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 .
Claims 1-3 and 5-21 are presented for examination.
Response to Amendment
The prior objections to the specification and the drawings have been obviated by the amendments. Therefore, those objections are withdrawn.
Claim Objections
Claims 1-3 and 5-21 are objected to because of the following informalities:
Claim 1: “the multiple event sequences that correspond to the assigned cluster” should read “multiple event sequences that correspond to the assigned cluster”; “lacks include an event level” should read “lacks an event level”; “one or more antecedents that matches” should read “one or more antecedents that match”; “wherein the at least one recommended event sequence including” should read “wherein the at least one recommended event sequence includes”; “one or more actions of the at least one recommended event sequences” should read “one or more actions of the at least one recommended event sequence”
Claim 12: “provide at least some of the extracted features features extracted as input data” should read “provide at least some of the extracted features as input data”; “the multiple event sequences that correspond to the assigned cluster” should read “multiple event sequences that correspond to the assigned cluster”; “one or more antecedents that matches” should read “one or more antecedents that match”; “wherein the at least one recommended event sequence including” should read “wherein the at least one recommended event sequence includes”; “one or more actions of the at least one recommended event sequences” should read “one or more actions of the at least one recommended event sequence”
Claim 17: “the multiple event sequences that correspond to the assigned cluster” should read “multiple event sequences that correspond to the assigned cluster”; “one or more antecedents that matches” should read “one or more antecedents that match”; “wherein the at least one recommended event sequence including” should read “wherein the at least one recommended event sequence includes”; “one or more actions of the at least one recommended event sequences” should read “one or more actions of the at least one recommended event sequence”
Claim 21: “the weak learner boosting include” should read “the weak learner boosting includes”; there should be a comma or semicolon following “operations”
Claims 2-3, 5-11, 13-16, and 18-21 are objected to due to dependency on an objected-to base claim.
Appropriate correction is required.
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 1-3 and 5-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”).
Claim 1
Step 1: The claim is directed to a method and therefore is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites:
“extracting…features from the event data to generate extracted features, wherein the extracted features indicate events represented by the event data, the parameters, and other information”; This limitation encompasses mentally extracting features from the event data that indicate events represented by the event data, the parameters, and other information.
“…assign the extracted features to an assigned cluster of multiple clusters”; This limitation encompasses mentally assigning the extracted features to an assigned cluster of multiple clusters.
“…assign input feature sets to the multiple clusters based on relationships between the input feature sets and features of members of the multiple clusters”; This limitation encompasses mentally assigning input feature sets to the multiple clusters based on relationships between the input feature sets and features of members of the multiple clusters.
“generating…multiple candidate event sequences that represent the multiple event sequences that correspond to the assigned cluster”; This limitation encompasses mentally generating multiple candidate event sequences that represent the multiple event sequences that correspond to the assigned cluster.
“generating… multiple incremental candidate event sub-sequences based on the multiple candidate event sequences”; This limitation encompasses mentally generating multiple incremental candidate event sub-sequences based on the multiple candidate event sequences.
“pruning… the multiple incremental candidate event sub-sequences based on a current event level derived from the event data, wherein the pruning includes discarding any candidate event sub-sequence that lacks include an event level or that lacks one or more antecedents that matches the events indicated by the event data and the parameters”; This limitation encompasses mentally pruning the multiple incremental candidate event sub-sequences based on a current event level derived from the event data, such as by mentally removing sub-sequences that lack an event level or that lack one or more antecedents from consideration.
“generating… at least one recommended event sequence based on the pruned multiple incremental candidate event sub-sequences, wherein the at least one recommended event sequence including one or more actions to be performed to complete the structured process”; This limitation encompasses mentally generating at least one recommended event sequence based on the pruned multiple incremental candidate event sub-sequences.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “obtaining, by one or more processors, event data from a client device corresponding to a partial performance of a structured process, wherein the event data includes parameters of one or more events that have been performed during the partial performance as part of an event sequence to complete the structured process” and “outputting, by the one or more processors, the at least one recommended event sequence, wherein the outputting comprises transmitting instructions to the client device to cause the client device to automatically perform the one or more actions of the at least one recommended event sequences…wherein the instructions cause the client device to capture measurements from a particular sensor to move the at least one of the robot or the drone to a particular location,” however these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP § 2106.05(g)). The claim additionally recites “providing, by the one or more processors, at least some of the extracted features as input data to one or more machine learning (ML) models to… [perform the judicial exception],” “wherein the one or more ML models are configured to… [perform the judicial exception],” and that the extracting, generating, and pruning steps analyzed as abstract ideas above are performed “by the one or more processors.” However, these limitations amount to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)). The claim further recites, “the structured process includes a process to control at least one of a robot or a drone,” however, this limitation amounts to generally linking the judicial exception to the field of use of robotics (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtaining event data and outputting the at least one recommended event sequence limitations, in addition to being insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP § 2106.05(d)(II)(i) 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)). Otherwise, the analysis at this step mirrors that of step 2A, prong 2. As an ordered whole, the claim is directed to an abstract idea of extracting features from event data corresponding to a partial performance of a structured process, assigning the extracted features to clusters, generating multiple candidate event sequences from an assigned cluster, generating and pruning multiple incremental candidate event sub-sequences, and generating recommended event sequences based on the pruned multiple incremental candidate event sub-sequences. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “initiating display of a graphical user interface (GUI) that includes the at least one recommended event sequence,” however this limitation amounts to the insignificant extra solution activity of mere data gathering and outputting (MPEP § 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The initiating display of a GUI limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP §2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 1 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “outputting one or more instructions to initiate performance of one or more actions indicated by the at least one recommended event sequence,” however this limitation amounts to the insignificant extra solution activity of mere data gathering and outputting (MPEP § 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The outputting one or more instructions limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP §2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“determining…one or more scores corresponding to the multiple incremental candidate event sub-sequences based on associative rules”; This limitation encompasses mentally determining one or more scores corresponding to the multiple incremental candidate event sub-sequences based on associative rules.
“filtering…the multiple incremental candidate event sub-sequences to remove candidate event sub-sequences for which the corresponding one or more scores fail to satisfy one or more thresholds”; This limitation encompasses mentally filtering the multiple incremental candidate event sub-sequences to remove candidate event sub-sequences for which the corresponding one or more scores fail to satisfy one or more thresholds.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the determining and filtering steps are performed “by the one or more processors,” however this amounts to mere instructions to apply a judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A prong 2.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“ranking… remaining candidate event sub-sequences based on the corresponding one or more scores”; This limitation encompasses mentally ranking remaining candidate event sub-sequences based on the corresponding one or more scores.
“selecting…a threshold number of highest ranking candidate event sub-sequences of the remaining candidate event sub-sequences as the at least one recommended event sequence”; This limitation encompasses mentally selecting a threshold number of highest ranking candidate event sub-sequences of the remaining candidate event sub-sequences as the at least one recommended event sequence.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the ranking and selecting steps are performed “by the one or more processors,” however this amounts to mere instructions to apply a judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A prong 2.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“the one or more scores comprise a support score, a confidence score, and a lift score”; This limitation merely further limits the scores determined in claim 5, and determining one or more scores is still mentally performable when the scores comprise a support score, a confidence score, and a lift score.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See analysis of claim 5.
Step 2B: The claim does not contain significantly more than the judicial exception. See analysis of claim 5.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“extracting…features from historical event data corresponding to one or more past performances of the structured process to generate training data, wherein the historical event data indicates parameters of events that have been performed during the one or more past performances of the structured process”; This limitation encompasses mentally extracting features from historical event data to generate training data.
“…assign event sequences to the multiple clusters based on extracted features corresponding to the event sequences”; This limitation encompasses mentally assigning event sequences to the multiple clusters based on extracted features.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the extracting features step is performed “by the one or more processors,” however this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)). The claim also further recites “providing, by the one or more processors, the training data to the one or more ML models to train the one or more ML models to perform unsupervised learning-based clustering to… [perform the judicial exception],” however, this limitation merely limits the use of the judicial exception to the technological environment of unsupervised model training (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“determining…an initial seeding of the multiple clusters based on dissimilarity coefficients between candidate members of the multiple clusters”; This limitation encompasses mentally determining an initial seeding of the multiple clusters based on dissimilarity coefficients between candidate members of the multiple clusters.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the determining step is performed “by the one or more processors,” however this amounts to mere instructions to apply a judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“performing… affinity analysis on the features extracted from the historical event data to identify a subset of the features for which variance satisfies a threshold as principal features”; This limitation encompasses mentally performing affinity analysis on the features extracted from the historical event data to identify a subset of the features for which variance satisfies a threshold as principal features.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the performing affinity analysis step is performed “by the one or more processors,” however this amounts to mere instructions to apply a judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 11
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“extracting…the multiple features from the event data”; This limitation encompasses mentally extracting the multiple features from the event data.
“discarding…one or more of the multiple features that do not correspond to the principal features to generate the at least some of the multiple features”; This limitation encompasses mentally discarding one or more of the multiple features that do not correspond to the principal features to generate the at least some of the multiple features.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the extracting and discarding steps are performed “by the one or more processors,” however this amounts to mere instructions to apply a judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 12
Step 1: The claim recites a system and therefore is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites:
“extract features from the event data to generate extracted features, wherein the extracted features indicate events represented by the event data, the parameters, and other information”; This limitation encompasses mentally extracting features from the event data that indicate events represented by the event data, the parameters, and other information.
“…assign the extracted features to an assigned cluster of multiple clusters”; This limitation encompasses mentally assigning the extracted features to an assigned cluster of multiple clusters.
“…assign input feature sets to the multiple clusters based on relationships between the input feature sets and features of members of the multiple clusters”; This limitation encompasses mentally assigning input feature sets to the multiple clusters based on relationships between the input feature sets and features of members of the multiple clusters.
“generate multiple candidate event sequences that represent the multiple event sequences that correspond to the assigned cluster”; This limitation encompasses mentally generating multiple candidate event sequences that represent the multiple event sequences that correspond to the assigned cluster.
“generate multiple incremental candidate event sub-sequences based on the multiple candidate event sequences”; This limitation encompasses mentally generating multiple incremental candidate event sub-sequences based on the multiple candidate event sequences.
“prune the multiple incremental candidate event sub-sequences based on a current event level derived from the event data, wherein the pruning includes discarding any candidate event sub-sequence that lacks an event level or that lacks one or more antecedents that matches the events indicated by the event data and the parameters”; This limitation encompasses mentally pruning the multiple incremental candidate event sub-sequences based on a current event level derived from the event data, such as by mentally removing sub-sequences that lack an event level or that lack one or more antecedents from consideration.
“generate at least one recommended event sequence based on the pruned multiple incremental candidate event sub-sequences, wherein the at least one recommended event sequence including one or more actions to be performed to complete the structured process”; This limitation encompasses mentally generating at least one recommended event sequence based on the pruned multiple incremental candidate event sub-sequences.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “obtain event data from a client device corresponding to a partial performance of a structured process, wherein the event data includes parameters of one or more events that have been performed during the partial performance as part of an event sequence to complete the structured process” and “output the at least one recommended event sequence, wherein the outputting comprises transmitting instructions to the client device to cause the client device to automatically perform the one or more actions of the at least one recommended event sequences…wherein the instructions cause the client device to capture measurements from a particular sensor to move the at least one of the robot or the drone to a particular location,” however these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP § 2106.05(g)). The claim additionally recites “a memory; and one or more processors communicatively coupled to the memory, the one or more processors configured to: [perform the method]”, “provide at least some of the extracted features features extracted as input data to one or more machine learning (ML) models to… [perform the judicial exception],” and “wherein the one or more ML models are configured to… [perform the judicial exception].” However, these limitations amount to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)). The claim further recites, “the structured process includes a process to control at least one of a robot or a drone,” however, this limitation amounts to generally linking the judicial exception to the field of use of robotics (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtain event data and output the at least one recommended event sequence limitations, in addition to being insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP § 2106.05(d)(II)(i) 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)). Otherwise, the analysis at this step mirrors that of step 2A, prong 2. As an ordered whole, the claim is directed to an abstract idea of extracting features from event data corresponding to a partial performance of a structured process, assigning the extracted features to clusters, generating multiple candidate event sequences from an assigned cluster, generating and pruning multiple incremental candidate event sub-sequences, and generating recommended event sequences based on the pruned multiple incremental candidate event sub-sequences. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 13
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites:
“preprocess the event data or training data prior to extracting the multiple features”; This limitation encompasses mentally preprocessing the event data or training data.
“preprocess the event data by removing empty data sets, validating the parameters of the one or more events included in the event data, converting at least a portion of the event data to a common format, or a combination thereof”; This limitation encompasses mentally preprocessing the event data by removing empty data sets, validating the parameters of the one or more events included in the event data, and/or converting at least a portion of the event data to a common format.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the one or more processers are configured to perform the preprocessing steps, however this limitation amounts to mere instructions to apply a judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 14
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites:
“filter the multiple incremental candidate event sub-sequences to remove candidate event sub-sequences that do not include the current event level and sub-sequences for which one or more association rule-based scores fail to satisfy one or more thresholds”; This limitation encompasses mentally filtering the multiple incremental candidate event sub-sequences to remove candidate event sub-sequences that do not meet the given criteria.
“select a threshold number of highest ranking remaining candidate event sub-sequences as the at least one recommended event sequence”; This limitation encompasses mentally selecting a threshold number of highest ranking remaining candidate event sub-sequences as the at least one recommended event sequence.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the one or more processors are configured to perform the above steps, however this amounts to mere instructions to apply a judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 15
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites:
“extract the multiple features from the event data”; This limitation encompasses mentally extracting the multiple features from the event data.
“discard one or more of the multiple features that do not correspond to principal features prior to providing the at least some of the multiple features as input data to the one or more ML models, wherein the principal features are identified based on an affinity analysis performed on features extracted from historical event data corresponding to one or more past performances of the structured process”; This limitation encompasses mentally discarding one or more of the multiple features that do not correspond to principal features.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. This claim further recites that the one or more processors are configured to perform the extracting and discarding steps, however this limitation amounts to mere instructions to apply a judicial exception on a generic computer (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 16
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 12.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that the structured process is an insurance claim process, and the recommended event sequence represents at least one sequence of actions to process an insurance claim in compliance with the insurance claim process, however these limitations amount to generally linking the use of a judicial exception to the field of use of processing insurance claims (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 17
Step 1: The claim recites a non-transitory computer-readable storage medium and therefore is directed to the statutory category of articles of manufacture.
Step 2A Prong 1: The claim recites:
“extracting features from the event data to generate extracted features, wherein the extracted features indicate events represented by the event data, the parameters, and other information”; This limitation encompasses mentally extracting features from the event data that indicate events represented by the event data, the parameters, and other information.
“…assign the extracted features to an assigned cluster of multiple clusters”; This limitation encompasses mentally assigning the extracted features to an assigned cluster of multiple clusters.
“…assign input feature sets to the multiple clusters based on relationships between the input feature sets and features of members of the multiple clusters”; This limitation encompasses mentally assigning input feature sets to the multiple clusters based on relationships between the input feature sets and features of members of the multiple clusters.
“generating multiple candidate event sequences that represent the multiple event sequences that correspond to the assigned cluster”; This limitation encompasses mentally generating multiple candidate event sequences that represent the multiple event sequences that correspond to the assigned cluster.
“generating multiple incremental candidate event sub-sequences based on the multiple candidate event sequences”; This limitation encompasses mentally generating multiple incremental candidate event sub-sequences based on the multiple candidate event sequences.
“pruning the multiple incremental candidate event sub-sequences based on a current event level derived from the event data, wherein the pruning includes discarding any candidate event sub-sequence that lacks an event level or that lacks one or more antecedents that matches the events indicated by the event data and the parameters”; This limitation encompasses mentally pruning the multiple incremental candidate event sub-sequences based on a current event level derived from the event data, such as by mentally removing sub-sequences that lack an event level or that lack one or more antecedents from consideration.
“generating at least one recommended event sequence based on the pruned multiple incremental candidate event sub-sequences, wherein the at least one recommended event sequence including one or more actions to be performed to complete the structured process”; This limitation encompasses mentally generating at least one recommended event sequence based on the pruned multiple incremental candidate event sub-sequences.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “obtaining event data from a client device corresponding to a partial performance of a structured process, wherein the event data includes parameters of one or more events that have been performed during the partial performance as part of an event sequence to complete the structured process” and “outputting the at least one recommended event sequence, wherein the outputting comprises transmitting instructions to the client device to cause the client device to automatically perform the one or more actions of the at least one recommended event sequences…wherein the instructions cause the client device to capture measurements from a particular sensor to move the at least one of the robot or the drone to a particular location,” however these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP § 2106.05(g)). The claim additionally recites “A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for automated action recommendation for structured processes, the operations comprising: [the method],” “providing at least some of the extracted features as input data to one or more machine learning (ML) models to… [perform the judicial exception],” and “wherein the one or more ML models are configured to… [perform the judicial exception].” However, these limitations amount to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)). The claim further recites, “the structured process includes a process to control at least one of a robot or a drone,” however, this limitation amounts to generally linking the judicial exception to the field of use of robotics (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The obtaining event data and outputting the at least one recommended event sequence limitations, in addition to being insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP § 2106.05(d)(II)(i) 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)). Otherwise, the analysis at this step mirrors that of step 2A, prong 2. As an ordered whole, the claim is directed to an abstract idea of extracting features from event data corresponding to a partial performance of a structured process, assigning the extracted features to clusters, generating multiple candidate event sequences from an assigned cluster, generating and pruning multiple incremental candidate event sub-sequences, and generating recommended event sequences based on the pruned multiple incremental candidate event sub-sequences. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 18
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites:
“…generate at least one channelized event sequence”; This limitation encompasses mentally generating at least one channelized event sequence.
“…channelize input event sequences into channelized event sequences that each correspond to one of multiple layers of the structured process”; This limitation encompasses mentally channelizing input event sequences into channelized event sequences that each correspond to one of multiple layers of the structured process.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “providing the at least one recommended event sequence as input data to one or more second ML models to… [perform the judicial exception]” and “wherein the one or more second ML models are configured to… [perform the judicial exception].” However, these limitations amount to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 19
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 18.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “routing the at least one channelized event sequence to multiple ML models of a virtual agent configured to automatically perform the structured process, wherein each ML model of the multiple ML models corresponds to a layer of the multiple layers of the structured process, and wherein the multiple ML models are ensembled to generate an output of the virtual agent,” however this limitation amounts to the insignificant extra solution activity of mere data gathering and outputting (MPEP § 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “routing the at least one channelized event sequence…” limitation, in addition to being insignificant extra solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Claim 20
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites:
“determining a first layer of the multiple layers that corresponds to a first channelized event sequence of the at least one channelized event sequence”: This limitation encompasses mentally determining a first layer of the multiple layers that corresponds to a first channelized event sequence of the at least one channelized event sequence.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “routing the first channelized event sequence to a subset of the multiple ML models that correspond to the first layer,” however this limitation amounts to the insignificant extra solution activity of mere data gathering and output (MPEP § 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “routing the first channelized event sequence…” limitation, in addition to being insignificant extra solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Claim 21
Step 1: A process, as claim 6 above.
Step 2A Prong 1: The claim recites:
“the remaining candidate event sub-sequences are ranked”; This limitation encompasses mentally ranking the remaining candidate event sub-sequences.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the remaining candidate event sub-sequences are provided for weak learner boosting, wherein the weak learner boosting include use of the one or more ML models or techniques to further strengthen previous operations,” however this limitation merely generally links the judicial exception to the technological environment of weak learner boosting (MPEP 2106.05(h)). The claim additionally recites “a particular number of highest ranking candidate event sub-sequences are output as recommended event sequences,” however, this limitation amounts to the insignificant extra solution activity of mere data gathering and output (MPEP § 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The “a particular number of highest ranking candidate event sub-sequences are output as recommended event sequences” limitation, in addition to being insignificant extra solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network (MPEP § 2106.05(d)(II)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)). Otherwise, the analysis at this step mirrors that of step 2A prong 2.
Response to Arguments
Applicant's arguments filed June 16, 2026 regarding the rejections under 35 U.S.C § 101 (Remarks, pages 20-29) have been fully considered but they are not persuasive.
Applicant argues on pages 21-23 that the amended claims are not directed to an abstract idea under Step 2A, Prong One. Applicant first argues that “providing, at least some of the extracted features as input data to one or more machine learning (ML) models… involves processor-implemented machine learning operations which cannot be performed in human mind.” Examiner submits that this limitation was not analyzed as reciting a mental process in Step 2A Prong One, but rather as amounting to mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithms (MPEP 2106.05(f)) in Step 2A Prong Two, as it merely involves using a generically recited machine learning model to perform a mentally performable process of assigning extracted features to an assigned cluster. Applicant further argues that “pruning, the multiple incremental candidate sub-sequence… involves rule-based pruning operation applied to machine-generated candidate event sub-sequences using event-level and antecedent- matching conditions derived from event data which cannot be performed in human mind.” Examiner respectfully disagrees that this operation is not mentally performable. A human can mentally compare candidate event sub-sequences with an event level and/or antecedents derived from event data, determine if the candidate event sub-sequences match the event level and/or antecedents, and remove from consideration the candidate event sub-sequences that do not meet the matching conditions. Applicant further contends that “outputting, the at least one recommended event… involves machine-executed and physical-world operations which cannot be performed in human mind.” Examiner submits that this limitation was not analyzed as reciting a mental process in Step 2A, Prong One, but rather as amounting to the insignificant extra-solution activity of mere data gathering and outputting (MPEP 2106.05(g)) and generally linking the judicial exception to the field of use of robotics (MPEP 2106.05(h)) in Step 2A Prong Two.
Applicant argues on pages 24-27 that the amended claims integrate any alleged judicial exception into a practical application under Step 2A Prong Two. Applicant asserts that the claimed features address a technical problem associated with complexity of structured processes by reducing the search space for determining recommended event sequences, “which in turn decreases the time required to generate recommendations and reduces processing and memory resource consumption.” However, the reduced search space comes from the limitation of pruning the multiple incremental candidate event sub-sequences based on a current event level, which is a mental process as analyzed in Step 2A Prong One. The judicial exception alone cannot provide the improvement (MPEP 2106.05(a)). Applicant further argues that grouping event sequences into clusters using unsupervised learning “enables the server to learn similarities between previously performed event sequences that may not be obvious to human analysts, at least without significant time and resources devoted to analyzing a large quantity of event sequences.” However, grouping event sequences into clusters is a mental process, and merely using a generic unsupervised learning model to perform the clustering amounts to mere instructions to apply a judicial exception using generic computer algorithms (MPEP 2106.05(f)), and thus cannot integrate the judicial exception into a practical application.
Applicant argues on pages 28-29 that amended claim 1 recites significantly more than any alleged abstract idea under Step 2B. Applicant asserts that the claimed features “reduce the number of candidate event sequences and candidate event sub-sequences that must be evaluated, thereby reducing processing time and memory/processing resource consumption.” Examiner submits that, as stated above, this asserted improvement is provided by the judicial exception, and Applicant has provided no clear nexus between any claimed additional elements beyond the judicial exception and the asserted improvement. Applicant further argues that the claimed output is not limited to presentation of information and is a concrete technological application that meaningfully limits the claim to a specific implementation. Examiner submits that “transmitting instructions to the client device” amounts to insignificant extra-solution activity and is well-understood, routine, and conventional. Causing the client device to automatically perform actions/capture measurements/move the robot or drone is merely recited as an intended result of the transmitting instructions step and does not amount to significantly more than the judicial exception.
Applicant’s arguments regarding the rejections under 35 U.S.C. § 103 (Remarks, pages 29-32) are moot due to the withdrawal of that ground of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/G.A.D./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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