DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office action is in response to correspondence received July 2, 2026.
Claims 1, 9, 10, and 18 are amended. Claims 23-26 are canceled. Claims 1, 2, 6-11, and 15-22 are pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 6-11, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1 recite(s):
A method to receive measurement data for determining carbon footprint for one or more products assembled by the assembly unit, the method comprising: continuously collecting real-time data generated by at least one product counter sensor configured to detect a number of products assembled by the assembly unit over time, and wherein the real-time measurement data generated by the at least one product counter sensor is collected during a period of time; determining a number of assembled products in the period of time, based on the measurement data generated by the at least one product counter sensor; continuously collecting real-time measurement data to measure an amount of energy supplied to the assembly unit during the period of time, storing the collected real-time measurement data in a [data store] using a [data store] protocol, wherein the stored real-time measurement data temporally correlates the energy supplied to the assembly unit with the products assembled during the period of time; determining an amount of supplied energy in the period of time based on the measurement data collected from the at least one energy supply sensor and stored in the [data store] ; obtaining, energy carbon footprint information representing an amount of greenhouse gas released into the atmosphere for generating the amount of energy being supplied to the assembly unit; estimating an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time, based on the energy carbon footprint information; obtaining reject product information associated with a number of rejected products over time, wherein the reject product information identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product; obtaining component carbon footprint information associated with the components comprised in the rejected products; determining a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the period of time, based on the number of assembled products in the period of time the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time and the component carbon footprint information associated with the components in the rejected products; and [presenting] a per-assembled-product carbon-footprint value derived from the real-time measurement data collected , thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions.
Claim 9 recites:
determining carbon footprint for one or more products assembled by an assembly unit, receive, real-time measurement data from the assembly unit; , and a [data store] wherein continuously collecting real-time measurement data to detect a number of products assembled by the assembly unit over time, wherein the real time measurement data generated by the at least one product counter sensor is collected during a period of time; wherein the determine a number of assembled products in the period of time, based on the measurement data; continuously collect real-time measurement data generated to measure an amount of energy supplied to the assembly unit during the period of time, and to store the collected real-time measurement data using a protocol, wherein the stored real-time measurement data temporally correlates the energy supplied to the assembly unit with the products assembled during the period of time; wherein determine an amount of supplied energy in the period of time based on the measurement data generated ; obtain energy carbon footprint information representing an amount of greenhouse gas released into the atmosphere for generating the amount of energy being supplied to the assembly unit; estimate an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time, based on the energy carbon footprint information; obtain reject product information associated with a number of rejected products over time, wherein the reject product information identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product; obtain component carbon footprint information associated with the components comprised in the rejected products; determine a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the period of time, based on the number of assembled products in the period of time, on the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time and the component carbon footprint information associated with the components in the rejected products; and, [presenting] a per-assembled-product carbon-footprint value derived from the real-time measurement data collected, thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions..
Claim 10 recites:
determining carbon footprint for one or more products assembled by an assembly unit, store real-time measurement data continuously collected wherein the stored real-time measurement data temporally correlates energy supplied to the assembly unit with products assembled during a period of time; and continuously receive measurement data collected in real-time to detect a number of products assembled by the assembly unit over time, and wherein the measurement data generated by the at least one product counter sensor is collected during the period of time; determine a number of assembled products in the period of time, based on the measurement data generated continuously receive, , measurement data collected in real-time and generated to measure an amount of energy being supplied to the assembly unit during the period of time, determine an amount of supplied energy in the period of time, based on the measurement data generated ; obtain energy carbon footprint information representing an amount of greenhouse gas released into the atmosphere for generating the amount of energy being supplied to the assembly unit; estimate an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time, based on the energy carbon footprint information; obtain reject product information associated with a number of rejected products over time, wherein the reject product information identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product; obtain component carbon footprint information associated with the components comprised in the rejected products; determine a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the period of time, based on the number of assembled products in the period of time, the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time and (iii) the component carbon footprint information associated with the components in the rejected products;; and [present] , a per-assembled-product carbon-footprint value derived from the real-time measurement data collected wherein the determination of the carbon footprint per assembled product and/or the aggregated carbon footprint for all the assembled products in the period of time is further based on component carbon footprint information associated with components in the rejected products, thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions.
Claim 18 recites:
determining carbon footprint for one or more products assembled by an assembly unit, store measurement data continuously collected from physical sensors, wherein the stored measurement data temporally correlates energy supplied to the assembly unit with products assembled during a period of time , and continuously receive, measurement data collected in real-time and generated detect a number of products assembled by the assembly unit over time, and wherein the measurement data generated by the at least one product counter sensor is collected during the period of time; ; determine a number of assembled products in the period of time, based on the measurement data generated ; continuously receive, , measurement data collected in real-time and generated to measure an amount of energy supplied to the assembly unit during the period of time, determine an amount of supplied energy in the period of time based on the measurement data generated ; obtain energy carbon footprint information representing an amount of greenhouse gas released into the atmosphere for generating the amount of energy being supplied to the assembly unit; estimate an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time, based on the energy carbon footprint information; obtain reject product information associated with a number of rejected products over time, wherein the reject product information identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product; obtain component carbon footprint information associated with the components comprised in the rejected products; determine a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the period of time, based at least on the number of assembled products in the period of time, on the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time and (iii) the component carbon footprint information associated with the components in the rejected products; and [present] a per-assembled-product carbon-footprint value derived from the real-time measurement data collected, thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions. .
Claims 1, 9, 10, and 18 recite an abstract idea that is a mental process. That is because the steps detailed above are steps of observation and judgment. The receiving obtaining steps are observation because under a broadest reasonable interpretation one could observe information coming from a sensor including real-time information which is simply information as it is happening, imperceptible to the time between the instant something occurs that is being measured and the measurement (a counter on an assembly line for example, counting items as they go by). The judgement steps are determining and estimating steps which one could do mentally or with pen and paper. Storing data in a data store is a further observation step that can be done with pen and paper. Protocols are simply rules and here, broadly claimed (“by a protocol”) could be rules that one follows mentally. Combined the steps are a mental process which one could take one step at a time and therefore the claims recite an abstract idea. Continuously collecting can be performed mentally. The following is continuous, and one could observe objects or other elements on a readout as follows: 1, 2, 3, 4, … ,n) mentally, in other words, continue to observe. The thereby limitation is merely an intended use as there is no actual optimization of an assembly unit, just that the information elements above make it possible for an assembly unit to be optimized.
This judicial exception is not integrated into a practical application. The additional elements are under a broadest reasonable interpretation taught by generic computing components that collect, store, analyzed, and display data. In combination they could be taught by a generic computer (generic computers collect, store, analyze, and display data) as described in pars 086-094. The sensors are recited performing in their ordinary capacity as data collection/generation elements that simply output the data they are designed to output. In combination, the additional elements amount to generic computing components coupled with sensors to receive the data from the sensors, which is no more than reciting instructions to apply the abstract idea to a computer connected to sensors. These are therefore instructions to apply the abstract idea to a computer with sensors, see MPEP 2106.05(f)(2), which is not a practical application of an abstract idea.
The additional elements of claim 1 are:
[steps] performed by a monitoring system comprising a gateway and a processor, the gateway configured to
[data from] from physical sensors, using a communication protocol,
from an assembly unit
via the gateway,
wherein the at least one product counter sensor comprises at least one of the physical sensors
generated by at least one energy supply sensor configured to
wherein the at least one energy supply sensor comprises at least one of the physical sensors;
by the monitoring system,
generated by the at least one product counter sensor and the at least one energy supply sensor
by the processor x several
via the gateway
database
dynamically updating and displaying, on a graphical user interface
The additional elements of claim 18 are:
A computer program product comprising a non-transitory, computer-readable medium storing computer-executable code for
the code executable by a computer monitoring system having a memory and at least one processor, wherein the memory is configured to
by a gateway
using a communication protocol
wherein execution of the code by the processor of the computer monitoring system causes the processor to:
from the memory,
by at least one product counter sensor configured to
wherein the at least one product counter sensor comprises at least one of the physical sensors
by the at least one product counter sensor
by at least one energy supply sensor configured to
wherein the at least one energy supply sensor comprises at least one of the physical sensors;
dynamically update and display, on a graphical user interface,
via the gateway,
Claim 9:
A monitoring system for
the system comprising: a gateway configured to
, from physical sensors, using a communication protocol
a processor; and a database
the gateway is configured to
generated by at least one product counter sensor configured
at least one product counter sensor comprises at least one of the physical sensors; the processor is configured to
generated by the at least one product counter sensor, the gateway is configured to
by at least one energy supply sensor configured
wherein the at least one energy supply sensor comprises at least one of the physical sensors;
the monitoring system is configured
generated by the at least one product counter sensor and the at least one energy supply sensor
database
the processor is further configured to:
by the at least one energy supply sensor
and dynamically update and display, on a graphical user interface,
via the gateway
Claim 10:
A monitoring system for
the system comprising: a memory configured to
by a gateway from physical sensors using a communication protocol
at least one processor coupled to the memory and configured to:
from the memory
by the gateway and generated by at least one product counter sensor configured
wherein the at least one product counter sensor comprises at least one of the physical sensors
by the at least one product counter sensor;
by at least one energy supply sensor configured
wherein the at least one energy supply sensor comprises at least one of the physical sensors;
by the at least one energy supply sensor
dynamically update and display, on a graphical user interface
via the gateway
Claim 18:
A computer program product comprising a non-transitory, computer-readable medium storing computer-executable code for
the code executable by a computer monitoring system having a memory and at least one processor, wherein the memory is configured to
by a gateway
using a communication protocol
wherein execution of the code by the processor of the computer monitoring system causes the processor to:
from the memory,
by at least one product counter sensor configured to
wherein the at least one product counter sensor comprises at least one of the physical sensors
by the at least one product counter sensor
by at least one energy supply sensor configured to
wherein the at least one energy supply sensor comprises at least one of the physical sensors;
dynamically update and display, on a graphical user interface,
via the gateway,
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because for the same reasons that the claims are not a practical application they are not significantly more than the abstract idea. In other words, instructions to apply an abstract idea to a computer is not significantly more than the abstract idea. See MPEP 2106.05(f)(2).
Per the dependent claims:
Claims 2 and 11, which are similar in scope, are a field of use limitation describing the kind of industrial environment measured, field of use under MPEP 2106.05(h) is not a practical application or significantly more.
Claims 7 and 16, which are similar in scope, further describe, as well, the abstract idea with further obtaining and determining steps, as well as displaying which is analyzed in the same way as in the independent claims, above.
Claims 6, 8 and 15, 17, which are similar in scope, describes displaying information such as a graph which is something that can be done with pen and paper and is therefore a mental process and displaying information on a computer screen is an apply it limitation for a computing device, which is not a practical application or significantly more.
Claim 19 recites categories of sensors that could count and these are further apply it limitations as they are performing in their ordinary capacity, to count objects as they go by, in some way. Likewise with a “reject counter sensor” which is a functional description of a sensor (ie, no structural limitation except “sensor”).
Claim 20 describes displaying various information where the information is patent ineligible, mental process (graphs, “breakdowns,” etc, is able to be presented with pen and paper) and displaying is using a computer in an apply it manner.
Claim 21 recites detecting number of products assembled, the detecting (receiving, observing, collecting, equivalent to these) counted objects is a part of the mental process of claim 1 and using a product sensor is an apply it limitation, analyzed the same way as the product sensor or counter sensor is analyzed in the independent claim rejection.
Claim 22, similar to claim 21, recites an energy sensor being used in its ordinary capacity and therefore is an apply it limitation and then the act of measuring energy is a further mental process limitation of observation.
Therefore claims 1, 2, 6-11, and 15-22 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 6-11, 15-17, 19, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wollack et al., US PGPUB 20220237628 A1 (“Wollack”), in view of Schoeneboom et al., US PGPUB 20220108327 A1 ("Schoeneboom"), further in view of Fu et al., US PGPUB 20120323797 A1 (“Fu”).
Per claims 1, 9,10, and 18, which are similar in scope, Per claim 10, Wollack teaches A monitoring system for determining carbon footprint for one or more products assembled by an assembly unit, the system comprising: a memory configured to store real-time measurement data collected by a gateway from physical sensors using a communication protocol; and at least one processor coupled to the memory and configured to: in par 020: “During production of the materials, greenhouse gases such as methane and/or carbon dioxide are destroyed (or sequestered) through the conversion of methane to carbon dioxide, carbon dioxide to oxygen, and other processes. Various sensors and user input devices may monitor a production process and record production details such as the weight of material produced, the amount of greenhouse gas destroyed or sequestered, the amount of power used (e.g., electricity, fuel, or other resources consumed during production), the amount of water used in production, working conditions (e.g., the average wages and average working hours of workers involved in production of the carbon-sequestering raw materials), other production inputs, an expected rate of decay of the carbon-sequestering matter, and batch information (e.g., identification information), as examples.”
Wollack then teaches continuously collecting, via the gateway, real-time measurement data generated by at least one product counter sensor configured to detect a number of products assembled by the assembly unit over time in par 030: “As yet more examples, the concentrator 116, treatment equipment 118, dryer 120, and/or scale 112 may include sensors for measuring relevant properties of the reaction products (e.g., the concentrator may record the weights of inputs and outputs and the scale 122 may record the weight of finished polymer). In at least some embodiments, the entirety (or nearly the entirety) of the production process is automatically monitored and relevant sensor readings automatically recorded.” Continuously taught in par 030: “. In at least some embodiments, the entirety (or nearly the entirety) of the production process is automatically monitored and relevant sensor readings automatically recorded.” See also par 058 continuous process.
Wollack then teaches wherein the at least one product counter sensor comprises at least one of the physical sensors and wherein the real-time measurement data generated by the at least one product counter sensor is collected during a period of time; n par 030: “As yet more examples, the concentrator 116, treatment equipment 118, dryer 120, and/or scale 112 may include sensors for measuring relevant properties of the reaction products (e.g., the concentrator may record the weights of inputs and outputs and the scale 122 may record the weight of finished polymer). In at least some embodiments, the entirety (or nearly the entirety) of the production process is automatically monitored and relevant sensor readings automatically recorded.” Production process teaches a period of time as it involves several steps as described in Wollack see par 058: “production of carbon-sequestering raw materials may occur in a continuous process as opposed to a batch process. In some embodiments with continuous production processes, the outputted carbon-sequestering raw materials may be divided into batches, each of which has at least one associated blockchain entry to facilitate a traceable link between units of final product and the batch of carbon-sequestering raw materials those units were produced from. In other embodiments with continuous production (or in some embodiments with batch production), blockchain entries may not identify specific batches of raw material and thus there may not be a traceable link between a given unit of final product to a particular batch of raw material.”
Wollack then teaches determining, by the processor, a number of assembled products in the period of time, based on the measurement data generated by the at least one product counter sensor in par 33: “In at least some embodiments, vendor 130 may send a materials packet such as second 150b. Second packet 150b may include any desired information including, but not limited to, the identity of the vendor (sometimes referred to as a fabricator), a unit count, an indication of the amount of polymer used, an indication of the amount of resins products, a production date, a ship date, a type of carbon-sequestering material or resin produced, and an amount of power consumed by the vendor during processing of the polymer.” Sensors which provide this information are taught in pars 65, 66, 68, 79.
Wollack then teaches continuously collecting, via the gateway, real-time measurement data generated by at least one energy supply sensor configured to measure an amount of energy being supplied to the assembly unit during the period of time in par 40: “In some embodiments, the packet 150c may additionally include mileage, fuel consumed, and/or other data associated with shipment and other movement of the product and its component parts up to the shipment of the product 168.” See also par 020 for the energy sensor and energy used in the production process, ie real time. See par 058: “In such embodiments, modified versions of the blockchain entries may be stored in blocks 304 and 308. As an example, the blockchain entry stored in block 304 may indicate an amount of carbon credit associated with the produced carbon-sequestering raw materials as a function of some measurable quantity such as volume or weight (e.g., the blockchain entry may indicate that every 100 grams of raw material is associated with a specified amount of carbon credits, which may be based on average production values for greenhouse gas consumption and power consumption).”
Wollack then teaches wherein the at least one energy supply sensor comprises at least one of the physical sensors in par 20 “Various sensors and user input devices may monitor a production process and record production details such as the weight of material produced, the amount of greenhouse gas destroyed or sequestered, the amount of power used (e.g., electricity, fuel, or other resources consumed during production),”
Wollack then teaches storing, by the monitoring system, the collected real-time measurement data generated by the at least one product counter sensor and the at least one energy supply sensor in a database using a database protocol in par 074 and references to storing in a blockchain in Wollack (see 54-69 but not limited to this) as blockchain teaches a database as it is a decentralized ledger.
Wollack then teaches wherein the stored real-time measurement data temporally correlates the energy supplied to the assembly unit with the products assembled during the period of time in par 058: “In such embodiments, modified versions of the blockchain entries may be stored in blocks 304 and 308. As an example, the blockchain entry stored in block 304 may indicate an amount of carbon credit associated with the produced carbon-sequestering raw materials as a function of some measurable quantity such as volume or weight (e.g., the blockchain entry may indicate that every 100 grams of raw material is associated with a specified amount of carbon credits, which may be based on average production values for greenhouse gas consumption and power consumption).” Carbon credit as a function of a measurable quantity of raw materials based on … power consumption correlates energy to the raw materials. See further par 058: “In such an example, the blockchain entry stored in block 308 may indicate the per-unit amount of carbon credit based on the per-unit weight (or volume) of carbon-sequestering raw material incorporated into each unit of goods and based on the amount of carbon credits per a specified weight (or volume) of the raw material.”
Wollack then teaches determining, by the processor, an amount of supplied energy in the period of time, based on the measurement data collected via the gateway from the at least one energy supply sensor and stored in the database in par 20 “Various sensors and user input devices may monitor a production process and record production details such as the weight of material produced, the amount of greenhouse gas destroyed or sequestered, the amount of power used (e.g., electricity, fuel, or other resources consumed during production),”
Wollack then teaches obtain component carbon footprint information associated with the components comprised in the … products in par 053: “The illustrative method 300 begins at block 302, where a blockchain node receives at least one data packet detailing carbon sequestered during production of raw materials such as a polymer or one or more proteins.”
Wollack then teaches determining, by the processor, a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the period of time, based at least on the number of assembled products in the period of time and on the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time, and (iii) the component carbon footprint information associated with the components in the … products; in par 53: “Another method for tracking carbon credits on a per-unit basis is shown in FIG. 3, which is a flow diagram of an illustrative method 300. The illustrative method 300 begins at block 302, where a blockchain node receives at least one data packet detailing carbon sequestered during production of raw materials such as a polymer or one or more proteins. The packet received in block 302 may be, as an example, a packet such as first packet 150a of FIG. 1A. The packet received in block 302 may include information such as a finished weight of raw materials, an amount of carbon-dioxide (or other greenhouse gas) consumed (e.g., destroyed or otherwise sequestered), an amount of power used, an amount of greenhouse gases produced as a result of the production, and identification information such as information identifying a batch of raw materials or identifying the producer of the raw materials, as examples (though not each is required in every embodiment).” Batch of raw materials teaches determining… component carbon footprint information.
Wollack then teaches thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions in par 037: “In other embodiments, carbon-sequestering materials other than polymers and resins may be created such as carbon-sequestering proteins. Additionally, final useful products, which may include anything that ends up in a final product, such as a leather good, cutlery, etc., may be created from carbon-sequestering proteins, carbon-sequestering polymers, carbon-sequestering resins, and/or other carbon-sequestering materials. In other embodiments, such embodiments may also be assigned with other environmental attributes, such as a quantifiable and product-specific credit associated for reductions in water, energy, and/or other environmentally impactful inputs or impacts.” Which teaches reducing greenhouse gas emissions, through reductions from the specific proteins and polymers that were used in the process.
The above italicized limitation is an “adapted to” “adapted for” “wherein” or “whereby” phrase because thereby is equivalent and this suggests or makes optional but does not require a step to be performed. Here, no real time optimization of the assembly unit is required to be performed, it is merely “enabled” which under a broadest reasonable interpretation means that it is an option that the assembly unit could be real time optimized. See MPEP 2111.04(I). This does not therefore further limit the claim to a particular structure or process. Though Wollack teaches this limitation, this limitation adds no limitation to a particular structure or process, based on this guidance.
Wollack does not teach obtaining, by the processor, energy carbon footprint information representing an amount of greenhouse gas released into the atmosphere for generating the amount of energy being supplied to the assembly unit
estimating, by the processor, an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time, based on the energy carbon footprint information;
displaying and dynamically updating, on a graphical user interface, a per-assembled-product carbon-footprint value derived from the real-time measurement data collected via the gateway.
Schoeneboom teaches a method for determining a carbon footprint of a product. See abstract.
Schoeneboom teaches obtaining, by the processor, energy carbon footprint information representing an amount of greenhouse gas released into the atmosphere for generating the amount of energy being supplied to the assembly unit in par 40: " The energy data is usually transformed into carbon footprints by taking into account the energy sources and their specific greenhouse gas emissions."
See also pars 046-047: “ "In some embodiments, the process according to the present invention further comprises (d) determining the carbon footprint of the product taking into account the process data, the carbon footprint of each raw material and the energy data.
Determining the carbon footprint of the product comprises summation of the carbon footprints of each raw material used in a particular process step as contained in the process data from step (a). If a process step requires an intermediate from a different process step, the sum of the carbon footprint of the raw material for this earlier process step is determined and used as input for the later process step. It may be necessary to repeat this if the earlier process step again uses an intermediate of an even earlier process step. If one process step yields more than one intermediate, for example two or three, it is necessary to share the carbon footprint of the raw materials among these intermediates. The share for each intermediate should reflect the raw material usage for each intermediate. In some cases, two intermediates are formed at the same amount, so the carbon footprint of the raw materials can be equally shared among them. In other cases, significantly more of one intermediate is formed than the other, for example 90% of intermediate 1 and 10% of intermediate 2. The carbon footprint should be shared accordingly. Hence, preferably, in the method of the present invention determining the carbon footprint involves, in some embodiments, calculating the carbon footprint for an intermediate produced in a preceding process step and using the carbon footprint of the intermediate as input for the calculation of the carbon footprint of a subsequent process step. In particular, in interconnected production processes, the calculation of the carbon footprint can be facilitated by subdividing it into analogous calculation parts, one for each process step. “
Schoeneboom then teaches estimating, by the processor, an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the period of time, based on the energy carbon footprint information in par 52: "Alternatively, preferably, the contribution of the energy for the product is determined for the product independent of the raw materials. To achieve this, the energy contribution for each process step is added according to the process data. If a process step yields more than one intermediate or the intermediate is used in more than one other process step, the contribution is shared among these, so only that part of the process step is taken into account which can be attributed to the product. For example, if one process step yields two intermediates at the same ratio and only one intermediate is used to produce the product, only half of the energy contribution of said process step is used for the determination of the energy contribution. "
See also pars 046-047: “ "In some embodiments, the process according to the present invention further comprises (d) determining the carbon footprint of the product taking into account the process data, the carbon footprint of each raw material and the energy data.
Determining the carbon footprint of the product comprises summation of the carbon footprints of each raw material used in a particular process step as contained in the process data from step (a). If a process step requires an intermediate from a different process step, the sum of the carbon footprint of the raw material for this earlier process step is determined and used as input for the later process step. It may be necessary to repeat this if the earlier process step again uses an intermediate of an even earlier process step. If one process step yields more than one intermediate, for example two or three, it is necessary to share the carbon footprint of the raw materials among these intermediates. The share for each intermediate should reflect the raw material usage for each intermediate. In some cases, two intermediates are formed at the same amount, so the carbon footprint of the raw materials can be equally shared among them. In other cases, significantly more of one intermediate is formed than the other, for example 90% of intermediate 1 and 10% of intermediate 2. The carbon footprint should be shared accordingly. Hence, preferably, in the method of the present invention determining the carbon footprint involves, in some embodiments, calculating the carbon footprint for an intermediate produced in a preceding process step and using the carbon footprint of the intermediate as input for the calculation of the carbon footprint of a subsequent process step. In particular, in interconnected production processes, the calculation of the carbon footprint can be facilitated by subdividing it into analogous calculation parts, one for each process step. “
Schoeneboom then teaches displaying and dynamically updating, on a graphical user interface, a per-assembled-product carbon-footprint value derived from the real-time measurement data collected via the gateway in par 052: “To arrive at the total carbon footprint of the product, the contribution of the raw materials and the contribution of the energy is added. Hence, preferably determining the carbon footprint of the product comprises determining the contribution of the energy in each process step and add shares of it according to the process data.” Then per displaying on a graphical user interface, par 066: “In some embodiments, the system or apparatus according to the present invention comprises (c) an output or output unit configured to output the carbon footprint of the product, preferably the carbon footprint of the product and each contribution to it as obtained from the processor or processing unit. Preferably, the output or output unit has an interface to an ERP system or a computing system or apparatus, such as a centralized or decentralized computing system or apparatus including processing and storage. Preferably, the output or output unit comprises a user interface, in particular a graphical user interface. The user interface is preferably configured to display the carbon footprint of the product and each contribution, preferably comprising the contribution of the raw materials, the contribution of the energy, and the contribution of the direct emissions of each process step. Preferably, the user interface is configured to use graph technology. The user interface may be configured to provide an overview of each process step, its raw materials and energy required, the connection with other process steps. The user interface may also provide the carbon footprint for each process step, in particular it may be configured to display the carbon footprint originating from the raw materials, from the energy consumption, and from the direct greenhouse gas emissions separately and in aggregated form.” Dynamically updated taught in par 067: “Preferably, the system is adapted to receive updated data at any time and can update the output or carbon footprint and/or its contributions in real time, which usually means within less than a few minutes, preferably within less than a minute, for example within 1 to 30 seconds.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the carbon footprint teaching of Wollack with the energy calculations for carbon footprint teaching of Schoeneboom because Schoeneboom teaches in pars 005-006 that multiple sources of data have to be used for carbon footprint and were calculated manually which takes a long time, therefore one would be motivated to modify Wollack with Schoeneboom in order to calculate more quickly and efficiently, which would improve understanding of carbon footprints.
Wollack does not teach obtain reject product information associated with a number of rejected products over time, wherein the reject product information identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product; rejected products
Fu teaches back tracing a component used in manufacturing. See abstract.
Fu teaches obtain reject product information associated with a number of rejected products over time, in par 030: “] FIG. 2D shows how the component tracking database 220 could be further linked to end products, such as, for example, a display unit in which the lightbars are installed… When the relational database structure depicted in FIG. 2D is employed, it allows all potentially defective LEDs to be flagged correlated directly to the associated display. For example, defective LED tracking column 234 allows the database to be queried for all displays containing an LED from an effected lot, in this case lot L1015.”
Fu then teaches wherein the reject product information identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product; rejected products in par 029: “FIG. 2C shows representative component tracking database 220 in accordance with the described embodiments. In particular, component tracking database 220 can include information for completed lightbar assemblies, and shows the lightbar substrate identification information 124 in a hierarchical parent child (one lightbar substrate to many LEDs) relationship with the LED identification information 122. The information can be accumulated during an LED installation process in which individual LEDs are placed in specific locations on the light bar substrate. In this way, all components used to manufacture the light bar assembly can be traced back to their originating entity. This is particularly useful in those situations where during, for example, an outgoing quality check operation, one or more defective LEDs are discovered. By noting the locations of the defective LEDs, the component tracking database 220 can be queried to resolve specific LED lot or batch information in which other defective LEDs may be found.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the tracking product teaching of Wollack with the back tracking for defect teaching of Fu because Fu teaches in par 023 that: “Conventional manufacturing processes involve assembling complex devices such as electronic devices from a large variety of different component manufacturers. Often times a specific part may be produced by different factories, or even completely different corporations. Quality control can vary significantly between the component manufacturers. Without some sort of method for tracking the origin of the parts, it is very difficult to make informed decisions about which component suppliers are responsible for a bad component in a particular electronic device.” As combining Fu would improve a manufacturing process which Wollack is also teaching (both the manufacturing and tracing of manufacturing) one would be motivated to combine the references based on the desire to improve quality control and minimize waste.
Per claim 2, Wollack, Schoeneboom, and Fu, teach the limitations of claims 1, above. Wollack further teaches wherein the assembly unit is an assembly station, an assembly line comprising multiple assembly stations, or an assembly factory comprising multiple assembly lines in par 32: “As shown in FIG. 1B, vendor 130 may receive a materials order 132 or a resin order 146, requesting shipment of specified carbon-sequestering materials or resins. Upon receipt of an order, the vendor may combine polymers from polymer inventory 136, with optional materials from materials inventory 138, and with various chemicals 140 in accordance with a resin formula 142 or some other recipe. The combined materials may be passed through an extruder 144 to produce carbon-sequestering materials such as resins 148. The resin order 152 may then be fulfilled by shipping 154 the appropriate amount of resins to a customer.”
Per claim 6, Wollack, Schoeneboom, and Fu, teach the limitations of claim 1, above. Wollack further teaches the aggregated carbon footprint for all the assembled products in the period of time in par 080: “The amount of carbon credit associated with the batch of raw material may be determined based at least on (a) the amount of carbon prevented from entering the atmosphere when producing the batch of raw material and (b) the amount of power used in producing the batch of raw material. The receiving from the remote computing device of the first data packet may include receiving from the remote computing device the first data packet along with a digital signature generated with a private key of the remote computing device.”
Wollack does not teach upon determining the carbon footprint per assembled product, determining carbon footprint for all the assembled products in the period of time, and displaying, on the graphical user interface, carbon footprint information
Schoeneboom teaches upon determining the carbon footprint per assembled product, determining carbon footprint for all the assembled products in the period of time, and displaying, on the graphical user interface, carbon footprint information in pars 056-057: “"In some embodiments, the process according to the present invention further comprises (e) outputting the carbon footprint of the product obtained in step (d). Outputting can mean writing the carbon footprint on a non-transitory data storage medium, displaying it on a user interface, providing it to an interface for further processing or any combination thereof. It is also possible to provide the output through an interface to a customer, for example to the customers supply chain system or ERP system. It is also possible to provide the output through an interface to the EPR system of the producer itself from where it can be distributed to where this information is needed. When the carbon footprint and each contribution to it is output onto a user interface, the user interface preferably uses graph technology. In this way, it is possible to analyze the contributions along the production process in order to optimize the production process and thereby minimize the carbon footprint for the products. It is also possible to monitor changes of the carbon footprint upon changes in the production process. In addition, the output can be used to simulate effects of changes, for example by manually changing certain values and see its effect on the carbon footprint of the product. For example, the effect of replacing a particular raw material by one having lower carbon footprint for each product may be analyzed.
Preferably, the process further comprises outputting the carbon footprint for each process step as it contributes to the carbon footprint of a certain product. In this way, it is possible to analyze the contribution of each step, in particular the contribution of raw materials and energy in each step. This allows the identification of potential to reduce the carbon footprint of the product."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the carbon footprint teaching of Wollack with the energy calculations for carbon footprint teaching of Schoeneboom because Schoeneboom teaches in pars 005-006 that multiple sources of data have to be used for carbon footprint and were calculated manually which takes a long time, therefore one would be motivated to modify Wollack with Schoeneboom in order to calculate more quickly and efficiently, which would improve understanding of carbon footprints.
Per claim 7, Wollack, Schoeneboom, and Fu teach the limitations of claim 1, above. Wollack further teaches for each of multiple periods of time, performing the following steps upon a respective period of time has elapsed: determining a number of assembled products in the respective period of time, based on the measurement data generated by the at least one product counter sensor in par 053 and 055, see par 055: “At block 306, a blockchain node receives at least one second data packet detailing fabrication of a plurality of goods from the raw materials. The packet received in block 306 may be, as an example, a packet such as third packet 150c of FIG. 1C. The packet received in block 306 may include information such as a plurality of unique product identifiers, a product type, a number of units of goods produced, and a production date, as examples. In some embodiments, the packet received in block 306 and the associated entry stored in block 308 may detail fabrication of an intermediary good, rather than a final product. In embodiments with multiple stages of intermediary goods, blocks 306 and 308 may be repeated for each stage of fabrication. In these and other embodiments, a final iteration of blocks 306 and 308 may be performed for the fabrication of final goods from the final intermediary product. In this manner, the final goods (and any intermediary goods) can be traced back to an associated entry stored in block 304, thus facilitating tracking of carbon sequestered in the final goods (and any intermediary goods).”
Wollack does not teach
determining an amount of supplied energy in the respective period of time, based on the measurement data generated by the at least one energy supply sensor
estimating an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the respective period of time, based on the energy carbon footprint information
determining a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the respective period of time, based at least on the number of assembled products in the respective period of time and on the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the respective period of time
and displaying, on the graphical user interface, the carbon footprint per assembled product and/or the aggregated carbon footprint for all the assembled products in the respective period of time.
Schoeneboom teaches determining an amount of supplied energy in the respective period of time, based on the measurement data generated by the at least one energy in par 042: “Often, a production plant has multiple sensors providing data about the energy consumption of a certain process step or certain equipment.”
Then, Schoeneboom teaches estimating an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the respective period of time, based on the energy carbon footprint information in par 052: “For example, if one process step yields two intermediates at the same ratio and only one intermediate is used to produce the product, only half of the energy contribution of said process step is used for the determination of the energy contribution. To arrive at the total carbon footprint of the product, the contribution of the raw materials and the contribution of the energy is added. Hence, preferably determining the carbon footprint of the product comprises determining the contribution of the energy in each process step and add shares of it according to the process data.”
Then, Schoeneboom teaches determining a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the respective period of time, based at least on the number of assembled products in the respective period of time and on the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the respective period of time in par 056: “In some embodiments, the process according to the present invention further comprises (e) outputting the carbon footprint of the product obtained in step (d). Outputting can mean writing the carbon footprint on a non-transitory data storage medium, displaying it on a user interface, providing it to an interface for further processing or any combination thereof. It is also possible to provide the output through an interface to a customer, for example to the customers supply chain system or ERP system.”
Then, Schoeneboom teaches and displaying, on the graphical user interface, the carbon footprint per assembled product and/or the aggregated carbon footprint for all the assembled products in the respective period of time in par 056: “ It is also possible to provide the output through an interface to the EPR system of the producer itself from where it can be distributed to where this information is needed. When the carbon footprint and each contribution to it is output onto a user interface, the user interface preferably uses graph technology. In this way, it is possible to analyze the contributions along the production process in order to optimize the production process and thereby minimize the carbon footprint for the products.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the carbon footprint teaching of Wollack with the energy calculations for carbon footprint teaching of Schoeneboom because Schoeneboom teaches in pars 005-006 that multiple sources of data have to be used for carbon footprint and were calculated manually which takes a long time, therefore one would be motivated to modify Wollack with Schoeneboom in order to calculate more quickly and efficiently, which would improve understanding of carbon footprints.
Per claim 8, Wollack, Schoeneboom, and Fu teach the limitations of claim 7, above. Wollack does not teach displaying, on the graphical user interface, a graph of the carbon footprints per assembled product and/or the aggregated carbon footprints for all the assembled products in the multiple periods of time.
Schoeneboom teaches displaying, on the graphical user interface, a graph of the carbon footprints per assembled product and/or the aggregated carbon footprints for all the assembled products in the multiple periods of time in par 066: “In some embodiments, the system or apparatus according to the present invention comprises (c) an output or output unit configured to output the carbon footprint of the product, preferably the carbon footprint of the product and each contribution to it as obtained from the processor or processing unit. Preferably, the output or output unit has an interface to an ERP system or a computing system or apparatus, such as a centralized or decentralized computing system or apparatus including processing and storage. Preferably, the output or output unit comprises a user interface, in particular a graphical user interface. The user interface is preferably configured to display the carbon footprint of the product and each contribution, preferably comprising the contribution of the raw materials, the contribution of the energy, and the contribution of the direct emissions of each process step. Preferably, the user interface is configured to use graph technology. The user interface may be configured to provide an overview of each process step, its raw materials and energy required, the connection with other process steps. The user interface may also provide the carbon footprint for each process step, in particular it may be configured to display the carbon footprint originating from the raw materials, from the energy consumption, and from the direct greenhouse gas emissions separately and in aggregated form. FIG. 5 shows schematically an example of how the user interface could be configured. The raw materials and the intermediates to the product are displayed according to the chain of interconnected process steps. The arrows represent process steps. Their width reflects the amount of greenhouse gases the respective process step contributes to the carbon footprint of the product. It may be possible to display further information when hovering over a box or an arrow with the mouse pointer, for example specifics about the raw material, intermediate or product or the exact value of the greenhouse gas emission. Preferably the carbon footprints are displayed in aggregated form showing the contributions of the raw materials, the energy usage and direct greenhouse gas emissions.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the carbon footprint teaching of Wollack with the energy calculations for carbon footprint teaching of Schoeneboom because Schoeneboom teaches in pars 005-006 that multiple sources of data have to be used for carbon footprint and were calculated manually which takes a long time, therefore one would be motivated to modify Wollack with Schoeneboom in order to calculate more quickly and efficiently, which would improve understanding of carbon footprints.
Per claim 11, Wollack, Schoeneboom, and Fu teach the limitations of claim 10, above. Wollack further teaches wherein the assembly unit is an assembly station, an assembly line comprising multiple assembly stations, or an assembly factory comprising multiple assembly lines in par 32: “As shown in FIG. 1B, vendor 130 may receive a materials order 132 or a resin order 146, requesting shipment of specified carbon-sequestering materials or resins. Upon receipt of an order, the vendor may combine polymers from polymer inventory 136, with optional materials from materials inventory 138, and with various chemicals 140 in accordance with a resin formula 142 or some other recipe. The combined materials may be passed through an extruder 144 to produce carbon-sequestering materials such as resins 148. The resin order 152 may then be fulfilled by shipping 154 the appropriate amount of resins to a customer.”
Per claim 15, Wollack, Schoeneboom, and Fu teach the limitations of claim 10, above. Wollack further teaches the aggregated carbon footprint for all the assembled products in the period of time in par 080: “The amount of carbon credit associated with the batch of raw material may be determined based at least on (a) the amount of carbon prevented from entering the atmosphere when producing the batch of raw material and (b) the amount of power used in producing the batch of raw material. The receiving from the remote computing device of the first data packet may include receiving from the remote computing device the first data packet along with a digital signature generated with a private key of the remote computing device.”
Wollack does not teach upon determining the carbon footprint per assembled product, determining carbon footprint for all the assembled products in the period of time, and displaying, on the graphical user interface, carbon footprint information
Schoeneboom teaches upon determining the carbon footprint per assembled product, determining carbon footprint for all the assembled products in the period of time, and displaying, on the graphical user interface, carbon footprint information in pars 056-057: “"In some embodiments, the process according to the present invention further comprises (e) outputting the carbon footprint of the product obtained in step (d). Outputting can mean writing the carbon footprint on a non-transitory data storage medium, displaying it on a user interface, providing it to an interface for further processing or any combination thereof. It is also possible to provide the output through an interface to a customer, for example to the customers supply chain system or ERP system. It is also possible to provide the output through an interface to the EPR system of the producer itself from where it can be distributed to where this information is needed. When the carbon footprint and each contribution to it is output onto a user interface, the user interface preferably uses graph technology. In this way, it is possible to analyze the contributions along the production process in order to optimize the production process and thereby minimize the carbon footprint for the products. It is also possible to monitor changes of the carbon footprint upon changes in the production process. In addition, the output can be used to simulate effects of changes, for example by manually changing certain values and see its effect on the carbon footprint of the product. For example, the effect of replacing a particular raw material by one having lower carbon footprint for each product may be analyzed.
Preferably, the process further comprises outputting the carbon footprint for each process step as it contributes to the carbon footprint of a certain product. In this way, it is possible to analyze the contribution of each step, in particular the contribution of raw materials and energy in each step. This allows the identification of potential to reduce the carbon footprint of the product."
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the carbon footprint teaching of Wollack with the energy calculations for carbon footprint teaching of Schoeneboom because Schoeneboom teaches in pars 005-006 that multiple sources of data have to be used for carbon footprint and were calculated manually which takes a long time, therefore one would be motivated to modify Wollack with Schoeneboom in order to calculate more quickly and efficiently, which would improve understanding of carbon footprints.
Per claim 16, Wollack, Schoeneboom, and Fu teach the limitations of claim 10, above. Wollack further teaches for each of multiple periods of time, performing the following steps upon a respective period of time has elapsed: determining a number of assembled products in the respective period of time, based on the measurement data generated by the at least one product counter sensor in par 053 and 055, see par 055: “At block 306, a blockchain node receives at least one second data packet detailing fabrication of a plurality of goods from the raw materials. The packet received in block 306 may be, as an example, a packet such as third packet 150c of FIG. 1C. The packet received in block 306 may include information such as a plurality of unique product identifiers, a product type, a number of units of goods produced, and a production date, as examples. In some embodiments, the packet received in block 306 and the associated entry stored in block 308 may detail fabrication of an intermediary good, rather than a final product. In embodiments with multiple stages of intermediary goods, blocks 306 and 308 may be repeated for each stage of fabrication. In these and other embodiments, a final iteration of blocks 306 and 308 may be performed for the fabrication of final goods from the final intermediary product. In this manner, the final goods (and any intermediary goods) can be traced back to an associated entry stored in block 304, thus facilitating tracking of carbon sequestered in the final goods (and any intermediary goods).”
Wollack does not teach
determining an amount of supplied energy in the respective period of time, based on the measurement data generated by the at least one energy supply sensor
estimating an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the respective period of time, based on the energy carbon footprint information
determining a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the respective period of time, based at least on the number of assembled products in the respective period of time and on the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the respective period of time
and displaying, on the graphical user interface, the carbon footprint per assembled product and/or the aggregated carbon footprint for all the assembled products in the respective period of time.
Schoeneboom teaches determining an amount of supplied energy in the respective period of time, based on the measurement data generated by the at least one energy in par 042: “Often, a production plant has multiple sensors providing data about the energy consumption of a certain process step or certain equipment.”
Then, Schoeneboom teaches estimating an amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the respective period of time, based on the energy carbon footprint information in par 052: “For example, if one process step yields two intermediates at the same ratio and only one intermediate is used to produce the product, only half of the energy contribution of said process step is used for the determination of the energy contribution. To arrive at the total carbon footprint of the product, the contribution of the raw materials and the contribution of the energy is added. Hence, preferably determining the carbon footprint of the product comprises determining the contribution of the energy in each process step and add shares of it according to the process data.”
Then, Schoeneboom teaches determining a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the respective period of time, based at least on the number of assembled products in the respective period of time and on the amount of greenhouse gas released into the atmosphere for generating the amount of supplied energy in the respective period of time in par 056: “In some embodiments, the process according to the present invention further comprises (e) outputting the carbon footprint of the product obtained in step (d). Outputting can mean writing the carbon footprint on a non-transitory data storage medium, displaying it on a user interface, providing it to an interface for further processing or any combination thereof. It is also possible to provide the output through an interface to a customer, for example to the customers supply chain system or ERP system.”
Then, Schoeneboom teaches and displaying, on the graphical user interface, the carbon footprint per assembled product and/or the aggregated carbon footprint for all the assembled products in the respective period of time in par 056: “ It is also possible to provide the output through an interface to the EPR system of the producer itself from where it can be distributed to where this information is needed. When the carbon footprint and each contribution to it is output onto a user interface, the user interface preferably uses graph technology. In this way, it is possible to analyze the contributions along the production process in order to optimize the production process and thereby minimize the carbon footprint for the products.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the carbon footprint teaching of Wollack with the energy calculations for carbon footprint teaching of Schoeneboom because Schoeneboom teaches in pars 005-006 that multiple sources of data have to be used for carbon footprint and were calculated manually which takes a long time, therefore one would be motivated to modify Wollack with Schoeneboom in order to calculate more quickly and efficiently, which would improve understanding of carbon footprints.
Per claim 17, Wollack, Schoeneboom, and Fu teach the limitations of claim 16, above. Wollack does not teach displaying, on the graphical user interface, a graph of the carbon footprints per assembled product and/or the aggregated carbon footprints for all the assembled products in the multiple periods of time.
Schoeneboom teaches displaying, on the graphical user interface, a graph of the carbon footprints per assembled product and/or the aggregated carbon footprints for all the assembled products in the multiple periods of time in par 066: “In some embodiments, the system or apparatus according to the present invention comprises (c) an output or output unit configured to output the carbon footprint of the product, preferably the carbon footprint of the product and each contribution to it as obtained from the processor or processing unit. Preferably, the output or output unit has an interface to an ERP system or a computing system or apparatus, such as a centralized or decentralized computing system or apparatus including processing and storage. Preferably, the output or output unit comprises a user interface, in particular a graphical user interface. The user interface is preferably configured to display the carbon footprint of the product and each contribution, preferably comprising the contribution of the raw materials, the contribution of the energy, and the contribution of the direct emissions of each process step. Preferably, the user interface is configured to use graph technology. The user interface may be configured to provide an overview of each process step, its raw materials and energy required, the connection with other process steps. The user interface may also provide the carbon footprint for each process step, in particular it may be configured to display the carbon footprint originating from the raw materials, from the energy consumption, and from the direct greenhouse gas emissions separately and in aggregated form. FIG. 5 shows schematically an example of how the user interface could be configured. The raw materials and the intermediates to the product are displayed according to the chain of interconnected process steps. The arrows represent process steps. Their width reflects the amount of greenhouse gases the respective process step contributes to the carbon footprint of the product. It may be possible to display further information when hovering over a box or an arrow with the mouse pointer, for example specifics about the raw material, intermediate or product or the exact value of the greenhouse gas emission. Preferably the carbon footprints are displayed in aggregated form showing the contributions of the raw materials, the energy usage and direct greenhouse gas emissions.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the carbon footprint teaching of Wollack with the energy calculations for carbon footprint teaching of Schoeneboom because Schoeneboom teaches in pars 005-006 that multiple sources of data have to be used for carbon footprint and were calculated manually which takes a long time, therefore one would be motivated to modify Wollack with Schoeneboom in order to calculate more quickly and efficiently, which would improve understanding of carbon footprints.
Per claim 19, Wollack, Schoeneboom, and Fu teach the limitations of claim 1, above. Wollack further teaches wherein the at least one product counter sensor comprises at least one light sensor, photoelectric barrier, pressure sensor, flow sensor, temperature sensor, a scale, a displacement sensor, a vision system, a good part counter sensor configured to count a number of products that are successfully assembled, or a reject counter sensor configured to count a number of products that are not successfully assembled and a stage of assembly associated with each not successfully assembled product in par 030: “As yet more examples, the concentrator 116, treatment equipment 118, dryer 120, and/or scale 112 may include sensors for measuring relevant properties of the reaction products (e.g., the concentrator may record the weights of inputs and outputs and the scale 122 may record the weight of finished polymer). In at least some embodiments, the entirety (or nearly the entirety) of the production process is automatically monitored and relevant sensor readings automatically recorded.”
Per claim 21, Wollack, Schoeneboom, and Fu teach the limitations of claim 1, above. Wollack further teaches detecting, by the at least one product counter sensor, the number of products assembled by the assembly unit in par 030: “As yet more examples, the concentrator 116, treatment equipment 118, dryer 120, and/or scale 112 may include sensors for measuring relevant properties of the reaction products (e.g., the concentrator may record the weights of inputs and outputs and the scale 122 may record the weight of finished polymer). In at least some embodiments, the entirety (or nearly the entirety) of the production process is automatically monitored and relevant sensor readings automatically recorded.”
Per claim 22, Wollack, Schoeneboom, and Fu teach the limitations of claim 1, above. Wollack further teaches comprising measuring, with the at least one energy supply sensor, the amount of energy supplied to the assembly unit in par 40: “In some embodiments, the packet 150c may additionally include mileage, fuel consumed, and/or other data associated with shipment and other movement of the product and its component parts up to the shipment of the product 168.”
See also in par 20 “Various sensors and user input devices may monitor a production process and record production details such as the weight of material produced, the amount of greenhouse gas destroyed or sequestered, the amount of power used (e.g., electricity, fuel, or other resources consumed during production).”
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wollack et al., US PGPUB 20220237628 A1 (“Wollack”), in view of Schoeneboom et al., US PGPUB 20220108327 A1 ("Schoeneboom"), further in view of Fu et al., US PGPUB 20120323797 A1 (“Fu”), further in view of Kienzle et al., US PGPUB 20130151303 A1 (“Kienzle”).
Per claim 20, Wollack, Schoeneboom, and Fu teach the limitations of claim 19, above. Wollack further teaches (i) energy consumption in par 028: “energy monitors may cryptographically sign data blocks indicating the amount of power consumed,”
Wollack further teaches (ii) a per- product carbon-footprint value in par 068: “an amount of carbon-credits associated with the produced products (e.g., an aggregate amount that can later be divided by a unit count or a per-product amount)”
Wollack does not teach displaying, on the graphical user interface; (iii) a breakdown of carbon contributions based on energy consumption and component carbon footprint information, based on the real-time measurement data collected by the gateway and the component carbon-footprint information.
Schoeneboom teaches displaying, on the graphical user interface in par 066. Schoeneboom then teaches (iii) a breakdown of carbon contributions based on energy consumption and component carbon footprint information, based on the real-time measurement data collected by the gateway and the component carbon-footprint information in par 066: “Their width reflects the amount of greenhouse gases the respective process step contributes to the carbon footprint of the product. It may be possible to display further information when hovering over a box or an arrow with the mouse pointer, for example specifics about the raw material, intermediate or product or the exact value of the greenhouse gas emission. Preferably the carbon footprints are displayed in aggregated form showing the contributions of the raw materials, the energy usage and direct greenhouse gas emissions.” Based on real-time measurement data is taught in par 067: “Preferably, the system is adapted to receive updated data at any time and can update the output or carbon footprint and/or its contributions in real time, which usually means within less than a few minutes, preferably within less than a minute, for example within 1 to 30 seconds.”
Wollack does not teach (i) a time-series representation [carbon footprint] for the period of time
Kienzle teaches carbon footprint computation. See abstract.
Kienzle teaches (i) a time-series representation [carbon footprint] for the period of time in Fig 3 and par 040: “The average carbon footprint computation module 208 determines an average carbon footprint value based on the carbon footprint values of manufacturing processes over a limited period of time. For example, a manufacturing process based on a same order may generate different levels of carbon footprint over a period of time due to a multitude of manufacturing factors. The different levels of carbon footprints over a time interval 302 are illustrated in a chart 300 in FIG. 3. The horizontal axis 304 represents orders 302 or tasks placed in the time interval 302. The vertical axis 306 represents the amount of carbon footprint associated with each order. The average carbon footprint 308 per order within the interval 203 is represented by the horizontal line across the chart 300.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the carbon footprint determination system of Wollack, as modified by the display and graph teaching of Schoeneboom, with the time series representation demonstration of Kienzle because Kienzle teaches in par 002 that: “Also, the carbon footprint value may not be consistent throughout the flow of logistic processes because different combinations of materials and different processes produce different carbon footprint emissions. Further, common manufacturing processes do not have the means to track in real time how much carbon is generated during a manufacturing process. As such, common manufacturing planning processes cannot optimize their manufacturing processes in terms of carbon emission restrictions and other restrictions because this information is missing from their logistic planning process.” As Kienzle teaches the problem is determining the amount of carbon footprint emissions during manufacturing, Kienzle would motivate one ordinarily skilled to modify Wollack in view of Schoeneboom with Kienzle to better track in real time carbon generated during manufacturing.
Therefore, claims 1, 2, 6-11, and 15-22 are rejected under 35 USC 103.
Response to remarks:
35 USC 101
A. The Claims Are Not Directed to an Abstract Idea Under Step 2A, Prong 1
The Action characterizes the claims as reciting a mental process, steps of “observation” and “judgment.” (Action, p. 6). However, the claims recite specific technical operations that cannot practically be performed in the human mind.
The USPTO guidance recognizes that claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, including when the human mind is not equipped to perform the claimed limitations. See MPEP §2106.04(a)(2)(III)(A). See SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims.)" See also, SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010), as directed to inventions that ‘‘could not, as a practical matter, be performed entirely in a human’s mind’’).
The claims here are analogous. A person may be able to observe a single counter or perform multiplication after the fact, but the human mind is not equipped to continuously collect real-time measurement data from multiple physical sensors via a gateway using a communication protocol, store data that temporally correlates energy supplied to an assembly unit with products assembled during the same period of time, account for rejected products using assembly-stage and component information, and dynamically update a graphical user interface with a per-assembled-product carbon footprint value. These claimed operations are not mental observation or judgment. They are real-time industrial monitoring operations performed using physical sensors, a gateway, a database, and a processor.
In particular, as amended, claim 1 recites, among other things, continuously collecting, via a gateway (using a communication protocol), real-time measurement data from an assembly unit generated by at least one physical sensor - a product counter sensor detecting a number of products assembled during a period of time and an energy supply sensor configured to measure an amount of energy supplied to the assembly unit during the same period of time , and storing this collected real-time measurement data generated by the at least one product counter sensor and the at least one energy supply sensor in a database using a database protocol, wherein the stored real-time measurement data temporally correlates the energy supplied to the assembly unit with the products assembled during the period of time .
Amended claim 1 further recites obtaining reject product information that identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product , obtaining component carbon footprint information associated with the components comprised in the rejected products, and determining a per-assembled-product carbon footprint based on three enumerated factors. A per-assembled-product carbon-footprint value is then dynamically updated and displayed on a graphical user interface, thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions .
This technical process cannot be performed mentally. As noted above, a human cannot mentally “continuously collect” real-time measurement data from physical sensors using a communication protocol. A human cannot mentally “temporally correlate” energy consumption data with assembled product counts during the same period of time. A human cannot mentally track rejected products during an assembly process, identify the specific assembly stage at which each rejection occurred, and determine which components are comprised in each rejected product to attribute their carbon footprints to the per-product calculation. A human also cannot “dynamically update” a graphical user interface display.
Examiner disagrees. These are basic counting steps (1, 2, 3, … and so on) and analysis steps (averaging, dividing, etc) and the data comes from the sensors and are passed through or processed, without technical detail in the claims, by generic computing components. Examiner met the burden of showing that Applicant’s additional elements are generic and Applicant has not provided sufficiently persuasive evidence otherwise. Applicant’s argument is that “sensor data” (1, 2, 3, or # of kWh) is “integrated,” but this is an attempt to patent data or information which was properly identified as the abstract idea. There is no legal support for an integration of data to be an exception to the abstract idea identification. A human mind can readily take sensor data (1, 2, 3, for counting, or a quantity of kWh, for energy) and perform math on it. Graphical interface is just a computer screen, seen on all generic computing devices, and as Examiner is following the guidance, this element is analyzed in 2B. The counting and observation elements, which constitute the abstract idea, were and are properly analyzed as an Abstract Idea. Applicant has not been able to show how one could not count mentally, which is what the abstract idea amounts to. Continuous collection is a mental process, it’s done by observing and performing basic math. Applicant’s argument is unpersuasive as it lacks evidence to show the contrary. Therefore this argument is unpersuasive.
Claims that apply mathematical analysis to sensor data are not abstract where the claims recite a specific technical configuration and use of sensors to improve a physical monitoring process. See Thales Visionix Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017) (claims eligible where using a specific configuration of inertial sensors to improve motion tracking were not directed to an abstract idea); see also CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358 (Fed. Cir. 2020) (claims for detecting cardiac arrhythmias using device-implemented techniques with particular technical features were patent-eligible).
The Action asserts that that converting energy in kWh to CO2 using a parameter is essentially multiplication (A*x=y), which can be done mentally (Action, p. 57). However, the claims do not merely recite mathematical conversion. The amended independent claims recite an integrated technical system comprising: (1) continuous collection of real-time data from physical sensors via a gateway using a communication protocol; (2) storage of that data in a database using a database protocol, wherein the stored data temporally correlates energy supplied with products assembled during the same period of time; (3) obtaining reject product information that identifies the assembly stage and components for each rejected product; (4) obtaining component carbon footprint information for those components; (5) determining carbon footprint based on three enumerated factors including energy consumption data and component carbon footprint information from rejected products; and (6) dynamically updating and displaying a per-assembled-product carbon footprint value on a graphical user interface to enable real-time optimization. This is an industrial monitoring system that accounts for waste and defects in the carbon footprint calculation with temporal correlation between energy consumption and product assembly, not a mental calculation.
Applicant is correct in that the claims do not recite conversion. They recite even less than that: obtaining information/data and then “determining.” Which may not even require a conversion step, so it’s even simpler, not that complexity has relevance to the applicable guidance. Applicant’s argument is persistently unpersuasive because Applicant has not shown that the claims do any more than what they say, obtaining data; making a determination; and then showing the results. This is not similar to Thales Visionix as there is no specific configuration of sensors (nor did Applicant show that). Applicant merely claims sensors, and no configuration. The identified abstract idea is similar to Electric Power Group, collecting, analyzing, and displaying the results of the analysis. Here the obtaining is collecting and the determining is analyzing. The final step is presenting the results and the additional elements were analyzed below: here, Applicant argues ahead of the rejection but one following guidance would assume that the GUI element (what the information was displayed on) is an additional element.
Even assuming arguendo that the claims recite an abstract idea, the claims integrate that idea into a practical application. The claims recite a specific technical solution to a specific technical problem: real-time monitoring of product carbon footprint during industrial assembly operations. The specification explains that “one goal of the present disclosure is to provide techniques to monitor carbon footprint during manufacturing processes, in particular during assembly processes” and “to provide real-time feedback of the product carbon footprint (PCF), so that impact of reduction measures and their consequences on the PCF can be evaluated efficiently.” (Specification ¶ [0005].)
The amended claims achieve this goal through a specific technical arrangement: physical sensors (product counter sensors and energy supply sensors) integrated with an assembly unit, a gateway using a communication protocol to continuously collect real-time measurement data, a database storing the data using a database protocol wherein the stored data temporally correlates energy supplied with products assembled during the same period of time, and a processor that obtains reject product information identifying the assembly stage and components for each rejected product, obtains component carbon footprint information for those components, and determines per-product carbon footprint values based on three enumerated factors: assembled product counts, energy-related greenhouse gas emissions, and component carbon footprints from rejected products. The amended claims further recite dynamically updating and displaying the per-product carbon footprint value, thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions.
This approach accounts for manufacturing waste and defects in the carbon footprint calculation with explicit temporal correlation between energy consumption and product assembly - a specific technical improvement that enables more accurate environmental impact assessment. This is not merely “applying” an abstract idea to generic computing components - it is a specific monitoring system architecture that enables real-time per-product carbon footprint determination during industrial assembly operations while accounting for rejected products. Under Step 2A, Prong Two, a claim that recites a judicial exception is not directed to the exception when the claim as a whole integrates the exception into a practical application. MPEP § 2106.04(d); see also 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 55 (Jan. 7, 2019). One indication of such integration is that the additional elements reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. MPEP §§ 2106.04(d)(1), 2106.05(a).
This is unpersuasive reframing that does not specifically identify where a technical solution to a technical problem is claimed. MPEP 2106.05(a) provides the following guidance:
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016).
Here, one ordinarily skilled in the art would see that the claims largely are collecting, analyzing, and displaying the results of the analysis, which is similar to Electric Power Group. While Applicant’s argument was carefully considered, the argument reinforced this view by stating that this is about “monitoring” which is equivalent to observing and then “calculating” which, see id., is analyzing the observed data. Therefore this is unpersuasive. Further notable is that Applicant is arguing the abstract idea elements in the section where the additional elements are to provide a practical application. But, as the additional elements are just applied generic computer elements, they were not relied upon to make this argument. Therefore, the argument is unpersuasive.
The temporal correlation and reject product limitations are particularly significant. The claims now explicitly require that energy measurement data and product count data are collected during the same period of time, and that the stored data temporally correlates the energy supplied with the products assembled. This explicit time-correlation distinguishes the claims from generic data collection and analysis. Furthermore, as now explicitly claimed, the processor obtains reject product information that identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product. This requires tracking rejected products during the assembly process with specific details about the assembly stage and component identification, a technical operation that improves the accuracy of per-product carbon footprint calculations by accounting for manufacturing waste. The specification explains at paragraphs [0027] and [0049] that the reject counter sensor detects the stage of assembly associated with each not successfully assembled product, and that the information related to each reject event is used to determine at which assembly stage the reject event took place and which components are comprised in each rejected product.
These citations have been reviewed and, as claimed, this is mere data collection, a part of the abstract idea.
This case is analogous to Diamond v. Diehr, 450 U.S. 175 (1981), where the Supreme Court held that claims using a mathematical equation in a process for rubber were patent-eligible because the claims were directed to an industrial process as a whole, not to the mathematical equation in isolation. Here, the amended claims likewise do not seek to monopolize a carbon-footprint calculation. Rather, the claims integrate the calculations into a specific industrial assembly monitoring process that continuously collects temporally correlated real-time sensor data from an assembly unit, accounts for rejected products using assembly stage and component information, and dynamically updates and displays a graphical user interface to enable real-time optimization of the assembly unit to reduce greenhouse gas emissions.
In the USPTO’s Appendix 1 to the October 2019 Update: Subject Matter Eligibility Life Sciences & Data Processing Examples, Example #45 (Controller for Injection Mold), is also instructive. In that example, certain claims using measured industrial process data and mathematical analysis were treated as eligible where the claimed controller used the information in the context of an industrial molding process to improve the operation of that process.
Similarly here, the claims do not merely calculate and display a standalone number. The claims integrate carbon-footprint determination into a specific industrial assembly-monitoring process that continuously collects temporally correlated real-time sensor data from an assembly unit, accounts for rejected products using assembly-stage and component information, and dynamically updates and displays a graphical user interface to enable real-time optimization of the assembly unit to reduce greenhouse gas emissions.
This case is not analogous to Diehr as there is not transformation of a material, see MPEP 2106.05(c):
. The nature of the transformation in terms of the type or extent of change in state or thing. A transformation resulting in the transformed article having a different function or use, would likely provide significantly more, but a transformation resulting in the transformed article merely having a different location, would likely not provide significantly more (or integrate a judicial exception into a practical application). For example, a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diamond v. Diehr, 450 U.S. 175, 184, 209 USPQ 1, 21 (1981)), provides significantly more (or integrate a judicial exception into a practical application).
Applicant did not identify the physical object transformed. Also, in this section, it is noted that transformation is limited to a physical object or substance. Here, and to be fair, is limited by the claim language, the only “transformation” is one explicitly ruled out in the guidance, that is the information (that which is displayed on the GUI). The data/information elements do not qualify as a transformation:
An "article" includes a physical object or substance. The physical object or substance must be particular, meaning it can be specifically identified. "Transformation" of an article means that the "article" has changed to a different state or thing. Changing to a different state or thing usually means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which thoughts or human based actions are "changed" are not considered an eligible transformation. For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).
Because transformation that could be argued are the data/information elements identified in the abstract idea above (which constitutes the entirety of the abstract idea) which is a mental process, Diehr is not applicable to the claims as written. Basically, there needs to be a physical article or substance changed from one thing to another, such as the output of an injection mold, Id.
Applicant respectfully submits that all pending claims are patentable over the cited prior art and comply with 35 U.S.C. § 101. The claims recite specific technical operations - including continuous collection of temporally correlated sensor data, assembly stage and component identification for rejected products, and dynamic updating of carbon footprint displays - that cannot practically be performed in the human mind. The claims integrate any alleged abstract idea into a practical application by providing a technical solution to a technical problem, and explicitly recite a practical application: “thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions.” The claims recite significantly more than any alleged abstract idea through an unconventional combination of technical elements.
For at least these reasons, Applicant submits that amended independent claim 1, as well as claims 2, 6-8, and 19-22 which depend from claim 1, are patent eligible. Independent claims 9, 10 and 18 have been similarly amended, and thus are also patent eligible, along with claims 11 and 15-17, which depend from claim 10.
In view of the foregoing, withdrawal of the 35 U.S.C. §101 rejections is respectfully requested.
Thereby is an optional limitation as identified in the 103 section above but also applies here. In plain English it simply means that something is possible. But in patents, per claim language, no positive transformation or other act was claimed. Therefore, this language is identified by following guidance as changing the scope very little and not integrating the abstract idea into a practical application. Further, the mere presence of information (what the abstract idea claims, the resultant info displayed) could enable real time optimization as someone could use it to optimize the machine. However, the actual optimization steps are not claimed, just that something is enabled to happen.
35 USC 103
Claims 1, 2, 6-9, 18, 19, and 21-22 are rejected under 35 U.S.C. §103 as being unpatentable over U.S. Patent Pub. No. 2022/0237628 to Wollack et al. (hereafter, ‘Wollack’) in view of U.S. Patent Pub. No. 2022/0108327 to Schoeneboom et al (hereafter, ‘Schoeneboom’). Claims 10, 11, 15-17, and 23-26 are rejected under 35 U.S.C. §103 as being unpatentable over Wollack and Schoeneboom in view of Dey et al. (“Carbon Emission and Waste Reduction of a Manufacturing-Remanufacturing System using Green Technology and Automated Inspection”) (hereafter, ‘Dey’). Claim 20 is rejected under 35 U.S.C. §103 as being unpatentable over Wollack and Schoeneboom in view of U.S. Pat. Pub. No. 2013/0151303 to Kienzle (hereafter, ‘Kienzle’).
Without conceding to the validity of these rejections, Applicant has amended independent claims 1, 9, 10, and 18, and submits that the amended claims are patentable over the cited art for at least the reasons set forth below.
The remarks are fully and carefully considered.
As noted above, amended claim 1 recites, inter alia:
…continuously collecting, via the gateway, real-time measurement data generated by at least one product counter sensor configured to detect a number of products assembled by the assembly unit over time, wherein the at least one product counter sensor comprises at least one of the physical sensors, and wherein the real-time measurement data generated by the at least one product counter sensor is collected during a period of time…
…continuously collecting, via the gateway, real-time measurement data generated by at least one energy supply sensor configured to measure an amount of energy supplied to the assembly unit during the period of time, wherein the at least one energy supply sensor comprises at least one of the physical sensors;
storing, by the monitoring system, the collected real-time measurement data generated by the at least one product counter sensor and the at least one energy supply sensor in a database using a database protocol, wherein the stored real-time measurement data temporally correlates the energy supplied to the assembly unit with the products assembled during the period of time…
…obtaining, by the processor, reject product information associated with a number of rejected products over time, wherein the reject product information identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product; obtaining, by the processor, component carbon footprint information associated with the components comprised in the rejected products;
determining, by the processor, a carbon footprint per assembled product and/or an aggregated carbon footprint for all the assembled products in the period of time, based on…(iii) the component carbon footprint information associated with the components in the rejected products; and
dynamically updating and displaying, on a graphical user interface, a per-assembled-product carbon-footprint value derived from the real-time measurement data collected via the gateway, thereby enabling real-time optimization of the assembly unit to reduce greenhouse gas emissions.
A. Wollack does not disclose or suggest the claimed reject product and component carbon footprint features.
Wollack is directed to blockchain tracking of carbon credits for materials with sequestered carbon, a fundamentally different technical field from the claimed invention. Wollack tracks carbon sequestration, namely carbon captured and stored in materials such as polymers produced from greenhouse gases, not carbon emissions from manufacturing operations. This is the opposite problem. Wollack tracks how much carbon was removed from the atmosphere through biological/chemical processes that convert greenhouse gases into polymers, while the present claims monitor how much carbon is released into the atmosphere during product assembly operations. Wollack contains no teaching of determining carbon footprint based on energy consumption during assembly, let alone continuously collecting temporally correlated energy and product data during the same period of time, or accounting for rejected products with assembly stage and component identification.
More importantly, Wollack does not teach or suggest the claimed combination of: (i) continuously collecting real-time measurement data from product counter sensors and energy supply sensors during the same period of time ; (ii) storing the collected real-time measurement data, wherein the stored real-time data temporally correlates the energy supplied with the products assembled during the period of time ; (iii) obtaining reject product information that identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product ; (iv) obtaining component carbon footprint information associated with the components comprised in the rejected products; and (v) determining a carbon footprint per assembled product based on three enumerated factors, including the component carbon footprint information associated with the components in the rejected products.
As shown in the rejection, par 030 teaches this. Par 030 was previously cited but not this section, the automatic monitoring and automatically recording teaches continuous under a broadest reasonable interpretation because Wollack is teaching an industrial process which happens more than once, see the assembly line in Fig 1B. The continuous modification submitted by Applicant does not overcome Wollack’s teaching.
Dey teaches carbon emission calculations for manufacturing-remanufacturing systems, and Kienzle teaches carbon footprint tracking in manufacturing processes. However, neither Dey nor Kienzle teaches or suggests: (i) continuously collecting temporally correlated energy and product data during the same period of time; (ii) obtaining reject product information that identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product; or (iii) basing the carbon footprint determination on component carbon footprint information in rejected products. While Dey addresses remanufacturing of defective products, Dey’s analysis is predictive and model-based, using assumed defect rates, not measurement-driven with real-time identification of the specific assembly stage and components for each rejected product. Dey does not teach real-time tracking of rejected products during assembly operations with assembly stage and component identification to attribute component carbon footprints to the per-product carbon footprint calculation for successfully assembled products.
These arguments change the scope of the claims. The only claimed are information elements (in other words, mere data). Further search and consideration has replaced Dye with Fu. Further, as the “rejected product” the modifier “rejected” is a mere information or data element that can be taught in combination as is shown above. Nothing distinguishes “rejected product” from “product” except that it is called “rejected” and “rejected product” is not positively claimed (that is the only aspect claimed are the information/data aspects of a rejected product, a physical rejected product is not in the scope). Further there are no limitations that further define what a rejected product is and therefore the above teaching is possible following Graham, KSR, etc.
; (iii) obtaining reject product information that identifies, for each rejected product, a stage of assembly at which the rejected product was rejected and components comprised in the rejected product ; (iv) obtaining component carbon footprint information associated with the components comprised in the rejected products;
Here there the “real time tracking of rejected products is “obtaining reject product information… obtaining component carbon footprint information.” Therefore what is claimed is collecting information.
Applicant also traverses the Action’s reliance on Official Notice on pages 41 and 50 of the Office Action. The Action takes Official Notice that “no manufacturing system is perfect” and relies on that noticed statement as part of the stated motivation to modify Wollack with Dey. Applicant respectfully submits that this generalized statement is insufficient to support the proposed combination. Even assuming manufacturing systems may experience imperfections in a general sense, that does not establish that a person of ordinary skill in the art would have modified Wollack’s blockchain/carbon-credit tracking system with Dey’s manufacturing/remanufacturing model to arrive at the claimed real-time assembly monitoring system, including continuous collection of temporally correlated product-count and energy-supply sensor data during the same period of time, and reject-product information identifying assembly stage and components for each rejected product. To the extent the Action relies on Official Notice for any broader proposition regarding the alleged motivation to combine Wollack and Dey or the alleged applicability of Dey’s reject/remanufacturing model to Wollack’s system, Applicant traverses that reliance and respectfully requests documentary evidence if the rejection is maintained.
Kienzle similarly does not teach obtaining reject product information that identifies assembly stage and components for each rejected product to factor their carbon footprints into the calculation. Accordingly, Dey and Kienzle do not cure the deficiencies of Wollack and Schoeneboom discussed above.
For at least these reasons, Applicant submits that amended independent claim 1 is patentable over Wollack, Schoeneboom, Dey, and Kienzle, individually and in combination. Claims 2, 6-8, and 19-22, which depend from claim 1, are also patentable over Wollack, Schoeneboom, Dey, and Kienzle for at least the same reasons as claim 1, and for reciting additional limitations that are neither taught nor suggested by the cited references. Independent claims 9, 10 and 18 have been similarly amended, and thus are also patentable over Wollack, Schoeneboom, Dey, and Kienzle, individually and in combination. Claims 11 and 15-17, which depend from claim 10, are also patentable over Wollack, Schoeneboom, Dey, and Kienzle for at least the same reasons as claim 10, and for reciting additional limitations that are neither taught nor suggested by the cited references.
To adequately traverse a finding based on official notice, an applicant must specifically point out the supposed errors in the examiner’s action, which would include stating why the noticed fact is not considered to be common knowledge or well-known in the art. A mere request by the applicant that the examiner provide documentary evidence in support of an officially-noticed fact is not a proper traversal. See 37 CFR 1.111(b). See also Chevenard, 139 F.2d at 713, 60 USPQ at 241. A general allegation that the claims define a patentable invention without any reference to the examiner’s assertion of official notice would be inadequate...
Here, Applicant supplied no specific point of a supposed error and actually agreed that manufacturing systems aren’t perfect. This is a request for a request and this does not follow the cited guidance above, and therefore while respectful, is not proper. At any rate Dye is replaced by Fu because further search and consideration was required.
Kienzle similarly does not teach obtaining reject product information that identifies assembly stage and components for each rejected product to factor their carbon footprints into the calculation. Accordingly, Dey and Kienzle do not cure the deficiencies of Wollack and Schoeneboom discussed above.
For at least these reasons, Applicant submits that amended independent claim 1 is patentable over Wollack, Schoeneboom, Dey, and Kienzle, individually and in combination. Claims 2, 6-8, and 19-22, which depend from claim 1, are also patentable over Wollack, Schoeneboom, Dey, and Kienzle for at least the same reasons as claim 1, and for reciting additional limitations that are neither taught nor suggested by the cited references. Independent claims 9, 10 and 18 have been similarly amended, and thus are also patentable over Wollack, Schoeneboom, Dey, and Kienzle, individually and in combination. Claims 11 and 15-17, which depend from claim 10, are also patentable over Wollack, Schoeneboom, Dey, and Kienzle for at least the same reasons as claim 10, and for reciting additional limitations that are neither taught nor suggested by the cited references.
Here, the arguments against Keinzle argue against Keinzle individually and does not argue the combination, therefore this argument is insufficient and unpersuasive. See MPEP 2145, ”One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Where a rejection of a claim is based on two or more references, a reply that is limited to what a subset of the applied references teaches or fails to teach, or that fails to address the combined teaching of the applied references may be considered to be an argument that attacks the reference(s) individually. Where an applicant’s reply establishes that each of the applied references fails to teach a limitation and addresses the combined teachings and/or suggestions of the applied prior art, the reply as a whole does not attack the references individually as the phrase is used in Keller and reliance on Keller would not be appropriate. This is because "[T]he test for obviousness is what the combined teachings of the references would have suggested to [a PHOSITA]." In re Mouttet, 686 F.3d 1322, 1333, 103 USPQ2d 1219, 1226 (Fed. Cir. 2012).”
Therefore, though the arguments were carefully considered, they are not persuasive and the rejections are maintained as modified per the amendments.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD W. CRANDALL whose telephone number is (313)446-6562. The examiner can normally be reached M - F, 8:00 AM - 5:00 PM.
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/RICHARD W. CRANDALL/ Primary Examiner, Art Unit 3619
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