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 .
Notice to Applicant
In response to the communication received on 11/15/2024, the following is a Non-Final Office Action for Application No. 18949964.
Status of Claims
Claims 21-40 are pending.
Claims 1-20 are cancelled.
Drawings
The applicant’s drawings submitted on 11/15/2024 are acceptable for examination purposes.
Information Disclosure Statement
The information disclosure statement(s) (IDS) filed 03/17/2025 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 18949964 filed 11/15/2024 is a Continuation of 17719195 , filed 04/12/2022 ,now U.S. Patent # 12147926 and having 1 RCE-type filing therein; 17719195 Claims Priority from Provisional Application 63174948 , filed 04/14/2021; 17719195 is a Continuation of 17102103 , filed 11/23/2020 ,now U.S. Patent # 12354045 and having 4 RCE-type filing therein; 17102103 Claims Priority from Provisional Application 63089542 , filed 10/08/2020, 17102103 Claims Priority from Provisional Application 62940014 , filed 11/25/2019.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: data transformer module, optimization module and visualization module in claim 31 and dependent claims.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 12147926 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims recite substantially similar limitations as follows: retrieving data from a customer enterprise data system; transforming the retrieved data to meet ingestion requirements of a machine learning model which calculates the cost of a supply chain subject to the following constraints: desired service levels, upper limits of the number of orders a supply site can service over a time period, carbon tax and offsets, cost of holding stock, site sensitivity to complexity, transportation cost, product level demand history, delivery performance and safety stock levels; optimizing cost across two variable parameters using the machine learning model; graphing the two variable parameters with current conditions and optimized conditions labeled; receiving a user selection of the variable parameters that are excluded from consideration; updating the graph with an updated optimized conditions responsive to the user selection; and improving the machine learning model by comparing actual outcomes to the modeled optimized conditions.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims fall within statutory class of process or machine; hence, the claims fall under statutory category of Step 1.
Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
A computerized method for optimizing a supply chain comprising: retrieving data from a customer enterprise data system; transforming the retrieved data to meet ingestion requirements of a machine learning model which calculates the cost of a supply chain subject to the following constraints: desired service levels, upper limits of the number of orders a supply site can service over a time period, carbon tax and offsets, cost of holding stock, site sensitivity to complexity, transportation cost, product level demand history, delivery performance and safety stock levels; optimizing cost across two variable parameters using the machine learning model; graphing the two variable parameters with current conditions and optimized conditions labeled; receiving a user selection of the variable parameters that are excluded from consideration; updating the graph with an updated optimized conditions responsive to the user selection; and improving the machine learning model by comparing actual outcomes to the modeled optimized conditions.
[or]
A computerized supply chain optimization system comprising:an interface for retrieving data from a customer enterprise data system; a data transformer module for transforming the retrieved data to meet ingestion requirements of a machine learning model which calculates the cost of a supply chain subject to the following constraints: desired service levels, upper limits of the number of orders a supply site can service over a time period, carbon tax and offsets, cost of holding stock, site sensitivity to complexity, transportation cost, product level demand history, delivery performance and safety stock levels;an optimization module for optimizing cost across two variable parameters using the machine learning model;a visualization module for graphing the two variable parameters with current conditions and optimized conditions labeled, receiving a user selection of the variable parameters that are excluded from consideration and updating the graph with an updated optimized conditions responsive to the user selection; anda future performance predictor for improving the machine learning model by comparing actual outcomes to the modeled optimized conditions.
The claim(s) recite(s) the following summarization of the abstract idea which includes improving the machine learning model by comparing actual outcomes to the modeled optimized conditions for supply chain optimization by the additional element(s) of modules and/or machine learning models. This falls into at least the Abstract Idea Grouping of Mental Processes since the information can be analyzed by an abstract evaluation judgment process. Thus, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity since the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion).
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion).
Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The module and/or machine learning model is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic module and/or machine learning model limitation is no more than mere instructions to apply the exception using a generic computer component. Further, improving the model by comparing actual outcomes to the modeled optimized conditions by a module and/or machine learning model is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The machine learning model is used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of the functions and do not include any details about how the functions via the machine learning model are accomplished. See MPEP 2106.05(f). Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B.
Per Step 2B, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: module and machine learning model. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. The additional element of a machine learning model is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Further, improving the model by comparing actual outcomes to the modeled optimized conditions by a module and/or machine learning model is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0124 wherein “Processor(s) 922 (also referred to as central processing units, or CPUs) are coupled to storage devices, including Memory 924.” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims).
The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements other than the addressed machine learning model that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Pace et al. (US 20200311657 A1) hereinafter referred to as Pace in view of Cella et al. (US 20220187847 A1) hereinafter referred to as Cella.
Pace teaches:
Claim 21. A computerized method for optimizing a supply chain comprising:
retrieving data from a customer enterprise data system (¶0041 Each of the suppliers 210, 220, and 230 often stores such information in its computing system (e.g., in databases). For example, the supplier 210 may supply items A, B, and C, and each item A, B, or C's unit price, minimum order and/or other related information may be stored in the data system 211. The supplier 220 may supply items D, E, and F, and each item D, E, or F's unit price, minimum order and/or other related information may be stored in the data system 221. Since the principles described herein is implemented in a computing system, throughout this application the term “supplier” and “supplier's computing system” may be interchangeable. Thus, in FIG. 2, each of the suppliers 210, 220 and 230 may also represent the supplier's computing system that includes user interfaces that allow employees to interact with the data stored in the computing system. ¶0045The service 240 also has access to each distributor 250 and/or 260's data systems 251 and/or 261. Based on the distributor's data, the service 240 determines the suppliers from which the distributor has made orders in the past, and the current inventories in stock which were ordered from these suppliers. The service 240 then retrieves each of the relevant suppliers' information from the data system 241.);
transforming the retrieved data to meet ingestion requirements of a machine learning model which calculates the cost of a supply chain subject to the following constraints (¶0060 The method 400 further includes maintaining and/or accessing a data system including information related to a plurality of suppliers (402). The information related to the suppliers may include information related to each of the items each of the suppliers supplies, and the minimum order requirement, etc. In some embodiments, the suppliers' information may be manually entered or updated by the service provider. In some embodiments, the suppliers' information may be input by each of the suppliers or automatically imported from the supplier's data system. Next, based on the distributor's data, each of the corresponding suppliers' information is retrieved from the data system (403). Using the distributor 250 of FIG. 2 as an example again, the distributor 250 has inventories A and E obtained from the suppliers 210 and 220. The data system 241 of the service 240 includes the suppliers 210 and 220's detailed information, such as the minimum order requirements for each of their items, the processing time for each order, etc. Thus, the service 240 can retrieve the minimum requirement of inventory A and E from the data system 241, and the processing time for the relevant order. Next, based on the distributor's data and the corresponding suppliers' information, future need of at least one of the distributor's inventory items is predicted (404). The prediction may be based on the sales of the inventory in stock, the minimum order requirement of the suppliers, and the processing time for each order, etc.):
desired service levels, upper limits of the number of orders a supply site can service over a time period, carbon tax and offsets, cost of holding stock, site sensitivity to complexity, transportation cost, product level demand history, delivery performance and safety stock levels (¶0085 Each purchase class can then accept a set of default supplier parameters or they can be changed according to specific needs. These parameters include the periods to supply, target service level, periods to include in forecasting calculations, whether or not to include or exclude zero usage periods, and the lower and upper bounds of automated updates for items within that class. ¶0099 The Item Ranking Tool (“IRT”) performs ABC classification based on a novel combination of the cumulative ranking of cost of sales and hits for each item by location and primary supplier. The ranking tool serves to group items into performance buckets which govern parameters such as periods to supply, safety stock, and service level percent. This tool may have no user interface of its own but returns information to the Inventory Replenishment Calculator (“IRC”) described in Attachment C. The interface is under development.);
optimizing cost across two variable parameters using the machine learning model (¶0098 The IER pulls a list of all the customers who have had invoiced sales since the date specified in the parameters. The list of items may include in the review process based on the parameters. Invoice information may be obtained from the ERP system. The IER performs final analysis and returns the results to the user interface. The Item Ranking Tool (“IRT”) performs ABC classification based on a novel combination of the cumulative ranking of cost of sales and hits for each item by location and primary supplier. The ranking tool serves to group items into performance buckets which govern parameters such as periods to supply, safety stock, and service level percent. This tool may have no user interface of its own but returns information to the Inventory Replenishment Calculator (“IRC”) described in Attachment C. The interface is under development. The IRT performs the analysis based on the supplier selected in the IRC and returns results to each item.);
graphing the two variable parameters with current conditions and optimized conditions labeled (¶0094 When the user has accessed the SPR, they are presented with a wealth of information. This information is show in FIGS. 14-20. FIG. 14 illustrates a main screen for the SPR including fields for rules. This template shows metrics for 3 different time periods. FIG. 15 illustrates a visualization that shows several details from the supplier performance details. This section shows the Turn/Earn Index for a particular supplier. FIG. 16 illustrates a visualization that shows comparison graphs showing changes in the Turn-Earn over time. This graph can be displayed for any of the metrics mentioned on the first page of this attachment. FIG. 17 illustrates a visualization that shows items that fall outside the desired range. The desired range can be defined by the user or automatically generated by the system. This type of analysis can be done for each metric mentioned in the specification. FIG. 18 illustrates a visualization that shows a list automatically generated by the system displaying items that have various issues. FIG. 19 illustrates a visualization that show a summary list of all supplier programs and current performance based on those programs);
receiving a user selection of the variable parameters that are excluded from consideration (¶0085 Each purchase class can then accept a set of default supplier parameters or they can be changed according to specific needs. These parameters include the periods to supply, target service level, periods to include in forecasting calculations, whether or not to include or exclude zero usage periods, and the lower and upper bounds of automated updates for items within that class ¶0087 As explained previously, the SB is configured to analyze and aggregate required item information stored in the existing ERP. Based on the analysis, the SB may then calculate the various buy requirements for each location and supplier combination. The SB may also implement a statistical elimination to exclude purchase orders with abnormal lead times from standard lead time calculations ¶0091 The IRC may also analyze each item in the dataset based on a user parameter that sets how many periods to include. Average usage information including and/or excluding the current period as well as including and/or excluding periods with zero usage may be analyzed. The IRC may use various statistical methods to calculate the recommended dynamic minimum and/or dynamic maximum that will be passed back to an existing ERP application);
updating the graph with an updated optimized conditions responsive to the user selection (¶0091 The IRC may also analyze each item in the dataset based on a user parameter that sets how many periods to include. Average usage information including and/or excluding the current period as well as including and/or excluding periods with zero usage may be analyzed. The IRC may use various statistical methods to calculate the recommended dynamic minimum and/or dynamic maximum that will be passed back to an existing ERP application. The IRC may also review the results of the previous results to determine which items require a user's attention and which items can be updated automatically. These automatic updates are based on thresholds initially established by the user. The IRC may also make periodic recommendations to changes to these automated thresholds.); and
improving the machine learning model by comparing actual outcomes to the modeled optimized conditions (¶0045 The principles described herein illustrates an improved supply chain and inventory management system. As illustrated in FIG. 2, in the supply chain environment 200, a service 240 is provided to help the distributors 250, 260 and 270 to manage their inventories. ¶0069 FIGS. 8-20 further illustrate example implementations of the supply chain optimization and inventory management system (herein after referred to as “the system” or “the platform”), though the claims are not limited by these embodiments. The system may include many different modules, for example, a supply barometer module, an inventory replenishment calculator module, a supplier performance report module, an item exposure module, an item ranking module, a null matrix module, an item health tool, etc.).
Although not explicitly taught by Pace, Cella teaches in the analogous art of robot fleet management for value chain networks:
calculating cost of a supply chain subject to the following constraints: carbon tax and offsets[, and] cost of holding stock (¶0559 in embodiments, the adaptive intelligent systems layer 614 may obtain data from other types of external data sources that are not necessarily a value chain entity but may provide insightful data. For example, weather data, stock market data, news events, and the like may be collected, crawled, subscribed to, or the like to supplement the outcome data (and/or training data and/or simulation data) ¶0564 the logistics design system determines the recommendations to optimize an outcome. Examples of outcomes can include manufacturing times, manufacturing costs, shipping times, shipping costs, loss rate, environmental impact, compliance to a set of rules/regulations, and the like. Examples of optimizations include increased production throughput, reduced production costs, reduced shipping costs, decreased shipping times, reduced carbon footprint, and combinations thereof ¶1996 In embodiments, the platform 10110 may process the inputs from a plurality of sources including, but not limited to, medical records (e.g., patient measurements, material allergies, use of other related medical devices, and the like), device specification data (e.g., manufacturing specifications from the party(ies) holding rights to the device, part or other object to be manufactured), patient-input data (e.g., aesthetic preferences such as color of the device), healthcare-provider-input data (e.g., medical office branding), or some other input. An artificial intelligence system (such as a robotic process automation system trained on a training set of expert medical devices or other data), to determine a recommended action, prototype, device, which in embodiments may involve production of a device and/or a component of a device..).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the robot fleet management for value chain networks of Cella with the system for supply chain optimization and inventory management system of Pace for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Pace ¶0002 teaches that it is desirable to have a system for coordination of multiple buyers being hired to manage a portion of the inventory items where the buyers need to review the number of inventory unit in stock while the distributors hire people to manage purchase orders;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Pace Abstract teaches optimizing supply chain and inventory management, and Cella Abstract teaches an intelligence layer controller coordinates performance of the artificial intelligence services on behalf of the intelligence service clients and performance of analyses corresponding to the artificial intelligence services based on the set of governance standards; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Pace at least the above cited paragraphs, and Cella at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the robot fleet management for value chain networks of Cella with the system for supply chain optimization and inventory management system of Pace. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Pace teaches:
Claim 22. The method of claim 21, wherein the two variable parameters are reordering frequency and safety stock levels (¶0099 The Item Ranking Tool (“IRT”) performs ABC classification based on a novel combination of the cumulative ranking of cost of sales and hits for each item by location and primary supplier. The ranking tool serves to group items into performance buckets which govern parameters such as periods to supply, safety stock, and service level percent. This tool may have no user interface of its own but returns information to the Inventory Replenishment Calculator (“IRC”) described in Attachment C. The interface is under development. ¶0100 The IRT performs the analysis based on the supplier selected in the IRC and returns results to each item. The IRT may require three parameters: (1) Supplier ID—This is the internal supplier identifier used throughout the P21™ application; (2) Analysis Periods—How many periods should be included in the analysis. The default may be 12; (3) Include Current Period—Should the current period be included in the analysis. Future versions will rank the item using a statistical model that will include the current period in analysis but will automatically select whether or not to rank an item based on the current period based on error.).
Pace teaches:
Claim 23. The method of claim 21, wherein the two variable parameters are selected from among the options of transport costs, factory changeover costs, cost of discards, cost of lost sales, and supply chain on-time-in-full (¶0092 The SPR may generate reports including (but are not limited to) the following information: (1) Total Purchases, Sales, Cost of Sales, and Gross Margin ($ and %), (2) Average Inventory Value, (3) Inventory Turns, (4) Turn and Earn Index (TE), (5) Gross Margin Return on Investment (GMROI), (6) Lead Time and Lead Time Issues, (7) Supplier Fill Rate. ¶0101 The IRT collects all items for the location and supplier being analyzed in the IRC. The IRT may further aggregate the total hits and hits rank for each item between the starting and ending periods as calculated using the analysis periods and include current period flag. The IRT may also aggregate the total cost of sales, total gross margin, total units sold, the average gross margin, and the cost of sales rank for each item between the starting and ending periods as calculated using the analysis periods and include current period flag).
Pace teaches:
Claim 24. The method of claim 21, wherein the optimization assumes a normal distribution of demand (¶0087 As explained previously, the SB is configured to analyze and aggregate required item information stored in the existing ERP. Based on the analysis, the SB may then calculate the various buy requirements for each location and supplier combination. The SB may also implement a statistical elimination to exclude purchase orders with abnormal lead times from standard lead time calculations. This is important in accurately forecasting the estimated arrival date (Est. Arrival Date in FIG. 9 above) of a purchase order once placed. The SB may also find the last actual purchase order date by location and supplier which is returned as the Last PO Date in FIG. 9 above. The SB may also aggregate the parameters mentioned in the description that are stored in the suppler. After all the information has been collected, the SB takes those results and performs the calculations necessary to produce the % Of Order column in FIG. 9 above. ¶0116 Another UDF may be directed to find net stock. This UDF may take as inputs the company ID, location ID, and inventory ID for a specific item. The system calculates returns the net stock based on a standardized net stock calculation. For example, the following equation may be used to calculate the net stock. Net Stock=On Hand−Allocated−Backordered+On PO+In Process−Protected+In Transit−Reserved).
Pace teaches:
Claim 25. The method of claim 21, wherein graphing is for a node in the supply chain (¶0043 In the supply chain environment 200, there are also many different distributors 250, 260 and 270. The ellipsis 270 represents that there may be any number of distributors that make orders from the suppliers 210, 220 and 230. Each of the distributors 250, 260 and 270 often stores its inventory and purchase order related information in its computing system. For example, the distributor 250 may distribute inventory items A and E, and each of items A and E's information including, but not limited to, number of unit in stock and information related to past purchase orders, is stored in the distributor 250's data system 251. The distributor 260 may distribute inventory items B and D, and each items B and D's information may be store in the distributor 260's data system 261. Similarly, each of the distributors 250, 260 and 270 may also represent the corresponding distributor's computing system that includes user interfaces allowing customer services, buyers, management and other personnel to interact with the data stored in the computing system.).
Pace teaches:
Claim 26. The method of claim 25, further comprising graphing a plurality of graphs for the two variable parameters, each graph corresponding to a respective node in the supply chain (¶0094 FIG. 16 illustrates a visualization that shows comparison graphs showing changes in the Turn-Earn over time. This graph can be displayed for any of the metrics mentioned on the first page of this attachment. FIG. 17 illustrates a visualization that shows items that fall outside the desired range. The desired range can be defined by the user or automatically generated by the system. This type of analysis can be done for each metric mentioned in the specification. FIG. 18 illustrates a visualization that shows a list automatically generated by the system displaying items that have various issues. FIG. 19 illustrates a visualization that show a summary list of all supplier programs and current performance based on those programs.).
Pace teaches:
Claim 27. The method of claim 21, wherein graphing is for an aggregation of all nodes in the supply chain (¶0094 When the user has accessed the SPR, they are presented with a wealth of information. This information is show in FIGS. 14-20. FIG. 14 illustrates a main screen for the SPR including fields for rules. This template shows metrics for 3 different time periods. FIG. 15 illustrates a visualization that shows several details from the supplier performance details. This section shows the Turn/Earn Index for a particular supplier. FIG. 16 illustrates a visualization that shows comparison graphs showing changes in the Turn-Earn over time. This graph can be displayed for any of the metrics mentioned on the first page of this attachment. FIG. 17 illustrates a visualization that shows items that fall outside the desired range.).
Pace teaches:
Claim 28. The method of claim 21, wherein sales forecasts and demand history is used to generate noise terms for use in regression calculations to generate posterior distributions (¶0066 FIG. 7 illustrates a flow chart of an example method 700 for generating a customized recommendation of a purchase order based on a particular distributor or buyer's past records, which may which may also correspond to an embodiment of step 405 of method 400. As described above in FIG. 6, the generated recommendation, the user's modification, and/or the final purchase order may be recorded as part of the order history of the inventory item. When a next recommendation is to be generated, the recommendation may take into account the past order history including the past recommendation, the user's modification and/or the past final purchase order (701). For example, a default recommendation may be generated, and the default recommendation may then be modified based on the order records (702)..).
Pace teaches:
Claim 29. The method of claim 28, wherein the posterior distributions are used as probability weighted future demand scenarios for the optimization (¶0101 The IRT collects all items for the location and supplier being analyzed in the IRC. The IRT may further aggregate the total hits and hits rank for each item between the starting and ending periods as calculated using the analysis periods and include current period flag. The IRT may also aggregate the total cost of sales, total gross margin, total units sold, the average gross margin, and the cost of sales rank for each item between the starting and ending periods as calculated using the analysis periods and include current period flag. The IRT may analyze the aggregated information and prepare them for return to the user interface. The essential analysis performed here may be a simple weighted average comparison between the hits rank and cost of sales rank from procUsage and procSales, respectively. The analysis may also include certain thresholds for ABCD class segregation are set previously and stored in a table that is dedicated to store supplier ranking information.).
Pace teaches:
Claim 30. The method of claim 21, wherein the sensitivities of input feature vectors into the optimization are quantified (¶0051 In some embodiments, the service system 240 may further include a user interface for suppliers, such that each supplier can input and/or update its own information into the data system 241. In some embodiments, the service system 240 may further include a communication module configured to query each distributor's data systems 211, 221 periodically. In some embodiments, each of the suppliers may store their information on their server and/or publish the data on a webpage, and the service system 240 only needs to maintain a list of the addresses that links to the supplier's information (e.g., URLs) ¶0105 The Item Health Tool (“IHT”) performs an inventory analysis on all items based on different input parameters, for example Location—the stocking location being analyzed Minimum Gross Margin Percentage—The minimum gross margin that should be made, on average, in order for an item to be considered a healthy stock item. Minimum Turns—The minimum inventory turnover required for an item to be considered a healthy stock item. Minimum Gross Margin Return On Investment (GMROI)—The minimum GMROI required for an item to be considered a healthy stock item. Start Date—The date that specifies how far back the analysis should look at history. Defaults can be set globally.).
As per claims 31-40, the system tracks the method of claims 21-30, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 21-30 are applied to claims 31-40, respectively. Pace discloses that the embodiment may be found as a system (Fig. 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624