DETAILED ACTION
Status of the Application
The following is a Final Office Action. In response to Examiner's communication of March 18, 2026, Applicant, on June 18, 2026, amended claim 1 and canceled 4-6. Claim 12 was previously canceled. Claims 1-3 and 7-11 are now pending in this application and have been rejected below.
The drawing amendment filed on June 18, 2026 has been accepted and entered.
The Information Disclosure Statement (IDS) filed on April 28, 2026 has been acknowledged.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Response to Amendment
Applicant’s drawing amendment has been accepted and entered.
Applicant's amendments are not sufficient to overcome the 35 USC 101 rejection set forth in the previous action. Therefore, this rejection is maintained below.
Applicant's amendments render moot the 35 USC 103 rejection set forth in the previous action. Therefore, new grounds for rejection necessitated by Applicant’s amendments is set forth below.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejection have been fully considered, but they are not persuasive.
Applicant argues the claimed invention is patent eligible because, under prong 2 of Step 2A, amended claim 1 specifies that the central hub calculates the predictive redesign while maintaining isolation, supported by the clear boundaries described between independent multi-tenant datasets, between the current proprietary data and the anonymized primary data harvested across multiple independent supply chains, this is not a generic computer executing a standard business practice, it is a specialized network engine designed to overcome the technological challenge of data silos and data-leak risks between competing entities, by maintaining strict isolation boundaries while concurrently allowing a rules engine to project part death rates based on macro-level competitive arena and/or geographic source point metrics, the claim improves the operational security and computational capabilities of risk management servers, and claims that focus on a specific, structured improvement to computer functionality, data organization, or secure network architectures are patent-eligible because they integrate any foundational concept into a practical technological application. Examiner respectfully disagrees.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
As noted above, under Prong 2 of Step 2A, we determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, integrate the judicial exception into a practical application. However, for the reasons discussed below, other than the generic central hub, memory, processor, and GUI, the features referred to by Applicant of calculating the recommended redesign to address the predictive non-competitive part related risk based on a baseline survival curve identifying an average percentage of parts which will die while maintaining isolation between the current proprietary product data and the anonymized primary product data of the plurality of supply chain nodes, are not additional elements beyond the recited abstract idea, but rather, these features are part of and directed to the recited abstract idea because these features referred to by Applicant manage commercial or legal interactions, including sales activities and business relations, relationships, interactions, and fundamental economic practices of people and business entities in a supply chain by managing commercial interactions, sales activity, and fundamental economic practice of calculating redesign of a part based on risks in supplying parts from supply chain nodes based on the average percentage of parts which will die while managing commercial interactions, obligations, and relationships in maintaining privacy of product information exchanged by supply chain business entities to manage the risk of the supply chain, and thus, the features referred to by Applicant recite a certain method of organizing human activity.
Further, but for the generic central hub, memory, processor, and GUI, the features referred to by Applicant of calculating a recommended redesign to address the part risk while isolating proprietary and anonymous product data could all be reasonably interpreted as a human making observations of information regarding the supply chain and product life cycles, including proprietary and anonymous data, a human using judgement to organize the proprietary and anonymous data, and a human performing an evaluation and using judgment based on the observed information to calculate a redesign to address the risk; therefore, the features referred to by Applicant recite mental processes
Accordingly, the features Applicant alleges amount to an improvement recite a certain method of organizing human activity and mental processes, which are abstract ideas.
Under Prong 1 of Step 2A, claim 1, and similarly claims 2, 3, and 7-11, recites “supply chain predictive risk manager for managing a supply chain comprising a plurality of supply chain nodes, comprising: a managing … comprising a plurality of primary data regarding anonymized hardware and software product data from the plurality of supply chain nodes across multiple independent supply chains, and life cycle data for all parts employed at the plurality of supply chain nodes; an input to the managing … comprising current proprietary data regarding a product made at one of the plurality of supply chain nodes; a plurality of rules … capable, based on the primary data, of recognizing a predictive non-competitive part related risk embedded in the proprietary data, the plurality of rules calculating, based on accumulated part related risk data from at least providers of parts and from the primary data, a recommended redesign to address the predictive non-competitive part related risk before it arises, wherein the predictive non-competitive part related risk is based on a baseline survival curve identifying an average percentage of parts which will die at different time increments into the future, wherein the predictive non-competitive part related risk is further based on a next most important factor that is significantly related to the death rate, wherein the survival curve is adjusted based on the next most important factor, wherein the next most important factor is derived from the anonymized hardware and software product data and comprises at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes; and an output …, wherein the non-competitive part related risk and the recommended redesign are presented … to prompt transition to alternate parts specified by the recommended redesign, … calculate the recommended redesign while maintaining isolation between the current proprietary data and the anonymized primary data of the plurality of supply chain nodes.” Claims 1-3 and 7-11, in view of the claim limitations, recite the abstract idea of managing supply chain risk by collecting product data from supply chain nodes and life cycle of parts at supply chain nodes, including proprietary and anonymous product data from supply chain nodes, generating rules for recognizing predictive non-competitive part risk based on a survival curve that is adjusted based on the next most importance factors, identifying an average percentage of parts which will die at time increments, and calculating a recommended redesign to address the part risk while isolating proprietary and anonymous product data, and outputting a result of the recommended redesign to address the part risk to prompt transition to alternate parts specified by the recommended redesign.
Aside from the generic computer component of the generic central hub, memory, processor, and GUI implementing the above limitations, each of the above limitations manage commercial or legal interactions, including sales activities and business relations, relationships, interactions, and fundamental economic practices of people and business entities in a supply chain by managing risks in supply parts from supply chain nodes based on the average percentage of parts which will die and private business information exchanged by the business entities to manage the risks; thus, the claims recite certain methods of organizing human activity. In addition, as a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited collecting product data from supply chain nodes and life cycle of parts at supply chain nodes, generating rules for recognizing predictive non-competitive part risk based on a survival curve that is adjusted based on the next most importance factors, identifying an average percentage of parts which will die at time increments, and calculating a recommended redesign to address the part risk while isolating proprietary and anonymous product data, and outputting a result of the recommended redesign to address the part risk to prompt transition to alternate parts specified by the recommended redesign could all be reasonably interpreted as a human making observations of information regarding the supply chain and product life cycles, a human using judgement to organize proprietary and anonymous data, a human performing an evaluation and using judgment based on the observed information to generate rules and apply the rules recognize a risk and calculate a redesign to address the risk, and a human outputting the result manually and/or with a pen and paper; therefore, the claims recite mental processes.
Accordingly, since the claims recite a certain method of organizing human activity and mental processes, the claims recite an abstract idea under the first prong of Step 2A.
Specifically with respect to Applicant’s assertions that the claimed invention is not a generic computer executing a standard business practice, it is a specialized network engine designed to overcome the technological challenge of data silos and data-leak risks between competing entities, by maintaining strict isolation boundaries while concurrently allowing a rules engine to project part death rates based on macro-level competitive arena and/or geographic source point metrics, the claim improves the operational security and computational capabilities of risk management servers, merely requiring that the claims use generic computer components, such as the generically recited “a managing central hub,” “stored in at least one memory,” “to a graphical user interface,” and “by a processor on the graphical user interface,” to implement the recited abstract idea does not make the claims directed to an improvement in computers or other technology or otherwise transform the abstract idea into a patent eligible invention. For the reasons discussed above, performing evaluations and using judgment to collect and organize proprietary and anonymized product data to maintain isolation between the proprietary and anonymized data and calculate redesigns using the collected and organized data are mental processes and maintaining proprietary product data and privacy of anonymous product data exchanged while calculating part risks in a supply chain is a certain methods of organizing human activity, and thus, implementing this with a computer platform, memory, a graphical user interface, and processor amounts to nothing more than requiring that the abstract idea is implemented with generic computer components, which is not sufficient to integrate an abstract idea into a practical application nor sufficient to amount to significantly more than an abstract idea.
As noted in the MPEP, "an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology." MPEP 2106.05(a). Further, mere automation of manual processes is not sufficient to show an improvement in computer-functionality. MPEP 2106.05(a). Therefore, because these alleged improvements are directed to and part of the abstract idea itself and amount to nothing more than automating the abstract idea, the claims do not recite an improvement in technology. See MPEP 2106.05(a).
The claims do not recite, nor does the specification disclose, any new or improved specific hardware, data structure, or other technology to maintain isolation between the proprietary and anonymous data. As noted above, this can be done mentally and manages commercial or legal interactions, including sales activities and business relations, relationships, and interactions of people and business entities in a supply chain. Like in Electric Power Group, the claims are not focused on a specific improvement in computers, but on certain independently abstract ideas that simply use computers as tools. Electric Power Group, LLC v. Alstom S.A., et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a).
Under the second prong of Step 2A, the only additional elements beyond the recited abstract idea are the recitations of “a managing central hub,” “stored in at least one memory element associated with the managing central hub,” “to a graphical user interface,” and “by a processor on the graphical user interface” in claim 1; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. In addition, these additional elements merely generally link the abstract idea to a field of use or environment, namely a generic computer platform environment with a generic user interface, which is not sufficient to integrate an abstract idea into a practical application.
Applicant argues that the elements considered as an ordered combination provide an inventive concept sufficient to amount to "significantly more" than the exception because the newly added ordered combination, specifically arranging a central hub to dynamically adjust an predictive part survival curve using cross-chain macro factors (geographic source points, geographic areas, or competitive arenas), and displaying a direct automated transition prompt on a GUI, while executing the entire calculation under strict data-isolation barriers, is completely absent from the conventional art, the Examiner's own prior art analysis (relying on a combination of two distinct references to address basic risk scoring and basic curve calculations) further supports that this specific integrated network and data architecture is non-conventional, non-routine, and unique to the industry, and the ordered combination provides a highly specialized predictive instrument that transforms the hardware into a patent-eligible machine. Examiner respectfully disagrees.
As noted above, under Step 2B, we determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. However, for the reasons discussed above, other than the generic hub and GUI, the features referred to by Applicant of the dynamically adjusting an predictive part survival curve using cross-chain macro factors (geographic source points, geographic areas, or competitive arenas), displaying a recommended design change to prompt transition to alternate parts, and executing the calculation while isolating proprietary and anonymous data are not additional elements beyond the recited abstract idea, but rather, these features are part of and directed to the recited abstract idea because these features referred to by Applicant recite a certain method of organizing human activity and recite mental processes.
Contrary to Applicant’s assertions regarding the prior art rejections, the search for an inventive concept under § 101 is distinct from demonstrating novel and non-obviousness. See SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2-3 (Fed Cir. May 15, 2018) (citing Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016). Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America at 2. Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at 3. In Step 2B, “[w]hat is needed is an inventive concept in the non-abstract application realm.” Id. at 11.
Under Step 2B because, as noted above under Prong 2 of Step 2Athe only additional elements beyond the recited abstract idea are the recitations of “a managing central hub,” “stored in at least one memory element associated with the managing central hub,” “to a graphical user interface,” and “by a processor on the graphical user interface” in claim 1; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0036] (describing the computer implemented engines and modules disclosed herein may include any number of tangibly-embodied software and/or hardware components having a transformative effect on at least a portion of a system and a computer program product in accordance with one embodiment comprises a tangible computer usable medium (e.g., standard RAM, an optical disc, a USB drive, or the like) having computer-readable program code embodied therein, wherein the computer-readable program code is adapted to be executed by a processor). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself.
Response to Arguments - Prior Art
Applicant’s arguments with respect to the prior art rejection have been fully considered, but they are now moot in view of new grounds for rejection necessitated by Applicant’s amendment set forth below.
Drawings
The drawings were received on June 18, 2026. These drawings are acceptable.
Claim Rejections - 35 USC § 112, First Paragraph
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3 and 7-11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1, and similarly claim 13 & 17, recites “the next most important factor is derived from the anonymized hardware and software product data and comprises at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes” and “wherein the managing central hub is configurated to calculate the recommended redesign while maintaining isolation between the current proprietary data and the anonymized primary data of the plurality of supply chain nodes” However, Applicant’s specification does not in expressly or inherently require the next most important factor comprises “at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes” nor “maintaining isolation between” the current proprietary data and the anonymized primary data of the plurality of supply chain nodes, as the claims require.
In order to satisfy the written description requirement, each claim limitation must be expressly or inherently supported by the disclosure. MPEP 2163 (emphasis added). “The 'written description' requirement implements the principle that a patent must describe the technology that is sought to be patented; the requirement serves both to satisfy the inventor's obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed.” Capon v. Eshhar, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). Further, the written description requirement promotes the progress of the useful arts by ensuring that patentees adequately describe their inventions in their patent specifications in exchange for the right to exclude others from practicing the invention for the duration of the patent's term. See MPEP 2163 (emphasis added).
For claims directed toward computer-implemented functions, like the presently claimed invention, “[i]f the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.” MPEP 2161.01 (emphasis added). It is not enough that one skilled in the art could write a program to achieve the claimed function because the written description requirement requires that the specification explains how the inventor intends to achieve the claimed function. Examining Claims for Compliance with 35 USC 112(a) - PowerPoint of Computer Based Training, Slides 20 & 21, (emphasis added) available at http://www.uspto.gov/ sites/default/files/documents/uspto_112a_ part1_17aug2015.pptx. The ability of one skilled in the art to make and use the claimed invention does not satisfy the written description requirement if details of how the function is to be performed are not disclosed. Id. at Slide 20.
With respect to the recitation of “the next most important factor is derived from the anonymized hardware and software product data and comprises at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes,” nothing in the Specification expressly or inherently requires the next most important factor comprises at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes.
Applicant cites to claims 4-6 and paragraphs [0009], [0060], [0065], [0083]-[0084], [0090]-[0091], and [0093] of the specification as support for the amendments. Claims 4-6 disclose “wherein the predictive non-competitive risk varies in accordance with a competitive arena of the one of the plurality of supply chain nodes,” “a geographic area of the one of the plurality of supply chain nodes,” and “a geographic source point of the parts” and paragraph [0065] discloses that “the model then selects the next most important factor that is significantly related to the death rate; in other words, as to how this curve differs for subsets of the data, i.e., for specific parts or parts having specific characteristics, which subsets are the most significantly different, and is this factor one such "very different" curve? The model then adds in the "very different" factors and adjusts the equation and consequently curves that are drawn based on the value of that factor, as shown in Figure 5A”; however, nothing in these sections disclose the next most important factor comprises a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes, but rather that the next most important factor related to the death rate is selected by the model and that the predictive non-competitive risk varies in accordance with a competitive arena, a geographic area of the one of the plurality of supply chain nodes, and a geographic source point. Paragraphs [0060], [0083]-[0084], [0090]-[0091], and [0093] are silent with respect to this feature.
Regarding the recitation of “wherein the managing central hub is configurated to calculate the recommended redesign while maintaining isolation between the current proprietary data and the anonymized primary data of the plurality of supply chain nodes,” nothing in the Specification expressly or inherently requires “maintaining isolation between” the current proprietary data and the anonymized primary data of the plurality of supply chain nodes.”
From the paragraphs cited as support, in relevant part, paragraph [0060] discloses “the end of life aspect may comprise a combination of global, anonymized data across all uses of that part and similar parts across all supply chains for which data is available, and of third party data sources available via accessible network links, and is configured to identify significant inflection points in a part's life cycle based on correlations to key features in the data that can be used to predict when a part can be expected to decline. Since the disclosed models and system rely on multiple data sources and a broad cross-section of global anonymized data (at primary node/hub 101 ), they benefit from a far more comprehensive information base than what is currently available, at least in that the learnings from the global data across many supply chains (each of which is likely proprietary, and would thus not be available in the known art for analysis) can both challenge or be augmented by complementary external data.” While this portion of the specification mentions anonymized data, there is no discussion regarding “maintaining isolation between” the current proprietary data and the anonymized primary data. Furthermore, on the contrary, the express disclosure of “the disclosed models and system rely on multiple data sources and a broad cross-section of global anonymized data (at primary node/hub 101), they benefit from a far more comprehensive information base than what is currently available, at least in that the learnings from the global data across many supply chains (each of which is likely proprietary, and would thus not be available in the known art for analysis)” evinces that the anonymized data includes the proprietary data, and thus, these data are not isolated. Claims 4-6 and Paragraphs [0060], [0083]-[0084], [0090]-[0091], and [0093] are silent with respect to this feature.
For the reasons set forth above, although the Specification generally discusses the next most important factor is selected, the risk varies based on competitive arena and geography of supply chain nodes and sources of parts, anonymized data, and proprietary data, the Specification does not inherently nor expressly support require the next most important factor derived from anonymized data comprises “at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes” nor “maintaining isolation between” the current proprietary data and the anonymized primary data of the plurality of supply chain nodes, as required by the claims.
Claims 2, 3, and 7-11 depend on claim 1 and do not cure the aforementioned deficiencies, and thus, these claims are rejected for the reasons set forth above.
Claim Rejections - 35 USC § 112, Second Paragraph
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3 and 7-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the anonymized primary data.” There is insufficient antecedent basis for this limitation in the claim.
Claims 2, 3, and 7-11 depend on claim 1 and do not cure the aforementioned deficiencies, and thus, these claims are rejected for the reasons set forth above.
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 1-3 and 7-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1, and similarly claims 2, 3, and 7-11, recites “supply chain predictive risk manager for managing a supply chain comprising a plurality of supply chain nodes, comprising: a managing … comprising a plurality of primary data regarding anonymized hardware and software product data from the plurality of supply chain nodes across multiple independent supply chains, and life cycle data for all parts employed at the plurality of supply chain nodes; an input to the managing … comprising current proprietary data regarding a product made at one of the plurality of supply chain nodes; a plurality of rules … capable, based on the primary data, of recognizing a predictive non-competitive part related risk embedded in the proprietary data, the plurality of rules calculating, based on accumulated part related risk data from at least providers of parts and from the primary data, a recommended redesign to address the predictive non-competitive part related risk before it arises, wherein the predictive non-competitive part related risk is based on a baseline survival curve identifying an average percentage of parts which will die at different time increments into the future, wherein the predictive non-competitive part related risk is further based on a next most important factor that is significantly related to the death rate, wherein the survival curve is adjusted based on the next most important factor, wherein the next most important factor is derived from the anonymized hardware and software product data and comprises at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes; and an output …, wherein the non-competitive part related risk and the recommended redesign are presented … to prompt transition to alternate parts specified by the recommended redesign, … calculate the recommended redesign while maintaining isolation between the current proprietary data and the anonymized primary data of the plurality of supply chain nodes.” Claims 1-3 and 7-11, in view of the claim limitations, recite the abstract idea of managing supply chain risk by collecting product data from supply chain nodes and life cycle of parts at supply chain nodes, including proprietary and anonymous product data from supply chain nodes, generating rules for recognizing predictive non-competitive part risk based on a survival curve that is adjusted based on the next most importance factors, identifying an average percentage of parts which will die at time increments, and calculating a recommended redesign to address the part risk while isolating proprietary and anonymous product data, and outputting a result of the recommended redesign to address the part risk to prompt transition to alternate parts specified by the recommended redesign.
Each of the above limitations manage commercial or legal interactions, including commercial or legal, sales activities, and business relations, relationships and interactions, and fundamental economic practice of people and business entities in a supply chain by managing risks in supply parts from supply chain nodes based on the average percentage of parts which will die and private business information exchanged by the business entities to manage the risks; thus, the claims recite certain methods of organizing human activity. In addition, as a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited collecting product data from supply chain nodes and life cycle of parts at supply chain nodes, generating rules for recognizing predictive non-competitive part risk based on a survival curve that is adjusted based on the next most importance factors, identifying an average percentage of parts which will die at time increments, and calculating a recommended redesign to address the part risk while isolating proprietary and anonymous product data, and outputting a result of the recommended redesign to address the part risk to prompt transition to alternate parts specified by the recommended redesign could all be reasonably interpreted as a human making observations of information regarding the supply chain and product life cycles, a human using judgement to organize proprietary and anonymous data, a human performing an evaluation and using judgment based on the observed information to generate rules and apply the rules recognize a risk and calculate a redesign to address the risk, and a human outputting the result manually and/or with a pen and paper; therefore, the claims recite mental processes. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2, 3, and 7-11 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper and recite a certain method of organizing human activity that manages business interactions. Accordingly, since the claims recite a certain method of organizing human activity and mental processes, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “a managing central hub,” “stored in at least one memory element associated with the managing central hub,” “to a graphical user interface,” and “by a processor on the graphical user interface” in claim 1; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2, 3, and 7-11 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0036] (describing the computer implemented engines and modules disclosed herein may include any number of tangibly-embodied software and/or hardware components having a transformative effect on at least a portion of a system and a computer program product in accordance with one embodiment comprises a tangible computer usable medium (e.g., standard RAM, an optical disc, a USB drive, or the like) having computer-readable program code embodied therein, wherein the computer-readable program code is adapted to be executed by a processor). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2, 3, and 7-11 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-3 and 7-11 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 7-11 are rejected under 35 U.S.C. 103 as being unpatentable over Bajaj, et al. (US 20200175630 A1), hereinafter Bajaj, in view of Brandstetter, et al. (US 20080154458 A1), hereinafter Brandstetter, in further view of Meszaros, et al. (US 20210334735 A1), hereinafter Meszaros.
Regarding claim 1, Bajaj discloses a supply chain predictive risk manager for managing a supply chain comprising a plurality of supply chain nodes, comprising ([0010]-[0012]):
a managing central hub ([0058]-[0059], computer system 100 is configured as a supply chain management (SCM) processing system, wherein primary processing node 101 is configured to contain an SCM platform for processing data from other nodes (104, 107), wherein each node 104, 107 may be network nodes comprising network servers 105, 108 and terminals 106, 109, respectively, nodes 104, 107 may be configured as assembly nodes, part nodes, supplier nodes, manufacturer nodes and/or any other suitable supply chain node, and each of these nodes may be configured to collect, store, and process relevant supply chain-related data and transmit the SCM data to primary node 101 via network 112, [0069], a SCM operating platform 307 may reside at a primary node 101 and is configured to perform and/or control SCM data processing on data received from external nodes 104, 107 and other data sources 110, 111, and [0116], supply chain analytics engine 1703 within platform 307, as is shown in FIG. 14 may be, include, be included within, or be distinct from the supply chain analytics module 304 discussed above, wherein the analytics engine 1703 may include large volume data 1711 from other supply chains, wherein the large volume data may be, include, be included within, or be distinct from analytics data 322, discussed above) comprising a plurality of primary data regarding anonymized hardware and software product data from the plurality of supply chain nodes across multiple independent supply chains, and life cycle data for all parts employed at the plurality of supply chain nodes ([0011], the a supply chain management operating platform may include a plurality of data inputs capable of receiving primary hardware and software data from at least one third party data source and at least one supply chain node upon indication by at least one processor, [0121], analytics may review a variety of variables indicated by data from data store 1711, including but not limited to lead time, alternate parts, product and part lifecycle, supplier and reseller alignments, product and part risk, a recommended solution may stem from data gained from numerous similar and dissimilar products, the parts used to make those products, the suppliers from which those parts are obtained, the manpower and geography necessary to make those parts, and the like, all of which data resides in data store 1711, as disclosed herein, a subscriber to the SaaS system set forth herein may be enabled to access anonymized content in order to optimize a design in light of the supply chain based on the performance of several, dozens, or hundreds of other relevant supply chains, [0117], the wealth of data 1711 available to the disclosed algorithms 1707 may be subjected to comparative analytics 1709 (which may be anonymized));
an input to the managing central hub comprising current proprietary data regarding a product made at one of the plurality of supply chain nodes ([0011], the a supply chain management operating platform may include a plurality of data inputs capable of receiving primary hardware and software data from at least one third party data source and at least one supply chain node upon indication by at least one processor, [0058]-[0059], computer system 100 is configured as a supply chain management (SCM) processing system, wherein primary processing node 101 is configured to contain an SCM platform for processing data from other nodes (104, 107), wherein each node 104, 107 may be network nodes comprising network servers 105, 108 and terminals 106, 109, respectively, nodes 104, 107 may be configured as assembly nodes, part nodes, supplier nodes, manufacturer nodes and/or any other suitable supply chain node, and each of these nodes may be configured to collect, store, and process relevant supply chain-related data and transmit the SCM data to primary node 101 via network 112, [0073], data inputs for the one of more modules may be associated with the platform 307, and thus may obtain data that is made available by the platform, such as may be obtained from hardware or software outputs provided from nodes 104, 107 and/or sources 110, 111, and data may be received in platform modules for risk management 311, analytics 312, information visualization 313 and exception management 314, [0104], attributes and weighting may be dependent upon data availability, i.e., algorithms and selected attributes may be modified based on manual inputs, [0117]-[0119], output from analytics may be weighted, prioritized, ranked in accordance with the preferences input by the user, any data input by the user, such as current parts for aspects of a products design, may be subjected to the analytics 1709 in order to make a completeness assessment, once data is entered to data store 1711 by the user and preferences are entered by the user to the optimizer 1705, automated searching may be performed, the disclosed design for supply chain may integrate supply chain design with product design provided by optimizer 1705 based on the priorities input by the user to the disclosed algorithms, e.g., an input product design may be optimized using the disclosed analytic algorithms);
a plurality of rules stored in at least one memory element associated with the managing central hub ([0116], the analytics engine 1703 may include an optimizer 1705 that may perform the functionality described in the Figures below, including optimization of computing resources and networking related to SCM such as may be indicated by analysis by a rules engine 1707 that applies at least a comparator 1709 having therein comparative algorithms, such as those discussed below, to the large volume data 1711 from other supply chains) and capable, based on the primary data, of recognizing a predictive non-competitive part related risk embedded in the proprietary data, the plurality of rules calculating, based on accumulated part related risk data from at least providers of parts and from the primary data ([0105], the platform 307, and/or the individual app, may receive primary data and generate therefrom secondary data, such as calculation of the risk attribute score, [0103]-[0104], the system may be configured to calculate a risk-in-supply chain (risk attribute) value, where a risk attribute value is based on a framework that analyzes various different risk categories of the supply chain, e.g., in FIG. 9 attributes 901-906, along with their respective descriptions 907, are assigned risk weight values 908 to calculate risk by a weighting algorithm (applied at platform 307 or one of its associated apps), wherein attributes include component lifecycle stages), a recommended redesign to address the predictive non-competitive part related risk before it arises ([0087], a risk calculation, as discussed in more detail below with regard to FIGS. 9-12, may indicate that a particular part is a high risk part (such as because it is from a small, sole source, foreign supplier), consequently, the presently disclosed SCM platform 307, may derive secondary data from the combinations of the optimal procurement secondary data, the risk associated with the part, and the cost of the part, that a 28 day buffer should be ordered for the part at each of the next two 14 day procurement windows—thereby increasing the buffer for this key, high risk part using the learning algorithms of the platform 307, [0106], in FIG. 11, if an alternative sourcing category contains a high risk attribute score, a user may investigate the parts where the user requires purchase from only one manufacturer (“sourced parts”) which may be causing a high risk attribute score, the user may configure the system to enable or suggest other manufacturers as suppliers which will lower the risk attribute score by diversifying the supply base, a user may configure the system to communicate or order suppliers to lower lead times, it is understood by those skilled in the art that the risk attribute calculations disclosed herein may be applied to any aspect or attribute of a supply chain system including parts, suppliers, manufacturers, geography, and so forth, [0121], analytics are algorithmically applied, such as both based on pre-stored, user input, and/or learning algorithms, in order to provide the disclosed optimizations and may review a variety of variables indicated by data from data store 1711, including but not limited to lead time, alternate parts, product and part lifecycle, supplier and reseller alignments, product and part risk assessment, and the like, wherein each of the foregoing may be weighted or otherwise balanced in accordance with the user's input objectives, in order to provide a recommended solution of the supply chain for the user's input product based on all of the foregoing, and the disclosed analytics may provide optimized suppliers, parts, source and assembly locations), wherein the predictive non-competitive part related risk is based on a baseline survival … parts which will die … ([0103]-[0104], the system may be configured to calculate a risk-in-supply chain (risk attribute) value, where a risk attribute value is based on a framework that analyzes various different risk categories of the supply chain, e.g., in FIG. 9 attributes 901-906, along with their respective descriptions 907, are assigned risk weight values 908 to calculate risk by a weighting algorithm (applied at platform 307 or one of its associated apps), wherein attributes include component lifecycle stages), wherein the predictive non-competitive part related risk is further based on a next most important factor ([0103]-[0104], the system may be configured to calculate a risk-in-supply chain (risk attribute) value, where a risk attribute value is based on a framework that analyzes various different risk categories of the supply chain, attributes 901-906 with their respective descriptions 907 are assigned risk weight values 908 to calculate risk, wherein a total risk attribute score may be determined by multiplying a score in each category by the associated weight, e.g., six attributes: alternative sourcing 901, part change risk 902, part manufacturing risk 903, lead time 904, spend leverage 905 and strategic status 906 are weighted by a weighting algorithm (applied at platform 307 or one of its associated apps) at 36%, 19%, 9%, 19%, 19%, and 0%) that is significantly related to the death rate ([0104], attributes may comprise defects per million and component life cycle stages, [0075], the disclosure thus provides a SCM operating platform 307 suitable for receiving base data from the supply chain, and based on the significant data available to the platform, the platform may “learn” from certain of the data received, such as trend data fail point data, [0114], the foregoing aspects may require significant data-driven aspects including analytics in order to assess likelihood of failure), wherein the survival [calculation] is adjusted ([0104], attributes and weighting may be dependent upon data availability, i.e., algorithms and selected attributes may be modified based on availability, and the data selection, [0107], the weighting algorithm may be adjusted over time, e.g., if actual risk is repeatedly indicated as higher than a generated risk attribute score, the weighting algorithm shown in FIG. 11 may be adjusted, and/or the primary and secondary data that is used in the risk attribute score calculation may be modified) based on the next most important factor ([0103], wherein attributes 901-906 with their respective descriptions 907 are assigned risk weight values 908 to calculate risk, wherein a total risk attribute score may be determined by multiplying a score in each category by the associated weight), wherein the next most important factor is derived from the … hardware and software product data ([0011], the platform receives primary hardware and software data and performing operations on the primary hardware and software data to produce secondary data, [0105], the platform 307, and/or the individual app, may receive primary data and generate therefrom secondary data, such as calculation of the risk attribute score, [0107], the weighting algorithm may be employed with regard to primary and/or secondary data to arrive at requested information) … at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes ([0121], analytics may review a variety of variables indicated by data from data store 1711, to provide a recommended solution of the supply chain for the user's input product based on all of the foregoing and other factors, such as geographic effects, and this recommended solution may typically stem not only from the user's input data, but from data gained from geography necessary to make those parts, and the like, all of which data resides in data store 1711, [0102], part of the embodiments disclosed herein, the system is further enabled to process and calculate risk(s), and various other factors and related factors, wherein supply chain risks may emanate from geographic risk and attribute-based risk, among others, for geographic risks, manufacturing locations are registered within the system for parts purchased so that when an area becomes volatile because of socio-political, geographic, (macro-) economic, and/or weather-related disruption, related variables may be processed to determine an effect on, or risk to, a supply chain); and
an output from the managing central hub to a graphical user interface, wherein the non-competitive part related risk and the recommended redesign are presented by a processor on the graphical user interface to prompt transition to alternate parts specified by the recommended redesign ([0120]-[0121], provided to the user by optimizer 1705 may be a compare and contrast of the user's original input design and its impact on the supply chain, as compared to the optimized design and optimized supply chain. Thereby, the user may be enabled to interact, such as through the graphical user interface shown and discussed herein and using known computing peripherals, with the comparison in order to assess whether the automated modifications made in accordance with the analytics have provided an improvement or a detriment to the original product design and supply chain in light of the user's objectives, wherein the disclosed analytics may provide optimized suppliers, parts, source and assembly locations, [0133], FIG. 17 illustrates design comparisons for supply chain performance of various designs for the same or a similar product. As illustrated and given particular user inputs, different design attributes—such as risk score, lead time, sourcing, supplier alignment, preferred suppliers, and part lifecycle—may be compared for each of different designs such that the user may make judgments regarding those different designs as to which will perform best in light of the prospective supply chain, [0106], in FIG. 11, if an alternative sourcing category contains a high risk attribute score, a user may investigate the parts where the user requires purchase from only one manufacturer (“sourced parts”) which may be causing a high risk attribute score, [0121], analytics are algorithmically applied to provide the disclosed optimizations and may review a variety of variables indicated by data from data store 1711, including but not limited to lead time, alternate parts, product and part risk assessment, and each of the foregoing may be weighted to provide a recommended solution of the supply chain for the user's input product based on all of the foregoing, and the disclosed analytics may provide optimized suppliers, parts, source and assembly locations), wherein the managing central hub is configurated to calculate the recommended redesign ([0087], a risk calculation, as discussed in more detail below with regard to FIGS. 9-12, may indicate that a particular part is a high risk part (such as because it is from a small, sole source, foreign supplier), derive secondary data from the combinations of the optimal procurement secondary data, the risk associated with the part, and the cost of the part, that a 28 day buffer should be ordered for the part at each of the next two 14 day procurement windows—thereby increasing the buffer for this key, high risk part using the learning algorithms of the platform 307, [0106], in FIG. 11, if an alternative sourcing category contains a high risk attribute score, a user may investigate the parts where the user requires purchase from only one manufacturer (“sourced parts”) which may be causing a high risk attribute score, [0121], analytics are algorithmically applied to provide the disclosed optimizations and may review a variety of variables indicated by data from data store 1711, including but not limited to alternate parts) while maintaining … the current proprietary data ([0011], the a supply chain management operating platform may include a plurality of data inputs capable of receiving primary hardware and software data from at least one third party data source and at least one supply chain node upon indication by at least one processor, [0058]-[0059], each node 104, 107 may be network nodes comprising network servers 105, 108 and terminals 106, 109, respectively, nodes 104, 107 may be configured as assembly nodes, part nodes, supplier nodes, manufacturer nodes and/or any other suitable supply chain node, and each of these nodes may be configured to collect, store, and process relevant supply chain-related data and transmit the SCM data to the primary processing node 101 via network 112) and the anonymized primary data of the plurality of supply chain nodes ([0121], analytics may review a variety of variables indicated by data from data store 1711, including but not limited to lead time, alternate parts, product and part lifecycle, supplier and reseller alignments, the parts used to make those products, the suppliers, the manpower, all of which data resides in data store 1711, as disclosed herein, a subscriber to the SaaS system set forth herein may be enabled to access anonymized content in order to optimize a design in light of the supply chain based on the performance of several, dozens, or hundreds of other relevant supply chains, [0117], the wealth of data 1711 available to the disclosed algorithms 1707 may be subjected to comparative analytics 1709 (which may be anonymized)).
While Bajaj discloses wherein the predictive non-competitive part related risk is based on a baseline survival … parts which will die …, wherein the predictive non-competitive part related risk is further based on a next most important factor that is significantly related to the death rate, wherein the survival [calculation] is adjusted based on the next most important factor (as above) and suggests the predictive non-competitive part related risk is based on a baseline survival curve ([0075], the disclosure makes use of data to allow for risk management, based on the significant data, the platform may “learn” from data such as trend data fail point data), Bajaj does not appear to expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Brandstetter.
Brandstetter teaches wherein the predictive non-competitive part related risk is based on a baseline survival curve identifying an average percentage of parts which will die at different time increments into the future ([0019]-[0020], in an analysis, curves 1080 for the vehicle and for individual parts and replacement parts, wherein the Weibull analysis produces a survival function for each part, and in a risk report, the analysis results to quantify expected part failures over a given time window, [0024]-[0025], in figs. 2A-2C, example part curves generated in the analysis shows conditional part survival probability curves, e.g., in fig, 2A, in one example, the likelihood of new part with survivability curve 124 surviving at least 9000 flight hours 126 is 44%, e.g., in fig. 2C, points 134, 136, 138, 140, 142, 144, 146, 148 indicate the likelihood of the LRU (the heat exchanger) failing in each corresponding aircraft in the next flight hours, [0027], in an example risk table 150 generated by plotting fleet aircraft on LRU curves, e.g., from FIG. 2C, BUNO 165897 is most likely to experience a heat exchanger failure within the next 100 flight hours with a 97% probability of failure, and the electric generator is likely to fail for this same aircraft within the next 100 flight hours with a 91% probability of failure), and
wherein the survival curve is [generated] based on the next … factor ([0022], each query 104 may be customized for a specific part and failure mode for each particular part (i.e. based on next factors), [0019]-[0020], step 104 constructs a query to extract maintenance event data for part removal data for vehicle units, subunits and components for a whole fleet, step 106 collects part removal data periodically (e.g., weekly or monthly) applying the query, and Weibull analysis 108 generates Weibull curves 1080 for the vehicle and for individual parts and replacement parts (i.e. based on next factors), wherein the Weibull analysis produces a survival function for each part selected in the search query from step 104, and the fleet is mapped 1082 onto the Weibull curves to provide individual fleet rate for each vehicle and for individual parts in each vehicle as well, and in a risk report, the analysis results to quantify expected part failures over a given time window).
Bajaj and Brandstetter are analogous fields of invention because both address the problem of managing risks in supply chains. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Bajaj the ability for the predictive non-competitive part related risk to be based on a baseline survival curve identifying an average percentage of parts which will die at different time increments into the future, as taught by Brandstetter, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of the predictive non-competitive part related risk is based on a baseline survival curve identifying an average percentage of parts which will die at different time increments into the future, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Baja with the aforementioned teachings of Brandstetter in order to produce the added benefit of improving reliability of products and inventory control. [0006], [0010].
While Bajaj discloses all of the above, including wherein the next most important factor is derived from the … hardware and software product data … at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes; and …
wherein the managing central hub is configurated to calculate the recommended redesign while maintaining … the current proprietary data and the anonymized primary data of the plurality of supply chain nodes (as above), Bajaj does not appear to expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Meszaros.
Meszaros teaches wherein the next most important factor is derived from the anonymized … product data (Abstract, the present disclosure leverage distributed ledger technology (DLT) to maintain the privacy of data associated with different supply chain participants and allow that data to be shared in a controlled manner to determine actions to optimize distribution of products within the supply chain, [0059], such supply and demand data and information identifying the entity or entities having a supply of a resource may be masked prior to that information being written to the blockchain, and the demand management device 110 may utilize the demand and supply data recorded to the blockchain to identify the entities having supply of resources that match demand being experienced by other entities, once matches or potential matches (e.g., matches based on equivalent products analysis) are identified, the matching engine 118 may then perform operations to fulfill the demand from the available supply) and comprises at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes ( [0009], using such techniques, the demand sensing network may dynamically respond to the changing demand so as to provide products to areas where demand is high, ensuring that adequate quantities of the product are available to customers in different geographic areas, [0040], the demand management device 110 may aid producers and manufacturers with identification of new distribution channels for bringing products to consumer-facing outlets (e.g., retail locations, franchisee locations, or other locations where consumers are able to purchase products or services), e.g., if the demand for the product 254 at the retailer currently or may soon exceed quantities of the product 254 available to the retailer, a quantity of the product 254 sufficient to meet the demand may be communicated to the producer node 250, and the demand management device 110 may provide information to the logistics provider via the logistics provider node 260 to arrange for transportation of the product from the producer to the retailer, [0064]-[0065], using the techniques disclosed herein products may be distributed from the warehouses 372, 374 to the plurality of retailers 310, 320 according to sensed demand, retailers 310 are located in a first geographic region (e.g., a particular city, county, state, etc.) and retailers 320 are located in a second geographic region (e.g., a different particular city, county, state, etc.), and the retailers experiencing the sudden spike in demand may utilize the above-described techniques to obtain additional quantities); and …
while maintaining isolation between the current proprietary data and the anonymized primary data of the plurality of supply chain nodes ([0048]-[0050], where the permissions enable demand or other information to be shared and utilized by third party nodes (e.g., to calculate demand or other information), such as the demand management device 110, the permissions may also specify the types of data that may be shared and whether any masking of the data should be utilized, e.g., masking of the data may involve removing price information or other data, altering price information (e.g., if the specific price charged by the retailer is $1.50 the shared data may be a range, such as $0-$5, or simply a NULL value), masking operations may also be used to hide certain information while allowing that information to be shared with other entities within the supply chain, e.g., the demand management device 110 may have permission to share a portion of the information shared by the grocery store, such as the description of the product (e.g., corn flake cereal), but not other portions of the information shared with the demand management device 110, such as the brand of corn flake cereal the grocery store carries, the identity of the grocery store may be masked, such as to just indicate a generic entity (e.g., grocer 1) or to not identify the entity at all, [0059], such supply and demand data may be masked (e.g., altered, abstracted, etc.) in accordance with the permission data prior to being recorded onto the blockchain, such as to hide an identity of an entity having demand for a resource (e.g., a product, etc.) or a specific quantity that entity needs to meet the demand (e.g., the recorded data may specify a range, such as 1000-2000 units of the resource), information identifying the entity or entities having a supply of that resource may be masked prior to that information being written to the blockchain).
Bajaj and Meszaros are analogous fields of invention because both address the problem of managing distributions of products in supply chains. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Bajaj the ability for the next most important factor to be derived from the anonymized data and comprises at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes and to maintain isolation between the current proprietary data and the anonymized primary data of the plurality of supply chain nodes, as taught by Meszaros, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of the next most important factor is derived from the anonymized hardware and software product data and comprises at least one of a geographic source point of the parts, a geographic area of the one of the plurality of supply chain nodes, or a competitive arena of the one of the plurality of supply chain nodes and maintaining isolation between the current proprietary data and the anonymized primary data of the plurality of supply chain nodes, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Baja with the aforementioned teachings of Meszaros in order to produce the added benefit of improving efficiency and ensuring that adequate quantities of the product are available to customers. [0009].
Regarding claim 2, the combined teachings of Bajaj, Brandstetter, and Meszaros teach the risk manager of claim 1 (as above). Further, Bajaj discloses wherein the predictive non-competitive part related risk comprises an end of life for the parts ([0121], analytics may review a variety of variables indicated by data from data store 1711, including but not limited to part lifecycle, product and part risk assessment, and the like, wherein each of the foregoing may be weighted or otherwise balanced in accordance with the user's input objectives, in order to provide a recommended solution of the supply chain for the user's input product based on all of the foregoing, [0133], FIG. 17 illustrates design comparisons for supply chain performance of various designs for the same or a similar product, as illustrated and given particular user inputs, different design attributes—such as part lifecycle—may be compared for each of different designs such that the user may make judgments regarding those different designs as to which will perform best in light of the prospective supply chain, [0103]-[0104], attributes are assigned risk weight values to calculate risk where a risk attribute value is based on a framework that analyzes various different risk categories of the supply chain, wherein additional attributes may comprise component life cycle stages).
Regarding claim 3, the combined teachings of Bajaj, Brandstetter, and Meszaros teach the risk manager of claim 1 (as above). Further, Bajaj discloses wherein the predictive non-competitive part related risk comprises a lack of availability for the parts ([0103]-[0104], attributes 901-906, along with their respective descriptions 907, are assigned risk weight values 908 to calculate risk, e.g., attributes 901-906, along with their respective descriptions 907, are assigned risk weight values 908 to calculate risk, wherein in fig 9, Examiner notes, alternative sourcing 901 is described 907 as assesses likelihood a part will cause a supply disruption as a result of there being no sourcing contingency).
Regarding claim 7, the combined teachings of Bajaj, Brandstetter, and Meszaros teach the risk manager of claim 1 (as above). Further, Bajaj discloses wherein the recommended redesign comprises alternate sources of ones of the parts ([0106], in FIG. 11, if an alternative sourcing category contains a high risk attribute score, a user may investigate the parts where the user requires purchase from only one manufacturer (“sourced parts”) which may be causing a high risk attribute score, the user may configure the system to enable or suggest other manufacturers as suppliers which will lower the risk attribute score by diversifying the supply base, a user may configure the system to communicate or order suppliers to lower lead times, [0121], these analytics may review a variety of variables indicated by data from data store 1711, including but not limited to lead time, alternate parts, product and part lifecycle, supplier and reseller alignments)
Regarding claim 8, the combined teachings of Bajaj, Brandstetter, and Meszaros teach the risk manager of claim 1 (as above). Further, Bajaj discloses wherein the recommended redesign comprises alternative ones of the parts ([0106], in FIG. 11, if an alternative sourcing category contains a high risk attribute score, a user may investigate the parts where the user requires purchase from only one manufacturer (“sourced parts”) which may be causing a high risk attribute score, the user may configure the system to enable or suggest other manufacturers as suppliers which will lower the risk attribute score by diversifying the supply base, a user may configure the system to communicate or order suppliers to lower lead times, and, it is understood by those skilled in the art that the risk attribute calculations disclosed herein may be applied to any aspect or attribute of a supply chain system including parts, [0104], fig. 9, attribute part change risk 902 is defined as assesses likelihood a part will cause a supply disruption as result of part transition activity, [0121], these analytics may review a variety of variables indicated by data from data store 1711, including but not limited to lead time, alternate parts, product, [0119], disclosed design for supply chain may integrate supply chain design with product design, as referenced above, such that product design may be tweaked or modified as necessary in order to both optimize the final designed product and optimize the supply chain used to create the final product).
Regarding claim 9, the combined teachings of Bajaj, Brandstetter, and Meszaros teach the risk manager of claim 1 (as above). Further, Bajaj discloses wherein the life cycle data is historic ([0071], the platform of FIG. 3A is configured to utilize extensive data across many primary and secondary nodes, …. the platform is that it is effective in identifying actual and potential opportunities of improvement, … based on analysis of extended historical data of similar or related supply chains, [0121], analytics may review a variety of variables indicated by data from data store 1711, including but not limited to … product and part lifecycle, …, in order to provide a recommended solution of the supply chain for the user's input product based on all of the foregoing and other factors, …. Moreover, this recommended solution may typically stem … from numerous similar and dissimilar products, …., the suppliers from which those parts are obtained, …, all of which data resides in data store 1711 [0116], the analytics engine 1703 may include an optimizer 1705 that may perform the functionality described in the Figures below, … therein comparative algorithms, such as those discussed below, to the large volume data 1711 from other supply chains, such as may include similar product verticals …, and to the supply chain of interest, [0118], once data is entered to data store 1711 by the user and preferences are entered by the user to the optimizer 1705, automated searching may be performed, such as using a comparative algorithm 1707 to the supply chains/part lists of similar product supply chains in the data store 1711, to assess the completeness of the design).
Regarding claim 10, the combined teachings of Bajaj, Brandstetter, and Meszaros teach the risk manager of claim 1 (as above). Further, Bajaj discloses wherein the life cycle data is predictive ([0103]-[0104], attributes 901-906, along with their respective descriptions 907, are assigned risk weight values 908 to calculate risk, wherein in fig 9, Examiner notes, alternative sourcing 901 is described 907 as assesses likelihood a part will cause a supply disruption as a result of there being no sourcing contingency and part change risk 902 is described as assesses likelihood a part will cause a supply disruption as result of part transition activity, and additional attributes may comprise component life cycle stages, [0004], supply chain risk, or the likelihood of supply chain disruptions, is emerging as a key challenge to SCM).
Regarding claim 11, the combined teachings of Bajaj, Brandstetter, and Meszaros teach the risk manager of claim 10 (as above). Further, Bajaj discloses wherein the prediction is based on similar parts ([0121], this recommended solution may typically stem not only from data gained from numerous similar and dissimilar products, the parts used to make those products, the suppliers from which those parts are obtained, the manpower and geography necessary to make those parts, and the like, all of which data resides in data store 1711).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES A GUILIANO whose telephone number is (571)272-9859. The examiner can normally be reached Mon-Fri 10:00 am - 6:00 pm.
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623
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