DETAILED ACTION
This action is in response to the original filing of 11-23-2023. Claims 1-2 and 4-18 are pending and have been considered below:
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 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.
Claims 1-2 and 4-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ertoz et al. (“Ertoz” 20060161592 A1) in view of Takacs 20190164245 A1 and CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances Jihoon Tack et al. (“Tack”), pages 1-25, 10-21-2020.
Claim 1: Ertoz discloses a method of training a model for inferring when a record is from a distribution, using a model comprising a first neural network and a second neural network, the method comprising:
receiving a training dataset having: a plurality of records comprising a plurality of ground truth records, wherein a record from the plurality of records comprises a first tabular segment, and a second tabular segment (Paragraph 20; features derived from different areas of records/tabular);
Ertoz may not explicitly disclose a plurality of synthetic records each generated by adjusting at least one value in either the first tabular segment or the second tabular segment of a member of the plurality of ground truth records; Ertoz does adjust data samples (Paragraphs 15 32; adjust data samples);
Takacs is provided because it discloses a functionality to produce synthetic data from non-synthetic and determine a distance (Takacs: Paragraphs 15 synthetic data and Paragraphs 9 and 12; distance/space determination);
Takacs also discloses in each of a plurality of iterations processing one of the plurality of records by: feeding the first tabular segment of the respective record into the first neural network to acquire a first vector representation to a metric space, having a distance measure (Takacs: Paragraphs 9, 40 and 87 Siamese neural network);
feeding the second tabular segment of the respective record into the second neural network to acquire a second vector representation to the metric space (Takacs: Paragraphs 15, 64-65 and Paragraphs 9, 40 and 87 Siamese neural network);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide synthetic data and Siamese networks for the training data found in Ertoz. One would have been motivated to provide the functionality for enhanced analysis and comparing data for determining linkage/similarities (Takacs: abstract).
Ertoz also may not explicitly disclose when the record is one of the plurality of ground truth records updating at least one neural network parameter of the first neural network or the second neural network so that the distance measure between the first vector representation and the second vector representation decreases. Tack is provided because it discloses detection via contrastive learning and further provides functionality for decreasing distance between samples (Page 23, Paragraphs 1-3; modified parameters reduce distance “one can also increase the overall norm of the features to reduce the relative distance of two samples.”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide sample distance reduction through adjustments in Ertoz. One would have been motivated to provide the functionality for enhanced analysis and comparing data for determining similar representations.
Ertoz also may not explicitly disclose and when the record is one of the plurality of synthetic records updating at least one neural network parameter of the first neural network or the second neural network so that the distance measure between the first vector representation and the second vector representation increases . Tack further discloses training scheme contrasts the sample with distributionally-shifted augmentations of itself. (abstract) which increases distance of representation (Page 4; section 2.3: Detection score for contrastive representation).
Claim 2: Ertoz, Takacs and Tack disclose a method of claim 1, further comprising applying at least one permutation to at least one record from the plurality of records (Ertoz: Paragraphs 20; plurality of records and Takacs: Paragraph 12 and 63-64).
Claim 4: Ertoz, Takacs and Tack disclose a method of claim 1 wherein the adjusting comprises permuting at least one element from the first tabular segment to the second tabular segment (Ertoz: Paragraphs 20; plurality of records with different elements can be overlapped and Takacs: Paragraphs 15 synthetic data).
Claim 5: Ertoz, Takacs and Tack disclose a method of inferring when a record is from a distribution using a model comprising a first neural network and a second neural network, comprising: receiving a record comprising tabular data; splitting the record to a first tabular segment and a second tabular segment (Ertoz: Paragraph 20; features derived from different areas of records/tabular);
Ertoz may not explicitly disclose feeding the first segment of the respective record into the first neural network to acquire a first vector representation to a metric space; feeding the second segment of the respective record into the second neural network to acquire a second vector representation to a metric space; Takacs is provided because it discloses tabular data (Paragraph 12) and Siamese neural network which would allow feeding of a plurality of data for determining a distance (space)(Takacs: Paragraphs 9, 12, 40 and 87 Siamese neural network);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide multiple neural networks to process the data found in Ertoz. One would have been motivated to provide the functionality because it offers enhanced analysis above the standard similarity analysis currently offered (Takacs: Paragraph 41).
Ertoz also may not explicitly disclose estimating when the record is from the distribution by applying a threshold on a distance measure between the first vector representation and the second vector representation (Takacs: Paragraph 12; apply distance measurement).
Tack is further provided because it discloses detection of in-distribution and out via contrastive learning and further provides functionality for determination of samples based on distance between samples (Page 16: Evaluation metrics (distribution determination) and Page 23, Paragraphs 1-3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide distribution determination in Ertoz. One would have been motivated to provide the functionality for enhanced analysis and comparison of data for determining valid representations.
Claim 6: Ertoz, Takacs and Tack disclose a The method of claim 5, wherein the first tabular segment is larger than the second tabular segment (Ertoz: Paragraph 20; features derived from different areas of record and Takacs: Paragraph 12; tabular data). The record/tabular data can be collected as different segment sizes.
Claim 7: Ertoz, Takacs and Tack disclose a method of claim 5, wherein the first neural network is substantially a fully connected neural network (Tackas: Paragraph 87). Understood that the Siamese layers can be fully connected to deep learning model.
Claim 8: Ertoz, Takacs and Tack disclose a method of claim 5, wherein the second neural network is substantially a fully connected neural network( (Tackas: Paragraph 87). Understood that the Siamese layers can be fully connected to deep learning model.
Claim 9: Ertoz, Takacs and Tack disclose a method of claim 7, wherein the first neural network comprises at least two layers, having a first layer and additional layers and the activation of the first layer differs from the activation of at least one of the additional layers (Tackas: Paragraph 87).
Claim 10: Ertoz, Takacs and Tack disclose a method of claim 5, further comprising applying normalization to at least one element of the tabular data (Ertoz: Paragraph 37; components which are associated with the data can be normalized and Takacs: Paragraph 12; data provided as 1/0).
Claim 11: Ertoz, Takacs and Tack disclose a method of claim 5, applied on a plurality of dataset records, and further comprising training an additional network using a method assigning lesser weight to records for which the distance measure exceeded the threshold (Ertoz: Paragraphs 7 (types of models) 24 and claim 24; new models developed when features differ).
Claim 12 is similar in scope to claim 5 and therefore rejected under the same rationale.
Claim 13 is similar in scope to claim 6 and therefore rejected under the same rationale.
Claim 14 is similar in scope to claim 7 and therefore rejected under the same rationale.
Claim 15 is similar in scope to claim 8 and therefore rejected under the same rationale.
Claim 16 is similar in scope to claim 9 and therefore rejected under the same rationale.
Claim 17 is similar in scope to claim 10 and therefore rejected under the same rationale.
Claim 18 is similar in scope to claim 11 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Byeon et al. (11727703 B2)
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148 8-17-2026
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