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 .
Notice of Pre-AIA or AIA Status
This is the first Non-Final Office Action in response to Application Serial Number: 18/436,811, filed on February 08, 2024. Claims 1-20 are pending in this application and have been rejected below.
Priority
The Examiner has noted this Application is a Continuation of Application No. 18/109,643 filed February 14, 2023.
Information Disclosure Statement
The information disclosure statement (IDS) filed on February 08, 2024 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 18 and 19 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 18 depends on the method of claim 17 and claim 19 depends on the method of claim 18, however claim 17 is currently disclosed as a system claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. For the purpose of examination, Examiner will interpret claim 18 to depend from system claim 17 and claim 19 to depend from the system of claim 18.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-12 are directed towards a method and claims 13-20 are directed towards a system, both of which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite assigning baggage drivers to flights at an airport to reduce missed bag quantities.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receiving a first set of flight parameters descriptive of a first flight; simulating, by a simulator and the first set of flight parameters, expected missed bag quantities for a plurality of baggage driver quantities to create a first predictive staffing model for the first flight by, for each baggage quantity of baggage drivers of the plurality of baggage driver quantities, predicting an expected missed bag quantity using the first set of flight parameters, the first predictive staffing model correlates the plurality of baggage driver quantities to a first plurality of expected missed bag quantities; and each baggage quantity of baggage drivers of the plurality of baggage driver quantities corresponds to one expected missed bag quantity of the first plurality of expected missed bag quantities; identifying a first recommended quantity of baggage drivers based on the first predictive staffing model and a threshold missed bag quantity, wherein the first recommended quantity of baggage drivers corresponds, according to the first predictive staffing model, to an expected missed bag quantity of the first plurality of expected missed bag quantities that is less than the threshold missed bag quantity; and modifying electronic data representative of driver schedules to assign the first recommended quantity of baggage drivers to the first flight constitutes methods based on commercial interactions, as well as, constitutes methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a computer-implemented machine-learning model and electronic driver scheduling system does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claim 13 recites certain method of organizing human activity and mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites electronic data stored by an electronic driver scheduling system, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, the electronic driver scheduling system is recited at a high-level of generality such that it amounts to no more than a generic computer used as a tool to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites using a computer-implemented machine-learning model and wherein: the computer-implemented machine-learning model is configured to relate driver quantities and flight parameters to expected missed bag quantities. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the computer-implemented machine-learning model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination with the claim limitations, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The system comprising: a flight database; an electronic driver scheduling system; a server electronically connected to the flight database and the electronic driver scheduling system, the server comprising: a processor; and a memory encoded with instructions executable by the processor recited in claim 13 also amounts to no more than mere instructions to apply the exception using a generic computer component; see MPEP 2106.05(f). Thus, the additional elements recited in claim 13 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including the electronic driver scheduling system, the system comprising: a flight database; an electronic driver scheduling system; a server electronically connected to the flight database and the electronic driver scheduling system, the server comprising: a processor; and a memory encoded with instructions executable by the processor amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0062]; [0064]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 7, 11, 19 and 20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 3-5 and 15-17 recite querying and obtaining limitations that are considered insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g). Claims 2, 4, 9, 10, 14, 16, 18 and 19 recites additional computer-implemented machine-learning model general use and technique limitations, respectively. The general use of machine learning techniques does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the computer-implemented machine-learning model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claims 2-6, 8, 10, 12 and 14-17 recite steps that further narrow the abstract idea. Therefore claims 2-12 and 14-20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed claim 1 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine references with a reasonable expectation of success as discussed below.
LaBorde (US 10014076 B1) teaches a baggage system including a data collection engine. Specifically, LaBorde discloses the server device stores one or more trained models which are used to predict an outcome of an event such as whether a baggage item is or is likely to be deviated (lost), stolen, etc. (baggage event). A representation of the process for creating, training and using the trained model is shown in FIG. 16. As shown in FIG. 17, the raw data and new data include sets of data [1, 2 . . . N] with known outcomes and properties of each of the data. For example, the data can be past baggage events with known deviation outcomes. The properties of the data can be attributes of the baggage, airport facilities, baggage handlers, etc., a classification task for predicting deviation risks of a baggage item is shown. The machine learning task entails a two-step supervised learning process which utilizes both input and output data in the model training process. Model construction is done using a representative training data set and the model, once trained is used for classifying new or unseen cases, for example a baggage item at risk of deviation—predicts nominal categorical assessment or assignment. The inputs are collected baggage item data attributes/properties. The output will be predicted categorical risk for deviation, no deviation, moderately deviated and severely deviated, and the backend devices (TMD and server) can use trained models such as the NNM and/or SOM to predict outputs (e.g., which events are at risk for deviation) as described. The backend devices are capable of using their trained models to determine to which, if any, events more resources should be allocated (i.e. the backend devices can determine whether there is an opportunity, or more specifically, a high probability, of successfully mitigating the likelihood of a given predicted deviation by allocating additional resource(s)). Particularly, to do this, the controller of the TMD may utilize a NNM that takes inputs such as deviation risk category (moderate or significant risk for delay) of the event, attributes of the baggage item and the departure airport, etc.
Mizoguchi et al. (US 20050010460 A1) teaches the non-transported status of the baggage is predicted when a departure airplane and an arrival airplane are delayed and a security is strengthened. In this case, it is possible to predict change of non-transported status of the baggages by changing the settings of the above node and the link and by carrying out the simulation. When a node or link is found where the non-transported status of baggage is predicted, an appropriate countermeasure can be applied to equipments corresponding to the node or link or equipments corresponding to upstream or downstream of the node or link. For example, a node or link is found where the non-transported state of baggage is assumed in the extraordinary situation of the database. In this case, The following countermeasusres are taken: the increase of throughput (operation percentage) of equipments at the node or link and equipments at the upstream or downstream of the node or link, increase of throughputs of staffs, increase of the number of staffs, and open of a spare space. Thus, it becomes possible to achieve elimination of the non-transported state of the baggages, the shortening of boarding time for the baggages, earlier notification of departure predicted time, improvement of satisfaction of passengers, and increase of the number of taking-off and landing airplanes.
Outwater et al. (US 20150029024 A1) teaches airline baggage tracking and more particularly to a system and method for locating missing airline baggage.
However, LaBorde, Mizoguchi and Outwater, individually or in combination, do not explicitly teach the combination of claim limitations as a whole as recited in independent claim 1. Thus, claim is found to be distinguishable over the prior art. Claim 13 is distinguishable over the prior art for similar reasons as cited for claim 1. Dependent claims 2-12 and 14-20 are distinguishable because they depend on claims 1 and 13 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Martin et al. (US 20030143944 A1) – Capable of assembling and broadcasting a plurality of different message types dictated by the needs of a given broadcast application. In the aforementioned airport application message types or templates can include, but are not limited to the following: final boarding call, gate change, ready for boarding, second boarding call, cancelled flight (with or without an accompanying explanation), snack service on flight, no smoking flight, carry-on item announcements, on time departure, pre-boarding for children and passengers needing special attention, flight over-booked, aircraft change, gate change (arrival or departure), delayed arrival time, on-time arrival, boarding by specified row numbers, boarding instructions, continued boarding by row, baggage claim with welcome, delayed baggage, carousel change, flight cancellation, apology for delay, baggage available at carousel, overnight delay of flight, delayed departure with expected time, or any other necessary user configured message.
Yano (WO 2012093618 A1) - Provided is a travel situation detection system that can inhibit the occurrence of delays during the travel of a person or a baggage in a transportation-means facility and that can enhance service quality for rendezvousing etc. The travel situation detection system specifies, with checking devices (21), the passage time instants at which a traveler or a baggage passes respective waypoints on a travel route provided in an airport, compares the passage time instant or the passage time between two waypoints with an average value, and notifies the comparison result to the traveler or an operator through the checking device(s) (21) or a terminal device (22). If the passage time instant or the passage time is late compared to the average value, the traveler can hurry up with his/her travel, and the operator transporting the baggage can hurry up with the transport of the baggage. The travel situation detection system may also notify the travel situation of the traveler to, for example, a person rendezvousing with the traveler through the use of a terminal device (22) or a mobile terminal device (23). The person rendezvousing with the traveler can grasp the travel situation of the traveler and make preparations right before the rendezvous, and thus, the rendezvousing situation can be improved.
Le et al., (A Generalised Data Analysis Approach for Baggage Handling Systems Simulation) - Airport baggage handling systems are a critical infrastructure component within major airports, and essential to ensure smooth luggage transfer while preventing dangerous material being loaded onto aircraft. This paper proposes a standard set of measures to assess the expected performance of a baggage handling system through discrete event simulation. These evaluation methods also have application in the study of general network systems. Results from the application of these methods reveal operational characteristics of the studied BHS, in terms of metrics such as peak throughput, in-system time and system recovery time.
Johnstone et al. (Status-based Routing in Baggage Handling Systems: Searching Verses Learning) - This study contributes to work in baggage handling system (BHS) control, specifically dynamic bag routing. Although studies in BHS agent-based control have examined the need for intelligent control, but there has not been an effort to explore the dynamic routing problem. As such, this study provides additional insight into how agents can learn to route in a BHS. This study describes a BHS status-based routing algorithm that applies learning methods to select criteria based on routing decisions. Although numerous studies have identified the need for dynamic routing, little analytic attention has been paid to intelligent agents for learning routing tables rather than manual creation of routing rules. We address this issue by demonstrating the ability of agents to learn how to route based on bag status, a robust method that is able to function in a variety of different BHS designs.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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