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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
an un-stationary feature detector in claim 15, and
a stationary model-based classification engine in claim 16.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 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.
Regarding Claim 1, it recites “supervised training data set” (line 2), “unlabeled data set” ( line 4), and “un-stationary feature” (line 9). These terms lack an article such as “an,” so it is unclear what they refer to. In the case of and “un-stationary feature,” it is particularly indefinite because this could refer to a single feature or multiple features. In addition, the present claim recites “corresponding features” (line 3). It is unclear what the features correspond to. The claim also recites “the corresponding features” in lines 4-5 and line 6; this too is indefinite because the correspondence is unclear. For the purposes of examination under prior art, the examiner will interpret corresponding features to mean that at least one feature in a supervised training data set corresponds to at least one feature in an unlabeled data set.
Regarding Claim 3, it recites multiple instances of “the corresponding feature,” which is indefinite as described for claim 1. The claim further recites “obtaining a first value distribution based on values of the corresponding feature in the supervised training data set” (lines 3-4). It is unclear how a distribution of (multiple) values can be obtained from a single “corresponding feature.” A single feature could have only one value, which does not comprise a distribution. The recitation of “obtaining a second value distribution based on values of the corresponding feature in the unlabeled data set” (lines 5-6) is similarly indefinite. The claim further recites “a distribution change between the first value distribution and the second value distribution of the corresponding feature” (lines 7-9). It is unclear how there can be a “change” between distributions of two different data sets. The two data sets simply exist; there is no recitation of a change over time, or a change from one data set to the other. So, the concept of a “change” does not make sense in this context. For the purposes of examination under prior art, the examiner will interpret the claim to mean that distributions of feature values of the supervised training data set and the unlabeled data set are compared, and a feature is determined to be an un-stationary feature if there is a difference.
Regarding Claim 4, it recites “the distribution change,” which is indefinite as described for claim 3.
Regarding Claim 7, it recites “each of the corresponding features,” which is indefinite as described for claim 1.
Regarding Claims 8 and 15, they recite limitations and terms substantially similar to those of claim 1, so they are indefinite for the same reasons.
Regarding Claims 10 and 17, they recite limitations and terms substantially similar to those of claim 3, so they are indefinite for the same reasons.
Regarding Claims 11 and 18, they recite limitations and terms substantially similar to those of claim 4, so they are indefinite for the same reasons.
Regarding Claims 14 and 20, they recite limitations and terms substantially similar to those of claim 7, so they are indefinite for the same reasons.
Regarding Claims 2, 5-6, 9, 12-13, 16, and 19, they are rejected as being dependent on rejected base claims.
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 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Han et al. (U.S. 2022/0405613, hereinafter “Han”).
Regarding Claim 1, Han teaches a method (Abstract and ¶ [0042]), comprising:
receiving supervised training data set, having data samples each of which includes values of corresponding features and a label representing a classification of the data sample (fig. 3; ¶ [0042] – [0044]—the supervised training dataset includes features X1-Xm and target variable {label} Y);
receiving unlabeled data set, having data samples each of which includes values of the corresponding features and is to be classified (fig. 3; ¶ [0042] – [0044]—the testing dataset includes the features X1-Xm, but does not include the target variable {label});
detecting whether any of the corresponding features is un-stationary in the supervised training data set based on values of features in the supervised training data set and values of features in the unlabeled data set to obtain an un-stationary feature detection result (fig. 3; ¶ [0047]—a statistical distribution test is performed to detect any of the corresponding features is un-stationary in the supervised training data set by computing differences in feature distributions between the two datasets);
if the un-stationary feature detection result indicates that un-stationary feature exists,
generating an adjusted supervised training data set based on the supervised training data set according to the un-stationary feature detection result,
training, via machine learning, a stationary classification model based on the adjusted supervised training data set; and
if no un-stationary feature exists, training, via machine learning, the stationary classification model based on the supervised training data set (fig. 3; ¶ [0048] – [0051]—additional statistical tests further determine which features of the supervised training dataset are consistent {stationary}. A consistent dataset is generated, which includes only the consistent {stationary} features. Un-stationary features are placed in a differential dataset. The consistent dataset is used for training a classification model. It is clear that if no un-stationary features exist, the consistent dataset will include all of the features of the supervised training dataset).
Regarding Claim 8, Han teaches a machine-readable and non-transitory medium having information recorded thereon (fig. 1; ¶ [0027] – [0028] and [0033]). Han teaches wherein the information, when read by the machine, causes the machine to perform the steps of the present claim in the same manner as for claim 1, above.
Regarding Claim 15, Han teaches a system (fig. 1; ¶ [0035]). Han teaches the system comprising the elements and operations of the present claim in the same manner as for claim 1, above.
Regarding Claims 2, 9, and 16, Han teaches classifying, based on the stationary classification model, each of the data samples in the unlabeled data set with a label determined according to the classification (¶ [0041] and [0044]—a machine learning model is built for classification, which implies that the data samples in the unlabeled dataset are classified by assigning a label).
Regarding Claims 3, 10, and 17, Han teaches wherein the step of detecting comprises: with respect to each of the corresponding features,
obtaining a first value distribution based on values of the corresponding feature in the supervised training data set, obtaining a second value distribution based on values of the corresponding feature in the unlabeled data set, and determining the corresponding feature to be an un-stationary feature if there is a distribution change between the first value distribution and the second value distribution of the corresponding feature (fig. 3; ¶ [0047] – [0051]—value distributions of the features are obtained for both data sets, and calculations determine if distributions of each feature differ between the supervised training data set and the unlabeled data set).
Regarding Claims 4, 11, and 18, Han teaches wherein the distribution change is detected via a statistical test (¶ [0047]).
Regarding Claims 5, 12, and 19, Han teaches wherein the generating the adjusted supervised training data set comprises: identifying one or more un-stationary features based on the un-stationary feature detection result; creating the adjusted supervised training data set with minimized influence from the one or more un-stationary features (fig. 3; ¶ [0047] – [0051]—influence of the un-stationary features is minimized by removing them from the consistent dataset that is used to train a machine learning model).
Regarding Claims 6 and 13, Han teaches wherein the minimized influence is realized by removing the one or more un-stationary features from the supervised training data set (fig. 3; ¶ [0047] – [0051]—influence of the un-stationary features is minimized by removing them from the consistent dataset that is used to train a machine learning model).
Regarding Claim 20, Han teaches wherein the minimized influence is realized by at least one of: removing the one or more un-stationary features from the supervised training data set; and weighing each of the corresponding features in the supervised training data set with minimal weights applied to the one or more un-stationary features (fig. 3; ¶ [0047] – [0051]—influence of the un-stationary features is minimized by removing them from the consistent dataset that is used to train a machine learning model).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Han, as applied to claims 5 and 12, above, in view of Sahiner, Berkman, et al. (“Data drift in medical machine learning: implications and potential remedies,” The British Journal of Radiology 96.1150 (2023): 20220878; hereinafter “Sahiner”).
Regarding Claims 7 and 14, Han does not specifically teach wherein the minimized influence is realized by weighing each of the corresponding features in the supervised training data set with minimal weights applied to the one or more un-stationary features. However, Sahiner teaches minimizing influence of features by weighing each of the features in a supervised training data set with minimal weights applied to one or more un-stationary features (p. 5, section (a)—a covariate shift in features {i.e. a change in distribution of feature values} is remedied by assigning lower importance weights to the features that have shifted {i.e. the un-stationary features}).
All of the claimed elements were known in Han and Sahiner and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the importance weighting of Sahiner with the minimizing influence of Han to yield the predictable result of wherein the minimized influence is realized by weighing each of the corresponding features in the supervised training data set with minimal weights applied to the one or more un-stationary features. One would be motivated to make this combination for the purpose of improving the accuracy of machine learning models when features drift over time (Sahiner, Introduction).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. This art includes:
Ba et al. (U.S. 2024/0289608) teaches a system and method that performs a statistical test to determine data drift of features and takes mitigation action to improve machine learning models
Khan et al. (U.S. 2025/0101855) teaches detecting data drift of features using a distribution and generating an alert when drift is detected
Lopatecki et al. (U.S. 2023/0186144) teaches using statistical methods to determine data drift of features as data change over time
Webb, Geoffrey I., et al. (“Analyzing concept drift and shift from sample data,” Data Mining and Knowledge Discovery 32.5 (2018): 1179-1199) teaches using statistical methods for measuring feature drift in datasets
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAL W SCHNEE whose telephone number is (571) 270-1918. The examiner can normally be reached M-F 7:30 a.m. - 6:00 p.m.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached at 303-297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAL SCHNEE/Primary Examiner, Art Unit 2129
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