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 .
DETAILED ACTION
This communication is in responsive to the Application 18/394,969 filed on 12/22/2023.
Claims 1-21 have been examined and are pending in this application. This Action is made Non-FINAL.
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.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Gururajan et al. (“Gururajan,” US 2017/0220545), published on Aug. 03, 2017, in view of Shukla et al., (“Shukla,” US 12,013,883), filed on May 23, 2023.
Regarding claim 1, Gururajan discloses a system for composing templates used for automatically generating communications (Figs. 2-5 and 7), the system comprising:
a computer memory storing, in association with a plurality of templates of a template type, a first layout information dataset comprising layout information for a plurality of template elements contained in the plurality of templates and a first document event dataset comprising user action events captured on documents electronically generated from the plurality of templates (pars. 0030-0035 and 0081; Figs. 2-5 and 8-10; document templates 120a-n; document templates may be saved/stored in storages/database);
a processor coupled to the computer memory (Figs. 2-5 and 7-10);
a non-transitory, computer-readable medium storing thereon a set of computer-executable instructions that are executable by the processor, the set of computer-executable instructions comprising instructions for (Figs. 2-5 and 7-10):
providing a graphical user interface for authoring a new template of the template type (pars. 0046-0050; Figs. 2-5 and 7; the application 106 generates a user interface (UI) 201 for generating document template 120);
receiving, based on a user interaction with the graphical user interface, a request to add a first template element of a first template element type to the new template (pars. 0034 and 0086; Figs. 2 and 7A; content or formatting properties of the template may be added, removed, or modified; the user 102 may add additional content to the document 108);
determining a [[recommended]] layout for the first template element based on a second document event dataset and a second layout information dataset, the second document event dataset comprising data, from the first document event dataset, for events captured on electronic documents generated from a first template from the plurality of templates, and the second layout information dataset comprising layout information for the first template from the first layout information dataset (Gururajan: pars. 0042-0043, 0060-0065 and 0075-0082; Figs. 7A-7B, step 706: ‘analyze and identify matching subsets of content and/or formatting elements,’ and step 708: ‘cluster documents with matching subsets of content and/or formatting elements’; the trend analyzer 612 analyzes the index 118, and identifies matching composition elements between documents 108. The trend analyzer 612 further determines meaningful subsets of composition elements (e.g., content elements, formatting elements) for building a document template 120 that is relevant and useful to the user 102; identify additional matching composition elements, such as page layout, alignment, spacing, margins, etc.)
Gururajan discloses determining a layout for the first template element based on a second document event dataset and a second layout information dataset as recited above, but does not explicitly disclose determining a recommended layout for the first template element.
However, Shukla discloses a template recommendation method/system including the steps of: determining a recommended layout for the first template element based on a second document event dataset and a second layout information dataset (Shukla: col. 21, lines 50-63; Figs. 6A and 7; determining and displaying a set of recommended replacement templates 116 having designs that are similar across a target feature 117-2; see also col. 13, lines 63-67 to col. 14, lines 1-10; Figs. 2-3; steps 320-330);
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Shukla with the system/method of Gururajan. One would have been motivated to provide users with a recommended template having similar designs across a target feature (Shukla: col. 21, lines 50-63).
The combination of Gururajan and Shukla further discloses:
presenting the recommended layout for the first template element in the graphical user interface as a first recommendation (Gururajan: pars. 0046 and 0081-0082; displaying template layout; Figs. 3-5 and 7; Shukla: col. 21, lines 50-63; Figs. 6A and 7; displaying a set of recommended replacement templates 116 having designs that are similar across a target feature 117-2);
adding the first template element to the new template formatted according to the recommended layout for the first template element (Gururajan: pars. 0034 and 0086; Fig. 7A; the user 102 may add additional content to the template 120; Shukla: col. 6, lines 56-64; the illustrator application interface 113 enables a user to edit template 114 by adding, deleting, modifying, or replacing one or more elements of the template 114; see also col. 11, lines 5-12); and
storing the new template in a database of templates used to electronically generate documents (Gururajan: pars. 0030-0035 and 0081; Figs. 2-5 and 8-10; document templates 120a-n; document templates may be saved/stored in storages/database; Shukla: col. 6, lines 45-47; Fig. 1; template 114 stored on data storage unit 112).
Regarding claim 2, Gururajan and Shukla disclose the system of Claim 1.
The combination of Gururajan and Shukla further discloses wherein determining the recommended layout for the first template element comprises:
determining candidate templates from the plurality of templates based on a template selection criterion, wherein the first template is a candidate template and wherein the second document event dataset includes data for events captured on electronic documents generated from each of the candidate templates (Gururajan: pars. 0042-0043, 0060-0065 and 0075-0082; Figs. 7A-7B, step 706: ‘analyze and identify matching subsets of content and/or formatting elements;’ the trend analyzer 612 further determines meaningful subsets of composition elements (e.g., content elements, formatting elements) for building a document template 120 that is relevant and useful to the user 102; identify additional matching composition elements, such as page layout, alignment, spacing, margins, etc.; Shukla: col. 13, lines 63-67 to col. 14, lines 1-10; Figs. 2-3; steps 320-330; method 300 involves determining layout features for the replacement template 116; to determine the layout features, the representation generation subsystem 123 applies neural network to the replacement template 116 that is trained to extract representations of a predefined dimensionality; see also col. 15, lines 49-62);
selecting the first template as layout source template from the candidate templates based on ranking the candidate templates using the second document event dataset (Gururajan: pars. 0071-0080; Figs. 7A-7B, step 740-744; the similarity matrix may be used to score the aligned sequences; a distance between documents may be based on the score of the aligned sequences; Shukla: col. 7, lines 37-43; Fig. 2; selecting the set of replacement templates 116 having highest similarity scores to the template 114 over the feature; col. 11, lines 64-67 to col 12, lines 1-2; the database of replacement templates 116 is ranked in a descending order of similarity scores computed using Equation (1) and a predefined number of previews 115 of replacement templates 116 having the greatest similarity scores are displayed to the user); and
selecting a layout of a respective template element of the first template element type in the first template as the recommended layout for the first template element in the new template (Gururajan: pars. 0071-0080; Figs. 7A-7B, step 740-744; the similarity matrix may be used to score the aligned sequences; a distance between documents may be based on the score of the aligned sequences; exact matches are scored high and inexact matches are penalized; Shukla: col. 7, lines 37-43; col. 11, lines 64-67 to col 12, lines 1-2; Fig. 2; selecting the set of replacement templates 116 having highest similarity scores to the template 114 over the feature).
The motivation is the same that of claim 1 above.
Regarding claim 3, Gururajan and Shukla disclose the system of Claim 1.
The combination of Gururajan and Shukla further discloses wherein determining the recommended layout for the first template element comprises:
determining candidate templates from the plurality of templates based on a template selection criterion, wherein the first template is a candidate template and wherein the second document event dataset includes data for events captured on electronic documents generated from each of the candidate templates (Gururajan: pars. 0042-0043 and 0075; Figs. 7A-7B; identify additional matching composition elements, such as page layout, alignment, spacing, margins, etc.; Shukla: col. 13, lines 63-67 to col. 14, lines 1-10; Figs. 2-3; steps 320-330; method 300 involves determining layout features for the replacement template 116; to determine the layout features, the representation generation subsystem 123 applies neural network to the replacement template 116 that is trained to extract representations of a predefined dimensionality (e.g., 512-dimensional layout representations) using outputs of a bidirectional encoder representations; see also col. 15, lines 49-62);
determining effective templates from the candidate templates based on the second document event dataset (Gururajan: pars. 0071-0080; Figs. 7A-7B, step 740-744; Shukla: col. 13, lines 63-67 to col. 14, lines 1-10; Figs. 2-3; steps 320-330; method 300 involves determining layout features for the replacement template 116; to determine the layout features, the representation generation subsystem 123 applies neural network to the replacement template 116 that is trained to extract representations of a predefined dimensionality (e.g., 512-dimensional layout representations) using outputs of a bidirectional encoder representations; see also col. 15, lines 49-62);
generating exemplar documents from the effective templates (Gururajan: pars. 0044, 0071-0080; Figs. 7A-7B, step 708-710; document template may be generated from each cluster of documents; beginning with the document with the lowest distance to its respective cluster centroid, a determination is made as to which sequences in the document will be kept, removed or modified in the document template; Shukla: col. 13, lines 63-67 to col. 14, lines 1-10; Figs. 2-3; steps 320-330);
determining a similarity between the exemplar documents (Gururajan: pars. 0044, 0052, 0062-0064 and 0070-0075; Figs. 7A-7B; the automated template generation system 110 groups of documents into two or more clusters based on similarities of the composition elements between them; the similarity matrix may be used to score the aligned sequences; Shukla: col. 7, lines 37-43; col. 11, lines 64-67 to col 12, lines 1-2; Fig. 2; selecting the set of replacement templates 116 having highest similarity scores to the template 114 over the feature);
based on a determination of the similarity between the exemplar documents, selecting as the recommended layout for the first template element, a layout of a template element of the first template element type contained in one of the effective templates (Gururajan: pars. 0044, 0052, 0062-0064 and 0070-0075; Figs. 7A-7B; the automated template generation system 110 groups of documents into two or more clusters based on similarities of the composition elements between them; the similarity matrix may be used to score the aligned sequences; Shukla: col. 7, lines 37-43; col. 11, lines 64-67 to col 12, lines 1-2; Fig. 2; selecting the set of replacement templates 116 having highest similarity scores to the template 114 over the feature).
The motivation is the same that of claim 1 above.
Regarding claim 4, Gururajan and Shukla disclose the system of claim 1.
The combination of Gururajan and Shukla further discloses wherein the set of computer-executable instructions comprises instructions for:
receiving a request to add a second template element of a second template element type to the new template (Gururajan: pars. 0034 and 0086; Figs. 2 and 7A; content or formatting properties of the template may be added, removed, or modified; the user 102 may add additional content to the document 108; Shukla: col. 21, lines 50-63; Figs. 6A and 7; determining and displaying a set of recommended replacement templates 116 having designs that are similar across a target feature 117-2; see also col. 13, lines 63-67 to col. 14, lines 1-10; Figs. 2-3; steps 320-330); and
responsive to the request to add the second template element to the new template, determine a revised recommended layout for the first template element and a recommended layout for the second template element based on a third document event dataset and a third layout information dataset, the third document event dataset comprising data, from the first document event dataset, for events captured on electronic documents generated from a first set of templates from the plurality of templates, and the third layout information dataset comprising layout information, from the first layout information dataset, for template elements of the first template element type and template elements of the second template element type, each template in the first set of templates containing a respective template element of the first template element type and a respective template element of the second template element type (Gururajan: pars. 0042-0043 and 0075; Figs. 7A-7B; identify additional matching composition elements, such as page layout, alignment, spacing, margins, etc.; Shukla: col. 13, lines 63-67 to col. 14, lines 1-10; Figs. 2-3; steps 320-330; method 300 involves determining layout features for the replacement template 116; to determine the layout features, the representation generation subsystem 123 applies neural network to the replacement template 116 that is trained to extract representations of a predefined dimensionality (e.g., 512-dimensional layout representations); see also col. 15, lines 49-62).
The motivation is the same that of claim 1 above.
Regarding claim 5, Gururajan and Shukla disclose the system of claim 1.
The combination of Gururajan and Shukla further discloses wherein the computer memory stores a machine learning model that represents the first layout information dataset, the first document event dataset, the second layout information dataset, the second document event dataset, wherein the request to add the first template element comprises editing event data, and wherein determining the recommended layout for the first template element comprises:
extracting editing event features from the editing event data, the editing event features comprising a template metadata feature and a template element metadata feature (Gururajan: par. 0064; transformed and weighted data is analyzed using the cosine-theta similarity metric, which is expected similarity of two vectors; Shukla: col. 8, lines 28-37; a representation comprises an embedding, a vector, or other representation that describes a feature (e.g., color, style, layout, text or object feature) based on elements in the replacement template 116);
generating a feature vector using the editing event features (Gururajan: par. 0064; Shukla: col. 8, lines 28-37; a representation comprises an embedding, a vector, or other representation that describes a feature (e.g., color, style, layout, text or object feature) based on elements in the replacement template 116); and
processing the feature vector with the machine learning model to output the recommended layout (Gururajan: pars. 0034 and 0064; Shukla: col. 10, lines 64-67 to col. 11, lines 1-10; he representation generation subsystem 123 applies respective machine learning models to the object, style, color, layout, and text features to determine the representation of the template 114).
The motivation is the same that of claim 1 above.
Regarding claim 6, Gururajan and Shukla disclose the system of claim 5.
The combination of Gururajan and Shukla further discloses wherein the layout information represented by the machine learning model includes a parameter indicating whether a user accepted a prior layout recommendation for the first template element (Gururajan: pars. 0065, 0085-0087; determining composition elements from a set of documents that may be of interested to a user, such as documents previously authored by the user; Shukla: col. 12, lines 22-30; Fig. 2; when a preview 115 is selected via the user interface 111, the replacement template 116 associated with the selected preview 115 replaces the template 114).
The motivation is the same that of claim 1 above.
Regarding claim 7, Gururajan and Shukla disclose the system of claim 1.
The combination of Gururajan and Shukla further discloses wherein the recommended layout comprises at least one of a recommended element location, a recommended element size, a recommended font, a recommended background, or a recommended color (Gururajan: pars. 0048 and 0056; content formatting and document-level formatting (e.g., alignment, spacing, margins, indentions, page numbering, headers, footers, columns, typeface, font size) in a plurality of documents 108; Shukla: col. 10, lines 58-64; Figs. 2, block 220; the representation generation subsystem 123 determines object features, style features, color features, layout features, and text features of the template 114).
The motivation is the same that of claim 1 above.
Regarding claim 8, claim 8 is directed to a computer program product corresponding to the system recited in claim 1. Claim 8 is similar in scope to claim 1, and is therefore rejected under similar rationale.
Regarding claim 9, claim 9 is directed to a computer program product corresponding to the system recited in claim 2. Claim 9 is similar in scope to claim 2, and is therefore rejected under similar rationale.
Regarding claim 10, claim 10 is directed to a computer program product corresponding to the system recited in claim 3. Claim 10 is similar in scope to claim 3, and is therefore rejected under similar rationale.
Regarding claim 11, claim 11 is directed to a computer program product corresponding to the system recited in claim 4. Claim 11 is similar in scope to claim 4, and is therefore rejected under similar rationale.
Regarding claim 12, claim 12 is directed to a computer program product corresponding to the system recited in claim 5. Claim 12 is similar in scope to claim 5, and is therefore rejected under similar rationale.
Regarding claim 13, claim 13 is directed to a computer program product corresponding to the system recited in claim 6. Claim 13 is similar in scope to claim 6, and is therefore rejected under similar rationale.
Regarding claim 14, claim 14 is directed to a computer program product corresponding to the system recited in claim 7. Claim 14 is similar in scope to claim 7, and is therefore rejected under similar rationale.
Regarding claim 15, claim 15 is directed to a method corresponding to the system recited in claim 1. Claim 15 is similar in scope to claim 1, and is therefore rejected under similar rationale.
Regarding claim 15, claim 15 is directed to a method corresponding to the system recited in claim 1. Claim 15 is similar in scope to claim 1, and is therefore rejected under similar rationale.
Regarding claim 16, claim 16 is directed to a method corresponding to the system recited in claim 2. Claim 16 is similar in scope to claim 2, and is therefore rejected under similar rationale.
Regarding claim 17, claim 17 is directed to a method corresponding to the system recited in claim 3. Claim 17 is similar in scope to claim 3, and is therefore rejected under similar rationale.
Regarding claim 18, claim 18 is directed to a method corresponding to the system recited in claim 4. Claim 18 is similar in scope to claim 4, and is therefore rejected under similar rationale.
Regarding claim 19, claim 19 is directed to a method corresponding to the system recited in claim 5. Claim 19 is similar in scope to claim 5, and is therefore rejected under similar rationale.
Regarding claim 20, claim 20 is directed to a method corresponding to the system recited in claim 6. Claim 20 is similar in scope to claim 6, and is therefore rejected under similar rationale.
Regarding claim 21, claim 21 is directed to a method corresponding to the system recited in claim 7. Claim 21 is similar in scope to claim 7, and is therefore rejected under similar rationale
.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968))
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH K PHAM whose telephone number is (571)270-3230. The examiner can normally be reached Monday-Thursday from 8:00 AM to 6:00 PM (EST).
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/LINH K PHAM/
Primary Examiner
Art Unit 2174