DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. 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 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.
3. Claims 1-20, filed on 3/27/2025, are pending in this office action.
Information Disclosure Statement
4. Initialed and dated copy of Applicant’s IDs form 1449, filed 3/27/2025, is attached to the instant Office action.
Claim Rejections - 35 USC § 101
5. 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.
6. Claim 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 16-20 are directed towards a system claim, but does not necessarily claim statutory hardware embodiments of the system claim. The entity linking system can be interpreted as software, as there are no terms in the limitations that are defined in the specification as being hardware components, nor are there any limitations in the claim that specifically discloses hardware, such as a processor or memory. The claim recites a graph generation moule, a graph neural network, an embedding space similarity module, and a recommendation module; the specification specifically states in paragraph [0027]: ”As used herein, the term "module" can refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution.”, meaning solely software. To make the system claim statutory, hardware embodiments supported in the specification, as well as a processor executing steps, right after the preamble are needed. Proper correction is required.
Claim Rejections - 35 USC § 102
7. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
8. Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dhama et al. (US Publication 2022/0100720 A1)
As per claim 1, Dhama teaches A computer-implemented method, comprising:
extracting, by an extraction module, a first attribute set from an information source, wherein the first attribute set corresponds to an unknown entity, and wherein the first attribute set comprises first attributes; (paragraph 0023, 0025, a first entity in a first dataset is identified in a transaction request made towards a payment network server, based on details of the first entity, the details interpreted as attribute set, paragraph 0052, entities, including a first entity, having attribute information)
generating, by a graph generation module, an unknown entity graph comprising first nodes corresponding to the first attributes; (paragraph 0023, 0052, 0105, a graph based on the details of the first entity is generated, the graph utilizing first dataset data)
retrieving, by the extraction module, a second attribute set from a database comprising known entities, wherein the second attribute set corresponds to one of the known entities, and wherein the second attribute set comprises second attributes; (paragraph 0043, 0049, a second dataset is utilized containing details of a plurality of other entities that are already stored in a payment network server)
generating, by the graph generation module, a known entity graph comprising second nodes corresponding to the second attributes; (paragraph 0023, 0052, 0053, 0105, a graph based on the details of the second entity is generated from among a plurality of entities already stored, the graph utilizing second dataset data)
generating, by a graph neural network model, an unknown entity graph embedding by applying the unknown entity graph to the graph neural network model; (paragraph 0045, 0054, graph embeddings for a first dataset are generated)
generating, by the graph neural network model, a known entity graph embedding by applying the known entity graph to the graph neural network model; (paragraph 0045, 0054, graph embeddings for a second dataset are generated)
generating, by an embedding similarity module, an embedding space similarity score based on the unknown entity graph embedding and the known entity graph embedding; (paragraph 0050, 0068, 0109, a similarity metric between a first entity and each of the set of nearest neighbors representing second entities is determined)
and assigning, by a recommendation module, the information source to one of the known entities based on the embedding space similarity score. (paragraph 0050, 0078, 0089, 0109, a first entity is associated with a second entity of the second dataset corresponding to the one of set of nearest neighbors based on similarity metric)
As per claim 2, Dhama teaches the graph neural network model is a graph convolutional network model. (paragraph 0075, convolution neural network)
As per claim 3, Dhama teaches the extracting the first attribute set from the information source comprises extracting, by the extraction module, the first attributes from at least one of a news article or a webpage. (paragraph 0033, merchant web application)
As per claim 4, Dhama teaches the extracting the first attribute set from the information source comprises extracting, by the extraction module, at least one of: a name of the unknown entity; an industry associated with the unknown entity; geographic information related to the unknown entity; a name of an entity that is not the unknown entity; a classification of an image associated with the unknown entity; or a classification of a video associated with the unknown entity. (paragraph 0040, 0048, transaction request in first dataset includes name and address of merchant)
As per claim 5, Dhama teaches the retrieving the second attribute set from the database comprises retrieving, by the extraction module, at least one of: a name of one of the known entities; an industry associated with one of the known entities; geographic information related to one of the known entities; a name of a parent entity of one of the known entities; a name of a subsidiary entity of one of the known entities; or a name of an entity known to transact business with one of the known entities. (paragraph 0040, 0049, second dataset includes name and address of merchant)
As per claim 6, Dhama teaches the generating the embedding space similarity score comprises: applying, by the embedding similarity module, the unknown entity graph embedding and the known entity graph embedding to a deep neural network model. (paragraph 0075, deep neural network)
As per claim 7, Dhama teaches A computer-implemented method, comprising: (see Abstract)
extracting, by an extraction module, a first attribute set from an information source, wherein the first attribute set corresponds to an unknown entity, and wherein the first attribute set comprises first attributes; (paragraph 0023, 0025, a first entity in a first dataset is identified in a transaction request made towards a payment network server, based on details of the first entity, the details interpreted as attribute set, paragraph 0052, entities, including a first entity, having attribute information)
generating, by a graph generation module, an unknown entity graph comprising first nodes corresponding to the first attributes; (paragraph 0023, 0052, 0105, a graph based on the details of the first entity is generated, the graph utilizing first dataset data)
retrieving, by the extraction module, a second attribute set from a database comprising known entities, wherein the second attribute set corresponds to one of the known entities, and wherein the second attribute set comprises second attributes; (paragraph 0043, 0049, a second dataset is utilized containing details of a plurality of other entities that are already stored in a payment network server)
generating, by the graph generation module, a known entity graph comprising second nodes corresponding to the second attributes; (paragraph 0023, 0052, 0053, 0105, a graph based on the details of the second entity is generated from among a plurality of entities already stored, the graph utilizing second dataset data)
generating, by a graph neural network model, an unknown entity graph embedding by applying the unknown entity graph to the graph neural network model; (paragraph 0045, 0054, graph embeddings for a first dataset are generated)
generating, by the graph neural network model, a known entity graph embedding by applying the known entity graph to the graph neural network model; (paragraph 0045, 0054, graph embeddings for a second dataset are generated)
generating, by an embedding similarity module, an embedding space similarity score based on the unknown entity graph embedding and the known entity graph embedding; (paragraph 0050, 0068, 0109, a similarity metric between a first entity and each of the set of nearest neighbors representing second entities is determined)
determining, by a recommendation module, an overall similarity score based on the embedding space similarity score; (paragraph 0065, 0066, 0095, a ranking of a set of nearest neighbors is determined based on a similarity metric to identify the most similar neighbor)
and assigning, by the recommendation module, the information source to one of the known entities based on the overall similarity score. and assigning, by a recommendation module, the information source to one of the known entities based on the embedding space similarity score. (paragraph 0050, 0078, 0089, 0109, a first entity is associated with a second entity of the second dataset corresponding to the one of set of nearest neighbors based on similarity metric)
As per claim 8, Dhama teaches each of the first attributes comprises at least one word, and wherein each of the second attributes comprises at least one word, (paragraph 0053, 0090, attribute nodes represent tokens)
the method further comprising: identifying, by a string similarity module, attribute pairs, wherein each of the attribute pairs comprises one of the first attributes and a corresponding one of the second attributes ;and generating, by the string similarity module, string similarity scores, wherein each of the string similarity scores is based on one of the attribute pairs, and wherein the overall similarity score is further based on the string similarity scores. (paragraph 0065, pair of embeddings, paragraph 0065, 0066, 0095, a ranking of a set of nearest neighbors)
As per claim 9, Dhama teaches the determining the overall similarity score comprises: applying, by the recommendation module, the embedding space similarity score and the string similarity scores to at least one of a regression model or a classification model. (paragraph 0057, regression classifier)
As per claim 10, Dhama teaches the generating the embedding space similarity score comprises: applying, by the embedding similarity module, the unknown entity graph embedding and the known entity graph embedding to a deep neural network model. (paragraph 0075, deep neural network)
As per claim 11, Dhama teaches training, by a training module, the graph neural network model, the deep neural network model, and the at least one of the regression model or the classification model end- to-end based on labeled data. (paragraph 0057, regression classifier, paragraph 0075, deep neural network)
As per claim 12, Dhama teaches the graph neural network model is a graph convolutional network model. (paragraph 0075, convolution neural network)
As per claim 13, Dhama teaches the extracting the first attribute set from the information source comprises extracting, by the extraction module, the first attributes from at least one of a news article or a webpage. (paragraph 0033, merchant web application)
As per claim 14, Dhama teaches the extracting the first attribute set from the information source comprises extracting, by the extraction module, at least one of: a name of the unknown entity; an industry associated with the unknown entity; geographic information related to the unknown entity; a name of an entity that is not the unknown entity; a classification of an image associated with the unknown entity; or a classification of a video associated with the unknown entity. (paragraph 0040, 0048, transaction request in first dataset includes name and address of merchant)
As per claim 15, Dhama teaches the retrieving the second attribute set from the database comprises retrieving, by the extraction module, at least one of: a name of one of the known entities; an industry associated with one of the known entities; geographic information related to one of the known entities; a name of a parent entity of one of the known entities; a name of a subsidiary entity of one of the known entities; or a name of an entity known to transact business with one of the known entities. (paragraph 0040, 0049, second dataset includes name and address of merchant)
As per claim 16, Dhama teaches An entity linking system, (see Abstract)
comprising: a graph generation module operable to generate: an unknown entity graph based on a first attribute set comprising first attributes, wherein the first attributes are extracted from an information source, wherein the first attribute set corresponds to an unknown entity, and wherein the unknown entity graph comprises first nodes corresponding to the first attributes; (paragraph 0023, 0025, a first entity in a first dataset is identified in a transaction request made towards a payment network server, based on details of the first entity, the details interpreted as attribute set, paragraph 0052, entities, including a first entity, having attribute information, paragraph 0023, 0052, 0105, a graph based on the details of the first entity is generated, the graph utilizing first dataset data)
and a known entity graph based on a second attribute set comprising second attributes, wherein the second attributes are retrieved from a database comprising known entities, wherein the second attribute set corresponds to one of the known entities, and wherein the known entity graph comprises second nodes corresponding to the second attributes; (paragraph 0043, 0049, a second dataset is utilized containing details of a plurality of other entities that are already stored in a payment network server, paragraph 0023, 0052, 0053, 0105, a graph based on the details of the second entity is generated from among a plurality of entities already stored, the graph utilizing second dataset data)
a graph neural network configured to generate: an unknown entity graph embedding based on the unknown entity graph; (paragraph 0045, 0054, graph embeddings for a first dataset are generated)
and a known entity graph embedding based on the known entity graph; (paragraph 0045, 0054, graph embeddings for a second dataset are generated)
an embedding space similarity module configured to generate an embedding space similarity score based on the unknown entity graph embedding and the known entity graph embedding; (paragraph 0050, 0068, 0109, a similarity metric between a first entity and each of the set of nearest neighbors representing second entities is determined)
and a recommendation module configured to assign the information source to one of the known entities based on the embedding space similarity score. (paragraph 0050, 0078, 0089, 0109, a first entity is associated with a second entity of the second dataset corresponding to the one of set of nearest neighbors based on similarity metric)
As per claim 17, Dhama teaches a string similarity module configured to generate string similarity scores based on attribute pairs, wherein each of the attribute pairs comprises one of the first attributes and a corresponding one of the second attributes, and wherein each of the string similarity scores is based on one of the attribute pairs; (paragraph 0065, pair of embeddings, paragraph 0065, 0066, 0095, a ranking of a set of nearest neighbors)
and wherein the recommendation module is further configured to assign the information source to one of the known entities based on the string similarity scores and the embedding space similarity score. (paragraph 0065, 0066, 0095, a ranking of a set of nearest neighbors is determined based on a similarity metric to identify the most similar neighbor)
As per claim 18, Dhama teaches the graph neural network is a graph convolutional network, (paragraph 0075, convolution neural network)
wherein the embedding space similarity module comprises a deep neural network, and wherein the recommendation module comprises at least one of a regression model or a classification model. (paragraph 0057, regression classifier)
As per claim 19, Dhama teaches the information source is at least one of a news article or a webpage, and wherein the first attribute set comprises at least one: a name of the unknown entity; an industry associated with the unknown entity; geographic information related to the unknown entity; a name of an entity that is not the unknown entity; a classification of an image associated with the unknown entity; or a classification of a video associated with the unknown entity. (paragraph 0040, 0048, transaction request in first dataset includes name and address of merchant)
As per claim 20, Dhama teaches the second attribute set comprises at least one of: a name of one of the known entities; an industry associated with one of the known entities; geographic information related to one of the known entities; a name of a parent entity of one of the known entities; a name of a subsidiary entity of one of the known entities; or a name of an entity known to transact business with one of the known entities. (paragraph 0040, 0049, second dataset includes name and address of merchant)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
McCreary (US Publication 2022/0358395 A1)
Tandecki (US Publication 2021/0319785 A1)
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/DANGELINO N GORTAYO/Primary Examiner, Art Unit 2168