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 .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 18/510,834 received on 01/28/2026.
In accordance with Applicant’s amendment. Claims 1-20 are amended, currently pending, and have been examined.
Priority
Acknowledgement is made of applicant’s claim for priority under 35 USC 119 and/or 35 USC 120
Response to Amendment
The amendment filed on 01/28/2026 has been entered.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Upon review of amendment, the claim objection previously applied to claim 2 is withdrawn.
Response to Arguments
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the claims are raised in support of the amendments, which are believed to be fully addressed in the updated §103 rejections below.
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 18): “When the claims are properly evaluated under the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) and controlling Federal Circuit precedent, the claims are not "directed to" an abstract idea. Instead, they recite a specific improvement to computer- implemented pipeline synchronization and coordination within enterprise systems. In particular, the claims recite automated diagnosis of operating parameters against predefined threshold values, correlation of such parameters to critical parameters governing initiation of contract-stage activities, orchestration of multi-pipeline coordination across concurrent processes, use of trained machine-learning models to generate feature values and sourcing predictions, and automatic propagation of pipeline information to trigger downstream computer-executed activities such as requisition creation, bid ranking, contract drafting, and sourcing event initiation, as described throughout the specification (e.g., 1 [0049], [0051], [0053]-[0054], [0058], [0176]-[0182], [0229], [0233], [0326]). These operations are not mental steps and cannot be performed as such, because they require system-level diagnosis, threshold correlation, machine-learning inference, and coordinated execution across parallel computer pipelines. Accordingly, the claims describe a technological solution to a technological orchestration problem, not a business method.”. In response, Examiner respectfully disagrees and notes that, as drafted, the limitations recited by claim 1 fall under the “Certain Methods of Organizing Human Activity” abstract idea grouping directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and “Mental Processes” abstract idea grouping by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion). For example, but for the additional elements recited in the following limitations (underlined), the steps for “providing, by at least one processor, a plurality of interacting pipelines for sourcing a concept to a contract, including a concept pipeline and a contract pipeline configured to operate concurrently and exchange pipeline data through a structured interface; initiating, by the at least one processor, the concept pipeline to automatically generate and manage activities for guiding organizational users to determine evaluating sourcing details associated with an organizational opportunity or need, the activities comprising: (i) obtaining operational data from one or more enterprise resource systems, the operational data including supplier performance metrics, contract histories, and market conditions; (ii) analyzing the operational data using a trained machine-learning model selected from regression or classification models to generate feature values representing sourcing conditions; (iii) and generating a total concept score representing a quantified quality of the concept by normalizing and weighting the feature values against evaluation criteria comprising at least cost-effectiveness, feasibility, and strategic alignment; diagnosing, by the at least one processor, a current state of the concept pipeline by analyzing operating parameters of a resource planning system and detecting deviations or anomalies indicative of sourcing exceptions, the diagnosing comprising correlating the operating parameters with predefined threshold values stored in a database; synchronizing, by the at least one processor, operation of the concept pipeline and the contract pipeline by: (i) determining that one or more operating parameters of the concept pipeline satisfy one or more critical parameters associated with the contract pipeline; (ii) and activating, in response to the determination, a plurality of contract- pipeline activities comprising at least one of drafting contract terms, assigning stakeholders, or populating contractual data fields using the pipeline data propagated from the concept pipeline; and refining, by the at least one processor, operation of the concept pipeline by updating at least one of the feature values or the threshold values based on performance feedback received from the contract pipeline.”, as currently drafted, could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper. Additionally, the limitations for “activating, in response to the determination, a plurality of contract- pipeline activities comprising at least one of drafting contract terms, assigning stakeholders, or populating contractual data fields using the pipeline data propagated from the concept pipeline;” and “and refining, by the at least one processor, operation of the concept pipeline by updating at least one of the feature values or the threshold values based on performance feedback received from the contract pipeline.”, as currently drafted, fall under the “Certain Methods of Organizing Human Activity” abstract idea grouping directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Therefore, the claims recite abstract steps that fall under the Mental Processes and Certain Methods of Organizing Human Activity abstract idea groupings. Furthermore, Applicant is reminded that the use of additional elements, including computing components, instructions/software, machine learning, and others, doesn’t prevent the claims from reciting abstract ideas.
Applicant argues (Remarks at pg. 19): “This coordination addresses a computer-orchestration problem arising from distributed enterprise systems, similar to the software workflow improvements found patent-eligible in cases such as McRO, Inc. v. Bandai Namco Games Am. Inc., Nos. 2015-1080, et al., 2016 U.S. App. LEXIS 16703 (Fed. Cir. Sep. 13, 2016) and Finjan, Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299 (Fed. Cir. 2018).”. In response, Examiner respectfully disagrees and notes that the present claims do not provide an analogous improvement to the computer to that of McRO. The present claims are not directed to the automation of specific tasks that previously could only be performed subjectively by humans. Therefore, Examiner respectfully disagrees with Applicant's assertion that the present claims are directed to statutory subject matter in view of McRO. Regarding Finjan, Examiner respectfully asserts that the present claims do not provide an analogous improvement. The claims of Finjan were directed to an enhanced computer memory system that was an improvement in computer capabilities and not an abstract idea. The present claims do not provide an analogous improvement to the computer processor of the claims. Therefore, Examiner respectfully disagrees with Applicant's assertion that the present claims are directed to statutory subject matter in view of Finjan. The claims are directed to performing supply chain steps, and more specifically, C2C, in healthcare. This type of abstract process is not an improvement to computer capabilities. Therefore, Examiner respectfully disagrees with Applicant’s assertions.
Applicant argues (Remarks at pg. 19): “Importantly, the claims do not attempt to transform business relationships, legal obligations, or risk positions of the type discussed in Bilski v. Kappos, 561 U.S. 593 (2010). Instead, the claims transform computational system state by updating machine-readable feature values, populating contract-stage data structures, generating requisitions and sourcing events, and synchronizing execution across enterprise systems. Under McRO, and Finjan, and earlier cases Diamond v. Diehr, 450 U.S. 175 (1981), DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014), transformation of computer system state and improvement of software operations constitutes technological improvements. The Examiner's assertion that the claims fail to improve any technology is therefore incorrect. The specification describes improvements to automated exception diagnostics, synchronization of concurrent computing pipelines, structured triggering of contract activities, and reduction of manual data processing and errors. These are improvements to computer-implemented supply-chain and contract-generation systems, which constitute a recognized technological field.”. In response, Examiner respectfully disagrees and notes that the present claims do not reflect any particular improvements to any technology or technical field. Applicant's purported improvement of improving supply chain contract generation, as drafted, would be an improvement reflected in the abstract limitations for consideration under Step 2A, Prong 1. This type of improvement would not be an improvement to the additional elements for consideration under Step 2A, Prong 2 or Step 2B. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”.
Therefore, the 101 rejections are maintained and updated to address the amendments.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claims 1-9 are directed to a computer-implemented method (i.e., Process), claims 10-17 are directed to a system (i.e., Machine), and claims 18-20 are directed to a non-transitory computer-readable medium (i.e., Item of Manufacture). Therefore, claims 1-20 are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04.
Independent claim 1 recites a method for defining advancement of agricultural products in breeding. As drafted, the limitations recited by claim 1 fall under the “Certain Methods of Organizing Human Activity” abstract idea grouping directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and “Mental Processes” abstract idea grouping by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion). Claim 1 recites computer-implemented method for resolving match exceptions in a supply chain with limitations for: “providing, by at least one processor, a plurality of interacting pipelines for sourcing a concept to a contract, including a concept pipeline and a contract pipeline configured to operate concurrently and exchange pipeline data through a structured interface; initiating, by the at least one processor, the concept pipeline to automatically generate and manage activities for guiding organizational users to determine evaluating sourcing details associated with an organizational opportunity or need, the activities comprising: (i) obtaining operational data from one or more enterprise resource systems, the operational data including supplier performance metrics, contract histories, and market conditions; (ii) analyzing the operational data using a trained machine-learning model selected from regression or classification models to generate feature values representing sourcing conditions; (iii) and generating a total concept score representing a quantified quality of the concept by normalizing and weighting the feature values against evaluation criteria comprising at least cost-effectiveness, feasibility, and strategic alignment; diagnosing, by the at least one processor, a current state of the concept pipeline by analyzing operating parameters of a resource planning system and detecting deviations or anomalies indicative of sourcing exceptions, the diagnosing comprising correlating the operating parameters with predefined threshold values stored in a database; synchronizing, by the at least one processor, operation of the concept pipeline and the contract pipeline by: (i) determining that one or more operating parameters of the concept pipeline satisfy one or more critical parameters associated with the contract pipeline; (ii) and activating, in response to the determination, a plurality of contract- pipeline activities comprising at least one of drafting contract terms; and refining, by the at least one processor, operation of the concept pipeline by updating at least one of the feature values or the threshold values based on performance feedback received from the contract pipeline.”. But for the additional elements – underlined – recited in this limitation, the steps recited in the claim limitations could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper. Additionally, the limitation for “activating, in response to the determination, a plurality of contract- pipeline activities comprising at least one of assigning stakeholders” and “and refining, by the at least one processor, operation of the concept pipeline by updating at least one of the feature values or the threshold values based on performance feedback received from the contract pipeline.”, as drafted, fall under the “Certain Methods of Organizing Human Activity” abstract idea grouping directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
Independent Claims 10, and 18, recite a system, and a computer-readable medium with limitations that are largely similar to the limitations of independent claim 1, but for the recitation of additional elements. Therefore, the same analysis applies to claims 10, and 18.
Dependent claims 7/16 further narrows the abstract idea and introduce the following additional elements for consideration under said steps below: C2C prediction engine.
Dependent claims 2-6, 8-9, 11-15, 17, and 19-20 further narrow the abstract idea and do not introduce any additional elements for consideration under said steps. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract ideas as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
Regarding the computing additional elements, namely by the at least one processor from claims 1/10/18, a memory from claim 10, and a non-transitory computer-readable medium and at least one computing device from claim 18, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements (based on Examiner’s interpretation set forth in Claim Interpretation section above) or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
With respect to the limitations for using a trained machine-learning model from claims 1/10/18 and C2C prediction engine from claims 7/16, these limitations fail to integrate the abstract idea into a practical application because they provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
With respect to the limitations for obtaining operational data from one or more enterprise resource systems from claims 1/10/18, these limitations fail to integrate the abstract idea into a practical application because they amount to insignificant extra-solution activity (e.g., mere data gathering), which does not integrate the abstract idea into a practical application, as noted in MPEP 2106.05(g).
Dependent claims 2-6, 8-9, 11-15, 17, and 19-20 recite the same abstract ideas (“Mental Processes” and “Certain Methods of Organizing Human Activity”) as the independent claims along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed above.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely by the at least one processor from claims 1/10/18, a memory from claim 10, and a non-transitory computer-readable medium and at least one computing device from claim 18, these additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment) and does not amount to significantly more than the abstract idea itself. Therefore, the computing additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations for using a trained machine-learning model from claims 1/10/18 and C2C prediction engine from claims 7/16, these limitations fail to add significantly more to the abstract idea because they provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations for obtaining operational data from one or more enterprise resource systems from claims 1/10/18, these additional elements at most amount to insignificant extra-solution activity (e.g., mere data gathering), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g). Additionally, the obtaining operational data from one or more enterprise resource systems extra-solution activity have been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more.
Dependent claims 2-6, 8-9, 11-15, 17, and 19-20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 1-7, 10-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Britton et al. (US 20140149170 A1, hereinafter “Britton”), in view of Makhida et al. (US 20220027826 A1, hereinafter “Makhida”), in further view of Wodetzki et al. (US 11416956 B2, hereinafter “Wodetzki”).
Regarding claims 1/10/18: Britton teaches a computer-implemented method, a system, and a computer-readable medium for resolving match exceptions in a supply chain ([0019] In general, the invention includes a system and method for facilitating procurement processes within an organization.; [0024] Furthermore, the invention may take the form of a computer program product on a computer-readable storage medium having computer-readable program code means embodied in the storage medium.) with limitations for:
providing, by at least one processor, a plurality of interacting pipelines for sourcing a concept to a contract, including a concept pipeline and a contract pipeline ([0021] In addition to the components described above, PE system 110 may further include one or more of the following: a host server or other computing systems including a processor for processing digital data; a memory coupled to the processor for storing digital data; an input digitizer coupled to the processor for inputting digital data; an application program stored in the memory and accessible by the processor for directing processing of digital data by the processor; a display device coupled to the processor and memory for displaying information derived from digital data processed by the processor; and a plurality of databases. Various databases used herein may include: user database 130, procurement database 145, as well as any number of other databases, both internal and external to PE system 110 useful in the operation of the invention as disclosed.; [0089] With reference to FIG. 4, a process for negotiating a contract 400 may include, for example, developing a negotiation strategy, conducting the negotiation, selecting a supplier based on the negotiation, obtaining funding, preparing a contract based on the negotiation, recording the contract, and/or conducting enablement activities for the supplier (FIG. 1B, step 400); [Fig. 2] process for procurement analysis.; [Fig. 1B] process illustrating a high level view of exemplary steps for facilitating strategic sourcing and vendor management. Examiner notes that one of ordinary skill in the art would reasonably interpret the process for procurement analysis as equivalent to a concept pipeline and the process for facilitating strategic sourcing and vendor analysis as equivalent to a contract pipeline.);
initiating, by the at least one processor, the concept pipeline to automatically generate and manage activities for evaluating sourcing details associated with an organizational opportunity or need, the activities comprising: ([0021] In addition to the components described above, PE system 110 may further include one or more of the following: a host server or other computing systems including a processor for processing digital data; a memory coupled to the processor for storing digital data; an input digitizer coupled to the processor for inputting digital data; an application program stored in the memory and accessible by the processor for directing processing of digital data by the processor; a display device coupled to the processor and memory for displaying information derived from digital data processed by the processor; and a plurality of databases. Various databases used herein may include: user database 130, procurement database 145, as well as any number of other databases, both internal and external to PE system 110 useful in the operation of the invention as disclosed.; [0017] any part of the functions or steps may be performed manually, automated, using hardware and software, and/or outsourced to or performed by one or more third parties.; [Fig. 2] Process 200).
(i) obtaining operational data from one or more enterprise resource systems, the operational data including supplier performance metrics, contract histories, and market conditions; ([0020] procurement utility 155 is invoked by application server 135 to query procurement database 145, retrieve data, and perform complex calculations and data formatting for presentation to user 100 and/or any other designated third party. Procurement database 145 maintains data related to, for example, business units, departments, managerial hierarchies, projects, templates, suppliers, contracts, and/or the like. Practitioners will appreciate that the invention may incorporate any number and configurations of databases both internal and external to PE system 110 for the purpose of storing any of the data elements described herein. In one embodiment, application server 135 may interface with a report engine 170 to create pre-configured and/or ad-hoc reports representing any data elements detailed herein.; [0049] The commodity manager identifies user needs, analyzes supplier and market data, reviews opportunities, and/or prepares a high level implementation plan.; [0050] The commodity manager develops a commodity profile (step 206). The commodity profile is constructed based on the process of developing an up-to-date view of spend in scope including analysis of spend, incumbent supplier performance, market drivers and dynamics, cost analysis and/or internal business requirements finalization. A commodity profile may include, for example, current spend, future spend, an acquisition cost model, a market analysis, a supplier profile, a supplier cost driver, and/or a supplier performance driver. The commodity manager may analyze current and future spends, build a total acquisition cost model, and/or perform an RFx development and analysis. The commodity manager may perform an analysis of the supply market, develop new supplier profiles, and/or identify supplier cost and performance drivers. Using the above steps, the commodity manager may update the strategic commodity plan to include data from the commodity profile.);
synchronizing, by the at least one processor, operation of the concept pipeline and the contract pipeline by: (i) determining that one or more operating parameters of the concept pipeline satisfy one or more critical parameters associated with the contract pipeline; ([0063] With reference to FIG. 3, the commodity manager assesses a sourcing strategy 300 through obtaining sponsorship, identifying a sourcing team, planning a sourcing event, selecting potential suppliers, determining a sourcing approach, developing evaluation criteria for an RFx, developing a request for transaction, developing a recipient list for the RFx, releasing the RFx, conducting supplier interviews/evaluations, and the like (FIG. 1B, step 300).);
and (ii) activating, in response to the determination, a plurality of contract- pipeline activities comprising at least one of drafting contract terms, assigning stakeholders, or populating contractual data fields using the pipeline data propagated from the concept pipeline; ([0006] finalize contractual documents; assings clear roles and responsibilities; [0067] the commodity manager assigns clear roles and responsibilities to each member based on the specific requirements of the project.; [0097] The commodity manager may prepare a contract based on the negotiation (step 410) by preparing contractual documentation. Preparing contractual documentation may further include, for example, preparing operational documentation, obtaining sponsor authorization the contractual documentation and operational documentation, obtaining supplier authorization, obtaining internal signatures, and/or informing the affected business unit of the completion of the contractual documentation. In one embodiment, the organization may prepare finalized contractual documents (step 412) based on the culmination of contractual documentation that was prepared for the various authorizations.);
and refining, by the at least one processor, operation of the concept pipeline by updating at least one of the feature values or the threshold values based on performance feedback received from the contract pipeline. ([0017] Moreover, any part of the functions or steps may be performed manually, automated, using hardware and software, and/or outsourced to or performed by one or more third parties.; [0021] In addition to the components described above, PE system 110 may further include one or more of the following: a host server or other computing systems including a processor for processing digital data; a memory coupled to the processor for storing digital data; an input digitizer coupled to the processor for inputting digital data; an application program stored in the memory and accessible by the processor for directing processing of digital data by the processor; a display device coupled to the processor and memory for displaying information derived from digital data processed by the processor; and a plurality of databases. Various databases used herein may include: user database 130, procurement database 145, as well as any number of other databases, both internal and external to PE system 110 useful in the operation of the invention as disclosed.; [0048] The strategic commodity plan may be further dependent on creating a communication plan, defining a business scope, and/or interviewing users for plan input.; [0050] The commodity manager develops a commodity profile (step 206). The commodity profile is constructed based on the process of developing an up-to-date view of spend in scope including analysis of spend, incumbent supplier performance, market drivers and dynamics, cost analysis and/or internal business requirements finalization. A commodity profile may include, for example, current spend, future spend, an acquisition cost model, a market analysis, a supplier profile, a supplier cost driver, and/or a supplier performance driver. The commodity manager may analyze current and future spends, build a total acquisition cost model, and/or perform an RFx development and analysis. The commodity manager may perform an analysis of the supply market, develop new supplier profiles, and/or identify supplier cost and performance drivers. Using the above steps, the commodity manager may update the strategic commodity plan to include data from the commodity profile.; [0065] To confirm resource availability, the commodity manager prepares a detailed resource plan. The resource plan may include, for example, an estimate of the personnel requirements to execute the project in light of a department's remaining fiscal year budget. Based on the resource plan, the project plan is updated to confirm resource availability and budget requirements. In one embodiment, the commodity manager may further build out, refine, and approve the project plan by eliminating redundant tasks with approval from commodity managers, strategic directors, and the effected business unit. The commodity manager updates the strategic commodity plan according to the analysis phase. Examiner notes that one of ordinary skill in the art would reasonably consider the strategic commodity plan disclosed by Britton as equivalent to a feature value from Applicant’s claim.).
Britton doesn’t teach:
configured to operate concurrently and exchange pipeline data through a structured interface;
(ii) analyzing the operational data using a trained machine-learning model selected from regression or classification models to generate feature values representing sourcing conditions;
and (iii) generating a total concept score representing a quantified quality of the concept by normalizing and weighting the feature values against evaluation criteria comprising at least cost-effectiveness, feasibility, and strategic alignment;
diagnosing, by the at least one processor, a current state of the concept pipeline by analyzing operating parameters of a resource planning system and detecting deviations or anomalies indicative of sourcing exceptions, the diagnosing comprising correlating the operating parameters with predefined threshold values stored in a database;
Makhida teaches:
configured to operate concurrently and exchange pipeline data through a structured interface; ([Abstract] a method, a system and a computer program product for Autonomous sourcing and Category management. The invention includes demand sensing and generation through a category workbench interface providing actionable insights for sourcing operation. The invention includes an AI engine configured for recommending a sourcing strategy through prediction analysis and auto negotiation in sourcing operation of Supply chain.; [0010] According to an embodiment, the present invention provides a method of autonomous sourcing in supply chain management. The method includes receiving a demand from at least one data source, triggering a sourcing module through a category workbench user interface for initiating at least one task based on the received demand, processing by an AI engine coupled to a processor, a plurality of historical data from a data lake based on one or more data models to generate code for a recommended strategy through prediction analysis, injecting by an intelligent bot, aggregated data patterns related to one or more object categories into the recommended sourcing strategy for generating at least one object characteristic data set. The method also includes identifying one or more suppliers for executing the recommended strategy based on the object characteristic data set; and encapsulating one or more recommended awarding scenario on the category workbench user interface for selection. The method includes receiving a response to a questionnaire based on the object characteristic data set from one or more recommended suppliers for identifying the one or more suppliers. The questionnaire is generated by the AI engine configured to process a historical query knowledge database based on a plurality of parameters and the object characteristic data set. The method also includes the step of injecting by a bot, one or more impact parameters capable of modifying at least one of the actionable insights, the recommended strategy, the data patterns or the awarding scenario. Further, the method includes recommending a negotiation strategy through an auto-negotiator based on a negotiation script generated by the AI engine wherein the awarding scenario is encapsulated based on execution of the negotiation strategy.);
(ii) analyzing the operational data using a trained machine-learning model selected from regression or classification models to generate feature values representing sourcing conditions; ([0092] In an embodiment, as part of the negotiation strategy the system generates a script to further negotiate with the suppliers, e.g., falling prices of crude, additional volume discounts from volume consolidation, sign on bonus for incumbents, pricing formula to use, best payment terms etc. The negotiation script is generated based on one or more negotiation data models trained through natural language processing (NLP) of a historical dataset with logistic regression and median calculations to predict recommendations. Further, the recommendations predict one or more optimum negotiation approach and most effective negotiation parameters.; [0095] In an embodiment, the present invention provides the autonomous sourcing method that includes bid optimization through analysis of constraints.; [0142] In an exemplary embodiment the auto-negotiator processes data scripts through the AI engine for predicting recommendation with logistic regression and median calculations. The data scripts adapt processing logic to each category enabling changing decision parameters and tool recommendations over time. The AI engine prediction caters to the possibilities of being selected by a user and average real savings. The recommendations predict one or more optimum negotiation approach (tender, auction, face to face etc.) and most effective negotiation levers/parameters (LPP, should cost, benchmarking).);
and (iii) generating a total concept score representing a quantified quality of the concept by normalizing and weighting the feature values against evaluation criteria comprising at least cost-effectiveness, feasibility, and strategic alignment; ([0069] In an embodiment, the recommended strategy is determined based on data points including evaluation of operational objectives, total Cost of Ownership and lifecycle, engagement and pricing models, compliance levels, analysis of historical policies and strategies, consumption patterns, behaviour and performance data, opportunities for consolidation of volumes across geographies, business units, product and service categories, volume tier discounts, new technologies, substitute products, low cost alternatives, standardization or reuse opportunities, benchmarks for resource qualifications and experience, intervals for price negotiations, futures, forwards, and options to fix or cap prices of commodity purchases in liquid markets, currency hedging for materials which are predominantly imported, Value chain for opportunities for Vertical integration, Should cost by leveraging data model to negotiate on billing rates, material and equipment price, supplier mark-up/profit, and current inventory management practices.; [Claim 6] The method of claim 5 wherein the AI engine makes a supplier recommendation based on a supplier score (Si)
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determined by: score for a Supplier
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where Si is the score of the supplier, Wi=Weights of the supplier on attribute Xi, Xi is the attributes or the criteria Xi is normalized using a scaler Zi as:
where μi is the normal central tendency (Mean/Average in this case) and σi is the standard deviation of the distribution of that attribute.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Britton with Makhida’s features listed above. One would’ve been motivated to do so in order to meet this demand, target quantity (number of units), for each such item across multiple suppliers with cheapest possible cost (Makhida; [0095]). By incorporating the teachings of Makhida, one would’ve been able to run the pipelines concurrently, perform analysis using trained machine learning to generate feature values representing sourcing conditions, and generate a total score to use for evaluation.
Wodetzki teaches:
diagnosing, by the at least one processor, a current state of the concept pipeline by analyzing operating parameters of a resource planning system and detecting deviations or anomalies indicative of sourcing exceptions, ([Column 27, Lines 42-45] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor.; [Table 1] Contract Status; [Column 7, Line 44-56] Next, gaining visibility into the contracting outcomes reflected in a party's contract portfolio enables that party to protect its business from dangerous contracts by providing users with information about how to improve future contract drafting and negotiation processes and outcomes. A platform that combines automated contract analysis and automated contracting processes allows a party to easily create high quality, low risk documents through an intuitive, interview-driven platform. This lowers costs, reduces bottlenecks, and empowers business users. For example, insights gained through contract portfolio analysis can be used to “harvest” clauses from legacy contracts and automatically feed a clause library.; [Column 19, Lines 20-21] analyze individual contracts and the trends across an entire contract portfolio; [Column 24, Lines 21-23] Contract risk scores are processed by a rules engine to trigger alerts for unacceptable deviation from risk guidelines.);
the diagnosing comprising correlating the operating parameters with predefined threshold values stored in a database; ([Column 2, Lines 37-44] determining prevailing terms of the contract by evaluating all child contract transaction objects, and the data contained therein, chronologically to build a single set of terms for storage in the contract object and updating the single set of terms as those terms are modified by active, chronologically-later contract transaction objects; [Column 24, Lines 9-10] Further, the contract management system can alert a user when a contract risk score exceeds a threshold value.; [Column 23, Line 60-Column 24, Line 1] Inputs to the score include external data, including obtaining from public and private sources updating in real time. For example, for the data model variable CounterpartyCountry (which describes the country of origin of a contract counterparty) the credit risk score associate with various countries can be modified in realtime based upon platform monitor news, government databases (for example, countries under embargo, countries identified as rogue states or along a spectrum of business friendliness).; [Column 26, Lines 20-31] In embodiments of the of the present platform, blockchain technology is incorporated to create a ledger of contract timeline and performance activities. Blockchain is a distributed database that maintains a continuously growing list of ordered records called blocks. Each block contains a timestamp and a link to a previous block. By design, blockchains are inherently resistant to modification of the data—once recorded, the data in a block cannot be altered retroactively. Blockchains are an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Britton with Wodetzki’s features listed above. One would’ve been motivated to do so in order to evaluate the words of each contract document containing legal classifications to determine a type of the legal classification, wherein at least one of the types of legal classification is an obligation, a right, a representation, an act or deed, and a definition (Wodetzki; [Column 3, Lines 55-59]). By incorporating the teachings of Wodetzki, one would’ve been able to diagnose a current state of the concept pipeline by analyzing operating parameters and detecting deviations or anomalies indicative of sourcing exceptions using predetermined threshold values.
Regarding claims 2/11/19: Britton further teaches:
wherein synchronizing the plurality of interacting pipelines further comprises: detecting the trigger; in response to the trigger, activating a signal to initiate one or more activities in the contract pipeline. ([Column 14, Lines 52-59] FormationMechanism Some contracts may be finished and signed but not intended to “start” until some pre- condition is satisfied, a concept lawyers call a “condition precedent to formation”, and in this scenario, you may find wording indicating the mechanism by which the “official” formation of the contract will be triggered. ExecutionMechanism A statement indicating how the parties may execute or sign the contract. NoticeMechanism A description of the agreed mechanisms for sending notices under the contract.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Britton with Wodetzki’s features listed above. One would’ve been motivated to do so in order to implement legal classifications (Wodetzki; [Column 14, Lines 1-2]). By incorporating the teachings of Wodetzki, one would’ve been able to synchronize the pipelines using triggers.
Regarding claims 3/12/20: Britton further teaches:
wherein the trigger occurs at a stage in the concept pipeline to ensure that activities associated with development of the concept align with development of contract terms or requirements ([0069] The commodity manager may select potential suppliers (step 308) by reviewing requirements, identifying potential suppliers, providing a list of potential suppliers to a relationship manager, conducting an internal analysis, ensuring compliance with procurement rules of engagement, creating an RFx, and/or evaluating a response to the request for information.),
and includes at least one of:
a transition trigger, wherein the concept progresses to a point where a formal contract is needed to formalize an agreement between parties;
an awards trigger, wherein one or more vendors are identified; ([0095] The commodity manager may select a supplier based on supplier negotiation (step 406) by ensuring that the selection complies with the needs of the relationship manager, a business unit sales team, a vendor relationship manager, and/or any other designated internal or external party. The commodity manager further finalizes terms and conditions to develop a final recommendation to present to a sponsor for approval. When the selection is approved, the commodity manager may notify the selected supplier as well as notify those suppliers that were not selected.);
a decision trigger, wherein a sourcing decision is generated;
a contract trigger, wherein it is determined that a formal contract is necessary; ([0079] When needed, an NDA template is intended to protect the organization's sensitive information and intentions during the sourcing event and ensure that confidential information is not disclosed by the suppliers. A NDA should be used for all new projects, even when the supplier is an incumbent supplier and has an existing NDA. Practitioners will appreciate that due to legal inconsistencies among differing nations, the NDA may be negotiated with the approval of an authorized company official and/or group (e.g., Legal, Contracts, Policy and Compliance Group, etc.).);
or a communication trigger, wherein synchronization between the plurality of interacting pipelines includes communication of one or more critical details, requirements, or information related to a procurement decision, a selected vendor, one or more terms, and/or one or more conditions. ([0098] When a contract is finalized, an electronic representation of the contract may be stored in a database record (step 414). The electronic representation may subsequently be distributed among stakeholders and/or any other designated internal or external recipient.).
Regarding claims 4/13: Britton further teaches:
wherein information gathered by the concept pipeline, including at least one of supplier evaluations, vendor selections, or procurement requirements, is transferred to the contract pipeline for drafting and negotiation. ([0088] a selection criteria template may be used when conducting supplier evaluations to narrow down a list of potential suppliers according to who is most likely able to deliver within the established business requirements.; [0047] An opportunity assessment (step 202) is performed by collecting input from an organization's commodity managers, strategic directors, business unit budget managers, internal customers, and any other entity related to an opportunity. An opportunity assessment further includes developing an existing commodity strategy, assessing procurement resource requirements, and obtaining approval from internal customers.; [0025] User 100 may be, for example, a department manager who accesses PE system 110 to perform analysis, negotiate and contract, select suppliers, procure goods and/or services, and reconcile procurement activities.).
Regarding claims 5/14: Britton doesn’t teach:
wherein the concept pipeline and the contract pipeline operate concurrently while at least one activation is made between the interacting pipelines to expedite a procurement.
Makhida teaches:
wherein the concept pipeline and the contract pipeline operate concurrently ([Abstract] a method, a system and a computer program product for Autonomous sourcing and Category management. The invention includes demand sensing and generation through a category workbench interface providing actionable insights for sourcing operation. The invention includes an AI engine configured for recommending a sourcing strategy through prediction analysis and auto negotiation in sourcing operation of Supply chain.; [0010] According to an embodiment, the present invention provides a method of autonomous sourcing in supply chain management. The method includes receiving a demand from at least one data source, triggering a sourcing module through a category workbench user interface for initiating at least one task based on the received demand, processing by an AI engine coupled to a processor, a plurality of historical data from a data lake based on one or more data models to generate code for a recommended strategy through prediction analysis, injecting by an intelligent bot, aggregated data patterns related to one or more object categories into the recommended sourcing strategy for generating at least one object characteristic data set. The method also includes identifying one or more suppliers for executing the recommended strategy based on the object characteristic data set; and encapsulating one or more recommended awarding scenario on the category workbench user interface for selection. The method includes receiving a response to a questionnaire based on the object characteristic data set from one or more recommended suppliers for identifying the one or more suppliers. The questionnaire is generated by the AI engine configured to process a historical query knowledge database based on a plurality of parameters and the object characteristic data set. The method also includes the step of injecting by a bot, one or more impact parameters capable of modifying at least one of the actionable insights, the recommended strategy, the data patterns or the awarding scenario. Further, the method includes recommending a negotiation strategy through an auto-negotiator based on a negotiation script generated by the AI engine wherein the awarding scenario is encapsulated based on execution of the negotiation strategy.);
while at least one activation is made between the interacting pipelines to expedite a procurement. ([0077] In an exemplary embodiment, the autonomous sourcing and category management system 100 of the present invention is configured for analyzing impact of a plurality of varying parameters (changes in pricing, supply demand) on sourcing decisions factors to predict the sourcing strategy. The varying parameters include market dynamics and internal spend drivers across suppliers, parts, products, commodities, and business units/plants across various Regions. The invention evaluates leading indicators in the market applicable for the Category in conjunction with correlated factors to provide predictions on changes in material costs, product margins, supply constraints, supplier financial risk, etc. to enable proactive procurement decisions.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Britton with Makhida’s features listed above. One would’ve been motivated to do so in order to avoid cost pressures, minimize new risks, or lock-in savings (Makhida; [0077]). By incorporating the teachings of Makhida, one would’ve been able to operate pipelines concurrently and expedite procurement.
Regarding claims 6/15: Britton further teaches:
wherein at least one activation is made between the interacting pipelines to expedite a procurement comprising at least one of:
identifying a need for a conceptual product or service; ([0046] With reference to FIG. 2, the disclosed procurement process analyzes 500 an organization's existing business and sourcing strategies, business unit requirements, spend and supply markets, and cost models in order to develop sourcing strategies for a commodity. Specifically, such analysis includes performing a commodity analysis using an opportunity assessment, a commodity plan, a commodity profile, and/or a commodity strategy (FIG. 1B, step 200).; [0048] The commodity manager develops a strategic commodity plan (step 204) which, in one embodiment, is developed following the opportunity assessment. The strategic commodity plan may include, for example, a user requirement, a specification, a populated template, a requirements document, a budget estimate, and/or the like.);
creating a request for the conceptual product or service; or ([0051] As used herein, an RFx may include a Request for Proposal (RFP), but may further include any type of request designed to collect information in the form of responses from suppliers.);
obtaining one or more approvals required within the concept pipeline; and ([0047] An opportunity assessment further includes developing an existing commodity strategy, assessing procurement resource requirements, and obtaining approval from internal customers. As used herein, "internal customers" include members of an organization such as, for example, employees, managers, officers, consultants, vendors, and any other third parties designated by the organization to participate in procurement activities.);
wherein the contract pipeline comprises: approving a requisition, contract drafting, or contract award. ([0089] With reference to FIG. 4, a process for negotiating a contract 400 may include, for example, developing a negotiation strategy, conducting the negotiation, selecting a supplier based on the negotiation, obtaining funding, preparing a contract based on the negotiation, recording the contract, and/or conducting enablement activities for the supplier (FIG. 1B, step 400).).
Regarding claims 7/16: Britton further teaches:
comprising: providing a concept to contract (C2C) prediction engine for generating scores; ([0087] A supplier analysis tool may be employed at this stage to conduct supplier financial assessment, reduce risk related to supplier financial stability, and to provide insight into supplier cost structures.);
scoring, by the C2C prediction engine, one or more concepts as part of an assessment process to streamline decision-making and provide a quantitative measurement of the concept; ([0087] The supplier analysis tool provides a score that based on a variety of inputs relating to the supplier's overall health. Such inputs may include, for example, current assets, current liabilities, total assets, long term debt, retained earnings, net sales revenue, earnings before interest and taxes, market value of equity, and the like. The tool provides a score based on whether the supplier is a public manufacturer, private manufacturer, or non-manufacturer and represents an overall estimated likelihood of bankruptcy.);
and prioritizing the one or more concepts based on a score for at least one of a viability of a concept, an alignment of a concept with at least one organizational goal, or a potential impact of a concept, and each score provides a quantitative measure to compare different concepts and to guide decision-makers in selecting a concept to proceed. ([0088] The selection criteria template also fosters the creation of a level playing field through the ranking of suppliers and delivering optimum sourcing solutions. In one embodiment, selection criteria include weighted hurdles and ratings based on how well the suppliers can provide the needed goods and/or services. Creating supplier selections can be further broken down into two stages. The first stage translates business requirements into specific selection criteria. The second state includes the weighted selection criteria and assessing the supplier responses.).
Claim(s) 8/9/17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Britton et al. (US 20140149170 A1, hereinafter “Britton”), in view of Makhida et al. (US 20220027826 A1, hereinafter “Makhida”), in further view of Wodetzki et al. (US 11416956 B2, hereinafter “Wodetzki”), as applied to claims 7 and 10 above, in further view of Davar et al. (US 20180158004 A1, hereinafter “Davar”).
Regarding claims 8: Britton further teaches:
obtaining a set of evaluation criteria ([0071] The commodity manager may develop evaluation criteria (step 312) for a RFx by reviewing existing RFx templates, identifying RFx criteria, modifying new sourcing strategy, and confirming that the evaluation criteria with stakeholders.);
and key performance indicators (KPIs) that align with one or more objectives related to cost-effectiveness, strategic alignment, feasibility, and potential impact; ([0016] FIG. 7 is a process flow diagram of exemplary steps for determining metrics to be collected and tracked over a determined period, analyzing the metrics and reporting the results.; [0052] Another valuable step in developing the commodity plan is to perform a Cost Benefit Analysis to calculate a financial impact of the project including all necessary investments.);
assigning each criterion a weight to reflect its relative importance; ([0072] evaluation criteria may include a weighting factor to emphasize criteria elements that are more influential to the selection process. For example, price may not be the primary consideration when selecting suppliers. The commodity manager consults with the business unit to define the weighting of selection criteria as well as to identify "show stoppers" and hurdles relating to suppliers that may interfere with the progression to the negotiation stage.);
assessing one or more concepts against each criterion; ([0088] a selection criteria template may be used when conducting supplier evaluations to narrow down a list of potential suppliers... The selection criteria template helps the sourcing team to evaluate potential suppliers against the business requirements expressed by stakeholders within the organization and translating the organization's needs into objective measures and matrices.);
assigning a plurality of numerical scores related to an alignment of the concept for each criterion; ([0087] A supplier analysis tool may be employed at this stage to conduct supplier financial assessment, reduce risk related to supplier financial stability, and to provide insight into supplier cost structures. The supplier analysis tool provides a score that based on a variety of inputs relating to the supplier's overall health.);
Britton doesn’t teach:
normalizing the plurality of numerical scores to ensure that different criterion are on a common scale;
and generating a total concept score comprising each of the plurality of numerical scores to quantify an overall quality of the concept.
Davar teaches:
normalizing the plurality of numerical scores to ensure that different criterion are on a common scale; ([0115] the UI may provide a scale, such as 1-10, for the user to quantify the response score.);
and generating a total concept score comprising each of the plurality of numerical scores to quantify an overall quality of the concept. ([0111] This Agent may calculate a vendor score as the sum of weighted scores for each answer, whereby each question is assigned a weight and each expected response value is assigned a score.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Britton with Davar’s features listed above. One would’ve been motivated to do so in order to automatically score the responses using data from the database (Davar; 0110]). By incorporating the teachings of Davar, one would’ve been able to use scores to support vendor selection decisions.
Regarding Claim 9: Britton further teaches:
and provides transparency to enhance buy-in or support for selected concepts. ([0057] A Point of Arrival (POA) vision statement clarifies precisely what the optimal result will look and feel like when it is working well. Everyone on the sourcing team should be aligned with a central vision of the optimal result. The POA vision statement answers the questions regarding where the organization wants to be, what the destination is, and what the best strategy is to get there. The POA vision statement helps to ensure that there is a clear understanding of how the supplier is behaving, how the sourcing results should appear, and how it is all measured.; [0067] With the sourcing team identified, the commodity manager assigns clear roles and responsibilities to each member based on the specific requirements of the project. Also, the commodity manager ensures that the team comprises an appropriate level of representation as well as future buy-in.).
Britton doesn’t teach:
wherein the total concept score allows decision-makers to prioritize concepts
Davar further teaches:
wherein the total concept score allows decision-makers to prioritize concepts ([0072] a recommendation engine selecting vendors that are a best fit; [0074] the system selects the vendors for the user by determining which vendors best match a user's query or are likely to be a good fit for the potential client. For example, the system may select the top five vendors ranked by the recommendation engine. [0118] The Agent may then tally the number of positives/negatives or sum the quantified scores to evaluate each vendor. The calculated scores may be displayed on a website in detail or used to rank the vendors overall.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Britton with Davar’s features listed above. One would’ve been motivated to do so in order to calculate the recommendation metric for each vendor based on the sum of similarity values between the user and each client of that vendor (Davar; [0078]). By incorporating the teachings of Davar, one would’ve been able to use scores to minimize risk and expedite the vendor selection process.
Regarding claim 17: Britton further teaches:
obtain a set of evaluation criteria ([0071] The commodity manager may develop evaluation criteria (step 312) for a RFx by reviewing existing RFx templates, identifying RFx criteria, modifying new sourcing strategy, and confirming that the evaluation criteria with stakeholders.);
and key performance indicators (KPIs) that align with one or more objectives related to factors like cost-effectiveness, strategic alignment, feasibility, and potential impact; ([0016] FIG. 7 is a process flow diagram of exemplary steps for determining metrics to be collected and tracked over a determined period, analyzing the metrics and reporting the results.; [0052] Another valuable step in developing the commodity plan is to perform a Cost Benefit Analysis to calculate a financial impact of the project including all necessary investments.);
assign each criterion a weight to reflect its relative importance; ([0072] Ithe evaluation criteria may include a weighting factor to emphasize criteria elements that are more influential to the selection process. For example, price may not be the primary consideration when selecting suppliers. The commodity manager consults with the business unit to define the weighting of selection criteria as well as to identify "show stoppers" and hurdles relating to suppliers that may interfere with the progression to the negotiation stage.);
assess one or more concepts against each criterion; ([0088] a selection criteria template may be used when conducting supplier evaluations to narrow down a list of potential suppliers... The selection criteria template helps the sourcing team to evaluate potential suppliers against the business requirements expressed by stakeholders within the organization and translating the organization's needs into objective measures and matrices.);
assign a plurality of numerical scores related to an alignment of the concept for each criterion; ([0087] A supplier analysis tool may be employed at this stage to conduct supplier financial assessment, reduce risk related to supplier financial stability, and to provide insight into supplier cost structures. The supplier analysis tool provides a score that based on a variety of inputs relating to the supplier's overall health.);
and provides transparency to enhance buy-in or support for selected concepts. ([0057] A Point of Arrival (POA) vision statement clarifies precisely what the optimal result will look and feel like when it is working well. Everyone on the sourcing team should be aligned with a central vision of the optimal result. The POA vision statement answers the questions regarding where the organization wants to be, what the destination is, and what the best strategy is to get there. The POA vision statement helps to ensure that there is a clear understanding of how the supplier is behaving, how the sourcing results should appear, and how it is all measured.; [0067] With the sourcing team identified, the commodity manager assigns clear roles and responsibilities to each member based on the specific requirements of the project. Also, the commodity manager ensures that the team comprises an appropriate level of representation as well as future buy-in.).
Britton doesn’t teach:
normalize the plurality of numerical scores to ensure that different criterion are on a common scale;
generating a total concept score comprising each of the plurality of numerical scores to quantify an overall quality of the concept,
wherein the total concept score allows decision-makers to prioritize concepts
Davar further teaches:
normalize the plurality of numerical scores to ensure that different criterion are on a common scale; ([0115] the UI may provide a scale, such as 1-10, for the user to quantify the response score.);
generate a total concept score comprising each of the plurality of numerical scores to quantify an overall quality of the concept, ([0111] This Agent may calculate a vendor score as the sum of weighted scores for each answer, whereby each question is assigned a weight and each expected response value is assigned a score.);
wherein the total concept score allows decision-makers to prioritize concepts, ([0072] a recommendation engine selecting vendors that are a best fit; [0074] the system selects the vendors for the user by determining which vendors best match a user's query or are likely to be a good fit for the potential client. For example, the system may select the top five vendors ranked by the recommendation engine. [0118] The Agent may then tally the number of positives/negatives or sum the quantified scores to evaluate each vendor. The calculated scores may be displayed on a website in detail or used to rank the vendors overall.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Britton with Davar’s features listed above. One would’ve been motivated to do so in order to automatically score the responses using data from the database (Davar; 0110]), and to calculate the recommendation metric for each vendor based on the sum of similarity values between the user and each client of that vendor (Davar; [0078]). By incorporating the teachings of Davar, one would’ve been able to use scores to support and expedite vendor selection decisions, and to minimize risk.
Conclusion
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 concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on (571)270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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