Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 1 is a system claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 1, following limitations recite a judicial exception:
“receiving a plurality of records comprising a set of features indicative of (a) user parameters for a plurality of users, (b) corresponding user-element interactions for each user parameter recorded during a period of time, wherein each feature comprises a plurality of values with each value corresponding to a record of the plurality of records, and (c) a focus parameter, wherein the focus parameter indicates a portion of the set of features for model concentration”
[Mental Process] – receiving multiple records comprising such information can be done by looking and hearing which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“generating from the set of features (1) a first subset of the set of features, the first subset comprising concentrated features selected based on the focus parameter and generating from the set of features (2) a second subset of the set of features, the second subset comprising foundational features having values recorded over time that provide a baseline for a training dataset”
[Mental Process] – generating subsets based on the condition or a standard simply requires sorting the data accordingly which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“performing feature extraction using the first subset to obtain dynamic features representative of features that influenced user-element interactions associated with the focus parameter and performing the feature extraction using the second subset to obtain stable features representative of the features that influenced the user-element interactions”
[Mental Process] – extracting features from the subsets according to a condition or a standard simply requires going through each data of the subsets to find a feature that matches the condition or the standard which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 1, the claim recites additional elements of
“one or more processors”
The processors are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“one or more non-transitory, computer-readable media comprising instructions”
The media is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“first machine learning model” and “second machine learning model”
These models are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1, 2] are considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II).
The additional element [3] is considered a mere instruction to apply an exception to the generic computer components or machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f))
These limitations remain a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional elements represent mere instruction to apply an exception which cannot provide an inventive concept.
Regarding Claim 2
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 2 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 2, following limitations recite a judicial exception:
“receiving a prediction request for a user, wherein the prediction request comprises parameters associated with the user”
[Mental Process] – receiving data comprising such information can be done by looking and hearing which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“inputting the parameters associated with the user into the first machine learning model to obtain a first set of object parameters based on a measure of likelihood of interaction by the user with each object corresponding to the first set of object parameters based on the dynamic features”
[Mathematical Calculations] – inputting the parameters or the data to compute the likelihood of interaction requires mathematical computation which recites to an abstract idea.
“inputting the parameters associated with the user into the second machine learning model to obtain a second set of object parameters based on the measure of the likelihood of interaction by the user with each object corresponding to the second set of object parameters based on the stable features”
[Mathematical Calculations] – inputting the parameters or the data to compute the likelihood of interaction requires mathematical computation which recites to an abstract idea.
“identifying, based on the first set of object parameters and the second set of object parameters, one or more objects for the user, wherein the one or more objects are identified using a combined determination based on alignment of object features associated with the one or more objects with predicted features from the first set of object parameters and the second set of object parameters”
[Mental Process] – identifying using the sets of parameters with the combined determination requires comparing the objects according to the parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“providing the one or more objects to the user”
[Mental Process] – providing one object to user simply requires to pick the items from the list which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 2, the claim recites additional elements of
“one or more processors”
The processors are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“first machine learning model” and “second machine learning model”
These models are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than {automate the mental processes and mathematical calculations that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1, 2] are considered a mere instruction to apply an exception to the generic computer components or machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 3
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 3 is a dependent claim of 2, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 3, following limitations recite a judicial exception:
“modifying a field indicative of an availability of the object”
[Mental Process] – modifying the data according to the changes involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 3, the claim recites additional elements of
“one or more processors”
The processors are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“transmitting a first command for generating and displaying an interactive interface for the one or more objects”
The interactive interface is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
Transmitting data or command is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
“responsive to receiving an indication of a first interaction of the user with an object of the one or more objects, transmitting a second command for modifying a field indicative of an availability of the object.”
Receiving and transmitting data or command is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
[Even when viewed in combination, the additional elements do no more than {automate the mental processes and mathematical calculations that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II).
The additional element [2a] is considered a mere instruction to apply an exception to the generic computer components. (see MPEP 2106.05(f))
The additional elements [2b, 3] are considered an insignificant extra solution activity and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II).
These limitations remain insignificant extra-solution activity and a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents insignificant extra-solution activity and a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 4
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 4 is a method claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 4, following limitations recite a judicial exception:
“receiving a prediction request for a user, wherein the prediction request comprises parameters associated with the user”
[Mental Process] – receiving data comprising such information can be done by looking and hearing which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“inputting the parameters associated with the user into the first machine learning model to obtain a first set of object parameters based on a measure of likelihood of interaction by the user with each object corresponding to the first set of object parameters based on the dynamic features wherein the first machine learning model is trained using the dynamic features to identify object parameters associated with the objects that users are likely to interact with based on user-element interactions corresponding to a focus parameter, wherein the focus parameter indicates a portion of a set of features for model concentration”
[Mathematical Calculations] – inputting the parameters or the data to compute the likelihood of interaction requires mathematical computation which recites to an abstract idea.
[Mental Process] – identifying object parameters based on the user-element interactions requires to sort the parameters and compare them accordingly to select that matches the condition which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“inputting the parameters associated with the user into the second machine learning model to obtain a second set of object parameters based on the measure of the likelihood of interaction by the user with each object corresponding to the second set of object parameters based on the stable features wherein the second machine learning model is trained using the stable features to identify the object parameters associated with the objects that the users are likely to interact with based on stable user-element interactions”
[Mathematical Calculations] – inputting the parameters or the data to compute the likelihood of interaction requires mathematical computation which recites to an abstract idea.
[Mental Process] – identifying object parameters based on the user-element interactions requires to sort the parameters and compare them accordingly to select that matches the condition which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“identifying, based on the first set of object parameters and the second set of object parameters, one or more objects for the user, wherein the one or more objects are identified using a combined determination based on alignment of object features associated with the one or more objects with predicted features from the first set of object parameters and the second set of object parameters;
[Mental Process] – identifying using the sets of parameters with the combined determination requires comparing the objects according to the parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“providing the one or more objects to the user”
[Mental Process] – providing one object to user simply requires to pick the items from the list which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 4, the claim recites additional elements of
“first machine learning model” and “second machine learning model”
These models are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than {automate the mental processes and mathematical calculations that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components or machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 5
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 5 is a dependent claim of 4, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 5, following limitations recite a judicial exception:
“modifying a field indicative of an availability of the object”
[Mental Process] – modifying the data according to the changes involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 5, the claim recites additional elements of
“one or more processors”
The processors are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“transmitting a first command for generating and displaying an interactive interface for the one or more objects”
The interactive interface is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
Transmitting data or command is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
“responsive to receiving an indication of a first interaction of the user with an object of the one or more objects, transmitting a second command for modifying a field indicative of an availability of the object.”
Receiving and transmitting data or command is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
[Even when viewed in combination, the additional elements do no more than {automate the mental processes and mathematical calculations that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II).
The additional element [2a] is considered a mere instruction to apply an exception to the generic computer components. (see MPEP 2106.05(f))
The additional elements [2b, 3] are considered an insignificant extra solution activity and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II).
These limitations remain insignificant extra-solution activity and a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents insignificant extra-solution activity and a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 6
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 6 is a dependent claim of 4, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 6, following limitations recite a judicial exception:
“receiving a plurality of records comprising a set of features indicative of (a) user parameters for a plurality of users, (b) corresponding user-element interactions for each user parameter recorded during a period of time, wherein each feature comprises a plurality of values with each value corresponding to a record of the plurality of records, and (c) a focus parameter”
[Mental Process] – receiving multiple records comprising such information can be done by looking and hearing which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“generating from the set of features (1) a first subset of the set of features, the first subset comprising concentrated features selected based on the focus parameter and generating from the set of features (2) a second subset of the set of features, the second subset comprising foundational features”
[Mental Process] – generating subsets based on the condition or a standard simply requires sorting the data accordingly which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“performing feature extraction using the first subset to obtain dynamic features representative of features that influenced user-element interactions associated with the focus parameter and performing the feature extraction using the second subset to obtain stable features representative of the features that influenced the user-element interactions that are non-specific to any one topic”
[Mental Process] – extracting features from the subsets according to a condition or a standard simply requires going through each data of the subsets to find a feature that matches the condition or the standard which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 6 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 7
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 7 is a dependent claim of 6, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 7 does not have any abstract idea by itself, thus uses all the limitations of Claim 6.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 7 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea of Claim 6.
Regarding Claim 8
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 8 is a dependent claim of 6, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 8, following limitations recite a judicial exception:
“inputting the first set of object parameters and the second set of object parameters into a context-specific machine learning model configured to identify the one or more objects ranking highest according to their alignment with the features from both the first set of object parameters and the second set of object parameters”
[Mental Process] – inputting the parameters to identify the objects based on the highest scores requires sorting the objects according the scores and selecting the ones with the highest scores which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 8, the claim recites additional elements of
“a context-specific machine learning model”
The machine learning model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components. (see MPEP 2106.05(f))
This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 9
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 9 is a dependent claim of 6, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 9, following limitations recite a judicial exception:
“receiving the first set of object parameters and the second set of object parameters”
[Mental Process] – receiving these sets of parameters can be done by looking and hearing which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“determining a set of objects, wherein each object of the set of objects is characterized by at least one object parameter comprised in both the first set of object parameters and the second set of object parameters”
[Mental Process] – determining a set of objects that has parameters from both sets simply requires comparing each object to the parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“computing, for each object of the set of objects, a score based on a number of object parameters of each object comprised in both the first set of object parameters and the second set of object parameters”
[Mathematical Calculations] – counting the number of parameters associated with each object from the sets simply requires mathematical addition which recites an abstract
“identifying a subset of the set of objects based on the score of each object”
[Mental Process] – identifying the objects based on the score simply requires comparing each object to the scores and sort them accordingly which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 9 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 10
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 10 is a dependent claim of 6, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 10, following limitations recite a judicial exception:
“determining a first object set based on the objects characterized by at least one object parameter of the first set of object parameters”
[Mental Process] – determining a set of objects based on their object parameters simply require comparing each object to the object parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“determining the one or more objects by filtering the objects of the first object set based on whether or not each object of the first object set is characterized by the at least one object parameter of the second set of object parameters”
[Mental Process] – determining a set of objects based on their object parameters simply require comparing each object to the object parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 10 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 11
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 11 is a dependent claim of 6, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 11, following limitations recite a judicial exception:
“determining a third set of object parameters based on the object parameters comprised in both the first set of object parameters and the second set of object parameters”
[Mental Process] – determining a set of objects based on their object parameters simply require comparing each object to the sets of object parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“selecting the one or more objects based on each object of the one or more objects being characterized by at least a threshold number of object parameters of the third set of object parameters”
[Mental Process] – selecting the objects based on the number of matching parameters that is higher than a predetermined number simply requires comparing each object’s matching count to the threshold number which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 11 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 12
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 12 is a dependent claim of 6, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 12, following limitations recite a judicial exception:
“determining at least one object parameter of the first set of object parameters is distinct from the object parameters of the second set of object parameters”
[Mental Process] – determining object parameters of the first set that are distinct from the second set simply require comparing each parameter sets which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“selecting the one or more objects based on the objects characterized by a highest number of object parameters of the first set of object parameters and the second set of object parameters”
[Mental Process] – selecting the objects based on the highest number of object parameters simply requires sorting each object by its number of parameters then select from the highest to lowest which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 12 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 13
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 13 is a dependent claim of 6, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 13 does not have any abstract idea by itself, thus uses all the limitations of Claim 6.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 13 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea of Claim 6.
Regarding Claim 14
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 14 is a non-transitory computer-readable medium claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 14, following limitations recite a judicial exception:
“receiving a prediction request for a user, wherein the prediction request comprises parameters associated with the user”
[Mental Process] – receiving data comprising such information can be done by looking and hearing which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“inputting the parameters associated with the user into the first machine learning model to obtain a first set of object parameters based on a measure of likelihood of interaction by the user with each object corresponding to the first set of object parameters based on the dynamic features wherein the first machine learning model is trained using the dynamic features to identify object parameters associated with the objects that users are likely to interact with based on user-element interactions corresponding to a focus parameter, wherein the focus parameter indicates a portion of a set of features for model concentration”
[Mathematical Calculations] – inputting the parameters or the data to compute the likelihood of interaction requires mathematical computation which recites to an abstract idea.
[Mental Process] – identifying object parameters based on the user-element interactions requires to sort the parameters and compare them accordingly to select that matches the condition which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“inputting the parameters associated with the user into the second machine learning model to obtain a second set of object parameters based on the measure of the likelihood of interaction by the user with each object corresponding to the second set of object parameters based on the stable features wherein the second machine learning model is trained using the stable features to identify the object parameters associated with the objects that the users are likely to interact with based on stable user-element interactions”
[Mathematical Calculations] – inputting the parameters or the data to compute the likelihood of interaction requires mathematical computation which recites to an abstract idea.
[Mental Process] – identifying object parameters based on the user-element interactions requires to sort the parameters and compare them accordingly to select that matches the condition which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“identifying, based on the first set of object parameters and the second set of object parameters, one or more objects for the user, wherein the one or more objects are identified using a combined determination based on alignment of object features associated with the one or more objects with predicted features from the first set of object parameters and the second set of object parameters;
[Mental Process] – identifying using the sets of parameters with the combined determination requires comparing the objects according to the parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“providing the one or more objects to the user”
[Mental Process] – providing one object to user simply requires to pick the items from the list which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 14, the claim recites additional elements of
“first machine learning model” and “second machine learning model”
These models are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than {automate the mental processes and mathematical calculations that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components or machine-learning components that simply runs mathematical calculations. (see MPEP 2106.05(f)) This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 15
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 15 is a dependent claim of 14, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 15, following limitations recite a judicial exception:
“modifying a field indicative of an availability of the object”
[Mental Process] – modifying the data according to the changes involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 15, the claim recites additional elements of
“one or more processors”
The processors are recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“transmitting a first command for generating and displaying an interactive interface for the one or more objects”
The interactive interface is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
Transmitting data or command is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
“responsive to receiving an indication of a first interaction of the user with an object of the one or more objects, transmitting a second command for modifying a field indicative of an availability of the object.”
Receiving and transmitting data or command is merely data gathering recited at a high level of generality, thus is insignificant extra-solution activity (See MPEP 2106.05(g)).
[Even when viewed in combination, the additional elements do no more than {automate the mental processes and mathematical calculations that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II).
The additional element [2a] is considered a mere instruction to apply an exception to the generic computer components. (see MPEP 2106.05(f))
The additional elements [2b, 3] are considered an insignificant extra solution activity and at best the equivalent of a mere data gathering recited at a high level of generality and amount to receiving or transmitting data over a network, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II).
These limitations remain insignificant extra-solution activity and a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents insignificant extra-solution activity and a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 16
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 16 is a dependent claim of 14, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 16, following limitations recite a judicial exception:
“receiving a plurality of records comprising a set of features indicative of (a) user parameters for a plurality of users, (b) corresponding user-element interactions for each user parameter recorded during a period of time, wherein each feature comprises a plurality of values with each value corresponding to a record of the plurality of records, and (c) a focus parameter”
[Mental Process] – receiving multiple records comprising such information can be done by looking and hearing which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“generating from the set of features (1) a first subset of the set of features, the first subset comprising concentrated features selected based on the focus parameter and generating from the set of features (2) a second subset of the set of features, the second subset comprising foundational features”
[Mental Process] – generating subsets based on the condition or a standard simply requires sorting the data accordingly which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“performing feature extraction using the first subset to obtain dynamic features representative of features that influenced user-element interactions associated with the focus parameter and performing the feature extraction using the second subset to obtain stable features representative of the features that influenced the user-element interactions that are non-specific to any one topic”
[Mental Process] – extracting features from the subsets according to a condition or a standard simply requires going through each data of the subsets to find a feature that matches the condition or the standard which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 16 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 17
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 17 is a dependent claim of 16, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
As Claim 17 does not have any abstract idea by itself, thus uses all the limitations of Claim 16.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 17 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea of Claim 16.
Regarding Claim 18
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 18 is a dependent claim of 16, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 18, following limitations recite a judicial exception:
“inputting the first set of object parameters and the second set of object parameters into a context-specific machine learning model configured to identify the one or more objects ranking highest according to their alignment with the features from both the first set of object parameters and the second set of object parameters”
[Mental Process] – inputting the parameters to identify the objects based on the highest scores requires sorting the objects according the scores and selecting the ones with the highest scores which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 18, the claim recites additional elements of
“a context-specific machine learning model”
The machine learning model is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional element [1] is considered a mere instruction to apply an exception to the generic computer components. (see MPEP 2106.05(f))
This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claim 19
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 19 is a dependent claim of 16, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 19, following limitations recite a judicial exception:
“receiving the first set of object parameters and the second set of object parameters”
[Mental Process] – receiving these sets of parameters can be done by looking and hearing which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“determining a set of objects, wherein each object of the set of objects is characterized by at least one object parameter comprised in both the first set of object parameters and the second set of object parameters”
[Mental Process] – determining a set of objects that has parameters from both sets simply requires comparing each object to the parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“computing, for each object of the set of objects, a score based on a number of object parameters of each object comprised in both the first set of object parameters and the second set of object parameters”
[Mathematical Calculations] – counting the number of parameters associated with each object from the sets simply requires mathematical addition which recites an abstract
“identifying a subset of the set of objects based on the score of each object”
[Mental Process] – identifying the objects based on the score simply requires comparing each object to the scores and sort them accordingly which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 19 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 20
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 20 is a dependent claim of 16, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 20, following limitations recite a judicial exception:
“determining a first object set based on the objects characterized by at least one object parameter of the first set of object parameters”
[Mental Process] – determining a set of objects based on their object parameters simply require comparing each object to the object parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“determining the one or more objects by filtering the objects of the first object set based on whether or not each object of the first object set is characterized by the at least one object parameter of the second set of object parameters”
[Mental Process] – determining a set of objects based on their object parameters simply require comparing each object to the object parameters which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 20 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (Ma), Chinese Patent Application No. CN-113495991-A in view of Li et al. (Li), Chinese Patent Application No. CN-110599280-A.
As to independent Claim 1,
Ma teaches a system for training machine learning models based on previous user-element interactions, the system comprising:
one or more processors (Ma, Pg7, Figure6, CPU 601); and
one or more non-transitory, computer-readable media comprising instructions that, when executed by the one or more processors (Ma, Pg7, Figure6, ROM(602) and RAM(603)), cause operations comprising:
receiving a plurality of records comprising a set of features indicative of (a) user parameters for a plurality of users, (b) corresponding user-element interactions for each user parameter recorded during a period of time, wherein each feature comprises a plurality of values with each value corresponding to a record of the plurality of records, and (c) a focus parameter, wherein the focus parameter indicates a portion of the set of features for model concentration (Ma, Pg24, Claim1, Lines1-3, "A recommendation method, characterized in that it includes: Obtain user characteristics, long-term behavior data, short-term behavior data, and object characteristics of the user to be recommended;"
Pg14, Paragraph3, Lines3-6, "the user characteristics of the user to be recommended can be divided into two categories. One is the static attribute characteristics of the user, such as the basic attributes of the user’s name, gender, and age;"
Pg15, Lines17-18, "Specifically, each training batch has N samples, ..."
Pg12, Lines5-6, "generally use users’ historical behaviors (including clicks, searches, additional purchases, purchases, etc.)"
Pg14, Lines9-15, "takes short-term behavior data such as the user’s recent click behavior as short-term interest features in consideration of the actual online calculation speed (usually the user’s browsing data in the last week is used, and the upper limit of the number of data 100); and use long-term user behavior data such as purchase behavior to reflect the user's long-term interest preferences (usually use the user's behavior data for purchasing goods in the past two years, and the upper limit of the number of data is 300)."
Pg14, Paragraph4, Lines1-2, "For a certain user u, the purchase sequence is P=[pt], t.(1, Npurchase), and the browsing sequence is B=[bt], t.(1, Nbrowse);"
Pg14, Paragraph3, Lines8-10, "Object characteristics refer to the static attribute characteristics of the object, which mainly include basic attributes such as the number, category, and brand of the object."
Pg14, Paragraph4, Lines3-5, "For example, the static attributes of commodities include commodity number (sku_id), category (cate), brand (brand), then commodity characteristics: Eitem = concat (esku, ebrand, ecate)", wherein the claim 1 of Ma discloses it receives the user characteristics such as name, age and gender, long/short term data and the object characteristic data of the N samples or users (the corresponding user parameters for a plurality of users). Ma also discloses the data includes historical behaviors (corresponding user-element interactions for each user parameter recorded during a period of time) such as purchase sequence, browsing sequence, recent click behaviors for short-term and long-term period with up to 100 values and 300 values respectively. Ma further discloses the static attribute (the corresponding focus parameter) which will concentrate the objects to be recommended within this boundary, thus it is functionally equivalent to the claimed invention.)
generating from the set of features (1) a first subset of the set of features, the first subset comprising concentrated features selected based on the focus parameter and generating from the set of features (2) a second subset of the set of features, the second subset comprising foundational features having values recorded over time that provide a baseline for a training dataset (Ma, Pg14, Lines9-15, "takes short-term behavior data such as the user’s recent click behavior as short-term interest features in consideration of the actual online calculation speed (usually the user’s browsing data in the last week is used, and the upper limit of the number of data 100); and use long-term user behavior data such as purchase behavior to reflect the user's long-term interest preferences (usually use the user's behavior data for purchasing goods in the past two years, and the upper limit of the number of data is 300)"
Pg14, Paragraph4, Lines1-2, "For a certain user u, the purchase sequence is P=[pt], t.(1, Npurchase), and the browsing sequence is B=[bt], t.(1, Nbrowse);"
Pg14, Paragraph3, Lines3-6, "the user characteristics of the user to be recommended can be divided into two categories. One is the static attribute characteristics of the user, such as the basic attributes of the user’s name, gender, and age;"
Pg14, Paragraph4, Lines3-5, "For example, the static attributes of commodities include commodity number (sku_id), category (cate), brand (brand), then commodity characteristics: Eitem = concat (esku, ebrand, ecate)", wherein Ma discloses about short-term interest features in consideration of the actual online calculation speed and purchase behavior to reflect the user's long-term interest preferences. The claimed invention's generation of first and second subsets of the set of features functionally means create two subsets based on the short-term and the long-term. Here, Ma shows that the long-term data or the user static attributes such as age, gender, name or the purchase sequence are used as a long-term or the second subset which is grouped as P=[pt] for the past two years which provides the baseline for a long-term training dataset. While B=[bt] or the recent click behaviors are used as the short-term data or the first subset, thus it is functionally equivalent to the claimed invention of creating two subsets where one for the short-term or the dynamic model and another for the long-term or the stable model);
performing feature extraction using the first subset to obtain dynamic features representative of features that influenced user-element interactions associated with the focus parameter and performing the feature extraction using the second subset to obtain stable features representative of the features that influenced the user-element interactions (Ma, Pg25, Claim5, Lines4-6, "Using the object feature to perform attention calculation with the short-term behavior sequence and the long-term interest preference to obtain the short-term interest feature and the long-term interest feature of the object to be recommended;"
Pg, 16, Lines4-6, "The user's interest changes over time, and the Self-Attention network is used to describe the evolution of user interest, and the user's dynamic interest feature UserDynamic can be
obtained ..."
Pg14, Paragraph4, Lines3-5, "For example, the static attributes of commodities include commodity number (sku_id), category (cate), brand (brand), then commodity characteristics: Eitem = concat (esku, ebrand, ecate)."
Pg9, Lines8-11, "using a network based on an attention mechanism as a feature extractor, performing feature extraction on the long-term behavior data, and performing feature extraction on the extracted features. Add and pool processing to obtain the long-term interest preference of the user to be recommended"
Pg14, Lines12-15, "use long-term user behavior data such as purchase behavior to reflect the user's long-term interest preferences (usually use the user's behavior data for purchasing goods in the past two years, and the upper limit of the number of data is 300)", wherein Ma discloses in the claim5 extracting the short-term interest feature (the corresponding dynamic features) where the user interest changes over time and the attention network is used to describe the evolution of user interest and the user's dynamic interest feature which the network uses this focused parameters or the static attributes. Ma also shows that extracting the long-term interest feature (stable features) which tracks the user's behavior data for purchasing goods in the past two years that is non-specific to any one topic, thus it is functionally equivalent to the claimed invention.);
However, Ma does not teach the following limitations, but from the same field of endeavor Li teaches training a first machine learning model using the dynamic features of the first subset of the set of features to identify object parameters associated with objects that users are likely to interact with based on user-element interactions associated with the focus parameter (Li, Pg35, Claim1, "Obtain short-term characteristic data of labeled users on the recent acceptance of the distribution channel; Training the short-term characteristic data to obtain a short-term model for predicting that the user has a near-term preference for the distribution channel;"
Li, Pg36, Claim5, "training the short-term characteristic data to obtain a short-term model for predicting a user's near-term preference for a distribution channel comprises: The short-term feature data is trained by using any of the following machine learning models to obtain the short-term model: a long-short-term memory model, an iterative decision tree, a logistic regression, and a support vector machine."
Li, Pg14, Lines3-6, "use a short-term model to fit a user's recent delivery channel / acquisition to a certain type of product. The degree of acceptance of the channel, because the user's acceptance of a distribution channel usually changes drastically over time;"
Li, Pg27, Lines6-9, "The long-term characteristic data and the short-term characteristic data are correspondingly input to a long-term model and a short-term model for prediction, and a user's preference for a specified product and an output result of a recent acceptance of a delivery channel are output."
Pg36, Claim4, Lines1-3, "the short-term characteristic data comprises: User basic attribute data and one of the distribution channel click data, marketing scenario behavior data, and location service-based positioning data recently generated by users;", wherein the short-term memory model (the corresponding model) trained using the short-term or the dynamic features of the first subset has have been extracted above to identify the object parameters or the user's preference for a specified product and acceptance of a delivery according to the attribute data or the focus parameter, which is functionally equivalent to the claimed invention); and
training a second machine learning model using the stable features of the second subset of the set of features to identify the object parameters associated with the objects that the users are likely to interact with based on stable user-element interactions (Li, Pg35, Claim1, "Obtain long-term characteristic data of labeled users about long-term preferences of specified products; Training the long-term feature data to obtain a long-term model for predicting a user's long-term preference for a specified product;"
Li, Pg36, Claim3, "The long-term feature data is trained by using any of the following machine learning models to obtain the long-term model: iterative decision tree, logistic regression, and support vector machine."
Pg14, Lines2-3, "fit a user's relatively stable mental preferences for a certain type of product object through a long-term model;", wherein the long-term model (the corresponding second machine learning model) is trained using the stable and long-term characteristic data to identify or predict the user's long-term preference for a specified product which is equivalent to the claimed invention of predicting or identifying the object parameters associated with the stable features.)
Ma and Li are analogous to the claimed invention as they are from the same field of endeavor of e-commerce machine learning recommendation system designed to predict user commodity preferences and optimize targeted information placement based on historical user interaction sequences. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the precise attention-based feature extraction mechanism that inputs specific target object characteristics (sku_id, brand, and cate) as queries to align with behavioral sequence of Ma with the overarching dual-pipeline ensemble training architecture, which separately trains a long-term model for stable preferences and a short-term model for recent acceptances) of Li. The motivation is as recited by Li (Li, Pg11, Background Technique, Paragraph1, “In many scenarios where product information is pushed, in order to achieve accurate product information placement and thereby improve the later product conversion rate, it is usually necessary to predict the user's preference for a certain product in advance, and then determine whether the user is required based on the prediction results Place product information to improve the accuracy and effectiveness of information placement”) such that by integrating Li’s dual-pipeline architecture can greatly improve the accuracy and the product conversion rate so that the prediction or the recommendation given to the user actually can lead to the purchase of the product.
As to dependent Claim 2,
The combination of Ma and Li teaches, as mentioned above, all the limitations of Claim 1. It teaches about using the data comprising user’s short-term and long-term preferences to generate the dynamic feature set and the stable feature set which are then used to train the first and the second machine learning models.
Ma further teaches the system of claim 1, wherein the instructions further cause the one or more processors to perform operations including:
identifying, based on the first set of object parameters and the second set of object parameters, one or more objects for the user, wherein the one or more objects are identified using a combined determination based on alignment of object features associated with the one or more objects with predicted features from the first set of object parameters and the second set of object parameters (Ma, Pg25, Claim5, Lines4-6, "Using the object feature to perform attention calculation with the short-term behavior sequence and the long-term interest preference to obtain the short-term interest feature and the long-term interest feature of the object to be recommended;”,
Pg15, Lines, 32-37, "the present invention uses the object feature and the user’s short-term behavior sequence B to perform attention calculation to obtain the user’s short-term interest feature for the object. Vecshort uses the object characteristics and the user's long-term interest preference ulong to perform attention calculation to obtain the user's long-term interest feature Veclong for the object, namely: Vecshort = Attention (itarget, B, B); Veclong = Attention (itarget, ulong, ulong)"
Pg25, Claim6, Lines4-7, "Splicing the dynamic interest feature, the object feature, and the user feature; After the spliced feature vector passes through the fully connected layer, the softmax operation is performed to obtain the preference of the user to be recommended for the object."
Pg16, Lines17-20, "Assuming that the vector obtained after splicing is x = concat (Userprofile, UserDynamic, itarget, Veccontext), the preference score of the user to be recommended for the object is: score=softmax(RELU(Wx+b)), where W and b are model parameters", wherein the attention calculation using the itarget is used to calculate the likelihood and similarity between two vectors, which is the corresponding alignment of object features. Ma also discloses about the concatenating the output from the attention alignment, Vecshort and Veclong (the corresponding combined determination), to recommend a product based on the preference score computed from the output, which is functionally equivalent to the claimed invention); and
providing the one or more objects to the user (Ma, Pg4, Figure1, S105, "Determine the recommended objects based on the user's preference for objects for recommendation.")
Ma, however, does not teach the following limitations, but from the same field of endeavor Li teaches receiving a prediction request for a user, wherein the prediction request comprises parameters associated with the user (Li, Pg36, Claim7, "A product information preference prediction method is
characterized in that it includes: Obtain long-term characteristic data of the user's long-term preferences and short-term characteristic data of the recent acceptance of the distribution channel;" Pg27, S510, Lines1-5, "S510: Obtain long-term characteristic data of a user's long-term preference situation and short-term characteristic data of a recent acceptance situation of a distribution channel. Reference may be made to the specific content of the long-term feature data and the specific content of the short-term feature data to obtain the feature data of the current user to be targeted and classify the corresponding feature categories", wherein obtaining the data, which includes long/short-term characteristic data of a user, to make a prediction for a user is functionally equivalent to the claimed invention);
inputting the parameters associated with the user into a first machine learning model to obtain a first set of object parameters based on a measure of likelihood of interaction by the user with each object corresponding to the first set of object parameters based on dynamic features, wherein the first machine learning model is trained using the dynamic features to identify object parameters associated with the objects that users are likely to interact with based on user-element interactions corresponding to a focus parameter, wherein the focus parameter indicates a portion of a set of features for model concentration (Li, Pg27, Lines6-9, "The long-term characteristic data and the short-term characteristic data are correspondingly input to a long-term model and a short-term model for prediction, and a user's preference for a specified product and an output result of a recent acceptance of a delivery channel are output",
Pg35, Claim1, Lines7-10, "Obtain short-term characteristic data of labeled users on the recent acceptance of the distribution channel; Training the short-term characteristic data to obtain a short-term model for predicting that the user has a near-term preference for the distribution channel;", Pg36, Claim5, Lines4-6, "The short-term feature data is trained by using any of the following machine
learning models to obtain the short-term model: a long-short-term memory model, an iterative decision tree, a logistic regression, and a support vector machine",
Pg36, Claim4, Lines1-3, "the short-term characteristic data comprises: User basic attribute data and one of the distribution channel click data, marketing scenario behavior data, and location service-based positioning data recently generated by users;", wherein the claims 1 and 5 show that the short-term data is trained to obtain the short-term model (the corresponding first model) that predicts the near-term preference (the corresponding first set of object parameters based on a measure of likelihood), which then takes the input data of the short-term characteristic data (the corresponding dynamic features) to output the user's preference for a specified product and acceptance of a delivery (the corresponding first set of object parameters). In claim4, Li discloses that the short-term characteristic data is limited to such attribute data (the corresponding focus parameter) to concentrate the data only associated with the attribute data, which is equivalent to the claimed invention);
inputting the parameters associated with the user into a second machine learning model to obtain a second set of object parameters based on the measure of the likelihood of interaction by the user with each object corresponding to the second set of object parameters based on stable features, wherein the second machine learning model is trained using the stable features to identify the object parameters associated with the objects that the users are likely to interact with based on stable user-element interactions (Li, Pg27, Lines6-9, "The long-term characteristic data and the short-term characteristic data are correspondingly input to a long-term model and a short-term model for prediction, and a user's preference for a specified product and an output result of a recent acceptance of a delivery channel are output”,
Pg35, Claim1, Lines3-6, "Obtain long-term characteristic data of labeled users about long-term preferences of specified products; Training the long-term feature data to obtain a long-term model for predicting a user's long-term preference for a specified product;",
Pg14, Lines2-3, "fit a user's relatively stable mental preferences for a certain type of product object through a long-term model;", wherein the claim1 shows that the long-term data is trained to obtain the long-term model (the corresponding second model) that predicts user's long-term preference for a specified product (the corresponding second set of object parameters based on a measure of likelihood). Li also discloses that the second model uses the stable mental preferences (the corresponding stable feature) for the training and the output is also based on this feature, which is functionally equivalent to the claimed invention);
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ma and Li as mentioned in Claim 2 in further view of Rauser et al. (Rauser), US Patent Application No. US-7461058-B1.
As to dependent Claim 3,
The combination of Ma and Li teaches, as mentioned above, all the limitations of Claim 2. It teaches about recommending a set of objects using the object parameters which were obtained from the user’s short-term and long-term preferences which were given to the two machine learning models respectively.
Ma and Li, however, do not teach the following limitations, but from the same field of endeavor Rauser teaches the method of claim 4, further comprising:
transmitting a first command for generating and displaying an interactive interface for the one or more objects (Rauser, Pg11, Figure 6.C
PNG
media_image1.png
818
990
media_image1.png
Greyscale
, Pg14, Column 4, Lines25-33, “Recommendation server 120 transmits and receives web pages from a browser on client computer 112 using hypertext markup language (HTML), Java or other techniques. These web pages may include images or instructions to obtain recommendation requests from a user. Recommendation server 120 also contains a database that stores various data, Such as constraint filters, recommendation filters and items, further described below”); and
responsive to receiving an indication of a first interaction of the user with an object of the one or more objects, transmitting a second command for modifying a field indicative of an availability of the object (Rauser, Pg11, Figure 6.C, “ClICK ON ITEM TO PURCHASAE”, Rauser Pg15, Column 5, Lines4-8, “database 324 with constraint table 326 that stores built constraints to use with recommendation software 312 and item table 328 with attribute information about each item. For example, item table 328 could store a category identification, item number, and number in stock”, wherein the system contains the database with each item’s availability which is then connected to the figure 6.C using the HTML server which will change the database accordingly as the user purchases the items, thus it is functionally equivalent to the claimed invention.
Ma, Li and Rauser are analogous to the claimed invention as they are from the same field of endeavor of computer-implemented personalized recommender systems and data processing filtering methods designed to capture user preference data and dynamically generate optimized item recommendation lists from an item catalog. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the temporal tracking model of Ma, the dual-pipeline profile framework of Li with the cost-optimized, multi-stage adaptive filtering architecture of Rauser. The motivation is as recited by Rauser (Rauser, Pg13, Column 1, Lines32-39, "Prefiltering requires a constraint system that discovers acceptable items and then Submits all discovered items to a prediction system that makes recommendations from this Subset. Prefiltering has some serious practical limitations, however. For example, gathering the list of acceptable items is difficult to accomplish efficiently as the list of acceptable items may be very large since it is selected from the whole item catalog") such that by incorporating Rauser's structural funneling method, the system can arrange the independently extracted temporal parameter sets into a sequential "two-stage funnel" rather than an expensive parallel fusion. Specifically, the system can execute one preference set as an initial constraint filter to instantly prune the vast whole item catalog, thereby efficiently determining a smaller object set which then can effectively filtered down according to another preference.
Claims 4, 6, 7, 8, 13, 14, 15, 16, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (Li), Chinese Patent Application No. CN-110599280-A in view of Ma et al. (Ma), Chinese Patent Application No. CN-113495991-A.
As to independent Claim 4,
Li teaches a method for identifying objects based on previous user-object interactions, the method comprising:
receiving a prediction request for a user, wherein the prediction request comprises parameters associated with the user (Li, Pg36, Claim7, "A product information preference prediction method is characterized in that it includes: Obtain long-term characteristic data of the user's long-term preferences and short-term characteristic data of the recent acceptance of the distribution channel;" Pg27, S510, Lines1-5, "S510: Obtain long-term characteristic data of a user's long-term preference situation and short-term characteristic data of a recent acceptance situation of a distribution channel. Reference may be made to the specific content of the long-term feature data and the specific content of the short-term feature data to obtain the feature data of the current user to be targeted and classify the corresponding feature categories", wherein obtaining the data, which includes long/short-term characteristic data of a user, to make a prediction for a user is functionally equivalent to the claimed invention);
inputting the parameters associated with the user into a first machine learning model to obtain a first set of object parameters based on a measure of likelihood of interaction by the user with each object corresponding to the first set of object parameters based on dynamic features, wherein the first machine learning model is trained using the dynamic features to identify object parameters associated with the objects that users are likely to interact with based on user-element interactions corresponding to a focus parameter, wherein the focus parameter indicates a portion of a set of features for model concentration (Li, Pg27, Lines6-9, "The long-term characteristic data and the short-term characteristic data are correspondingly input to a long-term model and a short-term model for prediction, and a user's preference for a specified product and an output result of a recent acceptance of a delivery channel are output",
Pg35, Claim1, Lines7-10, "Obtain short-term characteristic data of labeled users on the recent acceptance of the distribution channel; Training the short-term characteristic data to obtain a short-term model for predicting that the user has a near-term preference for the distribution channel;", Pg36, Claim5, Lines4-6, "The short-term feature data is trained by using any of the following machine learning models to obtain the short-term model: a long-short-term memory model, an iterative decision tree, a logistic regression, and a support vector machine",
Pg36, Claim4, Lines1-3, "the short-term characteristic data comprises: User basic attribute data and one of the distribution channel click data, marketing scenario behavior data, and location service-based positioning data recently generated by users;", wherein the claims 1 and 5 show that the short-term data is trained to obtain the short-term model (the corresponding first model) that predicts the near-term preference (the corresponding first set of object parameters based on a measure of likelihood), which then takes the input data of the short-term characteristic data (the corresponding dynamic features) to output the user's preference for a specified product and acceptance of a delivery (the corresponding first set of object parameters). In claim4, Li discloses that the short-term characteristic data is limited to such attribute data (the corresponding focus parameter) to concentrate the data only associated with the attribute data, which is equivalent to the claimed invention);
inputting the parameters associated with the user into a second machine learning model to obtain a second set of object parameters based on the measure of the likelihood of interaction by the user with each object corresponding to the second set of object parameters based on stable features, wherein the second machine learning model is trained using the stable features to identify the object parameters associated with the objects that the users are likely to interact with based on stable user-element interactions (Li, Pg27, Lines6-9, "The long-term characteristic data and the short-term characteristic data are correspondingly input to a long-term model and a short-term model for prediction, and a user's preference for a specified product and an output result of a recent acceptance of a delivery channel are output”,
Pg35, Claim1, Lines3-6, "Obtain long-term characteristic data of labeled users about long-term preferences of specified products; Training the long-term feature data to obtain a long-term model for predicting a user's long-term preference for a specified product;",
Pg14, Lines2-3, "fit a user's relatively stable mental preferences for a certain type of product object through a long-term model;", wherein the claim1 shows that the long-term data is trained to obtain the long-term model (the corresponding second model) that predicts user's long-term preference for a specified product (the corresponding second set of object parameters based on a measure of likelihood). Li also discloses that the second model uses the stable mental preferences (the corresponding stable feature) for the training and the output is also based on this feature, which is functionally equivalent to the claimed invention);
Li, however, does not teach the following limitations, but from the same field of endeavor Ma teaches identifying, based on the first set of object parameters and the second set of object parameters, one or more objects for the user, wherein the one or more objects are identified using a combined determination based on alignment of object features associated with the one or more objects with predicted features from the first set of object parameters and the second set of object parameters (Ma, Pg25, Claim5, Lines4-6, "Using the object feature to perform attention calculation with the short-term behavior sequence and the long-term interest preference to obtain the short-term interest feature and the long-term interest feature of the object to be recommended;”,
Pg15, Lines, 32-37, "the present invention uses the object feature and the user’s short-term behavior sequence B to perform attention calculation to obtain the user’s short-term interest feature for the object. Vecshort uses the object characteristics and the user's long-term interest preference ulong to perform attention calculation to obtain the user's long-term interest feature Veclong for the object, namely: Vecshort = Attention (itarget, B, B); Veclong = Attention (itarget, ulong, ulong)"
Pg25, Claim6, Lines4-7, "Splicing the dynamic interest feature, the object feature, and the user feature; After the spliced feature vector passes through the fully connected layer, the softmax operation is performed to obtain the preference of the user to be recommended for the object."
Pg16, Lines17-20, "Assuming that the vector obtained after splicing is x = concat (Userprofile, UserDynamic, itarget, Veccontext), the preference score of the user to be recommended for the object is: score=softmax(RELU(Wx+b)), where W and b are model parameters", wherein the attention calculation using the itarget is used to calculate the likelihood and similarity between two vectors, which is the corresponding alignment of object features. Ma also discloses about the concatenating the output from the attention alignment, Vecshort and Veclong (the corresponding combined determination), to recommend a product based on the preference score computed from the output, which is functionally equivalent to the claimed invention); and
providing the one or more objects to the user (Ma, Pg4, Figure1, S105, "Determine the recommended objects based on the user's preference for objects for recommendation.")
Li and Ma are analogous to the claimed invention as they are from the same field of endeavor of e-commerce machine learning recommendation system designed to predict user commodity preferences and optimize targeted information placement based on historical user interaction sequences. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the overarching dual-pipeline ensemble training architecture, which separately trains a long-term model for stable preferences and a short-term model for recent acceptances) of Li with the precise attention-based feature extraction mechanism that inputs specific target object characteristics (sku_id, brand, and cate) as queries to align with behavioral sequence of Ma. The motivation is as recited by Ma (Ma, Pg23, Paragraph3, Lines10-15, "Dividing user behaviors into long-
term behaviors and short-term behaviors according to types can help distinguish users' long-term interest preferences and short-term impulsive needs, and better reflect the impact of user interest changes on the recommendation results, making the recommendation results more interpretable. At the same time, it brings higher click estimation accuracy, and the recommendation effect is significantly improved") such that by integrating Ma's teaching of utilizing specific static object attributes as an attention anchor allows the system to accurately align a user's immediate impulsive needs with the exact attributes of a target product, thereby successfully achieving the shared functional goals of maximizing click-through rate estimation accuracy and optimizing overall recommendation performance.
As to dependent Claim 6,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 4. It teaches about recommending a set of objects using the object parameters which were obtained from the user’s short-term and long-term preferences which were given to the two machine learning models respectively.
Li, however, does not teach the following limitations, but Ma from the same field of endeavor teaches the method of claim 4, wherein the dynamic features and the stable features are obtained through feature extraction comprising:
receiving a plurality of records comprising a set of features indicative of (a) user parameters for a plurality of users, (b) corresponding user-element interactions for each user parameter recorded during a period of time, wherein each feature comprises a plurality of values with each value corresponding to a record of the plurality of records, and (c) the focus parameter (Ma, Pg24, Claim1, Lines1-3, "A recommendation method, characterized in that it includes: Obtain user characteristics, long-term behavior data, short-term behavior data, and object characteristics of the user to be recommended;"
Pg14, Paragraph3, Lines3-6, "the user characteristics of the user to be recommended can be divided into two categories. One is the static attribute characteristics of the user, such as the basic attributes of the user’s name, gender, and age;"
Pg15, Lines17-18, "Specifically, each training batch has N samples, ..."
Pg12, Lines5-6, "generally use users’ historical behaviors (including clicks, searches, additional purchases, purchases, etc.)"
Pg14, Lines9-15, "takes short-term behavior data such as the user’s recent click behavior as short-term interest features in consideration of the actual online calculation speed (usually the user’s browsing data in the last week is used, and the upper limit of the number of data 100); and use long-term user behavior data such as purchase behavior to reflect the user's long-term interest preferences (usually use the user's behavior data for purchasing goods in the past two years, and the upper limit of the number of data is 300)."
Pg14, Paragraph4, Lines1-2, "For a certain user u, the purchase sequence is P=[pt], t.(1, Npurchase), and the browsing sequence is B=[bt], t.(1, Nbrowse);"
Pg14, Paragraph3, Lines8-10, "Object characteristics refer to the static attribute characteristics of the object, which mainly include basic attributes such as the number, category, and brand of the object."
Pg14, Paragraph4, Lines3-5, "For example, the static attributes of commodities include commodity number (sku_id), category (cate), brand (brand), then commodity characteristics: Eitem = concat (esku, ebrand, ecate)", wherein the claim 1 of Ma discloses it receives the user characteristics such as name, age and gender, long/short term data and the object characteristic data of the N samples or users (the corresponding user parameters for a plurality of users). Ma also discloses the data includes historical behaviors (corresponding user-element interactions for each user parameter recorded during a period of time) such as purchase sequence, browsing sequence, recent click behaviors for short-term and long-term period with up to 100 values and 300 values respectively. Ma further discloses the static attribute (the corresponding focus parameter) which will concentrate the objects to be recommended within this boundary, thus it is functionally equivalent to the claimed invention);
generating from the set of features (1) a first subset of the set of features, the first subset comprising concentrated features associated with focus parameter and generating from the set of features (2) a second subset of the set of features, the second subset comprising foundational features (Ma, Pg14, Lines9-15, "takes short-term behavior data such as the user’s recent click behavior as short-term interest features in consideration of the actual online calculation speed (usually the user’s browsing data in the last week is used, and the upper limit of the number of data 100); and use long-term user behavior data such as purchase behavior to reflect the user's long-term interest preferences (usually use the user's behavior data for purchasing goods in the past two years, and the upper limit of the number of data is 300)"
Pg14, Paragraph4, Lines1-2, "For a certain user u, the purchase sequence is P=[pt], t.(1, Npurchase), and the browsing sequence is B=[bt], t.(1, Nbrowse);"
Pg14, Paragraph3, Lines3-6, "the user characteristics of the user to be recommended can be divided into two categories. One is the static attribute characteristics of the user, such as the basic attributes of the user’s name, gender, and age;"
Pg14, Paragraph4, Lines3-5, "For example, the static attributes of commodities include commodity number (sku_id), category (cate), brand (brand), then commodity characteristics: Eitem = concat (esku, ebrand, ecate)", wherein Ma discloses about short-term interest features in consideration of the actual online calculation speed and purchase behavior to reflect the user's long-term interest preferences. The claimed invention's generation of first and second subsets of the set of features functionally means create two subsets based on the short-term and the long-term. Here, Ma shows that the long-term data or the user static attributes such as age, gender, name or the purchase sequence are used as a long-term or the second subset which is grouped as P=[pt] for the past two years. While B=[bt] or the recent click behaviors are used as the short-term data or the first subset, thus it is functionally equivalent to the claimed invention of creating two subsets where one for the short-term or the dynamic model and another for the long-term or the stable model); and
performing feature extraction using the first subset to obtain dynamic features representative of features that influenced user-element interactions associated with the focus parameter and performing the feature extraction using the second subset to obtain stable features representative of the features that influenced the user-element interactions that are non-specific to any one topic (Ma, Pg25, Claim5, Lines4-6, "Using the object feature to perform attention calculation with the short-term behavior sequence and the long-term interest preference to obtain the short-term interest feature and the long-term interest feature of the object to be recommended;"
Pg, 16, Lines4-6, "The user's interest changes over time, and the Self-Attention network is used to describe the evolution of user interest, and the user's dynamic interest feature UserDynamic can be
obtained ..."
Pg14, Paragraph4, Lines3-5, "For example, the static attributes of commodities include commodity number (sku_id), category (cate), brand (brand), then commodity characteristics: Eitem = concat (esku, ebrand, ecate)."
Pg9, Lines8-11, "using a network based on an attention mechanism as a feature extractor, performing feature extraction on the long-term behavior data, and performing feature extraction on the extracted features. Add and pool processing to obtain the long-term interest preference of the user to be recommended"
Pg14, Lines12-15, "use long-term user behavior data such as purchase behavior to reflect the user's long-term interest preferences (usually use the user's behavior data for purchasing goods in the past two years, and the upper limit of the number of data is 300)", wherein Ma discloses in the claim5 extracting the short-term interest feature (the corresponding dynamic features) where the user interest changes over time and the attention network is used to describe the evolution of user interest and the user's dynamic interest feature which the network uses this focused parameters or the static attributes. Ma also shows that extracting the long-term interest feature (stable features) which tracks the user's behavior data for purchasing goods in the past two years that is non-specific to any one topic, thus it is functionally equivalent to the claimed invention.)
As to dependent Claim 7,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 6. It teaches about receiving records of user information, user-element interactions, and the focus parameter, which then the record is used to generate the two subsets, which the first one is about the concentrated features associated with the focus parameter whereas the second one is about foundational features. Then, using all these subsets to extract dynamic and stable features, respectively.
Li further teaches the method of claim 6, further comprising:
training a first machine learning model using the dynamic features of the first subset of the set of features to identify object parameters associated with the objects that users are likely to interact with based on user-element interactions associated with the focus parameter (Li, Pg35, Claim1, "Obtain short-term characteristic data of labeled users on the recent acceptance of the distribution channel; Training the short-term characteristic data to obtain a short-term model for predicting that the user has a near-term preference for the distribution channel;"
Li, Pg36, Claim5, "training the short-term characteristic data to obtain a short-term model for predicting a user's near-term preference for a distribution channel comprises: The short-term feature data is trained by using any of the following machine learning models to obtain the short-term model: a long-short-term memory model, an iterative decision tree, a logistic regression, and a support vector machine."
Li, Pg14, Lines3-6, "use a short-term model to fit a user's recent delivery channel / acquisition to a certain type of product. The degree of acceptance of the channel, because the user's acceptance of a distribution channel usually changes drastically over time;"
Li, Pg27, Lines6-9, "The long-term characteristic data and the short-term characteristic data are correspondingly input to a long-term model and a short-term model for prediction, and a user's preference for a specified product and an output result of a recent acceptance of a delivery channel are output."
Pg36, Claim4, Lines1-3, "the short-term characteristic data comprises: User basic attribute data and one of the distribution channel click data, marketing scenario behavior data, and location service-based positioning data recently generated by users;", wherein the short-term memory model or the corresponding model trained using the dynamic features extracted from the first subset to identify the object parameters or the user's preference for a specified product and acceptance of a delivery, which is functionally equivalent to the claimed invention); and
training a second machine learning model using the stable features of the second subset of the set of features to identify the object parameters associated with the objects that the users are likely to interact with based on stable user-element interactions (Li, Pg35, Claim1, "Obtain long-term characteristic data of labeled users about long-term preferences of specified products; Training the long-term feature data to obtain a long-term model for predicting a user's long-term preference for a specified product;"
Li, Pg36, Claim3, "The long-term feature data is trained by using any of the following machine learning models to obtain the long-term model: iterative decision tree, logistic regression, and support vector machine."
Pg14, Lines2-3, "fit a user's relatively stable mental preferences for a certain type of product object through a long-term model;", wherein the long-term model (the corresponding second machine learning model) is trained using the stable and long-term characteristic data to identify or predict the user's long-term preference for a specified product which is equivalent to the claimed invention of predicting or identifying the object parameters associated with the stable features.)
As to dependent Claim 8,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 6. It teaches about receiving records of user information, user-element interactions, and the focus parameter, which then the record is used to generate the two subsets, which the first one is about the concentrated features associated with the focus parameter whereas the second one is about foundational features. Then, using all these subsets to extract dynamic and stable features, respectively.
Li, however, does not teach the following limitations, but from the same field of endeavor, Ma teaches the method of claim 6, wherein identifying the one or more objects for the user comprises
inputting the first set of object parameters and the second set of object parameters into a context-specific machine learning model configured to identify the one or more objects ranking highest according to their alignment with the features from both the first set of object parameters and the second set of object parameters (Ma, Pg15, Lines, 32-37, "the present invention uses the object feature and the user’s short-term behavior sequence B to perform attention calculation to obtain the user’s short-term interest feature for the object. Vecshort uses the object characteristics and the user's long-term interest preference ulong to perform attention calculation to obtain the user's long-term interest feature Veclong for the object, namely: Vecshort = Attention (itarget, B, B); Veclong = Attention (itarget, ulong, ulong)"
Ma, Pg17, Paragraph4, Lines10-13, "then, input user dynamic interest features, object feature Targe Item, user static attribute feature User Profile Features, and user interest mining feature Context Feature into the fully connected layer FCN, and after the softmax operation, the output layer OUTPUT Output the user's preference score for the object."
Pg16, Lines17-27, "Assuming that the vector obtained after splicing is x = concat(Userprofile,UserDynamic,itarget,Veccontext), the preference score of the user to be recommended for the object is: score=softmax(RELU(Wx+b)), where W and b are model parameters. According to the above formula, each object can be scored to obtain the user's preference for the object. Finally, you can select and recommend recommended objects according to the set preference screening rules. For example, you can sort each object in descending order according to the user's preference for each object, and select the top specified number of objects as the recommended object ; According to the set preference limit, the object that meets the requirement of the preference limit can be determined as the recommended object”, wherein Ma discloses the both short-term and long-term object parameters as Veclong and Vecshort (or the object parameters extracted from Claim 6 which Li shows) which then the these parameters are inputted into the third FCN layer as the context features. Then the preference scores are calculated for each object which will be recommended to the user according to the score, which is functionally equivalent to the claimed invention.)
As to dependent Claim 13,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 6. It teaches about receiving records of user information, user-element interactions, and the focus parameter, which then the record is used to generate the two subsets, which the first one is about the concentrated features associated with the focus parameter whereas the second one is about foundational features. Then, using all these subsets to extract dynamic and stable features, respectively.
Li further teaches the method of claim 6, wherein the focus parameter relates to a cyclical period of time, and/or is based on categories of inventory available (Li, Pg16, Lines8-10, "the user's shopping behavior in a certain category within 30, 90, and 180 days (such as browsing, clicking, bookmarking, adding purchases, Payment) statistical characteristics;"
Pg25, Paragraph3, "Category time series behavior refers to the time series behavior of users in multiple categories, such as buying pumpkin at time t-2 and buying candy at time t-1. If it is a manual method to extract features, the most likely prediction for time t is that the user will buy vegetables and fruits next, and the time series model may predict that the user may buy Halloween costumes based on past time action sequence dependencies.")
As to independent Claim 14,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 4 and thus rejected under the same rationale.
As to dependent Claim 15,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 5 and thus rejected under the same rationale.
As to dependent Claim 16,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 6 and thus rejected under the same rationale.
As to dependent Claim 17,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 7 and thus rejected under the same rationale.
As to dependent Claim 18,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 8 and thus rejected under the same rationale.
Claims 9, 11, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li and Ma as mentioned in Claim 6 in further view of Li Yuhua et al. (Li_Y), Chinese Application No. CN-115422411-A.
As to dependent Claim 9,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 6. It teaches about receiving records of user information, user-element interactions, and the focus parameter, which then the record is used to generate the two subsets, which the first one is about the concentrated features associated with the focus parameter whereas the second one is about foundational features. Then, using all these subsets to extract dynamic and stable features, respectively.
Ma further teaches the method of claim 6, wherein identifying the one or more objects comprises:
receiving the first set of object parameters and the second set of object parameters (Ma, Pg25, Claim5, Lines4-6, "Using the object feature to perform attention calculation with the short-term behavior sequence and the long-term interest preference to obtain the short-term interest feature and the long-term interest feature of the object to be recommended;", wherein Ma discloses that the corresponding first and second sets of object parameters or the short-term and long-term features have been extracted for the further prediction procedure, which is functionally equivalent to of receiving these sets of parameters);
However, both Li and Ma do not teach the following limitations, but from the same field of endeavor, Li_Y teaches determining a set of objects, wherein each object of the set of objects is characterized by at least one object parameter comprised in both the first set of object parameters and the second set of object parameters (Li_Y, Pg20, Claim5, "The formula of the Jaccard-like similarity is as follows:
PNG
media_image2.png
60
209
media_image2.png
Greyscale
If the Jaccard-like similarity is greater than the user similarity threshold δ, it is determined that there is a preference relationship between the corresponding two users", wherein finding the intersection between i and j, where they represent two different sets of features, is functionally equivalent to the claimed invention of determining a set of objects given above from Claim 6 where the object comprises the common features from the two sets);
computing, for each object of the set of objects, a score based on a number of object parameters of each object comprised in both the first set of object parameters and the second set of object parameters (Li_Y, Pg20, Claim5, "The formula of the Jaccard-like similarity is as follows:
PNG
media_image2.png
60
209
media_image2.png
Greyscale
If the Jaccard-like similarity is greater than the user similarity threshold δ, it is determined that there is a preference relationship between the corresponding two users", wherein computing the similarity score, sim, as the corresponding score to filter out the desirable outputs is functionally equivalent to the claimed invention.);
Ma, as mentioned in Claim4, teaches about recommending objects (Ma, Pg4, Figure1, S105, "Determine the recommended objects based on the user's preference for objects for recommendation") while Ma does not explicitly teach that the recommendation is based on the score of each object. Li_Y teaches identifying a subset of the set of objects based on the score of each object (Li_Y, Pg20, Claim5, "The formula of the Jaccard-like similarity is as follows:
PNG
media_image2.png
60
209
media_image2.png
Greyscale
If the Jaccard-like similarity is greater than the user similarity threshold δ, it is determined that there is a preference relationship between the corresponding two users", wherein the combination of the computed similarity score, sim, which uses the scores as the ordering factor with the recommendation system of Ma is functionally equivalent to the claimed invention.)
Li, Ma and Li_Y are analogous to the claimed invention as they are from the same field of endeavor of machine learning-based personalized recommendation systems designed to analyze user historical behavioral logs to predict user preferences and output filtered target object recommendations. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the dual-pipeline structural framework (which separately processes and isolates long-term and short-term user behavioral preference feature tracks) of Li, the precise attention-based feature extraction mechanism that inputs specific target object characteristics (sku_id, brand, and cate) as queries to align with behavioral sequence of Ma with the rule-based intersection cardinality counting and Jaccard-like similarity filtering mechanism of Li_Y. The motivation is as recited by Li_Y (Li_Y, Abstract, Lines10-16, The technical problems of sign-in data sparsity, insufficient meta-information utilization breadth and inaccurate cold user recommendation are solved, hierarchical feature extraction is performed on a user sign-in sequence, geographic space features are increased, user social information features are enhanced, behavior features of users are comprehensively learned, the next interest point is accurately recommended, and the user experience is improved. And the use experience of the user is optimized") such that by incorporating the explicit mathematical teaching of evaluating preference association relationships via the exact cardinality (count) of the intersection between discrete sets, counting overlapping parameters provides a transparent and highly efficient filtering logic.
As to dependent Claim 11,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 6. It teaches about receiving records of user information, user-element interactions, and the focus parameter, which then the record is used to generate the two subsets, which the first one is about the concentrated features associated with the focus parameter whereas the second one is about foundational features. Then, using all these subsets to extract dynamic and stable features, respectively.
Li and Ma, however, do not teach the following limitations, but from the same field of endeavor, Li_Y teaches the method of claim 6, wherein identifying the one or more objects comprises:
determining a third set of object parameters based on the object parameters comprised in both the first set of object parameters and the second set of object parameters (Li_Y, Pg20, Claim5, "The formula of the Jaccard-like similarity is as follows:
PNG
media_image2.png
60
209
media_image2.png
Greyscale
If the Jaccard-like similarity is greater than the user similarity threshold δ, it is determined that there is a preference relationship between the corresponding two users", wherein the intersection of the two different sets, i and j, is functionally equivalent to creating a third object parameter set as the intersection will be a set comprising object parameters that are either in the two sets); and
selecting the one or more objects based on each object of the one or more objects being characterized by at least a threshold number of object parameters of the third set of object parameters (Li_Y, Pg20, Claim5, "The formula of the Jaccard-like similarity is as follows:
PNG
media_image2.png
60
209
media_image2.png
Greyscale
If the Jaccard-like similarity is greater than the user similarity threshold δ, it is determined that there is a preference relationship between the corresponding two users, wherein using the similarity threshold, delta, to filter out the desirable outcome is functionally equivalent to the claimed invention.)
As to dependent Claim 19,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 9 and thus rejected under the same rationale.
Claims 5, 10, 12, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li and Ma as mentioned in Claim 6 in further view of Rauser et al. (Rauser), US Patent Application No. US-7461058-B1.
As to dependent Claim 5,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 4. It teaches about recommending a set of objects using the object parameters which were obtained from the user’s short-term and long-term preferences which were given to the two machine learning models respectively.
Li and Ma, however, do not teach the following limitations, but from the same field of endeavor Rauser teaches the method of claim 4, further comprising:
transmitting a first command for generating and displaying an interactive interface for the one or more objects (Rauser, Pg11, Figure 6.C
PNG
media_image1.png
818
990
media_image1.png
Greyscale
, Pg14, Column 4, Lines25-33, “Recommendation server 120 transmits and receives web pages from a browser on client computer 112 using hypertext markup language (HTML), Java or other techniques. These web pages may include images or instructions to obtain recommendation requests from a user. Recommendation server 120 also contains a database that stores various data, Such as constraint filters, recommendation filters and items, further described below”); and
responsive to receiving an indication of a first interaction of the user with an object of the one or more objects, transmitting a second command for modifying a field indicative of an availability of the object (Rauser, Pg11, Figure 6.C, “ClICK ON ITEM TO PURCHASAE”, Rauser Pg15, Column 5, Lines4-8, “database 324 with constraint table 326 that stores built constraints to use with recommendation software 312 and item table 328 with attribute information about each item. For example, item table 328 could store a category identification, item number, and number in stock”, wherein the system contains the database with each item’s availability which is then connected to the figure 6.C using the HTML server which will change the database accordingly as the user purchases the items, thus it is functionally equivalent to the claimed invention.
Li, Ma and Rauser are analogous to the claimed invention as they are from the same field of endeavor of computer-implemented personalized recommender systems and data processing filtering methods designed to capture user preference data and dynamically generate optimized item recommendation lists from an item catalog. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the dual-pipeline profile framework of Li, the temporal tracking model of Ma with the cost-optimized, multi-stage adaptive filtering architecture of Rauser. The motivation is as recited by Rauser (Rauser, Pg13, Column 1, Lines32-39, "Prefiltering requires a constraint system that discovers acceptable items and then Submits all discovered items to a prediction system that makes recommendations from this Subset. Prefiltering has some serious practical limitations, however. For example, gathering the list of acceptable items is difficult to accomplish efficiently as the list of acceptable items may be very large since it is selected from the whole item catalog") such that by incorporating Rauser's structural funneling method, the system can arrange the independently extracted temporal parameter sets into a sequential "two-stage funnel" rather than an expensive parallel fusion. Specifically, the system can execute one preference set as an initial constraint filter to instantly prune the vast whole item catalog, thereby efficiently determining a smaller object set which then can effectively filtered down according to another preference.
As to dependent Claim 10,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 6. It teaches about receiving records of user information, user-element interactions, and the focus parameter, which then the record is used to generate the two subsets, which the first one is about the concentrated features associated with the focus parameter whereas the second one is about foundational features. Then, using all these subsets to extract dynamic and stable features, respectively.
Li and Ma, however, do not teach the following limitations, but from the same field of endeavor Rauser teaches the method of claim 6, wherein identifying the one or more objects comprises:
determining a first object set based on the objects characterized by at least one object parameter of the first set of object parameters (Rauser, Pg13, Column 1, Lines32-35, "Prefiltering requires a constraint system that discovers acceptable items and then Submits all discovered items to a prediction system that makes recommendations from this Subset", wherein Rauser discloses a prefiltering mechanism which first filters out the desirable items using the constraint system, which is functionally equivalent to the claimed invention of filtering out the items using the first constraint or the first set of object parameters); and
determining the one or more objects by filtering the objects of the first object set based on whether or not each object of the first object set is characterized by the at least one object parameter of the second set of object parameters (Rauser, Pg16, Column 7, Lines37-43, "Once an item has been discovered in item table 328, the item is evaluated (step 514). Evaluation occurs by applying the constraint filter to the item. Items that pass the constraint filter will be passed to the recommendation filter (step 516). An item passes the constraint filter when it satisfies the constraints conditions. If an item does not pass the constraint filter, the item is discarded and not recommended", wherein once the first filter is finished, then the filtered items will be passed to the second filter which has another constraint filter, the corresponding the second filter using the second set of object parameters, which is functionally equivalent to the claimed invention of double filtering mechanism when applied with the sets of object parameters found in Claim 6.)
As to dependent Claim 12,
The combination of Li and Ma teaches, as mentioned above, all the limitations of Claim 6. It teaches about receiving records of user information, user-element interactions, and the focus parameter, which then the record is used to generate the two subsets, which the first one is about the concentrated features associated with the focus parameter whereas the second one is about foundational features. Then, using all these subsets to extract dynamic and stable features, respectively.
Li and Ma do not teach the following limitations, but from the same field of endeavor, Rauser teaches the method of claim 6, wherein identifying the one or more objects comprises:
determining at least one object parameter of the first set of object parameters is distinct from the object parameters of the second set of object parameters (The combination of Li and Ma teaches determining the first and second object parameters in Claim 6. While it does explicitly recite counting the total number of items when these sets are distinct, Rauser discloses a recommendation engine configured to evaluate items against multiple discrete input constrains and filter parameters.
Rauser, Pg15, Column 5, Lines31-34, "Logical expression include, for example, AND, OR, or NOT boolean expressions. Relational expressions include, for example EQUAL TO, GREATER THAN, LESS THAN or ISA", wherein using these logical expressions, it is possible to build "NOT EQUAL TO" Boolean which functionally acts to find a distinct object parameter from the two sets such that is functionally equivalent to the claimed invention); and
selecting the one or more objects based on the objects characterized by a highest number of object parameters of the first set of object parameters and the second set of object parameters (Rauser Pg18, Claim28, "wherein generating the recommendation list comprises generating a list of recommendations based on a specified number of the items that pass the constraint filter and the recommendation filter with highest predicted rating values", wherein evaluated items from above using the complex Boolean rules to construct a finalized recommendation list by sorting items that yields the highest predicted values is functionally equivalent to the claimed invention of selecting the objects that has the most matching object parameters.)
As to dependent Claim 20,
it is a non-transitory computer-readable medium claim that contains similar limitations of Claim 10 and thus rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONG YOON JUNG whose telephone number is (571)270-0198. The examiner can normally be reached 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DONG YOON JUNG/Examiner, Art Unit 2145
/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
Prosecution Insights