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 .
Response to Arguments
Regarding the claim objection, the claim amendments are sufficient to overcome the claim objection. Therefore, the claim objection has been withdrawn.
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “A system processing the entire supply chain logic at once would become exponentially complex and difficult to verify as the chain grows. In contrast, Applicant's claimed invention simplifies the system by making each step's computation self-contained and modular, thereby improving the computer system's performance and reliability.,” Examiner respectfully disagrees. The present claims, under consideration of the broadest reasonable interpretation, do not reflect any improvements to the functioning of the computer, or any other technology or technical field. An improvement to supply chain computation, as drafted, would be an improvement related to the abstract limitations for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “This is not merely "using a blockchain as a tool." It is a specific method of structuring data and computations on a blockchain to achieve a more efficient and scalable distributed system. Therefore, the amended claims provide "significantly more" than an abstract idea and are patent- eligible under 35 U.S.C. § 101.,” Examiner respectfully disagrees with Applicant’s assertion. The claims merely recite the use of a blockchain to record supply chain process information. The present claims further recite the execution of a smart contract to distribute a total consideration. These limitations are not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application or anything significantly more than the judicial exception. See MPEP 2106.05(h).
Accordingly, the 35 USC 101 rejection is maintained.
Regarding the 35 USC 102 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The Examiner equated Applicant's "consideration" with Gruber's "intelligent suggestion." However, Gruber's "intelligent suggestion" is merely information or advice for process optimization. In contrast, Applicant's "consideration" is an economic value to be distributed.,” Examiner respectfully asserts that the original disclosure does not comprise any definition for the claimed consideration. Therefore, Examiner has viewed the claims under broadest reasonable interpretation. The broadest reasonable interpretation of the claimed consideration includes that of an intelligent suggestion. Additionally, Examiner notes that the specification does not recite “economic value,” or any particular definition for the claimed “consideration.” Therefore, Examiner respectfully maintains Gruber as teaching the claimed consideration.
Regarding Applicant’s assertion of “Gruber fails to teach, at a minimum, the specific proportional distribution of an economic consideration and the recursive data flow for subsequent calculations. For at least these reasons, at least the above-noted features are distinctions over Gruber. Accordingly, withdrawal of these rejections is respectfully requested.,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Lin reference to cure the deficiencies of the prior art combination of the record.
Accordingly, the 35 USC 102 rejection has been withdrawn; however, the present claims are rejected under 35 USC 103.
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 1-6 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-4 are directed to a non-transitory computer readable medium, claim 5 is directed to a method, and claim 6 is directed to an apparatus. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 5, and 6 recite calculating values related to a process, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Claim 1 recites limitations, similarly recited in claims 5 and 6, including “for each process included in a supply chain, recording process information that includes a single-unit value of the process identification information for specifying an immediately preceding process, and a cumulative value obtained by summing single-unit values from a most upstream process to the current process; and upon receiving a total consideration for a downstream process, executing for a current process to distribute the total consideration by calculating a distributed portion of the total consideration for the current process based on a ratio of the single-unit value of the current process to the cumulative value of the current process, and calculating a remaining cumulative consideration to be passed to the immediately preceding process, the remaining cumulative consideration being calculated based on a ratio of the cumulative value of the immediately preceding process to the cumulative value of the current process, and serves as a new total consideration for the immediately preceding process.” These limitations, as drafted, but for the recitations of the preamble, is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the preamble language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the preamble language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 2-4 further narrow the abstract idea identified in the independent claim 1 and do not introduce further additional elements for consideration.
Step 2A, Prong 2: Claims 1, 5, and 6 do not integrate the judicial exception into a practical application. Claim 1 recites “a non-transitory computer readable recording medium storing a consideration distribution program causing a computer to execute processing comprising” within the preamble of the claim. Claim 5 recites “a consideration distribution method implemented by a computer to execute processing comprising” within the preamble of the claim. Claim 6 is directed to an apparatus comprising “a memory; and a processor coupled to the memory and configured to.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Claims 1, 5, and 6 further recite the additional element of “for each process included in a supply chain, on a block chain, recording process information” and “upon receiving a total consideration for a downstream process, executing the smart contract for a current process…serves as a new total consideration for a subsequent execution of the smart contract for the immediately preceding process.” These limitations do not comprise anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-4 further narrow the abstract idea identified in the independent claim 1 and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Step 2B: Claims 1, 5, and 6 do not comprise anything significantly more than the judicial exception. Claim 1 recites “a non-transitory computer readable recording medium storing a consideration distribution program causing a computer to execute processing comprising” within the preamble of the claim. Claim 5 recites “a consideration distribution method implemented by a computer to execute processing comprising” within the preamble of the claim. Claim 6 is directed to an apparatus comprising “a memory; and a processor coupled to the memory and configured to.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Claims 1, 5, and 6 further recite the additional element of “for each process included in a supply chain, on a block chain, recording process information” and “upon receiving a total consideration for a downstream process, executing the smart contract for a current process…serves as a new total consideration for a subsequent execution of the smart contract for the immediately preceding process.” These limitations are not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 2-4 further narrow the abstract idea identified in the independent claim 1 and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Accordingly, claims 1-6 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Gruber et al. (US 20220343229 A1) in view of Lin et al. (US 20230297901 A1).
Regarding claim 1, Gruber teaches a non-transitory computer readable recording medium storing a consideration distribution program causing a computer to execute processing comprising ([0101-0102] teach the operating environment includes a processing unit, memory, and computer readable media that stores computer readable instructions, wherein the processor can access the instructions; see also: [0040]):
for each process included in a supply chain, on a block chain, recording process information that includes a single-unit value of the process identification information for specifying an immediately preceding process (Fig. 6 and [0071] teach receiving input data at a certain step in the supply chain, which can be step “N” or any number, wherein the input data may be from a stakeholder in the supply chain that characterizes the processing taking place at that step, wherein the input data includes measurement data, wherein [0072] teaches following the reception of the input data, a carbon intensity score is generated and/or updated, wherein if Step N is step #3, then at the last two previous steps, each previous step received an intermediate CI score, which would already be calculated at step #3, so the results of the manufacturing/processing data at step #3 will result in an updated IC score, which is an intermediate score #3, wherein [0073] teaches after the CI is generated/updated, the CI score is recorded on the blockchain, wherein the CI score may be recorded as a new block appended to the blockchain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded, wherein [0049] teaches a smart contract may be for generating a CI score, wherein the contract terms include calculating the immutable CI score, wherein the initial generation/calculation of the CI score may be triggered; see also: [0038, 0066-0070]), and
a cumulative value obtained by summing single-unit values from a most upstream process to the current process (Fig. 6 and [0075] teach the output of the ML model analysis in an intelligent suggestion to a participant in the supply chain or controller for certain manufacturing/processing changes that could potentially lower the CI score in the future, wherein after the product receives an intermediate score after completing step N, or step N+1, in the supply chain, the ML model may output a suggestion to that participant in the supply chain for tweaking its processes to potentially obtain a lower score in the next iteration through the supply chain, wherein [0076] teaches the ML model may generate an intelligent suggestion for the next step in the supply chain, wherein [0077] teaches the intelligent suggestions can be provided to the participant or participants in the supply chain, the controller, or other relevant parties that would benefit from receiving the intelligent suggestion based on the current state of the supply chain and the current intermediate CI scores of certain products traversing the supply chain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded; see also: [0066-0074]); and
upon receiving a total consideration for a downstream process, executing the smart contract for a current process to distribute the total consideration by calculating a distributed portion of the total consideration for the current process based on a ratio of the single-unit value of the current process to the cumulative value of the current process ([0037-0038] teach a participant in the supply chain may provide certain information to the system via client devices, wherein the constructed state of the process may be stored as a block on the blockchain, wherein one or more smart contracts may reside in the blockchain network, wherein a consumer who contracts with a supplier to buy a certain product with a certain CI score may receive a product with a higher or lower CI score, wherein a smart contract stored on the blockchain may automatically adjust payment between the supplier and customer based on the finalized score, wherein the CI scores may be aggregated, wherein Fig. 6 and [0075] teach the output of the ML model analysis in an intelligent suggestion to a participant in the supply chain or controller for certain manufacturing/processing changes that could potentially lower the CI score in the future, wherein after the product receives an intermediate score after completing step N, or step N+1, in the supply chain, the ML model may output a suggestion to that participant in the supply chain for tweaking its processes to potentially obtain a lower score in the next iteration through the supply chain, wherein [0076] teaches the ML model may generate an intelligent suggestion for the next step in the supply chain, wherein [0077] teaches the intelligent suggestions can be provided to the participant or participants in the supply chain, the controller, or other relevant parties that would benefit from receiving the intelligent suggestion based on the current state of the supply chain and the current intermediate CI scores of certain products traversing the supply chain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded; see also: [0066-0074]), and
calculating a remaining cumulative consideration to be passed to the immediately preceding process ([0037-0038] teach a participant in the supply chain may provide certain information to the system via client devices, wherein the constructed state of the process may be stored as a block on the blockchain, wherein one or more smart contracts may reside in the blockchain network, wherein a consumer who contracts with a supplier to buy a certain product with a certain CI score may receive a product with a higher or lower CI score, wherein a smart contract stored on the blockchain may automatically adjust payment between the supplier and customer based on the finalized score, wherein the CI scores may be aggregated, wherein Fig. 6 and [0075] teach the output of the ML model analysis in an intelligent suggestion to a participant in the supply chain or controller for certain manufacturing/processing changes that could potentially lower the CI score in the future, wherein after the product receives an intermediate score after completing step N, or step N+1, in the supply chain, the ML model may output a suggestion to that participant in the supply chain for tweaking its processes to potentially obtain a lower score in the next iteration through the supply chain, wherein [0076] teaches the ML model may generate an intelligent suggestion for the next step in the supply chain, wherein [0077] teaches the intelligent suggestions can be provided to the participant or participants in the supply chain, the controller, or other relevant parties that would benefit from receiving the intelligent suggestion based on the current state of the supply chain and the current intermediate CI scores of certain products traversing the supply chain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded; see also: [0066-0074]),
the remaining cumulative consideration being calculated based on a ratio of the cumulative value of the immediately preceding process to the cumulative value of the current process ([0037-0038] teach a participant in the supply chain may provide certain information to the system via client devices, wherein the constructed state of the process may be stored as a block on the blockchain, wherein one or more smart contracts may reside in the blockchain network, wherein a consumer who contracts with a supplier to buy a certain product with a certain CI score may receive a product with a higher or lower CI score, wherein a smart contract stored on the blockchain may automatically adjust payment between the supplier and customer based on the finalized score, wherein the CI scores may be aggregated, wherein Fig. 6 and [0075] teach the output of the ML model analysis in an intelligent suggestion to a participant in the supply chain or controller for certain manufacturing/processing changes that could potentially lower the CI score in the future, wherein after the product receives an intermediate score after completing step N, or step N+1, in the supply chain, the ML model may output a suggestion to that participant in the supply chain for tweaking its processes to potentially obtain a lower score in the next iteration through the supply chain, wherein [0076] teaches the ML model may generate an intelligent suggestion for the next step in the supply chain, wherein [0077] teaches the intelligent suggestions can be provided to the participant or participants in the supply chain, the controller, or other relevant parties that would benefit from receiving the intelligent suggestion based on the current state of the supply chain and the current intermediate CI scores of certain products traversing the supply chain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded; see also: [0066-0074]), and
serves as a new total consideration for a subsequent execution of the smart contract for the immediately preceding process ([0037-0038] teach a participant in the supply chain may provide certain information to the system via client devices, wherein the constructed state of the process may be stored as a block on the blockchain, wherein one or more smart contracts may reside in the blockchain network, wherein a consumer who contracts with a supplier to buy a certain product with a certain CI score may receive a product with a higher or lower CI score, wherein a smart contract stored on the blockchain may automatically adjust payment between the supplier and customer based on the finalized score, wherein the CI scores may be aggregated, wherein Fig. 6 and [0075] teach the output of the ML model analysis in an intelligent suggestion to a participant in the supply chain or controller for certain manufacturing/processing changes that could potentially lower the CI score in the future, wherein after the product receives an intermediate score after completing step N, or step N+1, in the supply chain, the ML model may output a suggestion to that participant in the supply chain for tweaking its processes to potentially obtain a lower score in the next iteration through the supply chain, wherein [0076] teaches the ML model may generate an intelligent suggestion for the next step in the supply chain, wherein [0077] teaches the intelligent suggestions can be provided to the participant or participants in the supply chain, the controller, or other relevant parties that would benefit from receiving the intelligent suggestion based on the current state of the supply chain and the current intermediate CI scores of certain products traversing the supply chain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded; see also: [0066-0074]).
While Gruber teaches generating a cumulative value of the current process, Gruber does not explicitly teach calculating a distributed portion of the total consideration for the current process based on a ratio of the single-unit value of the current process to the cumulative value of the current process, and the remaining cumulative consideration being calculated based on a ratio of the cumulative value of the immediately preceding process to the cumulative value of the current process.
From the same or similar field of endeavor, Lin teaches calculating a distributed portion of the total consideration for the current process based on a ratio of the single-unit value of the current process to the cumulative value of the current process (Fig. 7 and [0058] teach generating the final scores using weighted processing, wherein the feature values can be generates for a number of features each within multiple timeframes, wherein scaling can be performed to convert feature values of the features into scaled feature values, wherein the scales feature values are defined as percentages or other values within a scale ranging from zero to one, wherein the weights can be applied to the scaled feature values in order to generate intermediate values, where each intermediate value is determined by applying different weights to the scaled feature values in different timeframes, wherein the weights can be applied to the intermediate values in order to generate the final score for the specific pair or group of entities, as well as in [0054] teaches the scales feature values of the features within each timeframe can be weighted using the appropriate weights and the scales features values of the different types of features within each timeframe can also be weighted using the appropriate weights, wherein after weighting, the weighted feature values can be summed in order to produce the final scores, wherein [0065] teaches the processing device can store the final score, wherein the final score can be provided to a downstream processing task, and wherein [0020] teaches the enabling of various downstream processing tasks, wherein a downstream processing task may be configured to identify entities, which can be used as an input in the downstream processing tasks; see also: [0064, 0072]), and
the remaining cumulative consideration being calculated based on a ratio of the cumulative value of the immediately preceding process to the cumulative value of the current process (Fig. 7 and [0058] teach generating the final scores using weighted processing, wherein the feature values can be generates for a number of features each within multiple timeframes, wherein scaling can be performed to convert feature values of the features into scaled feature values, wherein the scales feature values are defined as percentages or other values within a scale ranging from zero to one, wherein the weights can be applied to the scaled feature values in order to generate intermediate values, where each intermediate value is determined by applying different weights to the scaled feature values in different timeframes, wherein the weights can be applied to the intermediate values in order to generate the final score for the specific pair or group of entities, as well as in [0054] teaches the scales feature values of the features within each timeframe can be weighted using the appropriate weights and the scales features values of the different types of features within each timeframe can also be weighted using the appropriate weights, wherein after weighting, the weighted feature values can be summed in order to produce the final scores, wherein [0065] teaches the processing device can store the final score, wherein the final score can be provided to a downstream processing task, and wherein [0020] teaches the enabling of various downstream processing tasks, wherein a downstream processing task may be configured to identify entities, which can be used as an input in the downstream processing tasks; see also: [0049, 0064, 0072]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Gruber to incorporate the teachings of Lin to include calculating a distributed portion of the total consideration for the current process based on a ratio of the single-unit value of the current process to the cumulative value of the current process, and the remaining cumulative consideration being calculated based on a ratio of the cumulative value of the immediately preceding process to the cumulative value of the current process. One would have been motivated to do so in order to enable various downstream processing tasks by providing more effective differentiation of various types of relationships among entities and providing better resolution of those entities (Lin, [0020]). By incorporating the teachings of Lin, one would have been able to enable features to be combined by weighting features over a timeframe in order to scale all of the values to help align the ranges of values for features (Lin, [0050, 0052]).
Regarding claims 5 and 6, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 5, Gruber teaches a consideration distribution method implemented by a computer to execute processing comprising (Figs. 4-6). Regarding claim 6, Gruber teaches a consideration distribution apparatus comprising: a memory ([0101-0102] teach the operating environment includes a processing unit and memory that stores computer readable instructions; see also: [0040]); and a processor coupled to the memory and configured to ([0101-0102] teach the operating environment includes a processing unit and memory that stores computer readable instructions, wherein the processor can access the instructions; see also: [0040]). Accordingly, claims 5 and 6 are rejected as being unpatentable over Gruber in view of Lin.
Regarding claim 2, the combination of Gruber and Lin teaches all the limitations of claim 1 above.
Gruber further teaches wherein a consideration for the process is calculated based on the value of the process included in the process information of the process and a value of the process performed immediately before included in the process information of the process performed immediately before (Fig. 6 and [0075] teach the output of the ML model analysis in an intelligent suggestion to a participant in the supply chain or controller for certain manufacturing/processing changes that could potentially lower the CI score in the future, wherein after the product receives an intermediate score after completing step N, or step N+1, in the supply chain, the ML model may output a suggestion to that participant in the supply chain for tweaking its processes to potentially obtain a lower score in the next iteration through the supply chain, wherein [0076] teaches the ML model may generate an intelligent suggestion for the next step in the supply chain, wherein [0077] teaches the intelligent suggestions can be provided to the participant or participants in the supply chain, the controller, or other relevant parties that would benefit from receiving the intelligent suggestion based on the current state of the supply chain and the current intermediate CI scores of certain products traversing the supply chain, as well as in [0055] teaches the ML suggestion module may be configured to automatically make intelligent suggestions for how to optimize, or lower, CI scores for the supply chain by providing suggestions directly to participants and stakeholders in the supply chain regarding improving processes to make them eco-friendlier in order to achieve lower CI scores for an end product, wherein since the CI score of the present product is at a certain threshold, the product needs to be processed in a different location in order to ensure the product’s CI score does not exceed the threshold; see also: [0032, 0044, 0048, 0064-0071, 0073]).
Regarding claim 3, the combination of Gruber and Lin teaches all the limitations of claim 1 above.
Gruber further teaches wherein the process information includes a cumulative value obtained by summing up values of respective processes from a most upstream process to the process in the supply chain ([0083] teaches each co-product or byproduct’s CI score may be verified using a checksum function that adds the intermediate CI scores together to reach a whole, wherein a final CI score may be the sum of each intermediate CI score that was assigned to the product through each step in the supply chain, wherein the CI scores associated with each component input may be summed into the CI score, wherein the aggregate, intermediate CI score may then be added to the CI scores associated with the inputs, wherein each step in the supply chain may product its own additional CI score that will be summed at the final supply chain step to obtain the final CI score, wherein the checksum function can refer to the previous CI scores that are stored in the blockchain, as well as in Fig. 8 and [0084] teaches automatically generating and tracking a CI score along a supply chain by summing each previous CI score stored as blocks on the blockchain by applying a checksum function; see also: [0085-0088]), and
a consideration for the processes calculated based on the value of the process included in the process information of the process and a cumulative value up to the process performed immediately before that is included in the process information of the process performed immediately before (Fig. 6 and [0075] teach the output of the ML model analysis in an intelligent suggestion to a participant in the supply chain or controller for certain manufacturing/processing changes that could potentially lower the CI score in the future, wherein after the product receives an intermediate score after completing step N, or step N+1, in the supply chain, the ML model may output a suggestion to that participant in the supply chain for tweaking its processes to potentially obtain a lower score in the next iteration through the supply chain, wherein [0083] teaches each co-product or byproduct’s CI score may be verified using a checksum function that adds the intermediate CI scores together to reach a whole, wherein a final CI score may be the sum of each intermediate CI score that was assigned to the product through each step in the supply chain, wherein the CI scores associated with each component input may be summed into the CI score, wherein the aggregate, intermediate CI score may then be added to the CI scores associated with the inputs, wherein each step in the supply chain may product its own additional CI score that will be summed at the final supply chain step to obtain the final CI score, wherein the checksum function can refer to the previous CI scores that are stored in the blockchain, as well as in Fig. 8 and [0084] teaches automatically generating and tracking a CI score along a supply chain by summing each previous CI score stored as blocks on the blockchain by applying a checksum function; see also: [0055-0058, 0085-0088]).
Regarding claim 4, the combination of Gruber and Lin teaches all the limitations of claim 1 above.
Gruber further teaches further causing the computer to execute processing comprising:
accepting input of the identification information for specifying the process performed immediately before and trail information used for calculation of the value of the process (Fig. 6 and [0071] teach receiving input data at a certain step in the supply chain, which can be step “N” or any number, wherein the input data may be from a stakeholder in the supply chain that characterizes the processing taking place at that step, wherein the input data includes measurement data, wherein [0072] teaches following the reception of the input data, a carbon intensity score is generated and/or updated, wherein if Step N is step #3, then at the last two previous steps, each previous step received an intermediate CI score, which would already be calculated at step #3, so the results of the manufacturing/processing data at step #3 will result in an updated IC score, which is an intermediate score #3, wherein [0073] teaches after the CI is generated/updated, the CI score is recorded on the blockchain, wherein the CI score may be recorded as a new block appended to the blockchain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded, as well as in [0078-0079] teach the inputs at the farm can each have an associated CI score, wherein the end -product may have a received final CI score, wherein [0080-0081] teach a single CI score can be generated for each particular step in the supply chain, wherein [0083] teaches each co-product or byproduct’s CI score may be verified using a checksum function that adds the intermediate CI scores together to reach a whole, wherein a final CI score may be the sum of each intermediate CI score that was assigned to the product through each step in the supply chain, wherein the CI scores associated with each component input may be summed into the CI score, wherein the aggregate, intermediate CI score may then be added to the CI scores associated with the inputs, wherein each step in the supply chain may product its own additional CI score that will be summed at the final supply chain step to obtain the final CI score, wherein the checksum function can refer to the previous CI scores that are stored in the blockchain, as well as in Fig. 8 and [0084] teaches automatically generating and tracking a CI score along a supply chain by summing each previous CI score stored as blocks on the blockchain by applying a checksum function; see also: [0066-0070]);
calculating the value of the process (Fig. 6 and [0071] teach receiving input data at a certain step in the supply chain, which can be step “N” or any number, wherein the input data may be from a stakeholder in the supply chain that characterizes the processing taking place at that step, wherein the input data includes measurement data, wherein [0072] teaches following the reception of the input data, a carbon intensity score is generated and/or updated, wherein if Step N is step #3, then at the last two previous steps, each previous step received an intermediate CI score, which would already be calculated at step #3, so the results of the manufacturing/processing data at step #3 will result in an updated IC score, which is an intermediate score #3, wherein [0073] teaches after the CI is generated/updated, the CI score is recorded on the blockchain, wherein the CI score may be recorded as a new block appended to the blockchain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded, as well as in [0078-0079] teach the inputs at the farm can each have an associated CI score, wherein the end -product may have a received final CI score, wherein [0080-0081] teach a single CI score can be generated for each particular step in the supply chain, wherein [0083] teaches each co-product or byproduct’s CI score may be verified using a checksum function that adds the intermediate CI scores together to reach a whole, wherein a final CI score may be the sum of each intermediate CI score that was assigned to the product through each step in the supply chain, wherein the CI scores associated with each component input may be summed into the CI score, wherein the aggregate, intermediate CI score may then be added to the CI scores associated with the inputs, wherein each step in the supply chain may product its own additional CI score that will be summed at the final supply chain step to obtain the final CI score, wherein the checksum function can refer to the previous CI scores that are stored in the blockchain, as well as in Fig. 8 and [0084] teaches automatically generating and tracking a CI score along a supply chain by summing each previous CI score stored as blocks on the blockchain by applying a checksum function; see also: [0066-0070]),
and a cumulative value obtained by summing up values of respective processes from a most upstream process to the process in the supply chain (Fig. 6 and [0071] teach receiving input data at a certain step in the supply chain, which can be step “N” or any number, wherein the input data may be from a stakeholder in the supply chain that characterizes the processing taking place at that step, wherein the input data includes measurement data, wherein [0072] teaches following the reception of the input data, a carbon intensity score is generated and/or updated, wherein if Step N is step #3, then at the last two previous steps, each previous step received an intermediate CI score, which would already be calculated at step #3, so the results of the manufacturing/processing data at step #3 will result in an updated IC score, which is an intermediate score #3, wherein [0073] teaches after the CI is generated/updated, the CI score is recorded on the blockchain, wherein the CI score may be recorded as a new block appended to the blockchain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded, as well as in [0078-0079] teach the inputs at the farm can each have an associated CI score, wherein the end -product may have a received final CI score, wherein [0080-0081] teach a single CI score can be generated for each particular step in the supply chain, wherein [0083] teaches each co-product or byproduct’s CI score may be verified using a checksum function that adds the intermediate CI scores together to reach a whole, wherein a final CI score may be the sum of each intermediate CI score that was assigned to the product through each step in the supply chain, wherein the CI scores associated with each component input may be summed into the CI score, wherein the aggregate, intermediate CI score may then be added to the CI scores associated with the inputs, wherein each step in the supply chain may product its own additional CI score that will be summed at the final supply chain step to obtain the final CI score, wherein the checksum function can refer to the previous CI scores that are stored in the blockchain, as well as in Fig. 8 and [0084] teaches automatically generating and tracking a CI score along a supply chain by summing each previous CI score stored as blocks on the blockchain by applying a checksum function; see also: [0066-0070]); and
generating process information that includes the value of the process and the cumulative value (Fig. 6 and [0071] teach receiving input data at a certain step in the supply chain, which can be step “N” or any number, wherein the input data may be from a stakeholder in the supply chain that characterizes the processing taking place at that step, wherein the input data includes measurement data, wherein [0072] teaches following the reception of the input data, a carbon intensity score is generated and/or updated, wherein if Step N is step #3, then at the last two previous steps, each previous step received an intermediate CI score, which would already be calculated at step #3, so the results of the manufacturing/processing data at step #3 will result in an updated IC score, which is an intermediate score #3, wherein [0073] teaches after the CI is generated/updated, the CI score is recorded on the blockchain, wherein the CI score may be recorded as a new block appended to the blockchain, as well as in Fig. 4 and [0064-0065] teach once the data is received, the system may analyze the input data, wherein the initial CI score may be the output of the combination of the smart contract terms and the input data received at supply chain step #1, wherein this initial score may be stored and record on the blockchain, wherein as the product enters the next step of the supply chain, the system receives input data at the next supply chain step, such as steps #2, #3…#N, wherein the data may be recorded, as well as in [0078-0079] teach the inputs at the farm can each have an associated CI score, wherein the end -product may have a received final CI score, wherein [0080-0081] teach a single CI score can be generated for each particular step in the supply chain, wherein [0083] teaches each co-product or byproduct’s CI score may be verified using a checksum function that adds the intermediate CI scores together to reach a whole, wherein a final CI score may be the sum of each intermediate CI score that was assigned to the product through each step in the supply chain, wherein the CI scores associated with each component input may be summed into the CI score, wherein the aggregate, intermediate CI score may then be added to the CI scores associated with the inputs, wherein each step in the supply chain may product its own additional CI score that will be summed at the final supply chain step to obtain the final CI score, wherein the checksum function can refer to the previous CI scores that are stored in the blockchain, as well as in Fig. 8 and [0084] teaches automatically generating and tracking a CI score along a supply chain by summing each previous CI score stored as blocks on the blockchain by applying a checksum function; see also: [0066-0070]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Dennis et al. (US 20230252390 A1) discloses generating a weighted score based on the impact of performance on upstream services
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.
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