DETAILED ACTION
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 .
Response to Arguments
101 Rejections
With respect to Applicant’s argument that “For instance, currently amended independent claim 1 recites a practical application by "generating, using a selection-prediction neural network, a selection prediction for the content item based on the content-level genealogical metrics, the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account; based on the selection prediction, determining a recommended content item to provide to the client device for display; and providing the recommended content item for display within the genealogical user interface on the client device”, Examiner respectfully disagrees.
Examiner cites that the limitation “generating,
-“based on the selection prediction, determining a recommended content item to provide to the client device for display” recites a mental process because human mind can determine a recommend content item to surface to the client device associated with the user account by evaluation and judgement of data.
-“the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account” and “using a selection-prediction neural network” “genealogical data system” are Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“and providing the recommended content item for display within the genealogical user interface on the client device” is insignificant extra-solution activity as mere data outputting. See, MPEP 2106.05(g) and well understood, routine and conventional activities (WURC) and is well-known, routine and conventional activities (WURC).
Therefore, the claims recite abstract idea.
With respect to Applicant’s argument that “In particular, amended claim 1 recites a practical application as it integrates the generation of a machine-learning-based "selection prediction" into a specific technological process for controlling how genealogical content is selected and displayed within a computerized genealogical system. For instance, the method determines genealogical metrics for content items, generates a likelihood that a particular content item will be selected when displayed in a genealogical user interface (using a selection-prediction neural network), and then uses that likelihood to automatically determine and surface a recommended content item for display on a client device”, Examiner respectfully disagrees.
Examiner cites that the claim does not recite any specific technological process that improves the operation of the computerized genealogical system, the client device, the user interface, or the neural network itself. The claim recites determining genealogical metrics, generating a prediction of whether a user is likely to select a content item, determining a recommended content item based on that prediction which are mental processes by evaluation and judgement of data and the limitations providing the content items to a user device is insignificant extra solution activities and performing the steps using a neural network and selection prediction indicating likelihood of user selecting a content item when displayed to the user are field of use. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
With respect to Applicant’s argument that “As described in the Specification, this approach improves the efficiency, accuracy, and flexibility of genealogical computing systems by enabling the system to automatically filter and prioritize relevant content items from among large volumes of genealogical records, thereby reducing the quantity of content that must be processed and displayed within the user interface. The use of the neural-network-generated prediction also improves the accuracy and flexibility of identifying relevant genealogical content items compared to traditional static or rule-based approaches. Accordingly, the claimed likelihood functions as a machine-generated control signal with a content-selection pipeline that filters and surfaces genealogical content items, thereby integrating any alleged abstract concept (such as mental processes) into a practical application that improves the efficiency and accuracy of computerized genealogical content presentation systems. See Specification at [0029]-[0032]”, Examiner respectfully disagrees.
Examiner respectfully disagrees. The claim does not recite a specific improvement to the neural network, genealogical database, user interface, client device, or display technology. The claim merely uses genealogical metrics to predict whether a user will select a content item, determine content based on that prediction, and provides the selected content for display. Determining information for presentation and generating a selection prediction using content metric is part of the abstract idea and can be performed mentally by evaluating/judgment which genealogical content is most relevant to a user. The recitation of a neural-network is field of use, and providing the recommended content for display is insignificant extra-solution activity. Therefore, the claim does not integrate the abstract idea into a practical application. Detailed description is provided in the 101 section below.
103 Rejections
With respect to Applicant’s argument that “generating, using a selection-prediction neural network, a selection prediction for the content item based on the content-level genealogical metrics, the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account”, Examiner cites that Sahni teaches the limitation “generating, using a selection-prediction neural network, a selection prediction for the content item based on the content-level genealogical metrics” in paragraph [0054, 0066] and for the new amended limitation “the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account” new prior art Jeon is cited.
Claim Rejections - 35 USC § 101
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.
Claims 1-14, 21-26 are rejected under 35 U.S.C. 101 because of the following reasons:
Claim 1, 21
At Step 1:
The claim is directed to a "computer-implemented method" “a system” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining content-level genealogical metrics for a content item from among a plurality of content items associated with a user account within a genealogical data system” recites a mental process because human mind can determine content level genealogical metrics for a content item associated with user account by evaluation and judgement of data.
-“generating, , a selection prediction for the content item based on the content-level genealogical metrics,
-“based on the selection prediction, determining a recommended content item to provide to the client device for display” recites a mental process because human mind can determine a recommend content item to surface to the client device associated with the user account by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“at least one processor; and a non-transitory computer readable medium storing instructions that, when executed by the at least one processor, cause that at least one processor to” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“using a selection-prediction neural network”, “genealogical data system” are Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“providing the recommended content item for display within the genealogical user interface on the client device” is insignificant extra-solution activity as mere data outputting. See, MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“using a selection-prediction neural network” “genealogical data system” are Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“providing the recommended content item for display within a genealogical user interface on the client device” is well-known, routine and conventional activities (WURC), see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claims 2, 22:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining tree-level genealogical metrics for a genealogy tree database associated with the user account” recites a mental process because human mind can determine tree level genealogical metrics for a genealogy tree database by evaluation and judgement of data.
-“generating the selection prediction for the content item further based on the tree-level genealogical metrics” recites a mental process because human mind can generate the selection prediction for the content item based on tree level genealogical metrics by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“using the selection-prediction neural network” “genealogy tree database” are Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“using the selection-prediction neural network” “genealogy tree database” are Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claims 3, 23:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining account-level genealogical metrics associated with the user account within the genealogical data system” recites a mental process because human mind can determine account level genealogical metrics by evaluation and judgment of data.
-“generating the selection prediction for the content item further based on the account-level genealogical metrics” recites a mental process because human mind can generate the selection prediction for the content item based on the account level genealogical metrics by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“using the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“using the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claims 4, 24:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining previous client device interactions with genealogical content items associated with the user account” recites a mental process because human mind can determine previous interactions associated with the user account by looking at the data by evaluation and judgment of data.
-“generating the selection prediction for the content item further based on the previous client device interactions” recites a mental process because human mind can generate selection prediction for the content item based on previous client device interactions by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“using the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“using the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claims 5, 25:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“identifying a node corresponding to the content item within a genealogy tree associated with the user account” recites a mental process because human mind can identify a node corresponding to the content item with genealogy tree associated with user account by evaluation and judgement of data.
-“and generating a kinship embedding associated with the node within the genealogy tree” recites a mental process because human mind can generate kinship embedding by evaluation and judgement of data
Claims 6, 26:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein generating the kinship embedding comprises utilizing a kinship embedding block of the selection-prediction neural network to process kinship data from the content-level genealogical metrics” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein generating the kinship embedding comprises utilizing a kinship embedding block of the selection-prediction neural network to process kinship data from the content-level genealogical metrics” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“comparing the selection prediction with a selection prediction threshold to determine that the selection prediction satisfies the selection prediction threshold” recites a mental process because human mind can compare the selection of the prediction with a threshold and determine that the selection satisfies the threshold by evaluation and judgment of data.
Claim 8:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determine content-level genealogical metrics for a plurality of content items associated with a user account within a genealogical data system” recites a mental process because human mind can determine content level genealogical metrics for a content item associated with user account by evaluation and judgement of data.
-“generate, , selection predictions for the plurality of content items based on the content-level genealogical metrics,
-“based on the selection predictions, select a set of content items from among the plurality of content items according to a content-diversity metric” recites a mental process because human mind can select a set of content items from the plurality of content items based on the selected predictions according to a content diversity metric by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“a non-transitory computer readable medium storing instructions which, when executed by at least one processor, cause the at least one processor to” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“using a selection-prediction neural network”, “genealogical data system” are Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“the selection predictions indicating likelihoods of receiving selections of respective content items of the plurality of content items upon display within a genealogical user interface on a client device associated with the user account” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“provide the set of content items for display within the genealogical user interface on the client device” is insignificant extra-solution activity as mere data outputting. See, MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“using a selection-prediction neural network”, “genealogical data system” are Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“the selection predictions indicating likelihoods of receiving selections of respective content items of the plurality of content items upon display within a genealogical user interface on a client device associated with the user account” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“provide the set of content items for display within a genealogical user interface on the client device” is well-known, routine and conventional activities (WURC), see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 9:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“to provide the set of content items for display within the genealogical user interface by: providing a selectable option for a content item within the genealogical user interface” is insignificant extra-solution activity as mere data outputting. See, MPEP 2106.05(g).
-“excluding an additional content item from the genealogical user interface based on an additional selection prediction for the additional content item” is insignificant extra-solution activity as mere data outputting. See, MPEP 2106.05(g) and/or selecting data to be manipulated.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“to provide the set of content items for display within the genealogical user interface by: providing a selectable option for a content item within the genealogical user interface” is well-known, routine and conventional activities (WURC), see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“excluding an additional content item from the genealogical user interface based on an additional selection prediction for the additional content item” is well-known, routine and conventional activities (WURC), see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9" and/or "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating content embeddings from the content-level genealogical metrics
-“generating, from the content embeddings, probabilities of user interaction with the plurality of content items
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“utilizing the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“utilizing the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 11:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generate a first selection prediction for a first content item and a second selection prediction for a second content item , wherein the first content item and the second content item are of different content types” recites a mental process because human mind can generate a first selection prediction for a first content item and second content item of different content types by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“provide the first content item and the second content item for display together within the genealogical user interface” is insignificant extra-solution activity as mere data outputting. See, MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“provide the first content item and the second content item for display together within the genealogical user interface” is well-known, routine and conventional activities (WURC), see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 12
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“to generate the selection predictions by to process previous client device interactions with genealogical content items” recites a mental process because human mind can generate predictions to process previous client device interactions with genealogical content items by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“using the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“using the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“ to process previous client device interactions with genealogical content items by selecting, for a user account, up to a threshold number of previous client device interactions for processing
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“when executed by the at least one processor, cause the at least one processor to utilize the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“when executed by the at least one processor, cause the at least one processor to utilize the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 14:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determine tree-level genealogical metrics for a genealogy tree database associated with the user account” recites a mental process because human mind can determine tree level genealogical metrics for a genealogy tree database associated with a user account by evaluation and judgment of data.
-“determine account-level genealogical metrics associated with the user account” recites a mental process because human mind can determine account level genealogical metrics associated with the user account by evaluation and judgement of data.
-“generate the selection predictions for the plurality of content items to process the content-level genealogical metrics, the tree- level genealogical metrics, and the account-level genealogical metrics” recites a mental process because human mind can generate the selection predictions for plurality of content items to process multiple level of genealogical metrics by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“by utilizing the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“by utilizing the selection-prediction neural network” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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, 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 1 negated by the manner in which the invention was made.
Claim(s) 1, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Jeon et al. (US 2018/0260840).
With respect to claim 1, Sahni teaches a computer-implemented method comprising ([0020, a method facilitates training of a machine learning model for predicting characteristics of a content item including an image or video associated with surgical applications], a method for predicting selection):
determining content-level metrics for a content item from among a plurality of content items associated with a user account within a data system ([0053, The data selector 206 selects, based on the prediction metrics 220, a set of selected content items 216 from the content database 116 for annotating in association with the job], [0054, the data selector 206 evaluates a prediction metric 220 for each content item 212 individually and determines to include the content item 212 in the set of selected content items 216 if the prediction metric 220 is below a threshold level], [0058, the annotation server 110 accesses a user profile associated with the user and identifies open annotation jobs for the user], each content item is associated with metric associated with user account);
generating, using a selection-prediction neural network, a selection prediction for the content item based on the content-level metrics ([0052, The ML prediction engine 232 receives unannotated content items 212 (e.g., from the content database 116) associated with a particular job and applies the relevant machine learning model 214 associated with the job to determine prediction metrics 220 associated with predictions of labels for the unannotated content items 212], [0054, the data selector 206 evaluates a prediction metric 220 for each content item 212 individually and determines to include the content item 212 in the set of selected content items 216 if the prediction metric 220 is below a threshold level], [0066, the annotation server 110 may apply the machine learning model to generate a prediction for the content item and a confidence metric associated with the prediction], generating using ML prediction model a selection predication for item based on the item metric);
based on the selection prediction, determining a recommended content item to provide to the client device for display ([0053, The data selector 206 selects, based on the prediction metrics 220, a set of selected content items 216 from the content database 116 for annotating in association with the job], [0054, the data selector 206 evaluates a prediction metric 220 for each content item 212 individually and determines to include the content item 212 in the set of selected content items 216 if the prediction metric 220 is below a threshold level], [0066, he annotation server 110 may apply the machine learning model to generate a prediction for the content item and a confidence metric associated with the prediction], based on the selection prediction determining a recommended content to deliver to the user); and
providing the recommended content item for display within the user interface on the client device ([0047, The UI engine 112 interfaces with the annotation application 122 and the administrative application 132 to enable presentation of various information displays, controls, and content items and to process inputs received from the annotation application 122 and administrative application 132 as described herein], the recommended item (content) is displayed to the user).
Sahni does not explicitly teach genealogical metrics, genealogical data system, genealogical user interface and the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account.
However, Folkman teaches genealogical metrics ([0055, the Jaro-Winkler distance is used as the metric between two strings and cosine-similarity is used as the metric between any two non-strings], genealogical data and metric associated with the data), genealogical data system ([0060, genealogical trees in a tree database], genealogical database), genealogical user interface ([0086, a user may examine training tree data 502 and create a label 550A through a computer interface indicating an individual-level similarity score based on the user's examination of training tree data 502], genealogical interface).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. genealogical data of Folkman into the invention of Sahni to have a system that will process genealogical data. Sahni and Folkman are analogous art because each art teaches processing data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Folkman’s functionalities into the system of Sahni to have a system will process multiple types of data to make the system more accurate, efficient and robust (Folkman, [0084, The ML models may be trained sequentially in the illustrated order so as to improve the functionality of each individual ML model]).
Sahni, Folkman do not explicitly teach the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account.
However, Jeon teaches the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account ([0026, Each user of the online system 140 is associated with a user profile, which is stored in the user profile store 205. A user profile includes declarative information about the user that was explicitly shared by the user and also may include profile information inferred by the online system 140.], [0049, The machine-learning module 245 may train a model to predict a likelihood that a user will perform an action in response to being presented with content items associated with a particular category based on patterns of the action previously performed by one or more additional users in response to being presented with content items associated with the same category], [0073, if the model 420 was trained 325 using a histogram 413 for the user that indicates that about 26% of the user's consecutive purchases made in association with presentations of content items associated with the music category occurred five days apart, the model 420 may predict a 26% likelihood that the user will make a purchase if presented with a content item associated with the music category that same day], selection prediction indicates that likelihood of users selection of the content items upon displayed to the user in the user interface associated with users account and Folkman teaches genealogical interface, [0086, a user may examine training tree data 502 and create a label 550A through a computer interface indicating an individual-level similarity score based on the user's examination of training tree data 502]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. selection prediction indicating a likelihood of receiving a selection of content items upon displayed to the user of Jeon into the invention of Sahni/Folkman to have a system that will indicate prediction likelihood to select the appropriate content item. Sahni, Folkman, Jeon are analogous art because each art teaches processing data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Jeon’s functionalities into the system of Sahni/Folkman to have a system will process prioritize content items that the user is more likely to select when displayed, thereby increasing user engagement and improving the usefulness of the recommendations of content items (Jeon, [0005, Therefore, online systems may maximize their revenue by presenting content items in which users are likely to have an interest and with which they are likely to interact]).
Claim 21 encompasses the same limitations of claim 1, in additions of
at least one processor; and a non-transitory computer readable medium storing instructions that, when executed by the at least one processor, cause that at least one processor (Fig. 1). Therefore, claim 21 is rejected on the same basis of rejection of claim 1.
Claim(s) 2, 3, 22, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Jeon et al. (US 2018/0260840) and in view Sharifi et al. (US 10,120,746).
With respect to claim 2, Sahni, Folkman, Jeon in combination teach the computer-implemented method of claim 1, Sahni teaches further comprising: generating the selection prediction for the content item using the selection-prediction neural network ([0052, The ML prediction engine 232 receives unannotated content items 212 (e.g., from the content database 116) associated with a particular job and applies the relevant machine learning model 214 associated with the job to determine prediction metrics 220 associated with predictions of labels for the unannotated content items 212], [0054, the data selector 206 evaluates a prediction metric 220 for each content item 212 individually and determines to include the content item 212 in the set of selected content items 216 if the prediction metric 220 is below a threshold level], [0066, the annotation server 110 may apply the machine learning model to generate a prediction for the content item and a confidence metric associated with the prediction], generating using ML prediction model a selection predication for item based on the item metric), Folkman teaches genealogical metric and genealogy tree database ([0053, genealogical entity resolution system 200 includes a tree person selector for selecting tree data corresponding to two tree persons], genealogy metric and genealogy tree database) but do not explicitly teach determining account-level genealogical metrics associated with the user account within the genealogical data system; and generating the selection prediction for the content item using the selection-prediction neural network further based on the account-level genealogical metrics.
However, Sharifi teaches determining account-level metrics associated with the user account within the data system ([col. 5, lines 60-67, “The leaf level comprises profiles for each user of the service, the intermediate level comprises profiles for each site of the organization, and the root level maintains a profile for the entire organization. The anomaly detection system accumulates and maintains metrics for the service-login events throughout the levels of the hierarchical behavioral profile. For example, when the server computer system 102 receives a service-login event for a particular user, a leaf-level node in the hierarchical behavioral profile is identified that corresponds to the particular user, and metrics are generated for the leaf-level node”], determining tree level metric and account level metric; Folkman teaches genealogy database in paragraph [0025]); further based on the account-level metrics ([col. 5, lines 60-67, “The leaf level comprises profiles for each user of the service, the intermediate level comprises profiles for each site of the organization, and the root level maintains a profile for the entire organization. The anomaly detection system accumulates and maintains metrics for the service-login events throughout the levels of the hierarchical behavioral profile. For example, when the server computer system 102 receives a service-login event for a particular user, a leaf-level node in the hierarchical behavioral profile is identified that corresponds to the particular user, and metrics are generated for the leaf-level node”], processing account level metrics and Sahni teaches predicting section content in paragraph [0054] and Folkman teaches genealogical metrics in paragraph [0084]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. account-level genealogical data of Sharifi into the invention of Sahni/Folkman/Jeon to have a system that will process genealogical data based on account level data. Sahni, Folkman, Jeon, Sharifi are analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Sharifi’s functionalities into the system of Sahni/Folkman/Jeon to have a system will process data faster and accurately (Sharifi, [col. 21, lines 5-7, “The event combiner 1218 improves the efficiency of the metrics engine 1204 by combining incoming events with similar events in the event queue 1216. In some implementations”]).
With respect to claim 3, Sahni, Folkman, Jeon in combination teach the computer-implemented method of claim 1, Sahni teaches generate the selection predictions for the plurality of content items by utilizing the selection-prediction neural network to process metrics, ([0052, The ML prediction engine 232 receives unannotated content items 212 (e.g., from the content database 116) associated with a particular job and applies the relevant machine learning model 214 associated with the job to determine prediction metrics 220 associated with predictions of labels for the unannotated content items 212], [0054, the data selector 206 evaluates a prediction metric 220 for each content item 212 individually and determines to include the content item 212 in the set of selected content items 216 if the prediction metric 220 is below a threshold level], [0066, the annotation server 110 may apply the machine learning model to generate a prediction for the content item and a confidence metric associated with the prediction], generating using ML prediction model a selection predication for item based on the item metric), Folkman further teaches genealogical metrics and genealogical data system ([0055, the Jaro-Winkler distance is used as the metric between two strings and cosine-similarity is used as the metric between any two non-strings]) but does not explicitly teach determine account-level genealogical metrics for a genealogy tree database associated with the user account; determine account-level genealogical metrics associated with the user account; and generate the selection predictions for the plurality of content items by utilizing the selection-prediction neural network to process the content-level genealogical metrics, the tree- level genealogical metrics, and the account-level genealogical metrics.
However, Sharifi teaches determine tree-level metrics for a tree database associated with the user account; determine account-level metrics associated with the user account ([col. 5, lines 60-67, “The leaf level comprises profiles for each user of the service, the intermediate level comprises profiles for each site of the organization, and the root level maintains a profile for the entire organization. The anomaly detection system accumulates and maintains metrics for the service-login events throughout the levels of the hierarchical behavioral profile. For example, when the server computer system 102 receives a service-login event for a particular user, a leaf-level node in the hierarchical behavioral profile is identified that corresponds to the particular user, and metrics are generated for the leaf-level node”], determining tree level metric and account level metric); to process the tree-level metrics, and the account-level metrics ([col. 5, lines 60-67, “The leaf level comprises profiles for each user of the service, the intermediate level comprises profiles for each site of the organization, and the root level maintains a profile for the entire organization. The anomaly detection system accumulates and maintains metrics for the service-login events throughout the levels of the hierarchical behavioral profile. For example, when the server computer system 102 receives a service-login event for a particular user, a leaf-level node in the hierarchical behavioral profile is identified that corresponds to the particular user, and metrics are generated for the leaf-level node”], processing tree level metrics and account level metric and Sahni teaches predicting section content in paragraph [0054]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. tree-level genealogical data of Sharifi into the invention of Sahni/Folkman/Jeon to have a system that will process genealogical data based on tree level data. Sahni, Folkman, Sharifi, Jeon are analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Sharifi’s functionalities into the system of Sahni/Folkman/Jeon to have a system will process data faster and accurately (Sharifi, [col. 21, lines 5-7, “The event combiner 1218 improves the efficiency of the metrics engine 1204 by combining incoming events with similar events in the event queue 1216. In some implementations”]).
Claim 22 is rejected on the same basis of rejection of claim 2.
Claim 23 is rejected on the same basis of rejection of claim 3.
Claim(s) 4, 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Jeon et al. (US 2018/0260840) and in view of Yang et al. (US 2023/0162018).
With respect to claim 4, Sahni, Folkman, Jeon in combination teach the computer-implemented method of claim 1, Folkman teaches genealogical content items ([0053, genealogical entity resolution system 200 includes a tree person selector for selecting tree data corresponding to two tree persons], genealogical content items) but do not explicitly teach determining previous client device interactions with genealogical content items associated with the user account; and generating the selection prediction for the content item using the selection-prediction neural network further based on the previous client device interactions.
However, Yang teaches determining previous client device interactions with content items associated with the user account ([0016, output a prediction of a movie the user is most likely to watch next, or to complete any other task that can be trained via a supervised learning approach], [0020, A strength of correlation (similarity) between a user embedding and a movie embedding is proportional to a probability of the corresponding user having a positive interaction with (e.g., enjoying) the movie], users previous interactions to process and Folkman teaches genealogical data in [0025]); and generating the selection prediction for the content item using the selection-prediction neural network further based on the previous client device interactions ([0016, output a prediction of a movie the user is most likely to watch next, or to complete any other task that can be trained via a supervised learning approach], [0020, A strength of correlation (similarity) between a user embedding and a movie embedding is proportional to a probability of the corresponding user having a positive interaction with (e.g., enjoying) the movie], users previous interactions to process and Folkman teaches genealogical data in [0025]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. determining previous interactions data of user account of Yang into the invention of Sahni/Folkman/Jeon to have a system that will determine users’ previous interactions and generate selection prediction based on the user previous interaction data. Sahni, Folkman, Yang are analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Yang’s functionalities into the system of Sahni/Folkman/Jeon to have a system will produce personalized results to make the system a personalized system (Yang, [0014, facilitates accurate embedding-based computations at a reduced computational cost]).
Claim 24 is rejected on the same basis of rejection of claim 4.
Claim(s) 5, 6, 25, 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Jeon et al. (US 2018/0260840) and in view of Qiu et al. (US 2022/0171760).
With respect to claim 5, Sahni, Folkman, Jeon in combination teach the computer-implemented method of claim 1, Folkman teaches wherein determining the content-level genealogical metrics for the content item comprises: identifying a node corresponding to the content item within a genealogy tree associated with the user account ([0037, a node connection may be generated between two nodes corresponding to the two persons in different trees. This technique may be used incrementally during the generation of the cluster database], [0057, a new node connection is generated in cluster database 214 between a first node corresponding tree person TP.sub.1 and a second node corresponding to tree person TP.sub.2. In contrast, when it is determined that tree persons TP.sub.1 and TP.sub.2 do not correspond to the same individual], identifying a node corresponding to a genealogy tree that is associated with user account) but do not explicitly teach and generating a kinship embedding associated with the node within the genealogy tree.
However, Qiu teaches generating a kinship embedding associated with the node within the genealogy tree ([0160, A social relationship graph or a social network may be constructed according to the social feature of the user. A node in the social relationship graph represents a user, and two users having a social relationship such as friendship, colleague relationship, kinship, or the like are connected by a line], kinship embedding associated with the node, Folkman teaches genealogy tree in para. [0054, 0055]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. generating kinship embedding of Qiu into the invention of Sahni/Folkman/Jeon to have a system that will generate kinship embedding for data. Sahni, Folkman, Qiu analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Qiu’s functionalities into the system of Sahni/Folkman/Jeon to have a system which will improve the accuracy of selection predictions (Qiu, [0084, improve model performance while reducing parameters and saving computational resources]).
With respect to claim 6, Sahni, Folkman, Qiu, Jeon in combination teach the computer-implemented method of claim 5, Folkman teaches genealogical metrics in para. [0054]. Sahni and Folkman do not explicitly teach wherein generating the kinship embedding comprises utilizing a kinship embedding block of the selection-prediction neural network to process kinship data from the content-level genealogical metrics.
However, Qiu teaches wherein generating the kinship embedding comprises utilizing a kinship embedding block of the selection-prediction neural network to process kinship data from the content-level genealogical metrics ([0059, Decoding is predicting a possible outputted item in combination with the vector generated by encoding. In the embodiments of the present disclosure, decoding is predicting a next item through a behavior encoding vector (that is, an object embedding vector) of a user], [0160, A social relationship graph or a social network may be constructed according to the social feature of the user. A node in the social relationship graph represents a user, and two users having a social relationship such as friendship, colleague relationship, kinship, or the like are connected by a line], kinship embedding associated with the node, Sahni teaches selection prediction neural network in para. [0046, The machine learning engine 126 may utilize techniques such as neural networks, classification, regression, or other computer-based learning techniques. Parameters (e.g., weights) associated with machine learning models are stored to the ML model database 124]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. utilizing kinship embedding block of Qiu into the invention of Sahni/Folkman/Jeon to have a system that will generate kinship embedding for data. Sahni, Folkman, Qiu, Jeon analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Qiu’s functionalities into the system of Sahni/Folkman/Jeon to have a system which will improve the accuracy of selection predictions (Qiu, [0084, improve model performance while reducing parameters and saving computational resources]).
Claim 25 is rejected on the same basis of rejection of claim 5.
Claim 26 is rejected on the same basis of rejection of claim 6.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Jeon et al. (US 2018/0260840) and in view of Chavali et al. (US 2019/0174691).
With respect to claim 7, Sahni, Folkman, Jeon in combination teach the computer-implemented method of claim 1, but do not explicitly teach further comprising comparing the selection prediction with a selection prediction threshold to determine that the selection prediction satisfies the selection prediction threshold.
However, Chavali teaches further comprising comparing the selection prediction with a selection prediction threshold to determine that the selection prediction satisfies the selection prediction threshold ([0047, the selection engine 110 may apply one or more thresholds to the prediction scores to retain hybrids having prediction scores that satisfy the one or more thresholds (e.g., are greater (or less) than the threshold(s), etc.)], comparing the data with selection prediction threshold to determine the data satisfy the threshold or not).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. utilizing kinship embedding block of Chavali into the invention of Sahni/Folkman/Jeon to have a system that will compare selection prediction with a threshold to process data. Sahni, Folkman, Chavali analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Chavali’s functionalities into the system of Sahni/Folkman/Jeon to have a system which will reduce erroneous or low confidence selections to have better selections of data (Chavali, [0079, resulting in a more efficient capture of the commercially viable hybrids from the universe of potential hybrids]).
Claim(s) 8, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Jeon et al. (US 2018/0260840) and in view of Chang et al. (US 9,330,093).
With respect to claim 8, Sahni teaches a non-transitory computer readable medium storing instructions which, when executed by at least one processor, cause the at least one processor to ([0036, a non-transitory computer-readable storage medium that stores instructions for execution by the processor in order to carry out the functions attributed to the annotation server 110 described herein], the non-transitory computer readable medium):
determine content-level metrics for a plurality of content items associated with a user account within a data system([0053, The data selector 206 selects, based on the prediction metrics 220, a set of selected content items 216 from the content database 116 for annotating in association with the job], [0054, the data selector 206 evaluates a prediction metric 220 for each content item 212 individually and determines to include the content item 212 in the set of selected content items 216 if the prediction metric 220 is below a threshold level], [0058, the annotation server 110 accesses a user profile associated with the user and identifies open annotation jobs for the user], each content item is associated with metric associated with user account);
generate, using a selection-prediction neural network, selection predictions for the plurality of content items based on the content-level metrics ([0052, The ML prediction engine 232 receives unannotated content items 212 (e.g., from the content database 116) associated with a particular job and applies the relevant machine learning model 214 associated with the job to determine prediction metrics 220 associated with predictions of labels for the unannotated content items 212], [0054, the data selector 206 evaluates a prediction metric 220 for each content item 212 individually and determines to include the content item 212 in the set of selected content items 216 if the prediction metric 220 is below a threshold level], [0066, the annotation server 110 may apply the machine learning model to generate a prediction for the content item and a confidence metric associated with the prediction], generating using ML prediction model a selection predication for item based on the item metric);
based on the selection predictions, select a set of content items from among the plurality of content items
provide the set of content items for display within the user interface on the client device ([0047, The UI engine 112 interfaces with the annotation application 122 and the administrative application 132 to enable presentation of various information displays, controls, and content items and to process inputs received from the annotation application 122 and administrative application 132 as described herein], the recommended item (content) is displayed to the user).
Sahni does not explicitly teach genealogical metrics, genealogical data system, genealogical user interface, content diversity metric and the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account.
However, Folkman teaches genealogical metric ([0055, the Jaro-Winkler distance is used as the metric between two strings and cosine-similarity is used as the metric between any two non-strings], genealogical data and metric associated with the data), genealogical data system ([0060, genealogical trees in a tree database], genealogical database), genealogical user interface ([0086, a user may examine training tree data 502 and create a label 550A through a computer interface indicating an individual-level similarity score based on the user's examination of training tree data 502]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. genealogical data of Folkman into the invention of Sahni to have a system that will process genealogical data. Sahni and Folkman are analogous art because each art teaches processing data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Folkman’s functionalities into the system of Sahni to have a system will process multiple types of data to make the system more accurate, efficient and robust (Folkman, [0084, The ML models may be trained sequentially in the illustrated order so as to improve the functionality of each individual ML model]).
Sahni and Folkman do not explicitly teach a content-diversity metric and the selection prediction indicating a likelihood of receiving a selection of the content item upon display within a genealogical user interface on a client device associated with the user account.
However, Chang teaches a content-diversity metric ([col. 10, lines 27-30, “the content management system 400 may detect a CGI parameters or other parameters in the referring URLs that have a certain diversity for a given website”], selecting content based on a content diversity metric).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. content diversity metric of Chang into the invention of Sahni/Folkman to have a system that will process data using content diversity metric. Sahni, Folkman, Chang are analogous art because each art teaches processing data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Chang’s functionalities into the system of Sahni/Folkman to have a system will process data to make the system more reliable, consistent and improve performance of processing of data (Chang, [col. 1, lines 20-23, These tools also allow a content provider to track the performance of various content items or content campaigns]).
Sahni, Folkman, Chang do not explicitly teach the selection predictions indicating likelihoods of receiving selections of respective content items of the plurality of content items upon display within a genealogical user interface on a client device associated with the user account.
However, Jeon teaches the selection predictions indicating likelihoods of receiving selections of respective content items of the plurality of content items upon display within a genealogical user interface on a client device associated with the user account ([0026, Each user of the online system 140 is associated with a user profile, which is stored in the user profile store 205. A user profile includes declarative information about the user that was explicitly shared by the user and also may include profile information inferred by the online system 140.], [0049, The machine-learning module 245 may train a model to predict a likelihood that a user will perform an action in response to being presented with content items associated with a particular category based on patterns of the action previously performed by one or more additional users in response to being presented with content items associated with the same category], [0073, if the model 420 was trained 325 using a histogram 413 for the user that indicates that about 26% of the user's consecutive purchases made in association with presentations of content items associated with the music category occurred five days apart, the model 420 may predict a 26% likelihood that the user will make a purchase if presented with a content item associated with the music category that same day], selection prediction indicates that likelihood of users selection of the content items upon displayed to the user in the user interface associated with users account and Folkman teaches genealogical interface, [0086, a user may examine training tree data 502 and create a label 550A through a computer interface indicating an individual-level similarity score based on the user's examination of training tree data 502]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. selection prediction indicating a likelihood of receiving a selection of content items upon displayed to the user of Jeon into the invention of Sahni/Folkman/Chang to have a system that will indicate prediction likelihood to select the appropriate content item. Sahni, Folkman, Jeon, Chang are analogous art because each art teaches processing data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Jeon’s functionalities into the system of Sahni/Folkman/Chang to have a system will process prioritize content items that the user is more likely to select when displayed, thereby increasing user engagement and improving the usefulness of the recommendations of content items (Jeon, [0005, Therefore, online systems may maximize their revenue by presenting content items in which users are likely to have an interest and with which they are likely to interact]).
With respect to claim 11, Sahni, Folkman, Chang, Jeon in combination teach the non-transitory computer readable medium of claim 8, Sahni further teaches generate a first selection prediction for a first content item and a second selection prediction for a second content item utilizing the selection-prediction neural network, wherein the first content item and the second content item are of different content types ([0053, The data selector 206 selects, based on the prediction metrics 220, a set of selected content items 216 from the content database 116 for annotating in association with the job. The data selector 206 selects content items 216 that it predicts can best contribute to improving performance of the machine learning model 214 if included in a training set]); and
provide the first content item and the second content item for display together within the genealogical user interface ([0058, FIG. 3 is an example sequence of user interfaces presented to an annotator via the annotation application 122 for obtaining annotations of surgical media content such as video clips, images, or animations (e.g., gifs). A login screen 302 enables a user to provide credentials (e.g., username and password) for logging into the annotation server 110. Upon receiving and authenticating the login credentials, the annotation server 110 accesses a user profile associated with the user and identifies open annotation jobs for the user], displaying items in the graphical user interface, Folkman teaches genealogical interface and displaying data, [0088, user may examine training tree data 502 and create a label 550C through a computer interface indicating whether the first tree person and the second tree person are a match or a plausible match).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Chang et al. (US 9,330,093) and in view of Jeon et al. (US 2018/0260840) and in view of Camhi et al. (US 2020/0219466).
With respect to claim 9, Sahni, Folkman, Chang, Jeon in combination teach the non-transitory computer readable medium of claim 8, Sahni teaches further storing instructions which, when executed by the at least one processor, cause the at least one processor to provide the set of content items for display within the genealogical user interface by ([0065, This screen provides a list of jobs 802 (e.g., identified by name and/or a unique identifier) and various parameters 804 associated with the job. An administrator may select a job to view additional information, delete the job, or edit parameters associated with the job], selecting content; Folkman teaches genealogical data/user interface in [0086, a user may examine training tree data 502 and create a label 550A through a computer interface indicating an individual-level similarity score based on the user's examination of training tree data 502]):
providing a selectable option for a content item within the genealogical user interface (Sahni, [0065, This screen provides a list of jobs 802 (e.g., identified by name and/or a unique identifier) and various parameters 804 associated with the job. An administrator may select a job to view additional information, delete the job, or edit parameters associated with the job], selecting content; Folkman teaches genealogical interface and displaying data, [0088, user may examine training tree data 502 and create a label 550C through a computer interface indicating whether the first tree person and the second tree person are a match or a plausible match).
Sahni, Folkman, Chang, Jeon do not explicitly teach and excluding an additional content item from the genealogical user interface based on an additional selection prediction for the additional content item.
However, Camhi teaches excluding an additional content item from the genealogical user interface based on an additional selection prediction for the additional content item ([0039, the vehicle based data processing system 110 can identify the predictive content item 145 that has been selected by a user and remove the respective predictive content item 145, here the first predictive content item 145, from the displays 140 allocated to the predictive interface 135 within the information cluster 105], [0061, The vehicle based data processing system 110 can generate instructions to remove a selected predictive content item 145 from the predictive interface 135 responsive to executing the application 155. The vehicle based data processing system 110 can remove the selected predictive content item 145 from at least one display 140 of the predictive interface 135.], removing (excluding) content based on the selection prediction; Folkman teaches genealogical interface and displaying data, [0088, user may examine training tree data 502 and create a label 550C through a computer interface indicating whether the first tree person and the second tree person are a match or a plausible match).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. excluding content from the geographical user interface of Camhi into the invention of Sahni/Folkman/Chang/Jeon to have a system that will exclude data. Sahni, Folkman, Chang are analogous art because each art teaches processing data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Camhi’s functionalities into the system of Sahni/Folkman/Chang/Jeon to have a system will only display relevant data to improve clarity, relevance and interaction efficiency of the system (Camhi, [0017, a single display to efficiently manage the allocation of computer resources within the vehicle).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Chang et al. (US 9,330,093) and in view of Jeon et al. (US 2018/0260840) and in view of Yang et al. (US 2023/0162018).
With respect to claim 10, Sahni, Folkman, Chang, Jeon in combination teach the non-transitory computer readable medium of claim 8, Sahni teaches further storing instructions which, when executed by the at least one processor, cause the at least one processor to generate the selection predictions for the plurality of content items by ([0046, The machine learning engine 126 may furthermore apply the machine learning models to generate predictions for unannotated content items], the selection predictions of items) but do not explicitly teach generating content embeddings from the content-level genealogical metrics utilizing the selection-prediction neural network; and generating, from the content embeddings, probabilities of user interaction with the plurality of content items utilizing the selection-prediction neural network.
However, Yang teaches generating content embeddings from the content-level genealogical metrics utilizing the selection-prediction neural network ([0020, illustrates an example similarity metric 108 that may be computed to determine a similarity between a first embedding 120 of a first object type and a second embedding 122 of a second object type], [0051, A similarity predictor 322 performs the task of predicting which ad in the advertisement database is most likely to appeal to a selected user (e.g., User 1). This operation is performed by measuring a similarity metric between an embedding for the user (e.g., V1) and an embedding for each ad in the database (e.g., V2, V3, V4 . . . )], generating embeddings from metrics using selection prediction neural network, Folkman teaches genealogical data [0012, wherein the first tree data corresponds to a first tree person from a first genealogical tree and the second tree data corresponds to a second tree person from a second genealogical tree]); and
generating, from the content embeddings, probabilities of user interaction with the plurality of content items utilizing the selection-prediction neural network ([0016, The neural network 102 is a deep learning network that may assume a variety of different forms in different implementations including that of a graph neural network (GNN), convolutional neural network (CNN), a recurrent neural network (RNN), an artificial neural network (ANN), etc. …output a prediction of a movie the user is most likely to watch next, or to complete any other task that can be trained via a supervised learning approach], [0020, For example, the first object type may be “users” and the second object type may be “movies.” A strength of correlation (similarity) between a user embedding and a movie embedding is proportional to a probability of the corresponding user having a positive interaction with (e.g., enjoying) the movie], generating probabilities of user interaction of items utilizing selection prediction neural network; Folkman teaches genealogical data [0012, wherein the first tree data corresponds to a first tree person from a first genealogical tree and the second tree data corresponds to a second tree person from a second genealogical tree]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. generating content embedding and determining probabilities of selection of content items of Yang into the invention of Sahni/Folkman/Chang/Jeon to have a system that will use content embedding and generating probabilities of selection of content item. Sahni, Folkman, Yang, Jeon are analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Yang’s functionalities into the system of Sahni/Folkman/Chang/Jeon to have a system will produce accurate results which will reduce the computational cost of a system (Yang, [0014, facilitates accurate embedding-based computations at a reduced computational cost]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Chang et al. (US 9,330,093) and in view of Jeon et al. (US 2018/0260840) and in view Zimovnov et al. (US 2020/0089724).
With respect to claim 12, Sahni, Folkman, Chang, Jeon in combination teach the non-transitory computer readable medium of claim 8, Sahni further teaches further storing instructions which, when executed by the at least one processor, cause the at least one processor to generate the selection predictions by using the selection-prediction neural network ([0046, The machine learning engine 126 may furthermore apply the machine learning models to generate predictions for unannotated content items], [0052, The ML prediction engine 232 receives unannotated content items 212 (e.g., from the content database 116) associated with a particular job and applies the relevant machine learning model 214 associated with the job to determine prediction metrics 220 associated with predictions of labels for the unannotated content items 212], processing data using neural network) but does not explicitly teach to process previous client device interactions with genealogical content items.
However, Zimovnov teaches to process previous client device interactions with genealogical content items ([0119, (i) track interactions of users with content items that were previously recommended by the recommendation service, and store the user interactions in the user interaction database 126], [0120, Examples of user events/interactions associated with previous users of the system 100 tracked by the analytics module 116 and stored in the user interaction database 126], [0170, as an example when the user 102 has a number of user interactions of a given one of the first type of content 302, the second type of content 304, the third type of content 306, and the fourth type of content 308, which is fewer than a respective predetermined threshold], users previous interactions to process and Folkman teaches genealogical data in [0025]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. determining previous interactions data of user account of Zimovnov into the invention of Sahni/Folkman/Chang/Jeon to have a system that will determine users’ previous interactions and generate selection prediction based on the user previous interaction data. Sahni, Folkman, Chang, Zimovnov are analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Zimovnov’s functionalities into the system of Sahni/Folkman/Chang/Jeon to have a system will produce personalized results to make the system a personalized system (Zimovnov, [000, allowing the user to discover content and, more precisely, to allow for discovering and/or recommending content that the user may not be expressly interested in searching for]).
With respect to claim 13, Sahni, Folkman, Chang, Jeon in combination teach the non-transitory computer readable medium of claim 12, Sahni further teaches further storing instructions which, when executed by the at least one processor, cause the at least one processor to utilize the selection-prediction neural network to process ([0046, The machine learning engine 126 may furthermore apply the machine learning models to generate predictions for unannotated content items], [0052, The ML prediction engine 232 receives unannotated content items 212 (e.g., from the content database 116) associated with a particular job and applies the relevant machine learning model 214 associated with the job to determine prediction metrics 220 associated with predictions of labels for the unannotated content items 212], processing data using neural network) but do not explicitly teach previous client device interactions with genealogical content items by selecting, for a user account, up to a threshold number of previous client device interactions for processing by the selection-prediction neural network.
However, Zimovnov teaches previous client device interactions with genealogical content items by selecting, for a user account, up to a threshold number of previous client device interactions for processing by the selection-prediction neural network ([0119, (i) track interactions of users with content items that were previously recommended by the recommendation service, and store the user interactions in the user interaction database 126], [0120, Examples of user events/interactions associated with previous users of the system 100 tracked by the analytics module 116 and stored in the user interaction database 126], [0170, as an example when the user 102 has a number of user interactions of a given one of the first type of content 302, the second type of content 304, the third type of content 306, and the fourth type of content 308, which is fewer than a respective predetermined threshold], users previous interactions to process based on a threshold and Folkman teaches genealogical data in [0025]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. determining previous interactions data of user account of Zimovnov into the invention of Sahni/Folkman/Chang/Jeon to have a system that will determine users’ previous interactions and generate selection prediction based on the user previous interaction data. Sahni, Folkman, Zimovnov, Jeon are analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Zimovnov’s functionalities into the system of Sahni/Folkman/Chang/Jeon to have a system will produce personalized results to make the system a personalized system (Zimovnov, [000, allowing the user to discover content and, more precisely, to allow for discovering and/or recommending content that the user may not be expressly interested in searching for]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahni et al. (US 2023/0343079) and in view of Folkman et al. (US 2021/0319003) and in view of Chang et al. (US 9,330,093) and in view of Jeon et al. (US 2018/0260840) and in view Sharifi et al. (US 10,120,746).
With respect to claim 14, Sahni, Folkman, Chang, Jeon in combination teach the non-transitory computer readable medium of claim 12, Sahni teaches generate the selection predictions for the plurality of content items by utilizing the selection-prediction neural network to process the content-level genealogical metrics,genealogical metrics and genealogical database ([0055, the Jaro-Winkler distance is used as the metric between two strings and cosine-similarity is used as the metric between any two non-strings]) but does not explicitly teach determine tree-level genealogical metrics for a genealogy tree database associated with the user account; determine account-level genealogical metrics associated with the user account; and generate the selection predictions for the plurality of content items by utilizing the selection-prediction neural network to process the content-level genealogical metrics, the tree- level genealogical metrics, and the account-level genealogical metrics.
However, Sharifi teaches determine tree-level genealogical metrics for a genealogy tree database associated with the user account; determine account-level genealogical metrics associated with the user account ([col. 5, lines 60-67, “The leaf level comprises profiles for each user of the service, the intermediate level comprises profiles for each site of the organization, and the root level maintains a profile for the entire organization. The anomaly detection system accumulates and maintains metrics for the service-login events throughout the levels of the hierarchical behavioral profile. For example, when the server computer system 102 receives a service-login event for a particular user, a leaf-level node in the hierarchical behavioral profile is identified that corresponds to the particular user, and metrics are generated for the leaf-level node”], determining tree level metric and account level metric); to process the tree- level genealogical metrics, and the account-level genealogical metrics ([col. 5, lines 60-67, “The leaf level comprises profiles for each user of the service, the intermediate level comprises profiles for each site of the organization, and the root level maintains a profile for the entire organization. The anomaly detection system accumulates and maintains metrics for the service-login events throughout the levels of the hierarchical behavioral profile. For example, when the server computer system 102 receives a service-login event for a particular user, a leaf-level node in the hierarchical behavioral profile is identified that corresponds to the particular user, and metrics are generated for the leaf-level node”], processing tree level metrics and account level metric and Sahni teaches predicting section content in paragraph [0054]).
One of ordinary skill in the art would recognize that incorporating functionalities i.e. account-level/tree level genealogical data of Sharifi into the invention of Sahni/Folkman/Chang/Jeon to have a system that will process genealogical data based on account level data and tree-level data. Sahni, Folkman, Chang, Sharifi are analogous art because each art teaches processing metrics data.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate Sharifi’s functionalities into the system of Sahni/Folkman/Chang/Jeon to have a system will process data faster and produce accurate results (Sharifi, [col. 21, lines 5-7, “The event combiner 1218 improves the efficiency of the metrics engine 1204 by combining incoming events with similar events in the event queue 1216. In some implementations”]).
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Kadioglu (US 2024/0232652) teaches predicting a likelihood of user selection of a content item when displayed to the user in paragraphs [0027, 0032].
Chaturvedi (US 2023/0316349) teaches predicting likelihood of content items and metrics in paragraphs [0026, 0032].
Putnam et al. (US 2022/0078503) teaches predicting likelihood of content items selections and metrics in paragraphs [0055, 0065].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/FATIMA P MINA/ Examiner, Art Unit 2159
/ALBERT M PHILLIPS, III/Primary Examiner, Art Unit 2159
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