Prosecution Insights
Last updated: May 29, 2026
Application No. 18/823,555

System and Method for Text Editing Using Large Language Models

Non-Final OA §103
Filed
Sep 03, 2024
Priority
Sep 03, 2023 — provisional 63/536,422 +1 more
Examiner
LU, WILLIAM
Art Unit
2624
Tech Center
2600 — Communications
Assignee
Jasper AI Inc.
OA Round
1 (Non-Final)
72%
Grant Probability
Favorable
1-2
OA Rounds
10m
Est. Remaining
79%
With Interview

Examiner Intelligence

Grants 72% — above average
72%
Career Allowance Rate
430 granted / 601 resolved
+9.5% vs TC avg
Moderate +7% lift
Without
With
+7.4%
Interview Lift
resolved cases with interview
Typical timeline
2y 6m
Avg Prosecution
30 currently pending
Career history
627
Total Applications
across all art units

Statute-Specific Performance

§103
96.7%
+56.7% vs TC avg
§102
0.9%
-39.1% vs TC avg
§112
1.2%
-38.8% vs TC avg
Black line = Tech Center average estimate • Based on career data from 601 resolved cases

Office Action

§103
DETAILED ACTION Claims 1-20 filed September 3rd 2024 are pending in the current 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 . Claim Rejections - 35 USC § 103 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 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. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (US6,512,522) in view of Tran et al. (US2022/0138185) Consider claim 1, where Miller teaches a system for applying one or more effects to source text, comprising: an input for receiving the source text; (See Miller col 7 lines 9-55 where there is a glyph manager that stores glyphs which contain a character code as well as associated properties) and a graph of one or more connected nodes, each node representing the one or more effects on the source text, wherein each node of the graph receives at least one of the source text or an output from another node of the graph. (See Miller col 7 line 55-col 8 line 55 where a stack is used to track the active properties for a node during traversal of the scene graph. Each node in the scene graph may have a property list such as shown at 74. A property list is a list of properties and associated values for each node which differ from default values in the property stacks associated with the root node. Example layout properties include the amount of additional kern between objects, the amount of additional leading above objects, the justification or alignment, margins, and scroll position. Thus, the scene graph comprising text may affect neighboring text) Miller teaches a graph with nodes; however Miller does not explicitly teach each node being connected to at least one other node by an edge. However, in an analogous field of endeavor Tran teaches each node being connected to at least one other node by an edge. (See Tran Fig. 7 and ¶104 where a scene graph comprises nodes connected by edges) Therefore, it would have been obvious for one of ordinary skill in the art that the nodes of Miller would similarly be connected by edges as taught by Tran. One of ordinary skill in the art would have been motivated to utilize known implementations of graphs to yield predictable results. Consider claim 2, where Miller in view of Tran teaches the system of claim 1, wherein the graph is a non-linear effect stack and at least one of the connected nodes is a cross-dimensional node that can receive more than one input or produce more than one output. (See Miller col 7 line 55-col 8 line 55 where a stack is used to track the active properties for a node during traversal of the scene graph. Each node in the scene graph may have a property list such as shown at 74. A property list is a list of properties and associated values for each node which differ from default values in the property stacks associated with the root node. The Examiner notes that the term “non-linear effect stack” does not appear in the specification, as such the Examiner interprets this new term to mean the combination of a linear stack with a non-linear graph as shown in Figure 1 of the applicant’s specification and corresponding ¶72-75) Consider claim 3, where Miller in view of Tran teaches the system of claim 2, wherein the at least one of the connected nodes is a splitter that receives a single input and splits the single input into more than one output edge. (See Tran Fig. 7 and ¶104 where a scene graph comprises nodes connected by edges such as the node “boy” 700 that has two edges 720 and 725 that split off from the node 700) Therefore, it would have been obvious for one of ordinary skill in the art that the nodes of Miller would similarly be connected by edges as taught by Tran. One of ordinary skill in the art would have been motivated to utilize known implementations of graphs to yield predictable results. Consider claim 4, where Miller in view of Tran teaches the system of claim 3, wherein the more than one output edge includes a content stream of the source text and a style stream of the source text. (See Miller col 7 line 55-col 8 line 55 where a stack is used to track the active properties for a node during traversal of the scene graph. Each node in the scene graph may have a property list such as shown at 74. A property list is a list of properties and associated values for each node which differ from default values in the property stacks associated with the root node as well as the character itself) Consider claim 5, where Miller in view of Tran teaches the system of claim 2, wherein the at least one of the connected nodes is a combiner that receives at least two inputs and combines the at least two inputs into a single output edge based on a combination type of the combiner. (See Tran Fig. 3 and ¶133 where according to some embodiments, there are two sources of information, which require an encoder for each of the two sources. One way is to incorporate two information sources through concatenation.) One of ordinary skill in the art would have been motivated to modify the scene graph of Miller to be edited using natural language processing as taught by Tran. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using existing semantic processing to improve usability. Consider claim 6, where Miller in view of Tran teaches the system of claim 5, wherein said combination type includes at least one of a semantically additive type, (See Tran Fig. 8 and ¶108) semantically subtractive type, (See Tran Fig. 9 and ¶114) a best along a predetermined criteria, (See Tran Fig. 10 and ¶115-116) and a blending mode. (See Tran Fig. 11 and ¶117) One of ordinary skill in the art would have been motivated to modify the scene graph of Miller to be edited using natural language processing as taught by Tran. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using existing semantic processing to improve usability. Consider claim 7, where Miller in view of Tran teaches the system of claim 1, wherein each connected node is defined by a stream type that defines the input that a particular node expects to receive. (See Tran ¶147 where formally, given a source graph custom-character and a modification query y, one can produce a target graph custom-character by maximizing the conditional probability p(custom-character). As a graph consists of a list of typed nodes and edges) Consider claim 8, where Miller in view of Tran teaches the system of claim 1, wherein each edge between connected nodes is defined by a stream weight, (See Tran ¶48-49 where weights are created on the edges) the stream weight providing a global multiplier on text transparency. (See Miller col 8 line 11-36 where opacity is a property captured by the scene graph) Consider claim 9, where Miller in view of Tran teaches the system of claim 1, wherein said one or more effects comprises at least one of a filter, (See Tran Fig. 9 and ¶114) a generator, (See Tran Fig. 8 and ¶108) and a viewer. (See Miller col 14 lines 13-31 where user interfaces may be provided for text editing) Consider claim 10, where Miller in view of Tran teaches the system of claim 1, further comprising a graphical user interface for editing the one or more effects. (See Miller col 14 lines 13-31 where user interfaces may be provided for text editing) Consider claim 11, where Miller teaches a method for applying one or more effects to source text, comprising: receiving the source text at an input; (See Miller col 7 lines 9-55 where there is a glyph manager that stores glyphs which contain a character code as well as associated properties) and applying the one or more effects to the received source text, wherein the one or more effects are represented by a graph of one or more connected nodes, each node representing the one or more effects on the text, wherein each node of the graph receives at least one of the source text or an output from another node of the graph. (See Miller col 7 line 55-col 8 line 55 where a stack is used to track the active properties for a node during traversal of the scene graph. Each node in the scene graph may have a property list such as shown at 74. A property list is a list of properties and associated values for each node which differ from default values in the property stacks associated with the root node. Example layout properties include the amount of additional kern between objects, the amount of additional leading above objects, the justification or alignment, margins, and scroll position. Thus, the scene graph comprising text may affect neighboring text) Miller teaches a graph with nodes; however Miller does not explicitly teach each node being connected to at least one other node by an edge. However, in an analogous field of endeavor Tran teaches each node being connected to at least one other node by an edge. (See Tran Fig. 7 and ¶104 where a scene graph comprises nodes connected by edges) Therefore, it would have been obvious for one of ordinary skill in the art that the nodes of Miller would similarly be connected by edges as taught by Tran. One of ordinary skill in the art would have been motivated to utilize known implementations of graphs to yield predictable results. Consider claim 12, where Miller in view of Tran teaches the method of claim 11, wherein the graph is a non-linear effect stack and at least one of the connected nodes is a cross-dimensional node that can receive more than one input or produce more than one output. (See Miller col 7 line 55-col 8 line 55 where a stack is used to track the active properties for a node during traversal of the scene graph. Each node in the scene graph may have a property list such as shown at 74. A property list is a list of properties and associated values for each node which differ from default values in the property stacks associated with the root node. The Examiner notes that the term “non-linear effect stack” does not appear in the specification, as such the Examiner interprets this new term to mean the combination of a linear stack with a non-linear graph as shown in Figure 1 of the applicant’s specification and corresponding ¶72-75) Consider claim 13, where Miller in view of Tran teaches the method of claim 12, wherein the at least one of the connected nodes is a splitter, said method further comprises receiving a single input at the splitter and splitting the received single input into more than one output edge. (See Tran Fig. 7 and ¶104 where a scene graph comprises nodes connected by edges such as the node “boy” 700 that has two edges 720 and 725 that split off from the node 700) Therefore, it would have been obvious for one of ordinary skill in the art that the nodes of Miller would similarly be connected by edges as taught by Tran. One of ordinary skill in the art would have been motivated to utilize known implementations of graphs to yield predictable results. Consider claim 14, where Miller in view of Tran teaches the method of claim 13, wherein said splitting the received single input into more than one output edge includes splitting the single input into a content stream of the source text and a style stream of the source text. (See Miller col 7 line 55-col 8 line 55 where a stack is used to track the active properties for a node during traversal of the scene graph. Each node in the scene graph may have a property list such as shown at 74. A property list is a list of properties and associated values for each node which differ from default values in the property stacks associated with the root node as well as the character itself) Consider claim 15, where Miller in view of Tran teaches the method of claim 12, wherein the at least one of the connected nodes is a combiner that receives at least two inputs, said method further comprising combining the at least two inputs into a single output edge at the combiner based on a combination type of the combiner. (See Tran Fig. 3 and ¶133 where according to some embodiments, there are two sources of information, which require an encoder for each of the two sources. One way is to incorporate two information sources through concatenation.) One of ordinary skill in the art would have been motivated to modify the scene graph of Miller to be edited using natural language processing as taught by Tran. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using existing semantic processing to improve usability. Consider claim 16, where Miller in view of Tran teaches the method of claim 15, wherein said combination type includes at least one of a semantically additive type, (See Tran Fig. 8 and ¶108) semantically subtractive type, (See Tran Fig. 9 and ¶114) a best along a predetermined criteria, (See Tran Fig. 10 and ¶115-116) and a blending mode. (See Tran Fig. 11 and ¶117) One of ordinary skill in the art would have been motivated to modify the scene graph of Miller to be edited using natural language processing as taught by Tran. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using existing semantic processing to improve usability. Consider claim 17, where Miller in view of Tran teaches the method of claim 11, wherein each connected node is defined by a stream type that defines the input that a particular node expects to receive. (See Tran ¶147 where formally, given a source graph custom-character and a modification query y, one can produce a target graph custom-character by maximizing the conditional probability p(custom-character). As a graph consists of a list of typed nodes and edges) Consider claim 18, where Miller in view of Tran teaches the method of claim 11, wherein each edge between connected nodes is defined by a stream weight, (See Tran ¶48-49 where weights are created on the edges) the stream weight providing a global multiplier on text transparency. (See Miller col 8 line 11-36 where opacity is a property captured by the scene graph) Consider claim 19, where Miller in view of Tran teaches the method of claim 11, wherein said one or more effects comprises at least one of a filter, (See Tran Fig. 9 and ¶114) a generator, (See Tran Fig. 8 and ¶108) and a viewer. (See Miller col 14 lines 13-31 where user interfaces may be provided for text editing) Consider claim 20, where Miller in view of Tran teaches the method of claim 1, further comprising providing a graphical user interface for editing the one or more effects. (See Miller col 14 lines 13-31 where user interfaces may be provided for text editing) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM LU whose telephone number is (571)270-1809. The examiner can normally be reached 10am-6:30pm. 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, Matthew Eason can be reached at 571-270-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. WILLIAM LU Primary Examiner Art Unit 2624 /WILLIAM LU/Primary Examiner, Art Unit 2624
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Prosecution Timeline

Sep 03, 2024
Application Filed
Feb 23, 2026
Non-Final Rejection mailed — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
72%
Grant Probability
79%
With Interview (+7.4%)
2y 6m (~10m remaining)
Median Time to Grant
Low
PTA Risk
Based on 601 resolved cases by this examiner. Grant probability derived from career allowance rate.

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