DETAILED ACTION
Applicant’s amendment filed on 12/02/2025 has been entered in the case. After further consideration a new interpretation is now relied upon examiner to reject the claims. Therefore, this action is non-final.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 3, 11, and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 2, Firstly, claim 2 recites the limitation "a next arrival time" in line 2. There is insufficient antecedent basis for this limitation in the claim. It is not understood how it is a “next arrival time” when there is no mention of a first (or current) arrival time.
Second, it is not understood by the examiner as to what exactly is “arriving” during the next arrival time as the applicant doesn’t mention how the “arrival time” has anything to do with the current invention. The examiner will interpret the claims as if the applicant means the “next arrival time of the next event.”
As to claim 3, Firstly, claim 3 recites the limitations "time probability" and “type probability” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Secondly, It is not understood how "time probability" and “type probability” are event related to the current limitations. Time probability of what exactly? Type probability of what exactly? The examiner will interpret the claims as if the "time probability" and “type probability” are associated with the event sequence.
Claim 11 has similar limitations as claim 2 and the claim is rejected for the same reasons.
Claim 12 has similar limitations as claim 3 and the claim is rejected for the same reasons.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 4, 5, 7 – 10, 14, and 16 - 18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 - 18, of copending Application No. 18/493,374 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because
Claim 1 of 18/493,331
Claim 1 of 18/493,374
A computer-implemented method for event prediction, comprising:
A computer-implemented method for anomaly detection, comprising:
encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector;
encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector;
performing event prediction using the feature vector to identify a next event to occur within a system;
performing anomaly detection by determining an anomaly score with a support vector data description (SVDD) loss using the feature vector to identify an anomaly within a system:
performing a corrective action responsive to the next event to prevent or mitigate an effect of the next event.
performing a corrective action responsive to the anomaly to correct or mitigate an effect of the anomaly.
Claim 4 of 18/493,331
Claim 8 of 18/493,374
wherein the transformers and the aggregation network are trained using deep learning, with a set of training data that includes synchronized time series information and timestamped event sequences.
wherein the transformers and the aggregation network are trained using deep learning, with a set of training data that includes synchronized time series information and timestamped event sequences.
Claim 5 of 18/493,331
Claim 5 of 18/493,374
wherein the aggregation network includes a stack of self- attention layers that convert outputs of the respective transformers to the feature vector
wherein the aggregation network includes a stack of self-attention layers that convert outputs of the respective transformers to the feature vector.
Claim 7 of 18/493,331
Claim 7 of 18/493,374
further determining a ranked list of past events and time series measurements that most influence the predicted event, according to according to attention weights from the aggregation network
wherein determining the ranked list is performed according to attention weights from the aggregation network
Claim 8 of 18/493,331
Claim 9 of 18/493,374
further comprising reporting the next event to a medical professional to support medical decision-making
further comprising reporting the detected anomaly to a medical professional to support medical decision-making
Claim 9 of 18/493,331
Claim 10 of 18/493,374
wherein performing the corrective action includes an action selected from the group consisting of changing a security setting for an application or hardware component, changing an operational parameter of an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface’s status or settings
wherein performing the corrective action includes an action selected from the group consisting of changing a security setting for an application or hardware component, changing an operational parameter of an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface’s status or settings
Claim 10 of 18/493,331
Claim 11 of 18/493,374
A system for event prediction, comprising:
A system for anomaly detection, comprising::
a hardware processor; and
a hardware processor; and
a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to:
a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to:
encode a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector;
encode a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector;
perform event prediction using the feature vector to identify a next event to occur within a system; and
perform anomaly detection by determining an anomaly score with a support vector data description (SVDD) loss using the feature vector to identify an anomaly within a system; and
perform a corrective action responsive to the next event to prevent or mitigate an effect of the next event.
perform a corrective action responsive to the anomaly to correct or mitigate an effect of the anomaly.
Claim 14 of 18/493,331
Claim 15 of 18/493,374
wherein the aggregation network includes a stack of self- attention layers that convert outputs of the respective transformers to the feature vector
wherein the aggregation network includes a stack of self-attention layers that convert outputs of the respective transformers to the feature vector.
Claim 16 of 18/493,331
Claim 16 of 18/493,374
further determining a ranked list of past events and time series measurements that most influence the predicted event
wherein the computer program further causes the hardware processor to determine a ranked list of past events and time series measurements that most influence the anomaly
Claim 17 of 18/493,331
Claim 17 of 18/493,374
wherein determining the ranked list is performed according to attention weights from the aggregation network
wherein the determination of the ranked list is performed according to attention weights from the aggregation network
Claim 18 of 18/493,331
Claim 18 of 18/493,374
wherein performing the corrective action includes an action selected from the group consisting of changing a security setting for an application or hardware component, changing an operational parameter of an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface’s status or settings
wherein performing the corrective action includes an action selected from the group consisting of changing a security setting for an application or hardware component, changing an operational parameter of an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface’s status or settings
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 – 18, 21, and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1 and 10,
Step 2A, Prong One
The claim recites in part:
performing event prediction using the feature vector to identify a next event to occur within a system;
performing a corrective action responsive to the next event to prevent or mitigate an effect of the next event.
Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The claim further recites hardware processor, memory, transformers, and aggregation network which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of multivariate time series and multi-type event sequence amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The hardware processor, memory, transformers, and aggregation network are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of multivariate time series and multi-type even sequence to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2 and 11, the limitations “wherein performing event prediction uses an intensity function that includes a softplus function of the feature vector and a next arrival time” are process steps that cover Mathematical Concepts. If a claim, under its broadest reasonable interpretation, covers a mathematical concept, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
As to claims 3 and 12, the limitations “wherein performing event prediction uses a density function that models time probability and type probability independently” are process steps that cover Mathematical Concepts. If a claim, under its broadest reasonable interpretation, covers a mathematical concept, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
As to claims 4 and 13, the limitations “wherein the transformers and the aggregation network are trained using deep learning, with a set of training data that includes synchronized time series information and timestamped event sequences” are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
As to claims 5 and 14, the limitations “wherein the aggregation network includes a stack of self- attention layers that convert outputs of the respective transformers to the feature vector” are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
As to claims 6 and 15, the limitations “wherein a hidden state of the transformer of the multivariate time series is used as a latent vector in the event prediction” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 7 and 17, Under the broadest reasonable interpretation, the limitations “further determining a ranked list of past events and time series measurements that most influence the predicted event, according to according to attention weights from the aggregation network” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
As to claim 8, the limitations “further comprising reporting the next event to a medical professional to support medical decision-making” are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
As to claims 9 and 18, the limitations “wherein performing the corrective action includes an action selected from the group consisting of changing a security setting for an application or hardware component, changing an operational parameter of an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface’s status or settings” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 16, Under the broadest reasonable interpretation, the limitations “further determining a ranked list of past events and time series measurements that most influence the predicted event, according to according to attention weights from the aggregation network” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
As to claim 21, the limitations “wherein the intensity function represents the expected number of events at an infinitesimally small time period to determine a probability of a time and type of a next event” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 22, the limitations “wherein the density function includes a density
distribution that gives a probability of a next happening at a time τ after a last event amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4 -10 and 13 -18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by PANG et al (US 2022/0293272).
As to claim 10, PANG et al teaches a system for event prediction, comprising:
a hardware processor (paragraph [0090]…network computing device); and
a memory (paragraph [0096]…the healthcare platform may also store a task(s) in local devices or in a server memory to execute later) that stores a computer program (paragraph [0117]…a computer program for performing the method according to any of the embodiments described above) which, when executed by the hardware processor, causes the hardware processor to:
encode (paragraph [0108]…encoding layer 410) a multivariate time series (paragraph [0108]…multivariate time series) and a multi-type event sequence (paragraph [0108]… A token sequence from multimodal input sources is embedded 409 in the core representation layer 405 after receiving positional information through a learned positional encoding layer 410, and the outputs are passed to specialized neural networks 411 downstream that are pre-trained and optimized for specific task groups) using respective transformers (paragraph [0108]…seq2seq Transformer architecture which includes both encoder and decoder stacks) and an aggregation network (paragraph [0108]…multiple decoder stacks downstream which share inputs from upstream neural network(s)) to generate a feature vector (paragraph [0105]…the tokens are embedded as continuous vectors with positional information);
perform event prediction using the feature vector to identify a next event to occur within a system (paragraph [0087]…the clinician reviews the report containing a plurality of predictions, recommendations and processed patient information made by the machine learning system ; paragraph [0108]…output predictions for future data points based on prior context); and
perform a corrective action responsive to the next event to prevent or mitigate an effect of the next event (paragraph [0087]…uses the clinical report to decide on appropriate management steps—whether this be issuing a prescription, asking for more information, booking an investigation or a virtual or physical appointment).
As to claim 13, PANG et al teaches the system, the system, wherein the transformers (paragraph [0108]…seq2seq Transformer architecture which includes both encoder and decoder stacks) and the aggregation network (paragraph [0108]…multiple decoder stacks downstream which share inputs from upstream neural network(s)) are trained using deep learning (paragraph [0065]… a trained machine learning (in our case, deep learning) model), with a set of training data that includes synchronized time series information and timestamped event sequences (paragraph [0090]…the clinician's actions and management proposals are communicated and agreed with the patients, through relaying the suggested management plan to a network computing device used by the patients. At step 111, the data associated with the clinical case, including clinical actions are added to the time series data associated with the patient in the cloud. The time series data is used as both a confidential longitudinal record for each specific patient and their associated medical professionals, and as the substrate for constructing training examples for the machine learning system through an anonymization and tokenization process (described in Step 143). The time series data includes information on the machine learning system's inference outputs and also actions taken by the human clinician. This data allows the level of disagreement to be quantified through cross-entropy loss, across multiple points in time).
As to claim 14, PANG et al teaches the system, wherein the aggregation network (paragraph [0108]…multiple decoder stacks downstream which share inputs from upstream neural network(s)) includes a stack of self-attention layers that convert outputs of the respective transformers to the feature vector (paragraph [0108]… seq2seq Transformer architecture which includes both encoder and decoder stacks).
As to claim 15, PANG et al teaches the system, wherein a hidden state of the transformer of the multivariate time series is used as a latent vector in the event prediction (paragraph [0082]…a stacked ensemble architecture is employed with an initial fusion layer in which three base models may be pre-trained and learn useful representations for different data types. Their outputs may be fed into the core Transformer network downstream, represented either as reserved tokens (only used by the multimodal ensemble or as latent space embeddings). For example, for free text, a BART-like denoising autoencoder (a variant of a transformer) may be pre-trained on labelled domain data for summarizing text, compressing very long sequences of audio transcripts or virtual consultation text into much smaller sequences of tokens (representing relevant features), or a series of vectors with dimensionality equal to the fixed length embedding size of the core transformer (latent space embeddings)).
As to claim 16, PANG et al teaches the system, further determining a ranked list of past events and time series measurements that most influence the predicted event (paragraph [0029]… It is widely accepted that the vast majority of effort and resource expenditure in machine learning is in the pre-processing of training datasets. It is further widely accepted that a key goal of this pre-processing work is to balance the training data such that poorly performing edge case examples are well represented in the training dataset: this is part of what improves model performance and pushes the model towards convergence to a high F-score).
As to claim 17, PANG et al teaches the system, wherein determining the ranked list is performed according to attention weights (paragraph [0106]… the neural network is then trained successively through epochs, with weights and biases of nodes updated with each epoch using the Adam backpropagation optimization, which attempts to minimize cross-entropy loss across successive epochs. In such a way, the neural network and machine learning system is trained, with human clinicians as the teachers) from the aggregation network (paragraph [0108]…multiple decoder stacks downstream which share inputs from upstream neural network(s)).
As to claim 18, PANG et al teaches the system, wherein performing the corrective action (paragraph [0087]…uses the clinical report to decide on appropriate management steps—whether this be issuing a prescription, asking for more information, booking an investigation or a virtual or physical appointment) includes an action selected from the group consisting of changing a security setting for an application or hardware component, changing an operational parameter of an application or hardware component (paragraph [0087]… he clinician clicks a “Manage” button, which opens a pop-up in the web app. The pop-up is pre-populated with what the machine learning system believes to be the appropriate actions to take—e.g. “prescribe omeprazole” or “book urgent head CT within 2/52” or “book physical appointment and examination within 2/52”. The clinician may de-select any number of these suggested actions or predictions (for example, the differential diagnosis), edit them or add completely new actions or predictions not suggested by the machine learning system. All actions are structured and are categorical and/or ordinal in nature, and unambiguous, such that they can be reliably and directly encoded in a consistent manner without further interpretation, and then mapped onto a consistent medical token ontology used by the machine learning system (described in the later step 143)), halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface’s status or settings.
Claim 1 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above.
Claim 4 has similar limitations as claim 13. Therefore, the claim is rejected for the same reasons as above.
Claim 5 has similar limitations as claim 14. Therefore, the claim is rejected for the same reasons as above.
Claim 6 has similar limitations as claim 15. Therefore, the claim is rejected for the same reasons as above.
Claim 7 has similar limitations as claim 17. Therefore, the claim is rejected for the same reasons as above.
As to claim 8, PANG et al teaches the system, further comprising reporting the next event to a medical professional to support medical decision-making (paragraph [0085]…at step 107, the machine learning system and policy system together produces outputs in the form of clinical reports comprising the plurality of predictions, recommendations and a processed version of the patient history taken by the DAG, and is sent as JSON objects to the web/mobile app dashboard for viewing by the clinical team on their user computing device. The report also contains the top differential diagnoses considered by the machine learning system, and the contributing reasons to each (through attention mechanisms outlined in the accompanying description of FIG. 4, in step 143)).
Claim 9 has similar limitations as claim 18. Therefore, the claim is rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed 12/02/2025 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 112
The examiner updated and held the provisional double-patenting rejection as per request of the applicant.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
The rejection rests on the assertion that the claims recite abstract ideas without significantly more. Notably the rejection, asserts that the steps of performing event prediction and performing a corrective action are mental processes.
Applicant respectfully disagrees, particularly with respect to the performance of the corrective action. A mental process cannot prevent or mitigate an effect of an event, as mental processes "can be performed in the human mind, or by a human using a pen and paper." MPEP 2106.04(a)(2)(III). Nothing process that is performed in the human mind, or using a pen and paper can prevent or mitigate the effect of an action, and so this step is not fairly characterized as reciting an abstract idea. MPEP 2106.04(a)(2)(III)(A) emphasizes, "Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for
instance when the human mind is not equipped to perform the claim limitations."
Applicant acknowledges that "a claim which requires a computer may still recite a mental process." MPEP 2106.04(a)(2)(C). However, Applicant maintains that the act of performing a corrective action, in and of itself, is not a mental process, regardless of whether the corrective action is performed with a computer as its object. On its face, a corrective action cannot be reasonably
understood as a mental process at all.
The examiner disagrees. Humans can often prevent or mitigate the effects of events through various proactive measures and interventions. The ability to do so depends heavily on the nature and predictability of the event. Further, humans use knowledge, technology, and collective action to influence outcomes. Humans have been preventing or mitigating the effects of events before computers where even invented.
A corrective action process involves mental processes like analysis, decision-making, and communication, as it requires identifying problems, determining root causes (mental/analytical work), choosing solutions, and guiding behavioral or performance changes (cognitive/emotional skills) to improve future outcomes. The goal of corrective action is to fix the underlying issue and prevent it from happening again. The applicant needs to go into a lot more detail what the event and corrective actions entail.
The applicant argues:
Applicant respectfully asserts that this corrective action integrates any abstract idea in the claims with a practical application. In particular, Example 47 of the Patent Eligibility Guidelines is instructive, notably Claim 3 thereof. The example claim recites a mental process in the detection of anomalies and the determination that the anomalies are associated with malicious network packets.
However, the analysis of this example goes on to point out that the steps of dropping determining a source address, dropping malicious network packets, and blocking future traffic from the source address improve network security.
Notably the steps of dropping malicious network packets and blocking future traffic are enacted using generic network hardware. The guidelines make no attempt to assert that these corrective acts are somehow mental processes, or that the use of generic computer hardware to implement them somehow undermined their status as "additional elements." The performance of these actions integrate an improvement to a technology and SO render the claim as a whole statutory.
The examiner disagrees. The examiner is confused as to how Example 47 and its “malicious packets” even are related to the applicant’s limitations as currently claimed. The examiner respectfully disagrees with the applicant’s position, as the arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the appellant assert, describe, or even suggest the limitation of “a malicious packet” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language. As claimed, the events and the resulting corrective actions can create infinite possibilities and the applicant needs to go into more explicit detail as to what events and corrections actions they are referring to.
The applicant argues:
MPEP 2106.04(d)(1) sets forth only two requirements to show an improvement to a technology. First, the specification must describe the improvement and, second, the claims must reflect that improvement. The present specification describes how event prediction can be used to plan maintenance of a system or improve health outcomes for a patient. See 13. Paragraphs 22-28 describe a cyber-physical system, where anomalous behavior can be detected and corrective actions can be performed to prevent or mitigate the problem. Paragraph 28 in particular emphasizes that the system can determine which information source contributes most to the prediction, so that the corrective action can be tailored to the root cause of the problem. The specification describes how this is done, using a feature vector that combines an event sequence and a time series to perform event prediction.
This improvement is reflected in the present claims. The time series and the event sequence are encoded to form the feature vector, that feature vector is used to predict a next event within a system, and the corrective action is performed to prevent or mitigate the effect of that next event.
Thus the present specification and claims meet the requirements of MPEP 2106.04(d)(1) and the claims as a whole are integrated with a practical application.
The examiner disagrees. The examiner respectfully disagrees with the applicant’s position, as the arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the appellant assert, describe, or even suggest the limitation of “used to plan maintenance of a system or improve health outcomes for a patient or is part of a cyber-physical system.” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language. As claimed, the events and the resulting corrective actions can create infinite possibilities and the applicant needs to go into more explicit detail as to what events and corrections actions they are referring to.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
The applicant argues:
Claims 9 and 18 go further, reciting specific actions that clearly cannot be performed in the human mind. During the interview of November 13, 2025, the Examiner asserted that these merely applied an abstract idea using generic computer hardware. As noted above however, the corrective action itself is not an abstract idea at all, and the computer hardware in question would be the object of that action. Instead the corrective actions recited in claims 9 and 18 provide further support for the assertion that the claims reflect the improvement that is set out in the present specification.
The examiner disagrees. The claim language referencing examples of corrective actions are directed to an abstract idea. Merely reciting a list of functional or generic "corrective actions" is viewed as abstract steps or a mental process, rather than a specific, technical solution that improves the functionality of a machine or specific technology. The actions are conventional and are not integrated into a specific non-generic technological improvement.
The applicant argues:
Finally, Applicant notes that the Office has been overly strict on subject matter eligibility, even when interpreting its own guidelines. This has prompted course corrections from within the Office itself. For example, the USPTO released a memo on August 4, 2025, titled, "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101." The memo emphasizes that the claim is to be considered as a whole, where "additional limitations should not be evaluated in a vacuum, complete separate from the recited judicial exception." The memo specifically addresses the "improvements" consideration, emphasizing the two requirements addressed above. The memo concludes by asserting that the Examiner should only make a rejection when it is "more likely than not" that the claim is ineligible, and that unpatentability must be established by a preponderance of the evidence.Applicant further notes the opinion of Director Squires in Ex parte Desjardins, Appeal No.2024-000567, dated on September 26, 2026. That decision focused on Step 2A, Prong 2, of the Alice inquiry, particularly on improvements to a technology. See Desjardins, pp. 7-10. The Director's analysis relied heavily on Enfish, LLC V. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir.2016), going through the same two-step analysis as above. The Director held, "Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable 'algorithm' and the remaining additional elements as "generic computer components,' without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality."
The examiner disagrees. The applicant must prove the invention is more than an abstract idea by showing it provides a practical, technical improvement or a specific physical application, often by amending claims to emphasize novel hardware, unique processing, or a concrete technological advantage over the prior art. Meaning the applicant needs to go into a lot more detail of the “prevent or mitigate the effects of events” and the “corrective actions” to overcome the 101 Rejection.
Claim Rejections - 35 USC § 102 & 103
The applicant argues:
Claim 1 recites, inter alia, "encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector." Claim 10 recites analogous language. The rejection asserts that Pang teaches this feature in paragraph 108, which discusses embedding a token sequence for use in time series prediction. Paragraph 107 provides detail about the token sequence, noting that it is generated by fusion layer 401 that combines information from multiple sources. The examples of information sources include imaging, omic sequences, and free text. While paragraph 108 describes using the embedding to perform time series prediction, Pang never discloses nor suggests using a multi-type event sequence and a multivariate time series to generate a feature vector.
The examiner disagrees. PANG et al clearly teaches a multi-type event sequence (paragraph [0108]… A token sequence from multimodal input sources is embedded 409 in the core representation layer 405 after receiving positional information through a learned positional encoding layer 410, and the outputs are passed to specialized neural networks 411 downstream that are pre-trained and optimized for specific task groups) and a a multivariate time series (paragraph [0108]…multivariate time series) to generate a feature vector (paragraph [0105]…the tokens are embedded as continuous vectors with positional information). The applicant doesn’t give any reason why they don’t think the PANG et al reference doesn’t teach the applicants broad claims and it is the examiner’s duty to examine the claims under the broadest reasonable interpretation. It is well known in the art that vector embeddings are a type of feature vector.
Claims 2, 3, 11, and 12 are now rejected under 35 USC § 112 and no longer rejected under 35 USC § 103
.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128