# US 8,572,087 B1 · Content identification > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,572,087 B1 - Document ID: US8572087B1 - Status: Granted - Kind: B1 - Application number: 11/873,790 - Filed: 2007-10-17 - Granted: 2013-10-29 - Earliest claimed: 2007-10-17 - Assignees: Google Inc. - Applicants: Jay Yagnik - Inventors: Jay Yagnik - Web page: https://patentgrep.com/patent/US8572087B1 ## CPC - G06F 16/583: using metadata automatically derived from the content - G06F 18/231: Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram - G06V 10/7625: {Hierarchical techniques, i.e. dividing or merging patterns to obtain a tree-like representation; Dendograms} - G06V 20/35: {Categorising the entire scene, e.g. birthday party or wedding scene} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems, computer program products, and methods can identify a training set of content, and generate one or more clusters from the training set of content, where each of the one or more clusters represent similar features of the training set of content. The one or more clusters can be used to generate a classifier. New content is identified and the classifier is used to associate at least one label with the new content. ## Claims ### Claim 1 A method implemented by data processing apparatus, the method comprising: generating a plurality of distinct clusters from training content wherein each of the plurality of clusters represents similar features of content items in the training content; identifying one or more conjunctions of the clusters based on a respective probability of observing features of each cluster of the conjunction in a collection of content items; scoring each of a plurality of the identified conjunctions based at least partly on a conditional probability that the conjunction is associated with a label; selecting a conjunction of the scored conjunctions that has a highest score as a current conjunction; until a stopping condition is reached: generating one or more higher-order child conjunctions for the current conjunction wherein each of the child conjunctions is a conjunction of the conjoined clusters of the current conjunction with one or more respective additional clusters that are not included in the conjoined clusters; scoring each of the child conjunctions based at least partly on a conditional probability that the child conjunction is associated with the label; if a highest scoring child conjunction has a score that is less than the score of the current conjunction, the stopping condition is reached, otherwise designating the highest scoring child conjunction as the current conjunction; and generating a classifier for the label from the current conjunction. ### Claim 2 (depends on 1) The method of claim 1 wherein identifying the conjunctions of clusters further comprises generating a distributed data set that indicates probabilities of observing the features in the collection of content items. ### Claim 3 (depends on 1) The method of claim 1 wherein the content items are images. ### Claim 4 (depends on 1) The method of claim 1 wherein the content items are text. ### Claim 5 (depends on 1) The method of claim 1 wherein a conjunction is a set of two or more clusters. ### Claim 6 (depends on 1) The method of claim 1 wherein a cluster represents a collection of content item portions that have similar features. ### Claim 7 (depends on 1) The method of claim 1 wherein the content items are images and wherein the images are examined at different scales and positions, and similar portions of the images are placed into respective clusters. ### Claim 8 (depends on 1) The method of claim 1 wherein content items are parsed into regions and those regions that have similar patterns are grouped into respective clusters. ### Claim 9 A system comprising: a machine-readable storage device with having instructions stored thereon; data processing apparatus configured to execute the instructions to perform operations comprising: generating a plurality of distinct clusters from training content wherein each of the plurality of clusters represents similar features of content items in the training content; identifying one or more conjunctions of the clusters based on a respective probability of observing features of each cluster of the conjunction in a collection of content items; scoring each of a plurality of the identified conjunctions based at least partly on a conditional probability that the conjunctions is associated with a label; selecting a conjunction of the scored conjunctions that has a highest score as a current conjunction; until a stopping condition is reached: generating one or more higher-order child conjunctions for the current conjunction wherein each of the child conjunctions is a conjunction of the conjoined clusters of the current conjunction with one or more respective additional clusters that are not included in the conjoined clusters; scoring each of the child conjunctions based at least partly on a conditional probability that the child conjunction is associated with the label; if a highest scoring child conjunction has a score that is less than the score of the current conjunction, the stopping condition is reached, otherwise designating the highest scoring child conjunction as the current conjunction; and generating a classifier for the label from the current conjunction. ### Claim 10 (depends on 9) The system of claim 9 wherein the operations wherein identifying the conjunctions of clusters further comprises generating a distributed data set that indicates probabilities of observing the features in the collection of content items. ### Claim 11 (depends on 9) The system of claim 9 wherein the content items are images. ### Claim 12 (depends on 9) The system of claim 9 wherein the content items are text. ### Claim 13 (depends on 9) The system of claim 9 wherein a conjunction is a set of two or more clusters. ### Claim 14 (depends on 9) The system of claim 9 wherein a cluster represents a collection of content item portions that have similar features. ### Claim 15 (depends on 9) The system of claim 9 wherein the content items are images and wherein the images are examined at different scales and positions, and similar portions of the images are placed into respective clusters. ### Claim 16 (depends on 9) The system of claim 9 wherein content items are parsed into regions and those regions that have similar patterns are grouped into respective clusters. ### Claim 17 A machine-readable storage device having instructions stored thereon, the instructions operable to cause data processing apparatus to perform operations comprising: generating a plurality of distinct clusters from training content wherein each of the plurality of clusters represents similar features of content items in the training content; identifying one or more conjunctions of the clusters based on a respective probability of observing features of each cluster of the conjunction in a collection of content items; scoring each of a plurality of the identified conjunctions based at least partly on a conditional probability that the conjunction is associated with a label; selecting a conjunction of the scored conjunctions that has a highest score as a current conjunction; until a stopping condition is reached: generating one or more higher-order child conjunctions for the current conjunction wherein each of the child conjunctions is a conjunction of the conjoined clusters of the current conjunction with one or more respective additional clusters that are not included in the conjoined clusters; scoring each of the child conjunctions based at least partly on a conditional probability that the child conjunction is associated with the label; if a highest scoring child conjunction has a score that is less than the score of the current conjunction, the stopping condition is reached, otherwise designating the highest scoring child conjunction as the current conjunction; and generating a classifier for the label from the current conjunction. ### Claim 18 (depends on 17) The storage device of claim 17 wherein identifying the conjunctions of clusters further comprises generating a distributed data set that indicates probabilities of observing the features in the collection of content items. ### Claim 19 (depends on 17) The storage device of claim 17 wherein the content items are images. ### Claim 20 (depends on 17) The storage device of claim 17 wherein the content items are text. ### Claim 21 (depends on 17) The storage device of claim 17 wherein a conjunction is a set of two or more clusters. ### Claim 22 (depends on 17) The storage device of claim 17 wherein a cluster represents a collection of content item portions that have similar features. ### Claim 23 (depends on 17) The storage device of claim 17 wherein the content items are images and wherein the images are examined at different scales and positions, and similar portions of the images are placed into respective clusters. ### Claim 24 (depends on 17) The storage device of claim 17 wherein content items are parsed into regions and those regions that have similar patterns are grouped into respective clusters.