# US 12,216,630 B2 · Accumulation-based data object processing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,216,630 B2 - Document ID: US12216630B2 - Status: Granted - Kind: B2 - Application number: 18/596,187 - Filed: 2024-03-05 - Published: 2024-06-27 - Granted: 2025-02-04 - Earliest claimed: 2020-08-31 - Assignees: Chicago Mercantile Exchange Inc. - Applicants: Chicago Mercantile Exchange Inc. - Inventors: Paul Oglesby; Kalle Tomas Thurin; Carl Erik Thornberg - Web page: https://patentgrep.com/patent/US12216630B2 ## CPC - G06F 16/22: Indexing; Data structures therefor; Storage structures - G06Q 40/04: Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange - G06F 16/285: {Clustering or classification} - G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling - H03M 7/30: Compression (speech analysis-synthesis for redundancy reduction G10L19/00; for image communication H04N); Expansion; Suppression of unnecessary data, e.g. redundancy reduction - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C) ## Abstract A system implements data compression for a plurality of data objects each having a respective fixed data constituent and a variable data constituent. The data compression includes selecting a first subset of the fixed data constituents and a second subset of the variable data constituents. The second subset of the variable data constituents having an end date in common and event timing in common. The system compresses the first subset of the fixed data constituents and the second subset of the variable data constituents by defining a representative data object for the fixed data constituent subset and the variable data constituent subset. ## Claims ### Claim 1 A computer-implemented method for data compression for a plurality of data objects, wherein each data object of the plurality of data objects have a respective fixed data constituent and a respective variable data constituent, the method including: obtaining, by a processor, a historical component group comprising a historical component for at least each variable data constituent in a variable data constituent subset comprising the respective variable data constituents of a first subset of the plurality of data objects, each of the variable data constituents of the variable data constituent subset having an end date in common and an event timing in common; obtaining, by the processor, a net historical magnitude for the variable data constituent subset based on the historical component group; and compressing, by the processor, a fixed data constituent subset and the variable data constituent subset into a representative data object, the fixed data constituent subset comprising the respective fixed data constituents of a second subset of the plurality of data objects, the representative data object characterized by: a set of matched common properties including the end date and the event timing, the net historical magnitude for the variable data constituent subset, and current net magnitudes for the fixed data constituent subset and the variable data constituent subset. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein at least a first variable data constituent of the variable data constituent subset has a different start date than a second variable data constituent of the variable data constituent subset. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein obtaining the historical component group includes obtaining records of a series of data events since a start date for a first data object of the plurality of data objects, the first data object corresponding to a first variable data constituent of the variable data constituent subset. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein each data event in the series of data events establishes a rate magnitude. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein the rate magnitude includes an interest rate. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein obtaining the historical component group includes obtaining a representation of a data event occurring on a start date for a first data object of the plurality of data objects, the first data object corresponding to a first variable data constituent of the variable data constituent subset. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, wherein obtaining the representation includes obtaining an index magnitude. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, wherein the index magnitude includes an inflation index. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein compressing the fixed data constituent subset and the variable data constituent subset into the representative data object includes executing an optimization procedure to define a plurality of representative data objects for plurality of pairs of fixed and variable constituent subsets. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, further including obtaining a current net magnitude for the fixed data constituent subset by obtaining a current magnitude for at least each fixed data constituent in the fixed data constituent subset. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein obtaining the current magnitude for at least each fixed data constituent in the fixed data constituent subset includes obtaining each current magnitude based on a notional and a coupon rate for a corresponding fixed data constituent. ### Claim 12 Non-transitory machine-readable media configured to store instructions thereon, the instructions configured to, when executed, cause a processor to: for data compression for a plurality of data objects, wherein each data object of the plurality of data objects have a respective fixed data constituent and a respective variable data constituent: obtain a historical component group comprising a historical component for at least each variable data constituent in a variable data constituent subset comprising the respective variable data constituents of a first subset of the plurality of data objects, each of the variable data constituents of the variable data constituent subset having an end date in common and an event timing in common; obtain a net historical magnitude for the variable data constituent subset based on the historical component group; and compress a fixed data constituent subset and the variable data constituent subset into a representative data object, the fixed data constituent subset comprising the respective fixed data constituents of a second subset of the plurality of data objects, the representative data object characterized by: a set of matched common properties including the end date and the event timing, the net historical magnitude for the variable data constituent subset, and current net magnitudes for the fixed data constituent subset and the variable data constituent subset. ### Claim 13 (depends on 12) The non-transitory machine-readable media of claim 12, wherein at least a first variable data constituent of the variable data constituent subset has a different start date than a second variable data constituent of the variable data constituent subset. ### Claim 14 (depends on 12) The non-transitory machine-readable media of claim 12, wherein the instructions are further configured to cause the processor to obtain the historical component group by obtaining records of a series of data events since a start date for a first data object of the plurality of data objects, the first data object corresponding to a first variable data constituent of the variable data constituent subset. ### Claim 15 (depends on 14) The non-transitory machine-readable media of claim 14, wherein each data event in the series of data events establishes a rate magnitude. ### Claim 16 (depends on 15) The non-transitory machine-readable media of claim 15, wherein the rate magnitude includes an interest rate. ### Claim 17 (depends on 12) The non-transitory machine-readable media of claim 12, wherein the instructions are further configured to cause the processor to obtain the historical component group by obtaining a representation of a data event occurring on a start date for a first data object of the plurality of data objects, the first data object corresponding to a first variable data constituent of the variable data constituent subset. ### Claim 18 (depends on 17) The non-transitory machine-readable media of claim 17, wherein the instructions are further configured to cause the processor to obtain the representation by obtaining an index magnitude. ### Claim 19 (depends on 18) The non-transitory machine-readable media of claim 18, wherein the index magnitude includes an inflation index. ### Claim 20 A system for data compression for a plurality of data objects, wherein each data object of the plurality of data objects have a respective fixed data constituent and a respective variable data constituent: means for obtaining a historical component group comprising a historical component for at least each variable data constituent in a variable data constituent subset comprising the respective variable data constituents of a first subset of the plurality of data objects, each of the variable data constituents of the variable data constituent subset having an end date in common and an event timing in common; means for obtaining a net historical magnitude for the variable data constituent subset based on the historical component group; and means for compressing a fixed data constituent subset and the variable data constituent subset into a representative data object, the fixed data constituent subset comprising the respective fixed data constituents of a second subset of the plurality of data objects, the representative data object characterized by: a set of matched common properties including the end date and the event timing, the net historical magnitude for the variable data constituent subset, and current net magnitudes for the fixed data constituent subset and the variable data constituent subset.