DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in responsive to communication(s): original application filed on 03/18/2024, said application claims a priority filing date of 03/18/2024.
Claims 1-20 are pending. Claims 1, 10 and 16 are independent.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fan et al. (U.S. Publication 2019/0311022; hereinafter “Fan”).
In regard to independent claims 1, 10 and 16, Fan teaches A computer-implemented method comprising: monitoring, using human-computer interaction (HCI) analysis, editing operations of a set of text of an electronic document; analyzing the editing operations of the set of text to identify an editing pattern; searching the electronic document to identify a similar set of text to be edited based on the identified editing pattern; generating, based on the editing pattern, an automated editing command script to perform the editing operations on the similar set of text; and deploying the automated editing command script to apply the editing operations automatically to the similar set of text of the electronic document (Figure 1A-1G; Figure 2A-2C; Paragraph 0017-0021; paragraph 0023-0024).
In regard to dependent claims 2, 11 and 17, Fan teaches monitoring, using HCI analysis, user interactions in response to the automated editing command script applying the editing operations to the similar set of text of the electronic document; identifying, based on the monitoring, that the user has invalidated one or more editing operations that were applied to a first text of the similar set of text; and restoring, in response to the user invalidating the one or more editing operations, the similar set of text to the previous format prior to deploying the automated editing command script (Paragraph 0017; “Note: notification implies user gets a prompt where user can go back to previous format, if declines the recommendation”).
In regard to dependent claims 3, 12 and 18, Fan teaches monitoring, using HCI analysis, user interactions in response to the automated editing command script applying the editing operations to the similar set of text of the electronic document; identifying, based on the monitoring, that the user has modified one or more editing operations that were applied to a first text of the similar set of text; and adjusting, dynamically and based on the identifying, the automated editing command script to apply the modified one or more editing operations to the similar set of text (Paragraph 0024; “Note: said system also applied to the ‘similar text’).
In regard to dependent claims 4, 13 and 19, Fan teaches monitoring, using HCI analysis, user interactions in response to the automated editing command script applying the editing operations to the similar set of text of the electronic document; and validating, in response to no manual changes being made to the editing operations of the similar set the text, the automated editing command script (Figure 1B-1G, Paragraph 0017; “Note: monitored user action being making changes to the first two lines and based on the user interactions said system modified rest of the text that falls under the pattern”).
In regard to dependent claims 5, 14 and 20, Fan teaches training a machine learning model to identify one or more editing operations by analyzing historical user editing operations of a plurality of electronic documents; generating, by the machine learning model and based on the training, an editing pattern repository comprising a plurality of editing patterns; and updating the editing pattern repository with the identified editing pattern (Paragraph 0025).
In regard to dependent claims 6 and 15, Fan teaches generating the automated editing command script to perform the editing operations on the similar set of text is performed by the trained machine learning model (Paragraph 0025; “Note: recommendation is done based on the trained machine learning component”).
In regard to dependent claim 7, Fan teaches the editing operations of the set of text includes at least one or more editing operations selected from a group of recurring editing operations consisting of: highlighting text; emphasizing text to bold, italics, and/or underlined; modifying a font type of the text; modifying a font size of the text; modifying a font color; aligning the text; and adjusting spacing of text (Paragraph 0026).
In regard to dependent claim 8, Fan teaches the set of text comprises a set of similar types of words and/or phrases (Paragraph 0024 and 0026; “Note: ‘similar text’ and ‘similar types of data’ meaning similar types of words/phrases”).
In regard to dependent claim 9, Fan teaches generating the automated editing command script is further based on analyzing a set of learned user specific editing preferences (Paragraph 0032; “Note: based on user’s preference, user can change generating editing command script”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ittycheriah et al. U.S. Publication 2023/0259781 - Teaches giving prompt while updating a document.
Edge et al. U.S. Publication 2022/0405465 - Teaches giving suggestions proactively while formatting electronic document.
Barik et al. U.S. Patent 11,941372 - Teaches automated electronic document editing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REZA NABI whose telephone number is (571)270-7592. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm EST.
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/Reza Nabi/
Primary Examiner, Art Unit 2174