DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This Office Action is in response to the application filed on 05/10/2024.
3. The IDS filed on 08/15/2025 is considered and entered into the application file.
4. Claims 1-25 are pending, all the pending claims are examined.
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.
5. Claims 1-17, 19, and 23-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kim et al (US 20250007870 A1).
Kim is directed to automated content creation and content services for collaboration platforms.
As per claim 1, Kim discloses a method comprising:
defining an identification event associated with a Graphical User Interface (GUI) of a page of a third-party application, the identification event indicates that a defined automation process is associated with text presented in the GUI ([0294] FIGS. 12A-13B depict examples of how a generative output engine can be used to provide a task or action item summary for content of a collaboration platform. For example, a generative output engine can be used to identify a set of tasks or predicted action items in either an entire document or in a set of selected text. The tasks or action items may be inserted into the content or they may be used to automatically generate a set of issues or tasks in an issue tracking platform or system);
defining source data associated to the automation process, the source data comprising at least a portion of the text ([0295] FIG. 12A depicts an example result of invocation of the editor assistant service that can be used as a list of tasks or action items in an editor region of a graphical user interface);
defining that, in response to identifying the identification event, an indication of the automation process is configured to be presented over the GUI ([0300] In response to selecting an item in the results region 1234 of the object selection interface 1230, link object 1226 is positioned in the user input region 1222 of the command prompt interface 1220. In the present example, the link object 1226 includes a link or path designating a location or endpoint at which the electronic document can be accessed);
defining that, in response to identifying a trigger event, the automation process is configured to be executed, wherein executing the automation process comprises generating a prompt to a Large Language Model (LLM) engine, wherein the prompt is configured to comprise a predefined structure of a text portion and a variable portion, wherein the variable portion is configured to be replaced with the source data every invocation of the trigger event ([0194] Suggestions rendered in UI can also include and/or may be associated with one or more “engineered template prompts” that are configured to add context to a given user input. The context may be an instruction describing how particular output of the LLM/engine should be formatted, how a particular data item can be retrieved by the engine, or the like. As one example, an engineered template prompt may be “${user prompt}. Provide output of any table in the form of a tab delimited table formatted according to the markdown specification.” In this example, the variable ${user prompt} may be replaced with the user prompt such that the entire prompt received by the generative engine service 116 can include the user prompt and the example sentence describing how a table should be formatted); and
defining a configuration for presenting in the GUI a result, wherein the result is based on an output from the LLM engine ([0185] In response to receiving a modified prompt as input, the generative engine service 116 can execute an instance of a generative output engine, such as an LLM. As noted above, in some cases, the prompt management service 114 can be configured to specify what engine, engine version, language, language model or other data should be used to continue a particular modified prompt. 0194] Suggestions rendered in UI can also include and/or may be associated with one or more “engineered template prompts” that are configured to add context to a given user input. The context may be an instruction describing how particular output of the LLM/engine should be formatted, how a particular data item can be retrieved by the engine, or the like. Also see [0215]).
As per claim 2, Kim further discloses that the method of Claim 1, wherein the source data comprises text associated to an element that is displayed in the GUI, wherein the automation process is defined to correspond to the element ([0344] In some cases, the system may also reference user graphs, project or team graphs, or other object graphs to obtain data or content that is used to select predetermined board-creation prompt text. The user input, alone or in combination with any of these other data sources may be used to construct a prompt that is predicted to align with the user's intent. Also see text 2314 in fig. 23 as an example of source data ).
As per claim 3, Kim further discloses that the method of Claim 1, wherein the source data comprises selected text that is displayed in the GUI, wherein the automation process corresponds to the selected text (see text 2314 in fig. 23 as an example of source data displayed in GUI 2310 ).
As per claim 4, Kim further discloses that the method of Claim 1, wherein the source data comprises all text that is displayed in the GUI, wherein the automation process corresponds to the text ([0082] More generally, a continuation produced as output by an LLM can include not only text, source code, pseudocode, structured data, and/or cross-links to other platforms, but it also may be formatted in a manner that includes titles, emphasis, paragraph breaks, section breaks, code sections, quote sections, cross-links to external resources, inline images, graphics, table-backed graphics, and so on. [0255] As shown in FIG. 6A, the command prompt interface 602 includes a user input region 604 which is configured to receive user input. The user input region 604 may receive user-entered text, which may specify a content modification action, prompt text, or source of content to be analyzed or modified).
As per claim 5, Kim further discloses that the method of Claim 1, wherein the page includes one or more fields reflecting a submission of another user, wherein the prompt is generated to instruct the LLM engine to perform a text generation task configured to assist an end user with responding to the submission ([0353] In response to a user selection of the “generate card” control 2604, the system may automatically generate a new task card using a generative output engine and the process outlined with respect to FIG. 24. For example, in response to a user selection of control 2604, the system may automatically generate a prompt configured to define a new task card. FIG. 27A depicts an example prompt 2700 that includes predetermined query prompt text 2710 that can be used to generate a new task card that is in accordance with the respective task stack or column and other, previously defined task cards).
As per claim 6, Kim further discloses that the method of Claim 1, wherein the page includes one or more fields reflecting a submission of another user, wherein the prompt is configured to instruct the LLM engine to perform a text analysis task analyzing text of the submission, whereby providing an end user with insights useful to handle the submission by the another user ([0255] As shown in FIG. 6A, the command prompt interface 602 includes a user input region 604 which is configured to receive user input. The user input region 604 may receive user-entered text, which may specify a content modification action, prompt text, or source of content to be analyzed or modified. In the example of FIG. 6A, the user input is facilitated by a series of menus and selectable elements that help guide the user in constructing the user-prompt input. Also see [0257, 0297]).
As per claim 7, Kim further discloses that the method of Claim 1, wherein a second automation process is defined for the source data, the method comprising configuring a second indication of the second automation process to be presented over the GUI in response to the identification event ([0337] FIG. 21 depicts an example visualization tool for tracking a source of a query term or clause of a generative structured query. As mentioned previously, the graphical user interface 2100 can be used to assist users in constructing their own structured query or modifying a machine-generated query. As shown in FIG. 21, the graphical user interface 2100 includes a user input region 2120 which includes a natural language user input 2122. As described with respect to the previous examples, the natural language user input 2122 can be used to automatically generate a structured query 1232 displayed in a query region 2030).
As per claim 8, Kim further discloses that method of Claim 1 further comprising configuring a target location for presenting the indication of the automation process ([0299] The object selection interface 1230 also includes a results region 1234, which may display a list of selectable elements, each element associated with a content item that was identified using user input provided to the input region 1232. In some cases, the results region 1234 displays recently selected, recently viewed content items, or another curated list of content items predicted to be relevant to the object link creation process).
As per claim 9, Kim further discloses that the method of Claim 8, wherein the source data comprises text associated to an element that is displayed in the GUI, wherein the target location is adjacent to the element ([0062] In these embodiments, a user may provide input to a software platform coupled to a network architecture as described herein. The user input may be in the form of interaction with a graphical user interface affordance (e.g., button or other UI element), or may be in the form of plain text. In some cases, the user input may be provided as typed string input provided to a command prompt triggered by a preceding user input. Also see [0063])
As per claim 10, Kim further discloses that the method of Claim 1 further comprising configuring a target location for presenting the result. 0042] FIGS. 33A-33B depict a graphical user interface rendering a summary of results and a list of links to identified content items. [0299] The object selection interface 1230 also includes a results region 1234, which may display a list of selectable elements, each element associated with a content item that was identified using user input provided to the input region 1232. In some cases, the results region 1234 displays recently selected, recently viewed content items, or another curated list of content items predicted to be relevant to the object link creation process).
As per claim 11, Kim further discloses that the method of Claim 10 further comprising, in response to said identifying the trigger event, performing an automatic scroll of the page until reaching a displayed portion of the page that depicts the target location ([0257] The additional user input may also include selected text within the graphical user interface, which may be provided by the user through a cursor drag gesture, that is , Examiner’s Note: scrolling or dragging the finger toward the target location).
As per claim 12, Kim further discloses that the method of Claim 1, wherein the trigger event comprises a user interaction with the indication of the automation process ([0257] In addition to insertion of graphical object 620 or other auto-populated user input as a result of a user selection of the command control 612, the user may provide further user input that may be used to supplement or replace the action indicated by the graphical object 620).
As per claim 13, Kim further discloses that the method of Claim 1, wherein the indication of the automation process comprises a widget presenting a text string, wherein the text string describes the automation process, wherein the widget comprises a launcher or a tooltip, wherein the widget is not defined by the third-party application ([0257] In addition to insertion of graphical object 620 or other auto-populated user input as a result of a user selection of the command control 612, the user may provide further user input that may be used to supplement or replace the action indicated by the graphical object 620. [0328] As shown in FIG. 19B, the query region 1930 also includes controls 1934 including feedback controls that can be used to provide positive or negative feedback with respect to a particular result. [0352] As shown in FIG. 26A, in response to a user input or selection of a corresponding control, the graphical user interface may include an action window 2602 which may include a set of selectable options or controls 2604 that may be used to initiate the creation of various types of content. The selectable controls 2604 also define other actions that can be taken with respect to content or objects of a task management board).
As per claim 14, Kim further discloses that the method of Claim 1, wherein the configuration of presenting the result comprises at least one of:
updating one or more properties of the GUI based on the result ([0327] The generative result or output produced by the generative output engine may be displayed in a query region or field 1930. As shown in FIG. 19B, the result is a structured query 1932 that is formatted in accordance with the issue query schema examples provided in the prompt. A list of results 1910 may be updated or generated on execution of the structured query 1932. Each result of the list of results 1910 may be selectable to cause redirection of the graphical user interface 1900 to an issue view or project view associated with the selected result or item. In some implementations, the generative result or output is not displayed and the list of results 1910 is generated automatically in response to entry of the natural language user input may change the user interface blocks 230 and the list of results 2040.
presenting the result in a textbox in the GUI, wherein the result is configured to be inserted into the textbox and displayed therein ([0257] In addition to insertion of graphical object 620 or other auto-populated user input as a result of a user selection of the command control 612, the user may provide further user input that may be used to supplement or replace the action indicated by the graphical object 620. [0297] The user input region 1222 may receive user-entered text, which may specify a content modification action, prompt text, or source of content to be analyzed or modified. In the present example, the user input includes a graphical object 1224 corresponding to an action to “find action items,” which was inserted as a result of the selection of the command control 1212.
presenting the result in the textbox, wherein the result is configured to be displayed by updating a visual characteristic of at least a portion of the textbox ([ 0042] FIGS. 33A-33B depict a graphical user interface rendering a summary of results and a list of links to identified content items. [0299] The object selection interface 1230 also includes a results region 1234, which may display a list of selectable elements, each element associated with a content item that was identified using user input provided to the input region 1232. In some cases, the results region 1234 displays recently selected, recently viewed content items, or another curated list of content items predicted to be relevant to the object link creation process);
presenting the result in a text input field, wherein the result is configured to be appended to pre-existing text in the text input field ([0305] Generally, the command controls 1322 that are rendered in the command selection interface window 1320 may be selected based on the previous user action (e.g., the selection of the portion of the text 1304) and may be limited to actions that can be performed or are likely to be most applicable to selected portion 1304 of the content);
presenting the result in the text input field, wherein the text input field is configured to be updated to present the result ([0042] FIGS. 33A-33B depict a graphical user interface rendering a summary of results and a list of links to identified content items);
presenting the result in a popup element, the popup element is configured to be displayed over the GUI, wherein the popup element is not part of the third-party application; ([0287] As shown in FIG. 10B, the generative output or response is displayed in a preview window 1030 which overlaps or is otherwise displayed over the content of the editor or content region 100); and
presenting the result in a chat widget, wherein the chat widget is not part of the third-party application ([038] FIG. 29 depicts a graphical user interface rendering an example exchange between a client messaging interface and a provider messaging interface, the provider messenger interface operated by an automated chat service. [0365] In response to receiving the response from the generative output engine 2830, the first automated chat service 2824 may provide the response as a search query to the knowledge base platform 2832 or other content store).
As per claim 15, Kim further discloses that the method of Claim 14, wherein the one or more properties comprise at least one of: a color of a text element, a highlighting of a text element, and a color of a non-text element. The visual indica may be highlighted, underlined, bolded, or any other visual effect added to the text. ([0337] The visual indicia may be a display of a framing border, change in color of a local background region, or other indicia that indicates the relative location of the respective portion or portions. Similarly, the respective portions 2134, 2136 may be displayed with a visual indicia in response to the user input).
As per claim 16, Kim further discloses that the method of Claim 1, wherein the text portion of the prompt is set to comprise a background section describing a background of the automation process, and a command section comprising a request for processing the variable portion according to the automation process ([0352] As shown in the example of FIG. 26A, there may be a separate list of controls 2604 that are associated with automation or machine-assisted content generation (see, e.g., automation designation 2606). The action window 2602 is associated with a list or task stack option. Other similar action windows may be used for other types of objects of the collaboration platform. [0374] FIG. 31E depicts an example graphical user interface 3100e that may also be produced in response to the request message 3102 of FIG. 31A. Specifically, the automated chat service may generate a response 3150 that includes a proposed answer that is generated using the generative output engine, in accordance with the examples described herein).
As per claim 17, Kim further discloses that the method of Claim 1, wherein the automation process is configured to be performed with respect to a content source, wherein the content source comprises a database or a repository, wherein the prompt to the LLM is determined based on at least a portion of retrieved data that is retrieved from the content source, wherein the retrieved data conforms to a similarity metric with respect to the source data or with respect to portion of the predefined structure of the text portion ([0190] In some embodiments, user input can be provided by text input that can be provided by a user typing a word or phrase into an editable dialog box such as a rich text editing frame rendered within a user interface of a frontend application on a display of a client device. For example, the user can type a particular character or phrase in order to instruct the frontend to enter a command receptive mode. In some cases, the frontend may render an overlay user interface that provides a visual indication that the frontend is ready to receive a command from the user. As the user continues to type, one or more suggestions may be shown in a modal UI window. [0191] These suggestions can include and/or may be associated with one or more “preconfigured prompts” that are engineered to cause an LLM to provide particular output. More specifically, a preconfigured prompt may be a static string of characters, symbols and words, that causes—deterministically or pseudo-deterministically—the LLM to provide consistent output. For example, a preconfigured prompt may be “generate a summary of changes made to all documents in the last two weeks.” Preconfigured prompts can be associated with an identifier or a title shown to the user, such as “Summarize Recent System Changes.” In this example, a button with the title “Summarize Recent System Changes” can be rendered for a user in a UI as described herein. Upon interaction with the button by the user, the prompt string “generate a summary of changes made to all documents in the last two weeks” can be retrieved from a database or other memory, and provided as input to the generative engine service 116. 0364] Returning to FIG. 28, in response to the intent metric or other content metric (determined by the analysis module 2822) failing to satisfy the criteria, the system may provide the user input to a recipient associated with the first automated chat service 2824).
As per claim 19, Kim further discloses that the method of Claim 1 further comprising:
determining the identification event occurring in the third-party application, the third-party application is being used by an end user ([0356] FIGS. 28-33b describe systems and techniques that utilize a generative output engine to provide content for an online messaging platform).
presenting the indication of the automation process, wherein the indication of the automation process is presented over the GUI of the third-party application ([0038] FIG. 29 depicts a graphical user interface rendering an example exchange between a client messaging interface and a provider messaging interface, the provider messenger interface operated by an automated chat service.
in response to the trigger event:
determining the source data ([0358] In the example system 2800, selected portions of the messaging session may be directed to different recipients depending on the content of the message or a triggering message (e.g., a request message);
generating the prompt ([0185] In response to receiving a modified prompt as input, the generative engine service 116 can execute an instance of a generative output engine, such as an LLM.
providing the prompt to the LLM engine, whereby obtaining the result from the LLM engine; 0266] In response to a user input indicating that the prompt is complete, the editor assistant service or related service may access the linked content item using the path of the link object 720 and obtain content from the linked content item. and
presenting the result over the GUI of the third-party application according to the configuration ([0327] The generative result or output produced by the generative output engine may be displayed in a query region or field 1930. As shown in FIG. 19B, the result is a structured query 1932 that is formatted in accordance with the issue query schema examples provided in the prompt. A list of results 1910 may be updated or generated on execution of the structured query 1932. Each result of the list of results 1910 may be selectable to cause redirection of the graphical user interface 1900 to an issue view or project view associated with the selected result or item. In some implementations, the generative result or output is not displayed and the list of results 1910 is generated automatically in response to entry of the natural language user input may change the user interface blocks 230 and the list of results 2040. [0334] In the present example, a structured query or other generative response may be obtained in response to a natural language prompt 2022 provided to a user input region or field 2020.
As per claim 23, Kim further discloses computerized apparatus (e.g. client device 104) having a processor (e.g. 104c, Fig .1). The claim comprises similar limitations as that of method claim 1, thus claim 23 is rejected under similar citations given to the method claim 1.
As per claim 24, Kim further discloses computerized apparatus of Claim 23, wherein a second automation process is defined for the source data, the processor is further adapted to configure a second indication of the second automation process to be presented over the GUI in response to the identification event ([0234] In yet further examples, one or more frontends or backends can be configured to automatically generate one or more prompts for continuation by generative output engines as described herein. More generally, in many cases, user input may not be required and prompts may be requested and/or engineered automatically. [0244] The content-creation and modification service may be operably coupled to or include a language model platform, as described herein, which may be used to automatically generate content in response to text-based prompts).
As per claim 25, Kim further discloses a computer program product comprising a non-transitory computer readable storage medium (memory 104b, memory 106b, etc.). The claim comprises similar limitations as that of method claim 1, thus claim 25 is rejected under similar citations given to the method claim 1.
Allowable Subject Matter
6. Claims 20-22 are allowed.
7. Claim 18 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250217769 A1 - Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a generative interface panel having multiple automated assistant services. Each assistant service may access a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms (Abstract).
US 20250217371 A1 - Embodiments described herein relate to systems and methods for automatically generating content for a generative content interface of a collaboration platform. The system may perform an intent analysis on a natural language user input to the generative content interface to determine an intent confidence score with respect to each of a set of request classifiers, the set of request classifiers comprising a first request classifier associated with a request for an action, a second request classifier associated with a request for information, and a third request classifier associated with a request for a contact. Based on the intent confidence scores of the request classifiers, the system may select a content store in which to search for content to satisfy a user's query (Abstract).
US 20250005263 A1 - Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms (Abstract).
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TADESSE HAILU whose telephone number is (571)272-4051; and the email address is Tadesse.hailu@USPTO.GOV. The examiner can normally be reached Monday- Friday 9:30-5:30 (Eastern time).
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, Bashore, William L. can be reached (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TADESSE HAILU/Primary Examiner, Art Unit 2174
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