DETAILED ACTION
This action is in response to the original filing on 04/03/2024. Claims 1-20 are pending and have been considered below.
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 .
Claim Objections
Claims 7-9, 16, 17, 19, and 20 are objected to because of the following informalities:
Claims 7-9, 16, and 17 recite ‘the input’; however, they should recite - - an input - -.
Claims 19 and 20 recite ‘The computer-readable medium’; however, they should recite - - The non-transitory computer-readable medium - -.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, claim 1 recites “a user being input or captured by a user interface”. It is unclear what is meant by this limitation. It is unclear whether the “being input or captures” relates to the user or the content. It is unclear whether the user interface is intended to refer only to the capturing. For the purposes of examination, this limitation is interpreted as: one or more items of content associated with a user, wherein the one or more items of content associated with the user are being input or the one or more items of content are being captured by a user interface
Claim 1 further recites “training data pre-trained, or trained in real-time, on one or more content items”. It is unclear how training data is pre-trained. It is unclear whether “pre-trained, or trained in real-time” applies to the training data or the machine learning model. For the purposes of examination, this limitation is interpreted as: a machine learning model comprising training data, wherein the machine learning model is pre-trained, or trained in real-time, on one or more content items comprising one or more content formats
Claim 1 further recites “determining at least one suggested content format applied to the one or more items of content in response to determining that at least a subset of the one or more items of content are similar to corresponding content items of a same or similar type associated with, or within, the training data”. It is unclear whether “in response to determining that at least a subset” is intended to modify “determining at least one suggested content format” or “applied to the one or more content items. It is further unclear “with, or within, the training data” is intended to modify corresponding content items or similar type. For the purposes of examination, this limitation is interpreted as: in response to determining that at least a subset of the one or more items of content are similar to corresponding content items associated with, or within, the training data, determining at least one suggested content format applied to the one or more items of content, wherein the corresponding content items are of a same or similar type
Regarding claims 10 and 18, claims 10 and 18 contain substantially similar limitations to those found in claim 1. Consequently, claims 10 and 18 are rejected for the same reasons.
Regarding claims 2-9, 11-17, 19, and 20, claims 2-9, 11-17, 19, and 20 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for depending on an indefinite parent claim. Regarding claims 5, 6, 14, and 15, these claims also recite “one or more items of content associated with the user being input or captured”. These limitations are likewise rejected and interpreted.
Regarding claim 2, claim 2 recites “determining, by the implementing the machine learning model, at least one alternate suggested content format applied to the one or more items of content in response to determining an expiration”. It is unclear whether “in response to determining an expiration” is intended to modify the “determining” or “applied to the one or more items of content”. For the purposes of examination, this limitation is interpreted as: in response to determining an expiration, determining, by the implementing the machine learning model, at least one alternate suggested content format applied to the one or more items of content
Regarding claims 11 and 19, claims 11 and 19 contain substantially similar limitations to those found in claim 2. Consequently, claims 11 and 19 are rejected for the same reasons.
Regarding claim 3, claim 3 recites “determining, by the implementing the machine learning model, at least one alternate suggested content format applied to the one or more items of content in response to detecting”. It is unclear whether “in response to detecting” is intended to modify the “determining” or “applied to the one or more items of content”. For the purposes of examination, this limitation is interpreted as: in response to detecting an indication of a selection of an option associated with generating the alternate suggested content format, determining, by the implementing the machine learning model, at least one alternate suggested content format applied to the one or more items of content
Regarding claims 12 and 20, claims 12 and 20 contain substantially similar limitations to those found in claim 3. Consequently, claims 12 and 20 are rejected for the same reasons.
Regarding claim 4, claim 4 recites “presenting, by the user interface or the display device, the alternate suggested content format applied to the one or more items of content in response to the determining”. It is unclear whether “in response to determining” is intended to modify the “presenting” or “applied to the one or more items of content”. For the purposes of examination, this limitation is interpreted as: in response to the determining, presenting, by the user interface or the display device, the alternate suggested content format applied to the one or more items of content
Regarding claim 13, claim 13 contains substantially similar limitations to those found in claim 4. Consequently, claim 13 is rejected for the same reasons.
Claim 4 further recites “the determining”. It is unclear which previously recited determining steps this limitation is intended to refer. For the purposes of examination, this limitation is interpreted as: the determining at least one alternate suggested content format
Regarding claim 5, claim 5 recites “in relation one or more items of content of the at least one suggested content”. It is unclear what is meant by this limitation. For the purposes of examination, this limitation is interpreted as: in relation to one or more items of content of the at least one suggested content
Claim 5 further recites “the one or items of content of the alternate suggested content format”. It is unclear what is meant by this limitation. For the purposes of examination, this limitation is interpreted as: the one or more items of content of the alternate suggested content format
Regarding claim 14, claim 14 contains substantially similar limitations to those found in claim 5. Consequently, claim 14 is rejected for the same reasons.
Regarding claim 9, claim 9 recites “performing the presenting of the at least one suggested content format applied to the one or more items of content in response to, or after, determination”. It is unclear whether “in response to, or after, determination” is intended to modify the “performing”, “the presenting”, or “applied to the one or more items of content”. For the purposes of examination, this limitation is interpreted as: in response to, or after, determination of completion of the input received or captured by the user interface, performing the presenting of the at least one suggested content format applied to the one or more items of content
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10, and 18
Step 1: Claims 1, 10, and 18 recite a method, an apparatus, and a medium; therefore, they are directed to the statutory categories of a method, a machine, and a manufacture.
Step 2A Prong 1: The claims recite, inter alia:
analyzing one or more items of content associated with a user; automatically determining at least one suggested content format applied to the one or more items of content in response to determining that at least a subset of the one or more items of content are similar to corresponding content items of a same or similar type associated with, or within, the training data; Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of analyzing content and suggesting a format based on similarity, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of “A method comprising”, “An apparatus comprising: one or more processors; and at least one memory storing instructions, that when executed by the one or more processors, cause the apparatus to”, “A non-transitory computer-readable medium storing instructions that, when executed, cause”, and “implementing a machine learning model comprising training data pre-trained, or trained in real-time” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). The claimed computer components are recited at a high level of generality and are merely invoked as tool to perform the abstract idea. The additional elements of “being input or captured by a user interface“, “on one or more content items comprising one or more content formats”, and “presenting, by a user interface or a display device, the at least one suggested content format applied to the one or more items of content” amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)). Even when viewed in combination, these additional element do not integrate the abstract idea into a practical application and the claims are thus directed to the abstract idea.
Step 2B: The claims do not contain significantly more than the judicial exception. “A method comprising”, “An apparatus comprising: one or more processors; and at least one memory storing instructions, that when executed by the one or more processors, cause the apparatus to”, “A non-transitory computer-readable medium storing instructions that, when executed, cause”, and “implementing a machine learning model comprising training data pre-trained, or trained in real-time” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements of “being input or captured by a user interface“, “on one or more content items comprising one or more content formats”, and “presenting, by a user interface or a display device, the at least one suggested content format applied to the one or more items of content” amounts to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”). Nothing in the claims provides significantly more than that abstract idea. As such, the claims are ineligible.
Claims 2-9, 11-17, 19, and 20
Step 1: Claims 2-9, 11-17, 19, and 20 recite a method, an apparatus, and a medium; therefore, they are directed to the statutory categories of a method, a machine, and a manufacture.
Step 2: claims 2-9, 11-17, 19, and 20 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claims 1, 10, and 18, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Claims 2, 11, and 19 further recite the additional element of “automatically determining at least one alternate suggested content format applied to the one or more items of content in response to determining an expiration of a predetermined time period”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining an alternate suggestion after a time, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. The additional elements of “by the implementing the machine learning model” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Claims 3, 12, and 20 further recite the additional element of “determining at least one alternate suggested content format applied to the one or more items of content in response to detecting an indication”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining an alternate suggestion in response to an indication, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. The additional elements of “by the implementing the machine learning model” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). The additional element of “an indication of a selection of an option associated with generating the alternate suggested content format” amounts to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)).
Claims 4 and 13 further recite the additional elements of “automatically presenting, by the user interface or the display device, the alternate suggested content format applied to the one or more items of content in response to the determining”. These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”).
Claims 5 and 14 further recite the additional element of “one or more items of content of the alternate suggested content format are in a different content format in relation one or more items of content of the at least one suggested content; and the one or items of content of the alternate suggested content format and the one more items of content of the at least one suggested content format are applied to the one or more items of content”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining an alternate suggestion, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. The additional elements of “the user being input or captured” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Claims 6 and 15 further recite the additional elements of “wherein the one or more items of content associated with the user being input or captured comprises text comprising alphabetic characters and/or numeric characters”. These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”).
Claims 7 and 16 further recite the additional elements of “the user interface is associated with at least one composer entity or editor entity configured to receive or capture the input”. These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”). The additional elements of “the composer entity or the editor entity is associated with at least one application” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Claims 8 and 17 further recite the additional elements of “performing the presenting of the at least one suggested content format applied to the one or more items of content in real-time while the input is being received or captured by the user interface”. These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”).
Claim 9 further recites the additional elements of “performing the presenting of the at least one suggested content format applied to the one or more items of content in response to, or after, determination of completion of the input received or captured by the user interface”. These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-10, 12-18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gil et al. (US 20230351091 A1, published 11/02/2023), hereinafter Gil.
Regarding claim 10, Gil teaches the claim comprising:
An apparatus comprising: one or more processors; and at least one memory storing instructions, that when executed by the one or more processors, cause the apparatus to (Gil Figs. 1-6; [0005], a data processing system having a processor and a memory in communication with the processor wherein the memory stores executable instructions that, when executed by the processor, cause the data processing system to perform multiple functions. The functions may include examining a document to identify an enhancement for a portion of content in the document, enabling display of a first user interface element for previewing the identified enhancement as applied to the content, receiving a request to select the identified enhancement, and upon receiving the request, enabling display of a second user interface element for accepting the identified enhancement):
analyze one or more items of content associated with a user being input or captured by a user interface (Gil Figs. 1-6; [0025], The server 110 may include and/or execute an enhancement service 116 for providing intelligent identification of potential enhancements to contents of a document; [0027], The server 110 may also include or be connected to one or more online applications 114 that provide access to or enable creation and/or editing of one or more documents; [0028], The application 126 may process the electronic document 128, in response to user input through an input device, to create and/or modify the content of the electronic document 128; [0031], FIG. 2A-2E are example GUI screens for enabling a user to request identification of suggested enhancements in a document, displaying the suggested enhancements and allowing the user to interactively choose which enhancements to apply to the document; [0032], As the user creates or edits the content of the content pane 220, a UI element may be provided for transmitting a request to receive suggestions for enhancing the content; a notification may be automatically displayed either within the application, or on a ribbon or a notifications' pane when suggested enhancements are available or when it is a good point at which suggested enhancements may be requested; the user may be able to choose a setting in the application that indicates when to run an automatic identification of suggested enhancements; [0033], the application may examine and provide suggested enhancements for the entire document. Alternatively, a user may be able to select a portion of the content and transmit a request for identifying suggested enhancements within the selected portion; [0034], a user may submit a request for and receive enhancement suggestions for the document at its current state during the authoring process. This may result in one or more suggestions that may be selected by the user. Later after adding more content and/or making other changes to the document, the user may submit a second request for suggestions which may result in different enhancement suggestions; [0035], the enhancement service or engine may examine the content, identify possible suggested enhancements and provide them for displaying in an enhancements pane 240, such as the one displayed in FIG. 2B;);
implement a machine learning model comprising training data pre-trained, or trained in real-time, on one or more content items comprising one or more content formats (Gil Figs. 1-6; [0021], As a general matter, the methods and systems described herein may include, or otherwise make use of, a machine-trained model to identify contents related to text, images, or other types of data; [0022], a training system may be used that includes an initial ML model (which may be referred to as an “ML model trainer”) configured to generate a subsequent trained ML model from training data obtained from a training data repository or from device-generated data; [0023], The training data may be continually updated, and one or more of the models used by the system can be revised or regenerated to reflect the updates to the training data; [0024], The data store 112 may function as a repository in which documents and/or data sets relating to training models for providing intelligent identification of potential enhancements to content may be stored; [0026], Each of the models used as part of the enhancement service may be trained by a training mechanism 118. The training mechanism 118 may use training datasets stored in the data store 112 to provide initial and ongoing training for each of the model(s); [0054], the application providing the suggested enhancements may collect information from the document and/or the user as the user interacts with the suggested enhancements to better train the ML models used in providing suggested enhancements. For example, the application may collect information relating to which one of the suggested enhancements was approved by the user. To ensure that context is taken into account when using the information, the sentence structure, style, and subject of the document may also be collected. Additionally, other information about the document and/or the user may be collected. For example, information about the type of document (e.g., word document, email, presentation document, etc.), the topic of the document);
automatically determine at least one suggested content format applied to the one or more items of content in response to determining that at least a subset of the one or more items of content are similar to corresponding content items of a same or similar type associated with, or within, the training data (Gil Figs. 1-6; [0057], Once a request to identify enhancements has been received, method 400 may proceed to examine the document, at 410, to identify potential enhancements for suggestion, at 415; [0058], a first level search may be conducted by examining a user specific database to determine if a relevant map, image or any other media item may be located for the user (e.g., by searching a user specific storage medium or searching enterprise and/or global databases). This may use an ML model and may be achieved by collecting and storing user history data from documents prepared by the user, analyzing the data to identify patterns in use, determining if the patterns indicate a desire by the user to insert a relevant item in the document for the selected segment; [0059], identifying enhancements may include examining the content of the document to identify potential areas for improvement; [0060], different types of documents may require different styles. Thus, by identifying and examining keywords, the enhancement service may determine the topic of the document, which in turn, may help determine the best style for the document; [0061], machine learning algorithms may be used to examine activity history of the user within the document or within the user's use of the application to identify patterns in the user's usage; [0062], in consulting the global database, the method identifies and uses data for users that are in a similar category as the current user. For example, the method may use history data from users with similar activities, similar work functions and/or similar work products; [0063], identifying relevant suggested enhancements may be achieved by utilizing two or more different types of trained ML models. One type could be a personal model which is trained based on each user's personal information and another could be a global model that is trained based on examination of a global set of other users' information. A hybrid model may be used to examine users similar to the current user and to generate results based on activities of other users having similar characteristics (same organization, having same or similar job titles, creating similar types of documents, and the like) as the current user. For example, it may examine users that create similar artifacts as the current user or create documents having similar topics. Another type of model that could be developed may be a domain and/or organization specific model. The ML models for such a model may be trained specifically based on documents the organization creates or documents focused on a specific domain. Any of the models may collect and store what is suggested and record how the user interacts with the suggestions (e.g., which suggestions they approve); [0065], After a number of suggested enhancements have been identified for the document, a relevance/enhancement score may be calculated for each identified enhancement to determine which one(s) to present to the user);
and present, by a user interface or a display device, the at least one suggested content format applied to the one or more items of content (Gil Figs. 1-6; [0036], presenting the entire post enhanced content on one page may enable the user to compare each portion of the content against the enhancement suggested for that portion on the same screen, while at the same time being able to view the overall change to the content. In this manner, the user can review the suggested enhancements within the context of both the original document and the entire enhanced document to determine if the suggestion is appropriate. For example, the user may be able to determine whether the suggestion fits the style of the original previous or next paragraphs, and/or if the suggestion fits the style of the enhanced previous or next paragraphs; [0038], the user can interact with the enhancements pane 240 to determine how the suggestions affect the document. For example, as illustrated in the GUI screen 200C of FIG. 2C, selecting a portion 245 of content of the enhancements pane 240 may result in displaying a UI element 250. The UI element 250 may be a pop-up menu option that provides additional information about the types of enhancements provided in the selected portion 245 of the enhancements pane 240; the UI element 250 may list the types of suggested enhancements made to the selected portion 245. This may include broadly stating that the enhancement is related to text or to visual aspects of the content (e.g., enactments are available for style, font, images, visual emphases and the like); [0041], Referring back to the UI element 250, in addition to providing information about the type of enhancements made to the selected area, the UI element 250 may also display a menu option 255 for previewing the suggested enhancements within the content pane 220; [0042], Once the user previews the changes in the preview screen 200D or if they choose to accept them without first previewing them within the content pane 220, the user can choose the menu option 260 of the UI element 250 to accept the suggested enhancements; [0044], FIGS. 3A-3I depict additional example GUI screens for presenting suggested enhancements and enabling the user to interact with those suggested enhancements; [0045], once the suggested enhancement is selected in the enhancements pane, the UI element 335 may enable the user to cycle through the possible suggested enhancements by utilizing the backward scrolling icon 315 and forward scrolling icon 325. In one implementation, determining which types of enhancements to suggest may be made in accordance with to a scoring mechanism whereby a score is calculated based on a plurality of parameters for each identified enhancement)
Regarding claims 1 and 18, claims 1 and 18 contain substantially similar limitations to those found in claim 10. Consequently, claims 1 and 18 are rejected for the same reasons.
Regarding claim 3, Gil teaches all the limitations of claim 1, further comprising:
determining, by the implementing the machine learning model, at least one alternate suggested content format applied to the one or more items of content in response to detecting an indication of a selection of an option associated with generating the alternate suggested content format (Gil Figs. 1-6; [0034], a user may submit a request for and receive enhancement suggestions for the document at its current state during the authoring process. This may result in one or more suggestions that may be selected by the user. Later after adding more content and/or making other changes to the document, the user may submit a second request for suggestions which may result in different enhancement suggestions; [0036], the user can review the suggested enhancements within the context of both the original document and the entire enhanced document to determine if the suggestion is appropriate; [0038], the UI element 250 may list the types of suggested enhancements made to the selected portion 245; [0041-0042], Once the user previews the changes in the preview screen 200D or if they choose to accept them without first previewing them within the content pane 220, the user can choose the menu option 260 of the UI element 250 to accept the suggested enhancements; [0044], FIGS. 3A-3I depict additional example GUI screens for presenting suggested enhancements and enabling the user to interact with those suggested enhancements; [0045], once the suggested enhancement is selected in the enhancements pane, the UI element 335 may enable the user to cycle through the possible suggested enhancements by utilizing the backward scrolling icon 315 and forward scrolling icon 325. In one implementation, determining which types of enhancements to suggest may be made in accordance with to a scoring mechanism whereby a score is calculated based on a plurality of parameters for each identified enhancement; the scoring process is achieved via a ML model; [0046], Once the highest score suggestions have been identified, the enhancement having the highest score may displayed within the UI element 335 when the UI element is first displayed. This would allow the user to view the highest-ranking suggested enhancement first. Lower scored suggested enhancements may then be accessed by utilizing the backward and forward scrolling icons 315 and 325. In an example, as the user moves through the suggested enhancements in the UI element 335, the selected portion 340 changes to reflect the suggested enhancement displayed within the UI element 335; [0047], FIG. 3C depicts an example GUI screen 300C for displaying the content pane 320 after the visualization enhancement has been applied to the content)
Regarding claims 12 and 20, claims 12 and 20 contain substantially similar limitations to those found in claim 3. Consequently, claims 12 and 20 are rejected for the same reasons.
Regarding claim 4, Gil teaches all the limitations of claim 3, further comprising:
automatically presenting, by the user interface or the display device, the alternate suggested content format applied to the one or more items of content in response to the determining (Gil Figs. 1-6; [0034], a user may submit a request for and receive enhancement suggestions for the document at its current state during the authoring process. This may result in one or more suggestions that may be selected by the user. Later after adding more content and/or making other changes to the document, the user may submit a second request for suggestions which may result in different enhancement suggestions; [0036], the user can review the suggested enhancements within the context of both the original document and the entire enhanced document to determine if the suggestion is appropriate; [0038], the UI element 250 may list the types of suggested enhancements made to the selected portion 245; [0041-0042], Once the user previews the changes in the preview screen 200D or if they choose to accept them without first previewing them within the content pane 220, the user can choose the menu option 260 of the UI element 250 to accept the suggested enhancements; [0044], FIGS. 3A-3I depict additional example GUI screens for presenting suggested enhancements and enabling the user to interact with those suggested enhancements; [0045], once the suggested enhancement is selected in the enhancements pane, the UI element 335 may enable the user to cycle through the possible suggested enhancements by utilizing the backward scrolling icon 315 and forward scrolling icon 325. In one implementation, determining which types of enhancements to suggest may be made in accordance with to a scoring mechanism whereby a score is calculated based on a plurality of parameters for each identified enhancement; the scoring process is achieved via a ML model; [0046], Once the highest score suggestions have been identified, the enhancement having the highest score may displayed within the UI element 335 when the UI element is first displayed. This would allow the user to view the highest-ranking suggested enhancement first. Lower scored suggested enhancements may then be accessed by utilizing the backward and forward scrolling icons 315 and 325. In an example, as the user moves through the suggested enhancements in the UI element 335, the selected portion 340 changes to reflect the suggested enhancement displayed within the UI element 335; [0047], FIG. 3C depicts an example GUI screen 300C for displaying the content pane 320 after the visualization enhancement has been applied to the content)
Regarding claim 13, claim 13 contains substantially similar limitations to those found in claim 4. Consequently, claim 13 is rejected for the same reasons.
Regarding claim 5, Gil teaches all the limitations of claim 3, further comprising:
one or more items of content of the alternate suggested content format are in a different content format in relation one or more items of content of the at least one suggested content; and the one or items of content of the alternate suggested content format and the one more items of content of the at least one suggested content format are applied to the one or more items of content associated with the user being input or captured (Gil Figs. 1-6; [0032], As the user creates or edits the content of the content pane 220, a UI element may be provided for transmitting a request to receive suggestions for enhancing the content; [0034], a user may submit a request for and receive enhancement suggestions for the document at its current state during the authoring process. This may result in one or more suggestions that may be selected by the user. Later after adding more content and/or making other changes to the document, the user may submit a second request for suggestions which may result in different enhancement suggestions; [0036], the user can review the suggested enhancements within the context of both the original document and the entire enhanced document to determine if the suggestion is appropriate; [0038], the UI element 250 may list the types of suggested enhancements made to the selected portion 245; [0041-0042], Once the user previews the changes in the preview screen 200D or if they choose to accept them without first previewing them within the content pane 220, the user can choose the menu option 260 of the UI element 250 to accept the suggested enhancements; [0044], FIGS. 3A-3I depict additional example GUI screens for presenting suggested enhancements and enabling the user to interact with those suggested enhancements; [0045], once the suggested enhancement is selected in the enhancements pane, the UI element 335 may enable the user to cycle through the possible suggested enhancements by utilizing the backward scrolling icon 315 and forward scrolling icon 325. In one implementation, determining which types of enhancements to suggest may be made in accordance with to a scoring mechanism whereby a score is calculated based on a plurality of parameters for each identified enhancement; the scoring process is achieved via a ML model; [0046], Once the highest score suggestions have been identified, the enhancement having the highest score may displayed within the UI element 335 when the UI element is first displayed. This would allow the user to view the highest-ranking suggested enhancement first. Lower scored suggested enhancements may then be accessed by utilizing the backward and forward scrolling icons 315 and 325. In an example, as the user moves through the suggested enhancements in the UI element 335, the selected portion 340 changes to reflect the suggested enhancement displayed within the UI element 335; [0047], FIG. 3C depicts an example GUI screen 300C for displaying the content pane 320 after the visualization enhancement has been applied to the content)
Regarding claim 14, claim 14 contains substantially similar limitations to those found in claim 5. Consequently, claim 14 is rejected for the same reasons.
Regarding claim 6, Gil teaches all the limitations of claim 1, further comprising:
wherein the one or more items of content associated with the user being input or captured comprises text comprising alphabetic characters and/or numeric characters (Gil Figs. 1-6; [0028], The electronic document 128 can contain any type of data, such as text (e.g., alphabets, numbers, symbols), emoticons, gifs, still images, video and audio; [0031], FIG. 2A-2E are example GUI screens for enabling a user to request identification of suggested enhancements in a document, displaying the suggested enhancements and allowing the user to interactively choose which enhancements to apply to the document; The content may be displayed to the user for viewing and/or editing purposes and may be created by the user. For example, the user may utilize an input device (e.g., a keyboard) to insert input such as text or an image into the content pane 220; [0032], As the user creates or edits the content of the content pane 220, a UI element may be provided for transmitting a request to receive suggestions for enhancing the content; a notification may be automatically displayed either within the application, or on a ribbon or a notifications' pane when suggested enhancements are available or when it is a good point at which suggested enhancements may be requested; the user may be able to choose a setting in the application that indicates when to run an automatic identification of suggested enhancements; [0035], the enhancement service or engine may examine the content, identify possible suggested enhancements and provide them for displaying in an enhancements pane 240, such as the one displayed in FIG. 2B; [0045], FIG. 3B depicts an alternative example GUI screen 300B in which selecting a suggested enhancement that provides visualization emphasis results in displaying a UI element 335. The UI element 335 may be displayed in instances where there is more than one type of suggested enhancement for a selected portion. For example, the enhancement service may determine that there are multiple manners in which visualization emphasis for the phrase “1.2 million people in 2001 to 3.2 people in 2018” may be made and at least two of those are likely to fit well with the content of the document; see also [0025-0028], [0033-0034])
Regarding claim 15, claim 15 contains substantially similar limitations to those found in claim 6. Consequently, claim 15 is rejected for the same reasons.
Regarding claim 7, Gil teaches all the limitations of claim 1, further comprising:
the user interface is associated with at least one composer entity or editor entity configured to receive or capture the input; and the composer entity or the editor entity is associated with at least one application (Gil Figs. 1-6; [0025-0027], The server 110 may also include or be connected to one or more online applications 114 that provide access to or enable creation and/or editing of one or more documents; [0028], The application 126 may process the electronic document 128, in response to user input through an input device, to create and/or modify the content of the electronic document 128; Examples of suitable applications include, but are not limited to, a word processing application, a presentation application, a note taking application, a text editing application, an email application, a spreadsheet application, a desktop publishing application, and a communications application; [0031], FIG. 2A-2E are example GUI screens for enabling a user to request identification of suggested enhancements in a document, displaying the suggested enhancements and allowing the user to interactively choose which enhancements to apply to the document; [0032], As the user creates or edits the content of the content pane 220, a UI element may be provided for transmitting a request to receive suggestions for enhancing the content; [0033], the application may examine and provide suggested enhancements for the entire document. Alternatively, a user may be able to select a portion of the content and transmit a request for identifying suggested enhancements within the selected portion; [0044], FIGS. 3A-3I depict additional example GUI screens for presenting suggested enhancements; see also [0033-0034])
Regarding claim 16, claim 16 contains substantially similar limitations to those found in claim 7. Consequently, claim 16 is rejected for the same reasons.
Regarding claim 8, Gil teaches all the limitations of claim 1, further comprising:
performing the presenting of the at least one suggested content format applied to the one or more items of content in real-time while the input is being received or captured by the user interface (Gil Figs. 1-6; [0028], The application 126 may process the electronic document 128, in response to user input through an input device, to create and/or modify the content of the electronic document 128; [0031], FIG. 2A-2E are example GUI screens for enabling a user to request identification of suggested enhancements in a document, displaying the suggested enhancements and allowing the user to interactively choose which enhancements to apply to the document; [0032], As the user creates or edits the content of the content pane 220, a UI element may be provided for transmitting a request to receive suggestions for enhancing the content; a notification may be automatically displayed either within the application, or on a ribbon or a notifications' pane when suggested enhancements; the user may be able to choose a setting in the application that indicates when to run an automatic identification of suggested enhancements; [0033], the application may examine and provide suggested enhancements for the entire document. Alternatively, a user may be able to select a portion of the content and transmit a request for identifying suggested enhancements within the selected portion; [0034], It should be noted that requesting and receiving suggested enhancements may occur more than once during the document creation and editing process. For example, a user may submit a request for and receive enhancement suggestions for the document at its current state during the authoring process. This may result in one or more suggestions that may be selected by the user. Later after adding more content and/or making other changes to the document, the user may submit a second request for suggestions which may result in different enhancement suggestions. Depending on the changes made to the document, the second set of enhancement suggestions may be complementary to the first set or they may be contradictory to them; [0044], FIGS. 3A-3I depict additional example GUI screens for presenting suggested enhancements; [0061], during the current session; see also [0025-0027])
Regarding claim 17, claim 17 contains substantially similar limitations to those found in claim 8. Consequently, claim 17 is rejected for the same reasons.
Regarding claim 9, Gil teaches all the limitations of claim 1, further comprising:
performing the presenting of the at least one suggested content format applied to the one or more items of content in response to, or after, determination of completion of the input received or captured by the user interface (Gil Figs. 1-6; [0028], The application 126 may process the electronic document 128, in response to user input through an input device, to create and/or modify the content of the electronic document 128; [0031], FIG. 2A-2E are example GUI screens for enabling a user to request identification of suggested enhancements in a document, displaying the suggested enhancements and allowing the user to interactively choose which enhancements to apply to the document; [0032], As the user creates or edits the content of the content pane 220, a UI element may be provided for transmitting a request to receive suggestions for enhancing the content; a notification may be automatically displayed either within the application, or on a ribbon or a notifications' pane when suggested enhancements; the user may be able to choose a setting in the application that indicates when to run an automatic identification of suggested enhancements; For example, the user may be able to choose a setting in the application that indicates when to run an automatic identification of suggested enhancements (e.g., upon completing the creation of a selected number of pages of a document); [0033], the application may examine and provide suggested enhancements for the entire document. Alternatively, a user may be able to select a portion of the content and transmit a request for identifying suggested enhancements within the selected portion; [0034], It should be noted that requesting and receiving suggested enhancements may occur more than once during the document creation and editing process. For example, a user may submit a request for and receive enhancement suggestions for the document at its current state during the authoring process. This may result in one or more suggestions that may be selected by the user. Later after adding more content and/or making other changes to the document, the user may submit a second request for suggestions which may result in different enhancement suggestions. Depending on the changes made to the document, the second set of enhancement suggestions may be complementary to the first set or they may be contradictory to them; [0044], FIGS. 3A-3I depict additional example GUI screens for presenting suggested enhancements; [0061], during the current session; see also [0025-0027])
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.
Claims 2, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Gil in view of Le (US 20220215244 A1, published 07/07/2022).
Regarding claim 2, Gil teaches all the limitations of claim 1, further comprising:
automatically determining, by the implementing the machine learning model, at least one alternate suggested content format applied to the one or more items of content in response to determining (Gil Figs. 1-6; [0034], a user may submit a request for and receive enhancement suggestions for the document at its current state during the authoring process. This may result in one or more suggestions that may be selected by the user. Later after adding more content and/or making other changes to the document, the user may submit a second request for suggestions which may result in different enhancement suggestions; [0036], the user can review the suggested enhancements within the context of both the original document and the entire enhanced document to determine if the suggestion is appropriate; [0038], the UI element 250 may list the types of suggested enhancements made to the selected portion 245; [0041-0042], Once the user previews the changes in the preview screen 200D or if they choose to accept them without first previewing them within the content pane 220, the user can choose the menu option 260 of the UI element 250 to accept the suggested enhancements; [0044], FIGS. 3A-3I depict additional example GUI screens for presenting suggested enhancements and enabling the user to interact with those suggested enhancements; [0045], once the suggested enhancement is selected in the enhancements pane, the UI element 335 may enable the user to cycle through the possible suggested enhancements by utilizing the backward scrolling icon 315 and forward scrolling icon 325. In one implementation, determining which types of enhancements to suggest may be made in accordance with to a scoring mechanism whereby a score is calculated based on a plurality of parameters for each identified enhancement; [0046], Once the highest score suggestions have been identified, the enhancement having the highest score may displayed within the UI element 335 when the UI element is first displayed. This would allow the user to view the highest-ranking suggested enhancement first. Lower scored suggested enhancements may then be accessed by utilizing the backward and forward scrolling icons 315 and 325. In an example, as the user moves through the suggested enhancements in the UI element 335, the selected portion 340 changes to reflect the suggested enhancement displayed within the UI element 335)
However, Gil fails to expressly disclose in response to determining an expiration of a predetermined time period. In the same field of endeavor, Le teaches:
in response to determining an expiration of a predetermined time period (Le Figs. 1-6; abs. Methods and systems are described herein for dynamically selecting alternative content based on real-time events during device sessions using a cross-channel, time-bound deep reinforcement machine learning. The use of this architecture allows for alternative content to be selected in a time-bound and continuous manner that provides predictions in a dynamic environment (e.g., an environment in which user data is continuously changing and new events are continuously occurring) and with an increased success rate (e.g., new data and events are factored into each prediction); [0001], The invention relates to dynamically selecting alternative content based on real-time events during device sessions using a cross-channel, time-bound deep reinforcement machine learning; [0006], the system may receive multiple events during a time interval between a dynamic update. These events may comprise user actions (or inactions); [0010], as the system is time-bound, predictions based on timeouts (e.g., a lack of user actions, additional data, events occurring, and/or state changes) are considered (e.g., indicating a rejection), for which the system decreases a likelihood of success of previously presented content; [0033], System 200 generates a feature input for agent 204 to update its predictions (e.g., predict alternative content for displaying in user interface 210) following a predetermined time interval and/or after a triggering event; [0038], For example, system 200 may use a set of per-task (e.g., per content impression) present value (e.g., as assigned by a content provider and/or third-party) that dynamically estimates the value of content 206 and content 208, current events (e.g., user clickstream data), and/or other time-bound measures to update its predictions (e.g., as described in FIG. 4 below); [0060], For example, the system may monitor for event time steps (e.g., event time step 406 and event time step 408). Event time steps may comprise a time (which may be a portion of a time interval at which an event was received). Each event time step may have a different length and/or may correspond to a different event (or lack of an event); Each time window may allow time for a user to process presented alternative content (e.g., response to the content by selecting and/or otherwise engaging with the content). If the end of a time window is reached, the system may record any events (or the lack thereof) and update user data (e.g., user data 202) based on the recorded events)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated in response to determining an expiration of a predetermined time period as suggested in Le into Gil. Doing so would be desirable because the use of this architecture allows for alternative content to be selected in a time-bound and continuous manner that provides predictions in a dynamic environment (e.g., an environment in which user data is continuously changing and new events are continuously occurring) and with an increased success rate (e.g., new data and events are factored into each prediction). For example, in the system each round of predictions considers both input features, which can change by a user's actions, state of a user interface, and/or previous responses and states (see Le abs.). In recent years, users are increasingly receiving content on numerous different platforms. Moreover, users are increasingly accessing this content through different channels. However, these increases in both available content and its accessibility creates issues for generating personalized content for users (see Le [0002]). In previous systems, the order and/or content of the of recommendations is determined once when a user is accessing an interface, but is not able to be dynamically updated afterwards (e.g., based on user actions (or inactions), subsequently received user data (e.g., from another source), and/or other real-time data) during the device session despite the system receiving additional user inputs and/or data. On a practical level, with respect to user interface applications, this means that a user is less likely to be engaged and valuable screen real estate is wasted (see Le [0004]). Accordingly, methods and systems are described herein for dynamically selecting alternative content based on real-time events during device sessions (see Le [0005]). To overcome these technical problems with respect to conventional machine learning models, methods and systems are described herein for dynamically selecting alternative content based on real-time events during device sessions through the use of a cross-channel, time-bound deep reinforcement machine learning. The use of this architecture allows for alternative content to be selected in a time-bound and continuous manner that provides predictions in a dynamic environment (e.g., an environment in which user data is continuously changing and new events are continuously occurring) and with an increased success rate (e.g., new data and events are factored into each prediction) (see Le [0010]).
Regarding claims 11 and 19, claims 11 and 19 contain substantially similar limitations to those found in claim 2. Consequently, claims 11 and 19 are rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gines Marin (US 20190138611 A1) see Figs. 1-12 and [0010-0018], [0115].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T REPSHER III whose telephone number is (571)272-7487. The examiner can normally be reached Monday - Friday, 8AM-5PM EST.
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/JOHN T REPSHER III/ Primary Examiner, Art Unit 2143