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 .
DETAILED ACTION
The instant application having Application No. 18/464,647 filed on 10/25/2023 is presented for examination.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax, Regular postal mail, or EFS Web.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submissions of the Information Disclosure Statement dated 10/25/2023 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
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 1-10 is be rejected under 35 USC 101 as being an abstract idea without significantly more.
Step 2A, Prong One:
Claim 1 recites " training a task-agnostic continual learning (CL) model using prompt pool parameters and scheduling identification parameters” These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "task-agnostic continual learning (CL) model" nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites the additional elements " monitoring a datastream that is provided to the CL model” and “identifying, based on analysis of the datastream, a task scheduling type embodied in the datastream” these limitations amount to a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
The features of these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations " identifying, based on analysis of the datastream, a task scheduling type embodied in the datastream " are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....
Accordingly, the claim does not appear to be patent eligible under 35 USC 101.
Claims 2-10 depend from claim 1 and inherit the deficiencies of claim 1 and do not resolve the issues discussed above and are therefore rejected for the same manner.
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, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (“Learning to Prompt for Continual Learning” cited in IDS) in view of Fly (US 2020/0082296).
As per claim 1, Wang discloses a method, comprising:
training a task-agnostic continual learning (CL) model using prompt pool parameters and scheduling identification parameters (Abstract “Our method learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions. In our proposed framework, prompts are small learnable parameters, which are maintained in a memory space. The objective is to optimize prompts to instruct the model prediction and explicitly manage task-invariant and task-specific knowledge while maintaining model plasticity. We conduct comprehensive experiments under popular image classification benchmarks with different challenging continual learning settings, where L2P consistently outperforms prior state-of the-art methods.”), wherein the CL model comprises a machine learning model that is operable to perform tasks (Page 141 “Continual learning is usually defined as training machine learning models on non-stationary data from sequential tasks.”)
monitoring a datastream that is provided to the CL model, and identifying every c instances of the datastream as a collection so that one or more collections are defined (Page 141 “One of recent techniques, Prompt Tuning (PT) [25], proposes to simply condition frozen T5-like language models [47] to perform down-stream NLP tasks by learning prompt parameters that are prepended to the input tokens to instruct the model prediction. Without loss of generality, here we introduce the definition of PT using the image modality transformer-based sequence models [10, 56]. The definition is easy to generalize to other modalities and sequence-based models”).
Wang does not expressly disclose but Fly discloses
identifying, based on analysis of the datastream, a task scheduling type embodied in the datastream (Paragraph 15 “However, once the offline and online environments are connected, the inventive system is enabled to detect drift in real-time or near real-time. The connected systems identify or detect performance degradation (caused by drift) in an “online” operational scoring environment. Specifically, the inventive system identifies degradation by establishing a baseline in an “online” operational scoring environment of trained and validated modeling dataset and scores, and by ensuring that new online data and scores match those that were established in the “offline” discovery environment when the model was trained.”).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Wang to include the teachings of Fly because it provides for the purpose of connecting offline machine learning training systems with online near-real time machine learning scoring systems. In this way, the combination benefits by having a better machine learning model.
As per claim 2, Wang further discloses wherein the prompt pool parameters comprise a number of selected prompts, and a size of the prompt pool (Paragraph 139 “Overview of the L2P framework. Compared with typical methods that adapt entire or partial model weights to tasks sequentially with a rehearsal buffer to avoid forgetting, L2P uses a single backbone model and learns a prompt pool to instruct the model conditionally. Task-specific knowledge is stored inside a prompt pool, thus a rehearsal buffer is no longer mandatory to mitigate forgetting. L2P automatically selects and updates prompts from the pool in an instance-wise fashion, thus task identity is not required at test time. Notably, our largest prompt space is smaller than the size of one 224 ⇥ 224 image”).
As per claim 3, Wang does not expressly disclose but Fly discloses wherein the scheduling identification parameters comprise a look-up size in a number of collections, and a number of instances inside each collection of samples of the datastream (Paragraphs 21-22).
As per claim 4, Wang does not expressly disclose but Fly discloses wherein the task scheduling type is identified as discrete (Paragraph 15 “However, once the offline and online environments are connected, the inventive system is enabled to detect drift in real-time or near real-time. The connected systems identify or detect performance degradation (caused by drift) in an “online” operational scoring environment. Specifically, the inventive system identifies degradation by establishing a baseline in an “online” operational scoring environment of trained and validated modeling dataset and scores, and by ensuring that new online data and scores match those that were established in the “offline” discovery environment when the model was trained.”).
As per claim 5, Wang does not expressly disclose but Fly discloses wherein the task scheduling type is identified as continuous (Paragraph 15 “However, once the offline and online environments are connected, the inventive system is enabled to detect drift in real-time or near real-time. The connected systems identify or detect performance degradation (caused by drift) in an “online” operational scoring environment. Specifically, the inventive system identifies degradation by establishing a baseline in an “online” operational scoring environment of trained and validated modeling dataset and scores, and by ensuring that new online data and scores match those that were established in the “offline” discovery environment when the model was trained.”).
As per claim 6, Wang does not expressly disclose but Fly discloses wherein identifying the task scheduling type is based on a comparison of one of the collections with another of the collections (Paragraphs 21-22).
As per claim 7, Wang further discloses wherein task boundaries pertaining to tasks implied by the datastream are unknown to the CL prior to identification of the task scheduling type (Page 140 “We propose L2P, a novel continual learning framework based on prompts for continual learning, providing a new mechanism to tackle continual learning challenges through learning a prompt pool memory space, which are served as parameterized “instructions” for pre-trained models to learn tasks sequentially. The method is applicable to handle the most challenging task-agnostic continual learning.”).
As per claim 8, Wang does not expressly disclose but Fly discloses wherein one or more parameters of the CL model are automatically adapted on-the-fly when a change in the task scheduling type is identified (Paragraph 7 “Data scientists have been trying to account for, recognize, debug, and determine the mitigation strategy for various types of drift by using a variety of different methodologies. In one instance, machine learning systems have adopted classifiers that can be updated incrementally with new data. But this methodology raises new concerns about whether a learning system can be designed to remain stable and remain immune to irrelevant events (e.g., outliers), while being adaptive to new, important data (e.g., changes in concepts).”).
As per claim 9, Wang further discloses wherein the prompt pool parameters comprise L2P parameters (Abstract).
As per claim 10, Wang does not expressly disclose but Fly discloses wherein the CL model is operable using both discrete task scheduling and continual task scheduling (Paragraph 15).
As per claims 11-20, they are medium claims having similar limitations as cited in claims 1-10 and are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tamir (US 20220036201) discloses real-time detection of real concept drift in predictive machine learning models, by processing high-speed streaming data. The computerized-method includes: receiving a real-time data stream having labeled and unlabeled instances. Obtaining a window of ‘n’ instances having a portion of the ‘n’ instances as reliable labels. Computing posterior distribution of the reliable labels; and operating a Drift-Detection (DD) module. The DD module is configured to: operate a kernel density estimation on the computed posterior distribution for sensitivity control of the DD module; operate an error rate function on the estimated kernel density to yield an error value; and train an incremental estimator module, according to the kernel density estimation. When the error value is not above a preconfigured drift threshold repeating operations (i) through (iii), else when the error value is above the preconfigured drift threshold, at least one concept drift related action takes place.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY A MUDRICK whose telephone number is (571)270-3374. The examiner can normally be reached 9am-5pm Central Time.
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/TIMOTHY A MUDRICK/Primary Examiner, Art Unit 2198 2/08/2026