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 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 8 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim is a claim to software per se, and that is not patent eligible subject matter. Claim 8 actually claims software per se as a computer program to implement a method. Claim 9 recites a device, but there is no structural recitation in the claim, so the device amounts to software per se.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mathematical relationship without significantly more. The claims recite a mathematical relationship of: determining a signature (which is a probability distribution); computing scores and picking the model with the “best” score; synthesizing a data set; and creating a synthetic signature. This judicial exception is not integrated into a practical application because the additional limitations of receiving data, synthesizing data and training on data merely link the abstract idea to the technical field of machine learning. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims that carry out the method on a “computer” are mere instructions to apply the abstract idea to a computer. MPEP 2106.05(f).
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.
Claims 1-6 and 9 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.
Claims 1 and 9 recite implementing the auxiliary model with “on the one hand, has a better auxiliary similarity score with the input signature than the current signature and which, on the other hand, has the best auxiliary similarity score.” It’s unclear what’s happening here.
The term “better” and “best” in claims 1 and 9 is a relative term which renders the claim indefinite. The term “better” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
In claims 2, 3 and 6, applicant claims “the input data”, but it was introduced as an “input data stream in claim 1. The input data lacks antecedent basis.
Claim 4 recites, “the auxiliary similarity score is the p-value under the null hypothesis ‘the auxiliary signature is identical to the current signature’.” However, Claim 1 recites, “compute a corresponding auxiliary similarity score between the input signature and the associated auxiliary signature…” These definitions contradict each other because the signatures are explicitly different and don’t overlap.
Claim 5 also contradicts claim 1 with the auxiliary similarity score being a function of a current signature and the auxiliary signature. In claim 1 the auxiliary similarity score is a function of the “input signature and the associated auxiliary signature.
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 1-9 are rejected under 35 U.S.C. 102(a)(1) as being described by RCD: A recurring concept drift framework by Goncalves et al.
Goncalves teaches claims 1 and 9. A method (20) for configuring a data processing chain (4), the processing chain (4) comprising a computing stage (10) for processing an input data stream (6), the method being carried out by computer and comprising:
determining (22) an input signature of at least a portion of the input data stream (6); (Goncalves abs “It creates a new classifier to each context found and stores a sample of data used to build it.” The context is the signature.
computing (24) a current similarity score, with regard to a predetermined similarity measure, between the determined input signature and a current signature, (Goncalves abs “the algorithm compares the new context to previous ones using a non-parametric multivariate statistical test to verify if both contexts come from the same distribution.” Output of the test is the similarity score. The current signature is the new context in Goncalves, the previous context is the input signature. Goncalves sec. 3 “The drift detector uses ca to identify if a concept drift is beginning to occur (line5).”)) said current signature being associated with a current training data set on the basis of which a current artificial intelligence model (12) implemented by the computing stage (10) for said processing of the input data stream (6) has been previously trained; and (Goncalves sec. 3 “The drift detector uses ca to identify if aconcept drift is beginning to occur (line5).While no drift is detected, ba is filledwith the examples used in the training of ca (lines28 and 31).”)
if the computed current similarity score is outside a predetermined acceptable range: (Goncalves sec. 3 “However, if ca continues to increase its error rate, it will reach the error level (line14) meaning adrift has been detected.”)
for each of at least one auxiliary artificial intelligence model (16), each auxiliary artificial intelligence model (16) having been previously trained based on an auxiliary training dataset having a corresponding auxiliary signature, compute a corresponding auxiliary similarity score between the input signature and the associated auxiliary signature; and (Goncalves sec. 1 “It works by storing classifiers and samples of data used to build them. At predefined intervals, RCD compares the data distribution of actual data to stored data samples and, if they are similar, the stored classifier associated to that specific data sample is reused. To compare two data distributions and check their similarity, RCD uses a multivariate non-para- metric statistical test.” Goncalves sec. 3 “A statistical test is performed comparing bn with all stored buffers, trying to identify if this new context has already occurred in the past (line 15). If the test is positive, it means this is an old context recurring. Then, the stored classifier and buffer are considered the new ca and ba, respectively (line16),and cn and bn are disposed (line23).”)
configuring (26) the computing stage so as to implement, for the processing of the input data stream, the auxiliary artificial intelligence model (16) associated with the auxiliary signature which, on the one hand, has a better auxiliary similarity score with the input signature than the current signature and which, on the other hand, has the best auxiliary similarity score. (Goncalves sec. 1 “It works by storing classifiers and samples of data used to build them. At predefined intervals, RCD compares the data distribution of actual data to stored data samples and, if they are similar, the stored classifier associated to that specific data sample is reused. To compare two data distributions and check their similarity, RCD uses a multivariate non-para- metric statistical test.” Goncalves sec. 3 “A statistical test is performed comparing bn with all stored buffers, trying to identify if this new context has already occurred in the past (line 15). If the test is positive, it means this is an old context recurring. Then, the stored classifier and buffer are considered the new ca and ba, respectively (line16),and cn and bn are disposed (line23).” Goncaclves sec. 3 “The classifier trained on data more similar to actual data based on a statistical test is used.)
Goncalves teaches claim 2. The method (20) according to claim 1, wherein the input signature is determined, at any given current moment, from the input data received in a time window of predetermined duration preceding the current moment. (Goncalves sec. 3 “a framework to compare the data distribution of samples to identify if anew context or an old con- text has occurred using a multivariate non-parametric statistical test.” S is a data stream in algorithm 1. Goncalves fig. 2 shows that this is real time data. Goncalves sec 1 also says that these tests are run on time intervals, “A real concept drift ‘’occurs when a set of examples has legitimate class labels at one time and different legitimate labels at another time’ … SPLICE-2 (Harries et al., 1998 ) also deals with categorical attributes but the classifier training is made in batch mode: it assumes that a concept will be stable over some time interval.”)
Goncalves teaches claim 3. The method (20) according to claim 1, wherein:
the input signature is a probability distribution of the input data;
the current signature is a probability distribution of the data of the current training data set; and/or
the auxiliary signature is a probability distribution of the data in the auxiliary training dataset. (Goncalves algorithm 1 and sec. 3 “RCD can deal with both categorical and numerical data and uses statistical tests to compare distributions (which offers significance levels of the similarity between distribution s)and tests for concept drift using sample data.”)
Goncalves teaches claim 4. The method (20) according to claim 3, wherein the current similarity score is the p-value under the null hypothesis “the input signature is identical to the current signature”, and the auxiliary similarity score is the p-value under the null hypothesis “the auxiliary signature is identical to the current signature”. (Goncalves algorithm 1 and sec 3 “s:the significance value (p-value).It informs the amount of similarity between distributions. The tested values were 0.01 and 0.05.”)
Goncalves teaches claim 5. The method (20) according to claim 3, wherein the current similarity score, respectively the auxiliary similarity score, is:
the result of a Student's t-test on the current signature, respectively on the auxiliary signature; (Goncalves sec. 3 “t: the rate the tests will be made in the testing phase. Smaller values mean tests are made more frequently, positively influencing the accuracy but reducing performance. Tested values ranged from b to 500 instances;”)
the result of a Wilcoxon-Mann-Whitney test representative of proximity between the current signature, respectively the auxiliary signature, and the input signature; or
the result of a Kolmogorov-Smirnov test representative of proximity between the current signature, respectively the auxiliary signature, and the input signature.
Goncalves teaches claim 6. The method (20) according to claim 1, further comprising the steps of:
synthesizing, from the input data, of at least one synthetic dataset; and
for each synthetic dataset, training an artificial intelligence model on the basis of said synthetic dataset to generate an additional auxiliary artificial intelligence model. (Goncalves “Anew classifier and an empty buffer, called actual classifier(ca)and actual buffer (ba), respectively, are created and stored in their respective lists (lines2and 3)…. Then, a new classifier (cn) is created along side with a new buffer(bn). Examples are used to train cn and ca and are stored in bn (lines 12 and 13)…”)
Goncalves teaches claim 7. The method (20) according to claim 6, wherein the synthesis step comprises the phases of:
determining a probability distribution of at least a portion of the input data stream;
modifying at least one parameter of the determined probability distribution to create at least one synthetic probability distribution; and (Goncalves sec. 4.1 “To create the abrupt concept drifts data sets, we used the fol- lowing scheme: 25,000 examples of each concept were generated. The first12,500 examples of each concept were appended together and used to form the training set.” The parameter that is modified is the number of examples.)
for each created synthetic probability distribution, generating, in accordance with said created synthetic probability distribution, a plurality of values forming a synthetic dataset. (Algorithm 1 and Goncalvez sec 3 tech the probability distribution for the training set drift detector, see above and sec 4.)
Goncalves teaches claim 8. A computer program comprising executable instructions which, when they are executed by computer, implement the steps of the method according to claim 1. (Goncalves algorithm 1)
Conclusion
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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