Claim Rejections - 35 USC § 103
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 1-12 are pending for examination.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/30/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Ganapavarapu (US 20230376825 A1) in view of Walters (US 20200012900 A1).
Regarding claim 1, Ganapavarapu teaches an information processing apparatus for applying to an artificial intelligence (AI) model, comprising processing circuitry configured to:
acquire a first data set having a first data trend and a second data set having a second data trend different from the first data trend (Fig. 1: inputs 120 130. [0017] The AI system has following drifts. The data drift is a drift in input data. The analysis of the data drift requires only the input data, e.g., timeseries of input sensor data (X) 120 or feature extracted data (X′) 130.), the first data set and the second data set being input to the AI model and discriminated by the AI model (Fig. 1: predictive model 110. Predictive model or AI model (M) 110 is trained on input sensor data (X) 120 or feature extracted data (X′) 130 and outputs predictions (Ŷ) 140).
Ganapavarapu does not explicitly teach the following limitations, however, in an analogous art, Walters teaches calculate a first feature vector based on the first data set and a second feature vector based on the second data set (In step 1005, system 100 can determine a difference vector connecting the first representative point and the second representative point. For example, as shown in FIG. 11A, system 100 can be configured to determine a vector 1105 from first representative point 1101 to second representative point 1103. Likewise, as shown in FIG. 11B, system 100 can be configured to determine a vector 1119 from first representative point 1113 to second representative point 1117.); and
generate augmented data having the second data trend based on the first feature vector and the second feature vector ([0125] Process 1000 can then proceed to step 1013. In step 1013, as depicted in FIG. 12B, system 100 can generate a translated dataset.).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Walters and apply them to Ganapavarapu. One would be motivated as such as to improve the detection of data drift.
Regarding claim 2, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 1. Walters teaches wherein the second data set includes a smaller number of pieces of data than the first data set (the synthetic data model may likely generate data maintaining a high level of correlation or similarity with the original data, likely allowing a data leak. Examiner note: intentional data leak would cause different data set sizes). The same motivation used to combine Ganapavarapu in view of Walters in claim 1 is applicable.
Regarding claim 3, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 1, Walters teaches wherein the processing circuitry is configured to:
generate one or more pieces of candidate data as candidates to belong to the second data set from the first data set; and generate the augmented data from the candidate data based on the first feature vector, the second feature vector, and the candidate data (Process 1000 can include the steps of generating encoder and decoder models that map between a code space and a sample space, identifying representative points in code space, generating a difference vector in code space, and generating extreme points or transforming a dataset using the difference vector. [0113]). The same motivation used to combine Ganapavarapu in view of Walters in claim 1 is applicable.
Regarding claim 4, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 3, Walters teaches wherein the processing circuitry is configured to:
calculate third feature vector relating to each of the one or more pieces of candidate data (Fig. 12A-B Code Space as a feature); and
generate the augmented data by selecting one or more pieces of data that has a predetermined relevance between the first vector and the third vector from among the one or more pieces of candidate data, and excluding one or more pieces of data that has the predetermined relevance between the second vector and the third feature vector from the selected one or more pieces of data (System 100 can be configured to use process 900 to generate synthetic data that is similar, but not too similar to the actual data [0093]). The same motivation used to combine Ganapavarapu in view of Walters in claim 1 is applicable.
Regarding claim 5, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 1. Ganapavarapu teaches wherein the processing circuitry is further configured to:
determine a decision boundary that classifies the first data set and the second data set ([0003] thresholds defines data the boundary); and
generate, from the first data set, one or more pieces of data as candidate data to be candidates to belong to the second data set over the decision boundary to the second data set, the one or more pieces of data having an index of variety of data equal to or larger than a threshold value (Fig.4: [0045] The new training data may be an entirely new training dataset. The new training data may be combination of the drifted training data and non-drifted training data (or the one or more computing devices or servers incorporate the drifted training data into new data). Selecting the new training data from the drifted period uses known sampling techniques.).
Regarding claim 6, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 1. Ganapavarapu teaches wherein the processing circuitry is further configured to evaluate, using the second data set and the augmented data, whether retraining of a trained model that has been trained with the first data set is necessary or not ([0040] In response to determining that the retaining the AI model is not required (NO branch of decision block 209), the one or more computing devices or servers does not perform the retraining of the AI model.).
Regarding claim 7, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 1. Ganapavarapu teaches wherein the processing circuitry is further configured to perform a control for displaying the first data set, the second data set, and a decision boundary that classifies the first data set and the second data set ([0066] Computing device or server 800 further includes I/O interface(s) 850. I/O interface(s) 850 allows for input and output of data with external device(s) 860 that may be connected to computing device or server 800. Examiner note: I/O interface includes displaying).
Regarding claim 8, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 1. Ganapavarapu teaches wherein the processing circuitry is further configured to perform a control for displaying the first feature vector, the second feature vector, and a third feature vector relating to one or more pieces of candidate data as candidates to belong to the second data set ([0066] Computing device or server 800 further includes I/O interface(s) 850. I/O interface(s) 850 allows for input and output of data with external device(s) 860 that may be connected to computing device or server 800. Examiner note: I/O interface includes displaying).
Regarding claim 9, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 1. Ganapavarapu teaches wherein the processing circuitry is further configured to perform a control for displaying, using the first data set and the augmented data, a performance evaluation of a trained model that has been trained with the first data set ([0066] Computing device or server 800 further includes I/O interface(s) 850. I/O interface(s) 850 allows for input and output of data with external device(s) 860 that may be connected to computing device or server 800. Examiner note: I/O interface includes displaying).
Regarding claim 10, Ganapavarapu in view of Walters teaches the information processing apparatus according to claim 1. Ganapavarapu teaches wherein the processing circuitry is further configured to perform a control for displaying a performance evaluation of a retrained model that has been retrained with the first data set and the augmented data ([0066] Computing device or server 800 further includes I/O interface(s) 850. I/O interface(s) 850 allows for input and output of data with external device(s) 860 that may be connected to computing device or server 800. Examiner note: I/O interface includes displaying).
Regarding claim 11, Ganapavarapu teaches an information processing method for applying to an artificial intelligence model, comprising:
acquiring a first data set having a first data trend and a second data set having a second data trend different from the first data trend (Fig. 1: inputs 120 130. [0017] The AI system has following drifts. The data drift is a drift in input data. The analysis of the data drift requires only the input data, e.g., timeseries of input sensor data (X) 120 or feature extracted data (X′) 130.), the first data set and the second data set being input to the AI model and discriminated by the AI model (Fig. 1: predictive model 110. Predictive model or AI model (M) 110 is trained on input sensor data (X) 120 or feature extracted data (X′) 130 and outputs predictions (Ŷ) 140).
Ganapavarapu does not explicitly teach the following limitations, however, in an analogous art, Walters teaches calculating a first feature vector based on the first data set and a second feature vector based on the second data set (In step 1005, system 100 can determine a difference vector connecting the first representative point and the second representative point. For example, as shown in FIG. 11A, system 100 can be configured to determine a vector 1105 from first representative point 1101 to second representative point 1103. Likewise, as shown in FIG. 11B, system 100 can be configured to determine a vector 1119 from first representative point 1113 to second representative point 1117.); and
generating augmented data having the second data trend based on the first feature vector and the second feature vector ([0125] Process 1000 can then proceed to step 1013. In step 1013, as depicted in FIG. 12B, system 100 can generate a translated dataset.).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Walters and apply them to Ganapavarapu. One would be motivated as such as to improve the detection of data drift.
Regarding claim 12, Ganapavarapu teaches a non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising:
acquiring a first data set having a first data trend and a second data set having a second data trend different from the first data trend (Fig. 1: inputs 120 130. [0017] The AI system has following drifts. The data drift is a drift in input data. The analysis of the data drift requires only the input data, e.g., timeseries of input sensor data (X) 120 or feature extracted data (X′) 130.), the first data set and the second data set being input to the AI model and discriminated by the AI model (Fig. 1: predictive model 110. Predictive model or AI model (M) 110 is trained on input sensor data (X) 120 or feature extracted data (X′) 130 and outputs predictions (Ŷ) 140).
Ganapavarapu does not explicitly teach the following limitations, however, in an analogous art, Walters teaches calculating a first feature vector based on the first data set and a second feature vector based on the second data set (In step 1005, system 100 can determine a difference vector connecting the first representative point and the second representative point. For example, as shown in FIG. 11A, system 100 can be configured to determine a vector 1105 from first representative point 1101 to second representative point 1103. Likewise, as shown in FIG. 11B, system 100 can be configured to determine a vector 1119 from first representative point 1113 to second representative point 1117.); and
generating augmented data having the second data trend based on the first feature vector and the second feature vector ([0125] Process 1000 can then proceed to step 1013. In step 1013, as depicted in FIG. 12B, system 100 can generate a translated dataset.).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Walters and apply them to Ganapavarapu. One would be motivated as such as to improve the detection of data drift.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HESHAM K ABOUZAHRA whose telephone number is (571)270-0425. The examiner can normally be reached M-F 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jamie Atala can be reached at 57127227384. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HESHAM K ABOUZAHRA/ Primary Examiner, Art Unit 2486
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