DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/26/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
101 Rejection Arguments
Applicant asserts:
Applicant argues, on page 12-13, that “The amended claims recite operations including calculating correlation coefficients for each attribute, assigning weighting factors based on the correlations, parallel training of two distinct machine learning models on the weighted datasets, ground-truth comparison to select a target model, updating the correlations based on additional data and new selling times observed in a second time period, reweighting the combined multi-period dataset based on the updated correlations, and retraining the selected model on the reweighted combined data. These computational operations are not practically performable in the human mind or with pen and paper.”
Examiner response:
Examiner respectfully disagrees. Applicant’s arguments with respect to whether the claims is directed to a judicial exception have been considered but they are not persuasive. With regards to the limitations being not able to be performed in the human mind, the claims do not recite specific detail/steps on how to calculate correlation coefficients for each attribute, assign weighting factors based on the correlations, selecting a model based on ground truth comparisons, updating the correlations based on new data points, and reweighting the combined dataset based on updated correlations such that a human cannot perform those steps mentally. The steps regarding obtaining datapoints and selling time and causing presentation of time estimations is interpreted as extra solution activity – mere data gathering. In addition, training a first and second machine learning model, retraining the target machine learning model, and executing the target machine learning model is interpreted as merely applying a generic computer to perform the abstract idea.
Applicant asserts:
Applicant argues, on page 13, that “the claims as a whole integrate the operations into a practical application.”
Examiner response:
Examiner respectfully disagrees. The claims do not recite specific detail/steps on how to calculate correlation coefficients for each attribute, assign weighting factors based on the correlations, selecting a model based on ground truth comparisons, updating the correlations based on new data points, and reweighting the combined dataset based on updated correlations such that a human cannot perform those steps mentally. The steps regarding obtaining datapoints and selling time and causing presentation of time estimations is interpreted as extra solution activity – mere data gathering. In addition, training a first and second machine learning model, retraining the target machine learning model, and executing the target machine learning model is interpreted as merely applying a generic computer to perform the abstract idea. As stated in MPEP 2106.04(d) “The courts have also identified limitations that did not integrate a judicial exception into a practical application… Merely reciting the words "apply it"… Adding insignificant extra-solution activity to the judicial exception” Therefore the combinations of operations do not integrate the operations into a practical application.
Applicant asserts:
Applicant argues, on page 14, that “the amended claims recite an inventive concept”
Examiner response:
Examiner respectfully disagrees. The claims do not recite specific detail/steps on how to calculate correlation coefficients for each attribute, assign weighting factors based on the correlations, selecting a model based on ground truth comparisons, updating the correlations based on new data points, and reweighting the combined dataset based on updated correlations such that a human cannot perform those steps mentally. The steps regarding obtaining datapoints and selling time and causing presentation of time estimations is interpreted as extra solution activity – mere data gathering. In addition, training a first and second machine learning model, retraining the target machine learning model, and executing the target machine learning model is interpreted as merely applying a generic computer to perform the abstract idea. As stated in MPEP 2106.05 “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include… Adding the words "apply it"… Adding insignificant extra-solution activity to the judicial exception” Therefore the additional elements to not amount to an inventive concept.
103 Rejection Arguments
Applicant asserts:
Applicant argues that the prior art combination does not teach the amended claims.
Examiner response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 3-8, 10-14, 16-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“weighting the data points based on a correlation of each of the one or more attributes to selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person weigh the datapoints based on evaluation of a correlation of each of the one or more attributes to selling time.
“the first machine learning model predicting a first time to sell an item based on the weighted data points associated with the first item,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict a first time to sell an item based on data points of the first item.
“the second machine learning model predicting a second time to sell an item based on the weighted data points associated with the first item,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict a second time to sell an item based on data points of the first item.
“comparing the first time to sell with the actual selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could compare the first time to sell with the actual selling time.
“comparing the second time to sell with the actual selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could compare the second time to sell with the actual selling time.
“selecting a target machine learning model between the first machine learning model and the second machine learning model based on the comparing the first time to sell with the selling time and the comparing the second time to actual sell with the actual selling time.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could make an evaluation on the comparisons and select a machine learning model.
“updating the correlation of each of the one or more attributes to the selling time based on the additional data points and the new selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could update the correlation of each of the attributes to the selling time based on evaluation of additional data points and the new selling time.
“weighting the data points associated with the first item during a first time period and the additional data points associated with the first item during the second time period based on the updated correlation of the one or more attributes to the selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could weigh the data points based on the updated correlation.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A method for predicting individual item selling time in an online marketplace platform, the method comprising: obtaining data points associated with a first item listed on the online marketplace platform during a first time period, each of the data points including one or more values of one or more attributes associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g)).
“Obtaining an actual selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
providing the data points associated with the first item to a first machine learning model; is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the first machine learning model with the weighted data points associated with the first item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the weighted data points associated with the first item to a second machine learning model;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the second machine learning model with the weighted data points associated with the first item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining additional data points associated with the first item during a second time period, the additional data points each including one or more additional values of the one or more attributes associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“obtaining a new selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“retraining the target machine learning model with the weighted data points during the first time period and the weighted data points during the second time period;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“Obtaining data points associated with a second item listed on the online marketplace platform” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“Executing the target machine learning model to determine a time estimate associated with selling the second item based on the data points associated with the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“And causing presentation via an interactive interface associated with the online marketplace platform, of the time estimate associated with selling the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A method for predicting individual item selling time in an online marketplace platform, the method comprising: obtaining data points associated with a first item listed on the online marketplace platform during a first time period, each of the data points including one or more values of one or more attributes associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
“Obtaining an actual selling time associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
providing the data points associated with the first item to a first machine learning model; is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the first machine learning model with the weighted data points associated with the first item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the weighted data points associated with the first item to a second machine learning model;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the second machine learning model with the weighted data points associated with the first item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining additional data points associated with the first item during a second time period, the additional data points each including one or more additional values of the one or more attributes associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
“obtaining a new selling time associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
“retraining the target machine learning model with the weighted data points during the first time period and the weighted data points during the second time period;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“Obtaining data points associated with a second item listed on the online marketplace platform” (well-understood, routine, conventional MPEP 2106.05(d))
“Executing the target machine learning model to determine a time estimate associated with selling the second item based on the data points associated with the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“And causing presentation via an interactive interface associated with the online marketplace platform, of the time estimate associated with selling the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“receiving an identification of the second item;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could receive an identification of the second item.
“and retrieving the data points associated with the second item from a database based on the second item identification.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could retrieve data points from a database with an identifier.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, or a quantity of the second item.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, or a quantity of the second item.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 5:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“where further time estimates determined by the selected machine learning model differ from the time estimate associated with selling the second item based on the data points associated with the third item.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine a time estimate based on the data points of the third item.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and training the selected machine learning model with the data points associated with the third item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
“and training the selected machine learning model with the data points associated with the third item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 7:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“assigning different weights to each of the data points associated with the first item” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could assign weights to each data point of the first item.
“to predict the time estimate associated with selling the second item.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict the time estimate associated with selling the second item.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“the weighted attributes being used to train each of the first machine learning model and the second machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“the weighted attributes being used to train each of the first machine learning model and the second machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 8:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“weighting the data points based on a correlation of each of the one or more attributes to selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person weigh the datapoints based on evaluation of a correlation of each of the one or more attributes to selling time.
“the first machine learning model predicting a first time to sell an item based on the weighted data points associated with the first item,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict a first time to sell an item based on data points of the first item.
“the second machine learning model predicting a second time to sell an item based on the weighted data points associated with the first item,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict a second time to sell an item based on data points of the first item.
“comparing the first time to sell with the actual selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could compare the first time to sell with the actual selling time.
“comparing the second time to sell with the actual selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could compare the second time to sell with the actual selling time.
“selecting a target machine learning model between the first machine learning model and the second machine learning model based on the comparing the first time to sell with the actual selling time and the comparing the second time to sell with the actual selling time.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could make an evaluation on the comparisons and select a machine learning model.
“updating the correlation of each of the one or more attributes to the selling time based on the additional data points and the new selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could update the correlation of each of the attributes to the selling time based on evaluation of additional data points and the new selling time.
“weighting the data points associated with the first item during a first time period and the additional data points associated with the first item during the second time period based on the updated correlation of the one or more attributes to the selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could weigh the data points based on the updated correlation.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A non-transitory machine-readable medium having instructions for predicting individual item selling time in an online marketplace platform embodied thereon, the instructions executable by a processor of a machine to perform operations comprising” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining data points associated with a first item listed on the online marketplace platform during a first time period, each of the data points including one or more values of one or more attributes associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g)).
“obtaining an actual selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
providing the weighted data points associated with the first item to a first machine learning model; is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the first machine learning model with the weighted data points associated with the first item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the weighted data points associated with the first item to a second machine learning model;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the second machine learning model with the weighted data points associated with the first item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining additional data points associated with the first item during a second time period, the additional data points each including one or more additional values of the one or more attributes associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“obtaining a new selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“retraining the target machine learning model with the weighted data points during the first time period and the weighted data points during the second time period;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining data points associated with a second item listed on the online marketplace platform;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“executing the target machine learning model to determine a time estimate associated with selling the second item based on the data points associated with the second item;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and causing presentation, via an interactive interface associated with the online marketplace platform, of the time estimate associated with selling the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“the instructions executable by a processor of a machine to perform operations comprising” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining data points associated with a first item listed on the online marketplace platform during a first time period, each of the data points including one or more values of one or more attributes associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d)).
“obtaining an actual selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
providing the weighted data points associated with the first item to a first machine learning model; is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the first machine learning model with the weighted data points associated with the first item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the weighted data points associated with the first item to a second machine learning model;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the second machine learning model with the weighted data points associated with the first item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining additional data points associated with the first item during a second time period, the additional data points each including one or more additional values of the one or more attributes associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“obtaining a new selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“retraining the target machine learning model with the weighted data points during the first time period and the weighted data points during the second time period;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining data points associated with a second item listed on the online marketplace platform;” (well-understood, routine, conventional MPEP 2106.05(d))
“executing the target machine learning model to determine a time estimate associated with selling the second item based on the data points associated with the second item;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and causing presentation, via an interactive interface associated with the online marketplace platform, of the time estimate associated with selling the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 10:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, and a quantity of the second item.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, and a quantity of the second item.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 11:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“where further time estimates determined by the selected trained machine learning model differ from the time estimate associated with selling the second item based on the data points associated with the third item.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine a time estimate based on the data points of the third item.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and training the selected machine learning model with the data points associated with the third item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
“and training the selected machine learning model with the data points associated with the third item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 12:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The non-transitory machine-readable medium of claim 8, wherein the one or more attributes associated with the first item comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, and a quantity of the first item.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The non-transitory machine-readable medium of claim 8, wherein the one or more attributes associated with the first item comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, and a quantity of the first item.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 13:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“assigning different weights to each of the data points associated with the first item” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could assign weights to each data point of the first item.
“to predict the time estimate associated with selling the second item.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict the time estimate associated with selling the second item.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“the weighted attributes being used to train each of the first machine learning model and the second machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“the weighted attributes being used to train each of the first machine learning model and the second machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 14:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“weighting the data points based on a correlation of each of the one or more attributes to selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person weigh the datapoints based on evaluation of a correlation of each of the one or more attributes to selling time.
“the first machine learning model predicting a first time to sell an item based on the weighted data points associated with the first item,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict a first time to sell an item based on data points of the first item.
“the second machine learning model predicting a second time to sell an item based on the weighted data points associated with the first item,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict a second time to sell an item based on data points of the first item.
“comparing the first time to sell with the actual selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could compare the first time to sell with the actual selling time.
“comparing the second time to sell with the actual selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could compare the second time to sell with the actual selling time.
“selecting a target machine learning model between the first machine learning model and the second machine learning model based on the comparing the first time to sell with the actual selling time and the comparing the second time to sell with the actual selling time.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could make an evaluation on the comparisons and select a machine learning model.
“updating the correlation of each of the one or more attributes to the selling time based on the additional data points and the new selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could update the correlation of each of the attributes to the selling time based on evaluation of additional data points and the new selling time.
“weighting the data points associated with the first item during a first time period and the additional data points associated with the first item during the second time period based on the updated correlation of the one or more attributes to the selling time;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could weigh the data points based on the updated correlation.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“a processor; and memory including instructions for predicting individual item selling time in an online marketplace platform that, when executed by the processor, cause the device to perform operations including:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining data points associated with a first item listed on the online marketplace platform during a first time period, each of the data points including one or more values of one or more attributes associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g)).
“obtaining an actual selling time associated with the first item” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
providing the weighted data points associated with the first item to a first machine learning model; is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the first machine learning model with the weighted data points associated with the first item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the weighted data points associated with the first item to a second machine learning model;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the second machine learning model with the weighted data points associated with the first item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining additional data points associated with the first item during a second time period, the additional data points each including one or more additional values of the one or more attributes associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“obtaining a new selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“retraining the target machine learning model with the weighted data points during the first time period and the weighted data points during the second time period;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“Obtaining data points associated with a second item listed on the online marketplace platform” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“Executing the target machine learning model to determine a time estimate associated with selling the second item based on the data points associated with the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“And causing presentation via an interactive interface associated with the online marketplace platform, of the time estimate associated with selling the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“a processor; and memory including instructions that, when executed by the processor, cause the device to perform operations including:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining data points associated with a first item listed on the online marketplace platform during a first time period, each of the data points including one or more values of one or more attributes associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d)).
“obtaining an actual selling time associated with the first item” (well-understood, routine, conventional MPEP 2106.05(d))
providing the weighted data points associated with the first item to a first machine learning model; is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the first machine learning model with the weighted data points associated with the first item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“providing the weighted data points associated with the first item to a second machine learning model;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“training the second machine learning model with the weighted data points associated with the first item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“obtaining additional data points associated with the first item during a second time period, the additional data points each including one or more additional values of the one or more attributes associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
“obtaining a new selling time associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
“retraining the target machine learning model with the weighted data points during the first time period and the weighted data points during the second time period;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“Obtaining data points associated with a second item listed on the online marketplace platform” (well-understood, routine, conventional MPEP 2106.05(d))
“Executing the target machine learning model to determine a time estimate associated with selling the second item based on the data points associated with the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“And causing presentation via an interactive interface associated with the online marketplace platform, of the time estimate associated with selling the second item” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 16:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“receiving an identification of the second item;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could receive an identification of the second item.
“and retrieving the data points associated with the second item from a database based on the second item identification.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could retrieve data points from a database with an identifier.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 17:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, and a quantity of the second item.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, and a quantity of the second item.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 18:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“where further time estimates determined by the selected trained machine learning model differ from the time estimate associated with selling the second item based on the data points associated with the third item.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine a time estimate based on the data points of the third item.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and training the selected machine learning model with the data points associated with the third item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item;” (well-understood, routine, conventional MPEP 2106.05(d))
“and training the selected machine learning model with the data points associated with the third item,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 19:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The device of claim 14, wherein the one or more attributes associated with the first item comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, and a quantity of the first item.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The device of claim 14, wherein the one or more attributes associated with the first item comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, and a quantity of the first item.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 20:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“assigning different weights to each of the data points associated with the first item” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could assign weights to each data point of the first item.
“to predict the time estimate associated with selling the second item.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could predict the time estimate associated with selling the second item.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“the weighted attributes being used to train each of the first machine learning model and the second machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“the weighted attributes being used to train each of the first machine learning model and the second machine learning model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 21:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“and wherein weighting the data points based on a correlation of each of the data points to the actual selling time comprises: determining, for each data point, a correlation coefficient indicating a degree of correlation between values of the data point across the plurality of transactions and the respective actual selling times of the plurality of transactions; and” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine a correlation coefficient indicating a degree of correlation between values of the data point across the plurality of transactions and the respective actual selling times of the plurality of transactions.
“assigning a weighting factor to each data point based on the correlation coefficient.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could mentally assign a weighting factor to each data point based on the correlation coefficient.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 1, wherein the data points associated with the first item comprise data from a plurality of transactions of the first item during the first time period, each of the plurality of transactions having a respective actual selling time,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 1, wherein the data points associated with the first item comprise data from a plurality of transactions of the first item during the first time period, each of the plurality of transactions having a respective actual selling time,” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 22:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“determining a mean of the values of the data point across the plurality of transactions;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
“determining a mean of the respective actual selling times across the plurality of transactions; and” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
“determining the correlation coefficient based on deviations of the values of the data point from the mean of the values and deviations of the respective actual selling times from the mean of the respective actual selling times.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 21, wherein determining the correlation coefficient for each data point comprises: for each of the plurality of transactions, obtaining a value of the data point and the respective actual selling time;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 21, wherein determining the correlation coefficient for each data point comprises: for each of the plurality of transactions, obtaining a value of the data point and the respective actual selling time;” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 23:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 21, wherein data points having either a more positive correlation coefficient or a more negative correlation coefficient are assigned a higher weighting factor than data points having a less positive coefficient or a less negative correlation coefficient.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
Claim 1, 3-8, 10-14, 16-21, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf; Moritzet al; US 12182680 B1 (hereinafter “Wolf”) in view of David Nicholson; “A Novel method for Predicting the End-Price of eBay Auctions” (hereinafter “David”) in further view of Liangxiao Jiang et al; “A Correlation-Based Feature Weighting Filter for Naive Bayes” published Feb 2019 (hereinafter “Jiang”)
Regarding Claim 1, Wolf teaches A method for predicting individual item selling time in an online marketplace platform, the method comprising: obtaining data points associated with a first item [listed on the online marketplace platform] during a first time period, each of the data points including one or more values of one or more attributes associated with the first item; (Wolf Page 11 Paragraph 3; “Training data 220 of model training system 110 database 114 comprises a selection of one or more periods of historical supply chain data aggregated or disaggregated at various levels of granularity and presented to machine learning model 204 to generate trained models. According to one embodiment, training data 220 comprises historic sales patterns, prices, promotions, weather conditions, and other factors influencing future demand of a particular item sold in a given store on a specific day. Training data 220 may also comprise time series data, such as, for example, a list of products sold at various locations or retailers at recorded dates and times.” Wolf Page 12 Paragraph 2; “In one embodiment, data retrieval module 240 receives historical supply chain data 250 from one or more supply chain planning and execution systems.” Examiner notes that receives/obtain historical supply chain data/datapoints associated with a first item during a first time period/time period of historical data; the historical supply chain data includes one or more values (date and time sold) of one or more attributes associated with the first data (location or retailer item, date and time sold).);
Obtaining an actual selling time associated with the first item; (Wolf Column 9 Paragraph 3; "Training data 220 may also comprise time series data, such as, for example, a list of products sold at various locations or retailers at recorded dates and times." Examiner notes that an actual selling time (products sold at recorded dates and times) associated with the first item)
providing the [weighted] data points associated with the first item to a first machine learning model; (Wolf Page 14 Paragraph 3; “In an embodiment, training module 206 accesses training data 220 and one or more specified product/location/date combinations that may be stored therein, and uses training data 220 to train machine learning model 204 and generate one or more trained models.” Examiner notes that the one or more machine learning model is a first machine learning model and uses/is provided training data/data points associated with the first item);
training the first machine learning model with the [weighted] data points associated with the first item, the first machine learning model predicting a first time to sell an item based on the [weighted] data points associated with the first item; (Wolf Page 10 Paragraph 3; “Training module 206 may train machine learning model 204 to predict one or more demand volumes for one or more product/location/date combinations using causal factors stored in causal factors data 222 and/or historical target time series data stored in training data 220.” Wolf Page 10 Paragraph 6; “According to embodiments, testing module 208 may automatically test all modified models hourly, daily, weekly, twice weekly, or at any other time interval.” Examiner notes that the training data is data points associated with the first item; one or more trained models includes the first machine learning model to predict a demand volume which shows the likelihood of a product to sell/selling time based on the data points; models can predict in a time based interval i.e. hourly.;);
providing the [weighted] data points associated with the first item to a second machine learning model; (Wolf Page 14 Paragraph 3; “In an embodiment, training module 206 accesses training data 220 and one or more specified product/location/date combinations that may be stored therein, and uses training data 220 to train machine learning model 204 and generate one or more trained models.” Examiner notes that the one or more machine learning model is a second machine learning model and uses/is provided training data/data points associated with the first item);
training the second machine learning model with the [weighted] data points associated with the first item, the second machine learning model predicting a second time to sell an item based on the data points associated with the first item; ((Wolf Page 10 Paragraph 3; “Training module 206 may train machine learning model 204 to predict one or more demand volumes for one or more product/location/date combinations using causal factors stored in causal factors data 222 and/or historical target time series data stored in training data 220.” Wolf Page 10 Paragraph 6; “According to embodiments, testing module 208 may automatically test all modified models hourly, daily, weekly, twice weekly, or at any other time interval.” Examiner notes that the training data is data points associated with the first item; one or more trained models includes the second machine learning model to predict a demand volume which shows the likelihood of a product to sell/selling time based on the data points; models can predict in a time based interval i.e. hourly);
comparing the first time to sell with the actual selling time; (Wolf Page 15 Paragraph 8 and Fig. 5; “illustrated by FIG. 5, to compare the predictions of the Easter Model and Master Model to known historical data truth.” Examiner notes that first time to sell is trained model prediction with historical data truth as actual selling time);
comparing the second time to sell with the actual selling time; (Wolf Page 15 Paragraph 8 and Fig. 5; “illustrated by FIG. 5, to compare the predictions of the Easter Model and Master Model to known historical data truth.” Examiner notes that second time to sell is modified model prediction with historical data truth as actual selling time);
and selecting a target machine learning model between the first machine learning model and the second machine learning model based on the comparing the first time to actual sell with the selling time and the comparing the second time to sell with the actual selling time. (Page 14 Paragraph 6; “In another embodiment, the testing model compares the predictions generated by the one or more trained models and modified models with known historical data truth, and measures the discrepancy between the truth and the predictions generated by the one or more trained models and modified models. In this embodiment, if training module 206 determines the trained model generated a more accurate prediction (closer to the truth) than the modified model, model training system 110 returns to action 306 and continues generating modified models. If, on the other hand, training module 206 determines that the modified model and the one or more new feature branches contained in the modified model generated a more accurate prediction (closer to the truth), model training system 110 proceeds to action 312.” Examiner notes that the second model will be selected as the target machine learning model based on the comparing of the first time to sell with the actual selling time and the comparing the second time to sell with the actual selling time)
obtaining additional data points associated with the first item during a second time period, the additional data points each including one or more additional values of the one or more attributes associated with the first item; (Wolf Column 9 Line 19; “According to one embodiment, training data 220 comprises historic sales patterns, prices, promotions, weather conditions, and other factors influencing future demand of a particular item sold in a given store on a specific day” Wolf Column 17 Line 34; “selects predictions and sales of scented candles at Store X during the month of April as the particular product/location/date combination to be evaluated. In other embodiments, model training system 110 performs the actions of method 300 on all available data. In this example, the Master Model does not identify the Easter holiday and the days immediately following the Easter holiday as one or more causal factors that may influence sales of scented candles at Store X in April months in which Easter occurs.” Wolf Column 18 Line 24; “training module 206 accesses the historical time series data stored in training data 220, three copies of which are illustrated in time-dependent cross-validation process 402 as horizontal bars 404, 406, and 408… and trains the Easter Model using training blocks 410a-410c.” Examiner notes that additional data points (historical time series data during the month of April) is obtained/accessed associated with the first item (scented candles) during a second time period (month of April), the additional data points includes additional values (date and time sold) of one or more attributes (location or retailer item, date and time sold) associated with the first data)
obtaining a new selling time associated with the first item; (Wolf Column 9 Paragraph 3; "Training data 220 may also comprise time series data, such as, for example, a list of products sold at various locations or retailers at recorded dates and times." Examiner notes that a new actual selling time (products sold at recorded dates and times during April) associated with the first item is obtained)
retraining the target machine learning model with the [weighted] data points during the first time period and the weighted data points during the second time period; (Wolf Column 19 Line 39; “After performing independent trainings for the different data sets from, in this example, three different retailers on the full historical data sets rather than the piecewise trainings of the time-dependent cross-validation, deployment module 210 updates the three Master Models stored in trained models data 224 for each retailer to incorporate the Easter holidays model new feature branch, and stores the newly-updated Master Models in trained models data 224.” Examiner notes that the target machine learning model (Master models) is retrained/updated with datapoints during the first time period (Master model trained on datapoints not including Easter) and the weighted datapoints during the second time period (Master model is updated to include data points during Easter))
Obtaining data points associated with a second item listed on the online marketplace platform (Wolf Page 14 Paragraph 4 and Fig. 4; “In an embodiment, after user interface module 212 makes one or more source code alterations to a trained model to create a new modified model, training module 206 trains the new modified model using a time-dependent selection of cross-validation historical data stored in training data 220, as illustrated by FIG. 4.” Examiner notes that the test segment 420a used for cross validation shown in fig 4 is data points associated with a second item; test segment is obtained from the training data.)
Executing the target machine learning model to determine a time estimate associated with selling the second item based on the data points associated with the second item (Wolf Page 14 Paragraph 4; “training module 206 trains the new modified model using a time-dependent selection of cross-validation historical data stored in training data 220, as illustrated by FIG. 4.” Wolf Page 15 Paragraph 7 and Fig 4; “In this way, training module 206 and testing module 208 train and test the Easter Model using different chronological segments of three copies of the same historical time series data (illustrated in time-dependent cross-validation process 402 as horizontal bars 404, 406, and 408) and compare the predictions generated by the Easter Model to the true results stored in the historical time series data.” Examiner notes that testing module executes the target machine learning model to determine a time estimate (comparing the results of the selected model to the historical time series show that it predicts a time estimate) associated with selling the second item based on the data points associated with the second item(test segment 420a))
And causing presentation via an interactive interface [associated with the online marketplace platform], of the time estimate associated with selling the second item (Wolf Column 18 Paragraph 3; "Testing module 208 and user interface module 212 generate prediction comparison display 502, illustrated by FIG. 5" Examiner notes that interactive interface (user interface module) [associated with the online marketplace platform], causes presentation (display) of the time estimate associated with selling the second item (Fig 5 shows selling time of an item with modified model prediction))
Wolf does not teach first item listed on the online marketplace platform
associated with the online marketplace platform
However, David does teach first item listed on the online marketplace platform (David Section II Paragraph 1; "Auction data is accessed via eBay’s application programming interface (API) [5] using the Python software development kit (SDK) [6]…Keyword searches are made using the findItemsAdvanced function, and specific item information is obtained using the GetSingleItem function." Examiner notes that datapoints is obtained from the online marketplace platform (eBay) associated with first item (specific item))
associated with the online marketplace platform (David Section II Paragraph 1; "
Auction data is accessed via eBay’s application programming interface” Examiner notes that interface presents data associated with online marketplace platform (eBay))
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, etc. One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Wolf in view of David does not teach weighting the data points based on a correlation of each of the one or more attributes to selling time;
updating the correlation of each of the one or more attributes to the selling time based on the additional data points and the new selling time;
weighting the data points associated with the first item during a first time period and the additional data points associated with the first item during the second time period based on the updated correlation of the one or more attributes to the selling time;
However, Jiang does teach weighting the data points based on a correlation of each of the one or more attributes to selling time; (Jiang Algorithm 1 and Page 3 Paragraph 4; “we argue that for naive Bayes highly predictive features should be highly correlated with the class (maximum mutual relevance), yet uncorrelated with other features (minimum mutual redundancy). Based on this premise, we propose a correlation-based feature weighting (CFW) filter for naive Bayes.” Examiner notes that the data points (predictive features) are weighted (correlation-based feature weighting) of each of the one or more attributes (each feature) to selling time (highly correlated with the class))
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updating the correlation of each of the one or more attributes to the selling time based on the additional data points and the new selling time; (Jiang Algorithm 1 and Page 3 Paragraph 4; “we argue that for naive Bayes highly predictive features should be highly correlated with the class (maximum mutual relevance), yet uncorrelated with other features (minimum mutual redundancy). Based on this premise, we propose a correlation-based feature weighting (CFW) filter for naive Bayes.” Jiang Page 3 Paragraph 5; “Then we use mutual information to measure the correlation between each pair of random discrete variables, and there fore the feature-class correlation and the feature-feature intercorrelation can be respectively defined as: … (8) (9) where C is the class variable, Ai and Aj are two different feature variables, c, ai and aj represent the values that they take, respectively.” Examiner notes that the correlation is updated (interpreted as recomputed with new additional data) of each of the one or more attributes to the selling time (measure the correlation between each pair of random discrete variables) based on the additional data points and the new selling time (data is added into pool of discrete variables to recompute correlation))
weighting the data points associated with the first item during a first time period and the additional data points associated with the first item during the second time period based on the updated correlation of the one or more attributes to the selling time; (Examiner refers to previous mapping to show that the data points and additional data points of the first item during a first time period and second time period (predictive features) are weighted (correlation-based feature weighting) based on the updated correlation of each of the one or more attributes (each feature) to selling time (highly correlated with the class))
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf, David, and Jiang. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, etc. Jiang teaches a correlation based feature weighting filter for Naïve Bayes. One of ordinary skill would have motivation to combine Wolf, David, and Jiang to lower computational complexity and outperform other feature weighting filters “Experimental results show that NB with CFW significantly outperforms NB and all the other existing state-of-the-art feature weighting filters used to compare. Compared to feature weighting wrappers for improving NB, the main advantages of CFW are its low computational complexity (no search involved) and the fact that it maintains the simplicity of the final model.” (Jiang Abstract).
Regarding Claim 3, Wolf does not teach The method of claim 1, further comprising: receiving an identification of the second item;
and retrieving the data points associated with the second item from a database based on the second item identification.
However, David does teach The method of claim 1, further comprising: receiving an identification of the second item; (David Page 2 Paragraph 1; “Calls to the findItemsAdvanced function were made daily, with parameters set to a blank keyword search and to return the most recently listed items with each call.” Examiner notes that calling findItemAdvanced function to get a list of items is receiving an identification of the second item);
and retrieving the data points associated with the second item from a database based on the second item identification. (David Page 2 Paragraph 1; “Likewise, we called the GetSingleItem function daily for each live auction in order to trace the temporal data” Examiner notes that using the GetSingleItem is retrieving the data points of the second item from the database based on the second item identification);
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, and etc One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Regarding Claim 4, Wolf does not teach The method of claim 1, wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, or a quantity of the second item.
However, David does teach The method of claim 1, wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, or a quantity of the second item. (David Page 1 Paragraph 5; “We gathered as much information per item as possible. Features stored included: item number, item title, start time, end time, text description, detailed item specifications1 , seller feedback percentage and total score, shipping costs, return policy, and image presence.” Examiner notes information per item includes data points associated with the second item; data points further comprise a feedback score of seller/seller feedback percentage and images associated with a listing/image presence)
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, and etc One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Regarding Claim 5, Wolf teaches The method of claim 1, the method further comprising: obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item; (Wolf Page 14 Paragraph 4; “In an embodiment, after user interface module 212 makes one or more source code alterations to a trained model to create a new modified model, training module 206 trains the new modified model using a time-dependent selection of cross-validation historical data stored in training data 220, as illustrated by FIG. 4.” Examiner notes that the test segment used for cross validation shown in fig 4 is data points associated with a third item; test segment is obtained from the training data; the second time period is anytime following the time period used for training data, so data points of third item occur during at least a portion of the second time period and have a selling time that is different with the first item.);
and training the selected machine learning model with the data points associated with the third item, where further time estimates determined by the selected machine learning model differ from the time estimate associated with selling the second item based on the data points associated with the third item. (Wolf Page 15 Paragraph 7 and Fig 4; “In this way, training module 206 and testing module 208 train and test the Easter Model using different chronological segments of three copies of the same historical time series data (illustrated in time-dependent cross-validation process 402 as horizontal bars 404, 406, and 408) and compare the predictions generated by the Easter Model to the true results stored in the historical time series data.” Examiner notes selected trained machine learning model predicts a time estimate based on the data points of the third item/test segment 420b; comparing the results of the selected model to the historical time series shows that it predicts a time estimate; Fig 4 shows that the time estimates of how likely it is to sell determined from the third item differs from that of the second item on April 18, 2017.);
Regarding Claim 6, Wolf does not teach The method of claim 1, wherein the one or more attributes associated with the first item comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, or a quantity of the first item.
However, David does teach The method of claim 1, wherein the one or more attributes associated with the first item comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, or a quantity of the first item. (David Page 1 Paragraph 5; “We gathered as much information per item as possible. Features stored included: item number, item title, start time, end time, text description, detailed item specifications1 , seller feedback percentage and total score, shipping costs, return policy, and image presence.” Examiner notes information per item includes the one or more attributes associated with the first item; one or more attributes comprise a feedback score of seller/seller feedback percentage and images associated with a listing/image presence)
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, and etc One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Regarding claim 7, Wolf teaches The method of claim 1, further comprising assigning different weights to each of the data points associated with the first item, the weighted attributes being used to train each of the first machine learning model and the second machine learning model to predict the time estimate associated with selling the second item. (Wolf Page 11 Paragraph 2; “to compare the quality of the predictions from the trained models with the predictions from the one or more modified models in a time-dependent cross-validation back-test scenario.” Wolf Page 13 Paragraph 5; “the exact values of the parameters of the model may be specific to a single product to be sold on a specific day in a specific location or sales channel and may depend on a wide range of frequently changing influencing causal factors.” Wolf Page 14 Paragraph 3; “At action 304, training module 206 generates one or more trained models using training data 220. In an embodiment, training module 206 accesses training data 220 and one or more specified product/location/date combinations that may be stored therein, and uses training data 220 to train machine learning model.” Examiner notes that the parameters of the models is assigning different weights to each of the data points of the first item/training set; the models will be trained to predict the time estimate associated with the second item during the cross validation back-test.)
Regarding Claim 8, Wolf teaches A non-transitory machine-readable medium having instructions for predicting individual item selling time in an online marketplace platform embodied thereon, the instructions executable by a processor of a machine to perform operations comprising: (Wolf Page 8 Paragraph 7; “One or more computers 150 may include one or more processors and associated memory to execute instructions and manipulate information according to the operation of supply chain network 100 and any of the methods described herein.”)
Claim 8 is machine claim of method claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
Regarding Claim 10, Wolf does not teach The non-transitory machine-readable medium of claim 8, wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, or a quantity of the second item.
However, David does teach The non-transitory machine-readable medium of claim 8, wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, and a quantity of the second item. (David Page 1 Paragraph 5; “We gathered as much information per item as possible. Features stored included: item number, item title, start time, end time, text description, detailed item specifications1 , seller feedback percentage and total score, shipping costs, return policy, and image presence.” Examiner notes information per item includes data points associated with the second item; data points further comprise a feedback score of seller/seller feedback percentage and images associated with a listing/image presence)
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, and etc One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Regarding Claim 11, Wolf teaches The non-transitory machine-readable medium of claim 8, the operations further comprising: obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item; (Wolf Page 14 Paragraph 4; “In an embodiment, after user interface module 212 makes one or more source code alterations to a trained model to create a new modified model, training module 206 trains the new modified model using a time-dependent selection of cross-validation historical data stored in training data 220, as illustrated by FIG. 4.” Examiner notes that the test segment used for cross validation shown in fig 4 is data points associated with a third item; test segment is obtained from the training data; the second time period is anytime following the time period used for training data, so data points of third item occur during at least a portion of the second time period and have a selling time that is different with the first item.);
and training the selected machine learning model with the data points associated with the third item, where further time estimates determined by the selected trained machine learning model differ from the time estimate associated with selling the second item based on the data points associated with the third item. (Wolf Page 15 Paragraph 7 and Fig 4; “In this way, training module 206 and testing module 208 train and test the Easter Model using different chronological segments of three copies of the same historical time series data (illustrated in time-dependent cross-validation process 402 as horizontal bars 404, 406, and 408) and compare the predictions generated by the Easter Model to the true results stored in the historical time series data.” Examiner notes selected trained machine learning model predicts a time estimate based on the data points of the third item/test segment 420b; comparing the results of the selected model to the historical time series shows that it predicts a time estimate; Fig 4 shows that the time estimates of how likely it is to sell determined from the third item differs from that of the second item on April 18, 2017.);
Regarding Claim 12, Wolf does not teach The non-transitory machine-readable medium of claim 8, wherein the one or more attributes associated with the first item comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, and a quantity of the first item.
However, David does teach The non-transitory machine-readable medium of claim 8, wherein the one or more associated with the first item comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, and a quantity of the first item. (David Page 1 Paragraph 5; “We gathered as much information per item as possible. Features stored included: item number, item title, start time, end time, text description, detailed item specifications1 , seller feedback percentage and total score, shipping costs, return policy, and image presence.” Examiner notes information per item includes data points associated with the first item; data points further comprise a feedback score of seller/seller feedback percentage and images associated with a listing/image presence)
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, and etc One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Regarding claim 13, Wolf teaches The non-transitory machine-readable medium of claim 8, the operations further comprising assigning different weights to each of the data points associated with the first item, the weighted attributes being used to train each of the first machine learning model and the second machine learning model to predict the time estimate associated with selling the second item. (Wolf Page 11 Paragraph 2; “to compare the quality of the predictions from the trained models with the predictions from the one or more modified models in a time-dependent cross-validation back-test scenario.” Wolf Page 13 Paragraph 5; “the exact values of the parameters of the model may be specific to a single product to be sold on a specific day in a specific location or sales channel and may depend on a wide range of frequently changing influencing causal factors.” Wolf Page 14 Paragraph 3; “At action 304, training module 206 generates one or more trained models using training data 220. In an embodiment, training module 206 accesses training data 220 and one or more specified product/location/date combinations that may be stored therein, and uses training data 220 to train machine learning model.” Examiner notes that the parameters of the models is assigning different weights to each of the data points of the first item/training set; the models will be trained to predict the time estimate associated with the second item during the cross validation back-test.)
Regarding Claim 14, Wolf teaches A device, comprising: a processor; and memory including instructions for predicting individual item selling time in an online marketplace platform that, when executed by the processor, cause the device to perform operations including: (Wolf Page 8 Paragraph 7; “One or more computers 150 may include one or more processors and associated memory to execute instructions and manipulate information according to the operation of supply chain network 100 and any of the methods described herein.”)
Claim 14 is machine claim of method claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
Regarding Claim 16, Wolf does not The device of claim 14, wherein the instructions further cause the device to perform operations including: receiving an identification of the second item; and retrieving the data points associated with the second item from a database based on the second item identification.
However, David teaches The device of claim 15, wherein the instructions further cause the device to perform operations including: receiving an identification of the second item; (David Page 2 Paragraph 1; “Calls to the findItemsAdvanced function were made daily, with parameters set to a blank keyword search and to return the most recently listed items with each call.” Examiner notes that calling findItemAdvanced function to get a list of items is receiving an identification of the second item);
and retrieving the data points associated with the second item from a database based on the second item identification. (David Page 2 Paragraph 1; “Likewise, we called the GetSingleItem function daily for each live auction in order to trace the temporal data” Examiner notes that using the GetSingleItem is retrieving the data points of the second item from the database based on the second item identification);
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, and etc One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Regarding Claim 17, Wolf does not teach The device of claim 14, wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, and a quantity of the second item.
However, David teaches The device of claim 15, wherein the data points associated with the second item further comprise a feedback score associated with a seller of the second item and one or more of a price of the second item, a number of images associated with a listing of the second item, and a quantity of the second item. (David Page 1 Paragraph 5; “We gathered as much information per item as possible. Features stored included: item number, item title, start time, end time, text description, detailed item specifications1 , seller feedback percentage and total score, shipping costs, return policy, and image presence.” Examiner notes information per item includes data points associated with the second item; data points further comprise a feedback score of seller/seller feedback percentage and images associated with a listing/image presence)
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, and etc One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Regarding Claim 18, Wolf teaches The device of claim 14, wherein the instructions further cause the device to perform operations including: obtaining data points associated with a third item during at least a portion of the second time period, the data points associated with the third item including a selling time associated with the third item where the selling time associated with the third item is different from the selling time associated with the first item; (Wolf Page 14 Paragraph 4; “In an embodiment, after user interface module 212 makes one or more source code alterations to a trained model to create a new modified model, training module 206 trains the new modified model using a time-dependent selection of cross-validation historical data stored in training data 220, as illustrated by FIG. 4.” Examiner notes that the test segment used for cross validation shown in fig 4 is data points associated with a third item; test segment is obtained from the training data; the second time period is anytime following the time period used for training data, so data points of third item occur during at least a portion of the second time period and have a selling time that is different with the first item.);
and training the selected machine learning model with the data points associated with the third item, where further time estimates determined by the selected trained machine learning model differ from the time estimate associated with selling the second item based on the data points associated with the third item. (Wolf Page 15 Paragraph 7 and Fig 4; “In this way, training module 206 and testing module 208 train and test the Easter Model using different chronological segments of three copies of the same historical time series data (illustrated in time-dependent cross-validation process 402 as horizontal bars 404, 406, and 408) and compare the predictions generated by the Easter Model to the true results stored in the historical time series data.” Examiner notes selected trained machine learning model predicts a time estimate based on the data points of the third item/test segment 420b; comparing the results of the selected model to the historical time series shows that it predicts a time estimate; Fig 4 shows that the time estimates of how likely it is to sell determined from the third item differs from that of the second item on April 18, 2017.);
Regarding Claim 19, Wolf does not teach The device of claim 14, wherein the data points associated with the first item further comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, and a quantity of the first item.
However, David does teach The device of claim 14, wherein the data points associated with the first item further comprise a feedback score associated with a seller of the first item and one or more of a price of the first item, a number of images associated with a listing of the first item, and a quantity of the first item. (David Page 1 Paragraph 5; “We gathered as much information per item as possible. Features stored included: item number, item title, start time, end time, text description, detailed item specifications1 , seller feedback percentage and total score, shipping costs, return policy, and image presence.” Examiner notes information per item includes data points associated with the first item; data points further comprise a feedback score of seller/seller feedback percentage and images associated with a listing/image presence)
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf and David. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, and etc One of ordinary skill would have motivation to combine Wolf and David to train and select machine learning models for predicting a selling date of an item “One of the more interesting insights we were able to draw from this project was the importance of particular features over others.” (David Page 4 Paragraph 3).
Regarding claim 20, Wolf teaches The device of claim 14, wherein the instructions further cause the device to perform operations including assigning different weights to each of the data points associated with the first item, the weighted attributes being used to train each of the first machine learning model and the second machine learning model to predict the time estimate associated with selling the second item. (Wolf Page 11 Paragraph 2; “to compare the quality of the predictions from the trained models with the predictions from the one or more modified models in a time-dependent cross-validation back-test scenario.” Wolf Page 13 Paragraph 5; “the exact values of the parameters of the model may be specific to a single product to be sold on a specific day in a specific location or sales channel and may depend on a wide range of frequently changing influencing causal factors.” Wolf Page 14 Paragraph 3; “At action 304, training module 206 generates one or more trained models using training data 220. In an embodiment, training module 206 accesses training data 220 and one or more specified product/location/date combinations that may be stored therein, and uses training data 220 to train machine learning model.” Examiner notes that the parameters of the models is assigning different weights to each of the data points of the first item/training set; the models will be trained to predict the time estimate associated with the second item during the cross validation back-test.)
Regarding claim 21, Wolf teaches The method of claim 1, wherein the data points associated with the first item comprise data from a plurality of transactions of the first item during the first time period, each of the plurality of transactions having a respective actual selling time, (Wolf Column 9 Line 19; “training data 220 comprises historic sales patterns, prices, promotions, weather conditions, and other factors influencing future demand of a particular item sold in a given store on a specific day. Training data 220 may also comprise time series data, such as, for example, a list of products sold at various locations or retailers at recorded dates and times.”)
Wolf does not teach and wherein weighting the data points based on a correlation of each of the data points to the actual selling time comprises: determining, for each data point, a correlation coefficient indicating a degree of correlation between values of the data point across the plurality of transactions and the respective actual selling times of the plurality of transactions;
And assigning a weighting factor to each data point based on the correlation coefficient.
However, Jiang does teach and wherein weighting the data points based on a correlation of each of the data points to the actual selling time comprises: determining, for each data point, a correlation coefficient indicating a degree of correlation between values of the data point across the plurality of transactions and the respective actual selling times of the plurality of transactions; (Jiang Page 3 Paragraph 4; “In CFW, the weight for a feature is a sigmoid transformation of the difference between the feature-class correlation (mutual relevance) and the average feature-feature intercorrelation (average mutual redundancy). Thus it can be seen that how to measure (define) the correlation between each pair of random variables is crucial and therefore our research should start from answering this question.” Examiner notes that a correlation coefficient indicating a degree of correlation (feature-class correlation (mutual relevance)) between values of the data point across the plurality of transactions (feature) and the respective actual selling times (class))
And assigning a weighting factor to each data point based on the correlation coefficient. (Examiner refers to previous mapping to show that a weighting factor (weight) to each data point (for a feature) is assigned based on the correlation coefficient (feature-class correlation (mutual relevance) and the average feature-feature intercorrelation))
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf, David, and Jiang. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, etc. Jiang teaches a correlation based feature weighting filter for Naïve Bayes. One of ordinary skill would have motivation to combine Wolf, David, and Jiang to lower computational complexity and outperform other feature weighting filters “Experimental results show that NB with CFW significantly outperforms NB and all the other existing state-of-the-art feature weighting filters used to compare. Compared to feature weighting wrappers for improving NB, the main advantages of CFW are its low computational complexity (no search involved) and the fact that it maintains the simplicity of the final model.” (Jiang Abstract).
Regarding claim 23, Wolf does not teach The method of claim 21, wherein data points having either a more positive correlation coefficient or a more negative correlation coefficient are assigned a higher weighting factor than data points having a less positive coefficient or a less negative correlation coefficient.
However, Jiang does teach The method of claim 21, wherein data points having either a more positive correlation coefficient or a more negative correlation coefficient are assigned a higher weighting factor than data points having a less positive coefficient or a less negative correlation coefficient. (Jiang Page 3 Paragraph 6; “Since a highly predictive feature should be highly correlated with the class, yet uncorrelated with other features, its weight should be proportional to the difference between the feature-class correlation and the average feature-feature intercorrelation defined” Examiner notes data points having a more positive correlation coefficient (difference between the feature-class correlation and the average feature-feature intercorrelation defined is larger meaning more predictive) are assigned a higher weighting factor than data points having a less positive coefficient (difference between the feature-class correlation and the average feature-feature intercorrelation defined is smaller meaning less predictive))
It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf, David, and Jiang. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, etc. Jiang teaches a correlation based feature weighting filter for Naïve Bayes. One of ordinary skill would have motivation to combine Wolf, David, and Jiang to lower computational complexity and outperform other feature weighting filters “Experimental results show that NB with CFW significantly outperforms NB and all the other existing state-of-the-art feature weighting filters used to compare. Compared to feature weighting wrappers for improving NB, the main advantages of CFW are its low computational complexity (no search involved) and the fact that it maintains the simplicity of the final model.” (Jiang Abstract).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Wolf; Moritzet al; US 12182680 B1 (hereinafter “Wolf”) in view of David Nicholson; “A Novel method for Predicting the End-Price of eBay Auctions” (hereinafter “David”) in further view of Liangxiao Jiang et al; “A Correlation-Based Feature Weighting Filter for Naive Bayes” published Feb 2019 (hereinafter “Jiang”) in further view of Zach; “Pearson Correlation Coefficient” available Nov 3, 2021 (hereinafter “Zach”)
Regarding claim 22, Wolf teaches The method of claim 21, wherein determining the correlation coefficient for each data point comprises: for each of the plurality of transactions, obtaining a value of the data point and the respective actual selling time; (Wolf Column 9 Line 19; “training data 220 comprises historic sales patterns, prices, promotions, weather conditions, and other factors influencing future demand of a particular item sold in a given store on a specific day. Training data 220 may also comprise time series data, such as, for example, a list of products sold at various locations or retailers at recorded dates and times.” Wolf Column 18 Line 24; “training module 206 accesses the historical time series data stored in training data 220, three copies of which are illustrated in time-dependent cross-validation process 402 as horizontal bars 404, 406, and 408… and trains the Easter Model using training blocks 410a-410c.” Examiner notes that a value of the data point (prices) and the respective actual selling time (sold at … recorded dates and times) is obtained)
Zach does not teach determining a mean of the values of the data point across the plurality of transactions;
determining a mean of the respective actual selling times across the plurality of transactions;
and determining the correlation coefficient based on deviations of the values of the data point from the mean of the values and deviations of the respective actual selling times from the mean of the respective actual selling times.
However, Zach does teach determining a mean of the values of the data point across the plurality of transactions; (Zach Paragraph 6; “For each (X, Y) pair in our dataset, we need to find the difference between the x value and the mean x value, the difference between the y value and the mean y value, then multiply these two numbers together.” Examiner notes that a mean of the values of the data point across the plurality of transactions is determined (mean value of x))
determining a mean of the respective actual selling times across the plurality of transactions; (Examiner refers to previous mapping to show that a mean of the respective actual selling times across the plurality of transactions is determined (mean value of y))
and determining the correlation coefficient based on deviations of the values of the data point from the mean of the values and deviations of the respective actual selling times from the mean of the respective actual selling times. (Zach Paragraph 2 and attached formula shows determining the correlation coefficient based on deviations of the values of the data point from the mean of the values (difference between x value and mean x value) and deviations of the respective actual selling times from the mean of the respective actual selling times (difference between y value and mean y value))
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It would have obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wolf, David, Jiang, and Zach. Wolf teaches comparing two models performance and choosing the best sales forecasting model for following training iterations. David teaches using data relating to an item listing such as seller feedback, image presence, etc. Jiang teaches a correlation based feature weighting filter for Naïve Bayes. Zach teaches Pearson Correlation Coefficient. One of ordinary skill would have motivation to combine Wolf, David, Jiang, and Zach to use Pearson Correlation Coefficient to assign large weights to variables that highly correlate with the target variable for better training data to improve the machine learning model “When using the Pearson correlation coefficient, keep in mind that you’re merely testing to see if two variables are linearly related. Even if a Pearson correlation coefficient tells us that two variables are uncorrelated, they could still have some type of nonlinear relationship.” (Zach Last Paragraph).
Conclusion
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/D.D.T./Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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