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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Langi, 759 F.2d 887,225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937,214 USPQ 761 (CCPA 1982); In re Vogel, 422F.2d 438,164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CPR 1.321(c) or l.32l(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CPR l.32l(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CPR 1.111(a). For a reply to final Office action, see 37 CPR l.113(c). A request for reconsideration while not provided for in 37 CPR l.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, ref er to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected under 35 U.S.C. 101 on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent No. 12380126 (reference patent).
Although the claims at issue are not identical, they are not patentably distinct from each other because both are directed to a similar invention with similar limitations as demonstrated in the table below.
Instant Application Number 19/268,787
Reference - US Patent No. 12380126
A method, comprising:
receiving, by a computing system, a set of values from data to be transmitted to a target system;
determining, by the computing system, a first plurality of bounded value sets based at least in part on a first subset of the set of values and a second plurality of bounded value sets based at least in part on a second subset of the set of values;
determining, by the computing system, a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets;
determining, by the computing system, a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets; and
partitioning, by the computing system, the data based at least in part on the first deviation value and the second deviation value; and
transmitting, by the computing system, the partitioned data to the target system.
1. A method, comprising:
receiving, by a computing device and from a source system, a first set of values from data to be transmitted to a target system and a second set of values from the data to be transmitted to the target system;
determining, by the computing device, a partition boundary value for the data based at least in part on:
determining, by the computing device, a first plurality of bounded value sets based at least in part on the first set of values and a second plurality of bounded value sets based at least in part on the second set of values;
determining, by the computing device, a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets;
determining, by the computing device, a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets; and determining, by the computing device, a first partition boundary value based at least in part on the first deviation value and the second deviation value;
partitioning, by the computing device, the data based at least in part on the first partition boundary value; and
transmitting, by the computing device, the partitioned data to the target system.
2. The method of claim 1, wherein the method further comprises: determining a partition boundary value based at least in part on the first deviation value and the second deviation value, wherein the data is partitioned based at least in part on the partition boundary value.
2. The method of claim 1, wherein determining a first deviation value based at least in part on the first plurality of bounded value sets and a second deviation value based at least in part on the second plurality of bounded value sets comprises: determining a first average value of a first value of a first bounded value set of the first plurality of bounded value sets and a second value of a second bounded value set of the second plurality of bounded value sets, the first value corresponding to a first candidate partition boundary value; determining a second average value of a third value of a third set of bounded values of the first plurality of bounded value sets and a fourth value of a fourth set bounded values of the second plurality of bounded values, the third value corresponding to a second candidate partition boundary value; determining the first deviation value of the first average value from the first value; and determining the second deviation value of the second average value from the third value.
3. The method of claim 1, wherein the method further comprises: determining a first value representing an average of a second value of the first plurality of bounded value sets and a third value of the second plurality of bounded value sets, wherein the first deviation value is based at least in part on the first value.
3. The method of claim 2, wherein the method further comprises: receiving a third set of values from the data to be transmitted to the target system; determining whether the values of the third set of values are uniformly distributed across a first partition generated using the first partition boundary value and across a second partition generated using the second partition boundary value; and determining whether to partition the data using the first candidate partition boundary value or the second candidate partition boundary value based at least in part on the determination of uniform distribution.
4. The method of claim 3, wherein the method further comprises: determining a difference between the first value and the second value; and determining an absolute value of the difference of the first value and the second value, wherein the first deviation value is the absolute value.
4. The method of claim 3, wherein determining whether the values of the third set of values are uniformly distributed across the first partition and across the second partition is based at least in part on determining a first number of values distributed across the first partition is greater by a threshold margin than a second number of values distributed across the second partition.
5. The method of claim 3, wherein the method further comprises: determining a fourth value representing an average of a fifth value of the first plurality of bounded value sets and a sixth value of the second plurality of bounded value sets, wherein the second deviation value is based at least in part on the fourth value
5. The method of claim 4, wherein comparing the first deviation value to the second deviation value comprises determining whether the first deviation value is greater than the second deviation value.
6.The method of claim 1, wherein the method further comprises: receiving a second set of values from the data to be transmitted to the target system; determining whether the second set of values is uniformly distributed; and determining whether to partition the data in accordance with a determination that the second set of values is uniformly distributed.
6. The method of claim 2, wherein the method further comprises: determining a difference between the first average value and the first value; determining an absolute value of the difference of the first average value and the first value, wherein the first deviation value is the absolute value; determining a difference between the second average value and the third value; and determining an absolute value of the difference of the second average value and the third value, wherein the second deviation value is the absolute value.
7. The method of claim 1, wherein the method further comprises: determining a midpoint of the first value of the first subset of the set of values and a second value of the second subset of the set of values; and determining the first plurality of bounded value sets and the second plurality of bounded value sets based at least in part on the midpoint.
7. The method of claim 1, wherein the method further comprises: identifying a fifth value of the first set of values and a sixth value of the second set of values;
determining a midpoint of the fifth value and the sixth value; and determining the first plurality of bounded value sets and the second plurality of bounded value sets based at least in part on the midpoint, wherein the first plurality of bounded values is determined by adjusting the fifth value based at least in part on the midpoint, and wherein the second plurality of bounded values is determined by adjusting the sixth value based at least in part on the midpoint.
8. A computing system, comprising: one or more processors; and one or more computer-readable media having stored thereon instructions that, when executed, configure the one or more processors to:
receive a set of values from data to be transmitted to a target system;
determine a first plurality of bounded value sets based at least in part on a first subset of the set of values and a second plurality of bounded value sets based at least in part on a second subset of the set of values;
determine a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets;
determine a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets; and
partition the data based at least in part on the first deviation value and the second deviation value; and
transmit the partitioned data to the target system.
8. A computing device, comprising: a processor; and a non-transitory computer-readable medium including instructions that, when executed by the processor, cause the processor to perform operations comprising:
receiving, from a source system, a first set of values from data to be transmitted to a target system and a second set of values from the data to be transmitted to the target system; determining a partition boundary value for the data based at least in part on: determining a first plurality of bounded value sets based at least in part on the first set of values and a second plurality of bounded value sets based at least in part on the second set of values; determining a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets;
determining a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets; and determining a first partition boundary value based at least in part on the first deviation value and the second deviation value; partitioning the data based at least in part on the first partition boundary value; and
transmitting the partitioned data to the target system
9. The computing system of claim 8, wherein the instructions that, when executed, further configure the one or more processors to: determine a partition boundary value based at least in part on the first deviation value and the second deviation value, wherein the data is partitioned based at least in part on the partition boundary value
computing device of claim 8, wherein determining a first deviation value based at least in part on the first plurality of bounded value sets and a second deviation value based at least in part on the second plurality of bounded value sets comprises: determining a first average value of a first value of a first bounded value set of the first plurality of bounded value sets and a second value of a second bounded value set of the second plurality of bounded value sets, the first value corresponding to a first candidate partition boundary value; determining a second average value of a third value of a third set of bounded values of the first plurality of bounded value sets and a fourth value of a fourth set bounded values of the second plurality of bounded values, the third value corresponding to a second candidate partition boundary value; determining the first deviation value of the first average value from the first value; and determining the second deviation value of the second average value from the third value.
10. The computing system of claim 8, wherein the instructions that, when executed, further configure the one or more processors to: determine a first value representing an average of a second value of the first plurality of bounded value sets and a third value of the second plurality of bounded value sets, wherein the first deviation value is based at least in part on the first value.
10. The computing device of claim 9, wherein the instructions that, when executed by the processor, further cause the processor to perform operations comprising: receiving a third set of values from the data to be transmitted to the target system; determining whether the values of the third set of values are uniformly distributed across a first partition generated using the first partition boundary value and across a second partition generated using the second partition boundary value; and determining whether to partition the data using the first candidate partition boundary value or the second candidate partition boundary value based at least in part on the determination of uniform distribution..
11. The computing system of claim 10, wherein the instructions that, when executed, further configure the one or more processors to: determine a difference between the first value and the second value; and determine an absolute value of the difference of the first value and the second value, wherein the first deviation value is the absolute value.
11. The computing device of claim 10, wherein determining whether the values of the third set of values are uniformly distributed across the first partition and across the second partition is based at least in part on determining a first number of values distributed across the first partition is greater by a threshold margin than a second number of values distributed across the second partition.
12. The computing system of claim 10, wherein the instructions that, when executed, further configure the one or more processors to: determine a fourth value representing an average of a fifth value of the first plurality of bounded value sets and a sixth value of the second plurality of bounded value sets, wherein the second deviation value is based at least in part on the fourth value.
12. The computing device of claim 9, wherein the instructions that, when executed by the processor, further cause the processor to perform operations comprising: determining a difference between the of the first average value and the first value; determining an absolute value of the difference of the first average value and the first value, wherein the first deviation value is the absolute value; determining a difference between the second average value and the third value; and determining an absolute value of the difference of the second average value and the third value, wherein the second deviation value is the absolute value.
13. The computing system of claim 8, wherein the instructions that, when executed, further configure the one or more processors to: receive a second set of values from the data to be transmitted to the target system; determine whether the second set of values is uniformly distributed; and determine whether to partition the data in accordance with a determination that the second set of values is uniformly distributed
13. The computing device of claim 8, wherein the instructions that, when executed by the processor, further cause the processor to perform operations comprising: identifying a fifth value of the first set of values and a sixth value of the second set of values; determining a midpoint of the fifth value and the sixth value; and determining the first plurality of bounded value sets and the second plurality of bounded value sets based at least in part on the midpoint, wherein the first plurality of bounded values is determined by adjusting the fifth value based at least in part on the midpoint, and wherein the second plurality of bounded values is determined by adjusting the sixth value based at least in part on the midpoint
14. The computing system of claim 8, wherein the instructions that, when executed, further configure the one or more processors to: determine a midpoint of the first value of the first subset of the set of values and a second value of the second subset of the set of values; and determine the first plurality of bounded value sets and the second plurality of bounded value sets based at least in part on the midpoint.
14. The computing device of claim 13, wherein comparing the first deviation value to the second deviation value comprises determining whether the first deviation value is greater than the second deviation value.
15. One or more non-transitory, computer-readable media having stored thereon instructions that, when executed, configure one or more processors to:
receive a set of values from data to be transmitted to a target system;
determine a first plurality of bounded value sets based at least in part on a first subset of the set of values and a second plurality of bounded value sets based at least in part on a second subset of the set of values;
determine a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets;
determine a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets; and
partition the data based at least in part on the first deviation value and the second deviation value; and transmit the partitioned data to the target system
15. A non-transitory computer-readable medium having stored thereon a sequence of instructions that, when executed by a processor, causes the processor to perform operations comprising:
receiving, from a source system, a first set of values from data to be transmitted to a target system and a second set of values from the data to be transmitted to the target system;
determining a partition boundary value for the data based at least in part on: determining a first plurality of bounded value sets based at least in part on the first set of values and a second plurality of bounded value sets based at least in part on the second set of values; determining a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets;
determining a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets; and determining a first partition boundary value based at least in part on the first deviation value and the second deviation value; partitioning the data based at least in part on the first partition boundary value; and transmitting the partitioned data to the target system
16. The one or more non-transitory, computer-readable media of claim 15, wherein the instructions that, when executed, further configure the one or more processors to: determine a partition boundary value based at least in part on the first deviation value and the second deviation value, wherein the data is partitioned based at least in part on the partition boundary value
16. The non-transitory computer-readable medium of claim 15, wherein determining a first deviation value based at least in part on the first plurality of bounded value sets and a second deviation value based at least in part on the second plurality of bounded value sets comprises: determining a first average value of a first value of a first bounded value set of the first plurality of bounded value sets and a second value of a second bounded value set of the second plurality of bounded value sets, the first value corresponding to a first candidate partition boundary value; determining a second average value of a third value of a third set of bounded values of the first plurality of bounded value sets and a fourth value of a fourth set bounded values of the second plurality of bounded values, the third value corresponding to a second candidate partition boundary value; determining the first deviation value of the first average value from the first value; and determining the second deviation value of the second average value from the third valu.
17. The one or more non-transitory, computer-readable media of claim 15, wherein the instructions that, when executed, further configure the one or more processors to: determine a first value representing an average of a second value of the first plurality of bounded value sets and a third value of the second plurality of bounded value sets, wherein the first deviation value is based at least in part on the first value
17. The non-transitory computer-readable medium of claim 16, wherein the instructions that, when executed by the processor, further cause the processor to perform operations comprising: receiving a third set of values from the data to be transmitted to the target system; determining whether the values of the third set of values are uniformly distributed across a first partition generated using the first partition boundary value and across a second partition generated using the second partition boundary value; and determining whether to partition the data using the first candidate partition boundary value or the second candidate partition boundary value based at least in part on the determination of uniform distribution.
18. The one or more non-transitory, computer-readable media of claim 17, wherein the instructions that, when executed, further configure the one or more processors to: determine a difference between the first value and the second value; and determine an absolute value of the difference of the first value and the second value, wherein the first deviation value is the absolute value.
18. The non-transitory computer-readable medium of claim 17, wherein determining whether the values of the third set of values are uniformly distributed across the first partition and across the second partition is based at least in part on determining a first number of values distributed across the first partition is greater by a threshold margin than a second number of values distributed across the second partition
19. The one or more non-transitory, computer-readable media of claim 17, wherein the instructions that, when executed, further configure the one or more processors to: determine a fourth value representing an average of a fifth value of the first plurality of bounded value sets and a sixth value of the second plurality of bounded value sets, wherein the second deviation value is based at least in part on the fourth value.
19. The non-transitory computer-readable medium of claim 16, wherein the instructions that, when executed by the processor, further cause the processor to perform operations comprising: determining a difference between the first average value and the first value; determining an absolute value of the difference of the first average value and the first value, wherein the first deviation value is the absolute value; determining a difference between the second average value and the third value; and determining an absolute value of the difference of the second average value and the third value, wherein the second deviation value is the absolute value.
20. The one or more non-transitory, computer-readable media of claim 15, wherein the instructions that, when executed, further configure the one or more processors to: receive a second set of values from the data to be transmitted to the target system; determine whether the second set of values is uniformly distributed; and determine whether to partition the data in accordance with a determination that the second set of values is uniformly distributed.
20. non-transitory computer-readable medium of claim 15, wherein the instructions that, when executed by the processor, further cause the processor to perform operations comprising: identifying a fifth value of the first set of values and a sixth value of the second set of values; determining a midpoint of the fifth value and the sixth value; and determining the first plurality of bounded value sets and the second plurality of bounded value sets based at least in part on the midpoint, wherein the first plurality of bounded values is determined by adjusting the fifth value based at least in part on the midpoint, and wherein the second plurality of bounded values is determined by adjusting the sixth value based at least in part on the midpoint.
As demonstrated by the mappings in the table above, US Patent No. 12380126 discloses or renders obvious all the features of the claims of the instant application.
Claim Rejection - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 8, and 15, the terms "first upper boundary value" and “second upper boundary value” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Claims must particularly point out and distinctly claim the invention, and must have clarity and precision.
Any claim not specifically addressed is being rejected for being incorporate of the claim it is dependent upon.
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 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gardner et al. (US 10963802 A1) in view of Xu et al. (CN 114817651 B).
Regarding Claim 1, Gardner discloses a method, comprising:
receiving, by a computing system, a set of values from data to be transmitted to a target system (Fig. 5; [Col. 12, lines 50-51]: The data stored in the input data may be received directly or indirectly from the source; Fig. 5; [Cols. 14-15]: In an operation 508, a fourth indicator may be received that indicates a plurality of variables of the input dataset to define x.sub.i and, optionally, target variable value y.sub.i; Fig. 2; [Col. 7, lines 36-37]: Communication interface 206 provides an interface for receiving and transmitting data);
determining, by the computing system, a first plurality of bounded value sets based at least in part on a first subset of the set of values and a second plurality of bounded value sets based at least in part on a second subset of the set of values ([Abstract]: A computing device selects decision variable values. A lower boundary value and an upper boundary value is defined for a decision variable; Fig. 5; [Col. 15, lines 3-28]: The input data may be partitioned or otherwise divided into training dataset subset 414 and validation dataset subset 416, training dataset subset 434 and validation dataset subset 436, and/or test datasets as part of training of the model);
determining, by the computing system, a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets (Fig. 5; [Col. 22, lines 33-35]: In an operation 518, an eighth indicator may be received that defines values for one or more bounding parameters. For example… a number of standard deviations n.sub.σ to limit the boundary);
determining, by the computing system, a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets (Fig. 7; [Cols. 39-40]: In an operation 716, a standard deviation σ of the decision variable values for the next decision variable is computed… where nσ is the number of standard deviations to limit the boundary defined in operation 518).
However, Gardner does not explicitly teach “partitioning, by the computing system, the data based at least in part on the first deviation value and the second deviation value; and transmitting, by the computing system, the partitioned data to the target system”.
On the other hand, in the same field of endeavor, Xu teaches
partitioning, by the computing system, the data based at least in part on the first deviation value and the second deviation value ([Page 5]: the deviation values are stored in the predetermined data structure to obtain index data… the data to be stored is divided into a plurality of data segments, and then the data to be stored in each data segment is represented by a fitted line segment and a corresponding deviation value; FIG. 4 schematically shows a flowchart of a method for dividing a data segment; Fig. 10; [Page 10]: The dividing module 1010 is configured to divide the plurality of data to be stored into a plurality of data segments… The deviation value determination module 1030 is configured to determine the deviation value between each to-be-stored data in the data segment); and
transmitting, by the computing system, the partitioned data to the target system (Fig. 11; [Pages 11-12]: The target data determination module 1130 is configured to determine target data according to the target line segment coefficient and the target deviation value… The processor, which may be a special purpose or general-purpose programmable processor, may… transmit data and instructions to the storage system).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Gardner to incorporate the teachings of Xu to include partitioning the data based on the first deviation value and the second deviation value, and transmitting the partitioned data to the target system.
The motivation for doing so would be to determine the smallest estimated amount of space consumption for storing the target data, as recognized by Xu ([Page 1] of Xu: A deviation value determination module is used to determine the deviation value between each to-be-stored data in the data segment; Fig. 3; [Page 6]: In operation S330b, target data is determined according to the target line segment coefficient and the target deviation value… for example, a candidate segmentation scheme with the smallest estimated amount of space consumption among a plurality of candidate segmentation schemes may be determined as a target segmentation scheme).
Regarding Claim 2, the combined teachings of Gardner and Xu disclose the method of claim 1.
Xu further teaches wherein the method further comprises: determining a partition boundary value based at least in part on the first deviation value and the second deviation value, wherein the data is partitioned based at least in part on the partition boundary value ([Page 5]: the deviation values are stored in the predetermined data structure to obtain index data… the data to be stored is divided into a plurality of data segments, and then the data to be stored in each data segment is represented by a fitted line segment and a corresponding deviation value; FIG. 4).
Regarding Claim 3, the combined teachings of Gardner and Xu disclose the method of claim 1.
Gardner further teaches wherein the method further comprises: determining a first value representing an average of a second value of the first plurality of bounded value sets and a third value of the second plurality of bounded value sets, wherein the first deviation value is based at least in part on the first value ([Col. 6, lines 61-67]: When cross-validation is performed, the input dataset is partitioned into F subsets (folds)… An objective function value is averaged over each set of training and scoring executions to obtain a single objective function value).
Regarding Claim 4, the combined teachings of Gardner and Xu disclose the method of claim 3.
Gardner further teaches wherein the method further comprises: determining a difference between the first value and the second value; and determining an absolute value of the difference of the first value and the second value, wherein the first deviation value is the absolute value ([Col. 49, lines 5-15]: define a lower bin boundary value and an upper bin boundary value for each bin of a plurality of bins for the at least one decision variable by dividing a difference between the upper boundary value and the lower boundary value by a number of the plurality of bins).
Regarding Claim 5, the combined teachings of Gardner and Xu disclose the method of claim 3.
Gardner further teaches wherein the method further comprises: determining a fourth value representing an average of a fifth value of the first plurality of bounded value sets and a sixth value of the second plurality of bounded value sets, wherein the second deviation value is based at least in part on the fourth value ([Col. 1, lines 335-55]: Each decision variable can be any value of a set of possible values that may be continuous or categorical; [Col. 6, lines 61-67]: When cross-validation is performed, the input dataset is partitioned into F subsets (folds)… An objective function value is averaged over each set of training and scoring executions to obtain a single objective function value).
Additionally, Xu teaches ([Page 5]: The index data includes a plurality of line segment coefficients and a plurality of deviation values).
Regarding Claim 6, the combined teachings of Gardner and Xu disclose the method of claim 1.
Gardner further teaches wherein the method further comprises:
receiving a second set of values from the data to be transmitted to the target system (Fig. 5; [Col. 14, 48-63]: In an operation 504, a second indicator may be received);
determining whether the second set of values is uniformly distributed; and determining whether to partition the data in accordance with a determination that the second set of values is uniformly distributed (Figs. 9A-C[Col. 26, lines 49-63]: For illustration, the Grid search method generates uniform decision variable values across the range of each decision variable and combines them across decision variables… the last set of values for the decision variable is uniformly sampled across the set of levels).
Regarding Claim 7, the combined teachings of Gardner and Xu disclose the method of claim 1.
Gardner further teaches wherein the method further comprises: determining a midpoint of the first value of the first subset of the set of values and a second value of the second subset of the set of values; and determining the first plurality of bounded value sets and the second plurality of bounded value sets based at least in part on the midpoint ([Abstract]: The value for the decision variable is between the lower boundary value and the upper boundary value; Fig. 5; [Col. 19, lines 14-44]: In an operation 516… Using the seventh indicator, the user may select one or more of the decision variables to evaluate using a lower bound value, an upper bound value, and an iteration value and/or a specific value instead of the default value… the user may identify one or more of the decision variables to evaluate using default bounds or a default list of possible values).
Regarding Claim 8, Gardner discloses the computing system, comprising: one or more processors; and one or more computer-readable media having stored thereon instructions that, when executed, configure the one or more processors to ([Col. 2, lines 62-64]: FIG. 2 depicts a block diagram of a user device of the decision variable selection system of FIG. 1):
receive a set of values from data to be transmitted to a target system (Fig. 5; [Col. 12, lines 50-51]: The data stored in the input data may be received directly or indirectly from the source; Fig. 5; [Cols. 14-15]: In an operation 508, a fourth indicator may be received that indicates a plurality of variables of the input dataset to define x.sub.i and, optionally, target variable value y.sub.i; Fig. 2; [Col. 7, lines 36-37]: Communication interface 206 provides an interface for receiving and transmitting data);
determine a first plurality of bounded value sets based at least in part on a first subset of the set of values and a second plurality of bounded value sets based at least in part on a second subset of the set of values ([Abstract]: A computing device selects decision variable values. A lower boundary value and an upper boundary value is defined for a decision variable; Fig. 5; [Col. 15, lines 3-28]: The input data may be partitioned or otherwise divided into training dataset subset 414 and validation dataset subset 416, training dataset subset 434 and validation dataset subset 436, and/or test datasets as part of training of the model);
determine a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets (Fig. 5; [Col. 22, lines 33-35]: In an operation 518, an eighth indicator may be received that defines values for one or more bounding parameters. For example… a number of standard deviations n.sub.σ to limit the boundary);
determine a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets (Fig. 7; [Cols. 39-40]: In an operation 716, a standard deviation σ of the decision variable values for the next decision variable is computed… where nσ is the number of standard deviations to limit the boundary defined in operation 518); and
However, Gardner does not explicitly teach “partition the data based at least in part on the first deviation value and the second deviation value; and transmit the partitioned data to the target system”.
On the other hand, in the same field of endeavor, Xu teaches
partition the data based at least in part on the first deviation value and the second deviation value ([Page 5]: the deviation values are stored in the predetermined data structure to obtain index data… the data to be stored is divided into a plurality of data segments, and then the data to be stored in each data segment is represented by a fitted line segment and a corresponding deviation value; FIG. 4 schematically shows a flowchart of a method for dividing a data segment; Fig. 10; [Page 10]: The dividing module 1010 is configured to divide the plurality of data to be stored into a plurality of data segments… The deviation value determination module 1030 is configured to determine the deviation value between each to-be-stored data in the data segment); and
transmit the partitioned data to the target system (Fig. 11; [Pages 11-12]: The target data determination module 1130 is configured to determine target data according to the target line segment coefficient and the target deviation value… The processor, which may be a special purpose or general-purpose programmable processor, may… transmit data and instructions to the storage system).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Gardner to incorporate the teachings of Xu to include partitioning the data based on the first deviation value and the second deviation value, and transmitting the partitioned data to the target system.
The motivation for doing so would be to determine the smallest estimated amount of space consumption for storing the target data, as recognized by Xu ([Page 1] of Xu: A deviation value determination module is used to determine the deviation value between each to-be-stored data in the data segment; Fig. 3; [Page 6]: In operation S330b, target data is determined according to the target line segment coefficient and the target deviation value… for example, a candidate segmentation scheme with the smallest estimated amount of space consumption among a plurality of candidate segmentation schemes may be determined as a target segmentation scheme).
Regarding Claim 9, the combined teachings of Gardner and Xu disclose the computing system of claim 8.
Xu further teaches wherein the instructions that, when executed, further configure the one or more processors to: determine a partition boundary value based at least in part on the first deviation value and the second deviation value, wherein the data is partitioned based at least in part on the partition boundary value ([Page 5]: the deviation values are stored in the predetermined data structure to obtain index data… the data to be stored is divided into a plurality of data segments, and then the data to be stored in each data segment is represented by a fitted line segment and a corresponding deviation value; FIG. 4).
Regarding Claim 10, the combined teachings of Gardner and Xu disclose the computing system of claim 8.
Gardner further teaches wherein the instructions that, when executed, further configure the one or more processors to: determine a first value representing an average of a second value of the first plurality of bounded value sets and a third value of the second plurality of bounded value sets, wherein the first deviation value is based at least in part on the first value ([Col. 6, lines 61-67]: When cross-validation is performed, the input dataset is partitioned into F subsets (folds)… An objective function value is averaged over each set of training and scoring executions to obtain a single objective function value).
Regarding Claim 11, the combined teachings of Gardner and Xu disclose the computing system of claim 10.
Gardner further teaches wherein the instructions that, when executed, further configure the one or more processors to: determine a difference between the first value and the second value; and determine an absolute value of the difference of the first value and the second value, wherein the first deviation value is the absolute value ([Col. 49, lines 5-15]: define a lower bin boundary value and an upper bin boundary value for each bin of a plurality of bins for the at least one decision variable by dividing a difference between the upper boundary value and the lower boundary value by a number of the plurality of bins).
Regarding Claim 12, the combined teachings of Gardner and Xu disclose the computing system of claim 10.
Gardner further teaches wherein the instructions that, when executed, further configure the one or more processors to: determine a fourth value representing an average of a fifth value of the first plurality of bounded value sets and a sixth value of the second plurality of bounded value sets, wherein the second deviation value is based at least in part on the fourth value ([Col. 1, lines 335-55]: Each decision variable can be any value of a set of possible values that may be continuous or categorical; [Col. 6, lines 61-67]: When cross-validation is performed, the input dataset is partitioned into F subsets (folds)… An objective function value is averaged over each set of training and scoring executions to obtain a single objective function value).
Additionally, Xu teaches ([Page 5]: The index data includes a plurality of line segment coefficients and a plurality of deviation values).
Regarding Claim 13, the combined teachings of Gardner and Xu disclose the computing system of claim 8.
Gardner further teaches wherein the instructions that, when executed, further configure the one or more processors to:
receive a second set of values from the data to be transmitted to the target system (Fig. 5; [Col. 14, 48-63]: In an operation 504, a second indicator may be received);
determine whether the second set of values is uniformly distributed; and determine whether to partition the data in accordance with a determination that the second set of values is uniformly distributed (Figs. 9A-C[Col. 26, lines 49-63]: For illustration, the Grid search method generates uniform decision variable values across the range of each decision variable and combines them across decision variables… the last set of values for the decision variable is uniformly sampled across the set of levels).
Regarding Claim 14, the combined teachings of Gardner and Xu disclose the computing system of claim 8.
Gardner further teaches wherein the instructions that, when executed, further configure the one or more processors to: determine a midpoint of the first value of the first subset of the set of values and a second value of the second subset of the set of values; and determine the first plurality of bounded value sets and the second plurality of bounded value sets based at least in part on the midpoint ([Abstract]: The value for the decision variable is between the lower boundary value and the upper boundary value; Fig. 5; [Col. 19, lines 14-44]: In an operation 516… Using the seventh indicator, the user may select one or more of the decision variables to evaluate using a lower bound value, an upper bound value, and an iteration value and/or a specific value instead of the default value… the user may identify one or more of the decision variables to evaluate using default bounds or a default list of possible values).
Regarding Claim 15, Gardner discloses one or more non-transitory, computer-readable media having stored thereon instructions that, when executed, configure one or more processors to (Fig. 2; [Col. 7, lines 49-51]: Computer-readable medium 208 is a non-transitory electronic holding place or storage for information):
receive a set of values from data to be transmitted to a target system (Fig. 5; [Col. 12, lines 50-51]: The data stored in the input data may be received directly or indirectly from the source; Fig. 5; [Cols. 14-15]: In an operation 508, a fourth indicator may be received that indicates a plurality of variables of the input dataset to define x.sub.i and, optionally, target variable value y.sub.i; Fig. 2; [Col. 7, lines 36-37]: Communication interface 206 provides an interface for receiving and transmitting data);
determine a first plurality of bounded value sets based at least in part on a first subset of the set of values and a second plurality of bounded value sets based at least in part on a second subset of the set of values ([Abstract]: A computing device selects decision variable values. A lower boundary value and an upper boundary value is defined for a decision variable; Fig. 5; [Col. 15, lines 3-28]: The input data may be partitioned or otherwise divided into training dataset subset 414 and validation dataset subset 416, training dataset subset 434 and validation dataset subset 436, and/or test datasets as part of training of the model);
determine a first deviation value based at least in part on a first upper boundary value of the first plurality of bounded value sets and a first value of the second plurality of bounded value sets (Fig. 5; [Col. 22, lines 33-35]: In an operation 518, an eighth indicator may be received that defines values for one or more bounding parameters. For example… a number of standard deviations n.sub.σ to limit the boundary);
determine a second deviation value based at least in part on a second upper boundary value of the second plurality of bounded value sets and a second value of the first plurality of bounded value sets (Fig. 7; [Cols. 39-40]: In an operation 716, a standard deviation σ of the decision variable values for the next decision variable is computed… where nσ is the number of standard deviations to limit the boundary defined in operation 518).
However, Gardner does not explicitly teach “partition the data based at least in part on the first deviation value and the second deviation value; and transmit the partitioned data to the target system”.
On the other hand, in the same field of endeavor, Xu teaches
partition the data based at least in part on the first deviation value and the second deviation value ([Page 5]: the deviation values are stored in the predetermined data structure to obtain index data… the data to be stored is divided into a plurality of data segments, and then the data to be stored in each data segment is represented by a fitted line segment and a corresponding deviation value; FIG. 4 schematically shows a flowchart of a method for dividing a data segment; Fig. 10; [Page 10]: The dividing module 1010 is configured to divide the plurality of data to be stored into a plurality of data segments… The deviation value determination module 1030 is configured to determine the deviation value between each to-be-stored data in the data segment); and
transmit the partitioned data to the target system (Fig. 11; [Pages 11-12]: The target data determination module 1130 is configured to determine target data according to the target line segment coefficient and the target deviation value… The processor, which may be a special purpose or general-purpose programmable processor, may… transmit data and instructions to the storage system).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Gardner to incorporate the teachings of Xu to include partitioning the data based on the first deviation value and the second deviation value, and transmitting the partitioned data to the target system.
The motivation for doing so would be to determine the smallest estimated amount of space consumption for storing the target data, as recognized by Xu ([Page 1] of Xu: A deviation value determination module is used to determine the deviation value between each to-be-stored data in the data segment; Fig. 3; [Page 6]: In operation S330b, target data is determined according to the target line segment coefficient and the target deviation value… for example, a candidate segmentation scheme with the smallest estimated amount of space consumption among a plurality of candidate segmentation schemes may be determined as a target segmentation scheme).
Regarding Claim 16, the combined teachings of Gardner and Xu disclose the one or more non-transitory, computer-readable media of claim 15.
Xu further teaches wherein the instructions that, when executed, further configure the one or more processors to: determine a partition boundary value based at least in part on the first deviation value and the second deviation value, wherein the data is partitioned based at least in part on the partition boundary value ([Page 5]: the deviation values are stored in the predetermined data structure to obtain index data… the data to be stored is divided into a plurality of data segments, and then the data to be stored in each data segment is represented by a fitted line segment and a corresponding deviation value; FIG. 4).
Regarding Claim 17, the combined teachings of Gardner and Xu disclose the one or more non-transitory, computer-readable media of claim 15.
Gardner further teaches wherein the instructions that, when executed, further configure the one or more processors to: determine a first value representing an average of a second value of the first plurality of bounded value sets and a third value of the second plurality of bounded value sets, wherein the first deviation value is based at least in part on the first value ([Col. 6, lines 61-67]: When cross-validation is performed, the input dataset is partitioned into F subsets (folds)… An objective function value is averaged over each set of training and scoring executions to obtain a single objective function value).
Regarding Claim 18, the combined teachings of Gardner and Xu disclose the one or more non-transitory, computer-readable media of claim 17. wherein the instructions that, when executed, further configure the one or more processors to: determine a difference between the first value and the second value; and determine an absolute value of the difference of the first value and the second value, wherein the first deviation value is the absolute value ([Col. 49, lines 5-15]: define a lower bin boundary value and an upper bin boundary value for each bin of a plurality of bins for the at least one decision variable by dividing a difference between the upper boundary value and the lower boundary value by a number of the plurality of bins).
Regarding Claim 19, the combined teachings of Gardner and Xu disclose the one or more non-transitory, computer-readable media of claim 17.
Gardner further teaches wherein the instructions that, when executed, further configure the one or more processors to: determine a fourth value representing an average of a fifth value of the first plurality of bounded value sets and a sixth value of the second plurality of bounded value sets, wherein the second deviation value is based at least in part on the fourth value ([Col. 1, lines 335-55]: Each decision variable can be any value of a set of possible values that may be continuous or categorical; [Col. 6, lines 61-67]: When cross-validation is performed, the input dataset is partitioned into F subsets (folds)… An objective function value is averaged over each set of training and scoring executions to obtain a single objective function value).
Additionally, Xu teaches ([Page 5]: The index data includes a plurality of line segment coefficients and a plurality of deviation values).
Regarding Claim 20, the combined teachings of Gardner and Xu disclose the one or more non-transitory, computer-readable media of claim 15.
Gardner further teaches wherein the instructions that, when executed, further configure the one or more processors to: receive a second set of values from the data to be transmitted to the target system (Fig. 5; [Col. 14, 48-63]: In an operation 504, a second indicator may be received);
determine whether the second set of values is uniformly distributed; and determine whether to partition the data in accordance with a determination that the second set of values is uniformly distributed (Figs. 9A-C[Col. 26, lines 49-63]: For illustration, the Grid search method generates uniform decision variable values across the range of each decision variable and combines them across decision variables… the last set of values for the decision variable is uniformly sampled across the set of levels).
Conclusion
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/S.D.H./Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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