DETAILED ACTION
This non-final office action is in response to Applicant’s amendment and request for continued examination filed September 19, 2025. In Applicant’s September 19th amendment amended claim 1, canceled claims 5-10, 12 and 15-21 and added new claims 22-26. Claims 1-4, 11, 13, 14 and 22-36 are pending. Claims 1 and 26 are the independent claims.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 19, 2025 has been entered.
Response to Amendment
The Objection to the Title in the previous office action is withdrawn in response to Applicant’s amendment to the Title.
The 35 U.S.C. 101 rejection of claims 1-4, 11, 13, and 14 in the previous office action is maintained.
The 35 U.S.C. 112a rejections of claims 2-4 in the previous office action are maintained.
The 35 U.S.C. 112b rejection of claims 2 and 4 in the previous office action is maintained.
Response to Arguments
Applicant's arguments filed September 19, 2025 have been fully considered but they are not persuasive. Specifically, Applicant argues that the claims are patent eligible under 35 U.S.C. 101 as the claims integrate abstract idea into practical application (e.g. technical improvement in the field of computer modeling and simulation; improve efficiency of ML models; using substantially real-time sensor data from physical value chain entities as a training set; solution to model drift/inefficiency using real world data; Remarks: Last Paragraph, Page 10; Paragraphs 1-3, Page 11); the claims recite significantly more than abstract ide (e.g. self-improvement digital representation of a complex real world system, refining input to the model, etc.;. Remarks: Last Two Paragraphs, Page 11), the claims are similar to McRo (Remarks: Paragraph 3, Page 12) and the claims are similar to Enfish (Remarks: Last Paragraph, Page 12).
Additionally, Applicant argues that Applicant’s specification provides sufficient disclosure to demonstrate possession (35 U.S.C. 112a) of at least the steps of “…machine learning model is configured to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof..” (Claim 2); “..make suggestions…regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system” (Claim 3) and “...prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof” (Claim 4) (Remarks: Figure 43 - value chain network management platform/system; Figure 38 - training/refinement - feedback look; Figures 13, 49; Paragraphs 39, 72, 75, 554; Remarks: Last Two Paragraphs, Page 13; Page 14).
Further Applicant argues that the claims phrase relevant (Claims 2 and 4) is not relative and the specification provides a clear standard making the scope of the phrase clear (Paragraph 554; dependent claims 11-15; Remarks: Page 16).
In response to Applicant’s argument that the phrase ‘relevant’ to dynamics as recited in claim 2 (“...machine learning model is configured to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof”) and claim 4 (“...prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof”) (and newly added Claims 27 and 29),, is not vague or indefinite / not a relative term, the examiner respectfully disagrees.
More specifically Applicant argues that dependent claims 11-15 and Specification Paragraph 554 provide a clear and specific standard to determine the meet and bounds (scope) of the phrase relevant to dynamics, the examiner respectfully disagrees.
With respect to Claim 11 merely recited that model success is based on at least ONE of least noise or lowest cost. This claim does not provide a specific definition or standard for the phrase relevant to dynamics as claimed (e.g. does not provide a standard by which one skilled in the art would understand what sensors are ‘relevant to dynamics’ of each value chain entities).
With respect to Claim 12 merely recited that generating a refined machine learned model includes determining whether the absence of an input creates a degradation in the output. This claim does not provide a specific definition or standard for the phrase ‘relevant to dynamics’ as claimed (e.g. does not provide a standard by one skilled in the art would understand prioritizing collection/transmission of sensor data ‘relevant to dynamics’ of the supply chain entities).
With respect to Claim 13 merely recited identifying impacts on the model effectiveness including identifying an input that generates a lower cost. This claim does not provide a specific definition or standard for the phrase ‘relevant to dynamics’ as claimed (e.g. does not provide a standard by which one skilled in the art would understand what sensors are ‘relevant to dynamics’ of each value chain entities).
With respect to Claim 14 merely recited comparing current output to additional outputs including identifying an input that generates a lower cost model….. This claim does not provide a specific definition or standard for the phrase ‘relevant to dynamics’ as claimed (e.g. does not provide a standard by one skilled in the art would understand prioritizing collection/transmission of sensor data ‘relevant to dynamics’ of the supply chain entities).
With respect to Claim 15 merely recited generating a refined modeling including pruning an input. This claim does not provide a specific definition or standard for the phrase ‘relevant to dynamics’ as claimed (e.g. does not provide a standard by which one skilled in the art would understand what sensors are ‘relevant to dynamics’ of each value chain entities).
Similarly, Specification Paragraph 554, which discloses that the machine learning model maybe tested to determine the impact of various inputs to the accuracy of the model, including the claimed features of withdrawn claims 11-15 (discussed above). This paragraph, like the remainder of Applicant’s disclosure, does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Examiner suggests, amending the claims to remove the phrase relevant to dynamics potentially replacing the phrase with the now withdrawn claimed features of claims 11-15 to overcome this rejection.
In response to Applicant’s argument that Applicant’s specification provides sufficient disclosure for the claimed limitations recited in Claims 2-4 (and newly added Claims 27-29), the examiner respectfully disagrees.
With respect to claim 2 (“…machine learning model is configured to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof..”), Applicant argues that Specification Paragraphs 75 and 554 provide the necessary support/disclosure. As discussed in the previous office action(s), specification Paragraphs 75 (below) tangentially mentions, in a single sentence, that the ML models is configured to learn which types of sensors are relevant (not defined) to dynamics (not defined) of each value chain entity this brief mention of an undisclosed machine learning model or machine learning model configuration that may be configured to learned which types of sensors are relevant to supply chain entity dynamics is insufficient to show possession of the invention as claimed. Paragraph 75, does not disclose a process for refining a model, does not disclose generating additional outputs using various training data subsets, comparing current output to additional outputs or the like as argued. Even, if Paragraph 75 discloses such steps – it is not clear HOW these steps would enable one skilled in the art to program a computer/configure a machine learning model to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof as claimed. The specification merely recites a desired result without any discussion as to how that result is actually implemented or achieved.
[0075] In embodiments, the machine learning model is configured to learn which types of sensor data are relevant to dynamics of each value chain entity of the value chain entities and simulation thereof. In embodiments, the machine learning model is configured to make suggestions to a user of the information technology system via an interface regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system. In embodiments, the machine learning model is configured to prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof.
With regards to argued Specification Paragraph 554 (below), this paragraph does not disclose or discuss configuring a machine learning model to learn which sensors are ‘relevant to dynamics’ of each value chain entities and simulation thereof as claimed. While this paragraph does disclose testing the impact of various variables on the accuracy of the (machine learning) model including removed inputs and determining if removing the inputs degrades the success of the model and the like. This paragraph does not disclose at any level of detail on HOW these steps would enable one skilled in the art to program a computer/configure a machine learning model to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof as claimed.
[0554] In embodiments, inputs to the machine learning model 3000 (such as a regression model, Bayesian network, supervised model, or other type of model) may be tested, such as by using a set of testing data that is independent from the data set used for the creation and/or training of the machine learning model, such as to test the impact of various inputs to the accuracy of the model 3000. For example, inputs to the regression model may be removed, including single inputs, pairs of inputs, triplets, and the like, to determine whether the absence of inputs creates a material degradation of the success of the model 3000. This may assist with recognition of inputs that are in fact correlated (e.g., are linear combinations of the same underlying data), that are overlapping, or the like. Comparison of model success may help select among alternative input data sets that provide similar information, such as to identify the inputs (among several similar ones) that generate the least “noise” in the model, that provide the most impact on model effectiveness for the lowest cost, or the like. Thus, input variation and testing of the impact of input variation on model effectiveness may be used to prune or enhance model performance for any of the machine learning systems described throughout this disclosure.
Accordingly, Applicant’s specification fails to provide a specific algorithm, models, flow-charts, steps, processes or the like for at least the configuring a machine learning model to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof as claimed. Applicant’s specification only describes an indication of a result that one might achieve. This is insufficient to show possession or enablement under 35 U.S.C. 112.
With regards to Claim 3 (“..make suggestions…regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system” Applicant argues that Specification Paragraph 75 and Figure 49 provide the necessary support/disclosure. As discussed in the previous office action(s), specification Paragraphs 75 (above) tangentially mentions, in a single sentence, “In embodiments, the machine learning model is configured to make suggestions to a user of the information technology system via an interface regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system.” this brief mention of a wished for feature of the system is insufficient to show possession of the invention as claimed. Specification Paragraph 75, does not disclose HOW the machine learning model is configured to make suggestions to a user regarding potential changes to the plurality of sensors. Specification Figure 49, does disclose a sensor recommendation block, however the ‘sensor recommendation’ block is merely a black box into which input is provided and magically output is generated. No where in Applicant’s disclosure is there any discussion of any level as to what logic, algorithm, models, flow-charts, steps, processes or the like is contained in the ‘sensor recommendation’ block.
Accordingly, Applicant’s specification fails to provide a specific algorithm, models, flow-charts, steps, processes or the like for at least the step of make suggestions…regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system as claimed. Applicant’s specification only describes an indication of a result that one might achieve. This is insufficient to show possession or enablement under 35 U.S.C. 112.
With regards to Claim 4 (“...prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof”, Applicant argues that the single sentence in Specification Paragraph 75 (“In embodiments, the machine learning model is configured to prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof.”); the disclosure of an adaptive intelligence system in Specification Paragraph 39 and the disclosure of an Edge Intelligence mechanism in Figure 13 are sufficient disclosure. Examiner respectfully disagrees.
As discussed in the previous office actions, the single sentence in Specification 75 merely describes a wished-for feature of the invention and does not provide a specific algorithm, models, flow-charts, steps, processes or the like for at least the step of prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof as claimed.
Specification 39 (below) does not discuss at any level of detail HOW to prioritize the collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof as claimed. The argued ‘adaptive intelligence facilities’ including an ‘edge intelligence system’ are each recited at a very high level of generality (e.g. RPA, self-configuring data collection system, smart contracts, and the like) and not described in any level of detail sufficient to demonstrate possession of the disclosed adaptive intelligence system, much alone the specific claimed feature directed to prioritizing collection and transmission of sensor data that is relevant to each of the value chain entities and simulation as claimed.
[0039] In embodiments, the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, wherein the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system. In embodiments, the set of adaptive intelligence includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a smart contract system that is configured to automate a set of interactions among a set of value chain network entities. In embodiments, the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. In embodiments, the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities. In embodiments, the set of monitoring includes an Internet of Things monitoring system. In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise. In embodiments, the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.
Accordingly, Applicant’s specification fails to provide a specific algorithm, models, flow-charts, steps, processes or the like for at least the step of prioritize the collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof as claimed. Applicant’s specification only describes an indication of a result that one might achieve. This is insufficient to show possession or enablement under 35 U.S.C. 112.
Examiner suggests, amending the claims to remove the phrase relevant to dynamics potentially replacing the phrase with the now withdrawn claimed features of claims 11-15 to overcome this rejection.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are similar to those in the Enfish LLC v. Microsoft decision, the examiner respectfully disagrees.
the U.S. Court of Appeals for the Federal Circuit (Federal Circuit) in Enfish, LLC v. Microsoft Corp. held that the claimed database software designed as a "self-referential" table is patent eligible under 35 U.S.C. § 101 because it is not directed to an abstract idea.
The claims of the patents at issue Enfish case describe the steps of configuring a computer memory in accordance with a self-referential table, in both method claims and system claims that invoke 35 U.S.C. § 112(t). The court asked whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database), or instead on a process that qualifies as an "abstract idea" for which computers are invoked merely as a tool. To make the determination of whether these claims are directed to an improvement in existing computer technology, the court looked to the teachings of the specification. Specifically, the court identified the specification's teachings that the claimed invention achieves other benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements.
The instant application does not recite a database much alone improvements to a database. The instant application does not disclose or recite improvements in any of the recited computing elements (e.g. sensors, processor, etc.) or another technology. Nowhere in Applicant’s disclosure is there any discussion at any level of a specific improvement in computer capabilities/functioning of a computer, computer networks or other computer technology. Nowhere in Applicant’s discloser is there any discussion at any level of a specific improvement in machine learning or the digital twins technology much alone a focus in the claims on a specific/asserted improvement to machine learning or digital twin technology. Improving the accuracy of a model, especially one recited at a high level of generality, does not represent an improvement in the functioning of the computer/processor executing the model or represent an improvement in ML and DT Technology.
Examiner suggest Applicant review the recent 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (2024 AI SME Update) in the Federal Register on July 17, 2024 (https://www.federalregister.gov/documents/2024/07/17/2024-15377/2024-guidance-update-on-patent-subject-matter-eligibility-including-on-artificial-intelligence) and the three new Subject Matter Eligibility Examples 47-49 (https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf).
Additionally, examiner suggest Applicant review the recent Federal Circuit precedential decision in Recentive Analytics, Inc. v. Fox Corp.,https://www.cafc.uscourts.gov/opinions-orders/23-2437.OPINION.4-18-2025_2500790.pdf
The instant application is not directed to improving computer capabilities nor does Applicant's disclosure specific identify improvements to conventional computers or technologies. In sharp contrast to the claims in the Enfish decision the claims of the instant application are not directed to hardware or software for improving the performance of a computer.
Accordingly, the claims are not similar to those found patentable in the Enfish LLC v. Microsoft decision and are therefore not patent eligible under 35 U.S.C. 101.
In response to Applicant’s arguments that the claims are patent eligible under 35 U.S.C. 101 as the claims improve technical field (e.g. machine learning, digital twin), represent an improvement to a technology and are similar to the McRO decision, the examiner respectfully disagrees.
In McRO, the Federal Circuit concluded that the claimed methods of automatic lip synchronization and facial expression animation using computer-implemented rules were not directed to an abstract idea. McRO, 837 F.3d at 1316, 120 USPQ2d at 1103. The basis for the McRO court's decision was that the claims were directed to an improvement in computer animation. The court relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated. 837 F.3d at 1313, 120 USPQ2d at 1101. The McRO court found that the claims clearly improved the functioning of the claimed computer and that the claims directed to recite improvement (e.g. rules). Further the court found that the specification clearly disclosed that the claimed improvement improved the functioning of the computer.
In sharp contrast to the McRO decision neither Applicant’s disclosure nor Applicant’s claim clearly and specifically disclose/claim an improvement to any of the underlying technology (e.g. processor, sensors, etc.). Nor does Applicant’s disclosure specifically disclose a process that was previously unable to be automated. At best, the claims apply well-known, conventional and routine digital twin and machine learning algorithms/technologies, each recited at a very high level of generality, as tools to perform the method steps (apply it, conduit for the abstract idea) which may result in improvements to the abstract idea itself (e.g. visualizing value chain data, using substantially real-time sensor data from physical value chain entities as a training set; solution to model drift/inefficiency using real world data).
Accordingly, the claims are not similar to those found patentable in the McRO decision and are therefore not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101, as the claims recite significantly more than the abstract idea, the examiner respectfully disagrees.
The claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of business modeling/simulation. In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 612 (Fed. Cir. 2016). The recited technology (e.g. processor, memories, etc.), are used as a “conduit for the abstract idea,” not to provide a technological solution to a specific technological problem. Id.; see also id. at 611–13 (holding claims reciting the use of a cellular telephone and a network server to classify an image and store the image based on its classification to be abstract because the patent did “not describe a new telephone, a new server, or a new physical combination of the two” and did not address “how to combine a camera with a cellular telephone, how to transmit images via a cellular network, or even how to append classification information to that data”).
Nothing in Applicant’s disclosures suggests that the Applicant intended to accomplish any of the steps recited in the claims through anything other than well understood technology used in a routine and conventional manner. Therefore, the claims lack an inventive concept. See also, e.g., Elec. Power Grp., 830 F.3d at 1355 (holding claims lacked inventive concept where “[n]othing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information”); Content Extraction, 776 F.3d at 1348 (holding claims lacked an inventive concept where the claims recited the use of “existing scanning and processing technology”).
Similar to the Uniloc USA, Inc. v. LG Electronics USA, Appeal No. 19-1835 (Fed. Cir. Apr. 30, 2020), in which the Federal Circuit reaffirmed that software inventions are patentable in the U.S. with a bright-line statement: “Our precedent is clear that software can make patent-eligible improvements to computer technology, and related claims are eligible as long as they are directed to non-abstract improvements to the functionality of a computer or network platform itself.” The instant application neither discloses nor claims improvements to computer, computer networks or other technical field.
Regarding the recited machine learned model that is trained/refined in order to ‘transform’ sensor data into simulation data and the digital replica (digital twin) of the value chain entities – each of the ML and DR/DT are recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic machine learned model and/or digital replica on a generic computer/processor, also recited at a high level of generality. The ML and DR/DT are used to generally apply the abstract idea without limiting how the machine learned model or digital replica functions. The machine learned model and digital replica are each described at a high level such that it amounts to using a generic computer with a generic machine learned model and digital replica to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s arguments that the claims are patent eligible under 35 U.S.C. 101 because the claims are directed to a practical application the examiner respectfully disagrees.
The claims are directed to a well-known business practice – business modeling – in this case providing substantially real-time representation (digital replica) of at least ONE of a set of value chain entities and simulation data of at least ONE of the set of value chain entities. While the claims may represent an improvement to the business process of modeling, digitally representing and/or simulating value chain entities they in no way either claimed or disclosed represent a practical application.
None of the argued wished-for benefits of the invention, as claimed, represent a integrating the abstract idea into a practical application. For example, using substantially real-time sensor data from physical value chain entities as a training set, as argued, does not represent a practical application (e.g. not a technical solution to a technical problem, does not improve the underlying computer/technology, etc.) as it a business solution to a business problem (i.e. modeling/simulating value chain entities – supply chains). Addressing model drift, model accuracy and/or model accuracy, as argued does not integrate the abstract idea into a practical application.
Under the 2019 Revised Guidance, the claims are evaluated to determine if additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure ("MPEP") §§ 2106.05(a)-(c), (e)- (h)). See 2019 Revised Guidance, 84 Fed. Reg. at 51-52, 55. A claim that integrates a judicial exception into a practical application applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. See 2019 Revised Guidance, 84 Fed. Reg. at 54.
For example, limitations that are indicative of "integration into a practical application" include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP § 2106.05(a);
Applying the judicial exception with, or by use of, a particular machine - see MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP § 2106.05(e).
In contrast, limitations that are not indicative of "integration into a practical application" include:
Adding the words "apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP § 2106.05(±);
Adding insignificant extra-solution activity to the judicial exception- see MPEP § 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
See 2019 Revised Guidance, 84 Fed. Reg. at 54-55 ("Prong Two").
In view of the 2019 Revised Guidance, one must consider whether there are additional elements set forth in the claims that integrate the judicial exception into a practical application. The identified additional non-abstract elements recited in the independent claims are the generic processor, adaptive intelligence system (software per se), digital replica (software per se) and processor. These generic computer hardware merely performs generic computer functions of receiving, processing and providing data and represent a purely conventional implementation of applicant’s value chain entities modeling/simulation in the general field of business modeling/simulation and do not represent significantly more than the abstract idea. See at least MPEP § 2106.05(a) ("Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field").
These recited additional elements are merely generic computer components. The claims do present any other issues as set forth in the 2019 Revised Guidance regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. See Revised Guidance, 84 Fed. Reg. at 55. Rather, the claims merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding the recited machine learned model that is trained/refined in order to ‘transform’ sensor data into simulation data and the digital replica (digital twin) of the value chain entities – each of the ML and DR/DT are recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic machine learned model and/or digital replica on a generic computer/processor, also recited at a high level of generality. The ML and DR/DT are used to generally apply the abstract idea without limiting how the machine learned model or digital replica functions. The machine learned model and digital replica are each described at a high level such that it amounts to using a generic computer with a generic machine learned model and digital replica to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), the claims do not integrate the judicial exception into a practical application.
There is a fundamental difference between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other — a distinction that the Federal Circuit applied in Enfish, in rejecting a § 101 challenge at the first stage of the Mayo/Alice framework because the claims at issue focused on a specific type of data structure, i.e., a self-referential table, designed to improve the way a computer stores and retrieves data in memory, and not merely on asserted advances in uses to which existing computer capabilities could be put. See Enfish, 822 F.3d at 1335-36. Here the claims simply use a computer as a tool and nothing more.
For the reasons outlined above, the claims recite a method of organizing human activity, i.e., an abstract idea, and that the additional element recited in the claim beyond the abstract idea (i.e., processor, sensor, etc.) is no more than a generic computer component used as a tool to perform the recited abstract idea. As such, it does not integrate the abstract idea into a practical application. See Alice Corp., 573 U.S. at 223-24 (“[Wholly generic computer implementation is not generally the sort of ‘additional featur[e]’ that provides any ‘practical assurance that the process is more than a drafting effort designed to monopolize the [abstract idea] itself.’” (quoting Mayo, 566 U.S. at 77)).
Accordingly, the claims are directed to an abstract idea.
Step Two of the Mayo/Alice Framework (2019 Revised Guidance, Step 2B)
Having determined under step one of the Mayo/Alice framework that the claims are directed to an abstract idea, we next consider under Step 2B of the Guidance, the second step of the Mayo/Alice framework, whether the claims include additional elements or a combination of elements that provides an “inventive concept,” i.e., whether an additional element or combination of elements adds specific limitations beyond the judicial exception that are not “well-understood, routine, conventional activity” in the field (which is indicative that an inventive concept is present) or simply appends well-understood, routine, conventional activities previously known to the industry to the judicial exception. 2019 Revised Guidance, 84 Fed. Reg. at 56.
Under step two of the Mayo/Alice framework, the elements of each claim are considered both individually and “as an ordered combination” to determine whether the additional elements, i.e., the elements other than the abstract idea itself, “transform the nature of the claim” into a patent-eligible application. Alice Corp., 573 U.S. at 217 (citation omitted); see Mayo, 566 U.S. at 72-73 (requiring that “a process that focuses upon the use of a natural law also contain other elements or a combination of elements, sometimes referred to as an ‘inventive concept,’ sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the natural law itself’ (emphasis added) (citation omitted)).
Here the only additional element recited in the claims beyond the abstract idea is a processor, sensors, adaptive intelligence system (software per se) and artificial intelligence system (software per se),” i.e., generic computer component. See Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Applicant has not identified any additional elements recited in the claim that, individually or in combination, provides significantly more than the abstract idea.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
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 2-4 and 27-29 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.
Regarding claims 2 and 27, the claim recites “...machine learning model is configured to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof” wherein Applicant’s specification does not provide a sufficient description to show possession of the invention. Specifically, Applicant’s specification fails to provide a specific algorithm, models, flow-charts, steps, processes or the like for at least the configuring a machine learning model to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof as claimed. Applicant’s specification only describes an indication of a result that one might achieve. This is insufficient to show possession or enablement under 35 U.S.C. 112.
While specification Paragraphs 75 and 1666 both tangentially mention, in a single sentence, that the ML models is configured to learn which types of sensors are relevant (not defined) to dynamics (not defined) of each value chain entity this brief mention of an undisclosed machine learning model or machine learning model configuration that may be configured to learned which types of sensors are relevant to supply chain entity dynamics is insufficient to show possession of the invention as claimed. The specification merely recites a desired result without any discussion as to how that result is actually implemented or achieved.
Accordingly, Applicant's specification fails to provide adequate written support to show possession as well as lacks written disclosure to enable one to use the invention without undue experimentation as claimed. Applicant's disclosure fails to disclose any specific method, algorithm, approach, process or working example for the step of “...machine learning model is configured to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof” as claimed nor the claimed embodiment as a whole.
While Applicant’s specification appears to suggest some potential capabilities of the claimed system/method, the Specification merely lists potential features and fails to disclose any specific method, mechanism, process, algorithm, or example for how to perform any of the claimed steps much alone the combination of the steps as claimed. Applicant’s specification simply represents a wish list of potential system/device capabilities without any disclosure as to HOW those wished for capabilities are actually performed or implemented (e.g. what types of sensors are there, HOW to configure a ML model to learning ‘relevant’ sensors to ‘dynamics’ of supply chain entities).
The Federal Circuit explained that “[t]he test for the sufficiency of the written description ‘is whether the disclosure of the application relied upon reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date.’” Id. at 682 (quoting Ariad, 598 F.3d at 1351). The Federal Circuit emphasized that “[t]he written description requirement is not met if the specification merely describes a ‘desired result.’” Vasudevan, 782 F.3d at 682 (quoting Ariad, 598 F.3d at 1349). Thus, in applying this standard to the computer implemented functional claim at issue, the Federal Circuit stated that “[t]he more telling question is whether the specification shows possession by the inventor of how [the claimed function] is achieved.” Vasudevan, 782 F.3d at 683.
It is noted that the written description requirement under 112(a) is not satisfied by stating that one of ordinary skill in the art could devise an algorithm to perform the specialized programmed functions. For written description, the specification as filed must describe the claimed invention in sufficient detail so that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention. An original claim may lack written description when the claim defines the invention in functional language specifying a desired result but the specification does not sufficiently identify how the inventor has devised the function to be performed or result achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient).
Further, the structure corresponding to claim limitations that are computer-implemented specialized functions must include a general-purpose computer or computer component along with the algorithms that the computer uses to perform each claimed specialized function.
It is not enough that one skilled in the art could theoretically write a program to achieve the claimed function, rather the specification itself must explain how the claimed function is achieved to demonstrate that the applicant had possession of it. See, e.g., Vasudevan, 782 F.3d at 682-83.
Applicant’s specification does not provide a disclosure of the computer and algorithms in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention that achieves the claimed result.
Accordingly, Applicant's specification fails to provide adequate written support to show possession as well as lacks written disclosure to enable one to use the invention without undue experimentation as claimed. Applicant's disclosure fails to disclose any specific method, algorithm, approach, process or working example for the step of “...machine learning model is configured to learn which sensors are relevant to dynamics of each value chain entities and simulation thereof” as claimed.
Regarding claims 3 and 28, the claim 3 recites “..make suggestions…regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system” wherein Applicant’s specification does not provide a sufficient description to show possession of the invention. Specifically, Applicant’s specification fails to provide a specific algorithm, models, flow-charts, steps, processes or the like for at least the step of making suggestions regarding potential changes to the plurality of sensors that would improve the simulation of the value chain system via the digital twin system as claimed. Applicant’s specification only describes an indication of a result that one might achieve. This is insufficient to show possession or enablement under 35 U.S.C. 112.
While specification Paragraphs 75 and 1666 both tangentially mention, in a single sentence, that the ML models is configured to make suggestions regarding potential changes a plurality of sensors that would improve simulation of the value chain entities via the digital twin system this brief mention of an undisclosed machine learning model or machine learning model configuration that may be configured to make suggestions on changes to a plurality of sensors is insufficient to show possession of the invention as claimed. The specification merely recites a desired result without any discussion as to how that result is actually implemented or achieved.
Accordingly, Applicant's specification fails to provide adequate written support to show possession as well as lacks written disclosure to enable one to use the invention without undue experimentation as claimed. Applicant's disclosure fails to disclose any specific method, algorithm, approach, process or working example for the step of “..make suggestions…regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system” as claimed nor the claimed embodiment as a whole.
While Applicant’s specification appears to suggest some potential capabilities of the claimed system/method, the Specification merely lists potential features and fails to disclose any specific method, mechanism, process, algorithm, or example for how to perform any of the claimed steps much alone the combination of the steps as claimed. Applicant’s specification simply represents a wish list of potential system/device capabilities without any disclosure as to HOW those wished for capabilities are actually performed or implemented (e.g. HOW to determine what changes in what kinds of sensors will improve the a digital twin simulation).
Further, the structure corresponding to claim limitations that are computer-implemented specialized functions must include a general-purpose computer or computer component along with the algorithms that the computer uses to perform each claimed specialized function.
It is not enough that one skilled in the art could theoretically write a program to achieve the claimed function, rather the specification itself must explain how the claimed function is achieved to demonstrate that the applicant had possession of it. See, e.g., Vasudevan, 782 F.3d at 682-83.
Applicant’s specification does not provide a disclosure of the computer and algorithms in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention that achieves the claimed result.
Accordingly, Applicant's specification fails to provide adequate written support to show possession as well as lacks written disclosure to enable one to use the invention without undue experimentation as claimed. Applicant's disclosure fails to disclose any specific method, algorithm, approach, process or working example for the step of “...make suggestions…regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system” as claimed.
Regarding claims 4 and 29, the claim 4 recites “...prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulati