DETAILED ACTION
Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. 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 § 103
3. 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.
4. 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 of this title, 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.
5. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
6. Claims 1-5, 8-12, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Colgan (US 2025/0077793) in view of Heiler et al. (US 2025/0061312) hereinafter Heiler.
In claim 1, Colgan discloses “A system comprising:
one or more processors; and
one or more non-transitory computer-readable media that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving a query indicating first content ([0032] The query embedding 42 can be an embedding of a query 44 (i.e., a question, a prompt, a statement, a request, an image, a video, a set of information, etc.). The query 44 can be received from a requesting entity 46, or can otherwise be determined based on prompt information 48 indicative of the query 44 received from the requesting entity 46);
generating a query representation based at least in part on a first embedding generated by a first encoder using the first content, the query representation being indicated in an embedding space ([0033] the embedding search module can, in some implementations, formulate a more efficient or optimal query 44 based on prompt information 48 received from the requesting entity 48. For example, if the prompt information 48 includes textual content descriptive of a question, the embedding search module 40 may parse the textual content to formulate a query 44 that is more optimally processed with a machine-learned model. For another example, if the prompt information 48 is an image, the embedding search module 40 can reformat the image to generate a query 44 that is in a proper format for processing by the vector determinator 34 [0035] the embedding search module 40 can implement an embedding space 55. An embedding space refers to a lower-dimensional space to which embeddings can be mapped. Prior to receiving the query 44, the embedding search module 40 can populate the embedding space 55 by mapping embeddings generated by the embedding generator 36 to the embedding space 55);
generating, by the first encoder, a second embedding based at least in part on the second content indicated by the data entity ([0034] The query embedding 42 can be an embedding of a vector representation of the query 44. For example, the vector determinator 34 can determine a vector representation of the query 44. The embedding generator 36 can process the vector representation of the query 44 with the machine-learned embedding model 38 to obtain query embedding 42);
generating, by a second encoder, a vector based at least in part on the first data type indicated by the data entity ([0042] the vector determinator 34 can determine a vector representation of the data item 74. The embedding generator 36 can process the vector for the data item 74 with the machine-learned embedding model 38 to generate an embedding. The vector database management system 16 can generate a data entry 20 for the data item 74 that includes an ID, the vector, and the embedding for the data item 74);
predicting that the data entity is related to the query based at least in part on determining that the contextual representation is closest to the query representation from among multiple contextual representations associated with other data entities, wherein determining that the contextual representation is closest to the query representation comprises determining a distance between the query representation and the contextual representation in the embedding space ([0035] The embedding search module 40 can then map the query embedding 42 to the embedding space 55 and perform a nearest neighbor search to identify one or more result embeddings 54 most similar to the query embedding 42. For example, the embedding search module 40 may identify any result embeddings 54 that are within a threshold distance of the query embedding 42 within the embedding space 55. For another example, the embedding search module 40 may identify a certain number of result embeddings 54 closest to the query embedding 42 in the embedding space 55 [0036] the embedding space 55 is populated by embeddings of vectors included in the data entries 20 of the vector database 18, any reference to “querying” the vector database 18 may refer to querying the embedding space 55 by performing a nearest-neighbor, or approximate nearest-neighbor (ANN), search for a query embedding [0037] the filtering module 56 can generate an accuracy filter 58 based on the query 44 and/or contextual entity information 60. The contextual entity information 60 can be information stored by the vector database management system 16 that describes various characteristics of the requesting entity 46, or various contextual information relevant to the query 44 or other queries received by the vector database management system 16. For example, assume that the requesting entity 46 is a user device, and the query 44 is a query for movie recommendations for a user of the user device. The contextual entity information 60 may indicate that the user device currently has age-restriction settings enabled. Based on the contextual entity information 60, the accuracy filter 58 can filter mature movies from the data entries 20 (and their corresponding embeddings in the embedding space 55) prior to performing a query)”.
Colgan does not appear to explicitly disclose however, Heiler discloses “receiving a data entity indicating second content and a first data type, the data entity being linked in a graph to a set of data entities ([0074] knowledge graph 116 can include a structured representation of data describing various subjects. For instance, a data object can be stored in a node of the graph. The data object can contain at least one parent node associated with a subject (e.g., a product or service). The data object can contain, attached to the parent node, at least one child node associated with an attribute of the subject (e.g., a color, a dimension, a cost, a quantity, etc.));
generating, by a graph neural network, a contextual representation of the data entity based at least in part on a portion of the graph, the second embedding, and the vector ([0079] input builder 106 can query knowledge graph 116 based on request data 104 and context data source(s) 114. For instance, input builder 106 can implement various graph query techniques to identify data objects in knowledge graph 116 that is relevant to request data 104 in view of context data source(s) 114. Input builder 106 can employ tree search techniques. Input builder 106 can employ distance-based similarity search techniques. Example similarity search techniques include searching based on similarities between embedded vectors, such as an embedding of the request and context data compared against an embedded vector for one or more portions of the graph. Input builder 106 can leverage message passing techniques on the graph to determine relevant data objects);
transmitting an indication of the data entity to a computing device for selection, viewing, auto-filling, or generation of additional content by a machine-learned model based at least in part on the second content ([0233] the task can be a data generation task. Machine-learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of data (e.g., data from various data domains, such as sensor data, image data, multimodal data, statistical data, etc.). The desired data can be, for instance, synthetic data for training other machine-learned models. The context can include arbitrary data type(s). Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent data that aligns with the desired data. For instance, machine-learned model(s) 1 can be configured to generate data values for populating a dataset. Values for the data object(s) can be selected based on the context (e.g., based on a probability determined based on the context)”.
Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Colgan and Heiler, the suggestion/motivation for doing so would have been to provide a method for serving machine-generated content by generating the content based on the request and the context of the request, so that end users can receive high-quality content that is customized for them while also satisfying communications requirements of the communicating entities ([0029]).
In claim 2, Heiler teaches
The system of claim 1, wherein generating the query representation comprises:
generating, by the second encoder, a second vector based at least in part on a second data type indicated by the query, wherein the second data type is a same as, or different than, the first data type; and scaling the first embedding by the second vector ([0047] The request for content can be explicit or implicit. For instance, request data 104 can explicitly indicate that content is desired and can indicate one or more preferred characteristics of such content (e.g., content type, file type, size, duration, etc.) [0079] Example similarity search techniques include searching based on similarities between embedded vectors, such as an embedding of the request and context data compared against an embedded vector for one or more portions of the graph).
In claim 3, Heiler teaches
The system of claim 1, wherein:
the query further indicates a second data type; the second data type is a same as, or different than, the first data type; and generating the query representation comprises: generating, by the second encoder and based at least in part on the second data type, a second vector; and generating, by the graph neural network, the query representation based at least in part on the graph, the first embedding, and the second vector ([0079] input builder 106 can query knowledge graph 116 based on request data 104 and context data source(s) 114. For instance, input builder 106 can implement various graph query techniques to identify data objects in knowledge graph 116 that is relevant to request data 104 in view of context data source(s) 114. Input builder 106 can employ tree search techniques. Input builder 106 can employ distance-based similarity search techniques. Example similarity search techniques include searching based on similarities between embedded vectors, such as an embedding of the request and context data compared against an embedded vector for one or more portions of the graph. Input builder 106 can leverage message passing techniques on the graph to determine relevant data objects).
In claim 4, Heiler teaches
The system of claim 1, wherein generating the contextual representation further comprises:
scaling the embedding by the vector as a first intermediate embedding; concatenating, as a concatenated embedding, the embedding with an average of the first intermediate embedding and one or more intermediate embeddings generated for the set of data entities linked to the data entity in the graph; and generating the contextual representation by processing the concatenated embedding by the graph neural network ([0162] Prediction layer(s) 6 can evaluate associations between portions of input sequence 5 and a particular output element. These associations can inform a prediction of the likelihood that a particular output follows the input context. For example, consider the textual snippet, “The carpenter's toolbox was small and heavy. It was full of ______.” Example prediction layer(s) 6 can identify that “It” refers back to “toolbox” by determining a relationship between the respective embeddings. Example prediction layer(s) 6 can also link “It” to the attributes of the toolbox, such as “small” and “heavy.” Based on these associations, prediction layer(s) 6 can, for instance, assign a higher probability to the word “nails” than to the word “sawdust.”).
In claim 5, Heiler teaches
The system of claim 1, wherein generating the contextual representation based at least in part on the graph comprises determining the set of data entities linked directly to the data entity in the graph or within n links from the data entity, wherein n is a positive integer ([0162] Prediction layer(s) 6 can evaluate associations between portions of input sequence 5 and a particular output element. These associations can inform a prediction of the likelihood that a particular output follows the input context. For example, consider the textual snippet, “The carpenter's toolbox was small and heavy. It was full of ______.” Example prediction layer(s) 6 can identify that “It” refers back to “toolbox” by determining a relationship between the respective embeddings. Example prediction layer(s) 6 can also link “It” to the attributes of the toolbox, such as “small” and “heavy.” Based on these associations, prediction layer(s) 6 can, for instance, assign a higher probability to the word “nails” than to the word “sawdust.”).
Claims 8-12 and 15-16 are essentially same as claims 1-5 except that they recite claimed invention as a non-transitory computer-readable media and are rejected for the same reasons as applied hereinabove.
Claims 17-18 and 20 are essentially same as claims 1, 4-5 and 15 except that they recite claimed invention as a method and are rejected for the same reasons as applied hereinabove.
Allowable Subject Matter
7. Claims 6, 13 and 19 are objected to as being dependent upon a rejected baseclaim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
None of the prior arts of record teaches "wherein the operations further comprise training at least one of the first encoder, the second encoder, or the graph neural network based at least in part on:
determining a cosine similarity between the contextual representation and a second contextual representation generated for a second data entity;
determining to indicate that the data entity and the second data entity are a positive pair based at least in part on determining that the data entity and the second data entity are within n links of each other in the graph, wherein n is a positive integer, or determining that the data entity and the second data entity are a negative pair based at least in part on determining that the data entity and the second data entity are disassociated in the graph;
determining a loss based at least in part on the cosine similarity and the positive pair indication or the negative pair indication; and
altering a parameter of the first encoder, the second encoder, or the graph neural network to reduce the loss" as recited in claims 6, 13 and 19.
8. Claims 7 and 14 are objected to as being dependent upon a rejected baseclaim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
None of the prior arts of record teaches "the graph indicates, via a first link, that the data entity is linked to a second data entity;
the graph indicates that the second data entity is linked to itself and, via a second link, to a third data entity; and
training at least one of the first encoder, the second encoder, or the graph neural network comprises:
removing the second link from the second data entity to the third data entity;
indicating the data entity and the second data entity are a positive pair based at least in part on the first link;
determining a loss based at least in part on the positive pair indication; and
altering a parameter of the first encoder, the second encoder, or the graph neural network to reduce the loss" as recited in claims 7 and 14.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed on 892 form.
Examiner’s Note: Examiner has cited particular figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
US 2020/0242444 discloses: question answering over knowledge graph using a Knowledge Embedding based Question Answering (KEQA) framework. KEQA embodiments target jointly recovering the question's head entity, predicate, and tail entity representations in the KG embedding spaces. In embodiments, a joint distance metric incorporating various loss terms is used to measure distances of a predicated fact to all candidate facts. In embodiments, the fact with the minimum distance is returned as the answer.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUAWEN A PENG whose telephone number is (571)270-5215. The examiner can normally be reached Mon thru Fri 9 am to 5 pm.
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/HUAWEN A PENG/Primary Examiner, Art Unit 2169
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