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
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1
Step 1, This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act. Thus, the claim is to a process/method, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A – Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Step “inputting, to an initial graph neural network, the sampling sub-graphs respectively corresponding to each query node and each media node in each first training sample pair, to obtain respective initial semantic features of each query node and each media node in each first training sample pair, and form a semantic feature pair corresponding to each first training sample pair” This limitation is directed to a mental process because the initial semantic features are obtained from the nodes of sample sub-graphs. The act of looking at information and determining its “semantic features” is something a human does mentally. For instance, one looks at a word “orange” [Wingdings font/0xF3] query and a photo of a fruit [Wingdings font/0xF3] media data and mentally identify the shared “semantic feature”
between them is “food”. Further, “form a semantic feature pair corresponding to each first training sample pair” is also a mental process of associating or pairing things together.
Step “training the initial graph neural network based on a difference between first semantic feature pairs corresponding to the one or more positive node pairs and second semantic feature pairs corresponding to the one or more negative node pairs, to obtain a target graph neural network, the target graph neural network being configured to determine a target semantic feature corresponding to a query node or a media node” This step is directed to a mental process because training based on difference is analogous to a human learning from experience. For instance, a person can be shown a correct answer and incorrect answer. By observing the difference between the two answers, one trains/learns to identify a correct answer in the future.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, steps mentioned above are considered together as a single abstract idea (i.e., mental [Wingdings font/0xF3] abstract idea) for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements/limitations
obtaining a media search graph, the media search graph comprising one or more query nodes corresponding to a query, one or more media nodes corresponding to media data, one or more object nodes corresponding to a search object, and one or more association nodes corresponding to association data, and the association data comprising data associated with at least one of the query or the media data [Wingdings font/0xF3] Obtaining a media search graph is considered as insignificant pre-solution activity because it is mere data gathering. Further, a graph is a logical collection of nodes and edges organized based on relationships between nodes (queries, media, object nodes, association)
obtaining a plurality of first training sample pairs from the media search graph, the plurality of first training sample pairs comprising one or more positive node pairs and one or more negative node pairs, the one or more positive node pairs each comprising a query node and a media node that are connected to each other in the media search graph, and the one or more negative node pairs each comprising a query node and a media node that are randomly combined and are not connected to each other in the media search graph” This step recites “obtaining… training sample pairs…comprising… positive node pairs and… negative node pairs. This limitation is considered as insignificant extra-solution activity because it is an instance of data gathering and categorization. A person can evaluate to mentally identify into sets such as positive and negative node pairs. Further, because this step mere identifies data to be used in training process, this step does not add any technical improvement to the computer functionality.
sampling the media search graph based on meta-paths respectively corresponding to the query and the media data, to obtain sampling sub-graphs respectively corresponding to each query node and each media node in each first training sample pair, each meta-path being a sampling path starting from a corresponding query node or a corresponding media node in the media search graph, wherein at least one meta-path corresponding to the query comprises sequentially connected type flags corresponding to a query node type, an object node type, and the query node type. This limitation is considered as insignificant extra-solution activity because a “meta-path” is simply a template used to select a portion of information from a larger collection of information. For example, a person can follow a mental rule to pick specific book based on author, subject, and so on. This step does not provide any technical solution to a computer-centric problem. Therefore, this step does not integrate the abstract idea into a practical application
for a target query, using the target graph neural network to determine a target semantic feature corresponding to current search data of the target query, and determining target search data from candidate search data based on a similarity between the target semantic feature corresponding to the current search data and target semantic features corresponding to the candidate search data, and outputting the target search data as a search result corresponding to the target query. This step is considered as insignificant post-solution activity and functional result. This step recites using the target graph neural network to find the search data based on the similarity and outputting results. Finding things that are similar is observations, evaluations, judgments that can be performed in human mind. Further, outputting the result does not provide any technical improvements to the computer itself. This step fails to integrate the abstract idea into a practical application.
Accordingly, the additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites a meta-path that is simply a template used to select a portion of information from a larger collection of information. For example, a person can follow a mental rule to pick specific book based on author, subject, and so on. Taking these limitations as an ordered combination adds nothing that is not already present when the elements are taken individually. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 2 depends on claim 1 and includes all the limitations of claim 1. Claim 2 recites “… wherein obtaining the media search graph comprises: obtaining historical search information corresponding to a plurality of search objects, the historical search information comprising a historical query and positive media data corresponding to the historical query, and the positive media data being media data corresponding to a positive feedback by the search object; generating an object node corresponding to a search object of the plurality of search objects, a query node corresponding to a historical query, and a media node corresponding to a piece of positive media data, establishing a connection relationship between the object node and a corresponding query node, establishing a connection relationship between the object node and a corresponding media node, and establishing a connection relationship between the query node and a corresponding media node; obtaining at least one type of association data corresponding to each of the historical query and the positive media data, the at least one type of association data comprising an associated entity, an associated tag, an associated category, or an associated publish object; generating an association node corresponding to a piece of association data, establishing a connection relationship between the query node and a corresponding association node, and establishing a connection relationship between the media node and a corresponding association node; obtaining a node feature corresponding to each node and a connection feature corresponding to each connection relationship; and
generating the media search graph based on the node, the node feature corresponding to the node, a connection relationship between nodes, and a connection feature corresponding to the connection relationship. The limitations of claim 2 are pre-solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 3 depends on claim 2 and includes all the limitations of claim 2. Claim 3 recites “… further comprising: obtaining supplementary media data, and generating a media node corresponding to the supplementary media data, the supplementary media data comprising at least one of first media data and second media data, the first media data being media data with media quality higher than a quality threshold, and the second media data being media data with a time interval between publish time and current time less than a first time interval threshold; and obtaining at least one type of association data corresponding to the supplementary media data. The limitations obtaining data of claim 3 are pre-solution activities. Further, comparing data to threshold is observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea). The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 4 depends on claim 2 and includes all the limitations of claim 2. Claim 4 recites “… further comprising: obtaining a rewritten query corresponding to the historical query; when a search time interval between the historical query and the corresponding rewritten query is less than a second time interval threshold and a similarity between the historical query and the corresponding rewritten query is greater than a similarity threshold, generating a query node corresponding to the rewritten query; and
establishing a connection relationship between the query node corresponding to the historical query and the query node corresponding to the rewritten query.
Comparing data to threshold is observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea) The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 5 depends on claim 1 and includes all the limitations of claim 1. Claim 5 recites “… wherein sampling the media search graph based on the meta-paths respectively corresponding to the query and the media data comprises: determining a current search meta-path from at least one meta-path corresponding to the query, the current search meta-path being a path formed by sequentially connecting type flags respectively corresponding to a search type, a first type, and a second type; sampling, by using a current query node as a search center node, at least two nodes that are directly connected to the search center node and that belong to the first type from the media search graph as a first-order neighbor node corresponding to the search center node, sampling at least two nodes that are directly connected to the first-order neighbor node and that belong to the second type from the media search graph as a second-order neighbor node corresponding to the search center node, and obtaining a sampling sub-graph corresponding to the search center node in the current search meta-path based on the search center node and the corresponding first neighbor node second neighbor node; determining a current media meta-path from at least one meta-path corresponding to the media data, the current media meta-path being a path formed by sequentially connecting type flags respectively corresponding to a media type, a third type, and a fourth type; and sampling, by using a current media node as a center media node, at least two nodes that are directly connected to the center media node and that belong to the third type from the media search graph as a first-order neighbor node corresponding to the center media node, sampling at least two nodes that are directly connected to the first-order neighbor node and that belong to the fourth type from the media search graph as a second-order neighbor node corresponding to the center media node, and obtaining a sampling sub-graph corresponding to the center media node in the current media meta-path based on the center media node and the corresponding first neighbor node second neighbor node. The limitations of claim merely are observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea). The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 6 depends on claim 1 and includes all the limitations of claim 1. Claim 6 recites “… wherein inputting, to the initial graph neural network, the sampling sub-graphs respectively corresponding to the query node and the media node in the first training sample pair comprises: inputting, to the initial graph neural network, sampling sub-graphs from at least two semantic perspectives that correspond to a current node, to obtain respective sub-graph features of the sampling sub-graphs corresponding to the current node, the current node being the query node or the media node in the first training sample pair, and different meta-paths corresponding to different semantic perspectives; and fusing the sub-graph features corresponding to the current node, to obtain an initial semantic feature corresponding to the current node. The limitations of claim merely input data to a machine learning model (i.e., mathematical function) to obtain expected outputs. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 7 depends on claim 6 and includes all the limitations of claim 6. Claim 7 recites “… wherein a sampling sub-graph corresponding to a node comprises the node and a first-order neighbor node and a second-order neighbor node that correspond to the node, and inputting, to the initial graph neural network, the sampling sub- graphs from the at least two semantic perspectives that correspond to the current node comprises: aggregating, to a first-order neighbor node by using the initial graph neural network, a node feature corresponding to a second-order neighbor node in a current sampling sub-graph corresponding to the current node, and a connection feature between the second-order neighbor node and the first-order neighbor node, to obtain a second-order aggregated feature corresponding to the first-order neighbor node;
aggregating, to the current node by using the initial graph neural network, a node feature corresponding to the first-order neighbor node, the second-order aggregated feature, and a connection feature between the first-order neighbor node and the current node, to obtain a first- order aggregated feature corresponding to the current node; and
obtaining, by using the initial graph neural network based on the node feature corresponding to the current node and the first-order aggregated feature, a sub-graph feature of the current sampling sub-graph corresponding to the current node.
Aggregating/obtaining data is pre add post solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 8 depends on claim 1 and includes all the limitations of claim 1. Claim 8 recites “… wherein training the initial graph neural network based on the difference between semantic feature pairs corresponding to the positive node pair and the negative node pair, to obtain a target graph neural network comprises: obtaining a node loss based on the difference between the semantic feature pairs corresponding to the positive node pair and the negative node pair; obtaining a plurality of second training sample pairs, the plurality of second training sample pairs comprising a positive sampling sub-graph pair and a negative sampling sub-graph pair, the positive sampling sub-graph pair comprising sampling sub-graphs from different semantic perspectives that correspond to a same node, and the negative sampling sub-graph pair comprising sampling sub-graphs corresponding to different nodes that belong to a same type;
inputting each sampling sub-graph in the second training sample pair to the initial graph neural network, to obtain a sub-graph feature of each sampling sub-graph in the second training sample pair, and form a sub-graph feature pair corresponding to the second training sample pair; obtaining a perspective loss based on a difference between sub-graph feature pairs corresponding to the positive sampling sub-graph pair and the negative sampling sub-graph pair; and training the initial graph neural network based on the node loss and the perspective loss to obtain the target graph neural network.
The limitations of the claim comprise steps obtaining data, inputting data in to math function (i.e., graph neural network), and manipulating/training the math function (i.e., graph neural network). The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 9 depends on claim 1 and includes all the limitations of claim 1. Claim 8 recites “… wherein obtaining the node loss based on the difference between the semantic feature pairs corresponding to the positive node pair and the negative node pair comprises: obtaining, based on a feature similarity between initial semantic features in a same semantic feature pair, a semantic similarity corresponding to the semantic feature pair; fusing semantic similarities respectively corresponding to a same positive node pair and corresponding negative node pairs, to obtain a fusion similarity corresponding to the positive node pair, a negative node pair corresponding to the positive node pair being a negative node pair having an overlapping node with the positive node pair; obtaining, based on a difference between a semantic similarity and a fusion similarity that correspond to a same positive node pair, a node sub-loss corresponding to the positive node pair; and obtaining the node loss based on a node sub-loss corresponding to each positive node pair. The limitations of claim 9 are pre-solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 10 depends on claim 8 and includes all the limitations of claim 8. Claim 10 recites “… wherein obtaining the perspective loss based on the difference between sub-graph feature pairs corresponding to the positive sampling sub-graph pair and the negative sampling sub-graph pair comprises: obtaining, based on a feature similarity between sub-graph features in a same sub-graph feature pair, a perspective similarity corresponding to the sub-graph feature pair; fusing perspective similarities respectively corresponding to a same positive sampling sub- graph pair and corresponding negative sampling sub-graph pairs, to obtain a fusion similarity corresponding to the positive sampling sub-graph pair; obtaining, based on a difference between a perspective similarity and a fusion similarity that correspond to a same positive sampling sub-graph pair, a perspective sub-loss corresponding to the positive sampling sub-graph pair; and
obtaining the perspective loss based on a perspective sub-loss corresponding to each positive sampling sub-graph pair. The limitations of obtaining/collecting data and fusing/combining data are merely pre post-solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claims 11-20 are similar to claim 1-10. The claims are rejected based on the similar reasons.
Response to Arguments
Section Objections to Claims 1, 2, 6-12, and 16-20 - pg. 21
The objections to claims 1, 2, 6-12, and 16-20 are withdrawn as necessitated by Amendment.
Section Rejections of Claims 1-20 under 35 U.S.C 101
Applicant argues that “apply the trained network to determine target search data and output search results, thus integrating any alleged mathematical concepts into a practical application (i.e., computerized search)” Examiner respectfully disagrees because simply applying a machine trained model within a particular technology field (e.g., computerized search) is not enough to constitute a practical application. The claim does not provide any technical solution to improve the computerized search technology. It merely uses a computer as a tool to identifying and retrieving information.
Further, the Applicant argues that the ordered combination of steps creates a specific pipeline that improve the search. Examiner respectfully disagrees. A pipeline is just a sequence of steps. In the independent claims, every step in the pipeline is a mental process. Even if these abstract steps are put in a specific ordered, they are still abstract. The pipeline in the claim does not describe a better way for a computer to work. Because the claim does not fix a technical problem in the computer itself, it just a generic computer implementation performing an abstract idea.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/Primary Examiner, Art Unit 2154
Prosecution Insights