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 9, that the claimed limitations cannot be performed in the human mind or by pen and paper, integrated into a practical application, and significantly more than a judicial exception.
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 the machine learning model makes the predication such that a human can not make that prediction mentally. The additional limitations are considered in following Step 2A Prong 2 and Step 2B. With regards to the limitations being integrated into a practical application, the additional limitations state steps to apply the abstract idea on a generic computer along with its field of use. Finally, with regards to the claimed invention recite significantly more than alleged judicial exception, the claimed steps is state generic processes and is linking it to a common field of use.
An updated 101 rejection is provided below with the amended claims.
102 Rejection Arguments
Applicant asserts:
Applicant argues, on page 11, that the prior art does not teach the amended limitation to independent claim 1.
Examiner response:
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. Updated prior art rejection is shown below.
103 Rejection Arguments
Applicant asserts:
Applicant argues that the prior art combination does not teach the amended claims.
Examiner response:
Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Updated 103 rejection is shown below.
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, and 16-20 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?
“the first machine learning model predicting a first time to sell an item based on 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 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 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.
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, the data points associated with the first item including 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 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)).
“the first machine learning model being updatable with additional data points associated with a 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)).
“providing the 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 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)).
“the second machine learning model being updatable with the additional data points associated with 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, the data points associated with the first item including 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 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)).
“the first machine learning model being updatable with additional data points associated with a 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)).
“providing the 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 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)).
“the second machine learning model being updatable with the additional data points associated with 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?
“the first machine learning model predicting a first time to sell an item based on 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 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 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.
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, the data points associated with the first item including 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 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)).
“the first machine learning model being updatable with additional data points associated with a 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)).
“providing the 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 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)).
“the second machine learning model being updatable with the additional data points associated with 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 during a first time period, the data points associated with the first item including a selling time 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 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 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)).
“the first machine learning model being updatable with additional data points associated with a 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)).
“providing the 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 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)).
“the second machine learning model being updatable with the additional data points associated with 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 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?
“the first machine learning model predicting a first time to sell an item based on 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 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 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.
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, the data points associated with the first item including 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 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)).
“the first machine learning model being updatable with additional data points associated with a 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)).
“providing the 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 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)).
“the second machine learning model being updatable with the additional data points associated with 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 during a first time period, the data points associated with the first item including a selling time 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 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)).
“the first machine learning model being updatable with additional data points associated with a 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)).
“providing the 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 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)).
“the second machine learning model being updatable with the additional data points associated with 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 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.
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 3, 4, 6, 10, 12, 16, and 17 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”).
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, the data points associated with the first item including 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 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 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 data points associated with the first item, the first machine learning model predicting a first time to sell an item based on the data points associated with the first item, the first machine learning model being updatable with additional data points associated with a second time period; (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.” Wolf Page 15 Paragraph 4; “generates a modified model based on the Master Model, with the addition of a new feature branch that identifies the Easter holiday and the days immediately following the Easter holiday as one or more causal factors that influence sales of scented candles at Store X in April months in which Easter occurs.” 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.; models are capable of being updateable with additional data points/new feature branch associated with a second time period/Easter holiday and the days immediately following.);
providing the 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 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, the second machine learning model being updatable with the additional data points associated with the second time period; ((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.” Wolf Page 15 Paragraph 4; “generates a modified model based on the Master Model, with the addition of a new feature branch that identifies the Easter holiday and the days immediately following the Easter holiday as one or more causal factors that influence sales of scented candles at Store X in April months in which Easter occurs.” 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.; models are capable of being updateable with additional data points/new feature branch associated with a second time period/Easter holiday and the days immediately following.);
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 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 present application 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).
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 present application 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 present application 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 present application 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.”)
obtaining data points associated with a first item [listed on the online marketplace platform] during a first time period, the data points associated with the first item including 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 series data for products sold/selling time associated to first item.);
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 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 data points associated with the first item, the first machine learning model predicting a first time to sell an item based on the data points associated with the first item, the first machine learning model being updatable with additional data points associated with a second time period; (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.” Wolf Page 15 Paragraph 4; “generates a modified model based on the Master Model, with the addition of a new feature branch that identifies the Easter holiday and the days immediately following the Easter holiday as one or more causal factors that influence sales of scented candles at Store X in April months in which Easter occurs.” 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.; models are capable of being updateable with additional data points/new feature branch associated with a second time period/Easter holiday and the days immediately following.);
providing the 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 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, the second machine learning model being updatable with the additional data points associated with the second time period; ((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.” Wolf Page 15 Paragraph 4; “generates a modified model based on the Master Model, with the addition of a new feature branch that identifies the Easter holiday and the days immediately following the Easter holiday as one or more causal factors that influence sales of scented candles at Store X in April months in which Easter occurs.” 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.; models are capable of being updateable with additional data points/new feature branch associated with a second time period/Easter holiday and the days immediately following.);
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 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 present application 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 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 present application 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 present application 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.”)
obtaining data points associated with a first item listed on the online marketplace platform during a first time period, the data points associated with the first item including 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 series data for products sold/selling time associated to first item.);
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 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 data points associated with the first item, the first machine learning model predicting a first time to sell an item based on the data points associated with the first item, the first machine learning model being updatable with additional data points associated with a second time period; (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.” Wolf Page 15 Paragraph 4; “generates a modified model based on the Master Model, with the addition of a new feature branch that identifies the Easter holiday and the days immediately following the Easter holiday as one or more causal factors that influence sales of scented candles at Store X in April months in which Easter occurs.” 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.; models are capable of being updateable with additional data points/new feature branch associated with a second time period/Easter holiday and the days immediately following.);
providing the 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 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, the second machine learning model being updatable with the additional data points associated with the second time period; ((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.” Wolf Page 15 Paragraph 4; “generates a modified model based on the Master Model, with the addition of a new feature branch that identifies the Easter holiday and the days immediately following the Easter holiday as one or more causal factors that influence sales of scented candles at Store X in April months in which Easter occurs.” 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.; models are capable of being updateable with additional data points/new feature branch associated with a second time period/Easter holiday and the days immediately following.);
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 sell with the actual 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 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 present application 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 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 present application 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 present application 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 present application 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.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.D.T./Examiner, Art Unit 2147
/JAMES T TSAI/Primary Examiner, Art Unit 2147