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 .
Applicant's election with traverse of Group II, claims 35-40 in the reply filed on July 31, 2026 is acknowledged. The traversal is on the ground(s) that the same classification was chosen in part for both groups above and because the Restriction Requirement does not establish a burden in searching for both groups. This is not found persuasive because Group I, claims 21-34 is classified in 707/722 and Group II, claims 35-40 is classified in 707/722 and 706/45. There is an overlap of the Groups in classification 707/722, however, there is a search burden when Group II is classified in 706/45 which distinct from the classification of Group I.
The requirement is still deemed proper and is therefore made FINAL.
IDS, filed March 24, 2025, has been considered.
Claims 1-34 are cancelled.
Claims 35-40, filed July 31, 2026, are examined on the merits.
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.
Claim(s) 35-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boutros et al. (US 2019/0121807 A1) in view of Ohm et al. (US 20240046144 A1).
Claim 35, Boutros discloses a method, comprising:
executing a first query, at a predefined time interval, against a target datastore for insight records, the first query including an entity identifier, an insight type, and an insight version (page 7, [0062], e.g. Interactive collaborative activity feed 383 depicts interactions over time with the datasets of the data project by collaborative users. Further to this example, @User_1 is shown to have uploaded a dataset identified as “4Stream_fish_data_into_Muttonchop.csv,” and @User_XX has published an insight relating to a query identified as “Species by Count,” which includes a user input 307 (e.g., via a hyperlink) that may be linked to a lower hierarchical level at which a query may be accessed in association with, for example, a workspace interface, and page 7, [0063], e.g. dataset identifiers 384c to 384g in data source activator 384 may be implemented as user inputs that are each configured to link to respective datasets, whereby selection of any of dataset identifiers 384c to 384g may trigger access to underlying levels of data in the datasets, including data representing a composite data dictionary);
storing results of the first query as a record set, wherein the record set includes one or more existing insight records, and wherein each existing insight record includes connection information for connecting to a corresponding insight datastore (page 7, [0062], e.g. @User_1 is shown to have uploaded a dataset identified as “4Stream_fish_data_into_Muttonchop.csv,” and @User_XX has published an insight relating to a query identified as “Species by Count,” which includes a user input 307 (e.g., via a hyperlink) that may be linked to a lower hierarchical level at which a query may be accessed in association with, for example, a workspace interface);
executing insight retrieval logic for each insight record in the record set, wherein the insight retrieval logic executes a second query against the corresponding insight datastore and retrieves the new insight value from the corresponding insight datastore, wherein the new insight value is input into a field for each insight record (page 7, [0063], e.g. a query is automatically performed, or run, each time a query is accessed, thereby providing, for example, a latest (or “freshest”) query result. Another user input 385c, upon activation, may cause access to a collaborative query editor via links to datasets for creating a new query);
persisting the new insight value in a new insight record in the target datastore (Figure 22, e.g. Insight 2292);
and sending the new insight record as a communication to a recipient (Figure 22, e.g. Insight 2292).
However, Boutros does not disclose selecting, by a machine learning engine, a new insight value that based on training of the machine learning engine based on prior explicit user feedback, rules, or collaborative filtering ([0249], e.g. a metric predictor model for predicting the data metric is selected. That is, the metric predictor model can be used for predicting values of the data metric. As described herein, the metric predictor model can be an ML model that is selected based on a training using historical data related to the data metric. That the historical data is related to the data metric can include that values of the data metric are derivable from the historical data. For example, the historical data may not directly explicitly include values for the data metric. Rather, such values may be obtained using aggregation, filtering, collation, or other operations on the historical data)…executing a loop iteratively for each new insight value returned including using an insight connection string stored in the record set and associated with the field to connect to an entity data source.
Ohm discloses selecting, by a machine learning engine, a new insight value that based on training of the machine learning engine based on prior explicit user feedback, rules, or collaborative filtering ([0249], e.g. a metric predictor model for predicting the data metric is selected. That is, the metric predictor model can be used for predicting values of the data metric. As described herein, the metric predictor model can be an ML model that is selected based on a training using historical data related to the data metric. That the historical data is related to the data metric can include that values of the data metric are derivable from the historical data. For example, the historical data may not directly explicitly include values for the data metric. Rather, such values may be obtained using aggregation, filtering, collation, or other operations on the historical data)…executing a loop iteratively for each new insight value returned including using an insight connection string stored in the record set and associated with the field to connect to an entity data source ([0218], e.g. the sample data table with sampled data, the data sampling tool 5040 receives (e.g., queries for) a sample of rows (e.g., 20 thousands, 100 thousand rows, or some other number of rows), for each (interpreted as an iterative loop) dimension of the dimensions in the sample data table. Sampling techniques can be used so that the sampled data is not skewed or is otherwise not representative of the historical data. For example, a predefined number of rows may be obtained for each time period in the historical data. To illustrate, and without limitations, assume that the historical data includes 10,000 rows for each day of the last 5 years. The data sampling tool 5040 may obtain 100 random rows for each of the days).
Ohm discloses an invention that overcomes existing database analytic tools that are inefficient, costly to utilize, and require substantial configuration and training ([0001]). One of ordinary skill in the art at the time before the effective filing date of the claimed invention would have been motivated by by Ohm to improve the method of Boutros. Therefore, it would have been obvious for one of ordinary skill in the art to use insight mining by machine learning of Ohm. The benefit would be to improve database analytic tools that are inefficient, costly to utilize, and require substantial configuration and training.
Claim 36, Boutros discloses persisting the new insight record with a new time stamp (Ohm, [0210], e.g. assume that the resolved request essentially translates to “weekly defects in the analytics module” and that each defect datum has associated a time dimension, reported timestamp, indicating a timestamp (which includes a date and a time) that the defect was reported).
Claim 37, Boutros discloses the one or more existing insight records each include a corresponding time stamp (Ohm, [0210], e.g. assume that the resolved request essentially translates to “weekly defects in the analytics module” and that each defect datum has associated a time dimension, reported timestamp, indicating a timestamp (which includes a date and a time) that the defect was reported).
Claim 38, Boutros discloses the new insight value was added to the insight datastore after the corresponding time stamp (Ohm, [0212], e.g. the timing granularity can be inferred from the insight request or the resolved request. To illustrate, and without limitations, assume that the resolved request essentially translates to “weekly defects in the analytics module” and that each defect datum has associated a time dimension, reported timestamp, indicating a timestamp (which includes a date and a time) that the defect was reported).
Claim 39, Boutros discloses determining the new insight value was added to the insight datastore based on existing insight values an existing insight record, the new insight record, the corresponding time stamp, and the new time stamp (Ohm, [0212], e.g. the timing granularity can be inferred from the insight request or the resolved request. To illustrate, and without limitations, assume that the resolved request essentially translates to “weekly defects in the analytics module” and that each defect datum has associated a time dimension, reported timestamp, indicating a timestamp (which includes a date and a time) that the defect was reported).
Claim 40, Boutros discloses providing a user interface for configuration and addition of an initial insight record to the target datastore (Ohm, [0042], e.g. client device 2320, 2340 may receive the response, including the response data or a portion thereof, and may store, output, or both, the response, or a representation thereof, such as a representation of the response data, or a portion thereof, which may include presenting the representation via a user interface on a presentation device of the client device 2320, 2340, such as to a user of the client device 2320, 2340).
CONCLUSION
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Dune Ly, whose telephone number is (571) 272-0716. The examiner can normally be reached on Monday-Friday from 8 A.M. to 4 PM ET.
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
9/3/2026
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