DETAILED ACTION
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 § 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.
Claims 1-3, 7-12, 15-18, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Creedon et al. (US 10,896,176 B1), hereinafter “Creedon”, and in view of Sadkin et al. (US 2014/0222793 A1), hereinafter “Sadkin”.
As per claim 1, Creedon teaches a method comprising:
“communicating with a data lake that integrate access to data stored in a plurality of different data sources” at Col. 2 lines 35-55 and Fig. 1;
(Creedon teaches the query engine 110 communicates with the data lake 150 to access data stored in a plurality of different data stores 160-1, 160-2, 160-3)
“correlating, via the data lake, data fields in data sets across the plurality of different data sources to identify relationships across the plurality of different data sources” at Col. 3 lines 5-65, Col. 4 lines 1-20 and Figs. 2-4;
(Creedon teaches processing of the dynamic federated query schema links (i.e., “correlating”) records by comparing the records from two or more data sources to determine which pairs of records represent the same real-world entity and discovering duplicate data. As shown in Fig. 2, the federated database 200 comprises customer table 210 and a dynamic federated schema 220 that map to, for example, a customer table 280-1 on a data store PostgreSQL data store 290-1 and a customer table 280-2 on a HIVE data store 290-2)
“obtaining a request to access data associated with the data entity” at Col. 3 lines 5-65, Col. 5 lines 1-35 and Fig. 6;
(Creedon teaches receiving a requested query)
“determining that the data for the request is stored in two or more data sources of the plurality of different data sources, based on the first and second correlation confidences” at Col. 4 line 3 to Col. 5 line 35;
(Creedon teaches identifying the query as federated query, if the dynamic federated schema information 450 has more than one external data source option. Creedon teaches comparing the records from two or more data sources to determine which pairs of records represent the same real-world entity. Duplicate schema data can be recorded as candidate matches. Deterministic or rule-based record linkage is performed to generate links based on the number of individual identifiers that match among the available metadata sets (i.e., “first and second correlation confidences”.))
“selecting a particular data source of the two or more data sources based on an efficiency metric associated with a respective type of hardware storage provided by each of the two or more data source” at Col. 4 line 3 to Col. 5 line 35;
(Creedon also teaches identifying data sources with spare capacity and that are efficient at executing specific types of queries, if the target data source is efficient at executing the targeted type of query and has capacity at the time of expected execution, then the target data source identified as a data movement instruction. Creedon also teaches calculating the cost of execution for each potential data source, the cost is measured as the total expected elapsed time (i.e., “efficient metric”) for answering the query. The lowest cost data source is then selected)
“retrieving the data for the request from the particular data source” at Col. 5 lines 35-55;
(Creedon teaches generating a query execution plan for the data source and the query execution plan is applied to an execution engine 760 that executes the query)
Creedon does not explicitly teach “wherein the relationships are represented by a graph with field names of the data fields as nodes and correlation confidences as links between nodes, and wherein a first correlation confidence between a first pair of nodes selected from the nodes and a second correlation confidence between a second pair of nodes selected from the nodes are used together to strengthen a confidence that a data entity associated with the first pair of nodes or the second pair of nodes is referenced across the plurality of different data sources” as claimed.
However, Sadkin teaches a method for comparing contact data from a plurality of data sources, including:
“wherein the relationships are represented by a graph with field names of the data fields as nodes and correlation confidences as links between nodes” at [0122]-[0126] and Figs. 11-12;
(Sadkin teaches at Fig. 11 the relationship between the two contact lists is represented by a graph with field names of the data fields as nodes and correlation confidences (e.g., “field correlation weights”) as links between nodes)
“wherein a first correlation confidence between a first pair of nodes selected from the nodes and a second correlation confidence between a second pair of nodes selected from the nodes are used together to strengthen a confidence that a data entity associated with the first pair of nodes or the second pair of nodes is referenced across the plurality of different data sources” at [0087]-[0092] and Figs. 5-6;
(Sadkin teaches the correlation weight is associated with each of the field, and the confidence scores is calculated based on the number of matching pair of fields. For example, row Number 1 represents the matching criteria where all five fields match in both the new and existing version of the contact record, and therefore having the highest confidence score, wherein row 31 represents the matching criteria where only one field matches and therefore having a lower confidence score)
Thus, it would have been obvious to one of ordinary skill the art to combine Sadkin with Creedon’s teaching in order to determine the degree of similarity between data records in different data source by comparing the similarity between multiple pairs of fields of the data record, as suggested by Sadkin at [0087]-[0092].
As per claim 2, Creedon-Sadkin teaches the method of claim 1 discussed above. Creedon also teaches: “selecting comprises selecting the particular data source based on cost of retrieval and/or capacities of the two or more data sources” at Col. 5 lines 15-30.
As per claim 3, Creedon-Sadkin teaches the method of claim 1 discussed above. Creedon also teaches: “correlating comprises determining similarity of key-value structured data to correlate field names in data sets across the plurality of different data sources” at Col. 3 lines 5-65, Col. 4 lines 1-20 and Figs. 2-4.
As per claim 7, Creedon-Sadkin teaches the method of claim 1 discussed above. Creedon also teaches: “based on the correlating, storing location information identifying two or more data sources of the plurality of different data sources that store similar data, wherein selecting is performed based on the location information” at Col. 3 lines 5-65, Col. 4 lines 1-20 and Figs. 2-4.
As per claim 8, Creedon-Sadkin teaches the method of claim 1 discussed above. Creedon also teaches: “obtaining the request comprises using natural language processing to derive the request from a text-based or audio-based query” at Col. 3 lines 5-65 and Figs. 3A-B.
As per claim 9, Creedon-Sadkin teaches the method of claim 1 discussed above. Creedon also teaches: “correlating is performed using unsupervised machine learning techniques” at Col. 4 lines 1-60.
Claims 10-12, 15-18 recite similar limitations as in claims 1-3, 7-9 and are therefore rejected by the same reasons.
As per claim 23, Creedon and Sadkin teach the method of claim 1 discussed above. Sadkin also teaches: “wherein the confidence is strengthened based on a plurality of patterns of similarity across the plurality of different data sources, and wherein the plurality of similarity is identified via a pattern-to-vec mode” at [0087]-[0093] and Fig. 6.
Claims 4-6, 13-14, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Creedon and Sadkin as applied to claims 1-3, 7-12, 15-18, 23 above, and further in view of Goodsitt et al. (US 2021/0056099 A1), hereinafter “Goodsitt”, and further in view of Zhang et al. (US 2015/0286713 A1), hereinafter “Zhang”.
As per claims 4, 13, 19, Creedon teaches the method of claim 3 discussed above. Creedon does not teach: “wherein correlating includes: discovering patterns representing field names in data sets across the plurality of different data sources; aggregating the patterns into a vector that describes all patterns observed for a given key in a given data source across the plurality of different data sources; computing a vector similarity that represents similarities among data field attributes across the plurality of different data sources; and analyzing the vector similarity for data field attributes between data sources of the plurality of different data sources to generate a confidence score” as claimed. However, Goodsitt teaches a method for determining pattern and data relation using a regex-based pattern recognition system, including the steps of “discovering patterns representing field names in data sets across the plurality of different data sources” at [0023]-[000026] and “aggregating the pattern into a vector that describes all patterns observed for a given key in a given data source across the plurality of different data sources” at [0027]-[0030] and Fig. 1. Thus, it would have been obvious to one of ordinary skill in the art to combine Goodsitt with Creedon’s teaching to use the regex-based pattern recognition because “the use of regex allows a more succinct description of the dataset for data type classification and may increase overall discernibility with respect to at least identifying various characters, words, text, numbers, etc. in the dataset”, as suggested by Goodsitt at [0016].
Creedon and Goodsitt does not explicitly teaches: “computing a vector similarity that represents similarities among data field attributes across the plurality of different data sources; and analyzing the vector similarity for data field attributes between data sources of the plurality of different data sources to generate a confidence score” as claimed. However, Zhang teaches a method for ontology matching including the steps of “computing a vector similarity that represents similarities among data field attributes across the plurality of different data sources, and analyzing the vector similarity for data field attributes between data sources of the plurality of different data sources to generate a confidence score” at [0036]-[0039], [0045]-[0052]. Thus, it would have been obvious to one of ordinary skill in the art to combine Zhang with Creedon-Goodsitt’s teaching in order to correctly determining “entities that are related by some other relationship”, as suggested by Zhang at [0003].
As per claims 5, 14, 20, Creedon-Sadkin-Goodsitt and Zhang teach the method of claim 4 discussed above. Zhang also teaches wherein “determining that the data for the request is stored in two or more data sources of the plurality of different data sources is based on the confidence store for similarity of data field attributes between data sources of the plurality of different data sources” at [0036]-[0039], [0045]-[0052].
As per claim 6, Creedon-Sadkin-Goodsitt and Zhang teach the method of claim 4 discussed above. Goodsitt also teaches: wherein “discovering patterns comprises discovering patterns in regular expressions” at [0023]-[0030].
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Creedon and Sadkin as applied to claims 1-3, 7-12, 15-18, 23 above, and further in view of Smaldone et al. (US 11,513,902 B1), hereinafter “Smaldone”.
As per claim 22, Creedon-Sadkin teaches the method of claim 1 discussed above. Creedon does not explicitly teach “wherein the efficiency metric is determined based on an age of the respective type of hardware storage, and wherein the respective type of hardware storage includes one or more of a hard disk drive storage or a solid-state memory storage” as claimed. However, Smaldone teaches at Col. 11 line 25 to Col. 12 line 4 a storage array having multiple storage tiers, including a highest performance tier implemented using newer storage devices such as flash memory and a lower performance tier implemented using older storage devices such as hard disk drives. Thus, it would have been obvious to one of ordinary skill in the art to combine Smaldone with Creedon’s teaching in order to allow an administrator to define Service Level Objective (SLO) targets for particular application or workload. The SLO targets define performance (e.g., latency, throughput, etc.) targets for servicing request to various tier”, as suggested by Smaldone at Col. 11 lines 30-60.
Response to Arguments
Applicant’s arguments filed 6/11/2025 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.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KHANH B PHAM/Primary Examiner, Art Unit 2166
July 29, 2025