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 .
Claims 1-20 are presented for examination.
The claims and only the claims form the metes and bounds of the invention. “Office personnel are to give claims their broadest reasonable interpretation in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969)” (MPEP p 2100-8, c 2, I 45-48; p 2100-9, c 1, l 1-4). The Examiner has full latitude to interpret each claim in the broadest reasonable sense. The Examiner will reference prior art using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning.
Response to Arguments
Applicant’s remarks/amendment was filed on 10 February 2026.
US Patent 6,026,391 by Osborn et al. is newly introduced to address the amended claims.
Applicant's arguments have been considered but they are moot in view of new ground(s) of rejection. However, the Examiner welcomes any suggestion(s) Applicant may have on moving prosecution forward.
Terminal Disclaimer
The terminal disclaimer filed on 10 February 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent Application No. 18/599,436 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 11 recite “… and provide a comparison between the updated output and one or more historical summary statistics of past queries”.
Applicant’s original specification discloses the following:
[0129] “… various statistical analysis may be automatically performed and provided to the query requester (Step 825). This information preferably includes generalized information about the data set for the given time window: the standard deviation in a field's values, the mean and/or median, the boundaries between quartiles/deciles/percentiles/other quantiles, etc. The information also preferably includes information on the most recent query invocation's output in relation to that generalized information: what is today's output's z-score, is today's output above or below median, what is the percentile/other quantile of today's output in relation to other outputs in the time window, etc.”
Information on the most recent query invocation’s output such as z-score and percentlile/other quantile are summary statistics. Generalized information, as disclosed by ¶0129, can be “standard deviation in a field's values, the mean and/or median, the boundaries between quartiles/deciles/percentiles/other quantiles, etc.” Therefore, the disclosed “information on the most recent query invocation's output in relation to that generalized information” seems to be a comparison between statistics of a data set and statistics of recent query invocation's output rather than comparison between output of a query and historical summary statistics of past queries.
Applicant’s original specification does not appear to teach the amended limitation of “… and provide a comparison between the updated output and one or more historical summary statistics of past queries” recited in claims 1 and 11.
Claims 2-10 and 12-20 depend from Claims 1 and 11, and are rejected for the same reason(s) under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ).
Claim Rejections - 35 USC § 103
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, 7, 11-12 and 17 are rejected under U.S.C 103 as being unpatentable over US Patent 11,169,997 by Debo et al. (“Debo”) in view of US Patent 10,761,813 by Echeverria et al. (“Echeverria”), and further in view of US Patent 6,026,391 by Osborn et al. (“Osborn”).
As to Claim 1, Debo teaches a system for dynamically generating scripts to be executed during a query of a data store (Debo: at least Col. 6 Lines 30-34; “SQL query scripts are generated as needed, or “on the fly” by database query devices 102 and/or database administrator devices 104, these SQL scripts are typically referred to as “ad-hoc” SQL scripts”), comprising: a server comprising one or more processors (Debo: at least Col. 4 Lines 53-56; “computing environment 100 may include one or more database query devices 102, one or more database administrator devices 104, one or more client devices 105”); and non-transitory memory comprising instructions that, when executed by the one or more processors of the server, cause the one or more processors to:
receive a query comprising files to be searched, key values to search on (Debo: at least Col. 17 Lines 6-12 further disclose “a user may enter and/or edit script text using a suitable input device (e.g., a keyboard) as shown in FIG. 2B. Additionally or alternatively, a user may utilize the SQL parameters in portion 254. In other words, a user may select the desired search parameters from portion 254 instead of manually typing these into portion 252”; note: 252 of Fig. 2B shows the receiving of a SQL SELECT query with WHERE clause) in two or more distinct datasets (Debo: at least Col. 5 Lines 18-20 & 34-35 & 52-63; “central data governance server 108 may be configured to access (execute scripts on, query, alter data stored on, etc.) each of the data storage devices 106.1-106.N” and “data storage devices 106, each storing any suitable number of data collections 110” and “data storage devices 106.1-106.N and their respective collections of data 110.1-110.N may include data stored in any suitable data type of structure, format, protocol, language, etc. For example, the collections of data 110.1-110.N may include structured data, semi-structured data, or unstructured data stored in relational databases, object-relational databases, hierarchical databases, document-oriented databases, etc. Central data access server 108 may query, scan, or otherwise interact with the collections of data 110.1-110.N via languages, protocols, scripts, etc., as defined according to any suitable type and number of database servers, applications, and/or systems”; Col. 11 Lines 5-9 & 46-49 & 55-56 also disclose “allow only a portion of the data requested via the SQL query script received via network interface 132 to be retrieved from data storage devices 106.1-106.N” and “SQL scripts may include a number of search parameters to use to retrieve data from data storage devices 106.1-106.N, which act as filters when applied to the data, returning only results that match the SQL script parameters” and “any other suitable parameter that may be used to search data stored in data storage devices 106.1-106.N”), and one or both of a filter selecting a subset of the files to be searched and an aggregation of data from all of the files or from all of the files that are filtered (Debo: at least Col. 5 Lines 18-20 & 34-35 & 52-63; “central data governance server 108 may be configured to access (execute scripts on, query, alter data stored on, etc.) each of the data storage devices 106.1-106.N” and “data storage devices 106, each storing any suitable number of data collections 110” and “data storage devices 106.1-106.N and their respective collections of data 110.1-110.N may include data stored in any suitable data type of structure, format, protocol, language, etc. For example, the collections of data 110.1-110.N may include structured data, semi-structured data, or unstructured data stored in relational databases, object-relational databases, hierarchical databases, document-oriented databases, etc. Central data access server 108 may query, scan, or otherwise interact with the collections of data 110.1-110.N via languages, protocols, scripts, etc., as defined according to any suitable type and number of database servers, applications, and/or systems”; Col. 11 Lines 5-9 & 46-49 & 55-56 also disclose “allow only a portion of the data requested via the SQL query script received via network interface 132 to be retrieved from data storage devices 106.1-106.N” and “SQL scripts may include a number of search parameters to use to retrieve data from data storage devices 106.1-106.N, which act as filters when applied to the data, returning only results that match the SQL script parameters” and “any other suitable parameter that may be used to search data stored in data storage devices 106.1-106.N”);
dynamically generate a script (Debo: at least Col. 6 Lines 30-34; “SQL query scripts are generated as needed, or “on the fly” by database query devices 102 and/or database administrator devices 104, these SQL scripts are typically referred to as “ad-hoc” SQL scripts”) based on contents of the query (Debo: at least Col. 16 Lines 54-56 & 62-67, Col. 17 Lines 1-2; “screenshot 250 may be displayed to a user upon a user selecting a query from portion 204 of screenshot 200” and “to allow a user to enter and/or edit SQL query scripts. Portion 254 may include any suitable graphic, information, label, prompt, etc., to indicate one or more parameters that may be used to create the SQL script entered in portion 252. Portion 256 may include any suitable graphic, information, label, prompt, etc., to allow a user to build and/or update SQL script queries”; Col. 17 Lines 6-12 further disclose “a user may enter and/or edit script text using a suitable input device (e.g., a keyboard) as shown in FIG. 2B. Additionally or alternatively, a user may utilize the SQL parameters in portion 254. In other words, a user may select the desired search parameters from portion 254 instead of manually typing these into portion 252”), optimizing the script from a template to include only features necessary to satisfy the query (Debo: at least Col. 11 Lines 40-45; “optimization of the SQL script may be based upon various factors that may be customized by a user (e.g., a database administrator) in accordance with the particular needs or characteristics of a company hosting the data stored in data storage devices 1016.1-106.N”);
distribute the generated script horizontally to a plurality of computing devices that will execute the query (Debo: at least Col. 8 Lines 20-21 and Col. 17 Lines 37-38; “central data governance server 108 may include a host processor 114” and “upon the SQL script being built, the SQL query script may be sent to central data access server 108”; Col. 8 Lines 35-38 & 41-45 further disclose “host processor 114 may be configured to receive ad-hoc SQL scripts via network interface 132 that were sent from another device, such as database query devices 102” and “for example, if an SQL script is received via network interface 132 corresponding to a database query, then host processor 114 may execute the query SQL script to retrieve data in accordance with the SQL query script parameters”; Col. 12 Lines 58-62 also disclose “when an SQL script is received and/or executed, a target data storage device 106.1-106.N to be searched when host processor 114 executes the SQL script, a number of data storage devices 106.1-106.N to be searched, etc.”; Col. 13 Lines 50-52 also disclose “processor 114 may cause network interface 132 to send saved SQL scripts to database query devices 102”; Col. 15 Lines 28-32 explains that “processors 114” and “central data access server 108 may be distributed among a plurality of servers in an arrangement known as “cloud computing,” and “the data stored in data storage devices 106.1-106.N and the configuration database 130 may be distributed among a plurality of data storage devices”) by calling the generated script on each of the files to be searched (Debo: at least Col. 4 Line 65 – Col. 5 Line 3, Col. 5 Lines 59-61; “… taking certain actions upon the collections of data 110.1-110.N in accordance with the execution of a received SQL script. The actions may include, for example, performing data queries and/or performing alterations on the collections of data 110.1-110.N” and “Central data access server 108 may query, scan, or otherwise interact with the collections of data 110.1-110.N via languages, protocols, scripts, etc.”; Col. 8 Lines 39-45; “Host processor 114 may process the received ad-hoc SQL script and perform a corresponding action upon its execution. For example, if an SQL script is received via network interface 132 corresponding to a database query, then host processor 114 may execute the query SQL script to retrieve data in accordance with the SQL query script parameters”; Col. 12 Lines 58-62 also disclose “when an SQL script is received and/or executed, a target data storage device 106.1-106.N to be searched when host processor 114 executes the SQL script, a number of data storage devices 106.1-106.N to be searched, etc.”; Col. 6 Lines 42-4; further disclose “once central data access server 108 receives the SQL query script from database administrator devices 104, central data access server 108 in turn may retrieve data stored in one or more of data storage devices 106.1-106.N”);
store output (Debo: at least Col. 8 Lines 46-49; “send the data retrieved from one or more data storage devices 106.1-106.N, as a result of the executed SQL script, back to the querying device”; Col. 11 Lines 46-49 also disclose “SQL scripts may include a number of search parameters to use to retrieve data from data storage devices 106.1-106.N, which act as filters when applied to the data, returning only results that match the SQL script parameters”).
Debo does not explicitly disclose, but Echeverria discloses said query comprising a time window of files to be searched (Echeverria: at least Col. 16 Lines 18-20 & 23-28; “each bucket can include raw machine data associated with a time stamp and additional information about the data or bucket” and “bucket data and information about the bucket data is stored in one or more files” and “… stored in respective files in or associated with a bucket. In certain cases, the group of files can be associated together to form the bucket”; Col. 39 Lines 23-26 & 30-32 further disclose “search manager 514 that has parsed a query to identify the following filter criteria that is used to identify the data to be processed: time range: past hour, partition: sales, tenant: ABC, Inc., keyword: Error” and “identify buckets associated with the sales partition and the tenant ABC, Inc. and that include data from the past hour”; Col. 50 Lines 55-60 also disclose “each bucket can include one or more files … raw machine data files” and “each bucket can store events including raw machine data associated with a timestamp”; Col. 52 Line 67 – Col. 53 Line 5 further disclose “provide the data store catalog 220 with at least a portion of the query or one or more filter criteria associated with the query. In response, the data store catalog 220 can provide the query system 214 with an identification of buckets that store data that satisfies at least a portion of the query”; Col. 80 Lines 3-10 further disclose “in response to the request from the search head 504, the data store catalog 220 can be used to identify and return identifiers of buckets in common storage 216 and/or location information of data in common storage 216 that satisfy at least a portion of the query or at least some filter criteria (e.g., buckets associated with an identified tenant or partition or that satisfy an identified time range, etc.)”) and one or more output fields to track over a period of time (Echeverria: at least Col. 120 Lines 17-20 & 52-62; “summarization table can be populated by running a periodic query that scans a set of events to find instances of a specific field-value combination, or alternatively instances of all field-value combinations for a specific field” and “each of these entries keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field. For example, an example entry in an inverted index can keep track of occurrences of the value “94107” in a “ZIP code” field of a set of events and the entry includes references to all of the events that contain the value “94107” in the ZIP code field. Creating the inverted index data structure avoids needing to incur the computational overhead each time a statistical query needs to be run on a frequently encountered field-value pair”; Col. 125 Lines 24-25 & 36-42 also disclose “a query is received” and “each of the entries in an inverted index keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field. In order to expedite queries, in some embodiments, the query system 214 employs the inverted index separate from the raw record data store to generate responses to the received queries”; note: fields output to table) and to generate one or more summary statistics of the one or more output fields (Echeverria: at least Col. 119 Lines 6-11 & 61-63; “modifies search query 3202 by substituting “stats” (create aggregate statistics over results sets received from the search nodes 506 at the search head 504) with “prestats” (create statistics by the search node 506 from local results set) to produce search query 3204” and “… entry in the summarization table to count instances of the specific value in the field”; Col. 122 Lines 52-53 also disclose “… entry in the summarization table to count instances of the specific value in the field”; Col. 107 Lines 14-15 & 25-35 also discloses “query can start with a search command and one or more corresponding search terms at the beginning of the pipeline” and “for example, commands may be used to filter unwanted information out of the results, extract more information, evaluate field values, calculate statistics, reorder the results, create an alert, create summary of the results, or perform some type of aggregation function. In some embodiments, the summary can include a graph, chart, metric, or other visualization of the data. An aggregation function can include analysis or calculations to return an aggregate value, such as an average value, a sum, a maximum value, a root mean square, statistical values”), and in response to receiving a second instance of the query at a later time, re-provide updated output to the query and the one or more summary statistics (Echeverria: at least Col. 120 Lines 17-18 and 20-24; “summarization table can be populated by running a periodic query” and “periodic query can be initiated by a user, or can be scheduled to occur automatically at specific time intervals. A periodic query can also be automatically launched in response to a query that asks for a specific field-value combination”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Echeverria’s feature of a query comprising a time window of files to be searched (Echeverria: at least Col. 16 Lines 18-20 & 23-28) and one or more output fields to track over a period of time (Echeverria: at least Col. 120 Lines 17-20 & 52-62, Col. 125 Lines 24-25 & 36-42) and to generate one or more summary statistics of the one or more output fields (Echeverria: at least Col. 107 Lines 14-15 & 25-35, Col. 119 Lines 6-11 & 61-63, Col. 122 Lines 52-53), and in response to receiving a second instance of the query at a later time, re-provide updated output to the query and the one or more summary statistics (Echeverria: at least Col. 120 Lines 17-18 and 20-24) with Debo’s system.
The suggestion/motivation for doing so would have been to enhance “real-time data exploration through elimination of duplicate query fragments in complex database queries” and “remove duplicate subqueries, and thereby avoids executing redundant query operations” (Echeverria: at least Col. 16 Lines 18-20 & 23-28).
Debo and Echeverria do not explicitly disclose, but Osborn discloses in response to receiving a second instance of the query at a later time, re-provide updated output to the query and the one or more summary statistics (Osborn: at least Col. 7 Lines 7-16; “cost estimate 44 and result set 45 for the present query 40” and “recorded estimated costs and result sets of past queries stored in the query statistics cache 48” and “if an exact match is found between the present query 40 and a recorded past query 58 stored in the cache 48”), and provide a comparison between the updated output and one or more historical summary statistics of past queries (Osborn: at least Col. 6 Lines 45-59; “present query” and “searching out those past queries having the same or similar estimated costs for accessing the same, or similar, tables and items in the database located in a query statistics cache 48”; Col. 7 Lines 7-10 further disclose “the QPP module 46 compares 80 the cost estimate 44 and result set 45 for the present query 40 to the recorded estimated costs and result sets of past queries stored in the query statistics cache 48”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Osborn’s feature of in response to receiving a second instance of the query at a later time, re-provide updated output to the query and the one or more summary statistics (Osborn: at least Col. 7 Lines 7-16), and provide a comparison between the updated output and one or more historical summary statistics of past queries (Osborn: at least Col. 6 Lines 45-59; Col. 7 Lines 7-10) with the system taught by Debo and Echeverria.
The suggestion/motivation for doing so would have been to estimate “response times for database queries” (Osborn: at least Col. 1 Lines 8-9).
Claim 11 (a system claim) corresponds in scope to Claim 1, and is similarly rejected.
As to Claim 2, Debo, Echeverria and Osborn teach the system of claim 1, wherein the comparison includes data on a relationship between output for the one or more output fields in the second instance of the query and the one or more summary statistics (Echeverria: at least Col. 120 Lines 17-20 & 52-62; “summarization table can be populated by running a periodic query that scans a set of events to find instances of a specific field-value combination, or alternatively instances of all field-value combinations for a specific field” and “each of these entries keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field. For example, an example entry in an inverted index can keep track of occurrences of the value “94107” in a “ZIP code” field of a set of events and the entry includes references to all of the events that contain the value “94107” in the ZIP code field. Creating the inverted index data structure avoids needing to incur the computational overhead each time a statistical query needs to be run on a frequently encountered field-value pair”; Col. 122 Lines 52-53 also disclose “… entry in the summarization table to count instances of the specific value in the field”; Col. 125 Lines 24-25 & 36-39 also disclose “a query is received” and “each of the entries in an inverted index keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field”; note: looking for specific field and value include comparison).
Claim 12 (a system claim) corresponds in scope to Claim 2, and is similarly rejected.
As to Claim 7, Debo, Echeverria and Osborn teach the system of claim 1, wherein, in response to receiving a third instance of the query, whether before or after the second instance of the query, and further in response to determining that a count of one or more entries in a data structure storing output for the period of time is less than a predetermined threshold for sample size, the one or more summary statistics are not calculated (Echeverria: at least Col. 123 Lines 10-20; “a periodic query can be initiated by a user, or can be scheduled to occur automatically at specific time intervals. A periodic query can also be automatically launched in response to a query that asks for a specific field-value combination. In some embodiments, if summarization information is absent from a search node 506 that includes responsive event records, further actions may be taken, such as, the summarization information may generated on the fly, warnings may be provided the user, the collection query operation may be halted, the absence of summarization information may be ignored, or the like, or combination thereof”).
Claim 17 (a system claim) corresponds in scope to Claim 7, and is similarly rejected.
Claims 3-4 and 13-14 are rejected under U.S.C 103 as being unpatentable over US Patent 11,169,997 by Debo et al. (“Debo”) in view of US Patent 10,761,813 by Echeverria et al. (“Echeverria”), and further in view of US Patent 6,026,391 by Osborn et al. (“Osborn”), and further in view of 2019/0102553 by Herwadkar et al. (“Herwadkar”).
As to Claim 3, Debo, Echeverria and Osborn teach the system of claim 2, wherein the one or more summary statistics include a standard deviation (Echeverria: at least Col. 43 Lines 55-59; “if the query includes a command that operates on a result set, or a partial result set, e.g., a stats command (e.g., a command that calculates one or more aggregate statistics over the results set, e.g., average, count, or standard deviation, as examples) …”).
Debo, Echeverria and Osborn do not explicitly disclose but Herwadkar discloses wherein the relationship is a z-score for each of the one or more output fields in the second instance of the query (Herwadkar: at least ¶¶0134-0135, 0151; “extracted value for the feature attributes” and “training process further comprises generating or updating the distribution model for each feature attribute” and “histogram may then track the distribution of values for the corresponding feature” and “attribute values extracted from query”; ¶0153 further discloses “the attribute value in the query is also classified as an outlier” and “outlier may be determined based on a probability cutoff (such as a Z-score cutoff, standard deviation threshold, or a percent area under a histogram or cumulative distribution curve)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Herwadkar’s features of wherein the relationship is a z-score for each of the one or more output fields in the second instance of the query (Herwadkar: at least ¶¶0134-0135, 0151, 0153) with the system disclosed by Debo, Echeverria and Osborn.
The suggestion/motivation for doing so would have been to detect "an anomaly in queries of a relational database” (Herwadkar: at least Abstract).
Claim 13 (a system claim) corresponds in scope to Claim 3, and is similarly rejected.
As to Claim 4, Debo, Echeverria and Osborn teach the system of claim 2.
Debo, Echeverria and Osborn do not explicitly disclose but Herwadkar discloses wherein the one or more summary statistics include a set of quantiles and wherein the relationship is which quantile each of the one or more output fields in the second instance of the query falls into (Herwadkar: at least ¶¶0134-0135, 0151; “extracted value for the feature attributes” and “training process further comprises generating or updating the distribution model for each feature attribute” and “histogram may then track the distribution of values for the corresponding feature” and “attribute values extracted from query”; ¶0153 further discloses “the attribute value in the query is also classified as an outlier” and “an outlier may be determined based on a probability cutoff (such as a Z-score cutoff, standard deviation threshold, or a percent area under a histogram or cumulative distribution curve). Thus, values that are not frequently observed may be flagged as outliers, whereas common values are classified as normal”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Herwadkar’s features of wherein the one or more summary statistics include a set of quantiles and wherein the relationship is which quantile each of the one or more output fields in the second instance of the query falls into (Herwadkar: at least ¶¶0134-0135, 0151, 0153) with the system disclosed by Debo, Echeverria and Osborn.
The suggestion/motivation for doing so would have been to detect "an anomaly in queries of a relational database” (Herwadkar: at least Abstract).
Claim 14 (a system claim) corresponds in scope to Claim 4, and is similarly rejected.
Claims 5-6 and 15-16 are rejected under U.S.C 103 as being unpatentable over US Patent 11,169,997 by Debo et al. (“Debo”) in view of US Patent 10,761,813 by Echeverria et al. (“Echeverria”), and further in view of US Patent 6,026,391 by Osborn et al. (“Osborn”), and further in view of US PGPUB 2014/0188928 by Singh et al. (“Singh”).
As to Claim 5, Debo, Echeverria and Osborn teach the system of claim 1, wherein one or more expected entries in a data structure storing output for the period of time is absent or not yet calculated (Echeverria: at least Col. 120 Lines 17-20 & 52-62; “summarization table can be populated by running a periodic query that scans a set of events to find instances of a specific field-value combination, or alternatively instances of all field-value combinations for a specific field” and “each of these entries keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field. For example, an example entry in an inverted index can keep track of occurrences of the value “94107” in a “ZIP code” field of a set of events and the entry includes references to all of the events that contain the value “94107” in the ZIP code field. Creating the inverted index data structure avoids needing to incur the computational overhead each time a statistical query needs to be run on a frequently encountered field-value pair”).
Debo, Echeverria and Osborn do not explicitly disclose but Singh discloses wherein the one or more expected entries are generated and backfilled with default values before the one or more summary statistics are calculated (Singh: at least ¶¶0004, 0021; “inference results, comprising probability distributions each for an individual table cell, are used to fill missing data, highlight errors, and for other purposes” and “achieve inference using data in a relational database and to use the inference results to completing missing cells in tables of the relational database”; note: filling of values prior to any statistics calculation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Singh’s feature of wherein the one or more expected entries are generated and backfilled with default values before the one or more summary statistics are calculated (Singh: at least ¶¶0004, 0021) with the system disclosed by Debo, Echeverria and Osborn.
The suggestion/motivation for doing so would have been to enable “completion, error detection, error correction of data in relational databases, including completion of missing foreign key values, to facilitate understanding of data in relational databases, to highlight data that it would be useful to add to a relational database and for other applications” (Singh: at least ¶0004).
Claim 15 (a system claim) corresponds in scope to Claim 5, and is similarly rejected.
As to Claim 6, Debo, Echeverria, Osborn and Singh teach the system of claim 5, wherein an additional one or more expected entries in a data structure storing output for the period of time is absent or not yet calculated, but backfilling does not occur because a predetermined criterion for backfilling values is not met (Singh: at least ¶0037; “another option is to add new columns and/or rows to enable the inference results to be incorporated in the relational database”; note: criterion is whether or not results are to be incorporated in relational database).
Claim 16 (a system claim) corresponds in scope to Claim 6, and is similarly rejected.
Claims 8-9 and 18-19 are rejected under U.S.C 103 as being unpatentable over US Patent 11,169,997 by Debo et al. (“Debo”) in view of US Patent 10,761,813 by Echeverria et al. (“Echeverria”), and further in view of US Patent 6,026,391 by Osborn et al. (“Osborn”), and further in view of US PGPUB 2019/0243747 by Ross.
As to Claim 8, Debo, Echeverria and Osborn teach the system of claim 1.
Debo, Echeverria and Osborn do not explicitly disclose but Ross discloses wherein one or more entries in a data structure storing output for the period of time are older than a length of the period of time (Ross: at least ¶0091; “… with the oldest data point removed”), and are deleted before the one or more summary statistics are calculated (Ross: at least ¶0091; “calculates a new KPSS statistic based on the data used to calculate the KPSS statistic calculated immediately prior, but with the oldest data point removed, and the new data point added”; ¶0078 discloses stats “mean, median, sum, and so on”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ross’ features of wherein one or more entries in a data structure storing output for the period of time are older than a length of the period of time (Ross: at least ¶0091; “… with the oldest data point removed), and are deleted before the one or more summary statistics are calculated (Ross: at least ¶0091) with the system disclosed by Debo, Echeverria and Osborn.
The suggestion/motivation for doing so would have been to calculate “ … statistic on a rolling window, where the window of the time series shifts as new points are added” and allow “new points to be incorporated into the statistic, while the influence of old points is removed from the statistic” (Ross: at least ¶¶0007-0008).
Claim 18 (a system claim) corresponds in scope to Claim 8, and is similarly rejected.
As to Claim 9, Debo, Echeverria and Osborn teach the system of claim 1.
Debo, Echeverria and Osborn do not explicitly disclose but Ross discloses wherein entries in a data structure storing output for the period of time are more numerous than a predetermined threshold (Ross: at least ¶0008; “when the first n points are added to the window, followed by a streaming phase, during which new data is added and old data is discarded to maintain size n of the window”; ¶¶0083, 0085 further disclose “receives 530 data of different data streams. In an embodiment, the interface module 210 waits for a fixed interval of time, for example, 1 second or a few seconds and collects data received from different data streams. In an embodiment, the quantization module 240 performs quantization of the data for each incoming data stream for each time interval. Accordingly, data from each data stream is aggregated into a single value associated with the data stream for that time interval” and “new data streams are stored in the time series data store 260”), and oldest entries are deleted before the one or more summary statistics are calculated on only a set of entries as numerous as the predetermined threshold (Ross: at least ¶¶0091, 0097; “calculates a new KPSS statistic based on the data used to calculate the KPSS statistic calculated immediately prior, but with the oldest data point removed, and the new data point added” and “rolling window of data of size n”; ¶0078 discloses stats “mean, median, sum, and so on”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ross’ features of wherein entries in a data structure storing output for the period of time are more numerous than a predetermined threshold (Ross: at least ¶¶0008, 0083, 0085), and oldest entries are deleted before the one or more summary statistics are calculated on only a set of entries as numerous as the predetermined threshold (Ross: at least ¶¶0078, 0091, 0097) with the system disclosed by Debo, Echeverria and Osborn.
The suggestion/motivation for doing so would have been to calculate “ … statistic on a rolling window, where the window of the time series shifts as new points are added” and allow “new points to be incorporated into the statistic, while the influence of old points is removed from the statistic” (Ross: at least ¶¶0007-0008).
Claim 19 (a system claim) corresponds in scope to Claim 9, and is similarly rejected.
Claims 10 and 20 are rejected under U.S.C 103 as being unpatentable over US Patent 11,169,997 by Debo et al. (“Debo”) in view of US Patent 10,761,813 by Echeverria et al. (“Echeverria”), and further in view of US Patent 6,026,391 by Osborn et al. (“Osborn”), and further in view of UG PGPUB 2006/0224579 by Zheng et al. (“Zheng”).
As to Claim 10, Debo, Echeverria and Osborn teach the system of claim 1.
Debo, Echeverria and Osborn do not explicitly disclose, but Zheng discloses wherein a machine learning classifier is trained on the output (Zheng: at least ¶0034; “to train the relevance classifier 300, a set of data is employed with both implicit feedback and explicit feedback at result level (each entry in the data set represent a result of a search)(can link to multiple interactions to the result from a user in a single search session, or a visit to an asset from a user browsing)”) and used to classify output generated after the second instance of the query (Zheng: at least ¶0024; “apply the relevance classifier on the clicks/results that users didn't provide explicit feedback to infer their satisfactions”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Zheng’s feature of wherein a machine learning classifier is trained on the output (Zheng: at least ¶0034; “to train the relevance classifier 300, a set of data is employed with both implicit feedback and explicit feedback at result level (each entry in the data set represent a result of a search)(can link to multiple interactions to the result from a user in a single search session, or a visit to an asset from a user browsing)”) and used to classify output generated after the second instance of the query (Zheng: at least ¶0024) with the system disclosed by Debo, Echeverria and Osborn.
The suggestion/motivation for doing so would have been to “automatically learn data relevance from past search activities and apply such learning to facilitate future search activities” (Zheng: at least Abstract).
Claim 20 (a system claim) corresponds in scope to Claim 10, and is similarly rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Huen Wong whose telephone number is (571) 270 3426. The examiner can normally be reached on Monday - Friday (10:30AM EST -6:30PM EST). If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Charles Rones can be reached on (571) 272-4085. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300 for regular communications and after final communications.
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/H.W/Examiner, Art Unit 2168 09 March 2026
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168