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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites the limitation "the future time period" in line 3 of Claim 13. There is insufficient antecedent basis for this limitation in the claim. Claim 13 does not previously recite “a future time period”, so it is ambiguous as to what “the future time period” refers to, rendering the claim indefinite.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 2, 6-9, 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sarin (U.S. Pub. No. 2020/0175503 A1) hereinafter referred to as “Sarin”.
Regarding Claim 2:
Sarin teaches the following limitations:
A method, comprising: accessing historical information associated with a mining of a blockchain in a first time period by a plurality of miners (Par. [0032], Par. [0043]). Sarin teaches storing mining data which contains resource information of miner devices through transactions on a blockchain. This resource information can be considered historical information under the broadest reasonable interpretation, as it includes continuous information about a miner’s performance.
evaluating, based on the historical information, a performance of each of the miners in the plurality of miners according to one or more specified criteria (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Sarin teaches evaluating the performance of the miners according to criteria such as efficiency thresholds.
determining, based on the evaluating, that the performance of one or more first miners of the plurality of miners exceeds a rest of the plurality of miners according to the one or more specified criteria (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Sarin selects a subset of miners from this performance evaluation, i.e. this performance of this subset of miners exceeds the rest of the miners.
and assigning the one or more first miners with a higher priority over the rest of the plurality of miners for the mining of the blockchain in a second time period that occurs after the first time period (Par. [0032], Par. [0037], Par. [0039], Par. [0043], [0046]-[0048]). Sarin then teaches the subset of miners being assigned mining tasks. This is in a second period after the first period, as the first time period was directed towards historical information, i.e. the past.
Regarding Claim 6:
Sarin teaches the following limitations:
wherein: the first time period corresponds to an exploration phase (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Note that the terms “exploration phase” and “exploitation phase” are not explicitly defined within the Applicant’s specification, and are therefore subject to the broadest reasonable interpretation. This concept appears to be directed towards the exploration-exploitation tradeoff as explained in Applicant’s specification in Par. [0030]-[0031], and it is recommended by the Examiner to incorporate these aspects into the claimed invention in order to overcome the prior art. Under the broadest reasonable interpretation, the act of blockchain mining itself can be considered a type of exploration, as it is well known to one of ordinary skill in the art that blockchain mining entails miners solving mathematical problems, or searching/exploring for the correct solution. Alternatively, one can consider the historical information collection of Sarin as a type of exploration for finding efficient miners.
the second time period corresponds to an exploitation phase (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Under the broadest reasonable interpretation, the act of blockchain mining itself can be considered a type of exploitation, as it is making use of, i.e. exploiting, miners for mining a blockchain.
and the method is performed in a plurality of cycles that each comprises the exploration phase and the exploitation phase (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Since the act of blockchain mining itself can be considered both phases of exploration and exploitation, Sarin teaches the claimed limitation since Sarin teaches continuous blockchain mining and repeating miner selection.
Regarding Claim 7:
Sarin teaches the following limitations:
wherein the mining tasks are assigned substantially evenly among the plurality of miners in the exploration phase (Par. [0046], Par. [0047], Par. [0048]). Sarin teaches all devices of the subset of miners receiving the transaction for processing; this suggests a substantially even assignment under the broadest reasonable interpretation.
Regarding Claim 8:
Sarin teaches the following limitations:
wherein the method is performed without the plurality of miners knowing whether they are mining in the exploitation phase or in the exploration phase (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Since the differentiation between an exploitation and exploration phase is arbitrary under the broadest reasonable interpretation as argued above, this suggests that the miner devices have no knowledge of whether they are in such a phase or not.
Regarding Claim 9:
Sarin teaches the following limitations:
wherein a new group of the one or more first miners is identified in each exploration phase (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Sarin teaches continuously updating miner historical information for the most current data. This suggests that repeated miner subset selection will result in a new group of miners.
Regarding Claim 12:
Sarin teaches the following limitations:
wherein the one or more first miners are prioritized by being assigned as one or more exclusive miners for mining the blockchain in the second time period (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). The miners of Sarin are exclusively selected for performing the mining task.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sarin as applied to claim 2 above, and further in view of NPL – “Mempool”, hereinafter referred to as “Mempool”.
Regarding Claim 3:
“Mempool” teaches the following limitations:
wherein: the mining of the blockchain comprises mining transactions that are in a mempool but have not been written to the blockchain yet (Page 1, Par. 1-3). “Mempool” teaches that it is normal in a blockchain for transactions to first go through a mempool before confirmation regarding data mining.
and the evaluating comprises evaluating the performance of each of the miners of the plurality of miners with respect to the mining of the transactions that are in the mempool (Page 1, Par. 1-3). “Mempool” teaches that it is normal in a blockchain for transactions to first go through a mempool before confirmation regarding data mining.
Sarin teach mining blockchain transactions, but not a mempool. “Mempool” however teaches that it is normal in blockchain systems for transactions to first go through an electronic mempool during mining. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin with the mempool of “Mempool” in order to gain the predictable result of the transactions of Sarin being buffered through the mempool before confirmation on the blockchain. One of ordinary skill in the art would have recognized that Sarin and “Mempool” are compatible as they both regard blockchain transaction processing, and that using such a mempool would have been a predictable result of implementing a blockchain.
Regarding Claim 4:
Sarin teaches the following limitations:
wherein: the one or more specified criteria comprise a time delay of each of the miners of the plurality of miners in the mining of the transactions that are in the mempool (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Sarin further teaches that the performance can be evaluated based on latency, i.e. time delay, or alternatively an average time taken, i.e. also a type of time delay, and these are transactions in a mempool in combination with “Mempool” above.
and the one or more first miners have a smaller time delay than the rest of the plurality of miners in the mining of the transactions that are in the mempool (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Sarin teaches that selecting miners with lower latency/time taken, i.e. smaller time delay.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Sarin/”Mempool” as applied to claim 3 above, and further in view of DiCross et al. (U.S. Pub. No. 2019/0333048 A1), hereinafter referred to as “DiCross”.
Regarding Claim 5:
Sarin teaches the following limitation:
wherein: the one or more specified criteria comprise a tendency of each of the miners of the plurality of miners (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Sarin teaches the speed of miners mining a block as a performance evaluation. This is considered a tendency to not mine transactions in combination with DiCross as explained below under the broadest reasonable interpretation.
(taught by DiCross below)
and the one or more first miners have a lower tendency than the rest of the plurality of miners (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Since Sarin prioritizes miners with lower latency/time taken, this suggests a lower tendency.
(taught by DiCross below)
DiCross teaches the following limitations:
to not mine transactions in the mempool that have a mining fee below a threshold (Par. [0008], Par. [0060]). DiCross teaches that a low transaction fee disincentivizes mining, causing transaction processing to be delayed. This suggests that a higher time delay, i.e. slower speed, is associated with a tendency of not mining transactions because a fee is too low, i.e. below a threshold, under the broadest reasonable interpretation, as a “tendency” is not defined within the Applicant’s specification and therefore subject to broad interpretation. In combination with Sarin/”Mempool”, this teaches the claimed limitation regarding transactions in the mempool.
to not mine the transactions in the mempool that have the mining fee below the threshold (Par. [0008], Par. [0060]).
Sarin/”Mempool” teach that a blockchain mining system in which miners with faster transaction processing speeds are selected, but do not teach miners not mining transactions below a fee threshold. DiCross however teaches that slower transaction processing speeds can be a result of miners not mining transactions because the fee is too low, i.e. below a threshold. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/”Mempool” with the fee threshold of DiCross in order to gain the predictable result of selecting miners based on a tendency of not mining transactions below a fee threshold. One of ordinary skill in the art would have recognized that DiCross and Sarin/”Mempool” are compatible as both relate to blockchain mining, and that Sarin’s selection of miners based on processing speed can also be considered a type of tendency, as DiCross teaches low transaction fees predictably resulting in slower processing time.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sarin as applied to claim 6 above, and further in view of Leung et al. (U.S. Pub. No. 2019/0303363 A1), hereinafter referred to as “Leung”, and further in view of NPL – “Epsilon-Greedy Algorithm in Reinforcement Learning”, hereinafter referred to as “Epsilon-Greedy Algorithm in Reinforcement Learning”.
Regarding Claim 10:
Leung teaches the following limitation:
wherein the exploration phase or the exploitation phase is entered into (Par. [0060], Par. [0093], Par. [0119], the predetermined rules for assigning the mining task may be implemented in a combined manner). Leung teaches that mining nodes can be randomly assigned including using predetermined groups and weighted based on their characteristics in a combined manner. This is done after a roster of miners have been already determined, i.e. the subset of miners. In combination with Sarin, this teaches assignment of the subset of miners based on a random chance, and can be considered a type of entering an exploration/exploitation phase since the two phases can be both considered to be blockchain mining in general in which the distinction between the two phases was arbitrary as argued above. Therefore, the entering of a phase can be considered based on randomness, as the selection of miners regarding entering a new phase is based on randomness. This randomness is further based on a random number in comparison with a threshold in combination with “Epsilon-Greedy Algorithm” below.
Sarin teaches a blockchain miner selection based on latency, but does not teach randomness as part of miner selection. Leung however teaches that a subset of blockchain miners can also be additionally chosen at random in combination with a set of rules. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin with the random miner selection of Leung in order to gain the predictable result of additionally selecting miners randomly. One of ordinary skill in the art would have recognized that Sarin and Leung are compatible as both relate to miner selection based on performance characteristics, and that such this additional random miner selection would have been a predictable implementation for selecting miners to mine transactions.
“Epsilon-Greedy Algorithm in Reinforcement Learning” teaches the following limitation:
based on a comparison between a randomly generated number and a specified threshold (Page 2, p = random(), p < ε). “Epsilon-Greedy Algorithm” teaches that comparison of this random number against a threshold, epsilon, allows for action determination in calculating a random probability.
Sarin/Leung teaches blockchain mining through miner selection phases including an element of randomness, but does not teach comparison with a threshold. “Epsilon-Greedy Algorithm” however teaches that comparison of a randomly generated number against a threshold can be used to implement probabilities/randomness. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/Leung with the threshold comparison of “Epsilon-Greedy Algorithm” in order to gain the predictable result of implementing randomness based on comparison of a randomly generated number against a threshold. One of ordinary skill in the art would have recognized that Sarin/Leung is compatible with “Epsilon-Greedy Algorithm” as Leung taught randomly/probabilistically selecting miners, and “Epsilon-Greedy Algorithm” teaches that using a random number compared against a threshold is a predictable implementation of randomness/probabilities for taking action.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Sarin/Leung/”Epsilon-Greedy Algorithm in Reinforcement Learning” as applied to claim 10 above, and further in view of NPL – “Hyperparameter Tuning for Machine Learning”, hereinafter referred to as “Hyperparameter Tuning for Machine Learning”.
Regarding Claim 11:
“Hyperparameter Tuning for Machine Learning” teaches the following limitation:
wherein the specified threshold is determined via a machine learning process (Page 1, perform this exploration and select the optimal model architecture automatically, Page 7, grid search). Previously, “Epsilon-Greedy Algorithm” used a threshold in the form of the hyperparameter epsilon. “Hyperparameter Tuning for Machine Learning” teaches that this hyperparameter can be tuned through automation/searching, i.e. machine learning, in order to find the most optimal model.
Sarin/Leung/“Epsilon-Greedy Algorithm” teach using hyperparameters in the form of epsilon, but not determining epsilon via machine learning. “Hyperparameter Tuning for Machine Learning” however teaches that it is beneficial to automatically search for hyperparameters in order to gain the most optimal model. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/Leung/“Epsilon-Greedy Algorithm” with the parameter determination of “Hyperparameter Tuning for Machine Learning” in order to gain the most optimal model for miner selection.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Sarin as applied to claim 2 above, and further in view of Back et al. (U.S. Pub. No. 2016/0330034 A1) hereinafter referred to as “Back”.
Regarding Claim 13:
Sarin teaches the following limitation:
wherein the one or more first miners are prioritized by being each assigned a disproportionate percentage of the mining tasks compared to the rest of the plurality of miners in the future time period (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Sarin was previously shown to teach prioritization by assigning tasks to the selected subset of miners.
(taught by Back below)
Back teaches the following limitation:
and wherein the disproportionate percentage is less than 50% ([0035]). Back teaches that attacks can be thwarted by malicious parties if they have less than 50% of total work capacity, i.e. task assignment in combination with Sarin.
Sarin teach mining blockchain transactions, but assigning less than 50% of a task to miners. Back however teaches that a malicious entity can be thwarted if they have less than 50% of work capacity/task assignment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin with the task assignment percentage of Back in order to gain the benefit of additional security. One of ordinary skill in the art would have recognized that Sarin and Back are compatible as they both regard blockchain systems, and that assigning tasks such that every miner has less than 50% of the mining tasks would gain the benefit of additional security by preventing attacks from a malicious entity.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sarin in view of Leung and further in view of “Epsilon-Greedy Algorithm in Reinforcement Learning”.
Regarding Claim 14:
Sarin teaches the following limitations:
A system, comprising: a non-transitory memory storing instructions; and a processor configured to execute the instructions to cause the system to perform operations comprising (Par. [0060]-[0064]).
accessing mining information associated with a mining of a blockchain by a plurality of miners, wherein the mining is performed during an exploration phase of a mining cycle (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
identifying, based on the mining information, a subset of the plurality of miners whose mining performance meets one or more specified criteria (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
(taught by Leung/“Epsilon-Greedy Algorithm in Reinforcement Learning” below)
and assigning the subset of the plurality of miners with a disproportionately higher number of mining tasks during the exploitation phase (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). The miners of Sarin are exclusively selected for performing the mining task, which can be considered disproportionately higher compared to the miners who aren’t selected.
Leung teaches the following limitation:
determining, at least in part based on a randomly (Par. [0060], Par. [0093], Par. [0119], the predetermined rules for assigning the mining task may be implemented in a combined manner). Leung was previously shown to teach entering phases based on an element of randomness in the rejection of Claim 10 above.
Sarin teaches a blockchain miner selection based on latency, but does not teach randomness as part of miner selection. Leung however teaches that a subset of blockchain miners can also be additionally chosen at random in combination with a set of rules. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin with the random miner selection of Leung in order to gain the predictable result of additionally selecting miners randomly. One of ordinary skill in the art would have recognized that Sarin and Leung are compatible as both relate to miner selection based on performance characteristics, and that such this additional random miner selection would have been a predictable implementation for selecting miners to mine transactions.
“Epsilon-Greedy Algorithm in Reinforcement Learning” teaches the following limitation:
randomly generated number (Page 2, p = random(), p < ε).
Sarin/Leung teaches blockchain mining through miner selection phases including an element of randomness, but does not teach comparison with a threshold. “Epsilon-Greedy Algorithm” however teaches that comparison of a randomly generated number against a threshold can be used to implement probabilities/randomness. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/Leung with the threshold comparison of “Epsilon-Greedy Algorithm” in order to gain the predictable result of implementing randomness based on comparison of a randomly generated number against a threshold. One of ordinary skill in the art would have recognized that Sarin/Leung is compatible with “Epsilon-Greedy Algorithm” as Leung taught randomly/probabilistically selecting miners, and “Epsilon-Greedy Algorithm” teaches that using a random number compared against a threshold is a predictable implementation of randomness/probabilities for taking action.
Regarding Claim 15:
Sarin teaches the following limitation:
wherein the operations further comprise repeating the mining cycle a plurality of times (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
wherein a new subset of the plurality of miners is identified and assigned with the disproportionately higher number of mining tasks in each new mining cycle (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Sarin/Leung/“Epsilon-Greedy Algorithm in Reinforcement Learning” as applied to Claim 14 above, and further in view of “Mempool”.
Regarding Claim 16:
Sarin teaches the following limitation:
wherein the one or more specified criteria comprise an amount of time delay less than a specified threshold when transactions are mined (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
(taught by “Mempool” below)
“Mempool” teaches the following limitation:
out of a mempool (Page 1, Par. 1-3).
Sarin/Leung/”Epsilon-Greedy Algorithm” teach mining blockchain transactions, but not a mempool. “Mempool” however teaches that it is normal in blockchain systems for transactions to first go through an electronic mempool during mining. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/Leung/”Epsilon-Greedy Algorithm” with the mempool of “Mempool” in order to gain the predictable result of the transactions of Sarin/Leung/”Epsilon-Greedy Algorithm” being buffered through the mempool before confirmation on the blockchain. One of ordinary skill in the art would have recognized that Sarin/Leung/”Epsilon-Greedy Algorithm” and “Mempool” are compatible as they both regard blockchain transaction processing, and that using such a mempool would have been a predictable result of implementing a blockchain.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Sarin/Leung/“Epsilon-Greedy Algorithm in Reinforcement Learning” as applied to Claim 14 above, and further in view of “Hyperparameter Tuning for Machine Learning”.
Regarding Claim 17:
“Epsilon-Greedy Algorithm in Reinforcement Learning” teaches the following limitation:
wherein the determining is performed at least in part by comparing the randomly generated number with a numerical value (Page 2, p = random(), p < ε).
(taught by “Hyperparameter Tuning for Machine Learning” below)
“Hyperparameter Tuning for Machine Learning” teaches the following limitation:
that was determined at least in part via machine learning (Page 1, perform this exploration and select the optimal model architecture automatically, Page 7, grid search).
Sarin/Leung/“Epsilon-Greedy Algorithm” teach using hyperparameters in the form of epsilon, but not determining epsilon via machine learning. “Hyperparameter Tuning for Machine Learning” however teaches that it is beneficial to automatically search for hyperparameters in order to gain the most optimal model. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/Leung/“Epsilon-Greedy Algorithm” with the parameter determination of “Hyperparameter Tuning for Machine Learning” in order to gain the most optimal model for miner selection.
Claims 18, 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Sarin in view of “Mempool”, and further in view of Leung, and further in view of “Epsilon-Greedy Algorithm in Reinforcement Learning”.
Regarding Claim 18:
Sarin teaches the following limitations:
A non-transitory machine-readable medium having instructions stored thereon, the instructions executable to cause a machine to perform operations comprising (Par. [0060]-[0064]).
gathering mining data associated with a mining of transactions (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
evaluating, based on the mining data, a performance of each of the miners of the plurality of miners during the first mining phase according to one or more specified criteria (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
selecting, based on a result of the evaluating, a subset of the plurality of miners that should be given a greater priority during a second mining phase (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
(taught by Leung/”Epsilon-Greedy Algorithm in Reinforcement Learning” below)
and giving the greater priority to the selected subset of the plurality of miners during the second mining phase (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
“Mempool” teaches the following limitation:
in a mempool of a blockchain (Page 1, Par. 1-3).
Sarin teach mining blockchain transactions, but not a mempool. “Mempool” however teaches that it is normal in blockchain systems for transactions to first go through an electronic mempool during mining. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin with the mempool of “Mempool” in order to gain the predictable result of the transactions of Sarin being buffered through the mempool before confirmation on the blockchain. One of ordinary skill in the art would have recognized that Sarin and “Mempool” are compatible as they both regard blockchain transaction processing, and that using such a mempool would have been a predictable result of implementing a blockchain.
Leung teaches the following limitation:
determining, (Par. [0060], Par. [0093], Par. [0119]).
Sarin/”Mempool” teaches a blockchain miner selection based on latency, but does not teach randomness as part of miner selection. Leung however teaches that a subset of blockchain miners can also be additionally chosen at random in combination with a set of rules. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/”Mempool” with the random miner selection of Leung in order to gain the predictable result of additionally selecting miners randomly. One of ordinary skill in the art would have recognized that Sarin/”Mempool” and Leung are compatible as both relate to miner selection based on performance characteristics, and that such this additional random miner selection would have been a predictable implementation for selecting miners to mine transactions.
“Epsilon-Greedy Algorithm in Reinforcement Learning” teaches the following limitation below:
based on a comparison of a randomly generated number and a specified threshold (Page 2, p = random(), p < ε).
Sarin/”Mempool”/Leung teaches blockchain mining through miner selection phases including an element of randomness, but does not teach comparison with a threshold. “Epsilon-Greedy Algorithm” however teaches that comparison of a randomly generated number against a threshold can be used to implement probabilities/randomness. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/”Mempool”/Leung with the threshold comparison of “Epsilon-Greedy Algorithm” in order to gain the predictable result of implementing randomness based on comparison of a randomly generated number against a threshold. One of ordinary skill in the art would have recognized that Sarin/”Mempool”/Leung is compatible with “Epsilon-Greedy Algorithm” as Leung taught randomly/probabilistically selecting miners, and “Epsilon-Greedy Algorithm” teaches that using a random number compared against a threshold is a predictable implementation of randomness/probabilities for taking action.
Regarding Claim 20:
Sarin teaches the following limitation:
wherein the greater priority is given by assigning a disproportionately higher number of mining tasks to the selected subset of the plurality of miners (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
Regarding Claim 21:
Sarin teaches the following limitation:
wherein the greater priority is given by assigning an entirety of available mining tasks to the selected subset of the plurality of miners (Par. [0032], Par. [0037], Par. [0039], Par. [0043]).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Sarin/”Mempool”/Leung/“Epsilon-Greedy Algorithm in Reinforcement Learning” as applied to Claim 18 above, and further in view of DiCross.
Regarding Claim 19:
Sarin teaches the following limitation:
wherein the one or more specified criteria comprise how quickly transactions (Par. [0032], Par. [0037], Par. [0039], Par. [0043]). Sarin was previously shown to teach the speed of miners being a performance criteria.
DiCross teaches the following limitation:
with a transaction fee below a specified amount (Par. [0008], Par. [0060]). DiCross previously was shown to teach that transaction fees can be too low, i.e. below a threshold, for miners to mine a transaction.
Sarin/”Mempool”/Leung/”Epsilon-Greedy Algorithm” teach that a blockchain mining system in which miners with faster transaction processing speeds are selected, but do not teach miners not mining transactions below a fee threshold. DiCross however teaches that slower transaction processing speeds can be a result of miners not mining transactions because the fee is too low, i.e. below a threshold. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the miner selection system of Sarin/”Mempool”/Leung/”Epsilon-Greedy Algorithm” with the fee threshold of DiCross in order to gain the predictable result of selecting miners based on a speed of mining transactions below a fee threshold. One of ordinary skill in the art would have recognized that DiCross and Sarin/”Mempool”/Leung/”Epsilon-Greedy Algorithm” are compatible as both relate to blockchain mining, and that Sarin’s selection of miners based on processing speed can also be considered to be an indication of how quickly transactions with a fee below a threshold are processed, as DiCross teaches low transaction fees predictably resulting in slower processing time.
Related Art
The following prior art made of record and cited on PTO-892, but not relied upon, is considered pertinent to applicant’s disclosure:
Coughlan et al. (U.S. Pub. No. 2022/0405748 A1) – Includes methods regarding blockchain transaction fees
Gleichauf (U.S. Pub. No. 2018/0109541 A1) – Includes methods regarding blockchain mining
Konik et al. (U.S. Pub. No. 2018/0107958 A1) – Includes methods regarding blockchain miner selection based on cost criteria
Wu et al. (U.S. Pub. No. 2021/0192619 A1) – Includes methods regarding cryptocurrency mining
Conclusion
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached on (571)272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.V.V./Examiner, Art Unit 2431
/LYNN D FEILD/Supervisory Patent Examiner, Art Unit 2431