# US 2025/0148822 A1 · Systems for Generating Indications of Relationships between Electronic Documents > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0148822 A1 - Document ID: US20250148822A1 - Status: Published application - Kind: A1 - Application number: 19/015,404 - Filed: 2025-01-09 - Published: 2025-05-08 - Earliest claimed: 2021-11-24 - Assignees: Adobe Inc. - Applicants: Adobe Inc. - Inventors: Natwar Modani; Vaidehi Ramesh Patil; Inderjeet Jayakumar Nair; Gaurav Verma; Anurag Maurya; Anirudh Kanfade - Web page: https://patentgrep.com/patent/US20250148822A1 ## CPC - G06V 30/413: Classification of content, e.g. text, photographs or tables - G06V 30/19173: {Classification techniques} - G06V 30/274: {Syntactic or semantic context, e.g. balancing} - G06V 30/414: Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text - G06V 30/418: Document matching, e.g. of document images - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract In implementations of systems for generating indications of relationships between electronic documents, a processing device implements a relationship system to segment text of electronic documents included in a document corpus into segments. The relationship system determines a subset of the electronic documents that includes electronic document pairs having a number of similar segments that is greater than a threshold number. The similar segments are identified using locality sensitive hashing. The electronic document pairs are classified as related documents or unrelated documents using a machine learning model that receives a pair of electronic documents as an input and generates an indication of a classification for the pair of electronic documents as an output. Indications of relationships between particular electronic documents included in the subset are generated based at least partially on the electronic document pairs that are classified as related documents. ## Claims ### Claim 1 A method for training a fully convolutional network, the method comprising: forming, by a processing device, positive training sets of electronic documents that each include a first version of an electronic document and a second version of the electronic document; generating, by the processing device, a first heatmap for each of the positive training sets by modeling lexical similarity between sentences included in the first version of the electronic document and sentences included in the second version of the electronic document; generating, by the processing device, a second heatmap for each of the positive training sets by modeling a similarity between entities of the sentences included in the first version of the electronic document and entities of the sentences included in the second version of the electronic document; compressing, by the processing device, the first heatmaps and the second heatmaps into feature vectors using an encoder of the fully convolutional network; and training, by the processing device, the fully convolutional network to classify pairs of electronic documents using the feature vectors and a loss function. ### Claim 2 (depends on 1) The method as described in claim 1, wherein the first heatmaps and the second heatmaps have different aspect ratios. ### Claim 3 (depends on 1) The method as described in claim 1, wherein the encoder is configured to receive a fixed image size. ### Claim 4 (depends on 1) The method as described in claim 1, wherein unused portions of the first heatmaps are padded with zeros. ### Claim 5 (depends on 1) The method as described in claim 1, further comprising: forming negative training sets of electronic documents that each include a first electronic document and a second electronic document, the first electronic document is not a version of the second electronic document and the second electronic document is not a version of the first electronic document; generating additional feature vectors based on the negative training sets; and training the fully convolutional network to classify pairs of electronic documents using the additional feature vectors and the loss function. ### Claim 6 (depends on 1) The method as described in claim 1, further comprising: classifying, by the processing device and using the fully convolutional network, electronic document pairs received as input to the fully convolutional network as semantically similar documents or not semantically similar documents to generate an indication of a classification for the electronic document pairs as an output. ### Claim 7 (depends on 6) The method as described in claim 6, further comprising: computing, by the processing device, containment scores for the electronic document pairs based on a number of similar segments and a length of a shorter electronic document included in each of the electronic document pairs; and generating, by the processing device, indications of relationships between particular electronic documents based at least partially on the electronic document pairs that are classified as semantically similar documents and the containment scores. ### Claim 8 (depends on 7) The method as described in claim 7, wherein the indications of the relationships between the particular electronic documents include at least one of a change summary, an explanation of similarity, or a relative ordering between the particular electronic documents. ### Claim 9 (depends on 1) The method as described in claim 1, wherein the similarity between entities included in the first version of the electronic document and entities included in the second version of the electronic document includes a Jaccard similarity between the entities included in the first version of the electronic document and the entities included in the second version of the electronic document. ### Claim 10 (depends on 9) The method as described in claim 9, wherein the entities are sentences or paragraphs. ### Claim 11 One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a processing device, causes the processing device to perform operations including: forming positive training sets of electronic documents to train a fully convolutional network that each include a first version of an electronic document and a second version of the electronic document; generating a first heatmap for each of the positive training sets by modeling lexical similarity between sentences included in the first version of the electronic document and sentences included in the second version of the electronic document; generating a second heatmap for each of the positive training sets by modeling a similarity between entities of the sentences included in the first version of the electronic document and entities of the sentences included in the second version of the electronic document; compressing the first heatmaps and the second heatmaps into feature vectors using an encoder of the fully convolutional network; training the fully convolutional network to classify pairs of electronic documents received as input using the feature vectors and a loss function; and generating indications of relationships between particular electronic documents based at least partially on electronic document pairs that are classified by the fully convolutional network as semantically similar documents. ### Claim 12 (depends on 11) The one or more computer-readable storage media as described in claim 11, wherein the first heatmaps and the second heatmaps have different aspect ratios. ### Claim 13 (depends on 11) The one or more computer-readable storage media as described in claim 11, wherein the fully convolutional network includes a hierarchical attention network. ### Claim 14 (depends on 11) The one or more computer-readable storage media as described in claim 11, wherein the operations further include classifying, using the fully convolutional network, the electronic document pairs as semantically similar documents or not semantically similar documents. ### Claim 15 (depends on 11) The one or more computer-readable storage media as described in claim 11, wherein the operations further include: forming negative training sets of electronic documents that each include a first electronic document and a second electronic document, the first electronic document is not a version of the second electronic document and the second electronic document is not a version of the first electronic document; generating additional feature vectors based on the negative training sets; and training the fully convolutional network to classify pairs of electronic documents using the additional feature vectors and the loss function. ### Claim 16 (depends on 11) The one or more computer-readable storage media as described in claim 11, wherein the operations further include: computing containment scores for the electronic document pairs based on a number of similar segments and a length of a shorter electronic document included in each of the electronic document pairs; and generating the indications of relationships between particular electronic documents based at least partially on the electronic document pairs that are classified as semantically similar documents and the containment scores. ### Claim 17 A system comprising: a processing device; and computer-readable storage media storing instructions that are executable by the processing system to perform operations including: forming positive training sets of electronic documents to train a fully convolutional network that each include a first version of an electronic document and a second version of the electronic document; generating a first heatmap for each of the positive training sets by modeling lexical similarity between sentences included in the first version of the electronic document and sentences included in the second version of the electronic document; generating a second heatmap for each of the positive training sets by modeling a similarity between entities of the sentences included in the first version of the electronic document and entities of the sentences included in the second version of the electronic document; compressing the first heatmaps and the second heatmaps into feature vectors using an encoder of the fully convolutional network; training the fully convolutional network to classify pairs of electronic documents using the feature vectors and a loss function; classifying, using the fully convolutional network, electronic document pairs received as input to the fully convolutional network as semantically similar documents or not semantically similar documents to generate an indication of a classification for the electronic document pairs as an output; and generating indications of relationships between the electronic document pairs based at least partially on the electronic document pairs that are classified as semantically similar documents. ### Claim 18 (depends on 17) The system as described in claim 17, wherein the relationships between the electronic document pairs include a version relationship, an aggregation relationship, a repurposed relationship, or a similarity relationship. ### Claim 19 (depends on 17) The system as described in claim 17, the operations further including determining a maximum spanning tree from a graph that includes a node for each electronic document included in the electronic document pairs that are classified as semantically similar documents, and the indications of the relationships between the electronic document pairs are generated at least partially based on the maximum spanning tree. ### Claim 20 (depends on 17) The system as described in claim 17, the operations further including: forming negative training sets of electronic documents that each include a first electronic document and a second electronic document, the first electronic document is not a version of the second electronic document and the second electronic document is not a version of the first electronic document; generating additional feature vectors based on the negative training sets; and training the fully convolutional network to classify pairs of electronic documents using the additional feature vectors and the loss function.