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 .
Drawings
The drawings are objected to because Figures 8-10 submitted on 11/22/2022 are still blurred and the text and numbers are unreadable. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 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 1-18 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 pre-AIA the applicant regards as the invention.
Independent Claim 1 recites the limitations:
“…
converting, by the processor, a format of the set of external supplies and the one or more external outputs into a converted format that is receivable by a heuristic application”,
…,
inputting, by the processor, a set of demands and one or more inputs, to the heuristic;
inputting, by the processor, the set of converted warm start data to the heuristic;
applying, by the processor, the heuristic to the set of demands, the one or more inputs, and the set of warm start data; and
generating, by the processor, a set of supplies and one or more outputs based on the heuristics applied to the set of demands, the one or more inputs, and the set of converted warm start data”.
It is unclear whether the “heuristic application” recited at the beginning of the claim is meant to be the same “the heuristic/ the heuristics” recited subsequently in the claim limitations; rendering the claims unclear and indefinite. Furthermore, dependent claims 2-3, 5-6 also recite “the heuristic”. For purposes of examination, Examiner will interpret all the subsequent recitations of “the heuristic” as “the heuristic application”. Similar issues appear at independent claims 7 and 13, and dependent claims 8-9, 11-12, 14-15, 17-18. Clarification 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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Katz et al (US 7,870,012- hereinafter Katz) in view of Sheble (US 10,770,899 - hereinafter Sheble).
Referring to Claim 1, Katz teaches a computer-implemented method comprising:
receiving, by a processor, a set of external supplies and one or more external outputs based on an external calculation (see Katz at Abstract: “Value Chain Intelligence (VCI) system, which integrates and analyzes internal data from enterprises and external data from suppliers, catalogs, and marketplaces in real time for their impact on supply chains processes”. Further at Col. 26: lines 7-11: “External data collection components 116 search, extract and transform external data (i.e., part catalogs, prices, availability, lead time, compatible parts, specifications, etc.) from a plurality of sources of external data 32, such as databases and Internet sources”. Examiner interprets the external data extracted as the claimed external supplies and outputs);
converting, by the processor, a format of the set of external supplies and the one or more external outputs into a converted format that is receivable by a heuristic application (see Katz at Col. 26: lines 7-11: “External data collection components 116 search, extract and transform external data (i.e., part catalogs, prices, availability, lead time, compatible parts, specifications, etc.) from a plurality of sources of external data 32, such as databases and Internet sources”. Further at Col. 28: 48-57: “Extract module-2 212 preferably sends external data 32 to transform module 213, which aggregates external data 32, so that extracted external data 32 conforms to a format compatible with the schema in discovery database 192 and analysis database 194 in data integration components 118. Once external data 32 is normalized by transform module 213, then transform module 213 sends external data 32, which may have been originally formatted in HTML, XML, PDF, etc., to load module 188 in data extraction components 116”);
generating, by the processor, warm start data from the converted format (see Katz at Col. 28: 48-57: “Extract module-2 212 preferably sends external data 32 to transform module 213, which aggregates external data 32, so that extracted external data 32 conforms to a format compatible with the schema in discovery database 192 and analysis database 194 in data integration components 118. Once external data 32 is normalized by transform module 213, then transform module 213 sends external data 32, which may have been originally formatted in HTML, XML, PDF, etc., to load module 188 in data extraction components 116”. Examiner interprets the external data being transformed and normalized being sent to load module as the generated warm data from the converted format);
converting, by the processor, the warm start data to a set of converted warm start data (see Katz at Col. 29: 24-28: “external data 32 is preferably received by transform modules 176-186 as either streaming data or in a single query/response. Therefore, external data 32 may take the form of batch updates or real-time updates, depending on the nature of the request and response”. Examiner interprets the transformed and normalized external data taking the form of either batch updates or real-time updated as the converted warm start data, since it is formed into either batch or real-time updates);
inputting, by the processor, a set of demands and one or more inputs, to the heuristic (see Katz at Col. 6: 38-46: “VCI system 28 obtains and discovers a wide variety of internal and external data for particular components or other items, with the data typically originating in widely disparate forms and formats, with the data transformed and stored in a manner so as to be flexibly queried (such as by part number, type or characteristic such as by manufacturer, memory density, speed, functional characteristics, and the like) and continuously updated, thereby enabling a more optimum strategic decision-making process”. Examiner interprets the part number, type or characteristic such as by manufacturer, memory density, speed, functional characteristics being queried as the claims set of demands and one or more inputs into the VCI, being the heuristics);
inputting, by the processor, the set of converted warm start data to the heuristic (see Katz at Col. 6: 38-46: “VCI system 28 obtains and discovers a wide variety of internal and external data for particular components or other items, with the data typically originating in widely disparate forms and formats, with the data transformed and stored in a manner so as to be flexibly queried (such as by part number, type or characteristic such as by manufacturer, memory density, speed, functional characteristics, and the like) and continuously updated, thereby enabling a more optimum strategic decision-making process”. Examiner interprets the external data used by the VCI as the claimed converted warm start data, as the VCI (being the heuristic) used it for performing further recommendations);
applying, by the processor, the heuristic to the set of demands, the one or more inputs, and the set of warm start data (see Col. 6: 50-55: “VCI system 28 is an enterprise system comprised of a plurality of applications and components that gather internal data and external data, analyze this data for specified tasks, make strategic recommendations based on the analyses, and execute various operations based on the recommendations”. Examiner interprets the analysis of the data for specified tasks as the claimed application of the heuristics); and
generating, by the processor, a set of supplies and one or more outputs based on the heuristics applied to the set of demands, the one or more inputs, and the set of converted warm start data (see Col. 6: 50-55: “VCI system 28 is an enterprise system comprised of a plurality of applications and components that gather internal data and external data, analyze this data for specified tasks, make strategic recommendations based on the analyses, and execute various operations based on the recommendations”. Examiner interprets the strategic recommendations based on the analysis as the claimed generation of supplies and one or more outputs based on the heuristics).
Even though Katz implicitly teaches generating warm start data, as Katz uses external data in combination with internal data to make an analysis and provide recommendations in the supply chain field, Sheble explicitly teaches, in an analogous system, generating warm data (see Sheble at Col. 6: 26-33: “Other supply chains may include, but are not limited to, natural gas, oil, uranium, food, pharmaceutical, financial contracts. Reliability analysis may include all means of transportation, including but not limited to rail, plane, interstate, barge, and ship. Optimum information from the initial selection of tree paths may enable warm starts to find the adjacent optimal solutions as the tree paths are expanded”. Further at Col. 47: 60-65: “In cases such as these where the next LP to be solved is substantially similar (but not identical) to a previous LP, then a warm start that makes use of the previous solution and basis may be effective. This usually means that you can arrive at a new feasible (and optimal) solution in only a few iterations”. Also at Col. 51: 55-59: “Interior-point iterates for the original instance are used to obtain warm-start points for the perturbed instance, so that when an interior-point method is started from this point, it finds the solution in fewer iterations than if no prior information were available”. Therefore, the previous LP and previous solution corresponds to the claimed “external calculation”, and the warm start points as the claimed “warm start data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Katz with the above teachings of Sheble by receiving external data related to supply chain and transform it for its use with new supply chain demands, as taught by Katz, and using it as warm start data, as taught by Sheble. The modification would have been obvious because one of ordinary skill in the art would be motivated to make use of previous solutions from similar but not identical supply chain demands in order to find a solution to new supply chain demands in fewer iterations, thereby arriving at optimal solutions in a faster manner (as suggested by Sheble at Col. 47: 60-65: “In cases such as these where the next LP to be solved is substantially similar (but not identical) to a previous LP, then a warm start that makes use of the previous solution and basis may be effective. This usually means that you can arrive at a new feasible (and optimal) solution in only a few iterations”).
Referring to Claim 2, the combination of Katz and Sheble teaches the computer-implemented method of claim 1, wherein prior to inputting the set of converted warm start data to the heuristic, the method further comprises:
loading, by the processor, the warm start data (see Katz at Col. 10: 1-11: “The functionalities of discovery services 24 preferably include: Extraction, transformation, loading and normalization/integration of internal data 30 and external data 32. Extract Transform Load (ETL) refers to software tools, which one of skill in the art will understand may be used in accordance with the present invention to extract data from a source data set, transform the data through a set of business and data rules, and load the data to a target data set”. Further, Sheble teaches explicitly the warm start data as previously explained);
converting, by the processor, the warm start data to a set of warm start demands (see Katz at Col. 3: 8-13: “Such VCI systems may be used to combine supply chain planning and execution functions with other services, such as data integration, demand forecasting, and continuous market analysis, enabling users to not only gain insights into their supply chain operations, but also share the data among all participants in the supply chain network”. Further at Col. 10: 1-11: “The functionalities of discovery services 24 preferably include: Extraction, transformation, loading and normalization/integration of internal data 30 and external data 32. Extract Transform Load (ETL) refers to software tools, which one of skill in the art will understand may be used in accordance with the present invention to extract data from a source data set, transform the data through a set of business and data rules, and load the data to a target data set”);
merging, by the processor, the set of warm start demands into a list comprising the set of demands and a set of calculated demands (see Katz at Col. 3: 8-13: “Such VCI systems may be used to combine supply chain planning and execution functions with other services, such as data integration, demand forecasting, and continuous market analysis, enabling users to not only gain insights into their supply chain operations, but also share the data among all participants in the supply chain network”. Further at Col. 10: 1-11: “The functionalities of discovery services 24 preferably include: Extraction, transformation, loading and normalization/integration of internal data 30 and external data 32. Extract Transform Load (ETL) refers to software tools, which one of skill in the art will understand may be used in accordance with the present invention to extract data from a source data set, transform the data through a set of business and data rules, and load the data to a target data set”); and
sorting, by the processor, the list according to a user-defined configuration (see Katz at Col. 9: 38-49: “VCI workflow process 73 includes discovery, analysis, recommendation, and execution. Accordingly, discovery services 76 assist the user in identifying a plurality of parameters for criteria that are important to the user's tasks, so that the user can obtain necessary data for making business decisions. Analysis services 78 use the input of the discovered data to produce a variety of reports intended to assist the user in analyzing the discovered data. The generated reports of analysis services 78 along with data from user-defined criteria may be used as input for recommendation services 80 to make recommendations for possible actions based on the analyzed data”. Therefore, Examiner interprets the reports of analysis services with data from user-defined criteria as analogous to the user-defined configuration”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Katz with the above teachings of Sheble by receiving external data related to supply chain and transform it for its use with new supply chain demands, as taught by Katz, and using it as warm start data, as taught by Sheble. The modification would have been obvious because one of ordinary skill in the art would be motivated to make use of previous solutions from similar but not identical supply chain demands in order to find a solution to new supply chain demands in fewer iterations, thereby arriving at optimal solutions in a faster manner (as suggested by Sheble at Col. 47: 60-65: “In cases such as these where the next LP to be solved is substantially similar (but not identical) to a previous LP, then a warm start that makes use of the previous solution and basis may be effective. This usually means that you can arrive at a new feasible (and optimal) solution in only a few iterations”).
Referring to Claim 3, the combination of Katz and Sheble teaches the computer-implemented method of claim 2, wherein converting the warm start data to the set of warm start demands comprises:
selecting, by the processor, a piece of warm start data (see Katz at Col. 26: 7-11: “External data collection components 116 search, extract and transform external data (i.e., part catalogs, prices, availability, lead time, compatible parts, specifications, etc.) from a plurality of sources of external data 32”. Examiner interprets extracting external data such as parts catalogs, compatible parts, specification from a plurality of source of external data as analogous to selecting a piece of warm data. As previously explained, Sheble explicitly teaches warm start data);
creating, by the processor, a warm start demand from the piece of warm start data, the warm start demand having a demand construct comprising basic demand information, the demand construct receivable by the heuristic (see Katz at Col. 26: 7-11: “External data collection components 116 search, extract and transform external data (i.e., part catalogs, prices, availability, lead time, compatible parts, specifications, etc.) from a plurality of sources of external data 32”. Further, at Col. 22: 26-37: “Both internal data 30 and external data 32 are normalized and transmitted to data integration components 118, where the aggregated data is stored into discovery database 192 and analysis database 194, and analyzed in OLAP server 198. The stored data is made available to services and application server 202 in data application components 120. Services and applications server 202 provides a plurality of functional applications that make decisions about VCI services, such as inventory levels, demand forecasts, contract commitments, spot market analysis, etc., based on the integration of internal data 30 and external data 32”); and
incorporating, by the processor, additional warm start demand information into the demand construct of the warm start demand (see Katz at Col. 26: 7-11: “External data collection components 116 search, extract and transform external data (i.e., part catalogs, prices, availability, lead time, compatible parts, specifications, etc.) from a plurality of sources of external data 32”. Further, at Col. 22: 26-37: “Both internal data 30 and external data 32 are normalized and transmitted to data integration components 118, where the aggregated data is stored into discovery database 192 and analysis database 194, and analyzed in OLAP server 198. The stored data is made available to services and application server 202 in data application components 120. Services and applications server 202 provides a plurality of functional applications that make decisions about VCI services, such as inventory levels, demand forecasts, contract commitments, spot market analysis, etc., based on the integration of internal data 30 and external data 32”. Therefore, the demand forecast done by the VCI using the external data extracted is analogous to the demand construct of the warm start demand).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Katz with the above teachings of Sheble by receiving external data related to supply chain and transform it for its use with new supply chain demands, as taught by Katz, and using it as warm start data, as taught by Sheble. The modification would have been obvious because one of ordinary skill in the art would be motivated to make use of previous solutions from similar but not identical supply chain demands in order to find a solution to new supply chain demands in fewer iterations, thereby arriving at optimal solutions in a faster manner (as suggested by Sheble at Col. 47: 60-65: “In cases such as these where the next LP to be solved is substantially similar (but not identical) to a previous LP, then a warm start that makes use of the previous solution and basis may be effective. This usually means that you can arrive at a new feasible (and optimal) solution in only a few iterations”).
Referring to Claim 4, the combination of Katz and Sheble teaches the computer-implemented method of claim 3, wherein the basic demand information comprises a part name, a part quantity and a due date (see Katz at Col. 11: 18-26: “Recommendation services 80 then preferably examine the analyzed data according to user-defined criteria (such as priorities and preferences) and make recommendations (such as what to buy, when to buy, how much to buy, from whom to buy, what to sell, when to sell, how much to sell, to whom to sell, etc.). Preferably recommendation services 80 apply a plurality of algorithms that optimize the analyzed data based on specific variables, such as price, quantity, time to delivery, client preferences, utility functions, business rules, etc. Recommendation services 80 then preferably run the data through its algorithms, making a recommendation or plurality of recommendations based on the resulting data, displaying it via a generated report or the user interface of VCI system 28”. Therefore, the user client criteria is analogous to the “demand information”, the what to buy is analogous to the “part name”, the parts quantity is analogous to the “part quantity”, and the time to deliver is analogous to the “due date”).
Referring to Claim 5, the combination of Katz and Sheble teaches the computer-implemented method of claim 1, wherein the external calculation is based on machine learning, optimization or a second heuristic (see Sheble at Col. 6: 26-33: “Other supply chains may include, but are not limited to, natural gas, oil, uranium, food, pharmaceutical, financial contracts. Reliability analysis may include all means of transportation, including but not limited to rail, plane, interstate, barge, and ship. Optimum information from the initial selection of tree paths may enable warm starts to find the adjacent optimal solutions as the tree paths are expanded”. Further at Col. 47: 60-65: “In cases such as these where the next LP to be solved is substantially similar (but not identical) to a previous LP, then a warm start that makes use of the previous solution and basis may be effective. This usually means that you can arrive at a new feasible (and optimal) solution in only a few iterations”. Also at Col. 51: 55-59: “Interior-point iterates for the original instance are used to obtain warm-start points for the perturbed instance, so that when an interior-point method is started from this point, it finds the solution in fewer iterations than if no prior information were available”. Therefore, the previous LP (which stands for “parametric programming” being a machine learning algorithm) corresponds to the claimed “external calculation”, and the warm start points as the claimed “warm start data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Katz with the above teachings of Sheble by receiving external data related to supply chain and transform it for its use with new supply chain demands, as taught by Katz, and using it as warm start data, as taught by Sheble. The modification would have been obvious because one of ordinary skill in the art would be motivated to make use of previous solutions from similar but not identical supply chain demands in order to find a solution to new supply chain demands in fewer iterations, thereby arriving at optimal solutions in a faster manner (as suggested by Sheble at Col. 47: 60-65: “In cases such as these where the next LP to be solved is substantially similar (but not identical) to a previous LP, then a warm start that makes use of the previous solution and basis may be effective. This usually means that you can arrive at a new feasible (and optimal) solution in only a few iterations”).
Referring to Claim 6, the combination of Katz and Sheble teaches the computer-implemented method of claim 1, wherein the external calculation is the heuristic, the heuristic having an input configuration that is different from the set of demands and the one or more inputs (see Sheble at Col. 6: 26-33: “Other supply chains may include, but are not limited to, natural gas, oil, uranium, food, pharmaceutical, financial contracts. Reliability analysis may include all means of transportation, including but not limited to rail, plane, interstate, barge, and ship. Optimum information from the initial selection of tree paths may enable warm starts to find the adjacent optimal solutions as the tree paths are expanded”. Further at Col. 47: 60-65: “In cases such as these where the next LP to be solved is substantially similar (but not identical) to a previous LP, then a warm start that makes use of the previous solution and basis may be effective. This usually means that you can arrive at a new feasible (and optimal) solution in only a few iterations”. Also at Col. 51: 55-59: “Interior-point iterates for the original instance are used to obtain warm-start points for the perturbed instance, so that when an interior-point method is started from this point, it finds the solution in fewer iterations than if no prior information were available”. Therefore, the previous LP (which stands for “parametric programming” being a machine learning algorithm) stated as being similar but not identical corresponds to the claimed “heuristic having an input configuration that is different from the set of demands and the one or more inputs”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Katz with the above teachings of Sheble by receiving external data related to supply chain and transform it for its use with new supply chain demands, as taught by Katz, and using it as warm start data, as taught by Sheble. The modification would have been obvious because one of ordinary skill in the art would be motivated to make use of previous solutions from similar but not identical supply chain demands in order to find a solution to new supply chain demands in fewer iterations, thereby arriving at optimal solutions in a faster manner (as suggested by Sheble at Col. 47: 60-65: “In cases such as these where the next LP to be solved is substantially similar (but not identical) to a previous LP, then a warm start that makes use of the previous solution and basis may be effective. This usually means that you can arrive at a new feasible (and optimal) solution in only a few iterations”).
Referring to independent Claim 7 and Claim 13, they are rejected on the same basis as independent claim 1, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 8 and Claim 14, they are rejected on the same basis as dependent claim 2, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 9 and Claim 15, they are rejected on the same basis as dependent claim 3, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 10 and Claim 16, they are rejected on the same basis as dependent claim 4, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 11 and Claim 17, they are rejected on the same basis as dependent claim 5, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 12 and Claim 18, they are rejected on the same basis as dependent claim 6, mutatis mutandis, since they are analogous claims.
Conclusion
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/LUIS A SITIRICHE/Primary Examiner, Art Unit 2126