DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Due to communications filed 2/6/25, the following is a non-final office action. Claims 1, 9 and 14 are amended. Claims 21-40 are new. Claims 1-40 are pending in this application and are rejected as follows. The previous rejection has been modified to reflect claim amendments.
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 1-40 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 enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or
with which it is most nearly connected, to make and/or use the invention. Due to a 112(f) invocation
caused by the "machine learning system", "artificial intelligence system", and "digital twin system",
Examiner suggests entering an amendment to correct the following limitations: "a machine learning
system" needs to be amended to recite "a machine learning system, comprising of one or more
processors; "an artificial intelligence system" needs to be amended to recite "an artificial intelligence
system, comprising of the one or more processors,"; and "a digital twin system" needs to be amended
to recite "a digital twin system, comprising of the one or more processors,". This amendment will
remove the 112(f) invocation
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 1-40 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 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being
incomplete for omitting essential structural cooperative relationships of elements, such omission
amounting to a gap between the necessary structural connections. See MPEP § 2172.01.
In this case, the disclosure of "a machine learning system", "an artificial intelligence system",
and "a digital twin system" invokes a 112(f).
As per independent claim 1, it recites, "a machine learning system that trains a machine-learned
model..." However, no corresponding structure/algorithm was disclosed by the specification on how the
training is performed. Mere reference to a general purpose computer with appropriate programming
without providing an explanation of the appropriate programming, or simply reciting "software" without
providing detail about the means to accomplish a specific software function, would not be an adequate
disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35
U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239; Finisar, 523 F.3d at
1340-41, 86 USPQ2d at 1623.
Dependent claims 2-40 inherit the deficiency noted for claim 1.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102
and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory
basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of
rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same
under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis
for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application
for patent published or deemed published under section 122(b), in which the patent or application, as
the case may be, names another inventor and was effectively filed before the effective filing date of
the claimed invention.
Claim(s) 1-30, 31-33, 36-40 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wolfgang's
"Digital Twins for decision making in Complex Production and Logistic Enterprises".
As per claim 1, Wolfgang discloses:
a machine learning system comprising one or more processors that trains a machine-learned model that outputs a logistics design recommendation given a respective set of input features relating to a specific respective logistics system, wherein the machine learning system trains the machine-learned model based on training data sets that define features of logistics systems and outcomes of the logistics systems, (Wolfgang: Pg. 263-264, "In production and logistic enterprises Digital Twins will enable to improve operations, asset management, operational efficiency, maintenance repair and operation and insights into how products and processes can be improved."; Pg. 265, "Aggregate - The real-time data have to be send into a data repository, processed and prepared for the analytics."; Pg. 266, "Machine Learning anomaly detectors -Machine learning algorithms can be used, ranging from multivariate multi-level survival models to baseline asset risk, to classification techniques like logistic regression, decision trees, random forest methods, neural networks and clustering methodologies. These models are usually derived using healthy and fault data based on a database of historical sensor and configuration data."; Pg. 268, "4.4 Machine learning is based on data, either from structured data and increasingly also from unstructured data without explicitly being programmed. Based on data and algorithm it is referred to as supervised learning, where the algorithm is trained using examples where the input data and the correct answers are known and in unsupervised learning, where the algorithm must discover patterns in the data on its own, and rein[1]forced learning, where the algorithm is rewarded or penalized for the actions it takes based on trial and error.";
an artificial intelligence system comprising one or more processors that receives a request for logistics system design and determines a logistics system design recommendation based on the machine- learned model and the request, (Wolfgang: Pg. 264, "With Artificial Intelligence based capabilities Digital Twins offer for specific questions advanced product, production or logistic simulation models, which allows to test drive several alternatives in a virtual environment before decisions are applied to the real-world system."; Pg. 265, "Insight - Based on the analysed data, models for decision making are created. Significant differences in the performance of the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin models visualizes these differences in order to enable recommendations."; Pg. 268, "4.3 Advanced Digital Twins employ Artificial Intelligence technologies that leverage data from equipment to generate insights and deeper understanding of operating environments. These includes unstructured data analytics, multi-modal data analytics, component analytics, pattern recognition, learning models, knowledge networks etc. [22]. One sub-field of AI is machine learning."; and
a digital twin system that generates an environment digital twin of a logistics environment that
incorporates the logistics system design recommendation and one or more physical asset digital twins of
physical assets, wherein the digital twin system: executes a logistics simulation based on the logistics
environment digital twin and the one or more physical asset digital twins, issues a logistics system
design request from the artificial intelligence system based on a state of the logistics simulation,
(Wolfgang: Pg. 263, "Digital Twins are virtual clones of real assets or processes. As the physical source is
changing, the data from the physical asset or process are collected in real-time and replicated into the
virtual equivalent in order to improve operative decision making."; Pg. 265, "Aggregate - The real-time
data have to be send into a data repository, processed and prepared for the analytics. The technologies
of data aggregation and processing have evolved tremendously over the last years and allow nowadays
to create massively scalable architectures with greater agility."; Pg. 265, "Insight - Based on the
analysed data, models for decision making are created. Significant differences in the performance of the
Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin
models visualizes these differences in order to enable recommendations. More advanced Digital Twin
models use iterative simulation to analyse with the virtual model the possible impact of changes for the
real world in order to generate insights for decision making."; Also see Fig. 4 of Wolfgang);
adjusts the state of the logistics simulation based on the logistics system design
recommendation output by the artificial intelligence system in response to the logistics system design
request, (Wolfgang: Pg. 265, "Act - The knowledge and recommendations from the insights step can be
fed back to the physical world in order to transform the real enterprise. The insights may feed directly
into actuators for movement and control, or into software systems on a higher level for operational or
supply chains behaviour. With this interaction Digital Twins complete a closed loop connection from the
physical world to the virtual model and back to the physical world.").
As per claim 2, Wolfgang discloses:
wherein the digital twin system outputs a graphical representation of the environment digital twin to a
display, whereby a user views the simulation via the display, (See Fig 3).
As per claim 3, Wolfgang discloses:
wherein the digital twin system outputs a simulation outcome of the simulation to the machine
learning system, and the machine learning system reinforces the machine-learned model used to
determine the logistics system design recommendation based on the simulation outcome, (Pg 264. "the
monitoring and analysis of real-time data and advanced product, production or logistic simulation
models allow to improve design, controls and strategies [15]. by use of simulation models the Digital
twins can be used to operate the enterprise or parts of it in advance and to test drive several
alternatives in a virtual environment before a decision is applied to the real-world system [16]. with
artificial intelligence based capabil-ities Digital twins offer for specific questions advanced product,
production or logistic simulation models, which allows to test drive several alternatives in a virtual
environment before decisions are applied to the real-world system [17]."; Pg. 265, Insight - Based on
the analysed data, models for decision making are created. Significant differences in the performance of
the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin
models visualizes these differences in order to enable recommendations. More advanced Digital Twin
models use iterative simulation to analyse with the virtual model the possible impact of changes for the
real world in order to generate insights for decision making).
As per claim 4, Wolfgang discloses: wherein the artificial intelligence system receives the
request from a logistics design system that designs logistics systems, wherein the request includes one
or more logistics factors corresponding to a proposed logistics solution of an organization, (Pg. 264 (3)
with artificial intelligence based capabil-ities Digital twins offer for specific questions advanced product,
production or logistic simulation models, which allows to test drive several alternatives in a virtual
environment before decisions are applied to the real-world system [17]).
As per claim 5, Wolfgang discloses: wherein the logistics factors include one or more of: a type
of product corresponding to the proposed logistics solution, one or more features of the type of
product, a location of a manufacturing site, a location of a distribution facility, a location of a
warehouse, a location of a customer base, proposed expansion areas of the organization, and supply
chain features, (Pg. 265, 3.3 Data Quality and reliability- Data are the key for the success of Digital twins
and any artificial intelligence use case. it is crucial to determine the type, quantity, and quality of data
These data are coming directly from smart products...).
As per claim 6, Wolfgang discloses: wherein the logistics design system provides outcome data
relating to the logistics system design recommendation to the machine learning system, and the
machine learning system reinforces the machine- learned model that are used to determine the logistics
system design recommendation based on the outcome data, (Pg. 266, 3.3 "Machine Learning anomaly
detectors - Machine learning algorithms can be used, ranging from multivariate multi-level survival
models to baseline asset risk, to classification techniques like logistic regression" Pg 268, 4.3, Due to
advances in computer processing power, nowadays machine learning can be integrated into the
decision-making processes implemented into enterprise software systems [21]).
As per claim 7, Wolfgang discloses: wherein the artificial intelligence system determines the
logistics system design recommendation to minimize delay times, (Pg 268, 4.3, Decision making can be
improved significantly by use of artificial intelligence technologies [20] By use of advanced decision-
making approaches the software will be enabled to contribute increasing levels of performance and
productivity; Pg. 267-268, 4.2, "a particular strategy can be built up from several priority classes, filtering
and sorting rules, that are defined as default rules in order to optimize the global system or at least to
improve local requirements. examples for such rules are e.g. "earliest Due Date", "critical ratio" (Job
with the least time to due date divided by total remaining processing time) or "critical path").
As per claim 8, Wolfgang discloses: wherein the artificial intelligence system determines the
logistics system design recommendation to comply with regulatory requirements, (Pg. 267-268, 4.2, "a
particular strategy can be built up from several priority classes, filtering and sorting rules, that are
defined as default rules in order to optimize the global system or at least to improve local
requirements. examples for such rules are e.g. "earliest Due Date", "critical ratio" (Job with the least
time to due date divided by total remaining processing time) or "critical path").
As per claim 9, Wolfgang discloses: wherein the digital twin system:
receives the logistics system design recommendation output by the artificial intelligence system
in response to the logistics system design request; and generates an updated logistics environment
digital twin of the logistics environment that incorporates the logistics system design recommendation
output by the artificial intelligence system in response to the logistics system design request, (Wolfgang:
Pg. 264, "With Artificial Intelligence based capabilities Digital Twins offer for specific questions advanced product, production or logistic simulation models, which allows to test drive several alternatives in a
virtual environment before decisions are applied to the real-world system."; Pg. 265, "Insight - Based on
the analysed data, models for decision making are created. Significant differences in the performance of
the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin
models visualizes these differences in order to enable recommendations."; Pg. 268, "4.3 Advanced
Digital Twins employ Artificial Intelligence technologies that leverage data from equipment to generate
insights and deeper understanding of operating environments. These includes unstructured data
analytics, multi-modal data analytics, component analytics, pattern recognition, learning models,
knowledge networks etc. [22]. One sub-field of Al is machine learning."); and
adjusts the state of the logistics simulation based on the logistics system design
recommendation output by the artificial intelligence system in response to the logistics system design
request by executing the logistic simulation based on the updated logistics environment digital twin and
the one or more physical asset digital twins, (Wolfgang: Pg. 265, "Act - The knowledge and
recommendations from the insights step can be fed back to the physical world in order to transform the
real enterprise. The insights may feed directly into actuators for movement and control, or into software
systems on a higher level for operational or supply chains behaviour. With this interaction Digital Twins
complete a closed loop connection from the physical world to the virtual model and back to the physical
world.").
As per claim 10, Wolfgang discloses: wherein each of the one or more physical asset digital twins
of physical assets comprises a digital representation of a physical object in the value chain system, (Page
263, "the goal of Digital twins is to monitor and improve equipment and processes in a virtual
environment. Digital twins are virtual clones of real assets or processes. as the physical source
is changing, the data from the physical asset or process are collected in real-time and replicated
into the virtual equivalent in order to improve operative decision making [12]").
As per claim 11, Wolfgang discloses: wherein the physical object is one or more of an asset, a
device, a product, a package, a container, a vehicle, a ship, a container, a good, a product, or a
component, (Page 263, "the goal of Digital twins is to monitor and improve equipment and processes in
a virtual environment. Digital twins are virtual clones of real assets or processes. as the physical source
is changing, the data from the physical asset or process are collected in real-time and replicated
into the virtual equivalent in order to improve operative decision making [12]).
As per claim 21, Wolfgang discloses: wherein the one or more physical asset digital twins of
physical assets includes a first physical asset digital twin corresponding to a package or container in
which a product is shipped, and a second physical asset digital twin corresponding to a vehicle or other
mode of transport utilized to ship the product, (Pg. 263, (3) The goal of Digital twins is to monitor and
improve equipment and processes in a virtual environment. Digital twins are virtual clones of real assets
or processes. as the physical source is changing, the data from the physical asset or process are
collected in real-time and replicated into the virtual equivalent in order to improve operative decision
making [12]; Pg. 269, (5) connect disparate systems such as backend business applications - Digital twin
models can be used to connect with the backend business applications to achieve business outcomes in
the context of supply chain operations including manufacturing, procurement, warehousing,
transportation and logistics, field service, etc. [12]. Digital twins offer for specific questions an advanced
product, production or logistic simulation model, which allows the monitoring and analysis of real-time
data to improve design, controls and strategies").
As per claim 13, Wolfgang discloses: wherein the logistics system design recommendation
includes one or more of supply chain recommendations, storage and transport recommendations,
proposed storage development, infrastructure recommendations, or combinations thereof, (Pg. 269, (5)
Digital Twin models can be used to connect with the backend business applications to achieve business
outcomes in the context of supply chain operations including manufacturing, procurement,
warehousing, transportation and logistics, field service, etc. [12]).
As per claim 14, Wolfgang discloses:
wherein the supply chain recommendations include at least one of proposed suppliers, or
implementations of a smart inventory systems that order on-demand parts from available suppliers, (Pg.
265-266 "3.3 Data Quality and Reliability - Data are the key for the success of Digital Twins and any
Artificial Intelligence use case. It is crucial to determine the type, quantity, and quality of data. Real-Time
Data are the key factor for Digital Twins. In recent years, real-time data generated across the
manufacturing value chain have grown dramatically in volume and variety. These data are coming
directly from 266 W. Kuehn, Int. J. of Design & Nature and Ecodynamics. Vol. 13, No. 3 (2018)
smart products, connected production equipment, core manufacturing processes, enterprise
IT systems, and external sources from customers or suppliers").
As per claim 15, Wolfgang discloses:
wherein the storage and transport recommendations include at least one of proposed shipping
routes, proposed shipping types, or proposed storage development, (Pg. 267, "nowadays increasing
computation power and storage possibilities of big data engines, the improvement of analytics
technologies and the integration of various data enables Digital twins to model much richer, less
isolated and much more sophisticated and realistic models than ever before a Digital twin conceptual
architecture has to be designed for flexibility and scalability in terms of the number of sensors and
messages, applied analytics and processing in order to enable the architecture to evolve rapidly with
growing demands [16].").
As per claim 16, Wolfgang discloses: wherein the infrastructure recommendations include at
least one of updates to machinery, adding cooled storage, or adding heated storage, (Pg. 264, (3.1)
create - with multiple sensors various inputs from the physical process and its environment are
measured by use of integrated smart components. these measurements can be classified into the
operational measurements sensing physical performance criteria of the asset and the measurement of
environmental or external data affecting the operations of a physical asset. signals from the
sensors may be augmented with process-based information from systems such as the manufacturing
execution systems, enterprise resource planning systems, caD models and supply chains systems in
order to provide the Digital twin with a wide range of continually updating information to be used as
input for the analysis).
As per claim 17, Wolfgang discloses:
wherein: (i) the digital twin system outputs a simulation outcome of the simulation to the
machine learning system, (ii) the logistics design system provides outcome data relating to the logistics
system design recommendation to the machine learning system, and (ili) the machine learning system
reinforces the machine-learned model used to determine the logistics system design recommendation
based on the simulation outcome and the outcome data, (Pg 264. "the monitoring and analysis of real-
time data and advanced product, production or logistic simulation models allow to improve design,
controls and strategies [15]. by use of simulation models the Digital twins can be used to operate the
enterprise or parts of it in advance and to test drive several alternatives in a virtual environment before
a decision is applied to the real-world system [16]. with artificial intelligence based capabilities Digital twins offer for specific questions advanced product, production or logistic
simulation models, which allows to test drive several alternatives in a virtual environment
before decisions are applied to the real-world system [17].' ;Pg. 265, Insight Based on the analysed
data, models for decision making are created. Significant differences in the performance of the Digital
Twin model and the physical world indicate potentials for improvement. Simple Digital Twin models
visualizes these differences in order to enable recommendations. More advanced Digital Twin models
use iterative simulation to analyse with the virtual model the possible impact of changes for the real
world in order to generate insights for decision making).
As per claim 18, Wolfgang discloses: wherein the artificial intelligence system determines the
logistics system design recommendation to optimize one or more outcomes based on the simulation
outcome and the outcome data, (Pg. 267-268, 4.2, "a particular strategy can be built up from several
priority classes, filtering and sorting rules, that are defined as default rules in order to optimize the
global system or at least to improve local requirements. examples for such rules are e.g. "earliest Due
Date", "critical ratio" (Job with the least time to due date divided by total remaining processing time) or
"critical path").
As per claim 19, Wolfgang discloses:
wherein the one or more outcomes include at least one of manufacturing times, manufacturing
costs, shipping times, shipping costs, loss rate, environmental impact, compliance to a set of
rules/regulations or combinations thereof, (Pg. 267-268, 4.2, "a particular strategy can be built up from
several priority classes, filtering and sorting rules, that are defined as default rules in order to optimize
the global system or at least to improve local requirements. examples for such rules are e.g. "earliest
Due Date", "critical ratio" (Job with the least time to due date divided by total remaining processing
time) or "critical path").
As per claim 20, Wolfgang discloses:
wherein the machine learning system reinforces the machine-learned model used to determine the
logistics system design recommendation based on the simulation outcome and the outcome data by
supplementing the training data sets with the simulation outcome and outcome data, (Wolfgang
discloses: Pg 264. "the monitoring and analysis of real-time data and advanced product, production or
logistic simulation models allow to improve design, controls and strategies [15]. by use of simulation
models the Digital twins can be used to operate the enterprise or parts of it in advance and to test drive
several alternatives in a virtual environment before a decision is applied to the real-world system [16].
with artificial intelligence based capabil-ities Digital twins offer for specific questions advanced product,
production or logistic simulation models, which allows to test drive several alternatives in a virtual
environment before decisions are applied to the real-world system [17].").
As per claim 21, this claim recites limitations similar to those disclosed in independent claim 1 and therefore the limitation of the claims are rejected for similar reasons.
In addition, Wolfgang discloses:
wherein said logistics recommendations include one or more of: supply chain recommendations, storage and transport recommendations, or routing recommendations, (See Page 264, (3.1), paragraph 2, line 10 and Page 265, “Create – ...These measurements can be classified into the operational measurements sensing physical performance criteria of the asset and the measurement of environmental or external data affecting the operations of a physical asset. Signals from the sensors may be augmented with process-based information from systems such as...supply chains systems in order to provide the Digital Twin with a wide range of continually updating information to be used as input for the analysis.”; paragraph 5, lines 1-4 “Act – The knowledge and recommendations from the insights step can be fed back to the physical world in order to transform the real enterprise. The insights may feed directly into actuators for movement and control, or into software systems on a higher level for operational or supply chains behaviour”
As per claim 22, please see the rejection for claim 2.
As per claim 23, please see the rejection for claim 3.
As per claim 24, Wolfgang discloses:
wherein adjusting the simulation state further includes modifying one or more simulation parameters based on the logistics recommendations, (Page 265, paragraph 4, “Insight – Based on the analysed data, models for decision making are created. Significant differences in the performance of the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin models visualizes these differences in order to enable recommendations. More advanced Digital Twin models use iterative simulation to analyse with the virtual model the possible impact of changes for the real world in order to generate insights for decision making”
As per claim 25, Wolfgang discloses:
wherein the one or more simulation parameters include at least one of: warehouse capacity, transportation routes, inventory levels, or delivery schedules, (Page 262, lines 1-2: “2.3 Enterprise Optimization In digital enterprise systems the layout of manufacturing and logistics, the control strategies and a huge variety of parameters have to be considered and optimized [11].”; Page 263, lines 4-5: “... The control optimization focuses on the improvement of the operative production and logistic control, such as scheduling and routing of orders”).
As per claim 26, please see the rejection of claim 3.
As per claim 27, Wolfgang discloses:
wherein the logistics recommendations optimize for cost reduction, (Page 266, (3.4), lines 1-6, “3.4 Virtual Models and Automated Model Generation For Digital Twins it is meaningful to create lightweight virtual models by selecting required geometry, characteristics, and attributes without carrying around unnecessary details. This reduces the size of the models and allows for faster processing. Light-weight models allow to simulate complex systems, including their physical behaviours, in real-time and with acceptable costs. The time and cost of communicating is substantially less and these models can be shared within the enterprise and throughout the supplier network.”).
As per claim 28, Wolfgang discloses:
wherein the logistics recommendations optimize for delivery time reduction. (Page 266, (3.4), lines 1-6, “3.4 Virtual Models and Automated Model Generation For Digital Twins it is meaningful to create lightweight virtual models by selecting required geometry, characteristics, and attributes without carrying around unnecessary details. This reduces the size of the models and allows for faster processing. Light-weight models allow to simulate complex systems, including their physical behaviours, in real-time and with accept able costs. The time and cost of communicating is substantially less and these models can be shared within the enterprise and throughout the supplier network.”).
As per claim 29, Wolfgang discloses:
wherein the logistics data includes historical shipping data.(Page 266, paragraph 6, lines 1-5).
As per claim 30, Wolfgang discloses:
wherein the logistics data includes real-time tracking data, (Page 263, paragraph 6, lines 1-4 “3 Digital Twin Concept The goal of Digital Twins is to monitor and improve equipment and processes in a virtual environment. Digital Twins are virtual clones of real assets or processes. As the physical source is changing, the data from the physical asset or process are collected in real-time”).
As per claim 32, Wolfgang discloses:
wherein the simulation of the logistics environment models physical infrastructure, (Page 263, paragraph 6, lines 1-4 “3 Digital Twin Concept The goal of Digital Twins is to monitor and improve equipment and processes in a virtual environment. Digital Twins are virtual clones of real assets or processes. As the physical source is changing, the data from the physical asset or process are collected in real-time”).
As per claim 33, Wolfgang discloses:
wherein the supply chain recommendations include supplier selection recommendations, (Page 265, paragraph 9, lines 1-Page 266, paragraph 1, lines 3, “3.3 Data Quality and Reliability Data are the key for the success of Digital Twins and any Artificial Intelligence use case. It is crucial to determine the type, quantity, and quality of data. Real-Time Data are the key factor for Digital Twins. In recent years, real-time data generated across the manufacturing value chain have grown dramatically in volume and variety. These data are coming directly from 266 W. Kuehn, Int. J. of Design & Nature and Ecodynamics. Vol. 13, No. 3 (2018) smart products, connected production equipment, core manufacturing processes, enterprise IT systems, and external sources from customers or suppliers.”).
As per claim 36, Wolfgang discloses:
wherein the routing recommendations include multi-modal transportation recommendations, (Page 268, paragraph 5, lines 1-3 “Advanced Digital Twins employ Artificial Intelligence technologies that leverage data from equipment to generate insights and deeper understanding of operating environments. These includes unstructured data analytics, multi-modal data analytics”.).
As per claim 37, please see the rejection of claim 3.
As per claim 38, Wolfgang discloses:
wherein the digital twin system receives real-time data from physical logistics operations, (Pg. 265, "Aggregate - The real-time data have to be send into a data repository, processed and prepared for the analytics.").
As per claim 39, Wolfgang discloses:
wherein adjusting the simulation state includes updating the simulation based on the real-time data and the logistics recommendations, (See Page 264, (3.1), paragraph 2, line 10 and Page 265, “Create – ...These measurements can be classified into the operational measurements sensing physical performance criteria of the asset and the measurement of environmental or external data affecting the operations of a physical asset. Signals from the sensors may be augmented with process-based information from systems such as...supply chains systems in order to provide the Digital Twin with a wide range of continually updating information to be used as input for the analysis.”).
As per claim 40, please see rejection for claim 3.
Claim(s) 31, 34, 35 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wolfgang's
"Digital Twins for decision making in Complex Production and Logistic Enterprises", and further in view of Elias et al (US 20210356576 A1).
As per claim 31, Wolfgang does not disclose:
wherein the logistics data includes warehouse inventory data.
However, Elias et al discloses: ([0166] In an example, the sensor system 100 may include warehousing twins (also referred to as warehouse digital twins). The warehousing twins may combine a 3D model of the warehouse with inventory and operational data including size, quantity, location, and demand characteristics of different products. The warehousing twins may be used to collect sensor data of a connected warehouse, as well as data on the movement of inventory and personnel within the warehouse. Warehousing twins may assist in optimizing space utilization and aid in identification and elimination of waste in warehouse operations. The simulation using warehousing twins of the movement of products, workers, and material handling equipment may enable warehouse managers to test and evaluate the potential impact of layout changes or the introduction of new equipment and new processes. The sensor system 100 may use this information from digital twins (e.g., warehousing twins or warehouse digital twins) to determine and provide value chain recommendations that improve value chain workflows).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the above limitations as taught by Elias et al in the systems of Wolfgang, 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 results of the combination were predictable.
As per claim 34, Wolfgang does not disclose: wherein the storage and transport recommendations include warehouse layout recommendations.
However, Elias et al discloses: ([0166] In an example, the sensor system 100 may include warehousing twins (also referred to as warehouse digital twins). The warehousing twins may combine a 3D model of the warehouse with inventory and operational data including size, quantity, location, and demand characteristics of different products. The warehousing twins may be used to collect sensor data of a connected warehouse, as well as data on the movement of inventory and personnel within the warehouse. Warehousing twins may assist in optimizing space utilization and aid in identification and elimination of waste in warehouse operations. The simulation using warehousing twins of the movement of products, workers, and material handling equipment may enable warehouse managers to test and evaluate the potential impact of layout changes or the introduction of new equipment and new processes. The sensor system 100 may use this information from digital twins (e.g., warehousing twins or warehouse digital twins) to determine and provide value chain recommendations that improve value chain workflows).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the above limitations as taught by Elias et al in the systems of Wolfgang, 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 results of the combination were predictable.
As per claim 35 Wolfgang does not disclose: wherein the routing recommendations include vehicle routing Recommendations.
However, Elias et al discloses: ([0026] In embodiments, the value chain recommendation is determined by a digital twin system that generates an environment digital twin of a logistics environment that incorporates a logistics system design recommendation, and one or more physical asset digital twins of physical assets, the digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins; [0154] The sensor system 100 may determine a value chain recommendation including productivity recommendations based on efficiency-related information. Productivity may be based on occupancy numbers hourly, daily, weekly, etc. based on the sensor system 100 monitoring workers during these time periods for output and efficiency. Further, the sensor system 100 may use RFID and asset tracking systems for tracking goods as they move through spaces of a supply chain and use occupancy data over time and routing systems to improve an efficiency of route selection within spaces and between locations (e.g., improve transportation route between distribution buildings).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the above limitations as taught by Elias et al in the systems of Wolfgang, 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 results of the combination were predictable.
Response to Arguments
Applicant's arguments filed 11/12/25 have been fully considered but they are not persuasive.
With regard to the 112 rejection, Applicant has amended the claims to recite “comprising one or more processors”. However, this amendment does not overcome the 112 rejection. The issue is not whether processors exist, but whether the claim scope is reasonably certain. Merely reciting a processor does not provide structural clarity when the function and interactions remain unclear.
Applicant’s arguments, see arguments/remarks/amendments, filed 11/12/25, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Elias et al (US 20210356576 A1).
Conclusion
THIS ACTION IS MADE FINAL. 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 Akiba Robinson whose telephone number is 571-272-6734 and email is Akiba.Robinsonboyce@USPTO.gov. The examiner can normally be reached on Monday-Thursday 6:30am-4:30pm.
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January 27, 2026
/AKIBA K ROBINSON/Primary Examiner, Art Unit 3628