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
This Office Action is in response to the application filed on 12/22/2023.
Claims 1-20 are pending.
Drawings
The drawings filed on 12/22/2023 are accepted.
Examiner Notes
Examiner cites particular columns, paragraphs, figures 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 their 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.
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dattatri et al. (US Patent 11,748229, hereinafter Dattatri), in view of Neumann et al. (US PGPUB 2023/0154591, hereinafter neumann).
As per as claim 1, Dattatri discloses:
An apparatus, comprising:
interface circuitry (Dattatri, e.g., [col. 1, lines 33-39], [cp;/ 3, lines 35-40], “…graphics interface systems, data storage systems, networking systems, and mobile communication systems…”);
machine readable instructions (Dattatri, e.g., [col. 10, lines 20-55], “…computer-readable medium is shown to be a single medium, the term “computer-readable medium” includes a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions…”); and
programmable circuitry to at least one of execute or instantiate the machine readable instructions (Dattatri, e.g., [col. 10, lines 20-55], “…computer-readable medium is shown to be a single medium, the term “computer-readable medium” includes a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions…”) to:
determine a data usage type for each one of a plurality of input user data features in a first dataset (Dattatri, e.g., fig. 3, associating with texts description, [col. 6, lines 5-39], “…perform one or more operations on the input data, such as the usage profiles, and determine a corresponding persona classification for the information handling system…”);
classify the data usage type associated with each user data feature of the plurality of input user data features into a feature category (Dattatri, e.g., fig. 3, associating with texts description, [col. 6, lines 5-39], “…one or more usage profiles and a persona classification associated with the one or more usage profiles, and a second set of data may include usage profiles and an associated persona classification…a particular usage profile may be associated within more than one persona classification… utilize the profiles and associated personas to learn usage profiles for each persona type…”);
apply at least one feature engineering mechanism to feature categories of the data usage types of the plurality of input user data features (Dattari, e.g., [col. 4-5, lines 52-67], “…collect telemetry data for one or more components in the information handling system. The telemetry data may be associated with any suitable components or operations of the information handling system including, but not limited to, the battery, CPU usage, HDD usage, software usage, and application usage…collection and distribution manner during audio/video/collaboration sessions…”);
select, based on application of feature engineering, a subset of the plurality of input user data features for a feature selection training model (Dattari, e.g., [col. 6, lines 14-38], “… training of machine learning system, the training of hidden layers may be performed in any suitable manner including, but not limited to, supervised learning, unsupervised learning, reinforcement learning, and self-learning…create multiple machine learning models and each machine learning model may be associated with a different persona classification…”); and
output a second dataset based on the subset of the plurality of input user data features for the feature selection training model, the second dataset to include fewer user data features than the first dataset (Dattari, e.g., fig. 3, associating with texts description, [col. 7, lines 45-51], “…Output layer may provide the persona classification for information handling system, such as an executive persona…” and [col. 9, lines 52-56], “…a machine learning system receiving the new metrics or usage profiles, performing one or more operations, and outputting the re-classification of the personas…”).
To make records clearer regarding to the language of “output a second dataset based on the subset of the plurality of input user data features for the feature selection training model” (although as stated above Dattari functional disclose the features of “output a second dataset based on the subset of the plurality of input user data features for selection” (Dattari, e.g., fig. 3, associating with texts description, [col. 7, lines 45-51]).
However Neumann, in an analogous art, discloses “output a second dataset based on the subset of the plurality of input user data features for the feature selection training model” (Neumann, e.g., [0050-0052], “…classifier 136 may contain data categorized for comparing a subject's biometric state threshold to thresholds for a subset of healthy individuals for determining the presence of a malady… Classification machine-learning process may accept an input of such training data and generate a classifier that categories accessory device 108 usage as a function of a biometric state threshold to generate an output that is a classifier 136 that may be used to search for a malady…” and see [0056], [0067-0070], “…sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a process whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204. Classification may be performed using…”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Neumann and Dattari to classify the biological extraction to a temporal attribute, generate a nutrimental recommendation, as a function of the biological extraction and the temporal attribute, wherein the nutrimental recommendation includes at least a temporal specificity for optimal nutrient consumption, and output the nutrimental recommendation (Neumann, e.g., [004-005]).
As per as claim 2, the combination of Neumann and Dattari disclose:
The apparatus of claim 1, wherein the data usage type associated with a user data feature is one of a binary feature, a specific feature, a categorical feature with a discrete value, or a numerical feature (Neumann, e.g., [0050], [0068-0070], “…organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data 204 may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 204 may be linked to descriptors of categories by tags, tokens, or other data elements…”).
As per as claim 3, the combination of Neumann and Dattari disclose:
The apparatus of claim 1, wherein the programmable circuitry is to classify a data usage type to one of a first partite, a second partite, interaction between the first partite and the second partite or individual context feature (Dattari, e.g., [col. 6, lines 20-65], disclose classify a data usage type/category of user profiles) and (Neumann, e.g., [0050], [0068-0070], classifier type/category).
As per as claim 4, the combination of Neumann and Dattari disclose:
The apparatus of claim 1, wherein the feature engineering mechanism to the feature categories of the data usage types includes target encoding using feature-to-feature encoding or multi-class target encoding (Dattari, e.g., [col. 6, lines 20-65], disclose classify a data usage type/category of user profiles) and (Neumann, e.g., [0050], [0068-0070], classifier type/category).
As per as claim 5, the combination of Neumann and Dattari disclose:
The apparatus of claim 1, wherein the programmable circuitry is to perform feature selection by extracting feature importance by identifying features with lowest importance scores for removal from the plurality of input user data features (Dattari, e.g., [col. 8-9, lines 24-14], “…update device 232 may perform one or more operations to determine whether to re-classify the persona classification. For example, update device 232 may determine a degree of association between usage profiles of information handling system 218 and a current assigned persona classification for the information handling system…a weight may be assigned to the current persona classification based on the degree of association. In different examples, the weight assigned to the persona classification may change either directly proportional or inversely proportional to a closeness of the degree of association…a new cluster of usage profiles, such as a new persona classification, is better than the current persona classification for the current user behavior or usage of information handling system…”) (the examiner asserts re-classify and remove data/portion/file/content with low weight or non-relevant data that is equivalent to features with lowest importance scores for removal from the plurality of user data features) and further see (Neumann, e.g., [0028], [0071], [0073], [0080]).
As per as claim 6, the combination of Neumann and Dattari disclose:
The apparatus of claim 5, wherein the programmable circuitry is to train a gradient boosted decision tree (GBDT) based on the features with the lowest importance scores (Neumann, e.g., [0070-0073], [0076], [0080], “…classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers…”).
As per as claim 7, the combination of Neumann and Dattari disclose:
The apparatus of claim 6, wherein the programmable circuitry is to train the GBDT until (1) a number of remaining features reaches a predefined size, (2) an importance score is below a target threshold, or (3) observation of a model performance regression (Neumann, e.g., [0070], [0076], [0080], “…classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers…”).
Claims 8-14 are essentially the same as claims 1-7 except that they set forth the claimed invention as a method rather a system, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claims 1-7.
Claims 15-20 are essentially the same as claims 1-7 except that they set forth the claimed invention as a non-transitory machine readable storage medium rather a system, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claims 1-7.
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to determine a data usage type for each one of a plurality of user data features in a first dataset, classify the data usage type associated with each user data feature of the plurality of user data feature into a feature category, apply at least one feature engineering mechanism to feature categories of the data usage types of the plurality of user data features, select, based on application of feature engineering, a subset of the plurality of user data features for a feature selection training model, and output a second dataset based on the subset of the plurality of user data for the feature selection training model, the second dataset to include fewer user data features than the first dataset.
a. Kang et al. (US PGPUB 2020/0077023, hereafter Kang); “Image Stabilization Using Machine Learning” discloses “machine-learning based image stabilization which obtains a sequence of frames captured by an image capture device during a period of time, and collects motion sensor measurements calculated by a motion sensor associated with the image capture device based on movement of the image capture device during the period of time and generates, using a deep learning network and the motion sensor measurements, parameters for counteracting motions in one or more frames in the sequence of frames, the motions resulting from the movement of the image capture device during the period of time”.
Kang teaches classify patterns of motion associated with usage of an image capture device [0099].
Kang also teaches machine learning (ML) user and usage classifier which can be trained to learn and classify different patterns of motion [0123-0124].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN A PHAM whose telephone number is (571)270-3173. The examiner can normally be reached M-F 7:45 AM - 6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUAN A PHAM/Primary Examiner, Art Unit 2163
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