DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-2 and 4-21 are pending for examination.
Claims 1, 11 and 19 are independent Claims.
Claims 1-2 and 4-21 are rejected under 35 U.S.C. §§101, 103.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2 and 4-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a Judicial Exception without significantly more.
Independent Claims
As Claim 1:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
The Claim recites:
A method comprising:
receiving, by a server computer, a dataset comprising data associated with a plurality of service providers;
extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features;
training, by the server computer, a machine learning model using training data based on the plurality of features of at least some of the service providers; and
for one or more candidate service providers of the plurality of service providers, determining, by the server computer, a predicted rank and a predicted value for each of the one or more candidate service providers using the trained machine learning model;
evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers;
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers; and
onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
“for one or more candidate service providers of the plurality of service providers, determining, by the server computer, a predicted rank and a predicted value for each of the one or more candidate service providers using the trained machine learning model;
evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers;
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers; and” is/are directed to a mental processes group of abstract idea. Mental processes are defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes includes observations, evaluations, judgements and opinions.
These steps are considered mental processes group of abstract idea.
Step 2A prong 2:
Limitations “training, by the server computer, a machine learning model using training data based on the plurality of features of at least some of the service providers; and” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f).
Limitations “receiving, by a server computer, a dataset comprising data associated with a plurality of service providers;
extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features;
onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer.” are insignificant extra solution activity. See MPEP §2106.05(g).
The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
Limitation “receiving, by a server computer, a dataset comprising data associated with a plurality of service providers; extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Limitation “onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(g)). This appears to be well-understood, routine, conventional as evidenced by Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. MPEP 2106.05(d)(II)(iv. Storing and retrieving information in memory).
The claim is directed to mental processes group of abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As Claim 11:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
The Claim recites:
A server computer comprising:
a processor; and
a computer-readable medium coupled to the processor, the computer-readable medium comprising code executable by the processor for implementing a method comprising:
receiving a dataset comprising data associated with a plurality of service providers; extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features;
training a machine learning model using training data based on the plurality of features of at least some of the service providers; and
for one or more candidate service providers of the plurality of service providers, determining a predicted rank and a predicted value for each of the one or more candidate service providers using the trained machine learning model;
evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers;
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers; and
onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
“for one or more candidate service providers of the plurality of service providers, determining a predicted rank and a predicted value for each of the one or more candidate service providers using the trained machine learning model;
evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers;
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers; and” is/are directed to a mental processes group of abstract idea. Mental processes are defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes includes observations, evaluations, judgements and opinions.
These steps are considered mental processes group of abstract idea.
Step 2A prong 2:
Limitations “a processor; and a computer-readable medium coupled to the processor, the computer-readable medium comprising code executable by the processor for implementing a method comprising:” is a generic component to apply an abstract idea under 2106.05(f).
Limitations “training a machine learning model using training data based on the plurality of features of at least some of the service providers; and ” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f).
Limitations “receiving a dataset comprising data associated with a plurality of service providers; extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features; onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer.” are insignificant extra solution activity. See MPEP §2106.05(g).
The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
Limitation “receiving a dataset comprising data associated with a plurality of service providers; extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Limitation “onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(g)). This appears to be well-understood, routine, conventional as evidenced by Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. MPEP 2106.05(d)(II)(iv. Storing and retrieving information in memory).
The claim is directed to mental processes group of abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As Claim 19:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
The Claim recites:
A system comprising:
a server computer comprising:
a first processor; and a first computer-readable medium coupled to the first processor, the first computer-readable medium comprising code executable by the first processor for implementing a method comprising:
receiving a dataset comprising data associated with a plurality of service providers; extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features;
training a machine learning model using training data based on the plurality of features of at least some of the service providers; and
for one or more candidate service providers of the plurality of service providers, determining a predicted rank and a predicted value for each of the one or more candidate service providers using the trained machine learning model; and
evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers;
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers; and
onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer.
a logistics platform comprising:
a second processor; and
a second computer-readable medium coupled to the second processor, the second computer-readable medium comprising code executable by the second processor.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
“for one or more candidate service providers of the plurality of service providers, determining a predicted rank and a predicted value for each of the one or more candidate service providers using the trained machine learning model; and
evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers;
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers; and” is/are directed to a mental processes group of abstract idea. Mental processes are defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes includes observations, evaluations, judgements and opinions.
These steps are considered mental processes group of abstract idea.
Step 2A prong 2:
Limitations “A system comprising:
a server computer comprising:
a first processor; and a first computer-readable medium coupled to the first processor, the first computer-readable medium comprising code executable by the first processor for implementing a method comprising:
a logistics platform comprising:
a second processor; and
a second computer-readable medium coupled to the second processor, the second computer-readable medium comprising code executable by the second processor. ” is a generic component to apply an abstract idea under 2106.05(f).
Limitations “training a machine learning model using training data based on the plurality of features of at least some of the service providers; and ” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f).
Limitations “receiving a dataset comprising data associated with a plurality of service providers; extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features; training a machine learning model using training data based on the plurality of features of at least some of the service providers; onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer.” are insignificant extra solution activity. See MPEP §2106.05(g).
The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
Limitation “receiving a dataset comprising data associated with a plurality of service providers; extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features; training a machine learning model using training data based on the plurality of features of at least some of the service providers” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Limitation “onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(g)). This appears to be well-understood, routine, conventional as evidenced by Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. MPEP 2106.05(d)(II)(iv. Storing and retrieving information in memory).
The claim is directed to mental processes group of abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claims
As Claim 2, the Claim recites “wherein the dataset comprises external data and internal data.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the dataset comprises external data and internal data” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 4, the Claim recites “evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers; and
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers to use the server computer to perform service processing.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers; and responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers to use the server computer to perform service processing,” is directed to mental processes group of abstract idea. Prong 2: There are no additional limitation(s). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitation(s). The Claim is not patent eligible.
As Claim 5, the Claim recites “further comprising:
initiating, by the server computer, use of the server computer to perform service processing for the selected candidate service provider;
receiving, by the server computer from an end user device, a fulfillment request message comprising one or more items and indicating to use the selected candidate service provider, the fulfillment request message associated with a fulfillment request;
providing, by the server computer, the fulfillment request message, or a derivative thereof, to the selected candidate service provider, wherein the selected candidate service provider initiates preparation of the one or more items;
determining, by the server computer, one or more transporter user devices;
providing, by the server computer, the fulfillment request message to the one or more transporter user devices, wherein the one or more transporter user devices determine whether or not to request to accept the fulfillment request message;
receiving, by the server computer, an acceptance message from a transporter user device of the one or more transporter user devices;
generating, by the server computer, an update message indicating the status of the fulfillment request; and
providing, by the server computer, the update message to the end user device.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation “determining, by the server computer, one or more transporter user devices” is directed to mental processes group of abstract idea. Prong 2:
The limitation “initiating, by the server computer, use of the server computer to perform service processing for the selected candidate service provider;” are Mere Instruction to Apply an Exception.
The limitations “receiving, by the server computer from an end user device, a fulfillment request message comprising one or more items and indicating to use the selected candidate service provider, the fulfillment request message associated with a fulfillment request;
providing, by the server computer, the fulfillment request message, or a derivative thereof, to the selected candidate service provider, wherein the selected candidate service provider initiates preparation of the one or more items;
providing, by the server computer, the fulfillment request message to the one or more transporter user devices, wherein the one or more transporter user devices determine whether or not to request to accept the fulfillment request message;
receiving, by the server computer, an acceptance message from a transporter user device of the one or more transporter user devices;
generating, by the server computer, an update message indicating the status of the fulfillment request; and
providing, by the server computer, the update message to the end user device” are insignificant extra solution activity. See MPEP §2106.05(g).
Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Limitations “receiving, by the server computer from an end user device, a fulfillment request message comprising one or more items and indicating to use the selected candidate service provider, the fulfillment request message associated with a fulfillment request;
providing, by the server computer, the fulfillment request message, or a derivative thereof, to the selected candidate service provider, wherein the selected candidate service provider initiates preparation of the one or more items;
providing, by the server computer, the fulfillment request message to the one or more transporter user devices, wherein the one or more transporter user devices determine whether or not to request to accept the fulfillment request message;
receiving, by the server computer, an acceptance message from a transporter user device of the one or more transporter user devices;
generating, by the server computer, an update message indicating the status of the fulfillment request; and
providing, by the server computer, the update message to the end user device” were considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i. Receiving or transmitting data over a network, e.g., using the Internet to gather data). The Claim is not patent eligible.
As Claim 5, the Claim recites “further comprising:
after a predetermined amount of time, obtaining, by the server computer, data from the one or more candidate service providers;
including, by the server computer, the data from the one or more candidate service providers into the dataset;
extracting, by the server computer, an additional plurality of features from the dataset;
training, by the server computer, the machine learning model using the training data that is further based on the additional plurality of features; and
for one or more additional candidate service providers of the plurality of service providers, determining, by the server computer, an additional predicted rank and an additional predicted value for each of the one or more additional candidate service providers using the trained machine learning model.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation “for one or more additional candidate service providers of the plurality of service providers, determining, by the server computer, an additional predicted rank and an additional predicted value for each of the one or more additional candidate service providers using the trained machine learning model” is directed to mental processes group of abstract idea. Prong 2:
The limitation “training, by the server computer, the machine learning model using the training data that is further based on the additional plurality of features; and ” are Mere Instruction to Apply an Exception.
The limitations “after a predetermined amount of time, obtaining, by the server computer, data from the one or more candidate service providers;
including, by the server computer, the data from the one or more candidate service providers into the dataset;
extracting, by the server computer, an additional plurality of features from the dataset” are insignificant extra solution activity. See MPEP §2106.05(g).
Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Limitations “after a predetermined amount of time, obtaining, by the server computer, data from the one or more candidate service providers;
including, by the server computer, the data from the one or more candidate service providers into the dataset;
extracting, by the server computer, an additional plurality of features from the dataset” were considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i. Receiving or transmitting data over a network, e.g., using the Internet to gather data). The Claim is not patent eligible.
As Claim 6, the Claim recites “wherein the machine learning model comprises a gradient boosted trees supervised with a squared error loss function.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the machine learning model comprises a gradient boosted trees supervised with a squared error loss function” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 7, the Claim recites “outputting, by the server computer, metrics associated with the machine learning model, wherein the metrics comprise at least a Shapley value of each of the plurality of features of the dataset.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “outputting, by the server computer, metrics associated with the machine learning model, wherein the metrics comprise at least a Shapley value of each of the plurality of features of the dataset” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 8, the Claim recites “wherein the user intent features include user search queries and user service provider requests, wherein the off-platform features include service provider performance history, service provider ratings, service provider reviews, and service provider hours, and wherein the on-platform features include previously selected service provider performance.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the user intent features include user search queries and user service provider requests, wherein the off-platform features include service provider performance history, service provider ratings, service provider reviews, and service provider hours, and wherein the on-platform features include previously selected service provider performance” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 9, the Claim recites “generating, by the server computer, a weighted decile cohort percentage error and a decile rank score using the predicated value.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “generating, by the server computer, a weighted decile cohort percentage error and a decile rank score using the predicated value” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 10, the Claim recites “evaluating, by the server computer, a newly selected service provider using the weighted decile cohort percentage error and the decile rank score to determine performance of the newly selected service provider.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “evaluating, by the server computer, a newly selected service provider using the weighted decile cohort percentage error and the decile rank score to determine performance of the newly selected service provider” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 13, the Claim is not patent eligible for the same analysis as Claim 4 and 10.
As Claim 14, the Claim recites “wherein the predetermined amount of time has a length between 4-6 months.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the predetermined amount of time has a length between 4-6 months” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 15, the Claim recites “wherein extracting the plurality of features from the dataset comprises: extracting the plurality of features using principal component analysis, independent component analysis, linear discriminant analysis, local linear embeddings, and/or autoencoders.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein extracting the plurality of features from the dataset comprises: extracting the plurality of features using principal component analysis, independent component analysis, linear discriminant analysis, local linear embeddings, and/or autoencoders” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 16, the Claim recites “wherein training the machine learning model using training data based on the plurality of features of at least some of the service providers comprises:
training a first machine learning model using first training data based on the plurality of features of a first group of the service providers; and
training a second machine learning model using second training data based on the plurality of features of a second group of the service providers.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein training the machine learning model using training data based on the plurality of features of at least some of the service providers comprises:
training a first machine learning model using first training data based on the plurality of features of a first group of the service providers; and
training a second machine learning model using second training data based on the plurality of features of a second group of the service providers” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 17, the Claim recites “wherein the first group includes service providers associated with a first characteristic and wherein the second group includes service providers associated with a second characteristic.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the first group includes service providers associated with a first characteristic and wherein the second group includes service providers associated with a second characteristic” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 18, the Claim recites “wherein the first characteristic is a chain service provider, wherein the second characteristic is a local service provider.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the first characteristic is a chain service provider, wherein the second characteristic is a local service provider” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 20, the Claim is not patent eligible for the same reasons as Claim 13.
As Claim 21, the Claim recites “wherein the machine learning model comprises a gradient boost trees supervised learning model.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the machine learning model comprises a gradient boost trees supervised learning model” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 5, 8, 11, 15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nenkova et al. (U.S. 2022/0027858 hereinafter Nenkova) in view of Kumar (U.S. 2022/0019946 hereinafter Kumar) in further view of Sheth (U.S. 2002/0026398 hereinafter Sheth).
As Claim 1, Nenkova teaches a method comprising:
receiving, by a server computer (Nenkova (¶0076 line 1-4), cloud computing environment), a dataset comprising data associated with a plurality of service providers (Nenkova (¶0019), location data includes data bout food or other facility at the location);
extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features (Nenkova (¶0020 line 1-3 line 1-8), system utilizes the machine learning to analyze the user specific data (user intent features) and location data. Nenkova (¶0019 line 1-8), location data includes ratings for the food providers (internally collected (on-platform) and from external website (off-platform)));
for one or more candidate service providers of the plurality of service providers, determining, by the server computer, a predicted rank and a predicted value for each of the one or more candidate service providers using the trained machine learning model (Nenkova (¶0020 last 6 lines), system “provide a recommendation score and ranking for each of one or more locations or items at the location (e.g., park attractions, rides, shows) for the user”).
Nenkova may not explicitly disclose:
training, by the server computer, a machine learning model using training data based on the plurality of features of at least some of the service providers;
Kumar teaches:
training, by the server computer, a machine learning model using training data based on the plurality of features of at least some of the service providers (Kumar (¶0031 line 1-11), recommendation engine analyzes sentiment analysis on the location and user’s feedbacks, both on locally stored and from external system. Kumar (¶0032 line 1-4), recommendation engine learns from the generated data); and
Nenkova discloses a system/method for using machine learning model to recommend service providers for a user. Predicted recommendation score and raking are generated from internal and external ratings of food providers. Kumar discloses a system/method to train machine learning system using internal and external ratings. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning system of Nenkova instead be a training module taught by Kumar, with a reasonable expectation of success. The motivation would be that user can solve the problem with “trips that should be seamless with reduced stress can turn stressful due to a lack of default options. The present disclosure provides systems and methods for generating travel itineraries at least to provide default options for individuals” (Kumar (¶0004 last 8 lines)).
Nenkov in view of Kumar may not explicitly disclose:
evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers;
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers; and
onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer.
Sheth teaches:
evaluating, by the server computer, the predicted rank and the predicted value for each of the one or more candidate service providers (Sheth (¶0079 line 16-17), “the storefront service module 1506 may request information from only those service providers with the highest ratings”, system evaluates and selects providers with highest ratings);
responsive to evaluating, selecting, by the server computer, at least one of the one or more candidate service providers (Sheth (¶0079 line 16-17), “the storefront service module 1506 may request information from only those service providers with the highest ratings”, system evaluates and selects providers with highest ratings); and
onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer (Sheth (¶0079 line 4-7), “adding additional service providers 1504 to the database if there are no available service providers 1504 to complete a new service request.” Sheth (¶0079 last 3 lines), “This information about the capacity of the service provider 1504 is received and stored, in step 1706, in the database 1508 of service providers”).
Nenkova in view of Kumar discloses a system/method for using machine learning model to recommend service providers for a user. Sheth discloses a system/method for providing service provider and expanding the service provider pool. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify system of Nenkova in view of Kumar instead be a server database taught by Sheth, with a reasonable expectation of success. The motivation would be to allow “adding additional service providers
1504 to the database if there are no available service providers 1504 to complete a new service
request” (Sheth (¶0079 line 4-7)).
As Claim 2, besides claim 1, Nenkova in view of Kumar in further view of Sheth teaches wherein the dataset comprises external data and internal data (Nenkova (¶0019 line 1-8), location data includes ratings for the food providers (internally collected (on-platform) and form external website (off-platform))).
As Claim 5, besides Claim 1, Nenkova in view of Kumar in further view of Sheth teaches further comprising:
after a predetermined amount of time, obtaining, by the server computer, data from the one or more candidate service providers (Kumar (¶0106 last 11 lines), in initial run, weight values may be set according to w1>w2>w3);
including, by the server computer, the data from the one or more candidate service providers into the dataset (Kumar (¶0031 line 1-11), recommendation engine analyzes sentiment analysis on the location and user’s feedbacks, both on locally stored and from external system. Kumar (¶0032 line 1-4), recommendation engine learns from the generated data);
extracting, by the server computer, an additional plurality of features from the dataset (Kumar (¶0106 last 11 lines), “after regression, weight proportions over the various time spans can settle to a different relationship, e.g., w 1 =w2>w3 , etc”);
training, by the server computer, the machine learning model using the training data that is further based on the additional plurality of features (Kumar (¶0106 last 11 lines), “leaming and/or training as described herein can be performed to determine weights in other situations described herein in the present disclosure ( e.g., for the other equations included in the present disclosure)”); and
for one or more additional candidate service providers of the plurality of service providers, determining, by the server computer, an additional predicted rank and an additional predicted value for each of the one or more additional candidate service providers using the trained machine learning model (Nenkova (¶0028 last 4 lines), refresh options allow visit recommendation or visit schedule to be revised in real-time).
As Claim 8, besides claim 1, Nenkova in view of Kumar in further view of Sheth teaches wherein the user intent features include user search queries and user service provider requests (Nenkova (¶0031), system queries user for user input data. User inputs include user preference), wherein the off-platform features include service provider performance history, service provider ratings, service provider reviews, and service provider hours, and wherein the on-platform features include previously selected service provider performance history (Nenkova (¶0019 line 4-8), internal and external rating includes types of food, length of food lines and estimated wait time for food).
As Claim 11, Nenkova teaches a server computer comprising:
a processor (Nenkova (¶0082, fig. 4 line 9), one or more CPUs 402); and
a computer-readable medium coupled to the processor, the computer-readable medium comprising code executable by the processor for implementing a method (Nenkova (¶0082, fig. 4 line 9), a memory subsystem 404) comprising:
The rest of the Claim is rejected for the same reasons as Claim 1.
As Claim 15, besides Claim 11, Nenkova in view of Kumar in further view of Sheth teaches wherein extracting the plurality of features from the dataset comprises:
extracting the plurality of features using principal component analysis, independent component analysis, linear discriminant analysis, local linear embeddings, and/or autoencoders (Kumar (¶0031 line 1-11), recommendation engine analyzes sentiment analysis on the location and user’s feedbacks, both on locally stored and from external system. Kumar (¶0032 line 1-4), recommendation engine learns from the generated data).
As Claim 19, Nenkova teaches a logistics platform comprising:
a second processor (Nenkova (¶0050 line 3-9, fig. 1 item 102, 104), system includes platform 102 and 104); and
a second computer-readable medium coupled to the second processor, the second computer-readable medium comprising code executable by the second processor (Nenkova (¶0050 line 3-9, fig. 1 item 102, 104), platform 104 is a computer).
The rest of the Claim is rejected for the same reasons as Claim 11.
Claim(s) 4, 13-14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nenkova and Kumar in view of Sheth in further view of Shang et al. (U.S. 2022/0138887 hereinafter Shang).
As Claim 4, besides Claim 1, Nenkova in view of Kumar in further view of Sheth teaches:
initiating, by the server computer, use of the server computer to perform service processing for the selected candidate service provider (Nenkova (¶0028 line 7-10), graphical user interface includes an option to select one of the suggestion(s));
Nenkova in view of Kumar in further view of Sheth may not explicitly disclose:
receiving, by the server computer from an end user device, a fulfillment request message comprising one or more items and indicating to use the selected candidate service provider, the fulfillment request message associated with a fulfillment request;
providing, by the server computer, the fulfillment request message, or a derivative thereof, to the selected candidate service provider, wherein the selected candidate service provider initiates preparation of the one or more items;
determining, by the server computer, one or more transporter user devices;
providing, by the server computer, the fulfillment request message to the one or more transporter user devices, wherein the one or more transporter user devices determine whether or not to request to accept the fulfillment request message;
receiving, by the server computer, an acceptance message from a transporter user device of the one or more transporter user devices;
generating, by the server computer, an update message indicating the status of the fulfillment request; and
providing, by the server computer, the update message to the end user device.
Shang teaches:
receiving, by the server computer from an end user device, a fulfillment request message comprising one or more items and indicating to use the selected candidate service provider (Shang (¶0044 line 4-9), platform accept passengers’ requests for transportation including trip data), the fulfillment request message associated with a fulfillment request (Shang (¶0044 line 4-9), platform accept passengers’ requests for transportation including trip data);
providing, by the server computer, the fulfillment request message, or a derivative thereof, to the selected candidate service provider (Shang (¶0044 line 4-9), platform accept passengers’ requests for transportation including trip data), wherein the selected candidate service provider initiates preparation of the one or more items (Shang (¶0044 line 4-9), the platform identify idle vehicles to arrange for pick-ups);
determining, by the server computer, one or more transporter user devices (Shang (¶0044 line 4-9), the platform identifies idle vehicles to arrange for pick-ups);
providing, by the server computer, the fulfillment request message to the one or more transporter user devices, wherein the one or more transporter user devices determine whether or not to request to accept the fulfillment request message (Shang (¶0065 line 23-28), driver reacts such as stay, relocate or accept an order);
receiving, by the server computer, an acceptance message from a transporter user device of the one or more transporter user devices (Shang (¶0065 line 23-28), driver reacts such as stay, relocate or accept an order);
generating, by the server computer, an update message indicating the status of the fulfillment request (Shang (¶0044 line 4-9), the platform identifies idle vehicles to arrange for pick-ups); and
providing, by the server computer, the update message to the end user device (Shang (¶0044 line 4-9), the platform identifies idle vehicles to arrange for pick-ups).
Nenkova in view of Kumar in further view of Sheth discloses a system/metho to recommend service providers to a user. Shang discloses a system for servicing fulfillment requests. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify ranking system of Nankova in view of Kumar instead be a recommendation system taught by Shang, with a reasonable expectation of success. The motivation would be to “facilitate transportation service by connecting drivers of vehicles with passengers” (Shang (¶0044 line 2-4)).
As Claim 13, besides Claim 11, the Claim is rejected for the same reasons as Claim 4.
As Claim 14, besides Claim 13, Nenkova in view of Kumar in further view of Shang teaches wherein the predetermined amount of time has a length between 4-6 months (Kumar (¶0024 line 10-16), a predetermined amount of time can include winter month or summer Olympic games).
As Claim 20, the Claim is rejected for the same reasons as Claim 13.
Claim(s) 6-7, 9-10, 12 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nenkova and Kumar in view of Sheth in further view of Forgaty et al. (U.S. 12125067 hereinafter Forgaty).
As Claim 6, besides Claim 1, Nenkova in view of Kumar in view of Sheth may not explicitly disclose wherein the machine learning model comprises a gradient boosted trees supervised learning model with a squared error loss function.
Forgarty teaches:
wherein the machine learning model comprises a gradient boosted trees supervised (Forgaty (col. 52 line 4), supervised model) learning model (Forgaty (col. 35 line 1-3), mode is a tree based gradient boosting model) with a squared error loss function (Forgaty (col. 15 line 46-52), model is evaluated based on RMSE).
Nenkova in view of Kumar in view of Sheth discloses a system/method to train a machine learning model. Forgaty suggests gradient boost trees learning technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning system of Nenkova in view of Kumar in view of Sheth instead be a machine learning system taught by Forgaty, with a reasonable expectation of success. The motivation would be to allow “a gradient boosting model is a tree-based model which improves the ability of model fitting, by finding a gradient of the error” (Forgaty (col. 35 line 1-3)).
As Claim 7, besides Claim 1, Nenkova in view of Kumar in further view of Sheth may not explicitly disclose outputting, by the server computer, metrics associated with the machine learning model, wherein the metrics comprise at least a Shapley value of each of the plurality of features of the dataset.
Forgarty teaches:
outputting, by the server computer, metrics associated with the machine learning model, wherein the metrics comprise at least a Shapley value of each of the plurality of features of the dataset (Forgaty (col. 42 line 1-13), metrics comprise Shapley value).
Nenkova in view of Kumar in view of Sheth discloses a system/method to train a machine learning model. Forgaty suggests gradient boost trees learning technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning system of Nenkova in view of Kumar in view of Sheth instead be a machine learning system taught by Forgaty, with a reasonable expectation of success. The motivation would be to allow “a gradient boosting model is a tree-based model which improves the ability of model fitting, by finding a gradient of the error” (Forgaty (col. 35 line 1-3)).
As Claim 9, besides Claim 1, Nenkova in view of Kumar in further view of Sheth may not explicitly disclose generating, by the server computer, a weighted decile cohort percentage error.
Forgarty teaches:
generating, by the server computer, a weighted decile cohort percentage error (Forgaty (col. 17 line 37-41), businesses are categorized into decile with the top decile being the highest rank) and a decile rank score using the predicated value (Forgaty (col. 27 line 39-41), output of the model is stored in decile).
Nenkova in view of Kumar in view of Sheth discloses a system/method to train a machine learning model. Forgaty suggests gradient boost trees learning technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning system of Nenkova in view of Kumar in view of Sheth instead be a machine learning system taught by Forgaty, with a reasonable expectation of success. The motivation would be to allow “a gradient boosting model is a tree-based model which improves the ability of model fitting, by finding a gradient of the error” (Forgaty (col. 35 line 1-3)).
As Claim 10, besides Claim 9, Nenkova and Kumar in view of Sheth in further view of Forgarty teaches:
evaluating, by the server computer, a newly selected service provider using the weighted decile cohort percentage error (Forgaty (col. 17 line 37-41), businesses are categorized into decile with the top decile being the highest rank) and the decile rank score to determine performance of the newly selected service provider (Forgaty (col. 27 line 39-41), output of the model is stored in decile).
As Claim 12, the Claim is rejected for the same reasons as Claim 6.
As Claim 21, the Claim is rejected for the same reasons as Claim 6.
Claim(s) 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nenkova and Kumar in view of Sheth in further view of Yu et al. (U.S. 2019/0108458 hereinafter Yu).
As Claim 16, besides Claim 11, Nenkova in view of Kumar in further view of Sheth may not explicitly disclose wherein training the machine learning model using training data based on the plurality of features of at least some of the service providers comprises:
training a first machine learning model using first training data based on the plurality of features of a first group of the service providers; and
training a second machine learning model using second training data based on the plurality of features of a second group of the service providers.
Yu teaches:
wherein training the machine learning model using training data based on the plurality of features of at least some of the service providers comprises:
training a first machine learning model using first training data based on the plurality of features of a first group of the service providers (Yu (¶0085), models are trained based on categorized type. There are multiple types of models); and
training a second machine learning model using second training data based on the plurality of features of a second group of the service providers (Yu (¶0085), models are trained based on categorized type. There are multiple types of models).
Nenkova in view of Kumar in view of Sheth discloses a system/method to train a machine learning model. Yu suggests to train multiple models for different provider groups. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning system of Nenkova instead be a machine learning system taught by Yu, with a reasonable expectation of success. The motivation would be to allow “a different model exists for different target audiences. For example, a model may exist for male customers and one for female customers” (Yu (¶0026 line 6-8)).
As Claim 17, besides Claim 16, Nenkova and Kumar in view of Sheth further view of Yu teaches wherein the first group includes service providers associated with a first characteristic and wherein the second group includes service providers associated with a second characteristic (Yu (¶0085), models are trained based on categorized type. There are multiple types of models).
As Claim 18, besides Claim 16, Nenkova and Kumar in view of Sheth in further view of Yu teaches wherein the first characteristic is a chain service provider, wherein the second characteristic is a local service provider (Yu (¶0085), model type could be urban (local) or Pacific Northwest (chain)).
Response to Arguments
§101 Rejections:
Step 2A, prong 1: Applicant argues that the amended claims are not directed to mental process because they solve the technical problem of efficiently and autonomously select and onboard high-potential service providers (last paragraph of page 10 in the remarks).
Applicant’s arguments are fully considered but are not persuasive. The efficiently and autonomously select and onboard high-potential service providers are not discussed in the Applicant’s Specification. Therefore, there is no way for examiner to determine the effect of current claim amendment(s).
Step 2A, prong 1: Applicant argues that “training a machine learning model …” and “onboarding, by the server computer, the selected candidate service provider …” cannot be performed in the mind (second paragraph of page 11 in the remarks).
Applicant’s arguments are fully considered but are not persuasive.
Limitations “training a machine learning model using training data based on the plurality of features of at least some of the service providers; and” are Mere Instruction to Apply an Exception. See MPEP §2106.05(f).
Limitations “onboarding, by the server computer, the selected candidate service provider in a platform offered by the server computer.” are insignificant extra solution activity. See MPEP §2106.05(g).
Step 2A prong 2: Applicant argues that “the amended claim adds specific technical step that go far beyond merely organizing human activity or generic data processing” (fourth paragraph of page 11 in the remarks).
Applicant’s arguments are fully considered but are not persuasive. The current claimed limitation(s) do not include any activity/steps that could not be performed by human mind. Further clarifying the claims to include such limitation(s) might advance the prosecution(s).
Step 2A prong 2: Applicant argues that “the onboarding of service provider is not a mere display of information, but a system level action that integrates the new provider into the platform’s operational workflow. This process improves the technical functioning of the platform by automating selection and onboarding, reducing manual intervention, and efficiently allocating computational and business resources only to those candidates with the highest probability of success”. Therefore, this is a practical application of machine learning (last paragraph of page 11 in the remarks).
Applicant’s arguments are fully considered but are not persuasive. The “onboarding …” limitation is construed as saving information in the database. Therefore, the feature itself does not reflect the technical improvement. Further clarification on how onboarding of service provider would efficiently allocate computational and business resources might advance the prosecution. Further on, the improvement is not discussed in the Applicant’s Specification. Therefore, there is no way for examiner to determine the effect of the “onboarding” limitation.
Step 2B: Applicant argues that the amended claim is directed to a non-routine and unconventional method for improving a digital delivery platform because the claim requires extracting three distinct features type. The combination of internal search data, external popularity data and internal performance is not “well-understood, routine and conventional” activity of generic data gathering (second paragraph of page 12 in the remarks).
Applicant’s arguments are fully considered but are not persuasive. The Claim is directed to receiving and extracting data. By the way it is claimed, these steps are construed as transmitting and receiving data which is well-understood, routine and conventional by MPEP 2106.05(d)(II)(i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Step 2B: Applicant argues that the amended claim is directed to a non-routine and unconventional method because the claim requires applying the model to a distinct set of features in order to predict both a rank and a value for each candidate provider. In addition, the onboard step is not a trivial or generic computer function (third paragraph of page 12 in the remarks).
Applicant’s arguments are fully considered but are not persuasive. Applicant admits that the claimed invention is “applying the model to a distinct set of features”. Therefore, the limitation(s) is reasonably construed as Mere Instruction to Apply an Exception. Further on, onboarding step does not convey any information rather than storing service provider on a database. Further clarification on onboarding limitation might advance the prosecution.
§101 Rejections:
Applicant argues that cited references fail to disclose the amended limitation(s).
Applicant’s arguments are moot because new reference Sheth teaches the limitation(s).
Applicant argues that cited reference does not disclose “extracting … a plurality of features … wherein the plurality of features includes user intent features, off-platform features, and on-platform features” and “training … a machine learning model based on the plurality of features …” (second paragraph of page 14 in the remarks).
Applicant’s arguments are fully considered but are not persuasive. Nenkova teaches extracting, by the server computer, a plurality of features from the dataset, wherein the features include user intent features, off-platform features, and on-platform features (Nenkova (¶0020 line 1-3 line 1-8), system utilizes the machine learning to analyze the user specific data (user intent features) and location data. Nenkova (¶0019 line 1-8), location data includes ratings for the food providers (internally collected (on-platform) and from external website (off-platform))). Kumar teaches training, by the server computer, a machine learning model using training data based on the plurality of features of at least some of the service providers (Kumar (¶0031 line 1-11), recommendation engine analyzes sentiment analysis on the location and user’s feedbacks, both on locally stored and from external system. Kumar (¶0032 line 1-4), recommendation engine learns from the generated data); and
Applicant argues that Applicant argues that “user intent features” includes user behavior such as search queries and active engagement signals. Further, “on-platform” and “off-platform” refer to dynamic, performance-base metric internal to the platform and external reputation data from third-party sources (fourth paragraph of page 14 in the remarks).
Applicant’s arguments are fully considered but are not persuasive. These limiatation(s) are not currently claimed. Further clarification on these aspect(s) might advance the prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kung. (U.S. 2020/0082347) teaches a service broker between client and service provider.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147
Prosecution Insights