DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed August 21, 2026, has been entered. Claims 1 – 20 are pending in the application. Applicant’s amendments to the Drawings, Specification, and Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed May 21, 2026.
Response to Arguments
Applicant’s arguments, August 21, 2026, with respect to claims 1 – 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1 – 2, 4, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dornbush et al. (US Patent Application Publication No. 2021/0165829), hereinafter Dornbush, in view of Riscutia et al. (US Patent Application Publication No. 2024/0419465), hereinafter Riscutia.
Regarding claim 1, Dornbush discloses a method, comprising:
identifying an action of a user of a cloud-based content management platform with respect to content of one or more first documents of a plurality of documents stored at the cloud-based content management platform (Paragraph 0006, lines 1-15, "In some other implementations, a system and method are disclosed for notifying a document to a user of a cloud-based content management platform. A first set of documents is identified. The first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user. One or more target documents from the first set of documents for the user are identified based on an amount of overlap in topicality between a respective document and a user's current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document. The user's current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform."; Determining a document is in a user's current working set of documents based on the user accessing a document within a last predetermined time period via the cloud-based content management platform reads on identifying an action of a user of a cloud-based content management platform with respect to content of one or more first documents of a plurality of documents stored at the cloud-based content management platform.);
predicting user interest in content of one or more second documents of the plurality of documents stored at the cloud-based content management platform (Paragraph 0033, lines 1-10, "Aspects and implementations of the present disclosure address the above deficiencies, among others, by predicting documents that are likely to be of interest to the user among documents associated with the user account. The documents associated with the user account refer to documents that can be accessed and/or viewed by the user. The prediction technique of the present disclosure may predict a document containing a comment thread that may be of interest to the user because of a comment recently added to the comment thread."; Predicting documents that are likely to be of interest to the user among documents associated with the user account reads on predicting user interest in content of one or more second documents of the plurality of documents.),
wherein predicting the user interest comprises: selecting, based on a selection criterion, the one or more second documents stored at the cloud-based content management platform (Paragraph 0047, lines 1-3, "The server 112 includes a prediction engine 116 to predict documents to be suggested to the user and to notify the user about these documents."; Paragraph 0089, lines 1-11, "The prediction engine 116 may measure the level of relatedness as an amount of overlap in topicality between a respective document and the user's current working set of documents. The overlap of topicality may represent a measure or similarity score of how related the document is to the user's current set of working documents, and may be determined, for example, according to a predefined set of criteria such as a degree of overlap, an occurrence rate of words or phrases identified in a stored database as relating to a common field or topic, Jaccard similarity, cosine distance, Euclidean distance, and/or the like."; An amount of overlap in topicality between a respective document and the user's current working set of documents reads on a selection criterion.),
selecting, based on the action of the user, a portion of a document of the one or more second documents (Paragraph 0006, lines 1-15, "In some other implementations, a system and method are disclosed for notifying a document to a user of a cloud-based content management platform. A first set of documents is identified. The first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user. One or more target documents from the first set of documents for the user are identified based on an amount of overlap in topicality between a respective document and a user's current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document. The user's current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform."; Paragraph 0055, lines 10-17, "The document thumbnail section 204 may include a thumbnail image of a specific portion of the document. The prediction engine 116 may select a portion that is associated with the event of the suggestion card 200. For example, when the event is a comment event, the prediction engine 116 may take a snippet of a paragraph to where the comment event is added."; Identifying a target document from a set of documents for the user based on an amount of overlap in topicality between a respective document and a user's current working set of documents, where the user's current working set of documents is determined based on the user accessing a document within a last predetermined time period via the cloud-based content management platform, reads on selecting a document based on the action of the user, and the prediction engine selecting a portion of a document reads on selecting a portion of a document of the one or more second documents.),
and generating, using a generative machine learning model (MLM), a first generative MLM prompt based on the portion of the document (Paragraph 0045, lines 1-12, "The cloud-based content management platform 115 may enable a user to access documents and view a home screen user interface (UI) of the cloud-based content management platform as a respective UI 124A-124Z. Additionally, in some embodiments, the cloud-based content management platform 115 may provide a UI 124A-124Z that includes suggestion cards 200 to notify the user of documents to be of interest to the user. The cloud-based content management platform 115 may further provide the suggestion card 200 to enable the user to quickly access documents or perform an action to a document without opening the document."; Paragraph 0047, lines 5-9, "The prediction engine 116 may adopt a heuristics approach or a machine learning approach to implement the prediction scenario model as discussed in details with relation to FIGS. 3-5."; Paragraph 0054, lines 4-13, "The suggestion card 200 represents a document and an event associated with the document. The prediction engine 116 may build the suggestion card 200 to notify the user about a suggestion of a document predicted to be of interest to the user. The suggestion card 200 may allow the user to quickly access the document represented in the suggestion card 200. In other implementations, the prediction engine 116 may compose the suggestion card 200 to notify the user as well as to enable the user to respond to the event represented by the suggestion card 200."; Paragraph 0055, lines 10-13, "The document thumbnail section 204 may include a thumbnail image of a specific portion of the document. The prediction engine 116 may select a portion that is associated with the event of the suggestion card 200.”; The prediction engine generating a suggestion card using machine learning, where the suggestion card enables the user to quickly access documents or perform an action to a document, reads on generating a first generative MLM prompt using a generative machine learning model (MLM), and the suggestion card enabling the user to respond to an event, where a portion of the document is associated with the event, reads on generating the prompt based on the portion of the document. Examiner’s note: The prompt being a generative MLM prompt is an intended use of the prompt and therefore is not given patentable weight.);
and causing the first generative MLM prompt to be presented to the user in relation to the action of the user (Paragraph 0006, lines 1-15, "In some other implementations, a system and method are disclosed for notifying a document to a user of a cloud-based content management platform. A first set of documents is identified. The first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user. One or more target documents from the first set of documents for the user are identified based on an amount of overlap in topicality between a respective document and a user's current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document. The user's current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform."; Paragraph 0045, lines 1-12, "The cloud-based content management platform 115 may enable a user to access documents and view a home screen user interface (UI) of the cloud-based content management platform as a respective UI 124A-124Z. Additionally, in some embodiments, the cloud-based content management platform 115 may provide a UI 124A-124Z that includes suggestion cards 200 to notify the user of documents to be of interest to the user. The cloud-based content management platform 115 may further provide the suggestion card 200 to enable the user to quickly access documents or perform an action to a document without opening the document."; Identifying a target document from a set of documents for the user based on an amount of overlap in topicality between a respective document and a user's current working set of documents, where the user's current working set of documents is determined based on the user accessing a document within a last predetermined time period via the cloud-based content management platform, reads on selecting a document based on the action of the user, and the cloud-based content management platform providing a user interface that includes suggestion cards to notify the user of documents to be of interest to the user reads on causing the first generative MLM prompt to be presented to the user in relation to the action of the user.).
Dornbush does not specifically disclose: wherein generating the first generative MLM prompt comprises inputting the portion of the document into the generative MLM and obtaining the first generative MLM prompt as an output of the generative MLM; wherein the first generative MLM prompt is presented as a selectable option for submitting the first generative MLM prompt as an input to the generative MLM or another generative MLM.
Riscutia teaches:
wherein generating the first generative MLM prompt comprises inputting the portion of the document into the generative MLM and obtaining the first generative MLM prompt as an output of the generative MLM (Paragraph 0020, lines 1-6, "In various implementations of the technology, suggestions prompted by the application component and generated by the LLM service are presented as graphical input devices (e.g., hyperlinks or graphical buttons) which a user can select to have the LLM service generate the suggested content."; Paragraph 0036, lines 6-10, "In an implementation, when the user opens a document in the application environment in user experience 111, collaborative prompt object 112 initiates an interaction with LLM service 150 by generating a prompt including at least a portion of the content of the document."; Suggestions generated by a large language model (LLM) service which a user can select to have the LLM service generate the suggested content read on obtaining a generative MLM prompt as an output of the generative MLM, and a prompt including at least a portion of the content of a document reads on inputting the portion of the document into the generative MLM.);
wherein the first generative MLM prompt is presented as a selectable option for submitting the first generative MLM prompt as an input to the generative MLM or another generative MLM (Paragraph 0020, lines 1-6, "In various implementations of the technology, suggestions prompted by the application component and generated by the LLM service are presented as graphical input devices (e.g., hyperlinks or graphical buttons) which a user can select to have the LLM service generate the suggested content."; Suggestions generated by a large language model (LLM) service being presented as graphical input devices which a user can select to have the LLM service generate the suggested content reads on the generative MLM prompt being presented as a selectable option for submitting the generative MLM prompt as an input to the generative MLM or another generative MLM.).
Riscutia is considered to be analogous to the claimed invention because it is in the same field of generative machine learning model prompting. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush to incorporate the teachings of Riscutia to generate suggestions by a large language model (LLM) service with a prompt including at least a portion of the content of a document and present the suggestions as graphical input devices which a user can select to have the LLM service generate the suggested content. Doing so would allow for anticipating the user's needs prior to receiving user input and offering suggestions to guide the user in accomplishing any of a number of different tasks (Riscutia; Paragraph 0029, lines 1-18).
Regarding claim 2, Dornbush in view of Riscutia discloses the method as claimed in claim 1.
Dornbush further discloses:
wherein the action of the user comprises inputting text into a user interface of the cloud-based content management platform (Paragraph 0086, lines 16-25, "Once the prediction engine 116 has identified the user's current working set of documents, the prediction engine 116 may determine a key text of each document in the user's current working set. The key text may be indexed and represent a topic of content of the document. The key text may be extracted from a title or content of the respective document. The key text may be a word, phrase, or sentence. The key text may be manually entered by users associated with the document or automatically generated via the cloud-based content management platform 115."; A user associated with a document manually entering key text reads on the action of the user comprises inputting text into a user interface of the cloud-based content management platform.).
Regarding claim 4, Dornbush in view of Riscutia discloses the method as claimed in claim 1.
Dornbush further discloses:
wherein selecting the one or more second documents based on the selection criterion comprises: ranking the plurality of documents based on the action of the user; and selecting a threshold number of highest ranked documents of the plurality of documents as the one or more second documents (Paragraph 0006, lines 1-15, "In some other implementations, a system and method are disclosed for notifying a document to a user of a cloud-based content management platform. A first set of documents is identified. The first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user. One or more target documents from the first set of documents for the user are identified based on an amount of overlap in topicality between a respective document and a user's current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document. The user's current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform."; Paragraph 0081, lines 1-8, "At block 406, the prediction engine 116 may rank each document in the set of documents identified at block 402, based on the document scores. At block 408, the prediction engine 116 may select one or more documents from the set of documents of block 402 based on the rankings. The prediction engine 116 may present the selected documents to the user for notification via the suggestion cards 200 at block 308 of FIG. 3."; Identifying target documents from a set of documents for the user based on an amount of overlap in topicality between a respective document and a user's current working set of documents, where the user's current working set of documents is determined based on the user accessing a document within a last predetermined time period via the cloud-based content management platform, reads on selecting a document based on the action of the user, and ranking each document in the set of documents and selecting a document from the set of documents based on the rankings reads on ranking the plurality of documents based on the action of the user and selecting a threshold number of highest ranked documents of the plurality of documents as the one or more second documents.).
Regarding claim 15, Dornbush discloses a method, comprising:
at a user interface of a client device, receiving an action of a user of a cloud-based content management platform (Paragraph 0004, lines 18-21, "A graphical user interface (GUI) of a cloud storage of the user hosted by the cloud-based content management platform for presentation to the user is provided."; Paragraph 0005, lines 1-7, "When the GUI identifying the one or more electronic documents and the respective selected comment(s) is presented to the user, the user may provide, via the GUI, one or more inputs relating to one or more of the documents and/or comments identified in the GUI, thereby enabling a continuous interaction between the user and the documents stored in the cloud-based content management platform."; Paragraph 0006, lines 1-15, "In some other implementations, a system and method are disclosed for notifying a document to a user of a cloud-based content management platform. A first set of documents is identified. The first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user. One or more target documents from the first set of documents for the user are identified based on an amount of overlap in topicality between a respective document and a user's current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document. The user's current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform."; Determining a document is in a user's current working set of documents based on the user accessing a document within a last predetermined time period via the cloud-based content management platform reads on receiving an action of a user of a cloud-based content management platform.);
sending, over a computer network in data communication with the client device, data identifying the action of the user to the cloud-based content management platform (Paragraph 0006, lines 1-15, "In some other implementations, a system and method are disclosed for notifying a document to a user of a cloud-based content management platform. A first set of documents is identified. The first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user. One or more target documents from the first set of documents for the user are identified based on an amount of overlap in topicality between a respective document and a user's current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document. The user's current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform."; Paragraph 0042, line 1-7, "The system architecture 100 includes a cloud-based environment 110 connected to user devices 120A-120Z via a network 130. The cloud-based environment 110 refers to a collection of physical machines that host applications providing one or more services (e.g., content management) to multiple user devices 120A-120Z via a network 130."; Determining a document is in a user's current working set of documents based on the user accessing a document within a last predetermined time period via the cloud-based content management platform reads on data identifying the action of the user to the cloud-based content management platform.);
receiving, over the computer network, a generative machine learning model (MLM) prompt from the cloud-based content management platform (Paragraph 0042, line 1-7, "The system architecture 100 includes a cloud-based environment 110 connected to user devices 120A-120Z via a network 130. The cloud-based environment 110 refers to a collection of physical machines that host applications providing one or more services (e.g., content management) to multiple user devices 120A-120Z via a network 130."; Paragraph 0045, lines 1-12, "The cloud-based content management platform 115 may enable a user to access documents and view a home screen user interface (UI) of the cloud-based content management platform as a respective UI 124A-124Z. Additionally, in some embodiments, the cloud-based content management platform 115 may provide a UI 124A-124Z that includes suggestion cards 200 to notify the user of documents to be of interest to the user. The cloud-based content management platform 115 may further provide the suggestion card 200 to enable the user to quickly access documents or perform an action to a document without opening the document."; The prediction engine generating a suggestion card, where the suggestion card is provided through a user interface and enables the user to quickly access documents or perform an action to a document, reads on receiving a generative machine learning model (MLM) prompt from the cloud-based content management platform. Examiner’s note: The prompt being a generative MLM prompt is an intended use of the prompt and therefore is not given patentable weight.);
and presenting, on the user interface, a selectable option to send the generative MLM prompt to the cloud-based content management platform over the computer network (Paragraph 0042, line 1-7, "The system architecture 100 includes a cloud-based environment 110 connected to user devices 120A-120Z via a network 130. The cloud-based environment 110 refers to a collection of physical machines that host applications providing one or more services (e.g., content management) to multiple user devices 120A-120Z via a network 130."; Paragraph 0045, lines 1-12, "The cloud-based content management platform 115 may enable a user to access documents and view a home screen user interface (UI) of the cloud-based content management platform as a respective UI 124A-124Z. Additionally, in some embodiments, the cloud-based content management platform 115 may provide a UI 124A-124Z that includes suggestion cards 200 to notify the user of documents to be of interest to the user. The cloud-based content management platform 115 may further provide the suggestion card 200 to enable the user to quickly access documents or perform an action to a document without opening the document."; The cloud-based content management platform providing a user interface that includes suggestion cards to notify the user of documents to be of interest to the user and enables the user to quickly access documents or perform an action to a document reads on presenting a selectable option to send the generative MLM prompt to the cloud-based content management platform.).
Dornbush does not specifically disclose: wherein the generative MLM prompt is an output of a generative MLM and is obtained by inputting, into the generative MLM, a portion of a document stored at the cloud-based content management platform; and presenting, on the user interface, a selectable option to send the generative MLM prompt to the cloud-based content management platform over the computer network for submission of the generative MLM prompt as an input to the generative MLM or another generative MLM.
Riscutia teaches:
wherein the generative MLM prompt is an output of a generative MLM and is obtained by inputting, into the generative MLM, a portion of a document stored at the cloud-based content management platform (Paragraph 0020, lines 1-6, "In various implementations of the technology, suggestions prompted by the application component and generated by the LLM service are presented as graphical input devices (e.g., hyperlinks or graphical buttons) which a user can select to have the LLM service generate the suggested content."; Paragraph 0036, lines 6-10, "In an implementation, when the user opens a document in the application environment in user experience 111, collaborative prompt object 112 initiates an interaction with LLM service 150 by generating a prompt including at least a portion of the content of the document."; Paragraph 0100, lines 4-11, "Examples of computing device 901 include, but are not limited to, desktop and laptop computers, tablet computers, mobile computers, and wearable devices. Examples may also include server computers, web servers, cloud computing platforms, and data center equipment, as well as any other type of physical or virtual server machine, container, and any variation or combination thereof."; Suggestions generated by a large language model (LLM) service which a user can select to have the LLM service generate the suggested content read on the generative MLM prompt being an output of a generative MLM, and a prompt including at least a portion of the content of a document reads on inputting a portion of a document into the generative MLM.);
presenting, on the user interface, a selectable option to send the generative MLM prompt to the cloud-based content management platform over the computer network for submission of the generative MLM prompt as an input to the generative MLM or another generative MLM (Paragraph 0020, lines 1-6, "In various implementations of the technology, suggestions prompted by the application component and generated by the LLM service are presented as graphical input devices (e.g., hyperlinks or graphical buttons) which a user can select to have the LLM service generate the suggested content."; Paragraph 0100, lines 4-11, "Examples of computing device 901 include, but are not limited to, desktop and laptop computers, tablet computers, mobile computers, and wearable devices. Examples may also include server computers, web servers, cloud computing platforms, and data center equipment, as well as any other type of physical or virtual server machine, container, and any variation or combination thereof."; Paragraph 0110, lines 1-4, "Communication interface system 907 may include communication connections and devices that allow for communication with other computing systems (not shown) over communication networks (not shown)."; Suggestions generated by a large language model (LLM) service being presented as graphical input devices which a user can select to have the LLM service generate the suggested content reads on presenting, on the user interface, a selectable option to send the generative MLM prompt for submission of the generative MLM prompt as an input to the generative MLM or another generative MLM.).
Riscutia is considered to be analogous to the claimed invention because it is in the same field of generative machine learning model prompting. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush to incorporate the teachings of Riscutia to generate suggestions by a large language model (LLM) service with a prompt including at least a portion of the content of a document and present the suggestions as graphical input devices which a user can select to have the LLM service generate the suggested content. Doing so would allow for anticipating the user's needs prior to receiving user input and offering suggestions to guide the user in accomplishing any of a number of different tasks (Riscutia; Paragraph 0029, lines 1-18).
Regarding claim 18, Dornbush in view of Riscutia discloses the method as claimed in claim 15.
Dornbush further discloses:
wherein: the user interface comprises a list of documents stored by the cloud-based content management platform (Paragraph 31, lines 7-10, "A home screen used herein refers to a graphical user interface (GUI) that lists documents associated with a user account that are stored in the cloud storage.");
and presenting the selectable option on the user interface comprises displaying the selectable option in response to a user selection, using the user interface, of a document of the list of documents (Paragraph 0045, lines 1-12, "The cloud-based content management platform 115 may enable a user to access documents and view a home screen user interface (UI) of the cloud-based content management platform as a respective UI 124A-124Z. Additionally, in some embodiments, the cloud-based content management platform 115 may provide a UI 124A-124Z that includes suggestion cards 200 to notify the user of documents to be of interest to the user. The cloud-based content management platform 115 may further provide the suggestion card 200 to enable the user to quickly access documents or perform an action to a document without opening the document."; Suggestion cards that enable the user to quickly access documents or perform an action to a document reads on the selectable option.).
Regarding claim 20, Dornbush in view of Riscutia discloses the method as claimed in claim 15.
Dornbush further discloses:
further comprising: sending the generative MLM prompt to the cloud-based content management platform over the computer network (Paragraph 0042, line 1-7, "The system architecture 100 includes a cloud-based environment 110 connected to user devices 120A-120Z via a network 130. The cloud-based environment 110 refers to a collection of physical machines that host applications providing one or more services (e.g., content management) to multiple user devices 120A-120Z via a network 130."; Paragraph 0045, lines 1-12, "The cloud-based content management platform 115 may enable a user to access documents and view a home screen user interface (UI) of the cloud-based content management platform as a respective UI 124A-124Z. Additionally, in some embodiments, the cloud-based content management platform 115 may provide a UI 124A-124Z that includes suggestion cards 200 to notify the user of documents to be of interest to the user. The cloud-based content management platform 115 may further provide the suggestion card 200 to enable the user to quickly access documents or perform an action to a document without opening the document."; The user accessing documents or performing an action to a document reads on sending the generative MLM prompt to the cloud-based content management platform.).
and receiving, over the cloud-based content management platform, a generative MLM response from the cloud-based content management platform, wherein the generative MLM response comprises a citation to a document stored by the cloud-based content management platform (Paragraph 0054, lines 38-41, "As illustrated in FIG. 2, the suggestion card 200 includes a plurality of sections such as a document information section 202, document thumbnail section 204, and event section 206."; Paragraph 0055, lines 1-11, "The document information section 202 may include information about the document, such as a title of the document, a type of the document (e.g., a word, spreadsheet, presentation slide), and an overflow menu option (e.g., a vertically oriented ellipse). Such document information may be presented as an icon or text. The prediction engine 116 may present more options when the user selects the overflow menu option, such as an option to share the document, add the document to favorites, locate the document in the cloud storage of the user, and remove the suggestion card 200."; The suggestion card including an option to locate the document in the cloud storage of the user reads on a generative MLM response comprises a citation to a document stored by the cloud-based content management platform.).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Dornbush in view of Riscutia, and further in view of Chaudhuri et al. (US Patent No. 12,411,857), hereinafter Chaudhuri.
Regarding claim 3, Dornbush in view of Riscutia discloses the method as claimed in claim 1, but does not specifically disclose: wherein generating the first generative MLM prompt using the generative MLM comprises: generating a second generative MLM prompt, comprising a context comprising the portion of the document, and a command for the generative MLM to generate the first generative MLM prompt about the portion of the document; and inputting, into the generative MLM, the second generative MLM prompt.
Chaudhuri teaches:
wherein generating the first generative MLM prompt using the generative MLM comprises: generating a second generative MLM prompt, comprising a context comprising the portion of the document, and a command for the generative MLM to generate the first generative MLM prompt about the portion of the document; and inputting, into the generative MLM, the second generative MLM prompt (Column 1, lines 26-32, "Embodiments of the present invention provide an approach for retrieving data by exploring interesting data patterns in structured tables through recommending aggregate questions in a conversational data exploration. Specially, interesting features and operators are selected that are used to frame aggregate questions based on user intent and the data."; Column 7, lines 51-59, "The proposed delivery mechanism is an artificial intelligence (AI) enabled system that explores data patterns and data fields across one or more tables or datasets through aggregate questions based on user intent. Given a tabular dataset, a determination can be made as to what the relevant questions are that can be generated using the metadata of the dataset (e.g., such as column headers, description of the dataset, title of the dataset) in addition to the cell values in the dataset."; Column 11, lines 44-52, "Model module 430 is configured to receive and pass the set of columns 522, the list of operators 524, and metadata 510 mentioned above through a pre-trained fine-tuned language model 512 (e.g., a T5 model) to generate one or more questions. T5, or Text-to-Text Transfer Transformer, is a transformer-based architecture that uses a text-to-text approach. Every task (including translation, question answering, and classification) is cast as feeding the model text as input and training it to generate some target text."; A pre-trained fine-tuned language model generating one or more relevant questions, where a set of columns and metadata from a data set is input into the language model, reads on a generative machine learning model generating a first generative MLM prompt from a second generative MLM prompt comprising a portion of a document and a command and inputting the second generative MLM prompt into the generative machine learning model, where a set of columns and metadata from a data set reads on a portion of a document and generating relevant questions reads on the command.).
Chaudhuri is considered to be analogous to the claimed invention because it is in the same field of content management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush in view of Riscutia to incorporate the teachings of Chaudhuri to use a pre-trained fine-tuned language model to generate one or more relevant questions, where a set of columns and metadata from a data set is input into the language model. Doing so would allow for dynamically adapting and improving the recommendation of interesting and relevant aggregate questions for a user to access database data based on user feedback (Chaudhuri; Column 1, lines 26-36).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Dornbush in view of Riscutia, and further in view of Dean et al. (US Patent No. 8,224,827), hereinafter Dean.
Regarding claim 5, Dornbush in view of Riscutia discloses the method as claimed in claim 1, but does not specifically disclose: wherein selecting the one or more second documents based on the selection criterion comprises selecting one or more documents whose metadata indicate that the one or more documents were last modified within a predetermined time threshold.
Dean teaches:
wherein selecting the one or more second documents based on the selection criterion comprises selecting one or more documents whose metadata indicate that the one or more documents were last modified within a predetermined time threshold (Column 3, lines 28-32, "In the context of the Internet, a common document is a web page. Web pages often include textual information and may include embedded information (such as meta information, images, hyperlinks, etc.) and/or embedded instructions (such as Javascript, etc.)."; Column 10, lines 27-38, "Using the time-varying behavior of links to (and/or from) a document, search engine 125 may score the document accordingly. For example, a downward trend in the number or rate of new links (e.g., based on a comparison of the number or rate of new links in a recent time period versus an older time period) over time could signal to search engine 125 that a document is stale, in which case search engine 125 may decrease the document's score. Conversely, an upward trend may signal a "fresh" document (e.g., a document whose content is fresh--recently created or updated) that might be considered more relevant, depending on the particular situation and implementation."; Column 12, lines 17-23, "In one exemplary implementation, search engine 125 may generate a score for a document based on the scores of the documents with links to the document for all versions of the documents within a window of time. Another version of this may factor a discount/decay into the integration based on the major update times of the document."; A search engine scoring documents based on a document being recently created or updated within a period of time reads on selecting one or more documents whose metadata indicate that the one or more documents were last modified within a predetermined time threshold.).
Dean is considered to be analogous to the claimed invention because it is in the same field of document retrieval. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush in view of Riscutia to incorporate the teachings of Dean to score documents based on a document being recently created or updated within a period of time. Doing so would allow for improving search results generated in connection with a search query (Dean; Column 1, lines 61-65).
Claims 8, 10 and 13 – 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dornbush in view of Bhattacharjee et al. (US Patent No. 12,423,345), hereinafter Bhattacharjee, and Riscutia.
Regarding claim 8, Dornbush discloses a system, comprising:
a memory (Paragraph 0106, lines 1-7, “In a further aspect, the computer system 800 may include a processing device 802, a volatile memory 804 (e.g., random access memory (RAM)), a non-volatile memory 806 (e.g., read-only memory (ROM) or electrically-erasable programmable ROM (EEPROM)), and a data storage device 816, which may communicate with each other via a bus 808.”);
and one or more processing devices, coupled to the memory (Paragraph 0106, lines 1-7, “In a further aspect, the computer system 800 may include a processing device 802, a volatile memory 804 (e.g., random access memory (RAM)), a non-volatile memory 806 (e.g., read-only memory (ROM) or electrically-erasable programmable ROM (EEPROM)), and a data storage device 816, which may communicate with each other via a bus 808.”), configured to perform operations (Paragraph 0105, lines 13-17, “Further, the term “computer” shall include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods described herein.”) comprising:
identifying an action of a user of a cloud-based content management platform with respect to content of one or more first documents of a plurality of documents stored at the cloud-based content management platform (Paragraph 0006, lines 1-15, "In some other implementations, a system and method are disclosed for notifying a document to a user of a cloud-based content management platform. A first set of documents is identified. The first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user. One or more target documents from the first set of documents for the user are identified based on an amount of overlap in topicality between a respective document and a user's current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document. The user's current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform."; Determining a document is in a user's current working set of documents based on the user accessing a document within a last predetermined time period via the cloud-based content management platform reads on identifying an action of a user of a cloud-based content management platform with respect to content of one or more first documents of a plurality of documents stored at the cloud-based content management platform.);
predicting user interest in content of one or more second documents of the plurality of documents stored at the cloud-based content management platform (Paragraph 0033, lines 1-10, "Aspects and implementations of the present disclosure address the above deficiencies, among others, by predicting documents that are likely to be of interest to the user among documents associated with the user account. The documents associated with the user account refer to documents that can be accessed and/or viewed by the user. The prediction technique of the present disclosure may predict a document containing a comment thread that may be of interest to the user because of a comment recently added to the comment thread."; Predicting documents that are likely to be of interest to the user among documents associated with the user account reads on predicting user interest in content of one or more second documents of the plurality of documents.),
wherein predicting the user interest comprises: selecting, based on a selection criterion, the one or more second documents stored in the cloud-based content management platform (Paragraph 0047, lines 1-3, "The server 112 includes a prediction engine 116 to predict documents to be suggested to the user and to notify the user about these documents."; Paragraph 0089, lines 1-11, "The prediction engine 116 may measure the level of relatedness as an amount of overlap in topicality between a respective document and the user's current working set of documents. The overlap of topicality may represent a measure or similarity score of how related the document is to the user's current set of working documents, and may be determined, for example, according to a predefined set of criteria such as a degree of overlap, an occurrence rate of words or phrases identified in a stored database as relating to a common field or topic, Jaccard similarity, cosine distance, Euclidean distance, and/or the like."; An amount of overlap in topicality between a respective document and the user's current working set of documents reads on a selection criterion.),
selecting a portion of a document of the one or more second documents (Paragraph 0055, lines 10-17, "The document thumbnail section 204 may include a thumbnail image of a specific portion of the document. The prediction engine 116 may select a portion that is associated with the event of the suggestion card 200. For example, when the event is a comment event, the prediction engine 116 may take a snippet of a paragraph to where the comment event is added."; The prediction engine selecting a portion of a document reads on selecting a portion of a document of the one or more second documents.),
and generating, using a generative machine learning model (MLM) a first generative MLM prompt based on the portion of the document (Paragraph 0045, lines 1-12, "The cloud-based content management platform 115 may enable a user to access documents and view a home screen user interface (UI) of the cloud-based content management platform as a respective UI 124A-124Z. Additionally, in some embodiments, the cloud-based content management platform 115 may provide a UI 124A-124Z that includes suggestion cards 200 to notify the user of documents to be of interest to the user. The cloud-based content management platform 115 may further provide the suggestion card 200 to enable the user to quickly access documents or perform an action to a document without opening the document."; Paragraph 0047, lines 5-9, "The prediction engine 116 may adopt a heuristics approach or a machine learning approach to implement the prediction scenario model as discussed in details with relation to FIGS. 3-5."; Paragraph 0054, lines 4-13, "The suggestion card 200 represents a document and an event associated with the document. The prediction engine 116 may build the suggestion card 200 to notify the user about a suggestion of a document predicted to be of interest to the user. The suggestion card 200 may allow the user to quickly access the document represented in the suggestion card 200. In other implementations, the prediction engine 116 may compose the suggestion card 200 to notify the user as well as to enable the user to respond to the event represented by the suggestion card 200."; Paragraph 0055, lines 10-13, "The document thumbnail section 204 may include a thumbnail image of a specific portion of the document. The prediction engine 116 may select a portion that is associated with the event of the suggestion card 200.”; The prediction engine generating a suggestion card using machine learning, where the suggestion card enables the user to quickly access documents or perform an action to a document, reads on generating a first generative MLM prompt using a generative machine learning model (MLM), and the suggestion card enabling the user to respond to an event, where a portion of the document is associated with the event, reads on generating the prompt based on the portion of the document. Examiner’s note: The prompt being a generative MLM prompt is an intended use of the prompt and therefore is not given patentable weight.);
and causing the first generative MLM prompt to be presented to the user in relation to the action of the user (Paragraph 0006, lines 1-15, "In some other implementations, a system and method are disclosed for notifying a document to a user of a cloud-based content management platform. A first set of documents is identified. The first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user. One or more target documents from the first set of documents for the user are identified based on an amount of overlap in topicality between a respective document and a user's current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document. The user's current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform."; Paragraph 0045, lines 1-12, "The cloud-based content management platform 115 may enable a user to access documents and view a home screen user interface (UI) of the cloud-based content management platform as a respective UI 124A-124Z. Additionally, in some embodiments, the cloud-based content management platform 115 may provide a UI 124A-124Z that includes suggestion cards 200 to notify the user of documents to be of interest to the user. The cloud-based content management platform 115 may further provide the suggestion card 200 to enable the user to quickly access documents or perform an action to a document without opening the document."; Identifying a target document from a set of documents for the user based on an amount of overlap in topicality between a respective document and a user's current working set of documents, where the user's current working set of documents is determined based on the user accessing a document within a last predetermined time period via the cloud-based content management platform, reads on selecting a document based on the action of the user, and the cloud-based content management platform providing a user interface that includes suggestion cards to notify the user of documents to be of interest to the user reads on causing the first generative MLM prompt to be presented to the user in relation to the action of the user.).
Dornbush does not specifically disclose: wherein the selection is based on a query embedding associated with the portion of the document.
Bhattacharjee teaches:
wherein the selection is based on a query embedding associated with the portion of the document (Column 2, lines 22-32, "This set of documents may then be evaluated and one or more candidate theme phrases may be extracted from the documents. These extracted phrases may be used to generate document embeddings and candidate embeddings to rank candidate phrases using one or more ranking techniques. In at least one embodiment, a query embedding is generated by concatenating all documents from the representative set of the cluster. The query embedding and the candidate embedding may then be used to rank candidate theme phrases to obtain a final set of themes similar to the representative set of the cluster."; Column 10, lines 30-31, "From the ranking, a set of retained candidate theme phrases may be selected 512."; Selecting candidate theme phrases using a query embedding, where the candidate theme phrases are extracted from documents, reads on selecting a portion of a document of the one or more second documents, wherein the selection is based on a query embedding associated with the portion of the document.).
Bhattacharjee is considered to be analogous to the claimed invention because it is in the same field of content management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush to incorporate the teachings of Bhattacharjee to select candidate theme phrases using a query embedding, where the candidate theme phrases are extracted from documents. Doing so would allow for identifying one or more underlying themes associated with a corpus of information (Bhattacharjee; Column 2, lines 5-11).
Dornbush in view of Bhattacharjee does not specifically disclose: wherein generating the first generative MLM prompt comprises inputting the portion of the document into the generative MLM and obtaining the first generative MLM prompt as an output of the generative MLM; wherein the first generative MLM prompt is presented as a selectable option for submitting the first generative MLM prompt as an input to the generative MLM or another generative MLM.
Riscutia teaches:
wherein generating the first generative MLM prompt comprises inputting the portion of the document into the generative MLM and obtaining the first generative MLM prompt as an output of the generative MLM (Paragraph 0020, lines 1-6, "In various implementations of the technology, suggestions prompted by the application component and generated by the LLM service are presented as graphical input devices (e.g., hyperlinks or graphical buttons) which a user can select to have the LLM service generate the suggested content."; Paragraph 0036, lines 6-10, "In an implementation, when the user opens a document in the application environment in user experience 111, collaborative prompt object 112 initiates an interaction with LLM service 150 by generating a prompt including at least a portion of the content of the document."; Suggestions generated by a large language model (LLM) service which a user can select to have the LLM service generate the suggested content read on obtaining a generative MLM prompt as an output of the generative MLM, and a prompt including at least a portion of the content of a document reads on inputting the portion of the document into the generative MLM.);
wherein the first generative MLM prompt is presented as a selectable option for submitting the first generative MLM prompt as an input to the generative MLM or another generative MLM (Paragraph 0020, lines 1-6, "In various implementations of the technology, suggestions prompted by the application component and generated by the LLM service are presented as graphical input devices (e.g., hyperlinks or graphical buttons) which a user can select to have the LLM service generate the suggested content."; Suggestions generated by a large language model (LLM) service being presented as graphical input devices which a user can select to have the LLM service generate the suggested content reads on the generative MLM prompt being presented as a selectable option for submitting the generative MLM prompt as an input to the generative MLM or another generative MLM.).
Riscutia is considered to be analogous to the claimed invention because it is in the same field of generative machine learning model prompting. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush in view of Bhattacharjee to incorporate the teachings of Riscutia to generate suggestions by a large language model (LLM) service with a prompt including at least a portion of the content of a document and present the suggestions as graphical input devices which a user can select to have the LLM service generate the suggested content. Doing so would allow for anticipating the user's needs prior to receiving user input and offering suggestions to guide the user in accomplishing any of a number of different tasks (Riscutia; Paragraph 0029, lines 1-18).
Regarding claim 10, Dornbush in view of Bhattacharjee and Riscutia discloses the system as claimed in claim 8.
Dornbush further discloses:
wherein the portion of the document comprises at least one of: a sentence in the document; a paragraph in the document; or a section in the document (Paragraph 0055, lines 10-17, "The document thumbnail section 204 may include a thumbnail image of a specific portion of the document. The prediction engine 116 may select a portion that is associated with the event of the suggestion card 200. For example, when the event is a comment event, the prediction engine 116 may take a snippet of a paragraph to where the comment event is added."; The prediction engine selecting a portion of a document reads on selecting a portion of a document of the one or more second documents, and taking a snippet of a paragraph reads on the portion of the document comprising a paragraph in the document.).
Regarding claim 13, Dornbush in view of Bhattacharjee and Riscutia discloses the system as claimed in claim 8.
Dornbush further discloses:
wherein the action of the user comprises a user selection of a document presented on a user interface, wherein the one or more first documents comprise the selected document (Paragraph 0004, lines 18-23, "A graphical user interface (GUI) of a cloud storage of the user hosted by the cloud-based content management platform for presentation to the user is provided. The GUI identifies the one or more documents. For each identified document, a respective selected comment is associated with the identified document."; Paragraph 0005, lines 1-7, "When the GUI identifying the one or more electronic documents and the respective selected comment(s) is presented to the user, the user may provide, via the GUI, one or more inputs relating to one or more of the documents and/or comments identified in the GUI, thereby enabling a continuous interaction between the user and the documents stored in the cloud-based content management platform."; The user providing one or more inputs relating to one or more of the documents via a graphical user interface reads on a user selection of a document presented on a user interface, and enabling a continuous interaction between the user and the documents stored in the cloud-based content management platform reads on the one or more first documents comprise the selected document.).
Regarding claim 14, Dornbush in view of Bhattacharjee and Riscutia discloses the system as claimed in claim 8.
Dornbush further discloses:
wherein selecting the portion of the document comprises selecting the entire document (Paragraph 0033, lines 1-10, "Aspects and implementations of the present disclosure address the above deficiencies, among others, by predicting documents that are likely to be of interest to the user among documents associated with the user account. The documents associated with the user account refer to documents that can be accessed and/or viewed by the user. The prediction technique of the present disclosure may predict a document containing a comment thread that may be of interest to the user because of a comment recently added to the comment thread."; Predicting documents that are likely to be of interest to the user among documents associated with the user account reads on selecting the entire document.).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Dornbush in view of Bhattacharjee and Riscutia, and further in view of Blohm et al. (US Patent Application Publication No. 2024/0346255), hereinafter Blohm.
Regarding claim 9, Dornbush in view of Bhattacharjee and Riscutia discloses the system as claimed in claim 8, but does not specifically disclose: wherein the generative MLM comprises a transformer-based large language model (LLM).
Blohm teaches:
wherein the generative MLM comprises a transformer-based large language model (LLM) (Paragraph 0003, lines 1-12, "The techniques disclosed herein provide systems for enhancing knowledge base (KB) operations through knowledge summarization and curation by a large language model (LLM). Large language models have seen widespread adoption due to their diverse processing capabilities in vision, speech, language, and decision making. Unlike other artificial intelligence (AI) models such as recurrent neural networks and long short-term memory (LSTM) models, transformer-based large language models make use of a native self-attention mechanism to identify vague context from limited available data and even synthesize new content from images and music to software.").
Blohm is considered to be analogous to the claimed invention because it is in the same field of content management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush in view of Bhattacharjee and Riscutia to incorporate the teachings of Blohm to use a transformer-based large language model as a generative machine learning model. Doing so would allow for identify vague context from limited available data (Blohm; Paragraph 0003, lines 1-12).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Dornbush in view of Bhattacharjee and Riscutia, and further in view of Chaudhuri.
Regarding claim 11, Dornbush in view of Bhattacharjee and Riscutia discloses the system as claimed in claim 8, but does not specifically disclose: wherein the generative MLM prompt includes a question.
Chaudhuri teaches:
wherein the first generative MLM prompt includes a question (Column 1, lines 26-32, "Embodiments of the present invention provide an approach for retrieving data by exploring interesting data patterns in structured tables through recommending aggregate questions in a conversational data exploration. Specially, interesting features and operators are selected that are used to frame aggregate questions based on user intent and the data."; Column 7, lines 51-59, "The proposed delivery mechanism is an artificial intelligence (AI) enabled system that explores data patterns and data fields across one or more tables or datasets through aggregate questions based on user intent. Given a tabular dataset, a determination can be made as to what the relevant questions are that can be generated using the metadata of the dataset (e.g., such as column headers, description of the dataset, title of the dataset) in addition to the cell values in the dataset."; A pre-trained fine-tuned language model generating one or more relevant questions reads on the generative MLM prompt including a question.).
Chaudhuri is considered to be analogous to the claimed invention because it is in the same field of content management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush in view of Bhattacharjee and Riscutia to incorporate the teachings of Chaudhuri to use a pre-trained fine-tuned language model to generate one or more relevant questions. Doing so would allow for dynamically adapting and improving the recommendation of interesting and relevant aggregate questions for a user to access database data based on user feedback (Chaudhuri; Column 1, lines 26-36).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Dornbush in view of Riscutia, and further in view of Filben et al. (US Patent No. 12,353,694), hereinafter Filben.
Regarding claim 16, Dornbush in view of Riscutia discloses the method as claimed in claim 15.
Dornbush further discloses:
wherein: the selectable option comprises a text box that includes the generative MLM prompt (Paragraph 0054, lines 1-10, "In some implementations, the suggestion card 200 may take a form of a card (i.e., a box resembling a card) as shown in FIG. 2, amongst other possible forms such as a list or a table. The suggestion card 200 represents a document and an event associated with the document. The prediction engine 116 may build the suggestion card 200 to notify the user about a suggestion of a document predicted to be of interest to the user. The suggestion card 200 may allow the user to quickly access the document represented in the suggestion card 200."; Paragraph 0060, lines 23-29, "In an implementation, in response to the prediction engine 116 detecting the user's selection of the reply intelligent button 210A, the prediction engine 116 may provide a text box inside the suggestion card 200 for the user to enter content of the reply and an add button to add the reply content to the respective comment thread."; The suggestion card reads on the selectable option.).
Dornbush in view of Riscutia does not specifically disclose: presenting the selectable option on the user interface comprises displaying the text box below a search field of the user interface.
Filben teaches:
presenting the selectable option on the user interface comprises displaying the text box below a search field of the user interface (Column 11, lines 41-51, "Continuing with FIG. 2B, in the illustrated example, selected quantity indicator 216 and quick action selection control 218 are positioned above search field 220, and selection instruction 214 and options list 212 are positioned below search field 220. While this arrangement might present certain organizational and technical advantages, such as efficiently presenting this information in an order that facilitates ease of interaction with multiselect component 146, including to a user of screen reader 132, this disclosure contemplates arranging the information of selection box 210 in any suitable manner."; An options list positioned below a search field reads on displaying the text box below a search field of the user interface.).
Filben is considered to be analogous to the claimed invention because it is in the same field of user interfaces. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush in view of Riscutia to incorporate the teachings of Filben to implement an options list positioned below a search field. Doing so would allow for efficiently presenting information in an order that facilitates ease of interaction (Filben; Column 11, lines 41-51).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Dornbush in view of Riscutia, and further in view of Wu et al. (US Patent No. 9,177,018), hereinafter Wu.
Regarding claim 17, Dornbush in view of Riscutia discloses the method as claimed in claim 15.
Dornbush further discloses:
wherein: the selectable option comprises a text box that includes the generative MLM prompt (Paragraph 0054, lines 1-10, "In some implementations, the suggestion card 200 may take a form of a card (i.e., a box resembling a card) as shown in FIG. 2, amongst other possible forms such as a list or a table. The suggestion card 200 represents a document and an event associated with the document. The prediction engine 116 may build the suggestion card 200 to notify the user about a suggestion of a document predicted to be of interest to the user. The suggestion card 200 may allow the user to quickly access the document represented in the suggestion card 200."; Paragraph 0060, lines 23-29, "In an implementation, in response to the prediction engine 116 detecting the user's selection of the reply intelligent button 210A, the prediction engine 116 may provide a text box inside the suggestion card 200 for the user to enter content of the reply and an add button to add the reply content to the respective comment thread."; The suggestion card reads on the selectable option.).
Dornbush in view of Riscutia does not specifically disclose: presenting the selectable option on the user interface comprises displaying the text box below one or more search results displayed on the user interface.
Wu teaches:
presenting the selectable option on the user interface comprises displaying the text box below one or more search results displayed on the user interface (Column 6, lines 5-11, "While the cross-language search options 210 are shown below the search results 204, 206, and 208 responsive to the query 202, the cross-language search options 210 can appear in other locations in the user interface 200, including, but not limited to, above the search results, to the left of the search results, to the right of the search results, or intermixed with the search results."; Search options positioned below the search results reads on displaying the text box below one or more search results displayed on the user interface.).
Wu is considered to be analogous to the claimed invention because it is in the same field of user interfaces. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush in view of Riscutia to incorporate the teachings of Wu to implement search options positioned below the search results. Doing so would allow for presenting a user interface including cross-language search options (Wu; Column 7, lines 23-28).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Dornbush in view of Riscutia, and further in view of Tu et al. (US Patent No. 11,971,860), hereinafter Tu.
Regarding claim 19, Dornbush in view of Riscutia discloses the method as claimed in claim 15, but does not specifically disclose: wherein: the user interface comprises a folder stored by the cloud-based content management platform; and presenting the selectable option on the user interface comprises displaying the selectable option in response to a user selection, using the user interface, of the folder.
Tu teaches:
wherein: the user interface comprises a folder stored by the cloud-based content management platform; and presenting the selectable option on the user interface comprises displaying the selectable option in response to a user selection, using the user interface, of the folder (Column 4, lines 10-14, "The content items can be stored in content storage 160. Content storage 160 can be a storage device, multiple storage devices, or a server. Alternatively, content storage 160 can be a cloud storage provider or network storage accessible through one or more communications networks."; Column 9, lines 5-14, "In some embodiments, GUI 400 can present a folder 402 managed by content management system 106. Continuing the example above, folder 402 can be a folder that the teacher uses to store lecture notes for the class the teacher is teaching. Upon receiving a user selection of folder 402, GUI 400 can present menu 404 that displays options for folder 402. For example, menu 404 can present a “publish” option that when selected by a user (e.g., the teacher) presents GUI 500 of FIG. 5 that allows the user to select options for sharing and/or publishing folder 402."; Presenting a menu that displays options for a folder upon receiving a user selection of the folder reads on displaying the selectable option in response to a user selection of the folder.).
Tu is considered to be analogous to the claimed invention because it is in the same field of user interfaces. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dornbush in view of Riscutia to incorporate the teachings of Tu to present a menu that displays options for a folder upon receiving a user selection of the folder. Doing so would allow for providing an embedded web view of a folder in a content management system (Tu; Column 1, lines 28-31).
Allowable Subject Matter
Claim 6 – 7 and 12 are 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.
The following is a statement of reasons for the indication of allowable subject matter:
The primary reason claim 6 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, is the inclusion of the limitations “the portion of the document comprises a plurality of portions of the one or more second documents” and “each portion of the plurality of portions is from a different document of the one or more second documents”, in combination with the limitations to identify an action of a user of a cloud-based content management platform with respect to content of one or more first documents of a plurality of documents stored at the cloud-based content management platform, predict user interest in content of one or more second documents of the plurality of documents stored at the cloud-based content management platform, select the one or more second documents stored at the cloud-based content management platform based on a selection criterion, select a portion of a document of the one or more second documents based on the action of the user, generate a prompt based on the portion of the document using a generative machine learning model (MLM), and cause the prompt to be presented to the user in relation to the action of the user.
The primary reason claim 7 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, is the inclusion of the limitations “generating, using an embedding model, a query embedding based on the portion of the document”, “retrieving a query embedding associated with the document that includes the portion of the document”, and “responsive to the query embedding of the portion of the document being within a threshold similarity from the retrieved query embedding associated with the document, selecting the portion of the document”, in combination with the limitations to identify an action of a user of a cloud-based content management platform with respect to content of one or more first documents of a plurality of documents stored at the cloud-based content management platform, predict user interest in content of one or more second documents of the plurality of documents stored at the cloud-based content management platform, select the one or more second documents stored at the cloud-based content management platform based on a selection criterion, select a portion of a document of the one or more second documents based on the action of the user, generate a prompt based on the portion of the document using a generative machine learning model (MLM), and cause the prompt to be presented to the user in relation to the action of the user.
The primary reason claim 12 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, is the inclusion of the limitations “the action of the user comprises inputting text data into a user interface of the cloud-based content management platform” and “selecting the portion of the document of the one or more second documents is based on a query embedding based on the text data and the query embedding associated with the portion of the document being within a threshold similarity”, in combination with the limitations to identify an action of a user of a cloud-based content management platform with respect to content of one or more first documents of a plurality of documents stored at the cloud-based content management platform, predict user interest in content of one or more second documents of the plurality of documents stored at the cloud-based content management platform, select the one or more second documents stored at the cloud-based content management platform based on a selection criterion, select a portion of a document of the one or more second documents based on the action of the user, generate a prompt based on the portion of the document using a generative machine learning model (MLM), and cause the prompt to be presented to the user in relation to the action of the user.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Boggs whose telephone number is (571)272-2968. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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, Daniel Washburn can be reached at (571)272-5551. 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.
/JAMES BOGGS/Examiner, Art Unit 2657
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