DETAILED ACTION
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 Amendment
This action is responsive to the Applicant’s Application filed on January 5, 2026.
Claims 21-22, 28, and 35 have been amended.
Applicant's amendments necessitated new grounds of rejection.
This action is made final in view of the new grounds of rejection.
Claims 21, 28, and 35 are independent. As a result claims 21-40 are pending in this office action.
Response to Arguments
Applicant's argument filed January 5, 2026 regarding the rejection of claims 21-40 under 35 U.S.C 101, has been fully considered and is persuasive.
Applicants argue in substance:
Regarding claims 21-40, the applicants submit that the steps are being performed are directed to statutory subject matter under 101 because the claims as a whole integrates the exception into a practical application and are a technical improvement.
The argument of claims 21-40 have been fully considered and is persuasive.
Therefore, the 35 U.S.C. 101 rejection of claims 21-40 have been withdrawn.
Applicant's arguments filed January 5, 2026 regarding the rejection of claims 21, 28, and 35 under 35 U.S.C 103 have been fully considered but they are moot in view of the new grounds of rejection.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21, 23, 28, 30, 35, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Fujimoto et al. (US 2021/0390704) (hereinafter Fujimoto) in view of MacPherson et al. (US 2017/0329924) (hereinafter MacPherson), and in further view of Arroyo et al. (US 2021/0406533)(hereinafter Arroyo).
Regarding claim 21, Fujimoto teaches a method, comprising: segmenting the digital image to generate bounding boxes separating the set of page elements depicted in the digital image (see Fig. 2B, Fig. 4, para [0036], para [0088], discloses a segmented image with identified articles (set of page elements) defined by bounding boxes for classifications); extracting a plurality of entities from the set of page elements by implementing an entity extraction model on the bounding boxes (see Figs. 8A-8B, para [0099], para [0112], discloses extracting particles (entities), including classification and bounding box data from images, utilizing a trained model in comparing extracted particles to training data that includes images with bounding boxes and classifications).
Fujimoto does not explicitly teach detecting, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types; determining relationships among the plurality of entities; and generating genealogy tree data from the plurality of entities and the relationships among the plurality of entities.
MacPherson teaches determining relationships among the plurality of entities (see Fig. 5A, para [0042], para [0049], discloses amount of DNA segments between users exceeding a threshold, determines potential relatives of a user and structure of a tree representing family relationships); and generating genealogy tree data from the plurality of entities and the relationships among the plurality of entities (see Fig. 5B, para [0042-0043], discloses generating a family tree (genealogy tree) and relationships among users that are family members).
Fujimoto/MacPherson are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto to include a genealogy tree from disclosure of MacPherson. The motivation to combine these arts is disclosed by MacPherson as “Family history information can improve the accuracy of estimating disease risk” (para [0001]) and including a genealogy tree is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Fujimoto/MacPherson do not explicitly teach detecting, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types.
Arroyo teaches detecting, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types (see Fig. 1, Fig. 3, para [0034], para [0036-0037], discloses detecting in an image, columns (set of page elements) in bounding boxes (document structure type) utilizing model (document structure detection model) corresponding to bounding boxes that correspond to regions of interest as columns).
Fujimoto/MacPherson/Arroyo are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto/MacPherson to utilize one or more document structure detection models from disclosure of Arroyo. The motivation to combine these arts is disclosed by Arroyo as “explainability to be able to determine which factors were important for the neural network based model in generating an output” (para [0019]) and utilizing one or more document structure detection models is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 28, Fujimoto teaches a system comprising: at least one processor; and one or more memory devices coupled to the at least one processor, the one or more memory devices storing instructions that, when executed by the at least one processor, cause the at least one processor to (see Fig. 7, para [0115], discloses processor): segmenting the digital image to generate bounding boxes separating the set of page elements depicted in the digital image (see Fig. 2B, Fig. 4, para [0036], para [0088], discloses a segmented image with identified articles (set of page elements) defined by bounding boxes for classifications); extract a plurality of entities from the set of page elements by implementing an entity extraction model on the bounding boxes (see Figs. 8A-8B, para [0099], para [0112], discloses extracting particles (entities), including classification and bounding box data from images, utilizing a trained model in comparing extracted particles to training data that includes images with bounding boxes and classifications).
Fujimoto does not explicitly teach detect, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types; determine relationships among the plurality of entities; and generate genealogy tree data from the plurality of entities and the relationships among the plurality of entities.
MacPherson teaches determine relationships among the plurality of entities (see Fig. 5A, para [0042], para [0049], discloses amount of DNA segments between users exceeding a threshold, determines potential relatives of a user and structure of a tree representing family relationships); and generate genealogy tree data from the plurality of entities and the relationships among the plurality of entities (see Fig. 5B, para [0042-0043], discloses generating a family tree (genealogy tree) and relationships among users that are family members).
Fujimoto/MacPherson are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto to include a genealogy tree from disclosure of MacPherson. The motivation to combine these arts is disclosed by MacPherson as “Family history information can improve the accuracy of estimating disease risk” (para [0001]) and including a genealogy tree is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Fujimoto/MacPherson do not explicitly teach detect, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types.
Arroyo teaches detect, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types (see Fig. 1, Fig. 3, para [0034], para [0036-0037], discloses detecting in an image, columns (set of page elements) in bounding boxes (document structure type) utilizing model (document structure detection model) corresponding to bounding boxes that correspond to regions of interest as columns).
Fujimoto/MacPherson/Arroyo are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto/MacPherson to utilize one or more document structure detection models from disclosure of Arroyo. The motivation to combine these arts is disclosed by Arroyo as “explainability to be able to determine which factors were important for the neural network based model in generating an output” (para [0019]) and utilizing one or more document structure detection models is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 35, Fujimoto teaches a non-transitory computer readable medium storing instructions that, when executed by at least one processor (see Fig. 7, para [0115], discloses processor), cause the at least one processor to perform operations comprising: segmenting the digital image to generate bounding boxes separating the set of page elements depicted in the digital image (see Fig. 2B, Fig. 4, para [0036], para [0088], discloses a segmented image with identified articles (set of page elements) defined by bounding boxes for classifications); extracting a plurality of entities from the set of page elements by implementing an entity extraction model on the bounding boxes (see Figs. 8A-8B, para [0099], para [0112], discloses extracting particles (entities), including classification and bounding box data from images, utilizing a trained model in comparing extracted particles to training data that includes images with bounding boxes and classifications).
Fujimoto does not explicitly teach detecting, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types; determining relationships among the plurality of entities; and generating genealogy tree data from the plurality of entities and the relationships among the plurality of entities.
MacPherson teaches determining relationships among the plurality of entities (see Fig. 5A, para [0042], para [0049], discloses amount of DNA segments between users exceeding a threshold, determines potential relatives of a user and structure of a tree representing family relationships); and generating genealogy tree data from the plurality of entities and the relationships among the plurality of entities (see Fig. 5B, para [0042-0043], discloses generating a family tree (genealogy tree) and relationships among users that are family members).
Fujimoto/MacPherson are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto to include a genealogy tree from disclosure of MacPherson. The motivation to combine these arts is disclosed by MacPherson as “Family history information can improve the accuracy of estimating disease risk” (para [0001]) and including a genealogy tree is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Fujimoto/MacPherson do not explicitly teach detecting, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types.
Arroyo teaches detecting, from a digital image, a set of page elements belonging to one or more document structure types of the digital image by utilizing respective document structure detection models corresponding to the one or more document structure types (see Fig. 1, Fig. 3, para [0034], para [0036-0037], discloses detecting in an image, columns (set of page elements) in bounding boxes (document structure type) utilizing model (document structure detection model) corresponding to bounding boxes that correspond to regions of interest as columns).
Fujimoto/MacPherson/Arroyo are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto/MacPherson to utilize one or more document structure detection models from disclosure of Arroyo. The motivation to combine these arts is disclosed by Arroyo as “explainability to be able to determine which factors were important for the neural network based model in generating an output” (para [0019]) and utilizing one or more document structure detection models is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claims 23, 30, and 37, Fujimoto/MacPherson/Arroyo teach a method of claim 1, system of claim 28, and medium of claim 35.
Fujimoto further teaches wherein: the digital image comprises a historical record defining genealogical information (see Figs. 2A-2B, para [0004], discloses newspaper images of historical records in a genealogical context); and detecting the set of page elements comprises utilizing a convolutional neural network to generating the bounding boxes within the historical record (see Figs. 2A-2B, para [0077], para [0088], discloses convolutional neural network generating bounding boxes within an image of an historical document).
Claims 22, 26-27, 29, 33-34, 36, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Fujimoto et al. (US 2021/0390704) (hereinafter Fujimoto) in view of MacPherson et al. (US 2017/0329924) (hereinafter MacPherson) and Arroyo as applied to claims 21, 28, and 35, and in further view of Sederberg et al. (US 2015/0039636) (hereinafter Sederberg).
Regarding claims 22, 29, and 36, Fujimoto/MacPherson/Arroyo teach a method of claim 1, system of claim 28, and medium of claim 35.
Fujimoto/MacPherson does not explicitly teach wherein determining the relationships among the plurality of entities comprises utilizing a dependency model to determine dependencies among the plurality of entities.
Sederberg teaches wherein determining the relationships among the plurality of entities comprises utilizing a dependency model to determine dependencies among the plurality of entities (see para [0009], para [0027], discloses genealogical tree includes a hierarchical model (dependency model) that interlinks the family members as nodes within the genealogical tree based on the relationship links).
Fujimoto/MacPherson/Arroyo/Sederberg are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto/MacPherson/Arroyo to include a dependency model from disclosure of Sederberg. The motivation to combine these arts is disclosed by Sederberg as “improved systems for creating genealogical models of family trees and for tools that can be used to facilitate navigation through the nodes of family trees spanning multiple pages” (para [0006]) and including a dependency model is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claims 26, 33, and 40, Fujimoto/MacPherson/Arroyo teach a method of claim 1, system of claim 28, and medium of claim 35.
Fujimoto/MacPherson/Arroyo does not explicitly teach modifying an existing genealogy tree by updating nodes and edges within the existing genealogy tree to reflect the relationships indicated by the genealogy tree data; or generating a new genealogy tree by generating nodes and edges reflecting the plurality of entities and the relationships indicated by the genealogy tree data.
Senderberg teaches modifying an existing genealogy tree by updating nodes and edges within the existing genealogy tree to reflect the relationships indicated by the genealogy tree data; or generating a new genealogy tree by generating nodes and edges reflecting the plurality of entities and the relationships indicated by the genealogy tree data (see Figs. 2-3, Fig. 32, para [0048-0051], discloses reformatting (updating) genealogical tree by identifying and removing duplicate branches and displaying a single time as grouping 3205).
Fujimoto/MacPherson/Arroyo/Sederberg are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto/MacPherson/Arroyo to include a dependency model from disclosure of Sederberg. The motivation to combine these arts is disclosed by Sederberg as “improved systems for creating genealogical models of family trees and for tools that can be used to facilitate navigation through the nodes of family trees spanning multiple pages” (para [0006]) and including a dependency model is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claims 27 and 34 , Fujimoto/MacPherson/Arroyo teach a method of claim 1 and system of claim 28.
Fujimoto/MacPherson/Arroyo does not explicitly teach modifying a cluster database storing data clusters corresponding to entities of the existing genealogy tree to include cluster data from the plurality of entities.
Sederberg teaches modifying a cluster database storing data clusters corresponding to entities of the existing genealogy tree to include cluster data from the plurality of entities (see Fig. 2, Fig. 34, para [0050-0051], discloses grouping (clusters) of ancestor nodes).
Fujimoto/MacPherson/Arroyo/Sederberg are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto/MacPherson/Arroyo to include a dependency model from disclosure of Sederberg. The motivation to combine these arts is disclosed by Sederberg as “improved systems for creating genealogical models of family trees and for tools that can be used to facilitate navigation through the nodes of family trees spanning multiple pages” (para [0006]) and including a dependency model is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Claims 24-25, 31-32, and 38-39 are rejected under 35 U.S.C. 103 as being unpatentable over Fujimoto et al. (US 2021/0390704) (hereinafter Fujimoto) in view of MacPherson et al. (US 2017/0329924) (hereinafter MacPherson) and Arroyo as applied to claims 21, 28, and 35, and in further view of Anderson et al. (US 2021/0019569) (hereinafter Anderson).
Regarding claims 24, 31, and 38, Fujimoto/MacPherson/Arroyo teach a method of claim 1, system of claim 28, and medium of claim 35.
Fujimoto/MacPherson/Arroyo does not explicitly teach wherein detecting the set of page elements comprises discretizing footnotes as a distinct element class utilizing a segmentation model.
Anderson teaches does not explicitly teach wherein detecting the set of page elements comprises discretizing footnotes as a distinct element class utilizing a segmentation model (see Fig. 5, Figs. 9-10, para [0052], para [0094], discloses entity tags (discretizing footnotes) utilizing the segmentation machine learning model).
Fujimoto/MacPherson/Arroyo/Anderson are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto/MacPherson/Arroyo to include a segmentation model from disclosure of Anderson. The motivation to combine these arts is disclosed by Anderson as “change weights associated with segmentation ML model 506 such that output 602B better approximates output label” (para [0059]) and including a segmentation model is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claims 25, 32, and 39, Fujimoto/MacPherson/Arroyo teach a method of claim 1, system of claim 28, and medium of claim 35.
Fujimoto/MacPherson/Arroyo does not explicitly teach wherein extracting the plurality of entities from the set of page elements comprises comparing text within the set of page elements with entity names stored in a genealogical database.
Anderson teaches wherein extracting the plurality of entities from the set of page elements comprises comparing text within the set of page elements with entity names stored in a genealogical database (see Fig. 5, Fig. 10, para [0056], para [0094-0095], discloses comparing marriage and obituary facts to genealogical database data to determine if sections contain article of interest).
Fujimoto/MacPherson/Arroyo/Anderson are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Fujimoto/MacPherson/Arroyo to include a segmentation model from disclosure of Anderson. The motivation to combine these arts is disclosed by Anderson as “change weights associated with segmentation ML model 506 such that output 602B better approximates output label” (para [0059]) and including a segmentation model is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Allowable Subject Matter
Claim 22 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art neither teaches nor suggest the specific models included in claim 22.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
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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/Courtney Harmon/Primary Examiner, Art Unit 2159