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 Amendment
Claims 1-20 were previously pending and subject to non-final action filed 07/15/2025. In the response filed on 10/15/2025, claim 1, 9 and 17 were amended. Therefore, claims 1-20 are currently pending and subject to the final action below.
Response to Arguments
Applicant's arguments filed 10/15/2025, with respect to 35 U.S.C. 103 of claims 1-20 have been fully considered but are moot because the arguments do not apply to the new combinations of references being used in the current rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 9 and 17 recite the limitation of “in response to the selection, presenting on the mobile version of the computing application a document-type chooser that offers at least a text-document type and a slide-document type;.”
The term “slide-up interface” is shown in Fig. 3, element 312 are discussed in the disclosure. The specification also discuss a slide presented in a slide show presentation. However, it is unclear if the slide-up interface or a slides in presentation is consider “a slide-document type”. Furthermore, the terms “a text-document type” and “a slide-document type” are not recited or explicitly defined according to the specification filed on 06/09/2022.
Dependent claims 2-8, 11-16, and 18-20 are rejected for fully incorporating the dependencies of their bases.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 7-9, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Jones (PGPUB: 20180225032 A1) in view of Liu (PGPUB: 20190213393 A1) in view of Kim (PGPUB: US 20180189338 A1).
Regarding independent claim 1, Jones teaches: A computer-implemented method comprising: (Jones − [0014] In addition, some implementations include one or more processors of one or more computing devices, where the one or more processors are operable to execute instructions stored in associated memory, and where the instructions are configured to cause performance of any of the aforementioned methods.)
presenting content of an electronic document on a mobile computing device within a mobile version of a computing application; (Jones − [0027] Now turning to FIG. 1, the computing device 120 is a portable mobile computing device such as a cellular phone, tablet computer, laptop computer, watch, head-mounted device (e.g., glasses), virtual or augmented reality device, other wearable device, an audio/video system, a navigation system, automotive and other vehicular system, etc. [0029] Computing device 120 is a client computing device and generates content for display to a user of the computing device 120 under various scenarios.)
classifying, using a first set of machine learning models, by the mobile computing device, (Jones − [0033-0036] [0034] Segmentation engine 123 segments screenshot images captured by screenshot capture engine 122, and/or other displayed content such as markup language documents or content captured in a viewfinder, into one or more semantic regions. [0035] the segmentation engine 123 includes, or is in communication with, a trained machine learning model (e.g., a convolutional neural network (“CNN”) model, a recurrent neural network (“RNN”) model) and the trained machine learning model may be utilized by the segmentation engine 123 to determine semantic regions and/or semantic types of the regions. CNN model and RNN model)
the content into a plurality of components; (Jones − [0033-0036] [0034] A semantic type of a semantic region classifies content that is included in that semantic region. Semantic types may include, for example, “image”, “text region”, “list items”, etc.—and/or more granular types such as “photographic image”, “image that is a painting”, etc.)
generating, at the mobile computing device, metadata based on the classifying the content into the plurality of components; (Jones − [0027] the computing device 120 is a portable mobile computing device [0034] the segmentation engine 123 analyzes a plurality of logical regions of a markup language document (e.g., images/text that fall within <div></div> tags) to determine one or more semantic regions of the displayed content and to assign a corresponding semantic type to each of the semantic regions. assign a corresponding semantic type to each of the semantic regions. A semantic type of a semantic region classifies content that is included in that semantic region. Semantic types may include, for example, “image”, “text region”, “list items”, etc.—and/or more granular types such as “photographic image”, “image that is a painting”, etc. The segmentation engine 123 is part of the mobile device., and the markup language and semantic type are metadata.)
transmitting the metadata to a server having a second set of machine learning models, (Jones − [0028] in some implementations all or aspects of the system 121 may be implemented by one or more computing devices that are remote from computing device 120.[0034] the segmentation engine 123 analyzes a plurality of logical regions of a markup language document (e.g., images/text that fall within <div></div> tags) to determine one or more semantic regions of the displayed content and to assign a corresponding semantic type to each of the semantic regions.) The segmentation engine 123 in a report server, the metadata being sent to the remote server.
after the classifying, highlighting the plurality of components within the mobile version of the computing application; (Jones − [0037] [0047] Render engine 125 manages the presentation of one or more so-called “interactive content items” generated based on determined semantic regions. In other implementations, the interactive content item(s) may be presented as visually-emphasized versions of the one or more semantic regions, such as visually-raised and/or floating graphical elements that resemble the one or more semantic regions. Visually-raised and/or floating the graphical element is the highlighting of the components.) Examiner Note: “after the classifying” under the broadest reason of interpretation is can be using a first set of machine learning models or a second set of machine learning models.
and adding, by the mobile computing device, the component to a component data store with a type of the component, (Jones − [0006] semantic region contains text, the interactive content may, for instance, depict all or a portion of the text (e.g., a title or heading included in the text), and may be actuable to save the text, copy the text to a pasteboard, [0067-0068] the segmentation engine 123 provides the one or more semantic regions, a region having a semantic type of “image”, the action determination engine 134 provides a computer device of a user to save (locally or remotely) content. [0076])
receiving a user input selecting a component of the second plurality of components; (Jones − [0057] FIG. 4C illustrates the example mobile computing device 420 of FIG. 4A and one example of interactive content item(s) that may be displayed by the mobile computing device 420. In some implementations of FIG. 4C a user of the computing device may have provided user interface input directed particularly to the region 463 (FIG. 4B) that encompasses the image of the flower, such as a “long tap” or “long click” of that region, an oral input directed to that region (e.g., speech input of “tell me more about the image on the screen”), or user actuation of a biosensor such as a fingerprint sensor (e.g., repeated actuation of the biosensor may toggle through the semantic regions 461A, 461B, 463, 465).)
updating the highlighting based on the [second] classifying; (Jones − [0081] Each time a new product enters the view finder, its corresponding pixels may be detected and used to generate a semantic region. An interactive content item may then be generated based on the semantic region and displayed to the user. a match, an interactive content item that matches a product on the user's shopping list may be further visually emphasized, e.g., highlighting))
surfacing, within the mobile version of the computing application, an intelligent-copy element that is selectable by a user; (Jones − [0007] [0037] Render engine 125 manages the presentation of one or more so-called “interactive content items” generated based on determined semantic regions. In various implementations, user interaction with a given interactive content item (e.g., tapping an icon) causes the computing device 120 to perform one or more actions that are tailored to the semantic region that corresponds to the given interactive content item. [0089] These interactive content items may include, for instance, graphical elements (e.g., icons, etc.).) The interactive content item is an intelligent-copy element.
receiving a user selection of the intelligent-copy element; (Jones − [0007] [0037] [0091] At block 612, the system may detect user interaction with an interactive content item. For example, a user may tap, long tap,)
Jones does not explicitly teach: server taking precedence over the results of the mobile computing device in the metadata; receiving, from the server, a second classifying from the second set of machine learning models of the presented content into a second plurality of components;
However, Liu teaches: transmitting the metadata to a server having a second set of machine learning models, (Liu − [0003] Fig. 1, Fig. 2, IOT hardware device, the initial results of the classifying, an initial user type of the user; and transmitting, by the processor to a server, the vector representing the facial feature attributes and data indicating the initial user type, [0016] Server 21 comprises a facial image database 21a and classification layers (software) 24a . . . 24n. [0021] FIG. 5 illustrates an algorithm detailing a process flow enabled by system 100 of FIG. 1 for improving facial recognition software technology associated with determining user identity . In step 512, the vector representing the facial feature attributes and data indicating the initial user type are transmitting to a server.)
server taking precedence over the results of the mobile computing device in the metadata; (Liu − [0003-0006] [0021] Fig, 1 Fig. 2, Fig. 5, In response, deep learning model software code is executed for inferring (via the server and with respect to the initial user type, the vector, and a plurality of images in a specified database associated with the initial user type) a final user type of the user. Additionally, an identity of the user is determined based on the inferring and the identity of the user is transmitted to the IOT device. In step 513, the identity of the user is received by the IOT device.) Examiner Note: The classifier from the cloud server (deep learning) is transmitted back to IOT device. Using the classifier from the cloud server is taking precedence
receiving, from the server, a second classifying from the second set of machine learning models of the presented content into a second plurality of components; (Liu − [0003-0006] [0021] Fig, 1 Fig. 2, Fig. 5, In response, deep learning model software code is executed for inferring (via the server and with respect to the initial user type, the vector, and a plurality of images in a specified database associated with the initial user type) a final user type of the user. Additionally, an identity of the user is determined based on the inferring and the identity of the user is transmitted to the IOT device. In step 513, the identity of the user is received by the IOT device.)
the type of the component based on an output of the second set of machine learning models. (Liu − [0003-0006] [0021] Fig, 1 Fig. 2, Fig. 5, In response, deep learning model software code is executed for inferring (via the server and with respect to the initial user type, the vector, and a plurality of images in a specified database associated with the initial user type) a final user type of the user. Additionally, an identity of the user is determined based on the inferring and the identity of the user is transmitted to the IOT device. In step 513, the identity of the user is received by the IOT device.)
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the teaching of Jones, and Liu as each inventions relates object classification of data within a document. Liu teaches deep learning algorithm residing on a server for improving classification of facial objects residing within a document. One of ordinary skill in the art would have been motivated to make the modification to improve automatic recognition of objects within a document. Therefore, providing the benefit of improving classifying and identifying content of interest to a user.
Jones does not explicitly teach: in response to the selection, presenting on the mobile version of the computing application a document-type chooser that offers at least a text-document type and a slide-document type;
However, Kim teaches; in response to the selection, presenting on the mobile version of the computing application a document-type chooser that offers at least a text-document type and a slide-document type; (Kim − [0044] Fig. 5, element 556, In response to a user issuing a copy command, a dropdown menu 556 appears with the following options: “PASTE INTO BLANK DOCUMENT,” “LOOK UP RELATED BIRDS,” “PASTE INTO ‘BIRD REPORT’,” and “PASTE INTO SLIDE PRESENTATION.” “PASTE INTO BLANK DOCUMENT,” is a text-document type, and “PASTE INTO SLIDE PRESENTATION” is a slide document type.)
receiving a user selection of one of the offered document types; (Kim − [0044] [0046] Fig. 5, element 556, “PASTE INTO SLIDE PRESENTATION” may be made available because the user currently has a slide presentation editor open, e.g., in the background.)
automatically generating, on the mobile computing device and without further user input, a new document of the selected document type that is pre-populated with the selected component; (Kim − [0044] Fig. 5, element 556, In response to a user issuing a copy command, a dropdown menu 556 appears with the following options: “PASTE INTO BLANK DOCUMENT,” “LOOK UP RELATED BIRDS,” “PASTE INTO ‘BIRD REPORT’,” and “PASTE INTO SLIDE PRESENTATION.” “PASTE INTO BLANK DOCUMENT,” is a text-document type, and “PASTE INTO SLIDE PRESENTATION” is a slide document type. [0057] At block 714, the system may receive user selection of a candidate action. For example the user may select one or more menu options of a pull down menu, as described above. At block 716, the system may perform the selected candidate action.) perform the selected candidate action. Paste into slide presentation the information from element 554 shown in Fig. 5.
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the teaching of Jones, Liu and Kim as each inventions relates object classification of data within a document. One of ordinary skill in the art would have been motivated for reducing the number of inputs required by a user to utilize copied/cut content to perform various operations within a user interface. Therefore providing a faster way of performing tasks on a mobile device.
Regarding dependent claim 7, depends on claim 1, Jones teaches: wherein classifying, using the first set of machine learning models, by the mobile computing device, the presented content into the plurality of components includes: transforming the presented content into an image file; and inputting the image file into the first set of machine learning models. (Jones − [0035] Segmentation engine 123 may utilize various techniques to determine semantic regions of displayed content and/or semantic types of the semantic regions. the segmentation engine 123 includes, or is in communication with, a trained machine learning model (e.g., a convolutional neural network (“CNN”) model, a recurrent neural network (“RNN”) model) and the trained machine learning model may be utilized by the segmentation engine 123 to determine semantic regions and/or semantic types of the regions. For example, the trained machine learning model may be trained, based on a plurality of labeled training examples (e.g., using backpropagation), to enable applying, as input to the model, a plurality of pixels of an image and to generate over the model, output that identifies semantic regions of an input image and semantic labels of those regions)
Regarding dependent claim 8, depends on claim 1, Jones teaches: further comprising: receiving a selection of one at least a text or an image from the updated highlighting; and generating a text document or a presentation slide document based on the selection of the text or the image. (Jones − [0081] Each time a new product enters the view finder, its corresponding pixels may be detected and used to generate a semantic region. An interactive content item may then be generated based on the semantic region and displayed to the user. a match, an interactive content item that matches a product on the user's shopping list may be further visually emphasized, e.g., highlighting))
Regarding independent claim 9, is directed to a system. Claim 9 have similar/same technical features/limitation as claim 1 and the claims are rejected under the same rationale.
Regarding dependent claim 15, depends on claim 9, Jones teaches: wherein classifying, using the first set of machine learning models, by the mobile computing device, the presented content into the plurality of components includes: transforming the presented content into an image file; and inputting the image file into the first set of machine learning models. (Jones − [0035] Segmentation engine 123 may utilize various techniques to determine semantic regions of displayed content and/or semantic types of the semantic regions. the segmentation engine 123 includes, or is in communication with, a trained machine learning model (e.g., a convolutional neural network (“CNN”) model, a recurrent neural network (“RNN”) model) and the trained machine learning model may be utilized by the segmentation engine 123 to determine semantic regions and/or semantic types of the regions. For example, the trained machine learning model may be trained, based on a plurality of labeled training examples (e.g., using backpropagation), to enable applying, as input to the model, a plurality of pixels of an image and to generate over the model, output that identifies semantic regions of an input image and semantic labels of those regions)
Regarding dependent claim 16, depends on claim 9, Jones teaches: receiving a selection of one at least a text or an image from the updated highlighting; and generating a text document or a presentation slide document based on the selection of the text or the image. (Jones − [0081] Each time a new product enters the view finder, its corresponding pixels may be detected and used to generate a semantic region. An interactive content item may then be generated based on the semantic region and displayed to the user. a match, an interactive content item that matches a product on the user's shopping list may be further visually emphasized, e.g., highlighting))
Regarding independent claim 17, is directed to a computer-readable medium, executed by at least one processor. Claim 17 have similar/same technical features/limitation as claim 1 and the claims are rejected under the same rationale.
Claim(s) 2-6, 10-14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jones Liu as applied to claims 1, 9 and 17 above, and further in view of Palm (PGPUB: 20200005065 A1).
Regarding dependent claim 2, depends on claim 1, Jones teaches: overlaying on the presented content, an intelligent copy element; receiving a selection of the intelligent copy element; (Jones − [0006] the interactive content may, for instance, depict all or a portion of the text (e.g., a title or heading included in the text), and may be actuable to save the text, copy the text to a pasteboard, share the text with others, search the text using a search engine, use the text to generate a list (e.g., a shopping list, a to do list), etc. [0059] The interactive content generation system 130 may utilize the indication of content to generate interactive content items such as graphical elements 474A, 474B, and 474C.)
Jones does not explicitly teach: receiving the selection, performing the highlighting
However, Palm teaches: and in response to receiving the selection, performing the highlighting. (Palm − [0021] The system can determine how to present the objects and/or text blocks of interest to the user based on the arrangement and/or other visual characteristics of the text or objects within the image. For example, based on user input specifying one or more objects or text of interest and user input specifying a presentation mode (e.g., highlight, illuminate, circle, etc.), the system determines whether the image data contains an object or text that the user was interested in. These and other characteristics described herein can provide insight into the context of the image and its text/objects contained therein.)
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the teaching of Jones, Liu and Palm as each inventions relates object classification within documents. Adding the teaching of Palm provides Jones a method for identifying, selecting and highlighting object within a document. Therefore, providing the benefit of improving classifying and identifying content of interest to a user.
Regarding dependent claim 3, depends on claim 2, Jones teaches: wherein the component is a first component and wherein the method further comprises: receiving a user input selecting a second component of the plurality of components; and in response to receiving the user input selecting the second component, updating the intelligent copy element to indicate two components were selected. (Jones – [0059] may generate the graphical element 474B so that selection of graphical element 474B causes the computing device 420 to perform one or more actions that cause a particular state of a “garden” application of applications 127 to be accessed. Selecting a second graphical element 474B after selection of 474A.)
Regarding dependent claim 4, depends on claim 3, Jones teaches: further comprising, in further response to receiving the user input selecting the second component: (Jones – [0059] may generate the graphical element 474B so that selection of graphical element 474B causes the computing device 420 to perform one or more actions that cause a particular state of a “garden” application of applications 127 to be accessed. Selecting a second graphical element 474B after selection of 474A.)
overlaying on the presented content a set of control elements with respect to the first component and the second component; (Jones – [0059] may Also, for example, the interactive content generation system 130 may generate the graphical element 474C so that selection of graphical element 474C causes the computing device 420 to retrieve and/or display one or more additional images of daisy flowers.)
receiving a selection of a document creation control element of the set of control elements; (Jones – [0059] that selection of graphical element 474C causes the computing device 420 to retrieve and/or display one or more additional images of daisy flowers.) and in response to receiving the selection of the document creation control element: presenting a set of document types. (Jones – [0060] The interactive content generation system 130 further generates non-interactive content 472 that provides an indication of the entity shown in the image (“Daisy”) and an indication of a class of that entity (“Flower”). In FIG. 4C, the content generated by interactive content generation system 130 is displayed in a graphical “card” 470 that overlays other content on the display screen 440.)
Regarding dependent claim 5, depends on claim 4, Jones teaches: further comprising: in response to the selection of a document type of the set of document types: generating a new document of the document type; and presenting representations of the first component and second component in the selection interface. (Jones – [0060] The interactive content generation system 130 further generates non-interactive content 472 that provides an indication of the entity shown in the image (“Daisy”) and an indication of a class of that entity (“Flower”). In FIG. 4C, the content generated by interactive content generation system 130 is displayed in a graphical “card” 470 that overlays other content on the display screen 440.)
Regarding dependent claim 6, depends on claim 3, Jones teaches: wherein the first component is a text element type and the second component is an image component type. (Jones – Fig. 4B 461A, 461B text elements, 463 image which is an image component type)
Regarding dependent claim 10, depends on claim 9, Jones teaches: wherein the storage device further comprises instructions, which when executed by the at least one processor, configure the at least one processor to perform operations comprising: overlaying on the presented content, an intelligent copy element; (Jones − [0006] the interactive content may, for instance, depict all or a portion of the text (e.g., a title or heading included in the text), and may be actuable to save the text, copy the text to a pasteboard, share the text with others, search the text using a search engine, use the text to generate a list (e.g., a shopping list, a to do list), etc. [0059] The interactive content generation system 130 may utilize the indication of content to generate interactive content items such as graphical elements 474A, 474B, and 474C.)
Jones does not explicitly teach: receiving the selection, performing the highlighting
However, Palm teaches: receiving a selection of the intelligent copy element; and in response to receiving the selection, performing the highlighting. (Palm − [0021] The system can determine how to present the objects and/or text blocks of interest to the user based on the arrangement and/or other visual characteristics of the text or objects within the image. For example, based on user input specifying one or more objects or text of interest and user input specifying a presentation mode (e.g., highlight, illuminate, circle, etc.), the system determines whether the image data contains an object or text that the user was interested in. These and other characteristics described herein can provide insight into the context of the image and its text/objects contained therein.)
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the teaching of Jones, Liu, Kim and Palm as each inventions relates object classification within documents. Adding the teaching of Palm provides Jones a method for identifying, selecting and highlighting object within a document. Therefore, providing the benefit of improving classifying and identifying content of interest to a user.
Regarding dependent claim 11, depends on claim 10, Jones teaches: wherein the component is a first component and wherein the storage device further comprises instructions, which when executed by the at least one processor, configure the at least one processor to perform operations comprising: receiving a user input selecting a second component of the plurality of components; and in response to receiving the user input selecting the second component, updating the intelligent copy element to indicate two components were selected. (Jones – [0059] may generate the graphical element 474B so that selection of graphical element 474B causes the computing device 420 to perform one or more actions that cause a particular state of a “garden” application of applications 127 to be accessed. Selecting a second graphical element 474B after selection of 474A.)
Regarding dependent claim 12, depends on claim 11, Jones teaches: wherein the storage device further comprises instructions, which when executed by the at least one processor, configure the at least one processor to perform operations comprising: in further response to receiving the user input selecting the second component: overlaying on the presented content a set of control elements with respect to the first component and the second component; receiving a selection of a document creation control element of the set of control elements; and in response to receiving the selection of the document creation control element: presenting a set of document types. (Jones – [0060] The interactive content generation system 130 further generates non-interactive content 472 that provides an indication of the entity shown in the image (“Daisy”) and an indication of a class of that entity (“Flower”). In FIG. 4C, the content generated by interactive content generation system 130 is displayed in a graphical “card” 470 that overlays other content on the display screen 440.)
Regarding dependent claim 13, depends on claim 12, Jones teaches: wherein the storage device further comprises instructions, which when executed by the at least one processor, configure the at least one processor to perform operations comprising: in response to the selection of a document type of the set of document types: generating a new document of the document type; and presenting representations of the first component and second component in the selection interface. (Jones – [0060] The interactive content generation system 130 further generates non-interactive content 472 that provides an indication of the entity shown in the image (“Daisy”) and an indication of a class of that entity (“Flower”). In FIG. 4C, the content generated by interactive content generation system 130 is displayed in a graphical “card” 470 that overlays other content on the display screen 440.)
Regarding dependent claim 14, depends on claim 11, Jones teaches: wherein the first component is a text element type and the second component is an image component type. (Jones – Fig. 4B 461A, 461B text elements, 463 image which is an image component type)
Regarding dependent claim 18, depends on claim 17, Jones teaches: wherein the instructions, which when executed by the at least one processor, further configure the at least one processor to perform operations comprising: overlaying on the presented content, an intelligent copy element; receiving a selection of the intelligent copy element; (Jones − [0006] the interactive content may, for instance, depict all or a portion of the text (e.g., a title or heading included in the text), and may be actuable to save the text, copy the text to a pasteboard, share the text with others, search the text using a search engine, use the text to generate a list (e.g., a shopping list, a to do list), etc. [0059] The interactive content generation system 130 may utilize the indication of content to generate interactive content items such as graphical elements 474A, 474B, and 474C.)
Jones does not explicitly teach: receiving the selection, performing the highlighting
However, Palm teaches: receiving a selection of the intelligent copy element; and in response to receiving the selection, performing the highlighting. (Palm − [0021] The system can determine how to present the objects and/or text blocks of interest to the user based on the arrangement and/or other visual characteristics of the text or objects within the image. For example, based on user input specifying one or more objects or text of interest and user input specifying a presentation mode (e.g., highlight, illuminate, circle, etc.), the system determines whether the image data contains an object or text that the user was interested in. These and other characteristics described herein can provide insight into the context of the image and its text/objects contained therein.)
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the teaching of Jones, Liu, Kim and Palm as each inventions relates object classification within documents. Adding the teaching of Palm provides Jones a method for identifying, selecting and highlighting object within a document. Therefore, providing the benefit of improving classifying and identifying content of interest to a user.
Regarding dependent claim 19, depends on claim 18, Jones teaches: wherein the component is a first component and wherein the instructions, which when executed by the at least one processor, further configure the at least one processor to perform operations comprising: receiving a user input selecting a second component of the plurality of components; and in response to receiving the user input selecting the second component, updating the intelligent copy element to indicate two components were selected. (Jones – [0059] may generate the graphical element 474B so that selection of graphical element 474B causes the computing device 420 to perform one or more actions that cause a particular state of a “garden” application of applications 127 to be accessed. Selecting a second graphical element 474B after selection of 474A.)
Regarding dependent claim 20, depends on claim 19, Jones teaches: wherein the instructions, which when executed by the at least one processor, further configure the at least one processor to perform operations comprising: in further response to receiving the user input selecting the second component: overlaying on the presented content a set of control elements with respect to the first component and the second component; receiving a selection of a document creation control element of the set of control elements; and in response to receiving the selection of the document creation control element: presenting a set of document types. (Jones – [0060] The interactive content generation system 130 further generates non-interactive content 472 that provides an indication of the entity shown in the image (“Daisy”) and an indication of a class of that entity (“Flower”). In FIG. 4C, the content generated by interactive content generation system 130 is displayed in a graphical “card” 470 that overlays other content on the display screen 440.)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARL E BARNES JR whose telephone number is (571)270-3395. The examiner can normally be reached Monday-Friday 9am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached at (571) 272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CARL E BARNES JR/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178