DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a non-final rejection in response to claims filed on 2/27/2025. Claims 1-20 are pending and are examined herein.
Priority
The present application is a 371 national stage application of PCT/US2023/031565, filed on 08/30/2023. The prior filed provisional application 63/402,866 was filed less than a year before the PCT filing date, making the effective filing date of the present disclosure 08/31/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/21/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 19 and 20 objected to because of the following informalities:
-As a representative claim, claim 1 line 14, contains the language “the adjusted simulation results,” however, there is no prior mention of “adjusted simulation results,” only “adjusted computation results.” The applicant is advised to amend the claim such that the language is consistent, for example, changing all mentions of adjusted computational results to “adjusted simulation results.” Appropriate correction is required. Claims 19 and 20 are also objected for the same informalities, and should be amended to correct the errors.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Fig. 1 reference number “126” referring to “performance metrics” has not been mentioned in the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections – 35 USC § 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 an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1 – 18: A computer-implemented method for distributing a pharmaceutical therapy to at least one patient, comprising:
Claim 19: A product for distributing a pharmaceutical therapy to at least one patient, the product comprising a non-transitory computer-readable storage medium having computer-readable program code executable by a computing device to perform computer modeling and communication operations, the computer modeling and communication operations comprising:
Claim 20: A system for distributing a pharmaceutical therapy to at least one patient, the system comprising an analytic engine and one or more computing systems configured to perform operations comprising:
Therefore, claims 1-18 is directed to at least one potentially eligible subject matter category, in this case a “process.” Claims 19 and 20 are directed to a “machine” and “manufacture.” Therefore, the claims are to be further analyzed under the full 2 step process.
Step 2a Prong 1: Is the claim reciting a Judicial Exception(A Law of Nature, a Natural Phenomenon (Product of Nature), or An Abstract Idea?)
The claims under the broadest reasonable interpretation in light of the specification are analyzed herein. Representative claims 1, 19, and 20 is marked up, isolating the abstract idea from additional elements, wherein the abstract idea is in bold and the additional elements have been italicized as follows:
Claim 1 Preamble: A computer-implemented method for distributing a pharmaceutical therapy to at least one patient, comprising:
Claim 19 Preamble: A product for distributing a pharmaceutical therapy to at least one patient, the product comprising a non-transitory computer-readable storage medium having computer-readable program code executable by a computing device to perform computer modeling and communication operations, the computer modeling and communication operations comprising:
Claim 20 Preamble: A system for distributing a pharmaceutical therapy to at least one patient, the system comprising an analytic engine and one or more computing systems configured to perform operations comprising:
Claim 1 Body (also representative of claims 19 and 20):
- configuring a computer-executable pharmaceutical distribution model that comprises a plurality of distribution constraints;
- calculating a time-varying behavior of at least one parameter of the computer- executable pharmaceutical distribution model to obtain computational results;
- quantifying a value of a first performance metric for the computational results;
- adjusting the plurality of distribution constraints based at least on the computational results to form a computer-executable adjusted pharmaceutical distribution model that comprises an adjusted plurality of distribution constraints;
- further calculating a time-varying behavior of at least one further parameter of the computer-executable adjusted pharmaceutical distribution model to obtain adjusted computational results;
- further quantifying a value of a second performance metric for the adjusted simulation results; and
- making the computer-executable adjusted pharmaceutical distribution model accessible to a third-party computing device.
When evaluating the bolded limitations of the claims under the broadest reasonable interpretation in light of the specification, it is clear that representative claims 1, 19 and 20 recites an abstract idea within the category of “certain methods of organizing human activity” outlined in MPEP 2106.05(a)(2). More specifically, the present claims fall under the sub-grouping “commercial or legal interactions,” including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. More specifically, the limitations of configuring a pharmaceutical distribution model, calculating a time-varying behavior of a parameter, quantifying a value of the first performance metric, adjusting distribution constraints based on the results to form an adjusted model, obtaining adjusted computational results, further quantifying a value of a second performance metric for adjusted simulation results, and making the adjusted pharmaceutical distribution model accessible to a third-party, is akin to several examples given in MPEP 2106.04(a)(2)(II)(B), “where the commercial or legal interaction is advertising, marketing or sales activities or behaviors includ(ing): i. structuring a sales force or marketing company, which pertains to marketing or sales activities or behaviors, In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1038 (Fed. Cir. 2009);ii. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979); and iii. offer-based price optimization, which pertains to marketing, OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092 (Fed. Cir. 2015).” Thus the claims are no more than obtaining information, analyzing data, and displaying the results of the analysis to a third-party, which is at least a marketing or sales activity, and a business relation. Therefore, the claims recite at least “certain methods of organizing human activity,” and therefore to be further analyzed under Prong 2.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claims 1, 19 and 20 recites the following additional elements:
- computer-implemented in claim 1
- non-transitory computer-readable storage medium having computer-readable program code executable by a computing device in claim 19
- an analytic engine in claim 20
-- computer-executable pharmaceutical distribution model in claims 1, 19, and 20
- third-party computing device in claims 1, 19, and 20
The additional elements listed above, when considered individually and in combination with the claim as a whole, no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on generic computing components as outlined in MPEP 2106.05(f). In this case, the abstract idea of “configuring a pharmaceutical distribution model, calculating a time-varying behavior of a parameter, quantifying a value of the first performance metric, adjusting distribution constraints based on the results to form an adjusted model, obtaining adjusted computational results, further quantifying a value of a second performance metric for adjusted simulation results, and making the adjusted pharmaceutical distribution model accessible to a third-party,” is performed on generic computing components such as computer, non-transitory computer-readable storage media having computer-readable program code, computing devices, and an analytic engine. This is the definition of merely providing instructions to perform the abstract idea on any “computer” as software instructions. Furthermore, limiting the pharmaceutical distribution model to be “computer-executable” is also equivalent to apply it because it merely adds the computer after the fact to the abstract idea. Furthermore, merely applying a computer to the abstract idea, without reciting an improvement to computers, technological environments or fields of use does not integrate the abstract idea into a practical application(see MPEP 2106.05(a)).
Whether considered individually or in combination, the additional elements fail to integrate the abstract idea into a practical application, therefore the claims are directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claims 1, 19 and 20 recites the following additional elements:
- computer-implemented in claim 1
- non-transitory computer-readable storage medium having computer-readable program code executable by a computing device in claim 19
- an analytic engine in claim 20
-- computer-executable pharmaceutical distribution model in claims 1, 19, and 20
- third-party computing device in claims 1, 19, and 20
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computing components such as a computer, non-transitory computer-readable storage media having computer-readable program code, computing devices, and an analytic engine, to perform the abstract idea of “configuring a pharmaceutical distribution model, calculating a time-varying behavior of a parameter, quantifying a value of the first performance metric, adjusting distribution constraints based on the results to form an adjusted model, obtaining adjusted computational results, further quantifying a value of a second performance metric for adjusted simulation results, and making the adjusted pharmaceutical distribution model accessible to a third-party,” amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, limiting the abstract idea to be performed on an automatically, using machine learning or on an application does not meaningfully limit the claim beyond generally linking the abstract idea to a particular technological environment or field of use. Accordingly, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Thus claims 1, 19 and 20 are not patent eligible because the claims are directed to an abstract without significantly more.
Dependent claims 2-18 are also given the full two part analysis both individually and in combination with the claims they depend on herein:
Claims 2, 3, 4, and 5 further limit the abstract idea by adding limitations that enable the user to interact with the system. For example, claim 2 comprises “receiving user input,” claim 3 “provides user access to a sandbox environment,” claim 4 “provides user access by a website interface, API and app, ” and claim 5 “provides a URL for onboarding a user, the URL providing access” to the model. This is more of the same abstract idea under “certain methods of organizing human activity,” because it merely limits the interactions to be between an individual and the system, however, MPEP 2106.04(a)(2)(II) specifically states, “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.” Furthermore, claims 2, and 3 do not include any additional elements, and the additional element of a “website interface, application programming interface, and app” in claim 4 and “URL” of claim 5 are “apply it” level elements because they are mere instructions to perform the abstract idea on generic computing components without meaningfully limit their use on the claims. Therefore, even when considered individually or in combination with the existing additional elements, the claims are no more than “apply it” and do not integrate the abstract idea into a practical application. Even when viewed as a whole, nothing meaningfully limits the claims to provide significantly more, therefore, claims 2, 3, 4 are also patent-ineligible under 35 U.S.C. 101.
Claims 6-11 add further limitations to the abstract idea by reciting further limitations to the computational results, or time-dependent scenarios. For example, claim 6 comprises results for a plurality of time-dependent scenarios that are a function of the parameter. Claim 7 calculates an average of results, claim 8 bases the scenarios on a random number generator, and claim 9 comprises a solution that optimizes a parameter by minimizing or maximizing a performance metric. Claims 10 and 11 limit the mathematical program to either a mixed-integer linear or nonlinear program. However, even when considering all of these limitations above, they are all merely techniques dictating how the data analysis is to be performed, but none of which are additional elements that lead to an improvement to any technology or technical field. Thus, even with the additional mathematical steps, the claims still recite “certain methods of organizing human activity,” because the claims would still fall under generic data gathering, data analysis and displaying the results to a business or user. Even when considering in combination with previous additional elements or viewed as a whole, nothing in the claims integrates the abstract idea into a practical application or provides significantly more.
Claim 13 and 14 recite more of the same abstract idea because they merely limit the source of the distribution constraints to derive from “(third-party proprietary data).” Limiting the data to a particular source or format does not impact its categorization as an abstract idea, as the steps themselves still recite the “certain method of organizing human activity.” Therefore, even when considering in combination with previous additional elements or viewed as a whole, nothing in the claims integrates the abstract idea into a practical application or provides significantly more.
Claims 15 and 18, recites more of the same abstract by forming the adjusted pharmaceutical model by “restricting” or using a “restricted mode” of the pharmaceutical distribution model. However, this still falls within “certain methods of organizing human activity,” because it still is considered a certain activity between a user and the model, but merely restricting the “pharmaceutical distribution model” broadly, or by any manner. Therefore, even when considering in combination with previous additional elements or viewed as a whole, nothing in the claims integrates the abstract idea into a practical application or provides significantly more.
Claims 16 and 17 merely further limits the abstract idea because it adds conditions to certain steps (adjusting, making) to be based on the first or second performance metric, however, this is still generic data analysis that falls within “certain methods of organizing human activity,” because adjusting a model or displaying performance metrics of a model is still a “marketing or sales activity,” especially when the outcome is making the metrics accessible to a third-party. In this case, the additional element of the values being displayed to a third-party computing device, is still a “apply it” level elements because it merely limits the abstract idea to a generic computer to display the results of the analysis. Therefore, even when considered individually or in combination with the existing additional elements, the claims are no more than “apply it” and do not integrate the abstract idea into a practical application. Even when viewed as a whole, nothing meaningfully limits the claims to provide significantly more, therefore, claims 16 and 17 are also patent-ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-8, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tourtellote et al. (US 11398298 B2) hereinafter Tourtellote.
Regarding Claims 1, 19 and 20:
Tourtellote discloses a demand and supply forecasting model for clinical trials which displays supply plan forecasts to users such as doctors, trial sponsors, and distribution facilities. Tourtellote teaches:
Claim 1 Preamble: A computer-implemented method for distributing a pharmaceutical therapy to at least one patient, comprising:(Tourtellote [Col. 12 Lines 49-64] (60) The method 110 may include a step 112 that includes calculating, for each of a plurality of hypothetical patients, a statistical distribution of demand. A demand may include the quantity of a drug, a drug dose, and/or a placebo needed for one or more points in time during the patient's treatment. The distribution of demand may be a discrete distribution driven by the randomization ratios, for example. In other embodiments, other statistical distribution types may be used to determine patient demand.)
Claim 19 Preamble: A product for distributing a pharmaceutical therapy to at least one patient, the product comprising a non-transitory computer-readable storage medium having computer-readable program code executable by a computing device to perform computer modeling and communication operations, the computer modeling and communication operations comprising: (Tourtellote [Col. 5 Lines 5-59] The supply forecasting system 12 may include a processor 28 and a non-transitory, computer-readable memory 30 including instructions that, when executed by the processor 28, cause the processor 28 to perform one or more methods, algorithms, processes, etc. of this disclosure. The memory 30 may store one or more modules in the form of executable instructions (e.g., software) for execution by the processor 28. Example modules are described below.)
Claim 20 Preamble: A system for distributing a pharmaceutical therapy to at least one patient, the system comprising an analytic engine and one or more computing systems configured to perform operations comprising: (Tourtellote [Col. 7 Lines 44-61] Second, known forecasting systems are generally separate from RTSM systems, and thus must acquire information from RTSM systems via inefficient file transfer, which generally requires manual user intervention and may include the exchange of more data than is necessary both of which may be less efficient than desired, and which presents the risk of an inaccurate mapping between the data from both systems. In contrast, in embodiments, a supply forecasting system according to the present disclosure may be integrated with the RTSM system via an API exposed by the RTSM system.)
Claim 1 Body (also representative of claims 19 and 20):
- configuring a computer-executable pharmaceutical distribution model that comprises a plurality of distribution constraints; (Tourtellote [Col. 6 Lines 16-25] The forecasting system 12 may provide an electronic user interface for use by the trial sponsor, one or more trial sites, one or more depots, one or more doctors, and/or one or more patients. For example, in embodiments, the forecasting system 12 may provide an electronic user interface including one or more dials to enable a user to set certain constraints on a supply plan to be calculated by the forecasting system by adjusting the dials. Based on user actuation of such dials or other input means, the supply forecasting system 12 may receive user input, as discussed below. [Col. 13 Lines 40-49] The method 110 may further include a step 128 that includes determining an appropriate manufacturing/packaging timing and quantities over the course of the study, as well as a trigger for calculating a re-forecast of the supply plan. This may be one of, or a combination of, a minimum frequency, a calculation based on expiry dating, external constraints provided by the user such as drug availability, and may rely on operations research techniques such as Constraint Programming, Neural Networks, Mixed Integer Programming, etc. [Col. 2 Lines 20-25] cause the processor to receive, from a user, one or more electronic files that contain parameters of a clinical trial, the parameters comprising a quantity of patients, a plurality of sites, and one or more confidence values, calculate, according to the quantity of patients, a respective demand profile for each of the plurality of sites, )
- calculating a time-varying behavior of at least one parameter of the computer- executable pharmaceutical distribution model to obtain computational results; (Tourtellote [Col. 6 Lines 26-42] In some embodiments, the forecasting system 12 may receive from a user a level of confidence at which, for example, the total projected demand (e.g., predictable, correlated unpredictable, uncorrelated unpredictable, etc.) should be supported... The forecasting system may also receive from the user one or more lengths of time for the study. For example, the user may provide a length of the trial, a length of one or more specific phases of the trial, etc. In some embodiments, the user may present various different lengths of time, and the forecasting system may calculate a supply plan forecast for each of those lengths of time. [Col. 12 Lines 49-64] The method 110 may include a step 112 that includes calculating, for each of a plurality of hypothetical patients, a statistical distribution of demand. A demand may include the quantity of a drug, a drug dose, and/or a placebo needed for one or more points in time during the patient's treatment. The distribution of demand may be a discrete distribution driven by the randomization ratios, for example. In other embodiments, other statistical distribution types may be used to determine patient demand. The result of the patient demand calculation step may be a time series for which each point expands the likelihood for the demand, cumulatively or for a given period of time, to be of a certain amount. For example, after 1 month, the cumulative demand for a new patient in kit type KT-12 mg may be of 0 with 40% chance, 1 with 30% chance, 2 with 25% chance, and 3 with 5% chance.) The demand is the parameter in which time-varying behavior is obtained, which varies depending on time such as lengths of time.
- quantifying a value of a first performance metric for the computational results; (Tourtellote [Col. 6 Lines 26-38] In some embodiments, the forecasting system 12 may receive from a user a level of confidence at which, for example, the total projected demand (e.g., predictable, correlated unpredictable, uncorrelated unpredictable, etc.) should be supported. In some embodiments, the user may present various different confidence levels, and the forecasting system may calculate a supply plan forecast for each of those confidence levels. In embodiments, the forecasting system may receive numerous confidence levels from the user directed to different trial parameters—for example, a first confidence level for total demand, a second confidence level for correlated unpredictable demand, etc.) Confidence levels are examples of a performance metric for the computation results.
- adjusting the plurality of distribution constraints based at least on the computational results to form a computer-executable adjusted pharmaceutical distribution model that comprises an adjusted plurality of distribution constraints; (Tourtellote [Col. 6 Lines 15-25] The forecasting system 12 may provide an electronic user interface for use by the trial sponsor, one or more trial sites, one or more depots, one or more doctors, and/or one or more patients. For example, in embodiments, the forecasting system 12 may provide an electronic user interface including one or more dials to enable a user to set certain constraints on a supply plan to be calculated by the forecasting system by adjusting the dials. Based on user actuation of such dials or other input means, the supply forecasting system 12 may receive user input, as discussed below. [Col. 7 Lines 10-43] A supply forecasting system according to one or more aspects of the present disclosure may improve upon known supply forecasting methods and systems. First, known supply forecasting systems are generally used to calculate a comprehensive forecast for the entirety of a trial. In contrast, in embodiments, a supply forecasting system according to the present disclosure may be configured to calculate a supply plan, or a portion thereof, based on whatever information is available to the user, and may be further configured to determine and inform the user of specific milestones (e.g., dates, availability of certain information, etc.) that may trigger a re-calculation of the supply plan, or a calculation of an additional aspect of the supply plan, and thus may enable users to make changes to a plan during a trial without expensive trial delays. A supply forecasting system according to the present disclosure may therefore offer more flexible and robust forecasting of a supply plan,..) Col. 6 covers the “adjusting” limitation, and Col. 7 covers the “based on computation results,” because the information made available to a user (supply plan) can trigger a “re-calculation of the supply plan, or calculation of additional aspect,” which satisfies the “adjusted pharmaceutical distribution model” limitation, with new constraints (dates, etc).
- further calculating a time-varying behavior of at least one further parameter of the computer-executable adjusted pharmaceutical distribution model to obtain adjusted computational results; (Tourtellote [Col. 8 Lines 44- Col. 9 Line 12] The method 40 may further include a step 46 that includes developing a trial supply plan based on the trial parameters determined from the specification documents. An example method for performing step 46 will be described with respect to FIG. 5. One or more trial supply plans may be determined at step 46. For example, multiple trial supply plans may be determined for different confidence levels associated with the duration of the trial, or different confidence levels associated with the number of patients needed for the trial, or different numbers of doses to be tested in the trial, etc. (40) The method 40 may further include a step 48 that includes generating one or more graphical representations of the trial supply plan and the input used to calculate it. FIG. 3A and FIG. 3B are example graphical representations 60, 70 of a trial supply plan or the input used to calculate it. Referring to FIG. 3A, a first graphical representation 60 may include a plurality of nodes 62 (three nodes 62.sub.1, 62.sub.2, 62.sub.3 are indicated in FIG. 3A; for clarity of illustration, not all nodes 62 are indicated), with each node 62 representative of an action in the trial of which the graphical representation 60 is representative, such as a randomization of a trial population, a dispensing of a drug, etc. The graphical representation may further include one or more branches 64 extending from each node. Each branch 64 may illustrate a result of the action of the node 62 from which the branch 64 extends. For example, three branches 64.sub.1, 64.sub.2, 64.sub.3 may extend from an initial node 62.sub.1 representative of randomizing a patient population, with a first branch 64.sub.1 indicating an assignment of fifty percent (50%) of the randomized patient population to the ABC portion of the trial, a second branch 64.sub.2 indicating that twenty-five percent (25%) of the patient population will be included in a placebo portion of the trial, and a third branch 64.sub.3 indicating that twenty-five percent (25%) of the patient population will be included in the XYZ portion of the trial.) Each of the additional “graphical representations” which are simulations for different possible parameters, are examples of “adjusted computational results.”
- further quantifying a value of a second performance metric for the adjusted simulation results; and(Tourtellote [Col. 8 Lines 44-54] The method 40 may further include a step 46 that includes developing a trial supply plan based on the trial parameters determined from the specification documents. An example method for performing step 46 will be described with respect to FIG. 5. One or more trial supply plans may be determined at step 46. For example, multiple trial supply plans may be determined for different confidence levels associated with the duration of the trial, or different confidence levels associated with the number of patients needed for the trial, or different numbers of doses to be tested in the trial, etc.) The different confidence levels are examples of second performance metrics for the adjusted results (more trial supply plans).
- making the computer-executable adjusted pharmaceutical distribution model accessible to a third-party computing device. (Tourtellote [Col. 9 Line 37-50] Referring again to FIG. 2, the method 40 may further include a step 50 that includes transmitting the trial supply plan to the user. Transmitting the supply plan to the user may include transmitting data comprising one or more graphical representations of the supply plan to a user computing device for display on the user computing device. Transmitting the supply plan to the user may additionally or alternatively include transmitting a table or textual form of the supply plan to the user, and/or some other form of the supply plan, or one or more portions thereof. The transmission may be in the form of a hosted webpage delivered to a user computing device, for example. Additionally or alternatively, the transmission may be in the form of an email or other electronic form, such as downloadable electronic files. [Col. 5 Lines 26-38] The RTSM system 14 may further be in communication with one or more distribution facilities 22, such as depots, warehouses, or other facilities involved in the distribution of drug lots and placebos involved in the trial to trial sites 20. The RTSM system 14 may issue shipping instructions to such a facility 22, for example. Such shipping instructions may be generated and/or transmitted automatically by the RTSM system 14, in embodiments, based on a supply plan. The RTSM system may also receive data from such facilities 22, such as shipping records and inventory information, such that the RTSM system 14 has data indicative of distributed inventories of drugs involved in the trial and remaining inventories, and the locations of those inventories.)
Regarding Claim 2:
Tourtellote teaches: The method of claim 1,
- further comprising receiving user input that is used in the configuring. (Tourtellote [Col. 4 Lines 45-55] The RTSM system 14 may comprise one or more computing devices and may perform many operations for organizing and orchestrating a pharmaceutical trial, such as patient randomization, blinding, drug dispensing, and automatic drug resupply, in embodiments. The RTSM system 14 may be in electronic communication with one or more user computing devices 16 to receive input from the user (e.g., parameters of a trial) and to provide output to a user (e.g., orders for one or more drugs, a status of a patient participating in the trial, information respective of a trial site, etc.).)
Regarding Claim 3:
Tourtellote teaches: The method of claim 2,
- further comprising providing user access to a sandbox environment for guiding one or more of the configuring, the adjusting, and the making. (Tourtellote [Col. 6 Lines 15-25] The forecasting system 12 may provide an electronic user interface for use by the trial sponsor, one or more trial sites, one or more depots, one or more doctors, and/or one or more patients. For example, in embodiments, the forecasting system 12 may provide an electronic user interface including one or more dials to enable a user to set certain constraints on a supply plan to be calculated by the forecasting system by adjusting the dials. Based on user actuation of such dials or other input means, the supply forecasting system 12 may receive user input, as discussed below.)
Regarding Claim 4:
Tourtellote teaches: The method of claim 3,
- wherein the user access is provided by one or more of a website interface, an application programming interface, and an app. (Tourtellote [Col. 6 Lines 46-52] The supply forecasting system 12 may also be in electronic communication with the RTSM system 14. During a trial, the supply forecasting system 12 may retrieve data respective of the trial from the RTSM system 14. For such information retrieval, the supply forecasting system 12 may communication with the RTSM system 14 via an application programming interface (API) 38 of the RTSM system 14. [Col. 5 Lines 6-18] The method 80 may include a step 82 that includes receiving a spreadsheet file and one or more text files from the user. The files may include the information about a study to be conducted that is available to the user at the time. The text files may be HTML files, for example. The spreadsheet and text files may be received via a file upload through a website provided by the supply forecasting system, for example. In embodiments, the supply forecasting system may provide an interface in which a user may enter freeform text to describe the proposed study (which entered freeform text may be or may become the one or more text files of step 82), and further upload one or more spreadsheets through the interface.)
Regarding Claim 6:
Tourtellote teaches: The method of claim 1,
- wherein the computational results comprises results for a plurality of time-dependent scenarios that are a function of the parameter. (Tourtellote [Col. 12 Lines 46-64] The method 110 may include a step 112 that includes calculating, for each of a plurality of hypothetical patients, a statistical distribution of demand. A demand may include the quantity of a drug, a drug dose, and/or a placebo needed for one or more points in time during the patient's treatment. The distribution of demand may be a discrete distribution driven by the randomization ratios, for example. In other embodiments, other statistical distribution types may be used to determine patient demand. The result of the patient demand calculation step may be a time series for which each point expands the likelihood for the demand, cumulatively or for a given period of time, to be of a certain amount. For example, after 1 month, the cumulative demand for a new patient in kit type KT-12 mg may be of 0 with 40% chance, 1 with 30% chance, 2 with 25% chance, and 3 with 5% chance. [Col. 9 Lines 1-12] Each branch 64 may illustrate a result of the action of the node 62 from which the branch 64 extends. For example, three branches 64.sub.1, 64.sub.2, 64.sub.3 may extend from an initial node 62.sub.1 representative of randomizing a patient population, with a first branch 64.sub.1 indicating an assignment of fifty percent (50%) of the randomized patient population to the ABC portion of the trial, a second branch 64.sub.2 indicating that twenty-five percent (25%) of the patient population will be included in a placebo portion of the trial, and a third branch 64.sub.3 indicating that twenty-five percent (25%) of the patient population will be included in the XYZ portion of the trial.) Each branch represents a different scenario, which in Col. 12, can be based on time-dependent scenarios of the parameter (demand).
Regarding Claim 7:
Tourtellote teaches: The method of claim 6,
- wherein the value of the first performance metric comprises an average of results determined from the plurality of time-dependent scenarios. (Tourtellote [Col. 1 Lines 21-29] Over the past decade, systems for managing the supply of pharmaceuticals in a clinical trial have been developed and are now a much relied on system for a number of pharmaceutical companies, for production planning, cost optimization, patient recruitment campaigns management and more. For example, known systems include custom or template Excel spreadsheets, average-based forecasting systems, and systems using Monte-Carlo simulations, which are generally the most precise. [Claim 2 Published on 2022-07-26] The method of claim 1, wherein calculating the demand profile for one of the plurality of sites comprises: calculating a respective average of the demand for the site and a respective statistical distribution of the demand at each of a plurality of time points. )
Regarding Claim 8:
Tourtellote teaches: The method of claim 6,
- wherein the plurality of time-dependent scenarios are generated at least in part using a random number generator. (Tourtellote [Col. 8 Lines 55 - 67] The method 40 may further include a step 48 that includes generating one or more graphical representations of the trial supply plan and the input used to calculate it. FIG. 3A and FIG. 3B are example graphical representations 60, 70 of a trial supply plan or the input used to calculate it. Referring to FIG. 3A, a first graphical representation 60 may include a plurality of nodes 62 (three nodes 62.sub.1, 62.sub.2, 62.sub.3 are indicated in FIG. 3A; for clarity of illustration, not all nodes 62 are indicated), with each node 62 representative of an action in the trial of which the graphical representation 60 is representative, such as a randomization of a trial population, a dispensing of a drug, etc. The graphical representation may further include one or more branches 64 extending from each node.)
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.
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.
Claims 5, 13-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Tourtellote et al. (US 11398298 B2) hereinafter Tourtellote, in view of Posey et al. (US 20210304272 A1) hereinafter Posey.
Regarding Claim 5:
Tourtellote teaches the method of claim 1:
Furthermore, Tourtellote teaches:
- wherein the making comprises onboarding a user, (Tourtellote [Col. 4 Lines 56- Col. 5 Line 9] The RTSM system 14 may be configured for communication and information exchange with a variety of different user types (that is, communication with one or more user computing devices respective of such user types). For example, in a given trial, the RTSM system 14 may provide an input/output interface for a clinical trial sponsor 18 (e.g., a pharmaceutical company). The trial sponsor 18 may input complete parameters of a clinical trial, or stage thereof, for the RTSM system 14, in embodiments. Such a complete set of parameters may include, for example, patient visits or other actions on patients, lots and operations on lots, inventory and operations on inventory at a site or depot level, shipments and operations on shipments, temperature excursions and their management, return of dispensed or non-dispensed drugs and related approvals and parameter, destruction status of inventory, actions on alerts or notifications, addition and parametrization of sites and depots, countries, setup of forecasting parameters such as a confidence dial and a long window dial, set up of site group enrolment rates, setup of users and their access rights, setup of cohorts,) Setup of a user and their access rights satisfies “onboarding a user.”
- providing access to the adjusted pharmaceutical distribution model. (Tourtellote [Col. 6 Lines 58-63] One or both of the RTSM system 14 and the supply forecasting system 12 may be implemented on a Software-as-a-Service (SaaS) basis, in an embodiment. Accordingly, the supply forecasting system and/or RTSM system 14 may be embodied in one or more servers, databases, etc., that are electronically accessible to users through the internet.)
However, Tourtellote fails to teach:
- wherein the making comprises providing a URL for onboarding a user,
- the URL providing access to the adjusted pharmaceutical distribution model.
However, Posey discloses generating an acquisition scenario for request for proposals, request for quotes, requests for bids, etc, including generating optimization algorithms to generate visualizations to aid a user in negotiations. Posey teaches:
- wherein the making comprises providing a URL(Posey [0165] As shown in FIG. 10C, the graphical user interface 1000 additionally includes a resources section 1040, which can be used by the user to identify various RFX-related information that can be accessed using a web browser or other device by suppliers. In this example, the resources section 1040 includes a list 1042 of currently-defined resources, each of which is identified using name and a uniform resource locator (URL).)
- the URL providing access to the visualization model.(Posey [0165] The user may select an “Add Resource” link in order to provide the name and URL of an additional resource, and the user may select an existing resource (such as by double-clicking the resource) to edit or delete the resource. An event timeline 1044 illustrates the different stages of the sourcing event creation process (which can match the stages shown in the graphical representation 505) and can identify the current stage of the sourcing event creation process, along with an identification of the previously-completed stages.)
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of Posey, particularly the use of URL (uniform resource locators) to provide access to users to a particular additional resource. For the present claims, the use of URLs are substituted into the onboarding of a user and providing access to the model, as one of ordinary skill would have been motivated by the benefit of enabling the user to access the additional resources, along with features such as tracking the event creation process. (Posey [0165] The user may select an “Add Resource” link in order to provide the name and URL of an additional resource, and the user may select an existing resource (such as by double-clicking the resource) to edit or delete the resource. An event timeline 1044 illustrates the different stages of the sourcing event creation process (which can match the stages shown in the graphical representation 505) and can identify the current stage of the sourcing event creation process, along with an identification of the previously-completed stages. Controls 1046 can be used to save the information obtained at that point through the graphical user interface 1000, proceed to the next step of the process, or cancel the process.)
Regarding Claim 13:
Tourtellote teaches the method of claim 1:
However, Tourtellote fails to teach:
- wherein the plurality of distribution constraints comprise statistical values derived from proprietary data.
Alternatively, Posey teaches:
- wherein the plurality of distribution constraints comprise statistical values derived from proprietary data.(Posey [0160] The general information section 1002 also includes a text box 1006 that allows the user to provide a textual description for this revision of the RFX. In addition, the general information section 1002 includes a drop-down menu 1008 that allows the user to select any security markings to be used with the RFX being generated, such as proprietary, internal use only, most private, approved for public release, and unrestricted (although these are examples only). [0275] A summary of award data section 2504 provides general information about the contract being awarded. In this example, the information includes an owner of the agreement (who is making the agreement), a creation date, a suitable sustainability clause, and agreement start and end dates. The information also includes a total value to be approved, a sourcing agreement type, a Universal Commercial Code (UCC) and related description, and an agreement pricing structure. The information further includes payment terms, delivery terms, an identification of related agreements (if any), a TINA statement, and an identification of an associated proprietary information agreement (PIA) (if any). [0259] In addition, text boxes 2308 can be used by the user to provide comments, constraints, or help escalation information related to the reporting requirement event. [0231] In this particular example, a competitive pricing module 1916 can compare prices associated with multiple suppliers who responded to the same RFX or similar RFXs issued in the past in order to analyze the prices in a specific RFX response being analyzed. A purchase order history pricing module 1918 can analyze purchase order data, such as prices associated with prior purchase orders submitted to suppliers by the originating organization, in order to determine how those prices compare to the prices in the specific RFX response being analyzed. A parametric pricing module 1920 can generate pricing data using parametric pricing or modeling methods in order to determine how those prices compare to the prices in the specific RFX response being analyzed.)
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of Posey particularly the use of proprietary data to derive statistical values used as constraints for the model of Tourtellote. One of ordinary skill would have been motivated to perform this combination by the benefit of complying with privacy policies regarding proprietary data, whilst enabling the system to still use the data to derive mathematical relationships. (Posey [0229] In this particular example, the data source 1902 is used to obtain competition-related data, such as prices associated with multiple suppliers who responded to the same RFX. The data source 1904 is used to obtain purchase order data, such as prices associated with prior purchase orders submitted to suppliers by the originating organization. The data source 1906 is used to obtain parametric data, which may represent pricing information estimated for the future based on past pricing information and one or more mathematical relationships. The data source 1908 is used to obtain published information, such as prices for materials that are similar to materials being sourced in an RFX response under analysis. The data source 1910 is used to obtain data from the U.S. government (USG) or other governmental source. The data source 1912 is used to obtain market and index data, such as catalog prices or other prices charged by competitors or suppliers. The data source 1914 represents any other suitable source of information that might be useful for price analysis.)
Regarding Claim 14:
The combination of Tourtellote and Posey teaches the method of claim 13:
Furthermore, Tourtellote teaches:
- wherein a distribution constraint of the plurality of distribution constraints comprises a statistical model(Tourtellote [Col. 12 Lines 49-64] The method 110 may include a step 112 that includes calculating, for each of a plurality of hypothetical patients, a statistical distribution of demand. A demand may include the quantity of a drug, a drug dose, and/or a placebo needed for one or more points in time during the patient's treatment. The distribution of demand may be a discrete distribution driven by the randomization ratios, for example. In other embodiments, other statistical distribution types may be used to determine patient demand. The result of the patient demand calculation step may be a time series for which each point expands the likelihood for the demand, cumulatively or for a given period of time, to be of a certain amount. For example, after 1 month, the cumulative demand for a new patient in kit type KT-12 mg may be of 0 with 40% chance, 1 with 30% chance, 2 with 25% chance, and 3 with 5% chance.)
However, Tourtellote fails to teach:
- the statistical model derived from third-party proprietary data.
Alternatively, Posey teaches:
- the statistical model derived from third-party proprietary data.(Posey [0160] The general information section 1002 also includes a text box 1006 that allows the user to provide a textual description for this revision of the RFX. In addition, the general information section 1002 includes a drop-down menu 1008 that allows the user to select any security markings to be used with the RFX being generated, such as proprietary, internal use only, most private, approved for public release, and unrestricted (although these are examples only). [0275] A summary of award data section 2504 provides general information about the contract being awarded. In this example, the information includes an owner of the agreement (who is making the agreement), a creation date, a suitable sustainability clause, and agreement start and end dates. The information also includes a total value to be approved, a sourcing agreement type, a Universal Commercial Code (UCC) and related description, and an agreement pricing structure. The information further includes payment terms, delivery terms, an identification of related agreements (if any), a TINA statement, and an identification of an associated proprietary information agreement (PIA) (if any). [0259] In addition, text boxes 2308 can be used by the user to provide comments, constraints, or help escalation information related to the reporting requirement event. [0051] The originating system 102 generally represents a computing system that is owned by, operated by, or otherwise associated with an originating organization that issues RFXs. In some cases, the originating organization may represent a government contractor or other private or commercial organization that uses the RFXs to source or obtain raw materials, individual components, subassemblies, parts, or other items (generally referred to as “materials”) for products that are assembled or otherwise manufactured by the organization.) The data source being a private organization is an example of third-party proprietary data.
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of Posey particularly the use of proprietary data to derive the statistical model of Tourtellote. One of ordinary skill would have been motivated to perform this combination by the benefit of complying with privacy policies regarding proprietary data, whilst enabling the system to still use the data to derive mathematical relationships. (Posey [0229] In this particular example, the data source 1902 is used to obtain competition-related data, such as prices associated with multiple suppliers who responded to the same RFX. The data source 1904 is used to obtain purchase order data, such as prices associated with prior purchase orders submitted to suppliers by the originating organization. The data source 1906 is used to obtain parametric data, which may represent pricing information estimated for the future based on past pricing information and one or more mathematical relationships. The data source 1908 is used to obtain published information, such as prices for materials that are similar to materials being sourced in an RFX response under analysis. The data source 1910 is used to obtain data from the U.S. government (USG) or other governmental source. The data source 1912 is used to obtain market and index data, such as catalog prices or other prices charged by competitors or suppliers. The data source 1914 represents any other suitable source of information that might be useful for price analysis.)
Regarding Claim 15:
Tourtellote teaches the method of claim 1:
However, Tourtellote fails to teach:
- wherein the adjusted pharmaceutical distribution model is formed at least in part by restricting a portion of the pharmaceutical distribution model.
Furthermore, Posey teaches:
- wherein the adjusted pharmaceutical distribution model is formed at least in part by restricting a portion of the pharmaceutical distribution model.(Posey [0059] An optional third-party platform 124 is also shown in FIG. 1. In some embodiments, the RFX-related functionality of the application server 112 and the storage of RFX-related data by the database server 114 and database 116 may be implemented partially or completely outside the originating system 102. [0065] The application server 112 can apply a trained machine learning model or other logic to gather data from multiple sources, perform all or user-selected price analyses related to RFX responses, and graphically display the results of the price analyses simultaneously to the user. The results can be presented in a format that allows the user to easily identify bad or dubious data points and annotate or exclude data points used by the application server 112, and automated generation of FAR-compliant price analysis reports or other reports can be supported. [0234] The functional architecture 1900 shown in FIG. 19 supports the use of a standard price analysis report format and helps to ensure that consistent approaches are used. Also, as described below, users can view visualizations of results from price analyses and optionally exclude data from use, and data can be meta-tagged as usable or unusable for future price analyses (with auditable explanations for exclusion).) Posey’s ability to exclude certain data points to re-perform the data analysis satisfies the limitation because it is a restricted portion of the model.
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of Posey particularly the restriction of certain portions of the distribution model, by excluding certain data points or parameters from being used. One of ordinary skill in the art would have been motivated by the benefit of simplifying the formatting of the visualization. (Posey [0189] Users are able to access desired graphical user interfaces, track tasks to be performed, perform tasks related to RFX generation, and control the generation of finalized RFXs for suppliers in a simplified manner. Moreover, users can do this across multiple RFX processes, stopping and starting the RFX processes for different programs, projects, and products as needed or desired. Further, machine learning can be leveraged in various areas to scrape data, prepopulate inputs, verify user selections, and perform other functions. As a result, this approach greatly simplifies the generation and management of RFXs, which can be extremely useful to a number of organizations and governments. In addition, functions such as BOM characterization and visualization can be used to support a number of additional non-RFX-related functions while providing improved usability.)
Regarding Claim 18:
Tourtellote teaches the method of claim 1:
However, Tourtellote fails to teach:
- wherein the adjusted pharmaceutical distribution model defines a restricted mode of the pharmaceutical distribution model.
Alternatively, Posey teaches:
- wherein the adjusted pharmaceutical distribution model defines a restricted mode of the pharmaceutical distribution model.(Posey [0238] The visualization 2000 may also allow the user to view specific information about each data point shown in the graph 2002. For example, the user may position a mouse cursor on a data point in the graph 2002, at which point source information about that data point can be presented over or near the data point (such as in a pop-up menu 2012). The user can also be given the option, such as in the pop-up menu 2012, to deselect or exclude that source data point in the graph 2002 from use in a final price analysis report generated by the report creation function 1934. These features may allow, for example, the user to review data points associated with distinctly-different price analysis results to determine why the price analysis results differed. If one or more data points are determined by the user to be unreliable, the user may exclude these data points and provide an annotation (meta-tag) for the exclusion. The relevant price analysis or analyses can then be repeated without the excluded data, and the graph 2002 can be updated by omitting the excluded data point(s). The user may similarly use a pop-up menu or other mechanism to re-include data points that were previously excluded. [0317] This may include, for example, the processing device 202 implementing at least part of the architecture 1900 receiving an indication from the user that certain data contained in the visualization 2000 should be excluded from use. As a particular example, the user may hover over a specific data point in the visualization 2000 to view a pop-up menu, which the user can use to provide an indication that the data point should be excluded and to provide an explanation why. This can cause at least one of the analyses to be repeated without using the excluded data, and updated results of the one or more analyses can be included in an updated visualization 2000.)
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of Posey particularly the restriction of certain portions of the distribution model, as defined by the adjusted pharmaceutical model. One of ordinary skill in the art would have been motivated by the benefit of simplifying the formatting of the visualization. (Posey [0189] Users are able to access desired graphical user interfaces, track tasks to be performed, perform tasks related to RFX generation, and control the generation of finalized RFXs for suppliers in a simplified manner. Moreover, users can do this across multiple RFX processes, stopping and starting the RFX processes for different programs, projects, and products as needed or desired. Further, machine learning can be leveraged in various areas to scrape data, prepopulate inputs, verify user selections, and perform other functions. As a result, this approach greatly simplifies the generation and management of RFXs, which can be extremely useful to a number of organizations and governments. In addition, functions such as BOM characterization and visualization can be used to support a number of additional non-RFX-related functions while providing improved usability.)
Claims 9, 10, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tourtellote et al. (US 11398298 B2) hereinafter Tourtellote, in view of McNamara et al. (US 20200065759 A1) hereinafter McNamara.
Regarding Claim 9:
Tourtellote teaches the method of claim 6:
Furthermore, Tourtellote teaches:
- wherein the computational results comprise a solution to a mathematical program(Tourtellote [Col. 13 Lines 28-39] The method 110 may further include a step 122 that includes calculating region demands based on site-group demands, a step 124 that includes calculating depot demands based on region demands, and a step 126 calculating a total supply demand based on depot demands. Steps 122, 124, and 126 may include simple arithmetic as well as statistical computations, in embodiments—e.g., adding the demands for each site-group in a region to determine the total demand for that region, adding the demands for each region serviced by a particular depot to determine the total demand for that depot, and so on, either in terms of expected value or in terms of statistical distributions.)
However, Tourtellote fails to teach:
- a solution to a mathematical program that optimizes the at least one parameter so as to maximize or minimize the value of the first performance metric subject to the distribution constraints.
Alternatively, McNamara discloses an engineering, manufacturing, supply chain, and logistics operation management system, that allows selectively and securely displaying data, enabling real-time monitoring, and optimizing performance based on certain performance metrics. McNamara teaches:
- a solution to a mathematical program that optimizes the at least one parameter so as to maximize or minimize the value of the first performance metric subject to the distribution constraints.(McNamara [0226] The supply chain and logistics analyzer 332 can use other variables and/or algorithms to determine and assign a relative health factor to the supply chain and/or logistics operation(s) and/or to recommend a change to the selected supply chain and/or logistics operation(s). For example, the supply chain and logistics analyzer 332 can employ a metric—CpX, which can be a measure of risk and capable of substantially optimizing the supply chain and/or logistics operation(s). The metric can be determined through the collection, aggregation, and transformation of supply chain and/or logistics operation(s) data, including performance information, and, when optimized, can modify system parameters of the logistic or supply chain system to reduce and/or optimize risk profiles for any selected supply chain parameter or object, typically a given product, a selected product line, and/or a customer account. [0069] Examples of heuristic algorithms include the modified due date scheduling heuristic (which assumes that the objective of the scheduling process is to minimize the total amount of time spent on tasks after their due dates) and shifting bottleneck heuristic (which minimize the time it takes to do work, or specifically, the makespan in a job shop, wherein the makespan is defined as the amount of time, from start to finish, to complete a set of multi-machine jobs where machine order is pre-set for each job, the jobs are assumed to be actually competing for the same resources (machines) resulting in one or more resources acting as a ‘bottleneck’ in the processing, whereby the heuristic, or ‘rule of thumb’ procedure substantially minimizes the effect of the bottleneck). [0228] To optimize the supply chain and/or logistics operation(s) (or substantially minimize the magnitude of the 1×M scalar), the supply chain and logistics analyzer 332 can apply a reverse transform function and fine tune the factors in the (1×N) linear array to effect the change—meaning for each optimal element 1 to . . . N, actual operations (e.g., order cycle, warehouse sizing, assembly line capacity, order aggregation, price, etc.) will be changed or modified to achieve the desired risk profile. )
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of McNamara by adding the minimization of a performance metric subject to constraints as the solution to the mathematical program of Tourtellote. One of ordinary skill would have been motivated to perform this combination by the benefit of using such a technique to allow users to balance risk protection and other metrics, based on customer need. (McNamara [0228] To optimize the supply chain and/or logistics operation(s) (or substantially minimize the magnitude of the 1×M scalar), the supply chain and logistics analyzer 332 can apply a reverse transform function and fine tune the factors in the (1×N) linear array to effect the change—meaning for each optimal element 1 to . . . N, actual operations (e.g., order cycle, warehouse sizing, assembly line capacity, order aggregation, price, etc.) will be changed or modified to achieve the desired risk profile. Alternatively, the supply chain and logistics analyzer 332 can set risk protection, configured or determined at various levels, by setting the scalar (high, medium, low) and changing the various offerings to meet the customer need—as some customers can deal with risk better than others).)
Regarding Claim 10:
The combination of Tourtellote and McNamara teaches the method of claim 9:
Furthermore, Tourtellote teaches:
- wherein mathematical program is a mixed-integer program.(Tourtellote [Col. 13 lines 40-49] The method 110 may further include a step 128 that includes determining an appropriate manufacturing/packaging timing and quantities over the course of the study, as well as a trigger for calculating a re-forecast of the supply plan. This may be one of, or a combination of, a minimum frequency, a calculation based on expiry dating, external constraints provided by the user such as drug availability, and may rely on operations research techniques such as Constraint Programming, Neural Networks, Mixed Integer Programming, etc.)
However, Tourtellote fails to teach:
- wherein mathematical program is a mixed-integer linear program.
Alternatively, McNamara teaches:
- wherein mathematical program is a mixed-integer linear program.(McNamara [0072] “Transfer Function” (also known as the system function or network function) is a mathematical representation, in terms of spatial or temporal frequency, of the relation between the input and output of a linear time-invariant system with zero initial conditions and zero-point equilibrium. Transfer functions are commonly used in the analysis of systems such as single-input single-output filters. The term is often used to refer to linear, time-invariant systems (LTI). Most real systems have non-linear input/output characteristics, but many systems, when operated within nominal parameters (not “over-driven”) have behavior that is close enough to linear that LTI system theory is an acceptable representation of the input/output behavior. [0214] Risk can be determined given a time series of data collected by the data collection and maintenance module. This data can be presented either as a first linear array (1×N) where N is the number of factors collected. The factors can include one or more of the factors, parameters, or supply chain and/or logistics operation(s) characteristics identified herein. The factors can include risk factors, such as economic risk, environmental risk, geopolitical risk, societal risk, and technological risk. A transfer function (N×M) can relate the collection of such (risk) factors to variability (risk) of critical factors,(be they cost, time to delivery, the same or another risk factor, etc.), which is the (1×M) linear array. The (1×N) linear array can also be transformed into a single number or factor or given a coloration indicative of an “overall” metric of risk (variability). The overall metric of risk can be a supply chain and/or logistics operation(s) health index or risk. )
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of McNamara, particularly, the use of linear programming such as LTI or optimizing a linear array on the mixed-variables. One would have been motivated to perform the combination by the benefit of accurately representing the input/output behavior, despite the system not being a fully linear system. (McNamara [0072] Most real systems have non-linear input/output characteristics, but many systems, when operated within nominal parameters (not “over-driven”) have behavior that is close enough to linear that LTI system theory is an acceptable representation of the input/output behavior. While any LTI system can be described by some transfer function or another, there are certain families of special transfer functions that are commonly used. )
Regarding Claim 16:
Tourtellote teaches the method of claim 1:
However, Tourtellote fails to teach:
- wherein the adjusting is further based at least on the value of the first performance metric
Alternatively, McNamara teaches:
- wherein the adjusting is further based at least on the value of the first performance metric(McNamara [0171] In one application, the analytical engine 712 determines, based on performance information received from the historical state module 708, a performance rating for each enterprise and/or organization in the supply chain and/or logistics operation(s). The performance rating can be based on a scale from lowest performance level to highest performance level. [0219] The supply chain and logistics analyzer 332 can identify problems or choke points or bottlenecks in the supply chain and/or logistics operation(s) and/or provide recommended changes to the supply chain and/or logistics operation(s) to provide greater reliability, more reliable and faster material and/or part and/or component and/or product manufacture and delivery cycles, more material turns, and reduced cost and waste. reconfiguring the layout and/or production unit operations within a selected facility. A performance risk can be associated with each recommendation based on factors, such as performance rating, ) Reconfiguring the logistics analyzer based on the performance rating satisfies the limitation.
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of McNamara in which the adjustment to the distribution model is based on a performance metric. One of ordinary skill in the art would have been motivated by the benefit of using such information to generate recommendations and improvements based on the performance metrics. (McNamara [0220] The supply chain and logistics analyzer 332 can use cost information received from the cost monitoring module 356 to identify potential modifications to a selected supply chain and/or logistics operation to save money. For example, labor arbitrage, or differences in labor rates, differences in raw material prices, differences in governmental regulations and restrictions, differences in facility capital and operating costs, and other cost differences can cause the supply chain and logistics analyzer 332 to recommend relocating part of the supply chain to a less expensive country.)
Regarding Claim 17:
Tourtellote teaches the method of claim 1:
However, Tourtellote fails to teach:
- wherein the making further comprises making the value of the second performance metric accessible to the third-party computing device, and
- the value of the second performance metric is greater than the value of the first performance metric.
Alternatively, McNamara teaches:
- wherein the making further comprises making the value of the second performance metric accessible to the third-party computing device, and (McNamara [0281] In step 2012, the supply chain and logistics analyzer 332 determines and assigns a score or ranking to the selected object. This can be done using input from the supply chain and logistics manager 336. The score or ranking can be a function, for example, of one or more of current and/or historic performance information for the selected object, a performance rating, a CpX metric, a risk score, a performance score, a location score, a financial score, and a geo-political score. Problems or choke points or bottlenecks in the selected supply chain or logistics operation can be determined, for example, using one or more of a critical path method algorithm, queueing theory, a scheduling algorithm, a simulation of the selected supply chain or logistics operation, pattern or template matching, manufacturing process management techniques, enterprise resource planning techniques, transportation theory, capacity planning techniques, and a transform function. [0282] In decision diamond 2016, the supply chain and logistics analyzer 332 determines whether the score is acceptable. The acceptability of the score can be determined relative to a predetermined score threshold, policy or rule set, or relative to an object of a supply chain or logistics operation used as a basis of comparison. [0181] The analytical engine 712 calculate a key performance indicator (KPI) based on the validated supply chain data indicative of the performance of the supply chain member relative to a predetermined supply chain management plan. Accordingly, the analytical engine 712 may include a dashboard, in which the KPI is graphically displayed by the reporting module to the user.) The reporting module of the user falls within the scope of the third-party computing device, wherein performance metrics are displayed.
- the value of the second performance metric is greater than the value of the first performance metric.(McNamara [0219] The supply chain and logistics analyzer 332 at the control tower service platform 150 can do this, for example, by analyzing the reported performance information using advanced planning and scheduling techniques by which raw materials and production capacity are optimally allocated to meet demand. Recommendations could include restructuring tier 1, 2, 3 and 4 relationships, using differently located facilities for lesser or greater production, using different freight modes and/or carriers, and reconfiguring the layout and/or production unit operations within a selected facility. A performance risk can be associated with each recommendation based on factors, such as performance rating, geographic location of the recommended enterprise and/or organization relative to the geographic locations of the upstream enterprise and/or organization (if any)) Performance metrics which are higher (greater production), are also displayed to the user to show optimal decisions for the user to make.
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of McNamara particularly generating a second performance metric which is higher than a first performance metric, and displaying the second higher metric to the user. One of ordinary skill would have been motivated by the benefit of providing a proactive instead of reactive way to manage the supply chain, by providing optimal decision making support to users. (McNamara [0169] The analytical engine 712 receives performance and other data from the data collection and maintenance module 700, scheduling information from the scheduling module 704, and historical information (such as a level of confidence) from the historical state module 708 and, based on the information, forecasts incoming shipment arrival times and outgoing shipment departure times and identifies any inability to meet distribution chain requirements, commitments or objectives (e.g., orders, contractual commitments, policies, objectives, etc.) (a “noncompliant event”). This information is provided, by the analytical engine 712 to the risk manager 716. The analytical engine 712 can be a type of situational awareness application that looks at aspects of the current state of the supply chain and/or logistics operation(s) as well as the structural relationships and considers the effect of both internal and external events on the supply chain and/or logistics operation(s). Both past events and forecasted events can be considered by the analytical engine 712. The application can determine not only what happened to the supply chain and/or logistics operation(s) but also what may happen to the supply chain and/or logistics operation(s), thereby providing not only a reactive but also proactive model for problem resolution.)
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Tourtellote et al. (US 11398298 B2) hereinafter Tourtellote, in view of McNamara et al. (US 20200065759 A1) hereinafter McNamara, further in view of Grinchnik et al. (US 20150109287 A1) hereinafter Grichnik.
Regarding Claim 11:
The combination of Tourtellote and McNamara teaches the method of claim 9:
Furthermore, Tourtellote teaches:
- wherein mathematical program is a mixed-integer program. (Tourtellote [Col. 13 lines 40-49] The method 110 may further include a step 128 that includes determining an appropriate manufacturing/packaging timing and quantities over the course of the study, as well as a trigger for calculating a re-forecast of the supply plan. This may be one of, or a combination of, a minimum frequency, a calculation based on expiry dating, external constraints provided by the user such as drug availability, and may rely on operations research techniques such as Constraint Programming, Neural Networks, Mixed Integer Programming, etc.)
However, neither Tourtellote nor McNamara teach or suggest:
- wherein mathematical program is a mixed-integer nonlinear program.
Alternatively, Grinchnik discloses a supply chain analysis algorithm that allows users to input parameters and perform sensitivity analysis. Grinchnik teaches:
- wherein mathematical program is a mixed-integer nonlinear program.(Grinchnik [0023] When customers 140-144 make demands to manufacturing facilities 120-122 or distributing facilities 130-133, the structure of the distribution network may be designed to fulfill the demand. The design of the distribution network may be determined according to a plurality of objectives including, for example, minimum inventory cost, maximum profit of the business, time required to fulfill the demand, environmental impact, resilience of the network, total route distance, etc. The determination may be carried out according to disclosed embodiments by an exemplary system as shown in FIG. 2. The system disclosed herein may consider one or more of these objectives simultaneously in determining the structure of the distribution network. The objectives considered by the system may be competing with one another. The system may use a nonlinear programming technique to balance the competing objectives.)
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of Grinchnik to user nonlinear programming techniques. One of ordinary skill would have been motivated by the benefit of balancing the variables, when the objectives of the variables are in competition, which is an improvement over conventional systems which cannot capture the nonlinearity of real world systems. (Grinchnik [0023] The system disclosed herein may consider one or more of these objectives simultaneously in determining the structure of the distribution network. The objectives considered by the system may be competing with one another. The system may use a nonlinear programming technique to balance the competing objectives. [0005] Conventional supply chain modeling techniques, such as that disclosed in the '960 application, may be inherently inaccurate, because the linear programming used in these techniques cannot correctly capture the non-linearity of critical factors in a supply chain network.)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Tourtellote et al. (US 11398298 B2) hereinafter Tourtellote, in view of Grinchnik et al. (US 20150109287 A1) hereinafter Grichnik.
Regarding Claim 12:
Tourtellote teaches the method of claim 1:
However, Tourtellote fails to teach:
- wherein the computational results comprise a sensitivity analysis with respect to the at least one parameter.
Alternatively, Grinchnik teaches:
- wherein the computational results comprise a sensitivity analysis with respect to the at least one parameter.(Grinchnik [0007] In another aspect, the present disclosure is directed to a system for analyzing supply chain sensitivity based on a supply chain model. [0054] The color codes presented by interface 400 provide the user with information about sensitivity of the optimization results in response to changes in the input parameters. For example, interface 400 may show that as the promise time (e.g., parameter 2) is reduced, the profit of supply chain 100 is generally reduced. This may be due to the fact that, although a smaller promise time generally increases customer satisfaction, it may place the entire supply chain under stress, thereby increasing the operational costs of the supply chain. Similarly, interface 400 may show that as the production volume (e.g., parameter 1) is increased, the profit of supply chain 100 is also generally reduced. This may be due to the fact that, although a high production volume increases utilization of the manufacturing facilities, it may also put the entire supply chain under stress, thereby increasing the operational costs of the supply chain. [0057] Similar to the color codes described above, the three-dimensional surface may provide the user with information about the sensitivity of the optimization results in response to changes in the input parameters. For example, the three-dimensional surface may guide the user to focus the analysis on a region that corresponds to globally optimal results. )
Therefore, it would have been obvious to one of ordinary skill in the art before effective filing date of the present disclosure to further modify Tourtellote by adding the teachings of Grinchnik in which the sensitivity of particular parameters are analyzed with regards to their impact on profits. One of ordinary skill in the art would have been motivated by the benefit of displaying sensitivity to users to allow users to modify particular parameters without greatly impacting the profits. (Grinchnik [0005] In addition, conventional techniques may not allow the user to analyze the sensitivity of the supply chain network and its non-linear behavior in response to variations of the constraints and conditions. Furthermore, conventional techniques may not effectively present the sensitivity analysis to a user for viewing or guide the user to select a particular optimization result. The supply chain management system of the present disclosure is directed toward solving the problem set forth above and/or other problems of the prior art.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
-Mahmood et al. (US-20210280287-A1) discloses an optimization algorithm for an individualized medicine supply chain and distributed scheduling system to optimize the capacity of logistics based on patient datasets, order sites, and prescriber data.
-Solakhyan et al. (US-20200410618-A1) discloses a negotiations platform between pharmaceutical companies and companies that allows for tracked bids and offers in a sandbox environment.
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/NICO L PADUA/ Junior Patent Examiner, Art Unit 3626
/JESSICA LEMIEUX/ Supervisory Patent Examiner, Art Unit 3626
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