DETAILED ACTION
Status of the Application
The following is a non-Final Office Action. In response to Examiner's communication of January 28, 2026, Applicant, on April 10, 2026, amended claims 1 & 16, canceled claims 14 & 19, and added claims 21-24. Claims 9 & 13 were previously canceled. Claims 1-8, 10-12, 15-18, & 20-24 are now pending in this application and have been rejected below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 10, 2026 has been entered.
Response to Amendment
Applicant's amendments are sufficient to overcome the 35 USC 112, second paragraph, rejections set forth in the previous action. Therefore, these rejections are withdrawn.
Applicant's amendments are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action. Therefore, these rejections are maintained below.
Applicant's amendments render moot the 35 USC 103 rejections set forth in the previous action. Therefore, new grounds for rejection necessitated by Applicant’s amendment are set forth below.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC § 101 rejections have been fully considered, but they are not persuasive.
Applicant argues that the claims are not directed to an abstract idea, but rather to a specific, concrete data processing pipeline that provides a technological improvement to computer systems, and the amended claims 1 and 16 recite the steps of utilizing an NLP model to translate an input scenario into questions, and then utilizing the NLP model to automatically extract parameters and inputs to dynamically configure a simulation model, this specific pipeline cannot be performed in the human mind, nor is it a conventional business practice, and a human cannot mentally execute a Natural Language Processing algorithm to parse unstructured text, translate it into discrete machine sub-queries, and algorithmically extract structured data parameters to instantiate a predictive neural network. Examiner respectfully disagrees.
The claims are not directed to an improvement in computer technology, but rather, for the reasons detailed below pursuant to the Prong 1 of Step 2A, the claims recite the abstract mental processes, certain method of organizing human activity, and mathematical concept of idea of simulating a pandemic response supply chain by receiving information regarding a pandemic scenario, including information defined as a question, inputs, parameters related to the pandemic scenario, and data related to previous pandemic scenarios, translating input scenario into questions, extracting parameters of the questions, applying a mathematical function to transform the data, normalizing the data, generating and/or load and apply a supply chain operation model to simulate the pandemic scenario based on data and received information, and presenting the results of the simulation. Simulating a pandemic response supply chain is not a problem specifically arising in or necessarily rooted in computer technology, instead simulating a pandemic response supply chain is a business problem that is a certain method of organizing human activity including sales activity of a supply chain and as claimed, but for the generic computer components implementing the abstract idea (e.g., the NLP referred to by Applicant), can be implemented mentally without the use of a computer.
Examiner notes, despite Applicant’s assertions, the claims do not recite limitations instantiating a predictive neural network.
The recitations of “processed by a natural language processing (NLP) model,” “based on applying the NLP model,” “automatically,” “with the NLP model,” and “via the user interface” are additional elements beyond the recited abstract idea, and individually and when viewed as an ordered combination, pursuant to the broadest reasonable interpretation, these additional elements are no more than applying the abstract idea with generic computer components, which is not sufficient to integrate an abstract idea into a practical Application nor amount to significantly more.
The recitation of a generic computer component of NLP and a user interface to implement the abstract idea does not make the claims directed to an improvement in computer technology. The MPEP makes clear “[m]ere automation of manual processes” is not an improvement in computer technology. See MPEP 2106.05(a). The limitations discussed above recite a mental process and certain method of organizing human activity regardless of whether the claims also recite generic computer components, such as NLP or a user interface, to implement abstract idea rather than performing the process mentally. Moreover, as in the claims at issue in Electric Power Group, the present claims are not focused on a specific improvement in computers or any other technology, but instead on certain independently abstract ideas that simply invokes computers as tools to implement the abstract idea. Electric Power Group, LLC v. Alstom S.A., et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a). Accordingly, the limitations and the alleged solution are directed to a mental process and certain method of organizing human activity.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
Under prong 1 of Step 2A, claim 1, and similarly claims 2-8, 10-12, 15-18, & 20-24, recites “[a] pandemic response supply chain simulation … for simulating multiple scenarios during a pandemic comprising: … receive inputs from a user and present information to the user, …: receive a pandemic scenario related to a pandemic …, wherein the pandemic scenario is defined as a question configured … model; translate the input pandemic scenario into one or more questions …; automatically extract one or more inputs and one or more parameters from the one or more questions by processing the one or more questions …; retrieve or receive one or more data related to previous pandemic scenarios; apply a mathematical function to transform the data related to previous pandemic scenarios into a useable format that is consistent for use in a supply chain operational model; normalise the transformed data for use in the supply chain operational model; generate and/or load the supply chain operation model based on the inputs, parameters and the normalized transformed data related to previous pandemic scenarios, automatically apply the supply chain operation model to process the inputs and parameters, wherein the model is configured to simulate the pandemic scenario based on the inputs, parameters and the data related to the previous pandemic scenarios, generate one or more results of the simulation by the supply chain operational model, present … simulation results from the model.” Claims 1-8, 10-12, 15-18, & 20-24, in view of the claim limitations, recite the abstract idea of simulating a pandemic response supply chain by receiving information regarding a pandemic scenario, including information defined as a question, inputs, parameters related to the pandemic scenario, and data related to previous pandemic scenarios, translating input scenario into questions, extracting parameters of the questions, applying a mathematical function to transform the data, normalizing the data, generating and/or load and apply a supply chain operation model to simulate the pandemic scenario based on data and received information, and presenting the results of the simulation.
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited simulating a pandemic response supply chain by receiving information regarding a pandemic scenario, including information defined as a question, inputs, parameters related to the pandemic scenario, and data related to previous pandemic scenarios, translating input scenario into questions, extracting parameters of the questions, applying a mathematical function to transform the data, normalizing the data, generating and/or load and apply a supply chain operation model to simulate the pandemic scenario based on data and received information, and presenting the results of the simulation could all be reasonably interpreted as a human observing information regarding a pandemic scenario, questions, inputs, parameters related to the pandemic scenario, and data related to previous pandemic scenarios, a human performing an evaluation on the data to translate the input into a questions, extract parameters from the questions, transform the data using a mathematical function and to normalize the data, a human using judgment to generating and/or load and apply a supply chain operation model to simulate the pandemic scenario based on the observed information, and a human outputting the results manually and/or with a pen and paper; therefore, the claims recite a mental processes. In addition, the claims manage the response supply chain based on the demand of a pandemic and question regarding a pandemic, which manages the sales and marketing activity of a supply chain and human behavior of the demand of people during a pandemic, and thus, the claims recite a certain method of organizing human activity. Moreover, the mathematical function is a recitation of a mathematical equation or formula, and thus, the claims recite a mathematical concept. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2-8, 10-12, 15, 17, 18, & 20-24, recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the generic computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper. Accordingly, since the claims recite mental processes, a certain method of organizing human activity, and a mathematical concept, the claims recite an abstract idea under the first prong of Step 2A.
Under the second prong of Step 2A the claims recite the additional elements beyond the recited abstract idea of This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] … system … comprising: a computing apparatus comprising a memory unit and a processing unit arranged in communication with the memory unit, a user interface operatively coupled to the processing unit, the user interface configured to …, the memory unit storing processing unit executable instructions that, the processing unit, configured to execute the instructions causing the system to,” “ via the user interface,” “processed by a natural language processing (NLP) model,” “based on applying the NLP model,” “automatically,” “with the NLP model,” and “on the user interface” in claim 1, and similarly in claims 2-8, 10-12, 15-18, & 20-24; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Further, these elements generally link the abstract idea to a field of use.
Under Step 2B, as noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s specification at p. 25, ln. 32-p. 26, ln. 2 (describing the invention can be embodied in a computer-implemented process or a machine, such as an electronic device, or a general-purpose computer or other device that provides a platform on which computer programs can be executed). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, electronic record keeping, storing and retrieving information in memory, and presenting offers, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used 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 claims as a whole amount to significantly more than the abstract idea itself.
Response to Arguments - Prior Art
Applicant’s arguments with respect to the prior art rejections have been fully considered, but they are now moot in view of new grounds for rejection necessitated by Applicant’s amendments set forth below.
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 1-8, 10-12, 15-18, & 20-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under prong 1 of Step 2A, claim 1, and similarly claims 2-8, 10-12, 15-18, & 20-24, recites “[a] pandemic response supply chain simulation … for simulating multiple scenarios during a pandemic comprising: … receive inputs from a user and present information to the user, …: receive a pandemic scenario related to a pandemic …, wherein the pandemic scenario is defined as a question configured … model; translate the input pandemic scenario into one or more questions …; automatically extract one or more inputs and one or more parameters from the one or more questions by processing the one or more questions …; retrieve or receive one or more data related to previous pandemic scenarios; apply a mathematical function to transform the data related to previous pandemic scenarios into a useable format that is consistent for use in a supply chain operational model; normalise the transformed data for use in the supply chain operational model; generate and/or load the supply chain operation model based on the inputs, parameters and the normalized transformed data related to previous pandemic scenarios, automatically apply the supply chain operation model to process the inputs and parameters, wherein the model is configured to simulate the pandemic scenario based on the inputs, parameters and the data related to the previous pandemic scenarios, generate one or more results of the simulation by the supply chain operational model, present … simulation results from the model.” Claims 1-8, 10-12, 15-18, & 20-24, in view of the claim limitations, recite the abstract idea of simulating a pandemic response supply chain by receiving information regarding a pandemic scenario, including information defined as a question, inputs, parameters related to the pandemic scenario, and data related to previous pandemic scenarios, translating input scenario into questions, extracting parameters of the questions, applying a mathematical function to transform the data, normalizing the data, generating and/or load and apply a supply chain operation model to simulate the pandemic scenario based on data and received information, and presenting the results of the simulation.
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited simulating a pandemic response supply chain by receiving information regarding a pandemic scenario, including information defined as a question, inputs, parameters related to the pandemic scenario, and data related to previous pandemic scenarios, translating input scenario into questions, extracting parameters of the questions, applying a mathematical function to transform the data, normalizing the data, generating and/or load and apply a supply chain operation model to simulate the pandemic scenario based on data and received information, and presenting the results of the simulation could all be reasonably interpreted as a human observing information regarding a pandemic scenario, questions, inputs, parameters related to the pandemic scenario, and data related to previous pandemic scenarios, a human performing an evaluation on the data to translate the input into a questions, extract parameters from the questions, transform the data using a mathematical function and to normalize the data, a human using judgment to generating and/or load and apply a supply chain operation model to simulate the pandemic scenario based on the observed information, and a human outputting the results manually and/or with a pen and paper; therefore, the claims recite a mental processes. In addition, the claims manage the response supply chain based on the demand of a pandemic and question regarding a pandemic, which manages the sales and marketing activity of a supply chain and human behavior of the demand of people during a pandemic, and thus, the claims recite a certain method of organizing human activity. Moreover, the mathematical function is a recitation of a mathematical equation or formula, and thus, the claims recite a mathematical concept. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2-8, 10-12, 15, 17, 18, & 20-24, recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the generic computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper. Accordingly, since the claims recite mental processes, a certain method of organizing human activity, and a mathematical concept, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] … system … comprising: a computing apparatus comprising a memory unit and a processing unit arranged in communication with the memory unit, a user interface operatively coupled to the processing unit, the user interface configured to …, the memory unit storing processing unit executable instructions that, the processing unit, configured to execute the instructions causing the system to,” “ via the user interface,” “processed by a natural language processing (NLP) model,” “based on applying the NLP model,” “automatically,” “with the NLP model,” and “on the user interface” in claim 1, and similarly in claims 2-8, 10-12, & 15, “machine learning model” in claim 3, “neural network” in claim 4, and further, “[a] … method comprising,” “ via the user interface,” “processed by a natural language processing (NLP) model,” “based on applying the NLP model,” “automatically,” “with the NLP model,” and “on the user interface” in claim 16, and similarly in claims 17, 18, & 20; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Further, these elements generally link the abstract idea to a field of use. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-8, 10-12, 15, 17, 18, & 20-24 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s specification at p. 25, ln. 32-p. 26, ln. 2 (describing the invention can be embodied in a computer-implemented process or a machine, such as an electronic device, or a general-purpose computer or other device that provides a platform on which computer programs can be executed). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, electronic record keeping, storing and retrieving information in memory, and presenting offers, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-8, 10-12, 15, 17, 18, & 20-24 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used 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 claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-8, 10-12, 15-18, & 20-24 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7, 8, 10-13, 15-17, & 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Silverstein, et al. (US 20220092492 A1), hereinafter Silverstein, in view of Smith, et al. (US 20210049532 A1), hereinafter Smith, in further view of Bhattacharyya, et al. (US 20210065091 A1), hereinafter Bhattacharyya.
Regarding claim 1, Silverstein discloses a pandemic response supply chain simulation system for simulating multiple scenarios during a pandemic comprising ([0015]):
a computing apparatus comprising a memory unit and a processing unit arranged in communication with the memory unit ([0015]),
a user interface operatively coupled to the processing unit, the user interface configured to receive inputs from a user and present information to the user ([0017], via user device 120, which may be a computing device, wherein controller 110 may receive an initial set of information (and/or a complete set of information) on the supply chain within this initial prompt from the user),
the memory unit storing processing unit executable instructions that, the processing unit, configured to execute the instructions causing the system to ([0015]):
receive a pandemic scenario related to a pandemic via the user interface, wherein the pandemic scenario is defined as a question ([0017], analyze a supply chain in response to a request and/or prompt from a user sent via user device 120, wherein controller 110 may receive an initial set of information (and/or a complete set of information) on the supply chain within this initial prompt from the user, [0022], controller 110 may receive a request from user device 120 that identifies a supply chain to analyze, after which controller 110 may gather (and/or receive) data on locations 136 and/or points in time 138 of supply chain, and then controller 110 may search repositories 140 for viral pandemics risks 132) to be processed by a natural language processing (NLP) model ([0022], wherein controller 110 may use natural language processing (NLP) techniques as known in the art and discussed herein to discover and/or ingest this data once discovered, [0042], NLP techniques 246 that controller 110 may use to identify whether data stored in repositories 140 relates to risks 132, e.g., ontological matching could be used to map semantic and/or syntactic features to determine that this viral pandemic outbreak is in a part of a country through which the supply chain exists to identify previous similar outbreaks in this region and/or of a similar type of pandemic outbreak);
translate the input pandemic scenario … based on applying the NLP model ([0022], controller 110 may receive a request from user device 120 that identifies a supply chain to analyze, after which controller 110 may gather (and/or receive) data on locations 136 and/or points in time 138 of supply chain, and then controller 110 may search repositories 140 for viral pandemics risks 132, wherein controller 110 may use natural language processing (NLP) techniques discussed herein to discover this data, [0042], NLP techniques 246 that controller 110 may use to identify whether data stored in repositories 140 relates to risks 132, e.g., ontological matching could be used to map semantic and/or syntactic features to determine that this viral pandemic outbreak is in a part of a country through which the supply chain exists to identify previous similar outbreaks in this region and/or of a similar type of pandemic outbreak);
automatically extract one or more inputs and one or more parameters … with the NLP model ([0022], controller 110 may search repositories 140 for viral pandemics risks 132, wherein controller 110 may use natural language processing (NLP) techniques discussed herein to ingest this data once discovered, [0042], NLP techniques 246 that controller 110 may use to identify whether data stored in repositories 140 relates to risks 132, e.g., ontological matching could be used to map semantic and/or syntactic features to determine that this viral pandemic outbreak is in a part of a country through which the supply chain exists to identify previous similar outbreaks in this region and/or of a similar type of pandemic outbreak);
retrieve or receive one or more data related to previous pandemic scenarios ([0030], controller 110 may determine that, e.g., risks 132 of a pandemic viral outbreak is starting in one portion of the world, and to execute simulations and identify likelihoods of impact, controller 110 may compare current data regarding the identified risks 132 against historical data regarding similar risks 132, [0046], Controller 110 may identify a plurality of risks 132 (304) that components 134 will move through locations 136 by requisite points in time 138, e.g., risks include viral pandemics, or the like, and controller 110 may further gather historical information on similar risks 132 from repositories 140 to simulate (e.g., using a neural network) how risks 132 spread across space and time);
… transform the data related to previous pandemic scenarios into a useable format that is consistent for use in a supply chain operational model;
… the transformed data for use in the supply chain operational model ([0030], controller 110 may gather data on the viral pandemic outbreak risk, controller 110 may further use machine learning models such as neural networks to compare current data regarding the identified risks 132 against historical data regarding similar risks 132 to execute these simulations and identify likelihoods of impact, [0037], risk data 240 may also include historical data that is related to risks (e.g., previous instances of events that were similar to a projected reality of risks, [0047], controller 110 may further gather historical information on similar risks 132 from repositories 140, and using this gathered information on risks 132, controller 110 may simulate (e.g., using a neural network) how risks 132 spread across space and time);
generate and/or load a supply chain operation model based on the extracted inputs, extracted parameters and the … transformed data related to previous pandemic scenarios ([0025], once controller 110 has identified (and gathered relevant data on) risks 132, controller 110 may simulate risks 132 through space and time to determine any overlaps and/or interrelations between how risks 132 propagate through space and time and how components 134 are at locations 136 at points in time 138, [0030]-[0031], controller 110 may further use machine learning models such as neural networks to compare current data regarding the identified risks 132 against historical data regarding similar risks 132 to execute these simulations and identify likelihoods of impact, and controller 110 may gather data on the viral pandemic outbreak risk, and execute simulations regarding how these risks 132 may impact supply chain 160, [0040], machine learning techniques 242 comprise algorithms or models that are generated by performing supervised, unsupervised, or semi-supervised training on a dataset, and subsequently applying the generated algorithm or model to identify risks to a supply chain and then simulating these risks over space and time to determine if these risks surpass a threshold likelihood of negatively impacting the supply chain, [0047], controller 110 calculates a likelihood of each of these risks 132 impacting the supply chain (306) spread across time and/or space such that any component 134 is not able to get to a respective location 136 by a respective point in time 138, e.g., controller 110 may gather information of risks 132 from one or more repositories 140 to determine a current state of risks 132 and/or potential evolutions of risks 132, and then using this gathered information on risks 132, controller 110 may simulate (e.g., using a neural network) how risks 132 spread across space and time);
automatically apply the supply chain operation model to process the extracted inputs and extracted parameters, wherein the model is configured to simulate the pandemic scenario based on the extracted inputs, extracted parameters and the data related to the previous pandemic scenarios ([0048], controller 110 may calculate the likelihood of risks 132 by simulating risks 132 and or components 134 as they move through locations 136 at times 138, controller 110 may execute a predetermined number of simulations, and/or controller 110 may execute simulations until controller 110 can calculate likelihoods with at least a threshold confidence score, and may simulate how risks 132 overlap with many components 134 across locations 136 and times 138, including how a delay to a first component 134 at a first location 136 at a first time 138 may impact other components 134 at other locations 136 at other times 138);
generate one or more results of the simulation by the supply chain operational model ([0049], controller 110 identifies one or more risks 132 that exceeds a threshold (308) that may indicate that the respective risk 132 is relatively likely to occur, such that at least one component 134 has less than a threshold chance to get to a respective location 136 by a respective point in time 138, and in response to identifying this risk 132, controller 110 identifies a first and second change to the supply chain to mitigate this risk (310), e.g., use a different supplier for component 134, to go to a different location 136, and/or alter an order of events to change required respective points in time 138 at which component 134 must be at a respective location 136 to satisfy supply chain); and
present on the user interface simulation results from the model ([0050], once controller 110 identifies a first and second change to the supply chain, controller 110 may provide these changes to a user (312), and controller 110 may provide these changes to user device 120 of user, such as causing user device 120 to provide a graphical indication via graphical user interface 122 of user device 12, [0026], once identified, controller 110 may provide a graphical indication of one or more of these changes to a user via graphical user interface 122 provided on user device 120, provide this graphical indication such that a likelihood of risk 132 of the original supply chain is displayed, a likelihood of risk 132 of the provided options is displayed, and/or an amount of determined inefficiency of the options is displayed in the graphical indication).
While Silverstein discloses all of the above, including receive a pandemic scenario related to a pandemic via the user interface, wherein the pandemic scenario is defined as a question to be processed by a natural language processing (NLP) model;
translate the input pandemic scenario … based on applying the NLP model;
automatically extract one or more inputs and one or more parameters … with the NLP model (as above), Silverstein does not appear to expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Smith.
Smith teaches translate the input [supply chain disruption] scenario into one or more questions based on applying the NLP model ([0021], supply advisor program 110 receives a query (step 204), via supply advisor user interface 106, and supply advisor program 110, uses one or more natural language processing (NLP) techniques to receive the query, e.g., if the user inputs a query such as “what should I do if we run out of PN 84F5691?,” [0017], supply advisor program 110 is a natural language based cognitive tool that receives a query from a user regarding a supply chain disruption);
automatically extract one or more inputs and one or more parameters from the one or more questions by processing the one or more questions with the NLP model ([0021], supply advisor program 110 uses one or more natural language processing (NLP) techniques to determine what the user is asking, e.g., if the user inputs a query such as “what should I do if we run out of PN 84F5691?” then supply advisor program 110 determines the query corresponds to a potential or upcoming shortage of part number 84F5691).
Silverstein and Smith are analogous fields of invention because both address the problem of analyzing the risks to supply chains. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Silverstein the ability to translate the input scenario into one or more questions based on applying the NLP model and extract one or more inputs and one or more parameters from the one or more questions by processing the one or more questions with the NLP model, as taught by Smith, 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 combination would produce the predictable results of translating the input scenario into one or more questions based on applying the NLP model and extracting one or more inputs and one or more parameters from the one or more questions by processing the one or more questions with the NLP model, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Silverstein with the aforementioned teachings of Smith in order to produce the added benefit of quickly analyzing available data to suggest solutions to a supply chain disruption, optimizing costs while minimizing length of time to resolution, and managing risk. [0011].
Further, while Silverstein discloses all of the above, including receive a pandemic scenario related to a pandemic via the user interface, wherein the pandemic scenario is defined as a question to be processed by a natural language processing (NLP) model;
… transform the data related to previous pandemic scenarios into a useable format that is consistent for use in a supply chain operational model;
… the transformed data for use in the supply chain operational model;
generate and/or load the supply chain operation model based on the inputs, parameters and the … transformed data related to previous pandemic scenarios (as above), Silverstein does not appear to expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Bhattacharyya.
Bhattacharyya teaches defined as a question ([0078], the system 100 may build a target business profile using the introductory questions),
apply a mathematical function to transform the data related to previous pandemic scenarios ([0173], [0181], [0196], in a process 2200 for determining an updated performance score of the target business, provides capability process data (step 2204), including the processes “Demographic Impacts of Pandemic”) into a useable format that is consistent ([0095]-[0097], after acquiring first data 215, including performance-related variable of an operational area or measurable output, in step 403, the system 100 can pre-process (e.g., reformat and clean) the first data 215, e.g., standardize the first data 215 to be on the same scale (e.g., 1-100, 1-10, etc.), [0101], [0106]-[0107], [0111], the data analysis (in step 405) may include performing a factor analysis process 800, wherein in step 801, the system 100 can determine a correlation matrix based on the variables 720, wherein the variance corresponds to the amount of variation between the variables 720 of each factor) for use in for use in ([0124], after the analysis weights have been determined using the factor analysis process 800, in step 425, the adaptive weights may be applied to the variables 720 for each competitor to determine the respective performance score) a supply chain operational model ([0281], [0289], a flowchart of process 3000 for determining an updated performance score of the target business, includes providing a pain-point and solution data (step 3004) including information, e.g., the business' supply chain issue);
normalise the transformed data ([0112], in step 804, the system can transform and apply the variance to the factors to determine the factor analysis weights, wherein the system 100 can apply a transformation function to the variance of each variable 720 to normalize the values, wherein the normalized variance value may represent a number on a scale having a minimum value of zero) for use in ([0124], after the analysis weights have been determined using the factor analysis process 800, in step 425, the adaptive weights may be applied to the variables 720 for each competitor to determine the respective performance score) the supply chain operational model ([0281], [0289], a flowchart of process 3000 for determining an updated performance score of the target business, includes providing a pain-point and solution data (step 3004) including information, e.g., the business' supply chain issue); and
the normalized transformed data ([0112], in step 804, the system can transform and apply the variance to the factors to determine the factor analysis weights, wherein the system 100 can apply a transformation function to the variance of each variable 720 to normalize the values, wherein the normalized variance value may represent a number on a scale having a minimum value of zero).
Silverstein and Bhattacharyya are analogous fields of invention because both address the problem of modeling performance of a supply chain and impact of a pandemic. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Silverstein the ability to apply a mathematical function to transform the data related to previous pandemic scenarios and normalize the transformed data for use in the supply chain operational model, as taught by Bhattacharyya, 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 combination would produce the predictable results of applying a mathematical function to transform the data related to previous pandemic scenarios and normalizing the transformed data for use in the supply chain operational model, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Silverstein with the aforementioned teachings of Bhattacharyya in order to produce the added benefit of assessing, evaluating a target business to provide strategy recommendations that improve the performance of the target business. [0002].
Regarding claim 2, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 1 (as above). Further, Silverstein discloses wherein the supply chain operational model is configured to simulate one or more effects on one or more elements of the supply chain based on the inputs, parameters for the pandemic scenario ([0048], controller 110 may calculate the likelihood of risks 132 by simulating risks 132 and or components 134 as they move through locations 136 at times 138, controller 110 may execute a predetermined number of simulations, and/or controller 110 may execute simulations until controller 110 can calculate likelihoods with at least a threshold confidence score, and may simulate how risks 132 overlap with many components 134 across locations 136 and times 138, including how a delay to a first component 134 at a first location 136 at a first time 138 may impact other components 134 at other locations 136 at other times 138).
Regarding claim 3, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 1 (as above). Further, Silverstein discloses the supply chain operation model is a machine learning model that is trained to simulate one or more effects on one or more elements of the supply chain based on the inputs, parameters for the pandemic scenario ([0040], machine learning techniques 242 comprise algorithms or models that are generated by performing supervised, unsupervised, or semi-supervised training on a dataset, and subsequently applying the generated algorithm or model to identify risks to a supply chain and then simulating these risks over space and time to determine if these risks surpass a threshold likelihood of negatively impacting the supply chain).
Regarding claim 4, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 2 (as above). Further, Silverstein discloses wherein the supply chain operation model is a neural network ([0040], Machine learning techniques 242 can include, but are not limited to artificial neural networks, [0047], using this gathered information on risks 132, controller 110 may simulate (e.g., using a neural network) how risks 132 spread across space and time).
Regarding claim 5, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 2 (as above). Further, Silverstein discloses wherein the simulation results comprise numerical data defining the effects on one or more elements of the supply chain for the specified pandemic ([0048], controller 110 may calculate the likelihood of risks 132 by simulating risks 132 and or components 134 as they move through locations 136 at times 138, controller 110 may execute a predetermined number of simulations, and/or controller 110 may execute simulations until controller 110 can calculate likelihoods with at least a threshold confidence score, [0031], via these simulations, controller 110 may calculate that there is a 5% likelihood of the viral pandemic impacting transportation of first component 134 through location 136 related to active node 170E, a 35% likelihood of the viral pandemic impacting a supply of fourth component 134 related to active node 170D, and a 25% likelihood of the hurricane impacting a final assembly at active node 170K. Controller 110 may identify that a risk threshold of the respective user and/or supply chain is 10%. Accordingly, controller 110 may identify that risk 132 related to node 170E may be ignored. Further, controller 110 may identify that risk 132 relating to node 170D exceeds the risk threshold and therein identify optional nodes 172A and 172B as potential changes to supply chain).
Regarding claim 7, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 1 (as above). Further, Silverstein discloses wherein the system is configured to: receive changes to inputs and/or parameters related to the pandemic scenario via the user interface ([0039], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, [0020]-[0021], once controller 110 gathers a sufficient amount of data on the supply chain such that controller 110 has fully defined the relationships between how components 134 need to move locations 136 and points in time 138 in order for the supply chain to be successful, controller 110 may identify one or more risks 132 to the supply chain, e.g., controller 110 may receive information regarding risks 132 from user device 120, e.g., controller 110 may receive a message from user device 120 that identifies and provides information on risks 132 which controller 110 is to analyze),
automatically update the supply chain operational model based on the received changes to the inputs and parameters ([0039-[0040], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, controller 110 may improve a process of calculating a likelihood of the risks impacting the supply chain, wherein machine learning techniques 242 can comprise algorithms or models that are generated by performing supervised, unsupervised, or semi-supervised training on a dataset, and subsequently applying the generated algorithm or model to identify risks to a supply chain and then simulating these risks over space and time to determine if these risks surpass a threshold likelihood of negatively impacting the supply chain, [0025], once controller 110 has identified (and gathered relevant data on) risks 132, controller 110 may simulate risks 132 through space and time to determine any overlaps and/or interrelations between how risks 132 propagate through space and time and how components 134 are at locations 136 at points in time 138 and calculate specific likelihoods of risks 132 impacting any given component 134 traversing through any given location 136 at any given point in time 138),
apply the updated model to generate new results related to an updated simulation of the pandemic scenario ([0039], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, controller 110 may improve a process of calculating a likelihood of the risks impacting the supply chain, e.g., controller 110 may use machine learning techniques 242 to improve a process of selecting changes to the supply chain that mitigate the risk and also are acceptable to a user, and controller 110 may use machine learning techniques 242 to improve a process of determining an amount of predicted inefficiency of an optional change to the supply chain, [0025], once controller 110 has identified (and gathered relevant data on) risks 132, controller 110 may calculate specific likelihoods of risks 132 impacting any given component 134 traversing through any given location 136 at any given point in time 138).
Regarding claim 8, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 1 (as above). Further, Silverstein discloses wherein the system is configured to update the supply chain operational model ([0039]-[0040], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, controller 110 may improve a process of calculating a likelihood of the risks impacting the supply chain, wherein machine learning techniques 242 can comprise algorithms or models that are generated by performing supervised, unsupervised, or semi-supervised training on a dataset, and subsequently applying the generated algorithm or model to identify risks to a supply chain and then simulating these risks over space and time to determine if these risks surpass a threshold likelihood of negatively impacting the supply chain) based on changes or updates to data related to the previous pandemic scenarios ([0039]-[0040], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, [0025], once controller 110 has identified (and gathered relevant data on) risks 132, controller 110 may simulate risks 132 through space and time to determine any overlaps and/or interrelations between how risks 132 propagate through space and time and how components 134 are at locations 136 at points in time 138 and calculate specific likelihoods of risks 132 impacting any given component 134 traversing through any given location 136 at any given point in time 138),
Regarding claim 10, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 7 (as above). Further, Silverstein discloses wherein the system is configured to update the supply chain operational model in real time model ([0039]-[0040], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, controller 110 may improve a process of calculating a likelihood of the risks impacting the supply chain, [0025], once controller 110 has identified (and gathered relevant data on) risks 132, controller 110 may simulate risks 132 through space and time to determine any overlaps and/or interrelations between how risks 132 propagate through space and time and how components 134 are at locations 136 at points in time 138 and calculate specific likelihoods of risks 132 impacting any given component 134 traversing through any given location 136 at any given point in time 138).
Regarding claim 11, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 1 (as above). Further, Silverstein discloses wherein simulation results are indicative of a supply chain response during the defined pandemic ([0025], once controller 110 has identified (and gathered relevant data on) risks 132, controller 110 may simulate risks 132 through space and time to determine any overlaps and/or interrelations between how risks 132 propagate through space and time and how components 134 are at locations 136 at points in time 138 and calculate specific likelihoods of risks 132 impacting any given component 134 traversing through any given location 136 at any given point in time 138, [0049], controller 110 identifies one or more risks 132 that exceeds a threshold (308) that may indicate that the respective risk 132 is relatively likely to occur, such that at least one component 134 has less than a threshold chance to get to a respective location 136 by a respective point in time 138, and in response to identifying this risk 132, controller 110 identifies a first and second change to the supply chain to mitigate this risk (310), e.g., use a different supplier for component 134, to go to a different location 136, and/or alter an order of events to change required respective points in time 138 at which component 134 must be at a respective location 136 to satisfy supply chain).
Regarding claim 12, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 7 (as above). Further, Silverstein discloses the system is configured to generate the new results ([0039]-[0040], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations) in real time ([0015], the controller may further identify one or more options that would maintain the supply chain with a minimized amount of inefficiency while avoiding the risk and autonomously execute one or more of these options in response to determining that a timing of the risk is relatively soon) based on changes to the inputs and/or parameters ([0039]-[0040], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, controller 110 may improve a process of calculating a likelihood of the risks impacting the supply chain, [0025], once controller 110 has identified (and gathered relevant data on) risks 132, controller 110 may simulate risks 132 through space and time to determine any overlaps and/or interrelations between how risks 132 propagate through space and time and how components 134 are at locations 136 at points in time 138 and calculate specific likelihoods of risks 132 impacting any given component 134 traversing through any given location 136 at any given point in time 138).
Regarding claim 15, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 1 (as above). Further, Silverstein discloses wherein the system is configured to: receive new inputs and parameters and/or a new scenario based on the results of the simulation ([0039], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, [0020]-[0021], once controller 110 gathers a sufficient amount of data on the supply chain such that controller 110 has fully defined the relationships between how components 134 need to move locations 136 and points in time 138 in order for the supply chain to be successful, controller 110 may identify one or more risks 132 to the supply chain, e.g., controller 110 may receive information regarding risks 132 from user device 120, e.g., controller 110 may receive a message from user device 120 that identifies and provides information on risks 132 which controller 110 is to analyze) and,
repeat the functions of claim 1 for the new inputs and parameters and/or new simulation scenario ([0039], machine learning techniques 242 may be used by controller 110 to improve a process of identify and simulating risk events for a supply chain over time and space across iterations, controller 110 may improve a process of calculating a likelihood of the risks impacting the supply chain, e.g., controller 110 may use machine learning techniques 242 to improve a process of selecting changes to the supply chain that mitigate the risk and also are acceptable to a user, and controller 110 may use machine learning techniques 242 to improve a process of determining an amount of predicted inefficiency of an optional change to the supply chain).
Regarding claims 16-18 & 20, these claims are substantially similar to claims 1, 7, 8, & 15, respectively, and thus, claims 16-18 & 20 are rejected for the reasons set forth above regarding claims 1, 7, 8, & 15. While claims 16-18 & 20 are directed to a method, Silverstein discloses a method as claimed. [0002], [0012].
Regarding claim 21, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 1 (as above). Further, Silverstein discloses wherein the system is further configured to: automatically interpret the data and alarm accordingly or send messages to other systems based on the results interpretation ([0050], once controller 110 identifies a first and second change to the supply chain, controller 110 may provide these changes to a user (312), and controller 110 may provide these changes to user device 120 of user, such as causing user device 120 to provide a graphical indication via graphical user interface 122 of user device 12, [0026], once identified, controller 110 may provide a graphical indication of one or more of these changes to a user via graphical user interface 122 provided on user device 120, provide this graphical indication such that a likelihood of risk 132 of the original supply chain is displayed, a likelihood of risk 132 of the provided options is displayed, and/or an amount of determined inefficiency of the options is displayed in the graphical indication).
Regarding claim 22, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 1 (as above). Further, while Silverstein discloses wherein the computing apparatus further comprises a model generator operatively coupled to the processing unit, wherein the model generator comprises a plurality of predefined models ([0030], controller 110 may further use machine learning models such as neural networks to compare current data regarding the identified risks 132 against historical data regarding similar risks 132 to execute these simulations and identify likelihoods of impact (and/or identifying potential inefficiencies of various options), [0039]-[0041], controller 110 may use machine learning techniques 242 comprising algorithms or models that are generated by performing supervised, unsupervised, or semi-supervised training on a dataset, and subsequently applying the generated algorithm or model to identify risks to a supply chain and then simulating these risks over space and time to determine if these risks surpass a threshold likelihood of negatively impacting the supply chain, wherein he machine learning techniques 242 can utilize one or more of the following techniques: K-nearest neighbor (KNN), learning vector quantization (LVQ), self-organizing map (SOM), logistic regression, ordinary least squares regression (OLSR), CNN), Silverstein does not appear to expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Bhattacharyya.
Bhattacharyya teaches wherein the computing apparatus further comprises a model generator operatively coupled to the processing unit, wherein the model generator comprises a plurality of predefined models ([0149]-[0150], structure data file 1401 can be used to train the domain-scoring models to determine benchmark competitor domain scores and/or to predict sentiments (e.g., positive or negative sentiments), wherein fig. 16 illustrates a flowchart of an exemplary training process 1105 for training domain-scoring models, wherein each domain 1430 may be associated with its own domain-scoring model), and wherein generating and/or loading the supply chain operation model comprises the model generator selecting the appropriate model from the plurality of predefined models ([0153], step 1619, the system 100 may dynamically select a best fit algorithm to train the domain-scoring models, the best fit algorithm may be selected based on one or more factors, such as accuracy, etc., the best fit algorithm may include, e.g., logistic regression, decision trees, random forests, naïve Bayes, support vector machines, neural networks, and the like, and in step 1621, the selected best fit algorithm may create and/or select one or more domain-scoring models).
Silverstein and Bhattacharyya are analogous fields of invention because both address the problem of modeling performance of a supply chain and impact of a pandemic. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to have modified Silverstein with the aforementioned teachings of Bhattacharyya in order to produce the added benefit of assessing, evaluating a target business to provide strategy recommendations that improve the performance of the target business. [0002].
Claims 6 is rejected under 35 U.S.C. 103 as being unpatentable over Silverstein, et al. (US 20220092492 A1), hereinafter Silverstein, in view of Smith, et al. (US 20210049532 A1), hereinafter Smith, in further view of Bhattacharyya, et al. (US 20210065091 A1), hereinafter Bhattacharyya, and Gupta, et al. (US 20210166819 A1), hereinafter Gupta.
Regarding claim 6, the combined teachings of Silverstein, Smith, and Bhattacharyya teaches a pandemic response supply chain simulation system as per claim 5 (as above). Further, while Silverstein discloses all of the above and wherein the one or more effects relate to the effect on the supply of one or more [supplies] during a predefined pandemic ([0031], via these simulations, controller 110 may calculate that there is a 5% likelihood of the viral pandemic impacting transportation of first component 134 through location 136 related to active node 170E, a 35% likelihood of the viral pandemic impacting a supply of fourth component 134 related to active node 170D, and a 25% likelihood of the hurricane impacting a final assembly at active node 170K) and wherein the inputs relate to facts about the one or more [supplies] ([0022], controller 110 may gather (and/or receive) data on locations 136 and/or points in time 138 of supply chain, [0036], gathered or predetermined data or techniques or the like as used by processor 220 to analyze risks to supply chains stored within memory 230 include component data 234 may include specs on what component 134 must be and time data 236 may include threshold timeframes at which component 134 must be at locations 136 as defined by location data 238) and the parameters relate to facts about the pandemic ([0046], controller 110 may identify a plurality of risks 132 (304), e.g., risks 132 may include viral pandemics, or the like, that may reduce a likelihood that components 134 will move through locations 136 by requisite points in time 138, such that a supply chain has a chance of not delivering goods according to a predetermined timeline, [0042], NLP techniques 246 that controller 110 may use to identify whether data relates to risks 132, and such concepts may also be used to identify previous similar outbreaks in this region and/or of a similar type of pandemic outbreak), Silverstein does not appear to expressly disclose that the supplies necessarily include medications, which however, is taught by further teachings in Gupta.
Gupta teaches wherein the one or more effects relate to the effect on the supply of one or more medications during a predefined pandemic ([0008], a method of predicting a spread of an infectious disease and evaluating pandemic response resources includes generating a predictive model, applying the predictive model to the set of patient data and the set of resource data, determining resource levels based on an output of the predictive model, and outputting the resource levels formatted for integration into an electronic health records system, wherein resource data includes the location, quantity, and/or availability of critical medications, [0015], the response engine disclosed herein may provide data crucial for pandemic risk management and real-time capacity planning to thereby facilitate a pandemic response, wherein pandemic response planning and execution require knowledge an understanding of the risk of potential supply chain disruptions and dependencies, such as components of key medications and essential resources, [0078], the model uses resource data (e.g., the location, quantity, and/or availability of critical medications, etc.) to identify resource levels) and wherein the inputs relate to facts about the one or more medications ([0008], a method of predicting a spread of an infectious disease and evaluating pandemic response resources includes obtaining a set of resource data (e.g., the location, quantity, and/or availability of critical medications ) and the parameters relate to facts about the pandemic ([0008], a method of predicting a spread of an infectious disease and evaluating pandemic response resources includes obtaining a set of patient data [0046], data sources 110 may provide may include, but are not limited to, patient data (e.g., age, whether a patient is chronically ill or has underlying health condition, whether a patient has been infected by the infectious disease, whether the patient is undergoing treatment for the infectious disease, whether the patient has developed an antibody for the infectious disease, etc.).
Silverstein and Gupta are analogous fields of invention because both address the problem of predicting the risk to supplies during a pandemic. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Silverstein the ability for the one or more effects to relate to the effect on the supply of one or more medications during a predefined pandemic and the inputs to relate to facts about the one or more medications, as taught by Gupta, 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 combination would produce the predictable results of the one or more effects relating to the effect on the supply of one or more medications during a predefined pandemic and the inputs relating to facts about the one or more medications, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Silverstein with the aforementioned teachings of Gupta in order to produce the added benefit of improving the ability of providing aid to save lives, halting the spread of the contagion, and treating the infected. [0058].
Allowable Subject Matter
While claims 1-8, 10-12, 15-18, & 20-24 are rejected pursuant to 35 USC 101, claims 23 & 24, which depend on claim 1, respectively, are potentially allowable if amended to overcome the 101 rejections since claims 23 & 24 are novel and non-obvious in view of 35 USC 102 and 35 USC 103.
Conclusion
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623
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