IP Library › Granted Patent US 12,332,892
Granted Patent B2
US 12,332,892 · App. 18/741,889 · Granted Jun 17, 2025

Power based query processing system and methods for use therewith

Inventors: S. Christopher Gladwin (Chicago, IL); Andrew D. Baptist (Mt. Pleasant, WI); George Kondiles (Chicago, IL); Jason Arnold (Chicago, IL)
Assignee: Ocient Holdings LLC
G06F16/24545
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,332,892
App. No.
18/741,889
Granted
Jun 17, 2025
Kind
B2
Abstract

A database system operates by: determining a query pricing scheme, wherein the query pricing scheme indicates a record valuation scheme; receiving a query request from a requesting entity that indicates access to a subset of a plurality of records in a database system; generating query cost data based on the subset of the plurality of records by utilizing the query pricing scheme and a power consumption, wherein generating the query cost data includes: calculating plurality of record valuations by calculating a record valuation for each of the plurality of records in the subset of the plurality of records, and aggregating over the plurality of record valuations to generate a query price total indicated in the query cost data; and transmitting the query cost data to the requesting entity.

Claims (43)

1. A method for execution by a query processing system, the method comprising:

determining a plurality of query pricing schemes, wherein each of the plurality of query pricing schemes corresponds to one of a plurality of data providers;

receiving a query request from a requesting entity that indicates access to a subset of a plurality of records in a database system;

determining the subset of the plurality of records corresponds to a first one of the plurality of data providers,

determining a query pricing scheme of the plurality of query pricing schemes, wherein the query pricing scheme indicates a record valuation scheme and wherein determining the query pricing scheme includes selecting the query pricing scheme from the plurality of query pricing schemes based on determining the query pricing scheme corresponds to the first one of the plurality of data providers;

generating query cost data based on the subset of the plurality of records by utilizing the query pricing scheme and a power consumption, wherein generating the query cost data includes:

calculating plurality of record valuations by calculating a record valuation for each of the plurality of records in the subset of the plurality of records, and

aggregating over the plurality of record valuations to generate a query price total indicated in the query cost data; and

transmitting the query cost data to the requesting entity.

2. The method of claim 1 , wherein determining the plurality of query pricing schemes includes receiving the plurality of query pricing schemes from a plurality of provider devices corresponding to the plurality of data providers, wherein each of the plurality of query pricing schemes was generated based on user input in response to a prompt displayed by a corresponding one of the plurality of provider devices via a graphical user interface.

3. The method of claim 1 , further comprising:

determining a field valuation for each one of a set of fields of the database system; and

determining at least one field included in the subset of the plurality of records;

wherein the query cost data is generated based on the field valuation of the at least one field.

4. The method of claim 1 , further comprising:

determining a field grouping valuation for each of a set of field groupings within a set of fields of the database system; and

determining a field grouping included in the subset of the plurality of records;

wherein the query cost data is generated based on the field grouping valuation of the field grouping.

5. The method of claim 1 , further comprising:

determining a temporal-based valuation of the subset of the plurality of records based on age timestamps of records included in the subset of the plurality of records;

wherein the query cost data is generated based on the temporal-based valuation of the subset of the plurality of records.

6. The method of claim 5 , wherein determining the temporal-based valuation includes calculating a temporal span across all of the plurality of records, wherein the temporal-based valuation is an increasing function of the temporal span.

7. The method of claim 5 , wherein the plurality of records were generated based on being collected by a data collection device.

8. The method of claim 7 , wherein determining the temporal-based valuation includes calculating a shortest temporal span between any pair of records in the plurality of records, and wherein the temporal-based valuation is a decreasing function of the shortest temporal span.

9. The method of claim 1 , wherein the record valuation is based on an age of the each of the plurality of records.

10. The method of claim 1 , wherein the record valuation is based on a level of data transformation utilized to generate the each of the plurality of records.

11. The method of claim 1 , wherein the query pricing scheme indicates a storage location-based valuation scheme, wherein determining the query cost data includes determining at least one location where the subset of the plurality of records is stored, and wherein the query cost data is generated based on the at least one location.

12. The method of claim 11 , wherein determining the query cost data included determining a number of geographic boundaries corresponding to the at least one location, where the query cost data is an increasing function of the number of geographic boundaries.

13. The method of claim 12 , wherein determining the query cost data further includes determining a location of the requesting entity, and where the query cost data is generated based on a difference between the location of the requesting entity and the at least one location.

14. The method of claim 1 , further comprising accessing usage data to determine at least one of: a first subset of records that have already been accessed, or a second subset of records that are new.

15. The method of claim 1 , further comprising generating billing data for the requesting entity based on the query cost data.

16. The method of claim 1 , further comprising facilitating a payment of a query price value of the query cost data by the requesting entity.

17. A query processing system of an analytics system includes:

at least one processor; and

a memory that stores operational instructions that, when executed by the at least one processor, cause the query processing system to perform operations that include:

receiving a query request from a requesting entity that indicates access to a subset of a plurality of records in a database system;

determining a plurality of query pricing schemes, wherein each of the plurality of query pricing schemes corresponds to one of a plurality of data providers;

determining the subset of the plurality of records corresponds to a first one of the plurality of data providers,

determining a query pricing scheme of the plurality of query pricing schemes, wherein the query pricing scheme indicates a record valuation scheme and wherein determining the query pricing scheme includes selecting the query pricing scheme from the plurality of query pricing schemes based on determining the query pricing scheme corresponds to the first one of the plurality of data providers;

determining a temporal-based valuation of the subset of the plurality of records based on age timestamps of records included in the subset of the plurality of records;

generating query cost data based on the subset of the plurality of records by utilizing the query pricing scheme and a power consumption, wherein the query cost data is generated based on the temporal-based valuation of the subset of the plurality of records; and

transmitting the query cost data to the requesting entity.

18. The query processing system of claim 17 , wherein determining the plurality of query pricing schemes includes receiving the plurality of query pricing schemes from a plurality of provider devices corresponding to the plurality of data providers, wherein each of the plurality of query pricing schemes was generated based on user input in response to a prompt displayed by a corresponding one of the plurality of provider devices via a graphical user interface.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 13, 2024
From: GLADWIN, S. CHRISTOPHER; BAPTIST, ANDREW D.; KONDILES, GEORGE; ARNOLD, JASON
To: OCIENT HOLDINGS LLC
Reel/Frame 067722/0010 →
Continuity (5)
Continuation 18532294 · Dec 7, 2023
Continuation 18165029 · Feb 6, 2023
Continuation 17150415 · Jan 15, 2021
Continuation 16665571 · Oct 28, 2019
Related Publication 20240330292A1 · Oct 3, 2024
References Cited (63)
US 5548770A · Bridges · 1996 [cited by applicant]
US 5778354A · Leslie · 1998 [cited by applicant]
US 6230200B1 · Forecast · 2001 [cited by applicant]
US 6633772B2 · Ford · 2003 [cited by applicant]
US 7499907B2 · Brown · 2009 [cited by applicant]
US 7908242B1 · Achanta · 2011 [cited by applicant]
US 8903803B1 · Aly · 2014 [cited by applicant]
US 10133775B1 · Ramalingam · 2018 [cited by applicant]
US 20010051949A1 · Carey · 2001 [cited by applicant]
US 20020032676A1 · Reiner · 2002 [cited by applicant]
US 20030171998A1 · Pujar · 2003 [cited by applicant]
US 20040162853A1 · Brodersen · 2004 [cited by applicant]
US 20080133456A1 · Richards · 2008 [cited by applicant]
US 20090063893A1 · Bagepalli · 2009 [cited by applicant]
US 20090183167A1 · Kupferschmidt · 2009 [cited by applicant]
US 20100082577A1 · Mirchandani · 2010 [cited by applicant]
US 20100241646A1 · Friedman · 2010 [cited by applicant]
US 20100274983A1 · Murphy · 2010 [cited by applicant]
US 20100312756A1 · Zhang · 2010 [cited by applicant]
US 20110219169A1 · Zhang · 2011 [cited by applicant]
US 20110295833A1 · Narasayya · 2011 [cited by applicant]
US 20110320434A1 · Carston · 2011 [cited by examiner]
US 20120109888A1 · Zhang · 2012 [cited by applicant]
US 20120151118A1 · Flynn · 2012 [cited by applicant]
US 20120185866A1 · Couvee · 2012 [cited by applicant]
US 20120254252A1 · Jin · 2012 [cited by applicant]
US 20120311246A1 · Mcwilliams · 2012 [cited by applicant]
US 20120320434A1 · Takeda · 2012 [cited by examiner]
US 20130246336A1 · Ahuja et al. · 2013 [cited by applicant]
US 20130332484A1 · Gajic · 2013 [cited by applicant]
US 20140047095A1 · Breternitz · 2014 [cited by applicant]
US 20140136510A1 · Parkkinen · 2014 [cited by applicant]
US 20140188841A1 · Sun · 2014 [cited by applicant]
US 20150205607A1 · Lindholm · 2015 [cited by applicant]
US 20150244804A1 · Warfield · 2015 [cited by applicant]
US 20150248366A1 · Bergsten · 2015 [cited by applicant]
US 20150293966A1 · Cai · 2015 [cited by applicant]
US 20150310045A1 · Konik · 2015 [cited by applicant]
US 20160034547A1 · Lerios · 2016 [cited by applicant]
US 20170353395A1 · Richardson et al. · 2017 [cited by applicant]
US 20180018727A1 · Abuelsaad et al. · 2018 [cited by applicant]
US 20180157711A1 · Lee · 2018 [cited by applicant]
US 20180336639A1 · Dziabiak et al. · 2018 [cited by applicant]
JP 2012155358A · 2012 [cited by applicant]
A new high performance fabric for HPC, Michael Feldman, May 2016, Intersect360 Research. [cited by applicant]
Alechina, N. (2006-2007). B-Trees. School of Computer Science, University of Nottingham, http://www.cs.nott.ac.uk/˜psznza/G5BADS06/lecture13-print.pdf. 41 pages. [cited by applicant]
Amazon DynamoDB: ten things you really should know, Nov. 13, 2015, Chandan Patra, http://cloudacademy. .com/blog/amazon-dynamodb-ten-thing. [cited by applicant]
An Inside Look at Google BigQuery, by Kazunori Sato, Solutions Architect, Cloud Solutions team, Google Inc., 2012. [cited by applicant]
Anonymous; Database; Wikipedia; Sep. 22, 2019; 21 pgs [Retrieved from the Internet: https://en.wikipedia.org/w/index.php?title=Databases&oldid=917129682; retrieved on Jul. 14, 2023]. [cited by applicant]
Big Table, a NoSQL massively parallel table, Paul Krzyzanowski, Nov. 2011, https://www.cs.rutgers.edu/pxk/417/notes/contentlbigtable.html. [cited by applicant]
Distributed Systems, Fall2012, Mohsen Taheriyan, http://www-scf.usc.edu/-csci57212011Spring/presentations/Taheriyan.pptx. [cited by applicant]
European Patent Office; extended EP Search Report; Application No. 20881989.6; Jul. 27, 2023; 10 pgs. [cited by applicant]
International Searching Authority; International Search Report and Written Opinion; International Application No. PCT/US2017/054773; Feb. 13, 2018; 17 pgs. [cited by applicant]
International Searching Authority; International Search Report and Written Opinion; International Application No. PCT/US2017/054784; Dec. 28, 2017; 10 pgs. [cited by applicant]
International Searching Authority; International Search Report and Written Opinion; International Application No. PCT/US2017/066145; Mar. 5, 2018; 13 pgs. [cited by applicant]
International Searching Authority; International Search Report and Written Opinion; International Application No. PCT/US2017/066169; Mar. 6, 2018; 15 pgs. [cited by applicant]
International Searching Authority; International Search Report and Written Opinion; International Application No. PCT/US2018/025729; Jun. 27, 2018; 9 pgs. [cited by applicant]
International Searching Authority; International Search Report and Written Opinion; International Application No. PCT/US2018/034859; Oct. 30, 2018; 8 pgs. [cited by applicant]
International Searching Authority; International Search Report and Written Opinion; International Application No. PCT/US2020/056315; Feb. 9, 2021; 11 pgs. [cited by applicant]
MapReduce: Simplified Data Processing on Large Clusters, OSDI 2004, Jeffrey Dean and Sanjay Ghemawat, Google, Inc., 13 pgs. [cited by applicant]
Rodero-Merino, L.; Storage of Structured Data: Big Table and HBase, New Trends In Distributed Systems, MSc Software and Systems, Distributed Systems Laboratory; Oct. 17, 2012; 24 pages. [cited by applicant]
Step 2: Examine the data model and implementation details, 2016, Amazon Web Services, Inc., http://docs.aws.amazon.com/amazondynamodb/latestldeveloperguide!Ti . . . . [cited by applicant]
European Patent Office; Communication pursuant to Article 94(3) EPC; Application No. 20881989.6; Sep. 6, 2024; 8 pgs. [cited by applicant]