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 .
DETAILED ACTION
Status of Claims
This action is a Final action on the merits in response to the application filed on 07/01/2026.
Claims 1-14 have been cancelled. Claim 15 has been amended. Claims 21-31 have been added. Claims 15– 31 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s remarks have been considered.
In the remarks Applicant argues, “ When viewed as a whole, the claims are not directed to a method of organizing human activity, such as the concept of managing a supply chain or making business decisions.” (pg. 2)
Examiner respectfully disagrees. The claims encompass Certain Methods of Organizing Human Activity related to commercial activities related to commercial or legal interactions and fundamental economic practices, but for the recitation of generic computer components (e.g. a server). For example, receiving supply chain data, generating historical data, receiving key element data, determining a risk score, determining an exception likelihood, determining a mitigation opportunity, etc. involve commercial interactions for supplying products/supply chain and risk mitigation. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
In addition, the claim could be seen as Mental Processes related to observation and evaluation of data (collecting supply chain data, determining risk scores and mitigation opportunities).
Applicant argues, “Accordingly, the pending claims integrate any alleged abstract idea into a practical application, and are not merely steps of data receipt, data analysis, and output display.
The judicial exceptions are not integrated into a practical application. The claims recite the additional elements of a user device, a supply chain server system, a processor and a memory. These are generic computer components recited at a high level of generality as performing generic computer functionality (Specification ¶0039 and ¶0041).
For instance, the steps of receiving supply chain data from inventory management hub and constraint data; receiving key element data and parameter component is data gathering functionality. The steps of generating historical data; determining at least one risk score; determining an exception likelihood for constraint data; determining said constraint data is not exception data; determining a plurality of mitigation opportunities; generating scenario datasets; determining an efficacy metric and generating a least one mitigation opportunity involve analyzing data. The step of generating visualization for display is generic display functionality.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a server). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a server). The additional elements do not integrate the abstract ideas into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Applicant argues similar to Bascom, “Such improvement is achieved through
the particular analytical architecture recited in the claims, rather than merely through automation of a business practice.” (pgs. 8-9)
In Bascom the court found that although the additional elements were generic computer elements when considered individually, when considered in combination an inventive concept was found in the non-conventional and non-generic arrangement of the additional elements. The claims in Bascom did not merely recite the abstract idea of
filtering content along with the requirement to perform it on the Internet, or to perform it on a set of generic computer components. The inventive concept is in the technical feature of network technology in a filtering system by associating individual accounts with their own filtering scheme and elements while locating the filtering.
Here, the claims are directed to steps such as receiving supply chain data, generating historical data, receiving key element data, determining a risk score, determining an exception likelihood, determining a mitigation opportunity, etc. performed by generic computer components (e.g. a server), which encompass abstract concepts.
Further, the claims appear to be related to an improved business process for providing greater visibility across an organization, predictive analyses, and develop prescriptive plans in response to a disruptive events (Spec ¶0007). Further, there is no support in the claims or Specification for an improvement in a technology or a technical field.
Applicant argues, “Accordingly, even if an abstract idea were present in the
current claims, the ordered combination of limitations provides significantly more than the alleged abstract idea itself.”(pg. 4)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a user device, a server, a processor and a memory are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The remainder of Applicant’s arguments are moot in view of new grounds of rejection as necessitated by amendment.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 15-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
[a data receipt module configured to] receive said supply chain data from said inventory management hub and said constraint data from said primary supplier site;
[a historical data module configured to] generate historical data, said historical data associated with at least one prior disruption event;
[a dashboard module] comprising a key element component configured to receive key element data from said at least one user device and a parameter component configured to receive parameter data from said at least one user device;
[an analytics system] comprising a risk and recovery module configured to determine, in response to said constraint data, at least one risk score and at least one risk classification according to a risk calculation routine performed on said supply chain data and said historical data;
[said analytics system] further comprising an exception module configured to determine an exception likelihood for said constraint data by comparing the constraint data to historical data associated with said at least one prior disruption event, the historical data comprising at least one response output associated with said at least one prior disruption event, said exception likelihood indicating whether to identify said constraint data as exception data;
[said analytics system further comprising a mitigation module, said mitigation module,] responsive to said exception module determining said constraint data is not exception data, configured to determine a plurality of mitigation opportunities,
[said mitigation module] further configured to determine a relevant mitigation opportunity from said plurality of mitigation opportunities according to said efficacy metric and said deployment metric; and
The limitations under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to commercial or legal interactions and fundamental economic practices including mitigating risk, but for the recitation of generic computer components (e.g. a server). For example, receiving supply chain data, generating historical data, receiving key element data, determining a risk score, determining an exception likelihood, determining a mitigation opportunity, etc. involve commercial interactions for supplying products and risk mitigation. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
In addition, the claim could be seen as Mental Processes related to observation and evaluation of data (collecting supply chain data, determining risk scores and mitigation opportunities).
Independent Claim 25 substantially recites the subject matter of Claim 1 and also include the abstract ideas identified above. The dependent claims encompass the same abstract ideas. For instance, Claim 16 is directed to a mitigation opportunity; Claim 17 is directed to generating a user interface; Claim 18 is directed to reporting; Claim 19 is directed to demand prioritization and Claim 20 is directed to inventory classification score and service level score.
The judicial exceptions are not integrated into a practical application. Claim 15 recite the additional elements of a user device and a supply chain server system. Claim 25 recites the additional elements of a processor and a memory. These are generic computer components recited at a high level of generality as performing generic computer functionality (Specification ¶0039 and ¶0041).
For instance, the steps of receiving supply chain data from inventory management hub and constraint data; receiving key element data and parameter component is data gathering functionality. The steps of generating historical data; determining at least one risk score; determining an exception likelihood for constraint data; determining said constraint data is not exception data; determining a plurality of mitigation opportunities; generating scenario datasets; determining an efficacy metric and generating a least one mitigation opportunity involve analyzing data. The step of generating visualization for display is generic display functionality.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a server). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a server). The additional elements do not integrate the abstract ideas into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a user device, a server, a processor and a memory are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 15-31 are not patent eligible.
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.
Claims 15-17 and 21-31 are rejected under 35 U.S.C. 103 as being unpatentable over Melancon et al. (US 12,260,370) in view of Patel et al. (US 2021/0056475).
Claim 15:
Melancon discloses:
A supply chain management system comprising: an inventory management hub communicatively configured in connection with a supply chain platform and a supplier system for a logistical structure, (see at least Figure 1 and associated text; see also column 2, lines 40-50, supply chain network comprises an inventory system)
said logistical structure comprising a primary supplier site and at least one secondary supplier site, said primary supplier site configured to generate constraint data in relation to a disruption event; (see at least column 17, lines 23-49, prediction features that feed into overall occurrence risk score which is calculated based on precision values; column 17, lines 50-65, trained machine learning prediction model asses samples of supply chain data to predict supply chain failures
said supply chain platform communicatively configured in connection with at least one customer system and at least one user device; (see at least Figure 1 and associated text; see also column 2, lines 40-56, supply chain network)
said supply chain platform comprising a supply chain server system configured to perform at least one process of said supply chain platform; (see at column 2, lines 57-58, a server and database; see also Figure 1 and associated text)
said supply chain platform further comprising: a data receipt module configured to receive said supply chain data from said inventory management hub and said constraint data from said primary supplier site; (see at least column 3, archiving system may comprise inventory data; see also column 4, lines 23-28, receive and transmit inventory data; see also column 17, lines 23-49, prediction features that feed into overall occurrence risk score which is calculated based on precision values)
a historical data module configured to generate historical data, said historical data associated with at least one prior disruption event; (see at least column 3, lines, 44-64, sales data, forecast data, production, stock levels, logistics, inventory, etc.; see also column 2, lines 64-67, historical data used to predict failures)
a dashboard module comprising a key element component configured to receive key element data from said at least one user device and a parameter component configured to receive parameter data from said at least one user device; (see at least column 20, lines 4-20, visualization dashboard; see also Figure 5; see also column 19, lines 9-14, prediction results with supply chain data and new contextual data to provide visibility)
an analytics system comprising a risk and recovery module configured to determine, in response to said constraint data, at least one risk score and at least one risk classification according to a risk calculation routine performed on said supply chain data and said historical data; (see at least column 10, lines 54-64, computation module analysis past performance of calculated values of predicted supply chain values which are termed occurrence risk score; see also column 15, lines 55-60)
said analytics system further comprising a mitigation module , said mitigation module, responsive to said exception module determining said constraint data is not exception data, configured to determining a plurality of mitigation opportunities from:
a scenario module configured to generate a plurality of scenario datasets; and (see at least column 13, lines 44 ; see also column 19, lines 22-49, identifying one or more corrective actions for implementation; see also column 19, lines 54-65, initiating a corrective action by instructing automated machinery)
a scenario efficacy module configured to determine an efficacy metric and a deployment metric for said plurality of scenario datasets, (see at least column 13, lines 44 ; see also column 19, lines 22-49, identifying one or more corrective actions for implementation; see also column 19, lines 54-65, initiating a corrective action by instructing automated machinery)
a report module configured to generate at least one interactive data visualization for display on said at least one user device, said at least one interactive data visualization based on said at least one risk score and said relevant mitigation opportunity, and configurable according to said at least one parameter input. (see at least column 11, lines 3-17, visualization predicted supply chain failures; see also column 16, lines 50-55, visualization prediction features displays failure data and suggests one or more corrective actions to prevent the supply chain event)
While Melancon discloses the above limitations, Melancon does not explicitly disclose the following limitations; however, Patel does disclose:
said analytics system further comprising an exception module configured to determine an exception likelihood for said constraint data by comparing the constraint data to historical data associated with said at least one prior disruption event, the historical data comprising at least one response output associated with said at least one prior disruption event, said exception likelihood indicating whether to identify said constraint data as exception data; (see at least ¶0019, probability of risk occurring; see also Abstract; see also ¶0003 and ¶0018, mitigate or corrective action)
said analytics system further comprising a mitigation module , said mitigation module, responsive to said exception module determining said constraint data is not exception data, configured to determining a plurality of mitigation opportunities from: (see at least ¶0033-¶0035, risk management actions using risk reduction values; see also ¶0019, residual risk; ¶0026-¶0028, prioritizing actions)
a scenario module configured to generate a plurality of scenario datasets; and (see at least ¶0033-¶0035, risk management actions using risk reduction values; see also ¶0019, residual risk; ¶0026-¶0028, prioritizing actions)
a scenario efficacy module configured to determine an efficacy metric and a deployment metric for said plurality of scenario datasets, the efficacy metric indicating a degree of efficacy associated with each of said plurality of mitigation opportunities, and the deployment metric indicating a deployment readiness associated with each of said plurality of mitigation opportunities; and (see at least ¶0033-¶0035, risk management actions using risk reduction values; see also ¶0019, residual risk; ¶0026-¶0028, prioritizing actions)
said mitigation module further configured to determine a relevant mitigation opportunity from said plurality of mitigation opportunities according to said efficacy metric and said deployment metric; (see at least ¶0033-¶0035, risk management actions using risk reduction values; see also ¶0019, residual risk; ¶0026-¶0028, prioritizing actions)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the supply chain management system including various data sources of Melancon with the prioritization of mitigation actions of Patel to provide management of appropriate actions to take (Abstract).
Claim 16:
Melancon and Patel discloses claim 15. Melancon further discloses:
wherein said at least one mitigation opportunity includes the use of said at least one secondary supplier site. (see at least column 19, lines 60-67, looking at alternative sourcing for one or more supply chain entities)
Claim 17:
Melancon and Patel discloses claim 15. Melancon further discloses:
wherein said dashboard module further comprises a UI component configured to generate at least one user interface according to at least one output of said report module for display on said at least one user device. (see at least Figures 5A-5F and associated text)
Claim 21:
Melancon and Patel disclose claim 15. Melancon further discloses:
wherein said parameter data is configured to apply at least one parameterization to said at least one interactive data visualization. (see at least column 19, lines 9-20, updating data)
Claim 22:
Melancon and Patel disclose claim 21. Melancon further discloses:
wherein the parameter data specifies at least one of an allowable lapse period, risk tolerance, risk hedging, and risk exemption. (see at least column 19, lines 35-50, planning horizons potential lapse)
Claim 23:
Melancon and Patel disclose claim 15. Melancon further discloses:
wherein said historical data further comprises validation data associated with said at least one prior disruption event. (see at least column 2, lines 56-67, machine learning model for prediction failures using snapshots of recent supply chain data; see at least column 3, lines 44-50)
Claim 24:
Melancon and Patel disclose claim 23. Melancon further discloses:
wherein said exception module comprises at least one machine learning model utilizing said validation data. (see at least column 2, lines 56-67, machine learning model for prediction failures using snapshots of recent supply chain data)
Claim 25:
Melancon discloses:
A supply chain management system comprising: a processor and a memory, the memory storing instructions executable by the processor, the instructions causing the processor to: (see at least Figure 1 and associated text; see also column 2, lines 55-58)
receive supply chain data and constraint data associated with one or more supply chain entities, the constraint data associated with a disruption event; (see at least column 17, lines 23-49, prediction features that feed into overall occurrence risk score which is calculated based on precision values; column 17, lines 50-65, trained machine learning prediction model asses samples of supply chain data to predict supply chain failures; see also column 19, lines 22-25, corrective actions)
generate historical data associated with at least one prior disruption event; (see at least column 2, lines 64-67, snapshots of historical supply chain data used to predict failures)
determine a risk score according to the supply chain data and the historical data for the constraint data determine an exception likelihood for the constraint data by comparing the constraint data to the historical data, the historical data including at least one response output associated with the at least one prior disruption event; (see at least column 10, lines 54-64, computation module analysis past performance of calculated values of predicted supply chain values which are termed occurrence risk score; see also column 15, lines 55-60)
determine, for each of the plurality of scenario datasets, an efficacy metric and a deployment metric; (see at least column 19, lines 22-40, corrected actions are feasible based on prediction horizon)
generate an interactive data visualization based upon the risk score and the relevant mitigation opportunity. see at least column 11, lines 3-17, visualization predicted supply chain failures; see also column 16, lines 50-55, visualization prediction features displays failure data and suggests one or more corrective actions to prevent the supply chain event)
While Melancon discloses the above limitations, Melancon does not explicitly disclose the following limitations; however, Patel does disclose:
generate, responsive to the exception likelihood indicating the constraint data should not be identified as exception data, a plurality of scenario datasets for the disruption event and determine a plurality of mitigation opportunities according to the plurality of scenario datasets; (see at least ¶0019, probability of risk occurring; see also Abstract; see also ¶0003 and ¶0018, mitigate or corrective action)
determine, for each of the plurality of scenario datasets, an efficacy metric and a deployment metric; (see at least ¶0033-¶0035, risk management actions using risk reduction values; see also ¶0019, residual risk; ¶0026-¶0028, prioritizing actions)
determine a relevant mitigation opportunity according to the efficacy metric and the deployment metric; and(see at least ¶0033-¶0035, risk management actions using risk reduction values; see also ¶0019, residual risk; ¶0026-¶0028, prioritizing actions)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the supply chain management system including various data sources of Melancon with the prioritization of mitigation actions of Patel to provide management of appropriate actions to take (Abstract).
Claim 26:
Melancon and Patel disclose claim 25. Melancon further discloses:
wherein comparing the constraint data to the historical data comprises comparing the constraint data to historical response outputs associated with the at least one prior disruption event. . (see at least column 17, lines 23-49, prediction features that feed into overall occurrence risk score which is calculated based on precision values; column 17, lines 50-65, trained machine learning prediction model asses samples of supply chain data to predict supply chain failures; see also column 19, lines 22-25, corrective actions)
Claim 27:
Melancon and Patel disclose claim 25. Melancon further discloses:
wherein the plurality of scenario datasets comprise a first scenario without mitigation and at least one additional scenario including an alternative mitigation opportunity. (see at least column 19, lines 35-50, identifying one or more possible corrective action and if the action cannot occur within planning horizon it is no longer considered)
Claim 28:
Melancon and Patel disclose claim 25. Melancon further discloses:
wherein the deployment metric indicates whether a scenario is ready for immediate deployment or future deployment. (see at least column 19, lines 35-50, identifying one or more possible corrective action and if the action cannot occur within planning horizon it is no longer considered)
Claim 29:
Melancon and Patel disclose claim 25. Melancon further discloses:
wherein the historical data comprises a performance history of a primary supplier site. (see at least column 3, lines 44-55, historical data includes product from a first location)
Claim 30:
Melancon and Patel disclose claim 25. Melancon further discloses:
wherein the interactive data visualization comprises a material risk report including an interactive heat map depicting top mitigable risks. (see at least column 11, lines 3-6, heatmap showing risk occurrence of supply chain failures; see also column 11, lines 30-35, materials)
Claim 31:
Melancon and Patel disclose claim 25. Melancon further discloses:
wherein the historical data further comprises validation data associated with an exception determination for the at least one prior disruption event, the validation data used when determining the exception likelihood for the constraint data. (see at least column 17, lines 23-49, prediction features that feed into overall occurrence risk score which is calculated based on precision values; column 17, lines 50-65, trained machine learning prediction model asses samples of supply chain data to predict supply chain failures; see also column 19, lines 22-25, corrective actions)
Claims 2 are rejected under 35 U.S.C. 103 as being unpatentable over Melancon et al. (US 12,260,370) in view of Patel et al. (US 2021/0056475) further in view of Cella et al. (US 2020/034866).
Claim 2:
Melancon discloses claim 1. Melancon further discloses:
wherein said supply chain data comprises sales order data, pick list data, sales forecast data, inventory data, purchase order data, work order data, EDP data, bill of materials data, dependent demand data, and MRP data. (see at least column 3, lines, 44-64, sales data, forecast data, production, stock levels, logistics, inventory, etc.)
While Melancon discloses the above limitations, Melancon does not explicitly disclose the following limitations; however, Cella does disclose:
…EDP data, bill of materials data, dependent demand data, and MRP data. (see at least ¶0060, electronic data set; see also ¶4666, mrp data)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the supply chain management system including various data sources of Melancon and the prioritization of mitigation actions of Patel with the data collection of Cella since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 18 are rejected under 35 U.S.C. 103 as being unpatentable over Melancon et al. (US 12,260,370) in view of Patel et al. (US 2021/0056475) further in view of Cella et al. (WO2024/233674 A2) further in view of Kenigsberg et al. (US 11861712)
Claim 18:
While Melancon discloses claim 17, neither Melancon nor Patel explicitly disclose the following limitations; however, Cella does disclose:
a material risk module configured to generate a material risk report; (see at least ¶0431, low risk materials)
a shipping status module configured to generate a shipping status report; (see at least ¶0353, shipping activities)
a quarterly status module configured to generate a quarterly status report; (see at least ¶0970, annual or quarterly report)
a surplus status module configured to generate a surplus status report; (see at least ¶2098, surplus)
an expiration status module configured to generate an expiration status report; (see at least ¶1438, expiration of a set duration output) and
a portfolio module configured to generate a portfolio report. (see at least ¶0757, portfolio overview)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the visualization dashboard of Melancon and the prioritization of mitigation actions of Patel with the reporting functionality of Cella since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
While Melancon, Patel and Cella disclose the above limitations, neither explicitly disclose the following limitations; however, Kenigsberg does disclose:
wherein said report module comprises: a glidepath module configured to generate a glidepath report; (see at least column 25, lines 27-41,risk preference include glide path preferences)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the visualization dashboard of Melancon, the prioritization of mitigation actions of Patel and the reporting functionality of Cella with the glide path preferences of Kenigsberg since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 19 are rejected under 35 U.S.C. 103 as being unpatentable over Melancon et al. (US 12,260,370) in view of Patel et al. (US 2021/0056475) further in view of Venkatasubra et al. (US 2011/0137708).
Claim 19:
While Melancon discloses claim 15, neither Melancon nor Patel explicitly disclose the following limitation; however Venkatasubra does disclose:
wherein said risk and recovery module further comprises a demand prioritization routine configured to determine a demand priority metric. (see at least ¶0010, demand priority; see also ¶0039)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the predicting and preventing service levels failures of Melancon and the prioritization of mitigation actions of Patel with the demand priority of Venlatasubra to assist with planning based on supply events for inventory (see Abstract).
Claims 20 are rejected under 35 U.S.C. 103 as being unpatentable over Melancon et al. (US 12,260,370) in view of Patel et al. (US 2021/0056475) further in view of Morris (US 2023/0316223).
Claim 20:
While Melancon discloses claim 15, and Melancon further discloses risk scoring (see column 10, lines 63-64), neither Melancon nor Patel explicitly disclose the following limitation; however , Morris does disclose:
wherein said analytics system further comprises an inventory entitlement module configured to determine an inventory classification score and a service level score according to an inventory quantity routine. (see at least 0018, classification score for a good; see also ¶0030, service level value)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the predicting and preventing service levels failures of Melancon and the prioritization of mitigation actions of Patel with the classification and service level values of Morris to assist with supply chain optimization.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Burleson et al. (US 2019/0213509) discloses An impact prediction component analyzes real-time context data associated with tasks being performed within a selected area to predict potential occurrence of incidents associated with tasks in a dependency chain
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://portal.uspto.gov/external/portal/pair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866.217.9197 (toll-free).
Any response to this action should be mailed to:
Commissioner of Patents and Trademarks
Washington, D.C. 20231
or faxed to 571-273-8300.
Hand delivered responses should be brought to the United States Patent and Trademark Office Customer Service Window:
Randolph Building
401 Dulany Street
Alexandria, VA 22314.
/Renae Feacher/
Primary Examiner, Art Unit 3625
Prosecution Insights