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 .
1. In response to the Office Action dated on 03/23/2026, applicant(s) amend the application as follow:
Claims amended: 1, 12 and 14
Clams canceled: none
Claims newly added: none
Claims pending: 1-20
Response to Arguments
2. Applicant’s arguments with respect to claim(s) 1, 12 and 14 and have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues “applicant respectfully traverses these rejections. Claim 1 as amended, recites, “a model architecture comprising a front-end neural network configured to perform feature extraction on content from the two or more dataset; and back-end neural network configured to output a similar score for the two or more data sets.”
Examiner respectfully disagree with the above argument. The amended language a model architecture comprising a front-end neural network configured to perform feature extraction on content from the two or more dataset; and back-end neural network configured to output a similar score for the two or more data sets were disclosed by Hamedi.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1.
For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto- processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to ww.uspto.gov/patents/apply/applying-online/e-terminal-disclaimer
3. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,164,503 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because both application including similar concept of receive one or more inputs to facilitate database management, the one or more inputs initiating a machine learning process configured to detect data redundancies of two or more datasets, process the entity data to conform with formatting requirements for the machine learning process, insert the training data into an iterative training and testing loop and train, based on weights and calculation, a model architecture using the training data in the iterative training and testing loop to detect the data redundancies, the training including predicting a target variable and iteratively adjusting the weights and the calculations during each subsequent iteration in order to improve predictability of the target variable, wherein the model architecture is trained to identify data similarity among the two or more database. Although the language is a bit different such that in in 503 include access entity data stored one or more entity data storage locations. Such difference is also disclosed in the instant application.
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify the 503 to arrive the same invention as claimed.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claim(s) 1-2, 4, 9-11, 15 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Zhang et al. (Pub. No. US 2023/0281564 a1) in view of Hamedi et al. (Pub. No. US. 2025/0165995) and further in view of MORIYASU et al. (Pub. No. US 2022/0114461 A1).
As to claim 1, Zhang discloses a computing system for database management of datasets, comprising: at least one processor (processor 0110); a communication interface communicatively coupled to the at least one processor; and a memory device (memory) (paragraph 0010) storing executable code (instructions) (paragraph 0010) that, when executed, causes the at least one processor to:
receive one or more inputs to facilitate dataset management, the one or more inputs initiating a machine learning process configured to detect data redundancies of two or more datasets (an input interface configured to receive an input profile) (paragraph 0010);
process the entity data to conform with formatting requirements for the machine learning process (parse the input profile data from the plurality of candidate profiles data format) (paragraph 0010);
validate the processed entity data, the validation ensuring the processed entity data satisfy the formatting requirements, wherein the validation produces training data (the first set of profiles being related to the standardized profile data) (paragraph 0010);
insert the training data into an iterative training and testing loop (using either or a combination of: (i) a trained Machine.. a similar score ) (paragraph 0010).
Zhang does not explicitly disclose train, based on weights and calculations, a model architecture comprising: a front-end neural network configured to perform feature extraction on content form the two or more datasets; and a back-end neural network configured to output a similarity score for the two or more datasets; using the training data in the interactive training and testing loop, the training including predicting a target variable and iteratively adjusting the weights and the calculation during each subsequent iteration in order to improve predictability of the target variable, wherein the model architecture is trained to identify data similarities among the two or more datasets, transmit, based on similarity score an electronic notification to a computing device associated with an administrator.
However, Hamedi discloses train, based on weights and calculations, a model architecture comprising: a front-end neural network configured to perform feature extraction on content form the two or more datasets (for example, for each of the generated images 1521, the content evaluation system 1005 may extract features from the image by inputting the image into a machine model (e.g., neural network)…) (paragraph 0856); and a back-end neural network configured to output a similarity score for the two or more datasets and transmit, based on similarity score an electronic notification to a computing device associated with an administrator (… a machine learning model (e.g., a neural network trained to output performance scores for a particular audiences, such as the identified target audience…) (paragraph 0856).
MORIYASU using the training data in the interactive training and testing loop, the training including predicting a target variable and iteratively adjusting the weights and the calculation during each subsequent iteration in order to improve predictability of the target variable, wherein the model architecture is trained to identify data similarities among the two or more datasets (this configuration enables a model that predicts an actual output of the system with high accuracy to be learnt by adjusting the weights W.sub.w and W.sub. and the biases b.sub.y and b.sub. in each layer of the multilayer neural network such as to cause the output y corresponding to the input variable V calculated by using the model to approach an actual output of the system. Accordingly, the model learning apparatus of this configuration learns a model that is capable of establishing a control apparatus configured to determine an input that further improves the correlation of an output to a target value) (paragraph 0017). This suggest the training process including predicting a target variable and iteratively adjusting the weights and the calculations during each subsequent iteration in order to improve predictability of the target variable
Therefore, it would have been obvious to one ordinary skill in the art before effective filing date of the instant application to modify teaching of Zhang to include train, based on weights and calculations, a model architecture comprising: a front-end neural network configured to perform feature extraction on content form the two or more datasets; and a back-end neural network configured to output a similarity score for the two or more datasets; using the training data in the interactive training and testing loop, the training including predicting a target variable and iteratively adjusting the weights and the calculation during each subsequent iteration in order to improve predictability of the target variable, wherein the model architecture is trained to identify data similarities among the two or more datasets, transmit, based on similarity score an electronic notification to a computing device associated with an administrator as disclosed by Hamedi and MORIYASU in order to provide training model to find similarity.
As to claim 2, Zhang discloses the computing system of claim 1, wherein the model architecture is trained to perform natural language processing on column names of the two or more datasets, comparing the column names of the two or more datasets, and determine a likelihood of similarity of the column names (similar profile) (paragraph 0010 and 0016-0017).
As to claim 4, Zhang discloses the computing system of claim 1, wherein the model architecture is trained to identify a percentage of similarity between at least one dataset and another dataset (confidence score) (paragraphs 0010 and 0016-0017).
As to claim 9, Zhang discloses the computing system of claim 1, wherein the model architecture is trained to determine from two or more datasets whether one dataset of the two or more datasets is a subset of another dataset of the two or more datasets based on a degree of
similarity of a portion of data (profile include subset information and similarities are determined based on information subset information of profile) (paragraph 0010 and 0016-0017).
As to claim 10, Zhang discloses the computing system of claim 1, wherein the model architecture is trained to evaluate changes of the two or more datasets over time to identify which of the two or more datasets may include more recent data (profile include past and most current data) (paragraphs 0010-0016-0017).
As to claim 11, Zhang discloses the computing system of claim 1, wherein the model architecture is trained to perform natural language processing on words included in the two or more datasets and derive similarities in meaning from the words in order to determine whether the two or more datasets are redundant (matching profile between target and standardized profile is determining similarity) (paragraphs 0010 and 0016-0017).
Claim 14 is rejected under the same reason as to claim 1, Zhang discloses a computer- implemented method (method) (paragraph 0008).
Claim 15 is rejected under the same reason as to claim 2.
Claim 17 is rejected under the same reason as to claim 4.
5. Claim(s) 3 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Zhang et al. (Pub. No. US 2023/0281564 a1) in view of Hamedi et al. (Pub. No. US. 2025/0165995) and further in view of MORIYASU et al. (Pub. No. US 2022/0114461 A1) in view of Cho et al. (Pub. No. US 2022/0292669 A1).
As to claim 3, Zhang, Hamedi and MORIYASU disclose the computing system of claim 1 excepting for wherein the model architecture is trained to ascertain and compare maximum data values, mean values, a range of values, minimum values, and standard deviations of data from the two or more datasets. However, Cho discloses wherein the model architecture is trained to ascertain and compare maximum data values, mean values, a range of values, minimum values, and standard deviations of data from the two or more datasets (a contour histogram extracted by a stochastic prediction model 10 (see FIG. 6A) in an OPC verification stage after a training stage may also include a mean, maximum value, a minimum value, arrange, a median value, a model, and/or standard deviation which are defined in a similar manner) (paragraph 0045). This suggests the claim language the model architecture is trained to ascertain and compare maximum data values, mean values, a range of values, minimum values, and standard deviations of data from the two or more datasets. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Zhang, Hamedi and MORIYASU to include wherein the model architecture is trained to ascertain and compare maximum data values, mean values, a range of values, minimum values, and standard deviations of data from the two or more datasets as disclosed by Cho in order to provide prediction based on computed values.
Claim 16 is rejected under the same reason as to claim 3.
6. Claim(s) 5-8 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Zhang et al. (Pub. No. US 2023/0281564 a1) in view of Hamedi et al. (Pub. No. US. 2025/0165995) and MORIYASU et al. (Pub. No. US 2022/0114461 A1) in view of Shimshock et al. (Patent No. US 12,332,878 B1).
As to clam 5, Zhang, Hamedi and MORIYASU disclose the computing system of claim 1 excepting wherein the model architecture is trained to identify sensitive information included in the two or more datasets and determine, based on one or more rules, that a security restriction needs to be added to the sensitive information. Shimshock discloses wherein the model architecture is trained to identify sensitive information included in the two or more datasets and determine, based on one or more rules, that a security restriction needs to be added to the sensitive information (... if yes, the process 100 proceeds to block 110, where the system determine whether the query needs to blocks. If yes, the process 100 proceeds to block 112 and terminates. If no (e.g., the query does not need to be blocked completely but contains sensitive information), the system can mask or anonymize sensitive information in the query (e.g., by removing or replace the sensitive information using a rule-based logic or using machine-learning model and then procced to black 114) (col 8, lines 22-49). This suggests masking is the security restriction to add on the sensitive data. Therefore, it would have been obvious to one ordinary skill in the art before the effective filling date of the instant application to modify teaching of Zhang, Hamedi and MORIYASU to include the model architecture is trained to identify sensitive information included in the two or more datasets and determine, based on one or more rules, that a security restriction needs to be added to the sensitive information as disclosed by Shimshock in order to provide data protection.
As to claim 6, Zhang, Hamedi and MORIYASU disclose the computing system of claim 5 excepting for wherein the security restriction comprises data tokenization. Shimshock discloses wherein the security restriction comprises data tokenization (the process 100 can comprise a validation layer 106, which includes blocks 108 and 110, for performing a preliminary check on the natural language query 102 (or a tokenized version thereof) based on one or more organization-specific rules...) (col. 8, lines 22-49). This suggests claim language wherein the security restriction comprises data tokenization. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Zhang, Hamedi and MORIYASU to include the security restriction comprises data tokenization as disclosed by Shimshock in order to protect data.
As to claim 7, Zhang, Hamedi and MORIYASU disclose the computing system of claim 5 excepting for wherein the security restriction comprises data masking. Shimshock discloses wherein the security restriction comprises data masking (... if yes, the process 100 proceeds to block 110, where the system determine whether the query needs to blocks. If yes, the process 100 proceeds to block 112 and terminates. If no (e.g., the query does not need to be blocked completely but contains sensitive information), the system can mask or anonymize sensitive information in the query (e.g., by removing or replace the sensitive information using a rule- based logic or using machine-learning model and then procced to black 114) (col 8, lines 22- 49). This suggests the concept of masking the sensitive data. Therefore, it would have been obvious to one ordinary skill in the art before the effective filling date of the instant application to modify teaching of Zhang, Hamedi and MORIYASU to include the security restriction comprises data masking as disclosed by Shimshock in order to provide data protection.
As to claim 8, Zhang discloses the computing system of claim 5, wherein the sensitive information comprises personally identifiable information (identification) (paragraph 0101).
Claim 18 is rejected under the same reason as to claim 5.
Claim 19 is rejected under the same reason as to claim 6.
Claim 20 is rejected under the same reason as to claim 7.
7. Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Scanian et al. (Pub. No. US 2024/0248765 A1) in view of Hamedi et al. (Pub. No. US. 2025/0165995) and further in view of Zhang et al. (Pub. No. US 2023/0281564 a1).
As to claim 12, Scanian discloses a computing system facilitating data redundancy detection, the computing system comprising:
at least one processor (processor) (paragraph 0062);
a communication interface communicatively coupled to the at least one processor; and a memory device (memory device) (paragraph 0062) storing executable code (computer readable instruction) (paragraph 0062) that, when executed, causes the at least one processor to:
train a model to interpret meaning from words (natural language) (paragraph 0003) included in datasets (, the training including:
iteratively predicting a target variable using a set of training data (a machine learning algorithm is trained (e.g., utilizing a training data set) prior to model the problem with...) (paragraph 0087);
comparing each prediction to a correct output (predict other values based on the new input data) (paragraph 0088); and
adjusting weight coefficients until any error in predicting the target variable is less than a predetermined level (the machine learning algorithm may adjust the weight coefficient until any error in the output data generated by algorithm is less than predetermined acceptable level) (paragraph 0088);
Scanian does not explicitly disclose deploy the model architecture; apply the deployed model architecture to at least two datasets to interpret language meaning included in the at least two datasets, wherein the model architecture, comprises a front-end neural network configured to perform feature attraction on content from the at least two datasets; and a back-end neural network configured to output a similarity score for the at least two datasets; and determine, from interpreting the language meaning and similarity score, a first portion of a first dataset of the at least two datasets is likely repetitive of a second portion of a second dataset of the at least two datasets to facilitate consolidation of either the first dataset or the second dataset, transmit, based on the determine an electronic notification to a computing device associated with an administrator.
However, Hamedi discloses deploy the model architecture; apply the deployed model architecture to at least two datasets to interpret language meaning included in the at least two datasets, wherein the model architecture, comprises a front-end neural network configured to perform feature attraction on content from the at least two datasets (for example, for each of the generated images 1521, the content evaluation system 1005 may extract features from the image by inputting the image into a machine model (e.g., neural network)…) (paragraph 0856); and a back-end neural network configured to output a similarity score for the at least two datasets (… a machine learning model (e.g., a neural network trained to output performance scores for a particular audiences, such as the identified target audience…) (paragraph 0856), transmit, based on the determine an electronic notification to a computing device associated with an administrator.
However, Zhang discloses determine, from interpreting the language meaning, a first portion of a first dataset of the at least two datasets is likely repetitive of a second portion of a second dataset of the at least two datasets to facilitate consolidation of either the first dataset or the second dataset (similarity score is associated with a measure of similarity between the standardized profile data form the first set of profile data in the first set of profile data and each respective profile data in the first set profile data) (paragraph 0010). The model in Zhang used to calculate and finding similarity between profile that also mean natural language analysis the matching of portion of profiles are output. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Scanian to include deploy the model architecture; apply the deployed model architecture to at least two datasets to interpret language meaning included in the at least two datasets, wherein the model architecture, comprises a front-end neural network configured to perform feature attraction on content from the at least two datasets; and a back-end neural network configured to output a similarity score for the at least two datasets; and determine, from interpreting the language meaning and similarity score, a first portion of a first dataset of the at least two datasets is likely repetitive of a second portion of a second dataset of the at least two datasets to facilitate consolidation of either the first dataset or the second dataset, transmit, based on the determine an electronic notification to a computing device associated with an administrator as disclosed by Hamedi and Zhang to identify data redundancies.
As to claim 13, Scanian discloses the computing system of claim 12, wherein the executable code, when executed, further causes the at least one processor to apply data classification to the at least two datasets, wherein the data classification categorizes each structured column of structured data according to respective categories and based thereon the deployed model is applied to the at least two datasets to provide greater surety that the first portion is likely repetitive of the second portion (the machine learning program may be configured to utilize a plurality of support vector machines to perform a single classification) (paragraph 0095).
Conclusion
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
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