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 the Application
Claims 1-12 are pending and have been examined in this application. This communication is the first action on the merits. The Information Disclosure Statements (IDS) filed on November 11, 2025 has been acknowledged
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 1-12 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-12 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-12 are directed toward at least abstract idea without significantly more. In accordance with MPEP § 2106, the rationale for this determination is explained below.
Representative claim 1 is directed towards a method; claim 12 is directed towards a device, which are statutory categories of invention.
Although, claim 1 is directed toward a statutory category of invention, the claim however, is directed toward a judicial exception namely an abstract idea. The limitations that set forth the abstract idea recites:
generating, from a repository of inventory data corresponding to a reservable inventory, a set of data samples corresponding to changes to the inventory data within a time window; determining, an anomaly score for each data sample in the set; determining an aggregate anomaly score for the window; comparing the aggregate anomaly score to a threshold; and initiating a mitigation action when the aggregate anomaly score exceeds the threshold. These limitations, entail commercial interactions including, marketing or sales activities and business relations. As such, the limitations are directed towards the abstract grouping of Certain Methods of Organizing Human Activity in prong one of step 2A of the Alice/Mayo test (see MPEP 2106.04(a)(2) II). And/or Mathematical Concepts grouping because they perform mathematical calculations to organize, manipulate and process data. (See MPEP 2106.04(a)(2) I).
This judicial exception is not integrated into a practical application because, when analyzed as a whole under prong two of step 2A of the Alice/Mayo test (see MPEP 2106.04(d)), the additional elements provided by the claim amount to merely using a computer as a tool to perform an abstract idea. In particular the claim recites the additional element: via execution of a machine learning module, which is recited at a high level of generality and is the mere use of a computer as a tool to perform the abstract ideas. See MPEP 2106.05(f). Simply applying the abstract idea by a computer is not a practical application of the abstract idea. Therefore, the claim does not, for example, purport to improve the functioning of a computer. Nor does it effect an improvement in any other technology or technical field. Accordingly, the additional element does not impose any meaningful limits on practicing the abstract idea, and the claim is directed to abstract ideas.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements of a machine learning module, a memory and a processor (Claim 12) which do not constitute significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment1. Viewing these limitations as a combination, the additional elements amounts to no more than merely applying the exception using generic computer components. Merely applying an exception using generic computer components cannot provide an inventive concept. See at least, TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (“It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea”) Therefore, the limitations of the claim as a whole, when viewed individually and as an ordered combination, do not amount to significantly more than the abstract idea.
A review of dependent claims 2-11, likewise, do not recite any limitations that would remedy the deficiencies outlined above. The claims only further add to the abstract idea, with no elements which integrate the abstract idea into a practical application or constitute significantly more. Thus, while they may slightly narrow the abstract idea by further describing it, they do not make it less abstract and are rejected accordingly. Further still, claims 12 suffers from substantially the same deficiencies as outlined with respect to claim 1 and is also rejected accordingly.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
Claims 1, 5-7 and-12 are rejected under 35 U.S.C. 103 as being unpatentable over Neystadt (US Publication 2026/0135874) in view of Jin (US Publication 2021/0286874).
A. In regards to Claims 1 and 12, Neystadt teaches method and device comprising:
a memory storing repository of inventory data corresponding to a reservable inventory; Neystadt [0136; 0056: dynamic updater unit ensures that the system continuously crawls organizational directories, resources, and logs to collect updated information; ensures that the system's analysis is always based on the most current data, capturing changes like data repositories in real time];
and a processors; Neystadt [0134];
generating, from a repository of inventory data corresponding to a reservable inventory, a set of data samples corresponding to changes to the inventory data within a time window; Neystadt [0049: Events Database stores the logged events in an organized and searchable format; enabling the grouping of events into time-series and performing anomaly detection, and it can serve as the central repository (inventory) for all resource interactions; 0112: obtaining data describing a set of events; identifying entities involved in each event; grouping the events into time-series based on entity types];
determining, via execution of a machine learning module, an anomaly score for each data sample in the set; Neystadt [0113: detecting an anomaly in event data in said time-series of events; applying a Machine Learning model to assign a risk score based on organizational context information that relates to said anomaly];
Neystadt does not specifically disclose, determining an aggregate anomaly score for the window; this is disclosed by Jin [0027: aggregate score for the event is calculated for the event based on multiple scores, and the aggregate score is used to determine whether the event is an anomaly];
comparing the aggregate anomaly score to a threshold; this is disclosed by Jin [0225: compares the aggregate scores of events with an anomaly score threshold];
and initiating a mitigation action when the aggregate anomaly score exceeds the threshold. This is disclosed by Jin [0225: comparing the aggregate scores of events with an anomaly score threshold and issues alerts identifying events as anomalies; the anomaly detector may increase the aggregate score for an event (indicating the event is of higher concern) when one or more conditions are satisfied].
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of Neystadt with the teachings from Jin with the motivation to provide a means to quickly and efficiently detect an anomalous event because such an event may correspond to a security threat to the network (e.g., unauthorized access, attack, etc.). Jin [0002].
B. In regards to Claim 5, Neystadt does not specifically disclose, wherein determining the aggregate anomaly score includes: determining a count of a subset of the data samples having anomaly scores that exceed a sample threshold. This is disclosed by Jin [0256: alternatively, the aggregate score for the event may be the maximum of a subset of the level-specific scores averaged with another level-specific score.]. The motivation being the same as stated in claim 1.
C. In regards to Claim 6, Neystadt does not specifically disclose, wherein the mitigation action includes transmitting a notification including an indicator of the time window. This is disclosed by Jin [0225: the anomaly detector may issue custom reports reporting events that occur with a specific count within a time window]. The motivation being the same as stated in claim 1.
D. In regards to Claim 7, Neystadt does not specifically disclose, wherein the mitigation action includes discarding reservation records in the repository that correspond to data samples having anomaly scores exceeding a sample threshold; this is disclosed by Jin
and releasing reserved inventory corresponding to the discarded reservation records. This is disclosed by Jin [0225: the anomaly detector may issue custom reports reporting events that occur with a specific count within a time window]. The motivation being the same as stated in claim 1.
Claims 2-3 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Neystadt (US Publication 2026/0135874) in view of Jin (US Publication 2021/0286874) in further view of Bornat (US Publication 2014/0379389).
A. In regards to Claim 2, Neystadt does not specifically disclose, wherein generating each data sample in the set includes: obtaining a transaction record defining a reserved portion of the inventory; this is disclosed by Bornat [0025: inventory system may monitor booking activity of reservation agents by collecting data for each booking transaction of the reservation agent in a reservation agent profile];
and extracting a feature from the transaction record. Bornat [0034: reservation agent data structure may store collected data for booking transactions, where the collected data is organized by reservation agent].
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of Neystadt with the teachings from Bornat with the motivation to provide an improved inventory system for travel merchants, as well as methods and computer program products for managing inventory systems for travel merchants. Bornat [0006].
B. In regards to Claim 3, Neystadt does not specifically disclose, wherein the reservable inventory includes a flight, and wherein the feature includes at least one of: an origin location of the flight; a destination location of the flight; a passenger name; or a number of seats in the reserved portion. This is disclosed by Bornat [0035: origin/destination module computes the itinerary of each passenger based on the content of each booking request received by the sell/rebook module]. The motivation being the same as stated in claim 2.
C. In regards to Claim 8, Neystadt does not specifically disclose, wherein generating each data sample in the set includes: obtaining, from the repository, an occupancy ratio corresponding to the inventory at a given time within the time window. This is disclosed by Bornat [0060: system may determine a ratio of uncommitted travel bookings to total travel bookings for the flight-date of the booking request]. The motivation being the same as stated in claim 2.
D. In regards to Claim 9, Neystadt does not specifically disclose, wherein the set of data samples include a time sequence of occupancy ratios. This is disclosed by Bornat [0048: outcome associated with undesirable booking activity is the unavailability of types of travel inventory items (e.g., seats on a flight, seats of a certain class, etc.); in some embodiments, the BTL calculation module may determine one or more raw values corresponding to availability statistics based on inventory data (sample) retrieved from the inventory database; example, the BTL calculation module may determine a raw value corresponding to occupancy rate]. The motivation being the same as stated in claim 2.
Claims 4 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Neystadt (US Publication 2026/0135874) in view of Jin (US Publication 2021/0286874) in further view of Popow (US Publication 2026/0093977).
A. In regards to Claim 4, Neystadt does not specifically disclose, wherein the machine learning module includes an isolation forest. This is disclosed by Popowt [0034: the machine learning model may be an auto encoder neural network, isolation forest];
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of Neystadt with the teachings from Popowt with the motivation to provide an on-demand, real-time anomaly detection through a machine learning model that is trained in real-time on collected data, that results in a real-time trainable anomaly detection engine. Popowt [0034].
B. In regards to Claim 10, Neystadt does not specifically disclose, wherein determining the anomaly score for each data sample in the set includes: generating a reconstruction of the set of data samples; this is disclosed by Popow [0043: machine learning model may receive data and analyze the data to determine a rule for the second dataset, then reconstruct the second dataset based on the rule];
and determining the anomaly score by comparing the reconstruction with the set of data samples. This is disclosed by Jin [0096: the model may predict anomalous datapoints in the dataset by comparing reconstructed data to the input data to generate an anomaly score]. The motivation being the same as stated in claim 4.
C. In regards to Claim 11, Neystadt does not specifically disclose, further comprising, for each of the set of data samples: determining a primary sample threshold; this is disclosed by Popow [0041: user may select a predetermined filter indicating the behavior between the first query and the second query];
and determining a secondary sample threshold based on a variability metric corresponding to differences between (i) the primary sample threshold for previous data samples in the time window, and (ii) anomaly scores for the previous data samples; this is disclosed by Popow [0042: second query (e.g., the second dataset) may be generated and/or displayed, i.e., a second dataset may be retrieved based on the first dataset, the predefined filter, and the second data set of data parameters corresponding to the second query linked to the first query; 0041: a predetermined filter indicating the behavior between the first query and the second query, may use, be based on, and/or accept as an input all of a first dataset corresponding to the first query, specific datapoints from the first dataset corresponding to the first query, or exclude datapoints of the first dataset corresponding to the first query;. iIn some embodiments, the predetermined filter may indicate a time parameter (e.g., window functionality); 0043: determine a percent score for how similar a datapoint within the original dataset provided to the machine learning model is to the corresponding datapoint];
weighting the anomaly score based on whether the anomaly score exceeds the secondary sample threshold. This is disclosed by Popow [0043: machine learning model may also determine a percent score for how similar a datapoint within the original dataset provided to the machine learning model is to the corresponding datapoint in the reconstructed dataset returned by the machine learning model, e.g. an anomaly score; for example, in FIG. 14A, the anomaly score is 100%; 0099: for example, Cashier had a fixed void $ value (−$6,254.11) that was much higher than the average ($−25.51)]. The motivation being the same as stated in claim 4.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Neystadt (US Publication 2026/0135874) in view of Jin (US Publication 2021/0286874) in further view of Skeen (US Publication 2019/0012612).
A. In regards to Claim 7, Neystadt does not specifically disclose, wherein the mitigation action includes: discarding reservation records in the repository that correspond to data samples having anomaly scores exceeding a sample threshold; this is disclosed by Skeen [0095: if the reservation group does not achieve the minimum subscriber level within a predetermined time frame (threshold), the reservation group is cancelled, the reservations expire];
and releasing reserved inventory corresponding to the discarded reservation records. This is disclosed by Popowt [0199: the event tickets become reclaimed inventory into the event ticket allotment pool];
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of Neystadt with the teachings from Skeen with the motivation to provide functionality for enabling a ticket reservation/purchasing system to manage, track and allocate event-associated reserved tickets for group reservations and real-time inventory tracking. Skeen [1033].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Errol CARVALHO whose telephone number is (571)272-9987. The
Examiner can normally be reached on M-F 9:30-7:00 Alt Fri
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on 571- 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E CARVALHO/
Primary Examiner, Art Unit 3622
1 See, Alice Corp. Pty Ltd. v. CLS Bank lnt'l, 134 S. Ct. 2347, 2360 (2014) (noting that none of the hardware recited “offers a meaningful limitation beyond generally linking ‘the use of the [method] to a particular technological environment,’ that is, implementation via computers” (citing Bilski v. Kappos, 561 U.S. 593, 610-11 (2010))).
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