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 1-20 are pending for examination.
Claims 1, 11 and 17 are independent Claims.
Claims 1-20 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a Judicial Exception without significantly more.
Independent Claims
As Claims 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 system comprising:
a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving activity data for a plurality of model features associated with a machine learning (ML) model, wherein the activity data is associated with past activities of a user;
generating, using an encoder associated with the ML model, a first plurality of embeddings associated with the plurality of model features from the activity data;
encoding, using the encoder, a vector from the first plurality of embeddings, wherein the encoding includes utilizing a forecasting ML layer for output of the vector;
calculating, using the ML model, a risk score based on the vector, wherein the ML model is trained based on a plurality of other past activity vectors generated by the encoder using training data associated with other past activities for a plurality of other users;
analyzing the risk score from the ML model; and
determining, based on the analyzing, a predicted likelihood of the user meeting or failing to meet a condition for a service offered to the user at a future time.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
“generating, using an encoder associated with the ML model, a first plurality of embeddings associated with the plurality of model features from the activity data;
encoding, using the encoder, a vector from the first plurality of embeddings, wherein the encoding includes utilizing a forecasting ML layer for output of the vector;
analyzing the risk score from the ML model; and
determining, based on the analyzing, a predicted likelihood of the user meeting or failing to meet a condition for a service offered to the user at a future time.” 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.
“calculating, using the ML model, a risk score based on the vector, wherein the ML model is trained based on a plurality of other past activity vectors generated by the encoder using training data associated with other past activities for a plurality of other users;” is directed to a mathematical concepts group of abstract ideas. Mathematical concepts are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations.
These steps are considered mental processes group of abstract idea.
Step 2A prong 2:
Limitations “a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving activity data for a plurality of model features associated with a machine learning (ML) model, wherein the activity data is associated with past activities of a user;” 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 “a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:” is well-understood, routine conventional activity (WURC).
Limitation “receiving activity data for a plurality of model features associated with a machine learning (ML) model, wherein the activity data is associated with past activities of a user” 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”.)
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 Claims 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 method comprising:
receiving activity data for a user, wherein the activity data comprises historical activities by the user over a time period ;
extracting model feature data for a plurality of model features associated with a machine learning (ML) model from the activity data;
generating a plurality of embeddings for the plurality of model features from the activity data, wherein the plurality of embeddings are each associated with individual activities from the historical activities by the user over the time period;
applying an attention layer to the plurality of embeddings, wherein the attention layer applies weights on particular features from the plurality of model features in the plurality of embeddings;
generating, using a long-short term memory (LSTM) model, a vector from the plurality of embeddings, wherein the generating includes utilizing an ML layer for output of the vector;
providing the vector to the ML model, wherein the ML model is trained using a plurality of other past activity vectors associated with additional historical activities by a plurality of other users; and
determining, using the ML model based on the providing, a risk score of the user for failing to meet a required stipulation of a service extended to the user at a future time.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
Limitations “extracting model feature data for a plurality of model features associated with a machine learning (ML) model from the activity data;
generating a plurality of embeddings for the plurality of model features from the activity data, wherein the plurality of embeddings are each associated with individual activities from the historical activities by the user over the time period;
generating, using a long-short term memory (LSTM) model, a vector from the plurality of embeddings, wherein the generating includes utilizing an ML layer for output of the vector;
determining, using the ML model based on the providing, a risk score of the user for failing to meet a required stipulation of a service extended to the user at a future time.” 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 “receiving activity data for a user, wherein the activity data comprises historical activities by the user over a time period ;
providing the vector to the ML model, wherein the ML model is trained using a plurality of other past activity vectors associated with additional historical activities by a plurality of other users; and” are insignificant extra solution activity. See MPEP §2106.05(g).
Limitations “applying an attention layer to the plurality of embeddings, wherein the attention layer applies weights on particular features from the plurality of model features in the plurality of embeddings;” is mere instruction to apply an exception. See MPEP §2106.05(f).
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 activity data for a user, wherein the activity data comprises historical activities by the user over a time period ;
providing the vector to the ML model, wherein the ML model is trained using a plurality of other past activity vectors associated with additional historical activities by a plurality of other users; and” 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”.)
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 Claims 17:
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 non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
receiving data associated with past activities of a user;
generating, using an encoding operation of a machine learning (ML) framework, a plurality of embeddings for activity features of the past activities based on the data;
generating, using an ML layer of the ML framework, a vector encoded from the plurality of embeddings; and
forecasting, using the ML framework, a likelihood of a user action by the user at a future time based on the vector, wherein the forecasting is performed using an ML model trained using a plurality of other vectors generated using additional past activities of a plurality of other users.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
Limitations “generating, using an encoding operation of a machine learning (ML) framework, a plurality of embeddings for activity features of the past activities based on the data;
generating, using an ML layer of the ML framework, a vector encoded from the plurality of embeddings; and
forecasting, using the ML framework, a likelihood of a user action by the user at a future time based on the vector, wherein the forecasting is performed using an ML model trained using a plurality of other vectors generated using additional past activities of a plurality of other users.” 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 “receiving data associated with past activities of a user; ” 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 data associated with past activities of a user;” 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”.)
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 operations further comprise: providing an offer of the service to the user based on whether the predicted likelihood meets or exceeds a threshold likelihood score.”
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 “ wherein the operations further comprise: providing an offer of the service to the user based on whether the predicted likelihood meets or exceeds a threshold likelihood score” 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 3, the Claim recites “wherein, prior to the providing the offer, the operations further comprise:
predicting an engagement score of the user based on the risk score and the past activities, wherein the engagement score is associated with a usage of a service provider corresponding to the service by the user, and wherein the providing the offer is further based on the engagement score.”
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 “ predicting an engagement score of the user based on the risk score and the past activities, wherein the engagement score is associated with a usage of a service provider corresponding to the service by the user, and wherein the providing the offer is further based on the engagement score” 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 4, the Claim recites “wherein the engagement score comprises a Recency, Frequency, and Monetary, Breadth, and Consistency (RFMBC) model score associated with a recency of each of the past activities, a frequency of the past activities, and a monetary value associated with each of the past activities.”
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 engagement score comprises a Recency, Frequency, and Monetary, Breadth, and Consistency (RFMBC) model score associated with a recency of each of the past activities, a frequency of the past activities, and a monetary value associated with each of the past activities” are field of use and technological environment. See MPEP §2106.05(h). 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 5, the Claim recites “wherein the generating the embedding comprises:
converting description data for the past activities to the plurality of first embeddings using at least one data embedding process, wherein the at least one data embedding process converts text data to numerical representations in the plurality of first embeddings.”
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 generating the embedding comprises:
converting description data for the past activities to the plurality of first embeddings using at least one data embedding process, wherein the at least one data embedding process converts text data to numerical representations in the plurality of first embeddings” 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 6, the Claim recites “wherein, prior to the generating the first plurality of embeddings, the operations further comprise:
in response to receiving the activity data, determining that the activity data is designated for processing by the ML model;
determining, for the ML model, a multi-layer ML architecture comprising the encoder and a decoder associated with the encoder, wherein the encoder includes at least an embedding layer that generates the first plurality of embeddings, an attention layer that applies weights to the first plurality of embeddings, and the forecasting ML layer; and
executing the encoder for the generating the first plurality of embeddings.”
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: Limitations “in response to receiving the activity data, determining that the activity data is designated for processing by the ML model;” are mental processes group of abstract idea. Prong 2: The limitation “determining, for the ML model, a multi-layer ML architecture comprising the encoder and a decoder associated with the encoder, wherein the encoder includes at least an embedding layer that generates the first plurality of embeddings, an attention layer that applies weights to the first plurality of embeddings, and the forecasting ML layer; and
executing the encoder for the generating the first plurality of embeddings” 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 “wherein the forecasting ML layer comprises a long-short term memory (LSTM) model configured to encode the vector, and wherein the attention layer comprises a multi-headed self-attention mechanism configured to apply the weights to the first plurality of embeddings based on time-based 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 forecasting ML layer comprises a long-short term memory (LSTM) model configured to encode the vector, and wherein the attention layer comprises a multi-headed self-attention mechanism configured to apply the weights to the first plurality of embeddings based on time-based 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 8, the Claim recites “wherein, prior to the receiving the activity data, the operations further comprise: training the ML model using the plurality of other past activity vectors in place of mode feature data from the training data for the plurality of model features, wherein the plurality of other past activity vectors are configured to reduce a dimensionality of the plurality of model features in the training data to a n-dimensional vector.”
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, prior to the receiving the activity data, the operations further comprise: training the ML model using the plurality of other past activity vectors in place of mode feature data from the training data for the plurality of model features, wherein the plurality of other past activity vectors are configured to reduce a dimensionality of the plurality of model features in the training data to a n-dimensional vector” 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 “wherein, prior to the training, the operations further comprise:
decoding, using a decoder associated with the encoder, the plurality of other past activity vectors to a second plurality of embeddings;
comparing the first plurality of embeddings to the second plurality of embeddings; and
determining whether to provide the plurality of other past activity vectors for the training of the ML model based on the comparing.”
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: Limitations “comparing the first plurality of embeddings to the second plurality of embeddings; and determining whether to provide the plurality of other past activity vectors for the training of the ML model based on the comparing” are mental processes group of abstract idea. Prong 2: The limitation “decoding, using a decoder associated with the encoder, the plurality of other past activity vectors to a second plurality of embeddings;” 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 “wherein the training data comprises time-based activity data for the plurality of users that are not associated with banking account information, and wherein the plurality of model features comprise at least a portion of default risk features for risk assessment.”
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 ideas. Prong 2: The limitation “wherein the training data comprises time-based activity data for the plurality of users that are not associated with banking account information, and wherein the plurality of model features comprise at least a portion of default risk features for risk assessment;” are field of use and technological environment. See MPEP §2106.05(h). 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 12, the Claim recites “wherein the attention layer comprises a multi-headed self-attention mechanism for the weights on the particular features.”
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 ideas. Prong 2: The limitation “wherein the attention layer comprises a multi-headed self-attention mechanism for the weights on the particular features;” are field of use and technological environment. See MPEP §2106.05(h). 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 recites “wherein the LSTM model is configured for a transaction forecasting associated with the activity data and the additional historical activities.”
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 ideas. Prong 2: The limitation “wherein the LSTM model is configured for a transaction forecasting associated with the activity data and the additional historical activities;” are field of use and technological environment. See MPEP §2106.05(h). 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 14, the Claim recites “further comprising:
generating an engagement score for the user based on the risk score and the historical activities, wherein the engagement score is associated with a usage of a service provider corresponding to the service by the user; and
providing an offer for the service to the user based on the engagement score.”
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: Limitations “generating an engagement score for the user based on the risk score and the historical activities, wherein the engagement score is associated with a usage of a service provider corresponding to the service by the user; and providing an offer for the service to the user based on the engagement score” are mental processes group of abstract idea. Prong 2: There are no additional limitations. 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, prior to the receiving the activity data, the method further comprises:
generating the plurality of other past activity vectors using an encoder comprising an embedding layer associated with generating the plurality of embeddings, the attention layer, and the LSTM model; and training the ML model using the plurality of other past activity vectors.”
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 ideas. Prong 2: The limitation “generating the plurality of other past activity vectors using an encoder comprising an embedding layer associated with generating the plurality of embeddings, the attention layer, and the LSTM model; and training the ML model using the plurality of other past activity vectors;” 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 “further comprising:
decoding the plurality of other past activity vectors; comparing the decoded plurality of other past activity vectors to the plurality of embeddings; and determining that the decoded plurality of other past activity vectors correlates to the plurality of embeddings within a similarity threshold.”
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: Limitations “decoding the plurality of other past activity vectors; comparing the decoded plurality of other past activity vectors to the plurality of embeddings; and determining that the decoded plurality of other past activity vectors correlates to the plurality of embeddings within a similarity threshold” are mental processes group of abstract idea. Prong 2: There are no additional limitations. 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, prior to the forecasting, the operations further comprise:
determining, using a decoding operation of the ML framework, a plurality of decoded embeddings from the vector; and
comparing the plurality of embeddings to the plurality of decoded embeddings.”
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: Limitations “comparing the plurality of embeddings to the plurality of decoded embeddings” are mental processes group of abstract idea. Prong 2: Limitations “determining, using a decoding operation of the ML framework, a plurality of decoded embeddings from the vector; and” are mere instruction to apply an exception. See MPEP §2106.5(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 19, the Claim recites “wherein, prior to the forecasting, the comparing is required to meet a similarity threshold.”
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: Limitations “wherein, prior to the forecasting, the comparing is required to meet a similarity threshold” are 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. The Claim is not patent eligible.
As Claim 20, the Claim recites “wherein the operations further comprise:
providing a notification associated with an encoding accuracy of the encoding operation based on the comparing.”
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: Limitations “providing a notification associated with an encoding accuracy of the encoding operation based on the comparing” are mental processes group of abstract idea. Prong 2: There are no additional limitations. 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, 5-9, 11-13 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ben Kimon et al. (U.S. 2021/0200955 hereinafter BenKimon) in view of Hong (U.S. 2019/0325514 hereinafter Hong).
As Claim 1, BenKimon teaches a system comprising:
a non-transitory memory (BenKimon (¶0030 line 2, fig. 2 item 220), a system memory 220); and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations (BenKimon (¶0029 line 9-10, fig. 2 item 214), a processor 214) comprising:
receiving activity data for a plurality of model features associated with a machine learning (ML) model, wherein the activity data is associated with past activities of a user (BenKimon (¶0051 line 1-4), “predictive models may be trained using account actions of known or previously determined fraudulent accounts and/or account actions of known benign user accounts.”);
generating, using an encoder associated with the ML model, a first plurality of embeddings associated with the plurality of model features from the activity data (BenKimon (¶0044 line 1-4 and 9-11, fig. 3 item 320), “Natural Language Processing (NLP) word embedding algorithm, such as autoencoder or word2vec, to the retrieved user log data to generate a low-dimensional embedding (embedding)… data structure (set of arrays, a document, etc.) may be used as input for vectorization. Log data may be mined and converted to words or symbols”);
encoding, using the encoder, a vector from the first plurality of embeddings, wherein the encoding includes utilizing a forecasting ML layer for output of the vector (BenKimon (¶0048 line 1-5, fig. 3), “word2vec is a shallow word embedding model that, in this instance, learns to map discrete user actions into a low-dimensional continuous vector-space based on distributional properties observed from the corpus ( e.g., historical data of network traffic).”);
calculating, using the ML model, a risk score based on the vector (BenKimon (¶0054 lste 10 lines), “sentiment score for a user account is determined for each user account based on a similarity to the trained user actions. In one example embodiment, the sentiment score may be determined based on an average of the vectors of the user actions analyzed (e.g., the entire account or for a particular time period).”), wherein the ML model is trained based on a plurality of other past activity vectors generated by the encoder using training data associated with other past activities for a plurality of other users (BenKimon (¶0051 line 1-4), “predictive models may be trained using account actions of known or previously determined fraudulent accounts and/or account actions of known benign user accounts.”);
analyzing the risk score from the ML model (BenKimon (¶0053 line 12-15), “the sentiment score for the user account may be compared to a threshold value to record the account as having a fraudulent account sentiment.”); and
BenKimon may not explicitly disclose:
determining, based on the analyzing, a predicted likelihood of the user meeting or failing to meet a condition for a service offered to the user at a future time.
Hong teaches:
determining, based on the analyzing (Hong (¶0014 last 4 lines), “obtaining a risk score of the target account in a next time interval by inputting the hidden state vectors into the LSTM decoder, wherein the next time interval is next to the last time interval in the plurality of time intervals”), a predicted likelihood of the user meeting or failing to meet a condition for a service offered to the user at a future time (Hong (¶0025 line 1-7), “For example, assuming that a credit risk of a target account of a user is to be predicted in the future six months based on the behavior data of the target account in the past 12 months, the performance window may be set as the future six months and the observation window may be set as the past 12 months.”).
BenKimon discloses a system/method for analyzing user action and generating a risk score. Hong discloses a LSTM system/method for predicting a risk score in a future time. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify a risk score predictor of BenKimon instead be a risk score predictor taught by Hong, with a reasonable expectation of success. The motivation would be to allow the system to “predict[ing] the credit risk of the target account in a future period of time based on the trained LSTM model.” (Hong (¶0023 last 3 lines)).
As Claim 5, besides Claim 1, BenKimon in view of Hong teaches wherein the generating the embedding comprises:
converting description data for the past activities to the plurality of first embeddings using at least one data embedding process, wherein the at least one data embedding process converts text data to numerical representations in the plurality of first embeddings (BenKimon (¶0049 line 7-9), “user actions are one-hot encoded, where each type of user action is sorted (e.g., based on usage) and then assigned a number.”).
As Claim 6, besides Claim 1, BenKimon in view of Hong teaches wherein, prior to the generating the first plurality of embeddings, the operations further comprise:
in response to receiving the activity data, determining that the activity data is designated for processing by the ML model (BenKimon (¶0051 line 1-4), “predictive models may be trained using account actions of known or previously determined fraudulent accounts and/or account actions of known benign user accounts. BenKimon (¶0043 last 5 lines), “where the usage logs are stored in a database, data of a particular user can be filtered and desired data extracted.”);
determining, for the ML model, a multi-layer ML architecture comprising the encoder and a decoder associated with the encoder (Hong (¶0100 last 3 lines), “use the one or more generated sequences of user behavior vectors as training samples to train an encoder decoder architecture based LSTM model”), wherein the encoder includes at least an embedding layer that generates the first plurality of embeddings (Hong (¶0029 line 1-4), “the hidden state vectors obtained from computation by the LSTM encoder may be used as risk features of the target account to be inputted into the LSTM model.”), an attention layer that applies weights to the first plurality of embeddings (Hong (¶0031 line 1-9), “the hidden state vectors ( also referred to as "hidden state variables") corresponding to the time intervals obtained by the LSTM encoder may be used as risk features to input into the LSTM decoder for risk prediction computation and thus a weight of a hidden state vector corresponding to one time interval may be obtained”), and the forecasting ML layer (Hong (¶0043 line 5-10), “The LSTM decoder may be used to predict credit risks at the data nodes in the performance window according to the risk features discovered by the LSTM encoder from the inputted sequence of user behavior vectors and the user's behaviors at the data nodes in the observation window, and to output a prediction result”); and
executing the encoder for the generating the first plurality of embeddings (Hong (¶0043 line 1-4), “The LSTM decoder may also include multiple data nodes corresponding to the time intervals in the performance window. For example, each time interval in the performance window corresponds to a data node in the LSTM decoder.”).
As Claim 7, besides Claim 6, BenKimon in view of Hong teaches wherein the forecasting ML layer comprises a long-short term memory (LSTM) model configured to encode the vector, and wherein the attention layer comprises a multi-headed self-attention mechanism configured to apply the weights to the first plurality of embeddings based on time-based data (Hong (¶0045 line 1-6), “the attention mechanism is used to mark features ( e.g., the risk features outputted by the data nodes of the LSTM encoder in the observation window) with weights (multi-headed) corresponding to the prediction results outputted by the data nodes of the LSTM decoder in the performance window.”).
As Claim 8, besides Claim 1, BenKimon in view of Hong teaches wherein, prior to the receiving the activity data, the operations further comprise:
training the ML model using the plurality of other past activity vectors in place of mode feature data from the training data for the plurality of model features (BenKimon (¶0048 line 2-5), “map discrete user actions into a low-dimensional continuous vector-space based on distributional properties observed from the corpus ( e.g., historical data of network traffic).”), wherein the plurality of other past activity vectors are configured to reduce a dimensionality of the plurality of model features in the training data to a n-dimensional vector (BenKimon (¶0009 line 3-6), “an auto-encoder or a word2vec algorithm may be used to reduce the dimensionality and create an embedding in which similar actions are mapped close to each other in the new (vector) space.”).
As Claim 9, besides Claim 8, BenKimon in view of Hong teaches wherein, prior to the training, the operations further comprise:
decoding, using a decoder associated with the encoder, the plurality of other past activity vectors to a second plurality of embeddings (Hong (¶0043 line 5-10), “The LSTM decoder may be used to predict credit risks at the data nodes in the performance window according to the risk features discovered by the LSTM encoder from the inputted sequence of user behavior vectors and the user's behaviors at the data nodes in the observation window, and to output a prediction result”);
comparing the first plurality of embeddings to the second plurality of embeddings (BenKimon (¶0053 line 4-9), “the prediction model is founded on historical user activity, any current user activity that appears to have a similar pattern as a previously analyzed session of interest (according to the prediction model) may be flagged for review or otherwise recorded in association with a user account that engaged in the activity”); and
determining whether to provide the plurality of other past activity vectors for the training of the ML model based on the comparing (Hong (¶0050 line 1-4), “predictive models may be trained using account actions of known or previously determined fraudulent accounts and/or account actions of known benign user accounts.” ).
As Claim 11, BenKimon teaches a method comprising:
extracting model feature data for a plurality of model features associated with a machine learning (ML) model from the activity data (BenKimon (¶0051 line 1-4), “predictive models may be trained using account actions of known or previously determined fraudulent accounts and/or account actions of known benign user accounts.”);
generating a plurality of embeddings for the plurality of model features from the activity data, wherein the plurality of embeddings are each associated with individual activities from the historical activities by the user over the time period (BenKimon (¶0044 line 1-4, fig. 3 item 320), “Natural Language Processing (NLP) word embedding algorithm, such as autoencoder or word2vec, to the retrieved user log data to generate a low-dimensional embedding (embedding)… data structure (set of arrays, a document, etc.) may be used as input for vectorization. Log data may be mined and converted to words or symbols”);
BenKimon may not explicitly disclose:
receiving activity data for a user, wherein the activity data comprises historical activities by the user over a time period;
applying an attention layer to the plurality of embeddings, wherein the attention layer applies weights on particular features from the plurality of model features in the plurality of embeddings;
generating, using a long-short term memory (LSTM) model, a vector from the plurality of embeddings, wherein the generating includes utilizing an ML layer for output of the vector;
providing the vector to the ML model, wherein the ML model is trained using a plurality of other past activity vectors associated with additional historical activities by a plurality of other users; and
determining, using the ML model based on the providing, a risk score of the user for failing to meet a required stipulation of a service extended to the user at a future time.
Hong teaches:
receiving activity data for a user, wherein the activity data comprises historical activities by the user over a time period (Hong (¶0025 line 1-6), “For example, assuming that a credit risk of a target account of a user is to be predicted in the future six months based on the behavior data of the target account in the past 12 months, the performance window may be set as the future six months and the observation window may be set as the past 12 months.”);
applying an attention layer to the plurality of embeddings, wherein the attention layer applies weights on particular features from the plurality of model features in the plurality of embeddings (Hong (¶0031 line 1-9), “the hidden state vectors ( also referred to as "hidden state variables") corresponding to the time intervals obtained by the LSTM encoder may be used as risk features to input into the LSTM decoder for risk prediction computation and thus a weight of a hidden state vector corresponding to one time interval may be obtained”);
generating, using a long-short term memory (LSTM) model, a vector from the plurality of embeddings, wherein the generating includes utilizing an ML layer for output of the vector (Hong (¶0043 line 5-10), “The LSTM decoder may be used to predict credit risks at the data nodes in the performance window according to the risk features discovered by the LSTM encoder from the inputted sequence of user behavior vectors and the user's behaviors at the data nodes in the observation window, and to output a prediction result”);
providing the vector to the ML model, wherein the ML model is trained using a plurality of other past activity vectors associated with additional historical activities by a plurality of other users (Hong (¶0025 line 1-6), “For example, assuming that a credit risk of a target account of a user is to be predicted in the future six months based on the behavior data of the target account in the past 12 months, the performance window may be set as the future six months and the observation window may be set as the past 12 months.”); and
determining, using the ML model based on the providing, a risk score of the user for failing to meet a required stipulation of a service extended to the user at a future time (Hong (¶0025 line 1-6), “For example, assuming that a credit risk of a target account of a user is to be predicted in the future six months based on the behavior data of the target account in the past 12 months, the performance window may be set as the future six months and the observation window may be set as the past 12 months.”).
BenKimon discloses a system/method for analyzing user action and generating a risk score. Hong discloses a LSTM system/method for predicting a risk score in a future time. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify a risk score predictor of BenKimon instead be a risk score predictor taught by Hong, with a reasonable expectation of success. The motivation would be to allow the system to “predict[ing] the credit risk of the target account in a future period of time based on the trained LSTM model.” (Hong (¶0023 last 3 lines)).
As Claim 12, besides Claim 11, BenKimon in view of Hong teaches wherein the attention layer comprises a multi-headed self-attention mechanism for the weights on the particular features (Hong (¶0045 line 1-6), “the attention mechanism is used to mark features ( e.g., the risk features outputted by the data nodes of the LSTM encoder in the observation window) with weights (multi-headed) corresponding to the prediction results outputted by the data nodes of the LSTM decoder in the performance window.”).
As Claim 13, besides Claim 11, BenKimon in view of Hong teaches wherein the LSTM model is configured for a transaction forecasting associated with the activity data (BenKimon (¶0039 last 10 lines), “Transaction information may also be included in the historical data, e.g., registering/opening an account, logging into an account, changing a setting associated with the account, purchases or sales, item or service bought or sold. Additional transaction feature data that may be logged and that may be used as part of the vocabulary for NLP may include price of an item or items purchased or location of a merchant from which an item was bought.”) and the additional historical activities (BenKimon (¶0048 line 2-5), “map discrete user actions into a low-dimensional continuous vector-space based on distributional properties observed from the corpus ( e.g., historical data of network traffic).” BenKimon (¶0039), “The historical data may include any combination of information regarding a user's use and access of a network-accessible software service including metadata about the context of the user's use of the service. For example, historical data may include data about the page/screen accessed (e.g., an address and/or title), page generation statistics ( e.g., page generation time and page loading time) information about the device accessing the software service ( e.g. device type, operating system”).
As Claim 15, besides Claim 11, BenKimon in view of Hong teaches wherein, prior to the receiving the activity data, the method further comprises:
generating the plurality of other past activity vectors using an encoder comprising an embedding layer associated with generating the plurality of embeddings (Hong (¶0029 line 1-4), “the hidden state vectors obtained from computation by the LSTM encoder may be used as risk features of the target account to be inputted into the LSTM model.”), the attention layer (Hong (¶0031 line 1-9), “the hidden state vectors ( also referred to as "hidden state variables") corresponding to the time intervals obtained by the LSTM encoder may be used as risk features to input into the LSTM decoder for risk prediction computation and thus a weight of a hidden state vector corresponding to one time interval may be obtained”), and the LSTM model (Hong (¶0043 line 5-10), “The LSTM decoder may be used to predict credit risks at the data nodes in the performance window according to the risk features discovered by the LSTM encoder from the inputted sequence of user behavior vectors and the user's behaviors at the data nodes in the observation window, and to output a prediction result”); and
training the ML model using the plurality of other past activity vectors (BenKimon (¶0048 line 2-5), “map discrete user actions into a low-dimensional continuous vector-space based on distributional properties observed from the corpus ( e.g., historical data of network traffic).”).
As Claim 16, besides Claim 11, BenKimon in view of Hong teaches further comprising:
decoding the plurality of other past activity vectors; comparing the decoded plurality of other past activity vectors to the plurality of embeddings (Hong (¶0043 line 5-10), “The LSTM decoder may be used to predict credit risks at the data nodes in the performance window according to the risk features discovered by the LSTM encoder from the inputted sequence of user behavior vectors and the user's behaviors at the data nodes in the observation window, and to output a prediction result”); and determining that the decoded plurality of other past activity vectors correlates to the plurality of embeddings within a similarity threshold (Hong (¶0050 line 1-4), “predictive models may be trained using account actions of known or previously determined fraudulent accounts and/or account actions of known benign user accounts.” ).
As Claim 17, the Claim is rejected for the same reasons as Claim 1.
As Claim 18, besides Claim 17, BenKimon in view of Hong teaches wherein, prior to the forecasting, the operations further comprise:
determining, using a decoding operation of the ML framework, a plurality of decoded embeddings from the vector (Hong (¶0043 line 5-10), “The LSTM decoder may be used to predict credit risks at the data nodes in the performance window according to the risk features discovered by the LSTM encoder from the inputted sequence of user behavior vectors and the user's behaviors at the data nodes in the observation window, and to output a prediction result”); and
comparing the plurality of embeddings to the plurality of decoded embeddings (Hong (¶0050 line 1-4), “predictive models may be trained using account actions of known or previously determined fraudulent accounts and/or account actions of known benign user accounts.” ).
As Claim 19, besides Claim 17, BenKimon in view of Hong teaches wherein, prior to the forecasting, the comparing is required to meet a similarity threshold (Hong (¶0050 line 8-11), “compare the sum of the risk scores with a preset risk threshold; if the sum of the risk scores is greater than the risk threshold, the LSTM decoder outputs 1 (engagement score), indicating that the target account has a credit risk in the performance window”).
As Claim 20, besides Claim 17, BenKimon in view of Hong teaches wherein the operations further comprise:
providing a notification associated with an encoding accuracy of the encoding operation based on the comparing (BenKimon (¶0055 last 10 lines), “For example, if a user's account is determined to have a fraudulent sentiment, an email or popup notification alerting the user that their account access has been limited may be sent.”).
Claim(s) 2-4, 10 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over BenKimon in view of Hong in further view of Laptiev et al. (U.S. 2021/0097545 Laptiev).
As Claim 2, besides Claim 1, BenKimon in view of Hong may not explicitly disclose wherein the operations further comprise:
providing an offer of the service to the user based on whether the predicted likelihood meets or exceeds a threshold likelihood score.
Laptiev teaches:
providing an offer of the service to the user (Laptiev (¶0007 last 3 lines), “control the client server to approve, hold, or deny the online application based on the fraud score that is determined.”) based on whether the predicted likelihood meets or exceeds a threshold likelihood score (Laptiev (¶0005 line 5-7), “providing real-time risk assessment and detecting high-risk application attempts, while enabling friction free processing of low-risk applications.”).
BenKimon in view of Hong disclose a system/method for calculating a risk score of the applicant and provide remedial actions. Laptiev disclose a system/method for providing approval or denial actions based on risk score. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify possible actions of BenKimon instead be a application decisions taught by Laptiev, with a reasonable expectation of success. The motivation would be to “provid[ing] real-time risk assessment and detecting high-risk application attempts, while enabling friction free processing of low-risk applications.” (Laptiev (¶0005 line 4-6)).
As Claim 3, besides Claim 2, BenKimon in view of Hong in further view of Laptiev teaches wherein, prior to the providing the offer, the operations further comprise:
predicting an engagement score of the user based on the risk score and the past activities (Hong (¶0050 line 8-11), “compare the sum of the risk scores (risk score) with a preset risk threshold; if the sum of the risk scores is greater than the risk threshold, the LSTM decoder outputs 1 (engagement score), indicating that the target account has a credit risk in the performance window”), wherein the engagement score is associated with a usage of a service provider corresponding to the service by the user (Hong (¶0025 line 1-6), “For example, assuming that a credit risk (risk score) of a target account of a user is to be predicted in the future six months based on the behavior data (past activity) of the target account in the past 12 months (the past activities), the performance window may be set as the future six months and the observation window may be set as the past 12 months.”), and
wherein the providing the offer is further based on the engagement score (Laptiev (¶0007 last 3 lines), “control the client server to approve, hold, or deny the online application based on the fraud score that is determined.”).
As Claim 4, besides Claim 3, BenKimon in view of Hong in further view of Laptiev teaches wherein the engagement score comprises a Recency, Frequency, and Monetary, Breadth, and Consistency (RFMBC) model score associated with a recency of each of the past activities (BenKimon (¶0052), recent user actions), a frequency of the past activities (BenKimon (¶0045 line 3), the frequency of each action), and a monetary value associated with each of the past activities (BenKimon (¶0046 line 11-16), system considers transaction amounts).
As Claim 14, besides Claim 1, BenKimon in view of Hong teaches further comprising:
generating an engagement score for the user based on the risk score and the historical activities (Hong (¶0050 line 8-11), “compare the sum of the risk scores (risk score) with a preset risk threshold; if the sum of the risk scores is greater than the risk threshold, the LSTM decoder outputs 1 (engagement score), indicating that the target account has a credit risk in the performance window”), wherein the engagement score is associated with a usage of a service provider corresponding to the service by the user (Hong (¶0025 line 1-6), “For example, assuming that a credit risk (risk score) of a target account of a user is to be predicted in the future six months based on the behavior data (past activity) of the target account in the past 12 months (the past activities), the performance window may be set as the future six months and the observation window may be set as the past 12 months.”); and
BenKimon in view of Hong may not explicitly disclose:
providing an offer for the service to the user based on the engagement score.
Laptiev teaches:
providing an offer for the service to the user based on the engagement score (Laptiev (¶0007 last 3 lines), “control the client server to approve, hold, or deny the online application based on the fraud score that is determined.”).
BenKimon in view of Hong disclose a system/method for calculating a risk score of the applicant and provide remedial actions. Laptiev disclose a system/method for providing approval or denial actions based on risk score. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify possible actions of BenKimon instead be a application decisions taught by Laptiev, with a reasonable expectation of success. The motivation would be to “provid[ing] real-time risk assessment and detecting high-risk application attempts, while enabling friction free processing of low-risk applications.” (Laptiev (¶0005 line 4-6)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zou (U.S. 20210406670) teaches a system and method for encoding user interactions.
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/NHAT HUY T NGUYEN/ Primary Examiner, Art Unit 2147
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