remapping history data structure stores, for each inventory class in the plurality of inventory classes, a cumulative count of priority-class bookings that were remapped into that inventory class over a plurality of prior intervals.
However, Campbell et al discloses in col. 21, lines 21-44: "FIG. 18 is a flow diagram of a method for
generically determining marginal values for use in a client-specific perishable resource management
system. Data is loaded using the client-specific load() function (block 210). As described hereinabove,
the load() function is defined in the client-specific layer 192 as a derived class member for a particular
client. The loaded data is mapped into the problem space for the industry-specific layer 191 and generic
layer 190 (block 211). This mapping is accomplished through inheritance whereby the parent and
grandparent classes that is, the industry-specific layer 191 derived abstract classes and generic layer 190
.ocr_line, .ocr_header display:block; } abstract class, instantiate client-specific objects to industry-
specific and finally generic objects. Generic marginal values are then determined using the generic
solve() function, such as shown in the perishable RMP class 194 (block 212) as further described herein
below in FIG. 19. The generic marginal values are mapped back into the client-specific problem space,
that is, to the particular client-specific layer 192 class (block 213). Finally, the marginal values as mapped
from generic marginal values to client-specific marginal values are stored using the store() function
contained in the client-specific layer 192 class (block 214).' "
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Campbell in the system of Cleaz-Savoyen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have
recognized that the results of the combination were predictable.
As per claim 13, Cleaz-Savoyen does not disclose:
wherein the selecting the lowest inventory class with a non-zero previously remapped count comprises searching the remapping history data structure in ascending value order of the plurality of inventory classes.
However, Campbell discloses in col. 3, lines 45-54: A further embodiment of the present invention is a method using a computer and a marginal value system for determining marginal values for perishable resources expiring at a future time. A locally optimal marginal value is evaluated for one of the perishable resources using a continuous optimization function dependent on the marginal values for the other perishable resources. The locally optimal marginal value is iteratively reevaluated until a globally optimal marginal value is attained.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Campbell in the system of Cleaz-Savoyen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have
recognized that the results of the combination were predictable.
As per claim 15, Cleaz-Savoyen does not disclose:
wherein the repeating comprises iteratively selecting a next-lowest inventory class in the plurality of inventory classes that has a non-zero previously remapped count and decrementing the count for that next-lowest inventory class until the remaining cancellation quantity is zero.
However, Campbell discloses in: (Col.. 3, lines 46-54, A further embodiment of the present invention is a method using a computer and a marginal value system for determining marginal values for perishable resources expiring at a future time. A locally optimal marginal value is evaluated for one of the perishable resources using a continuous optimization function dependent on the marginal values for the other perishable resources. The locally optimal marginal value is iteratively reevaluated until a globally optimal marginal value is attained.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Campbell in the system of Cleaz-Savoyen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have
recognized that the results of the combination were predictable.
As per claim 16, Cleaz-Savoyen does not disclose:
wherein the providing the updated remapped bookings table comprises transmitting the updated remapped bookings table to an external forecasting system or an external revenue-management system that generates or revises a demand forecast for the scheduled transportation service based at least in part on the updated remapped bookings table.
However, Campbell et al discloses in col. 21, lines 21-44: "FIG. 18 is a flow diagram of a method for
generically determining marginal values for use in a client-specific perishable resource management
system. Data is loaded using the client-specific load() function (block 210). As described hereinabove,
the load() function is defined in the client-specific layer 192 as a derived class member for a particular
client. The loaded data is mapped into the problem space for the industry-specific layer 191 and generic
layer 190 (block 211). This mapping is accomplished through inheritance whereby the parent and
grandparent classes that is, the industry-specific layer 191 derived abstract classes and generic layer 190
.ocr_line, .ocr_header display:block; } abstract class, instantiate client-specific objects to industry-
specific and finally generic objects. Generic marginal values are then determined using the generic
solve() function, such as shown in the perishable RMP class 194 (block 212) as further described herein
below in FIG. 19. The generic marginal values are mapped back into the client-specific problem space,
that is, to the particular client-specific layer 192 class (block 213). Finally, the marginal values as mapped
from generic marginal values to client-specific marginal values are stored using the store() function
contained in the client-specific layer 192 class (block 214).' "
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Campbell in the system of Cleaz-Savoyen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have
recognized that the results of the combination were predictable.
As per claim 17, Cleaz-Savoyen does not disclose:
further comprising combining, by the one or more processors, the updated remapped bookings table with booking information for the current interval to generate cumulative booking information for the scheduled transportation service.
However, Campbell et al discloses in col. 21, lines 21-44: "FIG. 18 is a flow diagram of a method for
generically determining marginal values for use in a client-specific perishable resource management
system. Data is loaded using the client-specific load() function (block 210). As described hereinabove,
the load() function is defined in the client-specific layer 192 as a derived class member for a particular
client. The loaded data is mapped into the problem space for the industry-specific layer 191 and generic
layer 190 (block 211). This mapping is accomplished through inheritance whereby the parent and
grandparent classes that is, the industry-specific layer 191 derived abstract classes and generic layer 190
.ocr_line, .ocr_header display:block; } abstract class, instantiate client-specific objects to industry-
specific and finally generic objects. Generic marginal values are then determined using the generic
solve() function, such as shown in the perishable RMP class 194 (block 212) as further described herein
below in FIG. 19. The generic marginal values are mapped back into the client-specific problem space,
that is, to the particular client-specific layer 192 class (block 213). Finally, the marginal values as mapped
from generic marginal values to client-specific marginal values are stored using the store() function
contained in the client-specific layer 192 class (block 214).' "
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to
include the above limitations as taught by Campbell in the system of Cleaz-Savoyen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have
recognized that the results of the combination were predictable.
Response to Arguments
Applicant's arguments filed 7/15/26 have been fully considered but they are not persuasive.
With regard to the 35 USC 101 rejection, Applicant argues that amended claim one does not recite a generic commercial practice, but does recite a specific multi-step computational methodology applied to electronic bookings tables and inventory-class data structures, where dependent claims 3-5 further specify a defined algorithmic calculation. However, Examiner respectfully disagrees. Although the claims recite a specific sequence of calculations applied to electronic booking and inventory-class data, the specificity of the algorithm does not remove the claims from the judicial exception. The recited demand forecast, probabilities, marginal and average class values, weighted combination, and joint expected value are mathematical calculations used to determine transportation inventory and booking decisions. The electronic bookings table, inventory-class data, processors, and transmission of an inventory-control instruction merely provide the environment and generic computer implementation for performing the calculations and do not recite a specific improvement to computer functionality or another technology. Thus, the claims remain directed to the identified abstract idea under Step 2A.
Applicant further argues that amended claim 10 recites a specific ordered reverse-remapping algorithm which is a particular algorithmic sequence applied to a defined data structure, not an abstract concept. However, Examiner respectfully disagrees. The fact that the claims recite a specific ordered sequence of calculations, including a reverse remapping algorithm applied to a defined data structure, does not, by itself, remove the claims from the judicial exception. The recited algorithm performs mathematical calculations on booking and inventory-class data to determine values and inventory-control decisions. The defined data structure and ordered processing merely specify how the abstract calculations are performed and do not recite an improvement to the functioning of the computer or another technology. Accordingly, the claims remain directed to an abstract idea under Step 2A.
Applicant argues that the claims recite transmitting an inventory-control instruction that causes a downstream system to adjust class availability, protected inventory quantity, or class value and therefore integrate the claim into a practical application. However, Examiner respectfully disagrees. These limitations merely apply the results of the recited calculations to the intended field of transportation inventory and revenue management. The claims do not recite a specific improvement to the operation of the downstream system or to another technology. Rather, the downstream system performs its conventional function of adjusting inventory-control parameters for booking decisions. Accordingly, the additional elements do not integrate the judicial exception into a practical application under Step 2A, Prong Two.
Applicant further argues that the specific ordered combination of limitations constitutes an inventive concept, and furthermore that the calculations of claims 1 and 3-5 is not conventional activity, and also that the iterative lowest-to-successively-higher reverse-remapping of claims 10 and 13-15 and 19 is likewise not conventional. However, Examiner respectfully disagrees. None of the cited references teach . The specificity of the claimed combination and the asserted nonconventional nature of the iterative lowest-to-successively-higher reverse remapping do not, by themselves, establish an inventive concept. Under Step 2B, the claim must recite additional elements that, individually or in combination, amount to significantly more than the judicial exception. Here, the reverse remapping and associated calculations merely define the manner in which the mathematical calculations are performed to determine transportation inventory and revenue-management values. The claims do not establish that these operations improve computer functionality or another technology or otherwise provide a nonconventional technological application. Accordingly, the ordered combination does not amount to significantly more than the abstract idea.
Applicant’s arguments, see arguments/remarks, filed 7/15/26, with respect to claims 1-20 have been fully considered and are persuasive. The Double Patenting Rejection of claims 1-20 has been withdrawn.
Applicant’s arguments, see arguments/remarks, filed 7/15/26, with respect to the rejection(s) of the claim(s) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made as show above in the office action.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Akiba Robinson whose telephone number is 571-272-6734 and email is Akiba.Robinsonboyce@USPTO.gov. The examiner can normally be reached on Monday-Thursday 6:30am-4:30pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor, Nathan Uber can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the receptionist whose telephone number is (703) 305-3900.
September 14, 2026
/AKIBA K ROBINSON/Primary Examiner, Art Unit 3626
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