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 Objections
Claim 1, 11, 20, 2, 12, 6, 16, 7, 17, 8, 18, 9, 19, and 10 are objected to because of the following informalities as below. Appropriate correction is required.
Claim 1
A search data processing method, performed by a computer device, the method comprising:
obtaining a media search graph, the media search graph comprising a query node corresponding to a query, a media node corresponding to media data, and an association node corresponding to association data, and the association data comprising data associated with at least one of the query or the media data;
<examiner note: a media search graph only has 3 nodes: i) a query node, ii) a media node, and an association node>
obtaining a plurality of first training sample pairs from the media search graph, the plurality of first training sample pairs comprising a positive node pair and a negative node pair, the positive node pair comprising a query node and a media node that are connected to each other in the media search graph, and the negative node pair comprising a query node and a media node that are randomly combined in the media search graph;
<examiner note: positive node pair comprises a query node that connects a media node and a negative node pair comprises the same query node that connects randomly the same media node. What are the different between the positive node pair and the negative node pair because they both are the same query node and media node. Further, how the same query node connects to the same media node and the same query node and the same media node randomly combined in the media search graph>
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 the query node and the media node in the first training sample pair, the meta-path being a sampling path starting from the query node or the media node in the media search graph;
<examiner note: because there is only one training sample pair, there is no way to obtain multiple sampling sub-graphs>
inputting, to an initial graph neural network, the sampling sub-graphs respectively corresponding to the query node and the media node in the first training sample pair, to obtain respective initial semantic features of the query node and the media node in the first training sample pair, and form a semantic feature pair corresponding to the first training sample pair; and
<examiner note: because there is only one training sample pair, there is only one sampling sub-graph>
training the initial graph neural network based on a difference between semantic feature pairs corresponding to the positive node pair and the negative node pair, 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.
<examiner note: The semantic feature pairs obtained in step d) are from the first training pair (i.e., either obtain from a positive node pair or a negative node pair). There is no way to train the initial graph neural network based on the difference between semantic feature pairs corresponding to the positive node pair and negative node pair>
Claim 11 and 20 are similar to claim 1. The claims are objected based on the same reason.
Claim 2: “… 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…”
<examiner note: It is unclear which node the media search graph is generated. Further, the generated media search graph is built ONLY one node. Therefore, there will be no connections between nodes>
Claim 12 is similar to claim 2. The claim is objected based on the same reason.
Claim 6: “… The method according to claim 1, 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…”
<examiner note: There is only one sampling sub-graph 1 claim 1>
Claim 16 is similar to claim 6. The claim is objected based on the same reason.
Claim 7: “… The method according to claim 6, 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…”
<examiner note: There is only one sampling sub-graph 1 claim 1>
Claim 17 is similar to claim 7. The claim is objected based on the same reason.
Claim 8: “… The method according to claim 1, 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.
<examiner note: The semantic feature pairs obtained in step d) of claim 1 are from the first training pair (i.e., either obtain from a positive node pair or a negative node pair). There is no way to train the initial graph neural network and obtain a node loss based on the difference between semantic feature pairs corresponding to the positive node pair and negative node pair.
“… inputting each sampling sub-graph in the second training sample pair…” it seems that the underlined phrase should be “sampling sub-graph pair”
Claim 18 is similar to claim 8. The claim is objected based on the same reason.
Claim 9: The method according to claim 8, 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; <examiner note: there is only one semantic similarity>
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; <examiner note: how to fuse multiple semantic similarities into a fusion similarity since there is only one semantic similarity in previous step>
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.
Claim 19 is similar to claim 9. The claim is objected based on the same reason.
Claim 10: The method according to claim 8, 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; <examiner note: there is only one persepctive similarity>
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; <examiner note: how to fuse multiple perspective similarities into a fusion similarity since there is only one perspective similarity in previous step>
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.
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.
Regarding to claims 1-11
Claim 1
A search data processing method, performed by a computer device, the method comprising:
obtaining a media search graph, the media search graph comprising a query node corresponding to a query, a media node corresponding to media data, and an association node corresponding to association data, and the association data comprising data associated with at least one of the query or the media data;
obtaining a plurality of first training sample pairs from the media search graph, the plurality of first training sample pairs comprising a positive node pair and a negative node pair, the positive node pair comprising a query node and a media node that are connected to each other in the media search graph, and the negative node pair comprising a query node and a media node that are randomly combined in the media search graph;
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 the query node and the media node in the first training sample pair, the meta-path being a sampling path starting from the query node or the media node in the media search graph;
inputting, to an initial graph neural network, the sampling sub-graphs respectively corresponding to the query node and the media node in the first training sample pair, to obtain respective initial semantic features of the query node and the media node in the first training sample pair, and form a semantic feature pair corresponding to the first training sample pair; and
training the initial graph neural network based on a difference between semantic feature pairs corresponding to the positive node pair and the negative node pair, 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.
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, including steps a) - e). 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 d) inputting, to an initial graph neural network, the sampling sub-graphs respectively corresponding to the query node and the media node in the first training sample pair, to obtain respective initial semantic features of the query node and the media node in the first training sample pair, and form a semantic feature pair corresponding to the first training sample pair.
An initial graph neural network (i.e., a machine learning model = a math function) is a mathematical representation of relationship between inputs and outputs. The inputs (i.e., sampling sub-graphs) are fed into the math function to obtain outputs (i.e., semantic features, semantic feature pairs). The limitation d) merely involves a math function to output data based on input data.
Step e) training the initial graph neural network based on a difference between semantic feature pairs corresponding to the positive node pair and the negative node pair, 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
The initial graph neural network (i.e., a machine learning model) is a mathematical representation of relationship between inputs and outputs. Given broadest reasonable interpretation, step e) is nothing more than mathematical process (i.e., mathematical concept [Wingdings font/0xF3] abstract idea) of adjusting parameters to create a math function that maps input data to an output data.
“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 b, c, and f fall within the mathematical process grouping of abstract ideas and steps d and e fall within the mental process grouping of abstract ideas. Limitations d) - e) are considered together as a single abstract idea (i.e., mathematical concept [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 a query node corresponding to a query, a media node corresponding to media data, and an association node corresponding to association data, and the association data comprising data associated with at least one of the query or the media data;
obtaining a plurality of first training sample pairs from the media search graph, the plurality of first training sample pairs comprising a positive node pair and a negative node pair, the positive node pair comprising a query node and a media node that are connected to each other in the media search graph, and the negative node pair comprising a query node and a media node that are randomly combined in the media search graph;
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 the query node and the media node in the first training sample pair, the meta-path being a sampling path starting from the query node or the media node in the media search graph;
a) MPEP § 2106.05(a) "Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field."
There is no improvement to Functioning of a Computer or to Any Other Technology or Technical Field. The limitation a) - c) are obtaining/collecting data (e.g., media search graph, training sample pairs, sampling sub-graphs). These limitations do not make any improvements to the functionalities of a computer, database technology, or any other technologies.
b) MPEP § 2106.05(b) Particular Machine. The judicial exception does not apply to any particular machine.
The claim is silent regarding specific limitations directed to an improved computer system, processor, memory, network, database, or Internet, nor do applicant direct examiner’s attention to such specific limitations. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. at 223; see also Bascom Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1348 (Fed. Cir. 2016) ("An abstract idea on 'an Internet computer network' or on a generic computer is still an abstract idea."). Applying this reasoning here, the claim is not directed to a particular machine, but rather merely implement an abstract idea using generic computer components such as “computing device”, “graph neural network.” Thus, the claim fails to satisfy the "tied to a particular machine" prong of the Bilski machine-or-transformation test.
c) MPEP § 2106.05(c) Particular Transformation.
The claim operates to obtaining/collecting data (e.g., media search graph, training sample pairs, sampling sub-graphs). The steps are not a "transformation or reduction of an article into a different state or thing constituting patent-eligible subject matter[.]" See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd sub nom, Bilski v. Kappas, 561 U.S. 593 (2010); see also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) ("The mere manipulation or reorganization of data ... does not satisfy the transformation prong."). Applying this guidance here, the claims fail to satisfy the transformation prong of the Bilski machine-or-transformation test.
d) MPEP § 2106.05(e) Other Meaningful Limitations.
This section of the MPEP guides: Diamond v. Diehr provides an example of a claim that recited meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. 450 U.S. 175, ... (1981). In Diehr, the claim was directed to the use of the Arrhenius equation ( an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78 .... The Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products. 450 U.S. at 184... In contrast, the claims in Alice Corp. v. CLS Bank International did not meaningfully limit the abstract idea of mitigating settlement risk. 573 U.S._ .... In particular, the Court concluded that the additional elements such as the data processing system and communications controllers recited in the system claims did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers") or were well-understood, routine, conventional activity. MPEP § 2106.05(e).
The limitations a) - c) are obtaining/collecting data (e.g., media search graph, training sample pairs, sampling sub-graphs) are not meaningful limitations because collecting/obtaining data are pre and post-solution activities. The limitations are not meaningful limitations.
e) MPEP § 2106.05(g) Insignificant Extra-Solution Activity.
The limitations a) - c) are obtaining/collecting data (e.g., media search graph, training sample pairs, sampling sub-graphs) are not meaningful limitations because collecting/obtaining data are pre and post-solution activities.
f) MPEP § 2106.05(h) Field of Use and Technological Environment.
[T]he Supreme Court has stated that, even if a claim does not wholly pre-empt an abstract idea, it still will not be limited meaningfully if it contains only insignificant or token pre- or post-solution activity-such as identifying a relevant audience, a category of use, field of use, or technological environment. Ultramercial, Inc. v. Hulu, LLC, 722 F.3d 1335, 1346 (Fed. Cir. 2013). “A computing device”, “graph neural network” limitations are simply a field of use that attempts to limit the abstract idea to a particular technological environment.
Accordingly, the additional limitations a) – c) 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 does not recite any non-convention or non-generic arrangement because collecting/obtaining data is all conventional activities. 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.
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, 11, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chang (U.S. Pub 2025/0036697 A1)
Claim 1
Chang discloses a search data processing method, performed by a computer device, the method comprising:
obtaining a media search graph, the media search graph comprising a query node corresponding to a query (a first start information node), a media node corresponding to media data (first other information node), and an association node (operation relationship) corresponding to association data, and the association data comprising data associated with at least one of the query or the media data ([0039], “…obtain a first heterogeneous graph… includes a start information node (also referred to as a “first start information node”) representing historical query information and at least one another information node (also referred to as “first other information node”) representing media information, the media information includes media content…” [0049, “… Q-V may have an operation relationship. The operation relationship refers to that the media content V is operated by the user as a query result under a condition that the query information is Q…”);
obtaining a plurality of first training sample pairs from the media search graph, the plurality of first training sample pairs comprising a positive node pair and a negative node pair, the positive node pair comprising a query node and a media node that are connected to each other in the media search graph, and the negative node pair comprising a query node and a media node that are randomly combined in the media search graph ([0106] Several (Q, V) pairs that have a correspondence may be selected as positive sample pairs from the general heterogeneous graph formed by the first heterogeneous graph and the second heterogeneous graph, and a negative sample pair is obtained by replacing V in the positive sample pair with V′ in the general heterogeneous graph that does not have a correspondence with Q in the positive sample pair, or by replacing Q in the positive sample pair with Q′ in the general heterogeneous graph that does not have a correspondence with V in the positive sample pair…” <examiner note: the negative sample pairs are combined randomly with Q’ or V’ nodes>);
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 the query node and the media node in the first training sample pair, the meta-path being a sampling path starting from the query node or the media node in the media search graph ([0110], “… the first sub-graph is extracted from the general heterogeneous graph, and includes historical query information and other information extracted along each first meta-path based on the historical query information; and the second sub-graph is extracted from the general heterogeneous graph, and includes media information corresponding to the historical query information and other information extracted along each second meta-path based on the media information corresponding to the historical query information…” [0027], “… Meta-path: That is, a meta-path. Simply, the meta-path refers to a specific path mode connecting two entities. For example, a meta-path “video->actor->video” may connect two videos…”);
inputting, to an initial graph neural network (feature extraction network), the sampling sub-graphs respectively corresponding to the query node and the media node in the first training sample pair, to obtain respective initial semantic features of the query node and the media node in the first training sample pair, and form a semantic feature pair corresponding to the first training sample pair ([0113], “… inputting the first sub-graph and the second sub-graph into the feature extraction network to perform graph feature extraction, to obtain first representation sub-information corresponding to historical query information in each first path, and second representation sub-information corresponding to media content corresponding to historical query information in each second path; [0114] merging pieces of first representation sub-information to obtain the first sample representation information; and [0115] merging pieces of second representation sub-information to obtain the second sample representation information. …”); and
training the initial graph neural network based on a difference between semantic feature pairs corresponding to the positive node pair and the negative node pair, to obtain a target graph neural network (the graph feature extraction model) ([0129], “… Adjust the parameter of the feature extraction network based on a first loss, to obtain the graph feature extraction model, where the first loss is configured for guiding the feature extraction network to enhance a correlation between first sample representation information and second sample representation information in the positive sample pair and reduce a correlation between first sample representation information and second sample representation information in the negative sample pair...”), the target graph neural network being configured to determine a target semantic feature (representation information) corresponding to a query node or a media node ([0142], “… After a graph feature extraction model is trained, representation information corresponding to each media content in a media content library may be obtained based on the graph feature extraction model… When target query information is received, information extraction processing is performed on the target query information, to obtain first target representation information corresponding to the target query information. The media content feature library is queried based on the first target representation information, and media content matching the target query information is determined based on a query result…”)
Claim 11 and 20 are similar to claim 1. The claims are rejected based on the same reasons.
Conclusion
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HAU HAI. HOANG
Primary Examiner
Art Unit 2167
/HAU H HOANG/Primary Examiner, Art Unit 2167