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 .
Claim Objections
Claim 14 is objected to because of the following informalities: Claim 14 currently depends on claim 1 but seems it should depend on claim 8. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Katz et al. [Katz] (US PGPub 2020/0074315) in view of Sitarski (US PGPub 2008/0133299).
As to claim 1
Katz discloses a system (system 100, see Fig. 1), comprising:
a computer (server 102, see Fig. 1), comprising a processor (processor 120, see Fig. 1) and a memory (memory 116, see Fig. 1), the computer configured to:
partition a planning problem (one or more planning problems; see paragraph 0025, lines 24-25) into ordered subsets (cluster of one or more sets of diverse plans; see paragraph 0049, lines 3-4) based on a prioritization scheme (organizational scheme; see paragraph 0044, line 9/cost; see paragraph 0049, line 10) (see paragraph 0049, lines 1-10);
apply a planning algorithm (reformulation algorithm 200, see Fig. 2/AI planner algorithm; see paragraph 0041, line 8) to optimize a first subset of the ordered subsets (see paragraph 0036, lines 9-11 and paragraph 0041, lines 5-13);
determine whether there are any remaining subsets that have not been optimized (see paragraph 0060, lines 1-9; repeated through multiple iterations to generate a plurality of diverse plans…therefore remaining subsets are determined to need optimization);
in response to the determining that there are any remaining subsets that have not been optimized, load a next subset (next plan) ordered according to the prioritization scheme (see paragraph 0049, lines 1-16 and paragraph 0060, lines 1-4; chooses next plan to add);
optimize the loaded subset (see paragraph 0059, lines 1-6); and
in response to determining that there are no remaining subsets to optimize, run a final pass of the planning algorithm to improve one or more global plan metrics (see paragraphs 0046 and 0047).
Though Katz discloses the system optimizing the ordered subsets; Katz fails to specifically disclose the system freezing a corresponding plan after each iteration and optimizing each subset without disturbing the frozen plan.
Sitarski discloses a system comprising:
a computer (computer 1200, see Fig. 12), comprising a processor (processor 1202, see Fig. 12) and a memory (memory 1204, see Fig. 12), the computer configured to:
freeze a corresponding plan (plan; see paragraph 0057, line 1) after optimizing (solving) a first subset of ordered subsets and optimizing each subset without disturbing the frozen plan (see paragraph 0057, lines 1-5).
Katz and Sitarski are analogous art because they are from the same field of endeavor, which is planning responses to events. At the time of the invention it would have been obvious to a person of ordinary skill in the art to modify Katz’s invention with Sitarski’s in order to freeze Katz’s optimized subsets, since doing so would make it possible to recompute the plan on a going-forward basis rather than recomputing for previous months/iterations (see Sitarski paragraph 0057, lines 9-11).
As to claim 2
Katz discloses the system of Claim 1, wherein the prioritization scheme is based on a relative priority of one or more tasks to be performed, a value (costs) of finished goods that are to be produced or one or more requirements regarding a use of resources (see paragraphs 0041 and 0042).
As to claim 3
Katz discloses the system of Claim 1, wherein each subset of the ordered subsets corresponds to a demand (see paragraph 0071, lines 1-5).
As to claim 4
Katz discloses the system of Claim 1, wherein the planning problem is based on one or more of:
an item master comprising one or more finished goods, one or more components, and one or more raw materials required to satisfy demand;
bill of materials data comprising one or more material relationships, one or more consumed items, one or more quantities, and one or more yields for each produced part;
routing data comprising allocation of production capacities;
a demand comprising one or more items, one or more quantities, one or more priorities, and a timing of finished good production requirements; and
one or more capacity calendars comprising availability of one or more production resources (see paragraphs 0071 and 0088).
As to claim 5
Katz discloses the system of Claim 1, wherein the planning problem is based on one or more of:
data comprising on hand, in transit, and planned procurements of one or more raw materials;
data comprising one or more work in process material supplies;
supplier data comprising one or more raw materials;
plan and schedule data comprising existing frozen schedules and plans that are constrained; and
demand requirements comprising one or more finished good items, one or more quantities, one or more due dates, one or more priorities and one or more customers (see paragraph 0088).
As to claim 6
Sitarski discloses the system of Claim 1, wherein the final pass of the planning algorithm rebalances all resources and repositions planned start times of tasks assigned to the resources to remove any remaining capacity overloads (see paragraph 0037, lines 1-16).
As to claim 7
Sitarski discloses the system of Claim 1, wherein the computer is further configured to:
provide time stamps to display progress and timing of the planning algorithm during execution (see paragraph 0072, lines 1-10).
As to claim 8
Katz discloses a computer-implemented method, comprising:
partitioning, by a computer (server 102, see Fig. 1) comprising a processor (processor 120, see Fig. 1) and a memory (memory 116, see Fig. 1), a planning problem (one or more planning problems; see paragraph 0025, lines 24-25) into ordered subsets (cluster of one or more sets of diverse plans; see paragraph 0049, lines 3-4) based on a prioritization scheme (organizational scheme; see paragraph 0044, line 9/cost; see paragraph 0049, line 10) (see paragraph 0049, lines 1-10);
applying, by the computer, a planning algorithm (reformulation algorithm 200, see Fig. 2/AI planner algorithm; see paragraph 0041, line 8) to optimize a first subset of the ordered subsets (see paragraph 0036, lines 9-11 and paragraph 0041, lines 5-13);
determining, by the computer, whether there are any remaining subsets that have not been optimized (see paragraph 0060, lines 1-9; repeated through multiple iterations to generate a plurality of diverse plans…therefore remaining subsets are determined to need optimization);
in response to the determining that there are any remaining subsets that have not been optimized, loading, by the computer, a next subset (next plan) ordered according to the prioritization scheme (see paragraph 0049, lines 1-16 and paragraph 0060, lines 1-4; chooses next plan to add);
optimizing, by the computer, the loaded subset (see paragraph 0059, lines 1-6); and
in response to determining that there are no remaining subsets to optimize, running, by the computer, a final pass of the planning algorithm to improve one or more global plan metrics (see paragraphs 0046 and 0047).
Though Katz discloses the computer-implemented method optimizing the ordered subsets; Katz fails to specifically disclose the computer-implemented method freezing a corresponding plan after each iteration and optimizing each subset without disturbing the frozen plan.
Sitarski discloses a computer-implemented method comprising:
a computer (computer 1200, see Fig. 12), comprising a processor (processor 1202, see Fig. 12) and a memory (memory 1204, see Fig. 12), the computer configured to:
freeze a corresponding plan (plan; see paragraph 0057, line 1) after optimizing (solving) a first subset of ordered subsets and optimizing each subset without disturbing the frozen plan (see paragraph 0057, lines 1-5).
Katz and Sitarski are analogous art because they are from the same field of endeavor, which is planning responses to events. At the time of the invention it would have been obvious to a person of ordinary skill in the art to modify Katz’s invention with Sitarski’s in order to freeze Katz’s optimized subsets, since doing so would make it possible to recompute the plan on a going-forward basis rather than recomputing for previous months/iterations (see Sitarski paragraph 0057, lines 9-11).
As to claim 9
Katz discloses the computer-implemented method of Claim 8, wherein the prioritization scheme is based on a relative priority of one or more tasks to be performed, a value (costs) of finished goods that are to be produced or one or more requirements regarding a use of resources (see paragraphs 0041 and 0042).
As to claim 10
Katz discloses the computer-implemented method of Claim 8, wherein each subset of the ordered subsets corresponds to a demand (see paragraph 0071, lines 1-5).
As to claim 11
Katz discloses the computer-implemented method of Claim 8, wherein the planning problem is based on one or more of:
an item master comprising one or more finished goods, one or more components, and one or more raw materials required to satisfy demand;
bill of materials data comprising one or more material relationships, one or more consumed items, one or more quantities, and one or more yields for each produced part;
routing data comprising allocation of production capacities;
a demand comprising one or more items, one or more quantities, one or more priorities, and a timing of finished good production requirements; and
one or more capacity calendars comprising availability of one or more production resources (see paragraphs 0071 and 0088).
As to claim 12
Katz discloses the computer-implemented method of Claim 8, wherein the planning problem is based on one or more of:
data comprising on hand, in transit, and planned procurements of one or more raw materials;
data comprising one or more work in process material supplies;
supplier data comprising one or more raw materials;
plan and schedule data comprising existing frozen schedules and plans that are constrained; and
demand requirements comprising one or more finished good items, one or more quantities, one or more due dates, one or more priorities and one or more customers (see paragraph 0088).
As to claim 13
Sitarski discloses the computer-implemented method of Claim 8, wherein the final pass of the planning algorithm rebalances all resources and repositions planned start times of tasks assigned to the resources to remove any remaining capacity overloads (see paragraph 0037, lines 1-16).
As to claim 14
Sitarski discloses the computer-implemented method of Claim 1, further comprising:
providing, by the computer, time stamps to display progress and timing of the planning algorithm during execution (see paragraph 0072, lines 1-10).
As to claim 15
Katz discloses a non-transitory computer-readable medium embodied with software, the software when executed is configured to:
partition, by a computer (server 102, see Fig. 1) comprising a processor (processor 120, see Fig. 1) and a memory (memory 116, see Fig. 1), a planning problem (one or more planning problems; see paragraph 0025, lines 24-25) into ordered subsets (cluster of one or more sets of diverse plans; see paragraph 0049, lines 3-4) based on a prioritization scheme (organizational scheme; see paragraph 0044, line 9/cost; see paragraph 0049, line 10) (see paragraph 0049, lines 1-10);
apply a planning algorithm (reformulation algorithm 200, see Fig. 2/AI planner algorithm; see paragraph 0041, line 8) to optimize a first subset of the ordered subsets (see paragraph 0036, lines 9-11 and paragraph 0041, lines 5-13);
determine whether there are any remaining subsets that have not been optimized (see paragraph 0060, lines 1-9; repeated through multiple iterations to generate a plurality of diverse plans…therefore remaining subsets are determined to need optimization);
in response to the determining that there are any remaining subsets that have not been optimized, load a next subset (next plan) ordered according to the prioritization scheme (see paragraph 0049, lines 1-16 and paragraph 0060, lines 1-4; chooses next plan to add);
optimize the loaded subset (see paragraph 0059, lines 1-6); and
in response to determining that there are no remaining subsets to optimize, run a final pass of the planning algorithm to improve one or more global plan metrics(see paragraphs 0046 and 0047).
Though Katz discloses the non-transitory computer-readable medium embodied with software, the software when executed configured to optimize the ordered subsets; Katz fails to specifically disclose the software configured to freeze a corresponding plan after each iteration and to optimize each subset without disturbing the frozen plan.
Sitarski discloses a non-transitory computer-readable medium embodied with software, the software when executed configured to:
a computer (computer 1200, see Fig. 12), comprising a processor (processor 1202, see Fig. 12) and a memory (memory 1204, see Fig. 12), the computer configured to:
freeze, by a computer (computer 1200, see Fig. 12), comprising a processor (processor 1202, see Fig. 12) and a memory (memory 1204, see Fig. 12), a corresponding plan (plan; see paragraph 0057, line 1) after optimizing (solving) a first subset of ordered subsets and optimize each subset without disturbing the frozen plan (see paragraph 0057, lines 1-5).
Katz and Sitarski are analogous art because they are from the same field of endeavor, which is planning responses to events. At the time of the invention it would have been obvious to a person of ordinary skill in the art to modify Katz’s invention with Sitarski’s in order to freeze Katz’s optimized subsets, since doing so would make it possible to recompute the plan on a going-forward basis rather than recomputing for previous months/iterations (see Sitarski paragraph 0057, lines 9-11).
As to claim 16
Katz discloses the non-transitory computer-readable medium of Claim 15, wherein the prioritization scheme is based on a relative priority of one or more tasks to be performed, a value (costs) of finished goods that are to be produced or one or more requirements regarding a use of resources (see paragraphs 0041 and 0042).
As to claim 17
Katz discloses the non-transitory computer-readable medium of Claim 15, wherein each subset of the ordered subsets corresponds to a demand (see paragraph 0071, lines 1-5).
As to claim 18
Katz discloses the non-transitory computer-readable medium of Claim 15, wherein the planning problem is based on one or more of:
an item master comprising one or more finished goods, one or more components, and one or more raw materials required to satisfy demand;
bill of materials data comprising one or more material relationships, one or more consumed items, one or more quantities, and one or more yields for each produced part;
routing data comprising allocation of production capacities;
a demand comprising one or more items, one or more quantities, one or more priorities, and a timing of finished good production requirements; and
one or more capacity calendars comprising availability of one or more production resources (see paragraphs 0071 and 0088).
As to claim 19
Katz discloses the non-transitory computer-readable medium of Claim 15, wherein the planning problem is based on one or more of:
data comprising on hand, in transit, and planned procurements of one or more raw materials;
data comprising one or more work in process material supplies;
supplier data comprising one or more raw materials;
plan and schedule data comprising existing frozen schedules and plans that are constrained; and
demand requirements comprising one or more finished good items, one or more quantities, one or more due dates, one or more priorities and one or more customers (see paragraph 0088).
As to claim 20
Sitarski discloses the non-transitory computer-readable medium of Claim 15, wherein the final pass of the planning algorithm rebalances all resources and repositions planned start times of tasks assigned to the resources to remove any remaining capacity overloads (see paragraph 0037, lines 1-16).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael J. Brown whose telephone number is (571)272-5932. The examiner can normally be reached Monday-Thursday from 5:30am-4:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Brown/
Primary Examiner, Art Unit 2115
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