DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Status of Claims
Claims 1-20 are pending in this application.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 and are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One: The independent claim 103 recites “training an enriched language model using an expanded ground truth, wherein the enriched language model is trained to perform natural language processing using a natural language vocabulary converted to embeddings comprising vectorial representations within an Enriched Embedding Space; generating with the enriched language model an enriched language model embedding in response to a user input, wherein the enriched language model embedding comprises a vectorial representation of the user input within the EES; computing a distance between the enriched language model embedding and a nearest embedding within the EES; detecting, based on the distance, whether the enriched language model is affected by drift; and responsive to detecting the enriched language model is affected by drift, diagnosing a type of the drift.”.
Claims 1, 9, and 16 recite correcting intent from non-speech cues after receiving the first speech input when the cues indicate a likely correction.
[Abstract idea indicators]
Collecting data and receiving data from the other source— a task that a human routinely performs mentally or with conventional tools.
Identifying the difference between two entities, i.e., a decision-making process.
Determining type of anomalies is a cognitive step that are mental processes.
Accordingly, the claims are directed to the judicial exception of a mental process.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. The computer is recited at a high-level of generality (i.e., as performing a generic computer function and being used as an applying, e.g., collecting data, calculating) such that it amounts no more than mere instructions to apply the exception using a generic computer. Accordingly, there 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. The claim is directed to an abstract idea.
Step 2B — Claims Do Not Recite an Inventive Concept That Transforms the Mental Process into Patent-Eligible Subject Matter
The claims add generic, well-understood computer components (memory and control circuitry) and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer amounts to no more than mere instructions to apply an exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Applying Alice step two and relevant Federal Circuit precedent:
The recitation of conventional computer components (memory and processor) performing routine functions does not supply an inventive concept.
The mere invocation of “training… model” without particularity does not demonstrate an unconventional machine or technique or a specific improvement in computer technology.
The claims recite high-level, result-oriented steps (e.g., “generating,” “computing”, “detecting”, “diagnosing”) that describe mental processes rather than specific technical means for performing those processes.
Because the claims lack limitations that tie the mental-process steps to a particular way of achieving a technological improvement (for example, a novel model architecture, specialized data representation, unique training regimen that yields demonstrable technical performance gains, a specialized streaming/decoding pipeline that reduces latency by a quantifiable amount, or hardware/software co-design), the additional elements do not transform the mental processes into significantly more.
Therefore, claims 1, 9, and 16 fail to recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
With respect to dependent claims 2, 10, and 17, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claims 3, 11, and 18, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claims 4, 12, and 19, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claims 5 and 13, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claims 6 and 14, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claims 7 and 15, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claim 8, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Conclusion — Rejection
Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to a judicial exception (mental processes) and failing to recite additional elements that amount to significantly more than the judicial exception.
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.
Claims 1, 2, 6, 9, 10, 14, 16, and 17 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Elango et al., (“Detect NLP data drift using custom Amazon SageMaker Model Monitor”, Published on 2022-Jan-18, https://aws.amazon.com/blogs/machine-learning/detect-nlp-data-drift-using-custom-amazon-sagemaker-model-monitor/).
Regarding claim 1, Elango discloses a computer-implemented method, comprising:
training an enriched language model using an expanded ground truth, wherein the enriched language model is trained to perform natural language processing (NLP) using a natural language vocabulary converted to embeddings comprising vectorial representations within an Enriched Embedding Space (EES) (page 1, 1st – 3rd paragraph, training machine learning models using collecting ground truth training data; Figure on page 3, page 2, Solution overview section, Figure on page 7, Left side, training model using raw dataset and creating sentence embedding baseline using fine-tuned model using embeddings);
generating with the enriched language model an enriched language model embedding in response to a user input, wherein the enriched language model embedding comprises a vectorial representation of the user input within the EES (Figure on page 3, page 2, Solution overview section, Figure on page 7, right side, capturing data in real time and creating sentence embedding baseline using fine-tuned model using embeddings);
computing a distance between the enriched language model embedding and a nearest embedding within the EES (Figure on page 3, page 2, Solution overview section, Figure on page 7, page 6, Evaluation script section, measuring the cosine angle between two sentence embedding vectors and calculating an average of all the cosine similarity scores); and
detecting, based on the distance, whether the enriched language model is affected by drift (Figure on page 3, page 2, Solution overview section, Figure on page 7, page 6, Evaluation script section, detecting drift if the cosine angle is less than the threshold); and
responsive to detecting the enriched language model is affected by drift, diagnosing a type of the drift (page 2, using embeddings to detect the covariate shift which is the distribution of inputs changes over time, but the conditional distribution P(y|x) doesn’t change; page 6, Evaluation script section, page 10, Data drift violation report section, indicating whether the data observed during inference is drifting beyond the established baseline).
Regarding claim 2, Elango discloses the computer-implemented method of claim 1, and Elango further discloses:
responsive to detecting the enriched language model is affected by drift, initiating action to alleviate the drift, wherein the action depends on the type of the drift detected
(page 1, 1st – 3rd paragraph, if any drift in model performance is observed, taking corrective actions; page 10, Data drift violation report section, “based on this observation, you can configure your model for retraining”).
Regarding claim 6, Elango discloses the computer-implemented method of claim 1, and Elango further discloses:
generating the expanded ground truth by expanding a predetermined ground truth for training an NLP model; wherein the expanding is performed by applying at least one of synonym and antonym expansion, semantically related keyword expansion, grammatical reordering, lexical semantic variation, or synthetic text generation (page 2, solution section, BERT models project high-dimensional data into low-dimensional spaces while preserving the semantic information of the text).
Regarding claims 9, 10, and 14, Claims 9, 10, and 14 are the corresponding system claims to method claims 1, 2, and 6. Therefore, claims 9, 10, and 14 are rejected using the same rationale as applied to claims 1, 2, and 6 above.
Regarding claims 16 and 17, Claims 16 and 17 are the corresponding medium claims to method claims 1 and 2. Therefore, claims 16 and 17 are rejected using the same rationale as applied to claims 1 and 2 above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-5, 7-8, 11-13, 15, and 18-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Elango et al., (“Detect NLP data drift using custom Amazon SageMaker Model Monitor”, Published on 2022-Jan-18, https://aws.amazon.com/blogs/machine-learning/detect-nlp-data-drift-using-custom-amazon-sagemaker-model-monitor/) in view of Ramamurthy et al., (US 12,333,442 B2).
Regarding claim 3, Elango discloses the computer-implemented method of claim 2.
Elango does not explicitly teach, however Ramamurthy does explicitly teach:
wherein the action comprises retraining the enriched language model in response to determining the type of drift is domain drift (Ramamurthy, Col. 3, line 42- Col. 4, line 58, after detecting model drift, retraining the corresponding models with each actual personal volume data set; model/concept drift is a change in a target variable or machine learning model prediction, e.g., classification, and indicative of “domain drift”).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the method of detecting data drift using monitoring model as taught by Elango with the method of Training model based on determining a type of drift e.g., data drift or model drift, as taught Ramamurthy to improve upon the scalability of existing models by mapping particular subsets of a larger pool of raw input data (e.g., stream data) to the particular models that need the input data and storing the raw input data to computer objects so that the corresponding machine learning models can make predictions according to any suitable policy or triggering event on any of the data located in the computer objects (Ramamurthy, Col. 5, lines 15-24).
Regarding claim 4, Elango discloses the computer-implemented method of claim 2.
Elango does not explicitly teach, however Ramamurthy does explicitly teach:
wherein the action comprises updating the EES by adding the enriched language model embedding thereto in response to determining the type of drift is in-domain drift (Ramamurthy, Col. 3, line 42- Col. 4, line 58, after detecting data drift, retraining the corresponding models with each actual personal volume data set; data drift is a change in an independent variable or input data used in a predictive task over time and indicative of “in-domain drift”).
The previous motivation statement as in claim 3 is still applied.
Regarding claim 5, Elango in view of Ramamurthy discloses the computer-implemented method of claim 4 and Ramamurthy further discloses:
wherein the adding is performed during runtime processing of multiple other user inputs live transactions (Figs. 1 and 3, Col. 13, lines 51-65, Col. 22, lines 22-50, updating model using streaming data from the data sources in real time).
The previous motivation statement as in claim 3 is still applied.
Regarding claim 7, Elango discloses the computer-implemented method of claim 1.
storing the enriched language model embedding in response to detecting that the enriched language model embedding is affected by [domain] drift; wherein the storing is performed during runtime processing of multiple user inputs comprising live transactions; and wherein the enriched language model embedding is stored with other enriched language model embeddings affected by [domain] drift for subsequently updating the expanded ground truth and retraining the enriched language model with the expanded ground truth as updated (page 1, 1st – 3rd paragraph, if any drift in model performance is observed. Early and proactive detection of these deviations enables you to take corrective actions, such as collecting new ground truth training data, retraining models, and auditing upstream systems).
Elango does not explicitly teach the bracketed limitation, however Ramamurthy does explicitly teach:
[domain] drift (Ramamurthy, Col. 3, line 42- Col. 4, line 58, after detecting model drift, retraining the corresponding models with each actual personal volume data set which are streamlining in real time).
The previous motivation statement as in claim 3 is still applied.
Regarding claim 8, Elango discloses the computer-implemented method of claim 1.
Elango does not explicitly teach, however Ramamurthy does explicitly teach:
validating the enriched language model using holdout data extracted from a predetermined ground truth (Ramamurthy, Col. 13, line 14 – Col. 14, line 30, Evaluating can additionally include the input data of old machine learning models versus new ones, such as the amount of data drift).
The previous motivation statement as in claim 3 is still applied.
Regarding claims 11-13 and 15, Claims 11-13 and 15 are the corresponding system claims to method claims 3-5 and 7. Therefore, claims 11-13 and 15 are rejected using the same rationale as applied to claims 3-5 and 7 above.
Regarding claims 18-20, Claims 18-20 are the corresponding medium claims to method claims 3-5. Therefore, claims 18-20 are rejected using the same rationale as applied to claims 3-5 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEONG-AH A. SHIN whose telephone number is (571)272-5933. The examiner can normally be reached 9 AM-3PM.
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Seong-ah A. Shin
Primary Examiner
Art Unit 2659
/SEONG-AH A SHIN/ Primary Examiner, Art Unit 2659
Prosecution Insights