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 1-20 have been examined.
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 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 18 reciting a “non-transitory computer readable medium”, is not limited to tangible storage devices in view of par. 0176-0177, in the instant specification, which suggests that such a medium may be a carrier wave or transmission medium (intangible). Accordingly, claim 18 does not recite tangible manufactures, and are non-statutory subject matter.
As per claims 19-20, these claims are rejected for failing to cure the deficiencies of the above rejected base claim 18.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sivakumar (US 2023/0169147).
Per Claim 11:
Sivakumar teaches:
- one or more non-transitory computer readable media comprising a digital data repository; and at least one computer processor configured to cause the system to: determine, via the digital data repository, a common data object comprising attribute values representing implementation details for a machine-learning model in connection with one or more data processes within a computing system; determine, based on the attribute values of the common data object, a data configuration validation of the machine-learning model according to a digital representation of a system requirements framework associated with the one or more data processes ([0115] Next, at block 804, the process 800 performs attribute value extraction techniques to collect attribute values from the retraining data for use in comparing information in the retraining data to information previously used to train the machine learning model. In some embodiments, the retraining data is primarily composed of numeric attributes. In some such embodiments, the process 800 generates a tensor that includes the attribute vectors extracted from the retraining data. In some embodiments, for example where the machine learning model is used for natural language-based analysis, the process 800 may retrieve one or more pre-defined taxonomies from a taxonomy repository, where the taxonomies each include a predefined hierarchical classification of various classification objects and their characteristics for the domain modeled by the machine learning model. In some such embodiments, the taxonomy is a tree-like structure that illustrates relationships between objects based on characteristics of the objects. In some embodiments, the process 800 derives rich semantic information underlying objects represented by data points in the retraining data to facilitate comparison of the retraining data to the machine learning model.)
- and generate instructions to perform the one or more data processes at one or more computing devices utilizing the machine-learning model in response to determining that the data configuration validation indicates that the common data object meets a set of data configuration requirements of the digital representation of the system requirements framework ([0116] Next, at block 806, the process 800 generates metadata for the retraining data. For example, in some embodiments where the attributes in retraining data include numeric values arranged in feature vectors for each of the data points, the process 800 assembles the feature vectors into a tensor that is stored as metadata for the retraining data. In some embodiments, the process 800 retrieves the predefined classes of variation distinguished by the machine learning model and maps the classes to the attributes in the retraining data feature vectors. In some such embodiments, the process 800 stores this mapping information with the metadata for the retraining data. In some embodiments, where the process 800 retrieves one or more pre-defined taxonomies from a taxonomy repository and uses the one or more taxonomies to derive semantic information underlying objects represented by data points in the retraining data, the process 800 includes this semantic information with the metadata for the retraining data. [0120] Finally, at block 814, the process 800 compares the information in the candidate retraining data to the information that has already been taught to the machine learning mode. In some such embodiments, the process accomplishes this by comparing the attribute values in the retraining data metadata to the attribute value bands in the model metadata. If the attribute values are within the attribute band for the corresponding class, this is not new information for the machine learning model, so training is not needed. If an attribute value is outside the attribute band for the corresponding class, the process 800 measures how far the attribute value is outside the attribute band. If the distance is within a predetermined threshold value, then this is still considered not new, or negligible, for the machine learning model, so training is not needed. If the distance is not within a predetermined threshold value, then this is considered new information for the machine learning model, so training is needed.).
Allowable Subject Matter
Claims 1-10 are allowed.
Claims 12-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 18-20 are rejected under 35 USC 101, but would be allowable if the 35 USC 101 rejection is overcome.
The following is a statement of reasons for the indication of allowable subject matter:
The cited prior art taken alone or in combination fail to teach, in combination with the other claimed limitations, generating, by the at least one computer processor for display via a graphical user interface of a computing device, an indication of a hold for implementing the machine-learning model in response to determining that the data configuration validation indicates a configuration gap relative to the digital representation of the system requirements framework as recited in independent claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Selvanandan (US 2022/0114151) teaches a method for updating data objects in a data store.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QAMRUN NAHAR whose telephone number is (571)272-3730. The examiner can normally be reached Monday - Friday 8-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lewis Bullock can be reached on (571)272-3759. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QAMRUN NAHAR/Primary Examiner, Art Unit 2199
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