Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending for examination. Claims 1, 8, and 15 are independent.
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.
Step 1
According to the first part of the analysis, in the instant case, claims 1-7 are directed to a method, claims 8-14 are directed to a computer device, and claims 15-20 are directed to non-transitory computer-readable storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
2A Prong 1:
(This step for predicting a conversion index is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A data processing method performed by a computer device, the method comprising: (The computing device is understood to be a generic computer element - See MPEP 2106.05(f).)
obtaining a heterogeneous conversion graph, the heterogeneous conversion graph comprising N object nodes and M resource nodes, each object node representing an object, each resource node representing a resource, N and M being positive integers, wherein a connecting edge between an object node and a resource node represents a conversion behavior from an object corresponding to the object node to a resource corresponding to the resource node; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
obtaining a homogeneous object graph corresponding to each of the N objects, the homogeneous object graph comprising a plurality of object feature nodes, each object feature node being configured to represent an object feature of the corresponding object; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
obtaining a homogeneous resource graph corresponding to each of the M resources, the homogeneous resource graph comprising a plurality of resource feature nodes, each resource feature node being configured to represent a resource feature of the corresponding resource; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).) and
training a prediction network based on the heterogeneous conversion graph, the homogeneous object graph of each object, and the homogeneous resource graph of each resource, to obtain a trained prediction network, the trained prediction network being configured to predict a conversion index of an object of interest for a resource of interest. (Training a prediction network is understood as mere instructions to implement an abstract idea (e.g., generate predictions) on a computer - see MPEP 2106.05(f).))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A data processing method performed by a computer device, the method comprising: (The computing device is understood to be a generic computer element - See MPEP 2106.05(f).)
obtaining a heterogeneous conversion graph, the heterogeneous conversion graph comprising N object nodes and M resource nodes, each object node representing an object, each resource node representing a resource, N and M being positive integers, wherein a connecting edge between an object node and a resource node represents a conversion behavior from an object corresponding to the object node to a resource corresponding to the resource node; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
obtaining a homogeneous object graph corresponding to each of the N objects, the homogeneous object graph comprising a plurality of object feature nodes, each object feature node being configured to represent an object feature of the corresponding object; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
obtaining a homogeneous resource graph corresponding to each of the M resources, the homogeneous resource graph comprising a plurality of resource feature nodes, each resource feature node being configured to represent a resource feature of the corresponding resource; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i)))) and
training a prediction network based on the heterogeneous conversion graph, the homogeneous object graph of each object, and the homogeneous resource graph of each resource, to obtain a trained prediction network, the trained prediction network being configured to predict a conversion index of an object of interest for a resource of interest. (Training a prediction network is understood as mere instructions to implement an abstract idea (e.g., generate predictions) on a computer - see MPEP 2106.05(f).))
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
well, understood, routine and conventional activity as disclosed in combination
of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 8: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A computer device, comprising a memory and a processor, the memory having a computer program stored therein, the computer program, when executed by the processor, causing the computer device to perform a data processing method including:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 15: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A non-transitory computer-readable storage medium, having a computer program stored therein, the computer program being adapted to be loaded by a processor of a computer device and causing the computer device to perform a data processing method including:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 2, 9, and 16
2A Prong 1:
(This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
(This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
(This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
2A Prong 2 & 2B:
wherein the training a prediction network based on the heterogeneous conversion graph, the homogeneous object graph of each object, and the homogeneous resource graph of each resource, to obtain a trained prediction networks comprises: (Training a prediction network is understood as mere instructions to implement an abstract idea (e.g., generate predictions) on a computer - see MPEP 2106.05(f).))
invoking the prediction network to (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic prediction network as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
training the prediction network based on the first object embedding feature and the second object embedding feature of each object, the first resource embedding feature and the second resource embedding feature of each resource, to obtain the trained prediction network. (Training a prediction network is understood as mere instructions to implement an abstract idea (e.g., generate predictions) on a computer - see MPEP 2106.05(f).))
Regarding Claims 3 and 10
2A Prong 1:
converting the heterogeneous conversion graph into a relationship matrix; (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
propagating the object features of the N objects and the resource features of the M resources between the N objects and the M resources based on the feature propagation matrix and the relationship matrix, to generate an embedding feature matrix corresponding to the N objects and the M resources; (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).) and
generating the first object embedding feature of each object and the first resource embedding feature of each resource based on the embedding feature matrix. (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
2A Prong 2:
wherein the invoking the prediction network to generate a first object embedding feature of each object and a first resource embedding feature of each resource based on the heterogeneous conversion graph comprises: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic prediction network as a tool to perform the abstract idea (i.e., generate embedding) - see MPEP 2106.05(f).)
invoking the prediction network to obtain a feature propagation matrix for the heterogeneous conversion graph; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
wherein the invoking the prediction network to generate a first object embedding feature of each object and a first resource embedding feature of each resource based on the heterogeneous conversion graph comprises: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic prediction network as a tool to perform the abstract idea (i.e., generate embedding) - see MPEP 2106.05(f).)
invoking the prediction network to obtain a feature propagation matrix for the heterogeneous conversion graph; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Regarding Claims 4, 11, and 17
2A Prong 1:
(This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
(This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
performing feature propagation processing on object features of the target object in a plurality of dimensions based on the first activation subgraph, to obtain a node feature in the first activation subgraph corresponding to each object feature node of the target object; (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).) and
generating the second object embedding feature of the target object based on the node feature corresponding to each object feature node of the target object. (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
2A Prong 2 & 2B:
wherein a connecting edge exists between any two object feature nodes in the homogeneous object graph of a target object of the N objects; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the homogeneous graph - See MPEP 2106.05(h).)
the invoking the prediction network to (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic prediction network as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
Regarding Claims 5, 12, and 18
2A Prong 1:
(This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
(This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
performing feature propagation processing on resource features of the target resource in a plurality of dimensions based on the second activation subgraph, to obtain a node feature corresponding to each resource feature node of the target resource in the second activation subgraph; (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).) and
generating the second resource embedding feature of the target resource based on the node feature corresponding to each resource feature node of the target resource. (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
2A Prong 2 & 2B:
wherein a connecting edge exists between any two resource feature nodes in the homogeneous resource graph of a target resource of the M resources; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the homogeneous graph - See MPEP 2106.05(h).)
invoking the prediction network to (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic prediction network as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
Regarding Claims 6, 13, and 19
2A Prong 1:
generating a predicted loss value of the prediction network based on the first object embedding feature and the second object embedding feature of each object, the first resource embedding feature and the second resource embedding feature of each resource; (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation) or mathematical calculations.) and
correcting a network parameter of the prediction network based on the predicted loss value, to obtain the trained prediction network. (This step is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation) or mathematical calculations.)
2A Prong 2 & 2B:
wherein the training the prediction network based on the first object embedding feature and the second object embedding feature of each object, the first resource embedding feature and the second resource embedding feature of each resource, to obtain the trained prediction network comprises: (Training a prediction network is understood as mere instructions to implement an abstract idea (e.g., generate predictions) on a computer - see MPEP 2106.05(f).))
Regarding Claims 7, 14, and 20
2A Prong 1:
(This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2:
wherein the method further comprises:
obtaining a prediction object and a prediction resource; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
invoking the trained prediction network to (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic prediction network as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).) and
pushing the prediction resource to the prediction object when the conversion index of the prediction object for the prediction resource is greater than or equal to a conversion index threshold. (This step is directed to transmitting information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
2B:
obtaining a prediction object and a prediction resource; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
invoking the trained prediction network to predict a conversion index of the prediction object for the prediction resource; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic prediction network as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).) and
pushing the prediction resource to the prediction object when the conversion index of the prediction object for the prediction resource is greater than or equal to a conversion index threshold. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6, 8-10, 13, 15-16, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. ("Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation", hereinafter "Zhang").
Regarding Claim 1
Zhang discloses: A data processing method performed by a computer device ([Section 5 Experiments and Abstract] describes conduct experiments and evaluating benchmarks, it would be obvious that a computing device is utilized.), the method comprising:
obtaining a heterogeneous conversion graph, the heterogeneous conversion graph comprising N object nodes and M resource nodes, each object node representing an object, each resource node representing a resource, N and M being positive integers, wherein a connecting edge between an object node and a resource node represents a conversion behavior from an object corresponding to the object node to a resource corresponding to the resource node; ([Section 4.3 Heterogeneous Graph Neural Network and Fig 1] discloses a heterogeneous graph with user-item interactions (i.e. conversion behavior). The graph comprises user nodes (i.e. object nodes), item nodes (i.e. resource nodes), and edges between the user and item nodes.)
obtaining a homogeneous object graph corresponding to each of the N objects, the homogeneous object graph comprising a plurality of object feature nodes, each object feature node being configured to represent an object feature of the corresponding object; ([Section 2.2, Sections 4.1 – 4.2, and Fig 1-2] describes a user-user subgraph with embedding based on node features.)
obtaining a homogeneous resource graph corresponding to each of the M resources, the homogeneous resource graph comprising a plurality of resource feature nodes, each resource feature node being configured to represent a resource feature of the corresponding resource; ([Section 2.2, Sections 4.1 – 4.2, and Fig 1-2] describes an item-item subgraph with embedding based on node features.) and
training a prediction network based on the heterogeneous conversion graph, the homogeneous object graph of each object, and the homogeneous resource graph of each resource, to obtain a trained prediction network, the trained prediction network being configured to predict a conversion index of an object of interest for a resource of interest. ([Section 4.5 Model Training, Section 4.1, and Fig 1] describes training HGNN (i.e. prediction network) with heterogeneous, user-user, and item-item graphs. The trained network predicts a score (i.e. conversion index) that target user (i.e. object of interest) will rate a candidate item (i.e. resource of interest).)
Regarding Claim 8
Zhang discloses: A computer device, comprising a memory and a processor, the memory having a computer program stored therein, the computer program, when executed by the processor ([Section 5 Experiments and Abstract] describes conduct experiments and evaluating benchmarks, it would be obvious that a computing device is utilized.), causing the computer device to perform a data processing method including: (Claim 8 is a device claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 15
Zhang discloses: A non-transitory computer-readable storage medium, having a computer program stored therein ([Section 5 Experiments and Abstract] describes conduct experiments and evaluating benchmarks, it would be obvious that a computing device is utilized.), the computer program being adapted to be loaded by a processor of a computer device and causing the computer device to perform a data processing method including: (Claim 15 is a non-transitory computer-readable storage medium claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 2
Zhang discloses: The method according to claim 1, wherein the training a prediction network based on the heterogeneous conversion graph, the homogeneous object graph of each object, and the homogeneous resource graph of each resource, to obtain a trained prediction networks comprises: ([Section 4.5 Model Training, Section 4.1, and Fig 1], Zhang describes training HGNN (i.e. prediction network) with heterogeneous, user-user, and item-item graphs.)
invoking the prediction network to generate a first object embedding feature of each object and a first resource embedding feature of each resource based on the heterogeneous conversion graph; ([Sections 4.3 - 4.5, and Fig 1], Zhang describes user and item embeddings for the heterogeneous graph.)
invoking the prediction network to generate a second object embedding feature of each object based on the homogeneous object graph of the object; ([Section 4.2 -4.5 and Fig 1-2], Zhang describes final user embedding propagated representation and used by the predictor for recommendation.)
invoking the prediction network to generate a second resource embedding feature of each resource based on the homogeneous resource graph of the resource; ([Section 4.2 -4.5 and Fig 1-2], Zhang describes item embedding for the item-item subgraph.) and
training the prediction network based on the first object embedding feature and the second object embedding feature of each object, the first resource embedding feature and the second resource embedding feature of each resource, to obtain the trained prediction network. ([Section 4.5 Model Training, Section 4.1, and Fig 1], Zhang describes training HGNN (i.e. prediction network) with heterogeneous, user-user, and item-item graphs with embeddings.)
Regarding Claim 3
Zhang discloses: The method according to claim 2, wherein the invoking the prediction network to generate a first object embedding feature of each object and a first resource embedding feature of each resource based on the heterogeneous conversion graph comprises: ([Sections 4.3 - 4.5, and Fig 1], Zhang describes user and item embeddings based on the heterogeneous graph.)
converting the heterogeneous conversion graph into a relationship matrix; ([Section 4.5, Section 4.2, and Fig 1] describes the heterogenous graph represented using adjacency matrix (i.e. relationship matrix).)
invoking the prediction network to obtain a feature propagation matrix for the heterogeneous conversion graph; ([Section 4.3 and Fig 1] describes aggregating features or propagation (see equation 12).)
propagating the object features of the N objects and the resource features of the M resources between the N objects and the M resources based on the feature propagation matrix and the relationship matrix, to generate an embedding feature matrix corresponding to the N objects and the M resources; ([Section 4.3 – 4.5, and Fig 1] describes aggregating features of users and items.) and
generating the first object embedding feature of each object and the first resource embedding feature of each resource based on the embedding feature matrix. ([Section 4.3 – 4.5, and Fig 1] describes generating a user and item embedding for predictions.)
Regarding Claim 6
Zhang discloses: The method according to claim 2, wherein the training the prediction network based on the first object embedding feature and the second object embedding feature of each object, the first resource embedding feature and the second resource embedding feature of each resource, to obtain the trained prediction network ([Section 4.5 Model Training and Fig 1], Zhang describes obtaining the trained network.) comprises:
generating a predicted loss value of the prediction network based on the first object embedding feature and the second object embedding feature of each object, the first resource embedding feature and the second resource embedding feature of each resource; ([Section 4.5 and equations 18-23] Zhang describes a loss calculation.) and
correcting a network parameter of the prediction network based on the predicted loss value, to obtain the trained prediction network. ([Sections 4.4 - 4.5 and equations 18-23] Zhang describes learning parameters with loss.)
Regarding Claim 9
(Claim 9 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.)
Regarding Claim 16
(Claim 16 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.)
Regarding Claim 10
(Claim 10 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.)
Regarding Claim 13
(Claim 13 recites analogous limitations to claim 6 and therefore is rejected on the same ground as claim 6.)
Regarding Claim 19
(Claim 19 recites analogous limitations to claim 6 and therefore is rejected on the same ground as claim 6.)
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.
Claim(s) 4-5, 11-12, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Chen et al. (US 20240046075 A1, hereinafter "Chen").
Regarding Claim 4
Zhang discloses: The method according to claim 2, wherein a connecting edge exists between any two object feature nodes in the homogeneous object graph of a target object of the N objects ([Section 2.2, Sections 4.1 – 4.2, and Fig 1-2] show the connecting edges between nodes in the user-user and item-item subgraphs.); and
the invoking the prediction network to generate a second object embedding feature of each object based on the homogeneous object graph of the object ([Section 4.2 -4.5 and Fig 1-2], describes the prediction network with user embeddings for the subgraphs.) comprises:
performing feature propagation processing on object features of the target object in a plurality of dimensions based on the first activation subgraph, to obtain a node feature in the first activation subgraph corresponding to each object feature node of the target object ([Sections 4.3 – 4.5 and Fig 1] describes a multi-layer graph that propagates the node user features over the learned graph.); and
generating the second object embedding feature of the target object based on the node feature corresponding to each object feature node of the target object. ([Section 4.2 - 4.5 and Fig 1-2], Zhang describes final user embedding propagated representation and used by the predictor for recommendation.)
Zhang does not explicitly disclose: invoking the prediction network to delete the connecting edge in the
However, Chen discloses in the same field of endeavor: invoking the prediction network to delete the connecting edge in the ([Para 0056, 0084-0088, Fig 4] describes a graph convolutional networks applies a stochastic binary mask to prune insignificant edges (i.e. delete edges), and generating a first subgraph.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of a Stochastic mask disclosed by Chen into the method of Graph Neural Network for Social Recommendation disclosed by Zhang to delete edges of a graph. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Stochastic mask disclosed by Chen as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to generate more optimal graphs by pruning noisy and insignificant edges.
Regarding Claim 5
Zhang in view of Chen discloses: The method according to claim 2, wherein a connecting edge exists between any two resource feature nodes in the homogeneous resource graph of a target resource of the M resources ([Section 2.2, Sections 4.1 – 4.2, and Fig 1-2], Zhang show the connecting edges between nodes in the user-user and item-item subgraphs.); and
the invoking the prediction network to generate a second resource embedding feature of each resource based on the homogeneous resource graph of the resource ([Section 4.2 -4.5 and Fig 1-2] Zhang describes the prediction network with item embeddings for the subgraphs.) comprises:
invoking the prediction network to delete the connecting edge in the homogeneous resource graph of the target resource, to obtain a second activation subgraph of the homogeneous resource graph of the target resource; ([Para 0056, 0084-0088, Fig 4] Chen describes a graph convolutional networks applies a stochastic binary mask to prune insignificant edges (i.e. delete edges), and generating a first subgraph.)
performing feature propagation processing on resource features of the target resource in a plurality of dimensions based on the second activation subgraph, to obtain a node feature corresponding to each resource feature node of the target resource in the second activation subgraph; ([Sections 4.3 – 4.5 and Fig 1] Zhang describes a multi-layer graph that propagates the node item features over the learned graph.) and
generating the second resource embedding feature of the target resource based on the node feature corresponding to each resource feature node of the target resource. ([Section 4.2 - 4.5 and Fig 1-2], Zhang describes final item embedding propagated representation and used by the predictor for recommendation.)
Regarding Claim 11
(Claim 11 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 17
(Claim 17 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 12
(Claim 12 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Regarding Claim 18
(Claim 18 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Claim(s) 7, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Prakash et al. (US 20220180391 A1, hereinafter "Prakash").
Regarding Claim 7
Zhang discloses: The method according to claim 1, wherein the method further comprises:
obtaining a prediction object and a prediction resource; invoking the trained prediction network to predict a conversion index of the prediction object for the prediction resource; ([Section 4.5 Model Training, Section 4.1, and Fig 1], Zhang describes the trained network predicts a score (i.e. conversion index) that target user (i.e. object) will rate a candidate item (i.e. resource).) and
Zhang does not explicitly disclose: pushing the prediction resource to the prediction object when the conversion index of the prediction object for the prediction resource is greater than or equal to a conversion index threshold.
However, Prakash discloses in the same field of endeavor: pushing the prediction resource to the prediction object when the conversion index of the prediction object for the prediction resource is greater than or equal to a conversion index threshold. ([Para 0076, 0093, and Fig 1-4] describes presenting a product to the user when the product recommendations relevance scores are above a preset threshold.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Item Recommender disclosed by Prakash into the method of Graph Neural Network for Social Recommendation disclosed by Zhang to push a prediction resource to a prediction object. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Item Recommender disclosed by Prakash as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to recommend items to a user that have a better chance for conversion.
Regarding Claim 14
(Claim 14 recites analogous limitations to claim 7 and therefore is rejected on the same ground as claim 7.)
Regarding Claim 20
(Claim 20 recites analogous limitations to claim 7 and therefore is rejected on the same ground as claim 7.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhao et al. (US 20240289647 A1) describes homogeneous and heterogeneous graphs. Gombolay (US 20220226994 A1) describes tasks using heterogeneous graph attention network models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30.
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, ABDULLAH KAWSAR can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TEWODROS E MENGISTU/ Examiner, Art Unit 2127
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