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 .
DETAILED ACTION
1. This action is responsive to application communication filed on 12/27/2023.
2. Claims 1-20 are pending in the case.
3. Claims 1, 19 and 20 are independent claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-16 and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Neervannan et al. (hereinafter “Neervannan”), U.S. Published Application No. 20250061141 A1 which claims priority to U.S. Provisional Application No. 63520505 20230818.
Claim 1:
Neervannan teaches A computing device for generating content, comprising:
one or more memories configured to store instructions; and one or more processors configured to execute the instructions to perform operations, the operations comprising: (e.g., processor and memory of computing devices 22A of Figure 1 or remote computer 1810 of Figure 18 par. 73; The system 20 may be one or more computing devices 22 (such as computing devices 22a, 22b, . . . , 22n) that connect to, communicate with and/or exchange data over a link 24 to a search system 26 that interact with each other to provide the contextual and sentiment-aware deep search within a piece of content. Each computing device may be a processing unit based device with sufficient processing power, memory/storage and connectivity/communications capabilities to connect to and interact with the system 26)
providing, in response to a selection of a plurality of items of content, a user interface including a first portion and a second portion, the first portion including a summary description generated via one or more machine-learned models based on the plurality of items of content and the second portion including a plurality of user interface elements configured to perform an operation with respect to at least one of the summary description or the plurality of items of content. (e.g., in response to selecting documents and/or snippets (i.e., plurality of items of content), generating a summery description via AI models and then selecting filtering operations with respect to the summary (i.e., perform an operation with respect to the summary) to be fed into the AI model for creating additional varying summaries par. 61; The subject matter described herein includes a market intelligence platform for searching financial documents to identify relevant documents and/or relevant snippets of text within the documents, and to generate summaries of those documents. The identified snippets of text may be characterized within the documents, and, in some embodiments, the snippets of text may be used to identify particular documents to summarize using one or more trained AI models. The summaries of the individual documents may then be fed into one or more AI models for creating summaries across multiple companies, sectors, industries, peer groups, and the like. Par. 62; The summaries and/or analyses provide graphical information to the user that allows for rapid understanding and decision-making, while likewise providing citations to underlying sources for the information presented in the summaries that allows the user to further investigate (i.e., “drill-down”) on any desired aspect of the summaries. Par. 68; The user interface includes a multi-pane user interface configured to display (1) summaries relevant to one or more of user queries; (2) citations to the underlying documents for each summary; and (3) the underlying documents, with the snippets that were used for the summarization identified within the underlying documents. )
Claim 2 depends on claim 1:
Neervannan teaches wherein the operations further comprise:
receiving a selection of the plurality of items of content;
and implementing the one or more machine-learned models to generate the summary description based on the plurality of items of content. (e.g., selecting documents and/or snippets (i.e., plurality of items of content) to generate a summary description via AI models par. 61; The subject matter described herein includes a market intelligence platform for searching financial documents to identify relevant documents and/or relevant snippets of text within the documents, and to generate summaries of those documents. The identified snippets of text may be characterized within the documents, and, in some embodiments, the snippets of text may be used to identify particular documents to summarize using one or more trained AI models. The summaries of the individual documents may then be fed into one or more AI models for creating summaries across multiple companies, sectors, industries, peer groups, and the like.)
Claim 3 depends on claim 1:
Neervannan teaches wherein the plurality of user interface elements include a first user interface element comprising a suggested query, and the operations further comprise: receiving a selection of the first user interface element; and implementing the one or more machine-learned models to generate a response to the suggested query based on at least one of the summary description or the plurality of items of content. (e.g., displaying topics (i.e., suggested query) associated with a subject matter of searched documents (e.g., summaries) and selecting the topics to further narrow the criteria of the search par. 69; Summaries across documents, companies, sectors, and industries with a conversational chat-based user interface allow users to ask questions in real-time about documents to gather market intelligence. The summaries are generated in response to a user query, and they provide the user with underlying supporting documentation to allow the user to follow-up and/or confirm as necessary. par. 171; The user interface 80 may also have a topics portion 86 that lists the topics associated with the particular subject matter area, the financial industry documents in this example. When the system is used in a different subject matter area, such as the law, the system would like a different set of topics in the list. The list of topics portions can be used at any time during the viewing of documents to change the topics of interest. Any time that the user changes the topics, the viewing portion 84 is dynamically updated as the user changes the topics par. 210; Thus, the models identify the topic of commercial real estate as relevant across the industry. This information and topic may then be used to analyze trends as they relate to commercial real estate in one or more industries.)
Claim 4 depends on claim 3:
Neervannan teaches wherein the operations further comprise providing the user interface a third portion to provide for display a dialogue including the suggested query and the response. (e.g., chat interface integrates with the document search and provides for dialogue with user Examiner considers the retrieved snippets to be examples of the suggested query and the response par. 216; The chat-based interactions may use or include the following components: (1) an internal proprietary model (such as a large-language model (LLM)); (2) vector/hybrid search; (3) dialog management; and (4) chat user interface. The chat user interface integrates with the document search. Par. 219; The vector/hybrid search is integrated into the dialog management to retrieve snippets relevant to user questions. )
Claim 5 depends on claim 4:
Neervannan teaches wherein the third portion includes a citation user interface element which indicates a number of items of content from among the plurality of items of content referenced by the one or more machine-learned models to generate the response. (e.g., including citations in the user interface Par. 68; The user interface includes a multi-pane user interface configured to display (1) summaries relevant to one or more of user queries; (2) citations to the underlying documents for each summary; and (3) the underlying documents, with the snippets that were used for the summarization identified within the underlying documents. )
Claim 6 depends on claim 5:
Neervannan teaches wherein the operations further comprise: in response to receiving a selection of the citation user interface element, providing, for display in a fourth portion of the user interface, content from one or more items of content from among the plurality of items of content used to generate the response. (e.g., selecting citations to get a complete view of the relevant information and summaries par. 215; In various embodiments, the systems and methods described herein provide a multi-pane user-interface that allows users to drill into relevant content and underlying source documentation for summaries, for example, through citations provided to the underlying source documents, to get a complete view of the relevant information and summaries. Thus, in one or more embodiments, the subject matter described herein maintains an audit trail for the underlying documents that provide the content for the summaries. This audit trail allows the user to access the source documents to confirm the accuracy and/or to dig deeper into the source documents)
Claim 7 depends on claim 4:
Neervannan teaches wherein the third portion includes a note generation user interface element, and the operations further comprise: in response to receiving a selection of the note generation user interface element, generating a note which includes content from the suggested query and the response, and storing the note. (e.g., storage features that support storing conversations between user and AI system as a note par. 223; In various embodiments, after receiving a summary as a result of a search, users may ask one or more freeform follow-up questions. In one embodiment, the conversation is bounded by the active search. Par. 224; In one or more embodiments, historical conversations may be loaded to allow a user to return to previous conversations. par. 226; In one or more embodiments, the stored conversation history is provided as a dataset for analysis and/or summarization. Conversation threads may be stored so that they can be provided to the user. Feature storage supports fetching the most recent conversations for a user. Users may return to conversations, for example, conversations in which they asked follow-up questions.)
Claim 8 depends on claim 1:
Neervannan teaches wherein the plurality of user interface elements include a first user interface element configured to generate a note, and the operations further comprise: (e.g., generate a follow up question and response par. 223; In various embodiments, after receiving a summary as a result of a search, users may ask one or more freeform follow-up questions.)
providing the user interface a third portion which includes content from one or more items of content from among the plurality of items of content; (e.g., area for displaying responses to document search request par. 216; The chat-based interactions may use or include the following components: (1) an internal proprietary model (such as a large-language model (LLM)); (2) vector/hybrid search; (3) dialog management; and (4) chat user interface. The chat user interface integrates with the document search. Par. 219; The vector/hybrid search is integrated into the dialog management to retrieve snippets relevant to user questions. )
receiving a selection of a portion of the content from the one or more items of content from among the plurality of items of content; receiving a selection of the first user interface element; and in response to receiving the selection of the portion of the content and the first user interface element, generating, via the one or more machine-learned models based on the portion of the content, the note which includes a summary of the portion of the content. (e.g., selecting documents and/or snippets (i.e., plurality of items of content) to generate a summary description via AI models or displaying topics (i.e., suggested query) associated with a subject matter of searched documents (e.g., summaries) and selecting the topics to further narrow the criteria of the search par. 61; The subject matter described herein includes a market intelligence platform for searching financial documents to identify relevant documents and/or relevant snippets of text within the documents, and to generate summaries of those documents. The identified snippets of text may be characterized within the documents, and, in some embodiments, the snippets of text may be used to identify particular documents to summarize using one or more trained AI models. The summaries of the individual documents may then be fed into one or more AI models for creating summaries across multiple companies, sectors, industries, peer groups, and the like. par. 69; Summaries across documents, companies, sectors, and industries with a conversational chat-based user interface allow users to ask questions in real-time about documents to gather market intelligence. The summaries are generated in response to a user query, and they provide the user with underlying supporting documentation to allow the user to follow-up and/or confirm as necessary. par. 171; The user interface 80 may also have a topics portion 86 that lists the topics associated with the particular subject matter area, the financial industry documents in this example. When the system is used in a different subject matter area, such as the law, the system would like a different set of topics in the list. The list of topics portions can be used at any time during the viewing of documents to change the topics of interest. Any time that the user changes the topics, the viewing portion 84 is dynamically updated as the user changes the topics par. 210; Thus, the models identify the topic of commercial real estate as relevant across the industry. This information and topic may then be used to analyze trends as they relate to commercial real estate in one or more industries.)
Claim 9 depends on claim 1:
Neervannan teaches wherein the plurality of user interface elements include a first user interface element configured to add content to an existing note, and the operations further comprise: (e.g., storage features that support storing conversations between user and AI system as a note or adding content to an existing conversation (i.e., existing note) par. 223; In various embodiments, after receiving a summary as a result of a search, users may ask one or more freeform follow-up questions. In one embodiment, the conversation is bounded by the active search. Par. 224; In one or more embodiments, historical conversations may be loaded to allow a user to return to previous conversations. par. 226; In one or more embodiments, the stored conversation history is provided as a dataset for analysis and/or summarization. Conversation threads may be stored so that they can be provided to the user. Feature storage supports fetching the most recent conversations for a user. Users may return to conversations, for example, conversations in which they asked follow-up questions.)
providing the user interface a third portion which includes content from one or more items of content from among the plurality of items of content; (e.g., area for displaying responses to document search request par. 216; The chat-based interactions may use or include the following components: (1) an internal proprietary model (such as a large-language model (LLM)); (2) vector/hybrid search; (3) dialog management; and (4) chat user interface. The chat user interface integrates with the document search. Par. 219; The vector/hybrid search is integrated into the dialog management to retrieve snippets relevant to user questions. )
receiving a selection of a portion of the content from the one or more items of content from among the plurality of items of content;
receiving a selection of the first user interface element;
(e.g., selecting documents and/or snippets (i.e., plurality of items of content) to generate a summary description via AI models or displaying topics (i.e., suggested query) associated with a subject matter of searched documents (e.g., summaries) and selecting the topics to further narrow the criteria of the search par. 61; The subject matter described herein includes a market intelligence platform for searching financial documents to identify relevant documents and/or relevant snippets of text within the documents, and to generate summaries of those documents. The identified snippets of text may be characterized within the documents, and, in some embodiments, the snippets of text may be used to identify particular documents to summarize using one or more trained AI models. The summaries of the individual documents may then be fed into one or more AI models for creating summaries across multiple companies, sectors, industries, peer groups, and the like. par. 69; Summaries across documents, companies, sectors, and industries with a conversational chat-based user interface allow users to ask questions in real-time about documents to gather market intelligence. The summaries are generated in response to a user query, and they provide the user with underlying supporting documentation to allow the user to follow-up and/or confirm as necessary. par. 171; The user interface 80 may also have a topics portion 86 that lists the topics associated with the particular subject matter area, the financial industry documents in this example. When the system is used in a different subject matter area, such as the law, the system would like a different set of topics in the list. The list of topics portions can be used at any time during the viewing of documents to change the topics of interest. Any time that the user changes the topics, the viewing portion 84 is dynamically updated as the user changes the topics par. 210; Thus, the models identify the topic of commercial real estate as relevant across the industry. This information and topic may then be used to analyze trends as they relate to commercial real estate in one or more industries.)
and in response to receiving the selection of the portion of the content and the first user interface element, adding the portion of the content to the existing note. (e.g., storage features that support storing conversations between user and AI system as a note or adding content to an existing conversation (i.e., existing note) par. 223; In various embodiments, after receiving a summary as a result of a search, users may ask one or more freeform follow-up questions. In one embodiment, the conversation is bounded by the active search. Par. 224; In one or more embodiments, historical conversations may be loaded to allow a user to return to previous conversations. par. 226; In one or more embodiments, the stored conversation history is provided as a dataset for analysis and/or summarization. Conversation threads may be stored so that they can be provided to the user. Feature storage supports fetching the most recent conversations for a user. Users may return to conversations, for example, conversations in which they asked follow-up questions.)
Claim 10 depends on claim 1:
Neervannan teaches wherein the first portion further includes at least one key topic user interface element comprising at least one key topic relating to the summary description of the plurality of items of content, and the operations further comprise receiving a selection of the at least one key topic user interface element; and implementing the one or more machine-learned models to generate an output relating to the at least one key topic based on at least one of the summary description or the plurality of items of content. (e.g., selection of a topic to generate summaries or filter search results par. 62; The systems and methods are configured to perform a document search across a collection of documents in one or more electronic data sources, in the financial domain, over time, guided by concepts, topics, and/or trends identified by one or more AI models. Par. 75; The user interface of the search system (implemented as a user interface unit/portion) allows a user to conduct topical and sentiment filter based deep searches as described below in more detail. Par. 78; the topic tags at the sentence, sub-sentence and/or paragraph level provide the user with granular search capabilities par. 79; filters or underlines the sentences that match the topic(s), and brings back the result in an easy-to-read format to the user. The users may be given the choice to quickly drill down to the specific portions and find out the sentiment level, with matching topics and retrieve relevant text par. 171; The list of topics portions can be used at any time during the viewing of documents to change the topics of interest. Any time that the user changes the topics, the viewing portion 84 is dynamically updated as the user changes the topics,)
Claim 11 depends on claim 10:
Neervannan teaches wherein the operations further comprise providing the user interface a third portion to provide for display a dialogue including the key topic and the output relating to the at least one key topic. (e.g., chat interface integrates with the document search and provides for dialogue with user having option to select from the provided list of topics by the system par. 216; The chat-based interactions may use or include the following components: (1) an internal proprietary model (such as a large-language model (LLM)); (2) vector/hybrid search; (3) dialog management; and (4) chat user interface. The chat user interface integrates with the document search. Par. 219; The vector/hybrid search is integrated into the dialog management to retrieve snippets relevant to user questions. par. 171; The user interface 80 may also have a topics portion 86 that lists the topics associated with the particular subject matter area, the financial industry documents in this example. When the system is used in a different subject matter area, such as the law, the system would like a different set of topics in the list. The list of topics portions can be used at any time during the viewing of documents to change the topics of interest. Any time that the user changes the topics, the viewing portion 84 is dynamically updated as the user changes the topics,)
Claim 12 depends on claim 1:
Neervannan teaches wherein the operations further comprise: providing the user interface a third portion to provide for display at least one note generated via the one or more machine-learned models based on the plurality of items of content. (e.g., area for displaying responses to document search request par. 216; The chat-based interactions may use or include the following components: (1) an internal proprietary model (such as a large-language model (LLM)); (2) vector/hybrid search; (3) dialog management; and (4) chat user interface. The chat user interface integrates with the document search. Par. 219; The vector/hybrid search is integrated into the dialog management to retrieve snippets relevant to user questions. )
Claim 13 depends on claim 12:
Neervannan teaches wherein the third portion includes a citation user interface element which indicates a number of items of content from among the plurality of items of content referenced by the one or more machine-learned models to generate the at least one note. (e.g., including citations in the user interface Par. 68; The user interface includes a multi-pane user interface configured to display (1) summaries relevant to one or more of user queries; (2) citations to the underlying documents for each summary; and (3) the underlying documents, with the snippets that were used for the summarization identified within the underlying documents. )
Claim 14 depends on claim 13:
Neervannan teaches wherein the operations further comprise: in response to receiving a selection of the citation user interface element, providing, for display in a fourth portion of the user interface, content from one or more items of content from among the plurality of items of content used to generate the note. (e.g., selecting citations to get a complete view of the relevant information and summaries par. 215; In various embodiments, the systems and methods described herein provide a multi-pane user-interface that allows users to drill into relevant content and underlying source documentation for summaries, for example, through citations provided to the underlying source documents, to get a complete view of the relevant information and summaries. Thus, in one or more embodiments, the subject matter described herein maintains an audit trail for the underlying documents that provide the content for the summaries. This audit trail allows the user to access the source documents to confirm the accuracy and/or to dig deeper into the source documents)
Claim 15 depends on claim 14:
Neervannan teaches wherein the operations further comprise: in response to receiving the selection of the citation user interface element, providing, for display in the fourth portion of the user interface, contextual content about the content from the one or more items of content from among the plurality of items of content used to generate the note. (e.g., selecting citations to get a complete view of the relevant information and summaries par. 215; In various embodiments, the systems and methods described herein provide a multi-pane user-interface that allows users to drill into relevant content and underlying source documentation for summaries, for example, through citations provided to the underlying source documents, to get a complete view of the relevant information and summaries. Thus, in one or more embodiments, the subject matter described herein maintains an audit trail for the underlying documents that provide the content for the summaries. This audit trail allows the user to access the source documents to confirm the accuracy and/or to dig deeper into the source documents)
Claim 16 depends on claim 12:
Neervannan teaches wherein the plurality of user interface elements include a first user interface element configured to generate new content based on one or more notes, and the operations further comprise: (e.g., return to previous conversations adding content to an existing conversation (i.e., existing note) par. 223; In various embodiments, after receiving a summary as a result of a search, users may ask one or more freeform follow-up questions. In one embodiment, the conversation is bounded by the active search. Par. 224; In one or more embodiments, historical conversations may be loaded to allow a user to return to previous conversations. par. 226; In one or more embodiments, the stored conversation history is provided as a dataset for analysis and/or summarization. Conversation threads may be stored so that they can be provided to the user. Feature storage supports fetching the most recent conversations for a user. Users may return to conversations, for example, conversations in which they asked follow-up questions.)
providing the user interface a third portion to provide for display a plurality of notes generated via the one or more machine-learned models based on the plurality of items of content; (e.g., area for displaying responses to document search request par. 216; The chat-based interactions may use or include the following components: (1) an internal proprietary model (such as a large-language model (LLM)); (2) vector/hybrid search; (3) dialog management; and (4) chat user interface. The chat user interface integrates with the document search. Par. 219; The vector/hybrid search is integrated into the dialog management to retrieve snippets relevant to user questions. )
receiving a selection of the plurality of notes; receiving a selection of the first user interface element; and in response to receiving the selection of the plurality of notes and the first user interface element, generate the new content based on the plurality of notes. (e.g., selecting documents and/or snippets (i.e., plurality of items of content) to generate a summary description via AI models or displaying topics (i.e., suggested query) associated with a subject matter of searched documents (e.g., summaries) and selecting the topics to further narrow the criteria of the search par. 61; The subject matter described herein includes a market intelligence platform for searching financial documents to identify relevant documents and/or relevant snippets of text within the documents, and to generate summaries of those documents. The identified snippets of text may be characterized within the documents, and, in some embodiments, the snippets of text may be used to identify particular documents to summarize using one or more trained AI models. The summaries of the individual documents may then be fed into one or more AI models for creating summaries across multiple companies, sectors, industries, peer groups, and the like. par. 69; Summaries across documents, companies, sectors, and industries with a conversational chat-based user interface allow users to ask questions in real-time about documents to gather market intelligence. The summaries are generated in response to a user query, and they provide the user with underlying supporting documentation to allow the user to follow-up and/or confirm as necessary. par. 171; The user interface 80 may also have a topics portion 86 that lists the topics associated with the particular subject matter area, the financial industry documents in this example. When the system is used in a different subject matter area, such as the law, the system would like a different set of topics in the list. The list of topics portions can be used at any time during the viewing of documents to change the topics of interest. Any time that the user changes the topics, the viewing portion 84 is dynamically updated as the user changes the topics par. 210; Thus, the models identify the topic of commercial real estate as relevant across the industry. This information and topic may then be used to analyze trends as they relate to commercial real estate in one or more industries.)
Claim 18 depends on claim 1:
Neervannan teaches wherein the second portion includes a text entry box to receive a query from a user, and the operations further comprise: implementing the one or more machine-learned models to generate a response to the query based on at least one of the summary description or the plurality of items of content. (e.g., displaying interface for receiving query to generate a response based on the summaries via AI models par. 62; (c) provides a human-like chat interface to allow users to query information about one or more companies or industries in a conversational manner; par. 68; The user interface includes a multi-pane user interface configured to display (1) summaries relevant to one or more of user queries; (2) citations to the underlying documents for each summary; and (3) the underlying documents, with the snippets that were used for the summarization identified within the underlying documents. Par. 69; The user query is used to perform a search to identify relevant documents and relevant snippets within the documents. The relevant snippets and/or documents are then fed into an AI model (e.g., LLM) for summarization. The AI model uses metadata associated with the documents from the document search to rank the snippets for summarization. )
Independent Claim 19:
Neervannan teaches A computing device for generating content, comprising: one or more memories configured to store instructions; and one or more processors configured to execute the instructions to perform operations, the operations comprising: (e.g., processor and memory of computing devices 22A of Figure 1 or remote computer 1810 of Figure 18 par. 73; The system 20 may be one or more computing devices 22 (such as computing devices 22a, 22b, . . . , 22n) that connect to, communicate with and/or exchange data over a link 24 to a search system 26 that interact with each other to provide the contextual and sentiment-aware deep search within a piece of content. Each computing device may be a processing unit based device with sufficient processing power, memory/storage and connectivity/communications capabilities to connect to and interact with the system 26)
receiving an input to create a notebook; (e.g., input to create a custom report (i.e., notebook) ; par. 176; However, using the processes of the deep search system, the user can have a custom report generated as shown in FIGS. 10A and 10B which is a user-defined summary of what a selection of companies said about a topic of interest to the user. The example in FIGS. 10A and 10B shows what chip makers said about inventory during the past quarter, an indicator of the business cycle in that industry. As shown, the portions of the documents for each company are shown side-by-side 120-124 with the of interest sentences (based on the user's expressed interest) are highlighted so that the user can quickly review the documents of the three companies in the same industry.)
receiving a selection of a plurality of items of content to add to the notebook; (e.g., selection of documents to add to the platform and used to generate a custom report (e.g., adding relevant document content to the custom report) par. 71; The market intelligence platform described herein allows for user-selection of one or more of the multiple documents sources to include/exclude when performing a search or other type of user query. In one or more embodiments, the market intelligence platform is configured to automatically determine one or more of the document sources to include/exclude when performing a search or other type of user query based on the search or query itself. )
in response to receiving the selection of the plurality of items of content, implementing one or more machine-learned models to generate a summary description based on the plurality of items of content and at least one of a key topic user interface element indicative of a topic of the plurality of items of content or a selectable user interface element indicative of a query relating to the plurality of items of content; (e.g., selection of search terms and document content related to a topic to generate summaries of the document content via AI models par. 62; The systems and methods are configured to perform a document search across a collection of documents in one or more electronic data sources, in the financial domain, over time, guided by concepts, topics, and/or trends identified by one or more AI models. Par. 75; The user interface of the search system (implemented as a user interface unit/portion) allows a user to conduct topical and sentiment filter based deep searches as described below in more detail. Par. 78; the topic tags at the sentence, sub-sentence and/or paragraph level provide the user with granular search capabilities par. 79; filters or underlines the sentences that match the topic(s), and brings back the result in an easy-to-read format to the user. The users may be given the choice to quickly drill down to the specific portions and find out the sentiment level, with matching topics and retrieve relevant text par. 171; The list of topics portions can be used at any time during the viewing of documents to change the topics of interest.)
and providing a user interface including a first portion and a second portion, the first portion including the summary description and the second portion including the selectable user interface element. (e.g., interface displaying summaries and selectable snippets and search terms used to generate the summaries par. 68; The user interface includes a multi-pane user interface configured to display (1) summaries relevant to one or more of user queries; (2) citations to the underlying documents for each summary; and (3) the underlying documents, with the snippets that were used for the summarization identified within the underlying documents.)
Independent Claim 20:
Claim 20 is substantially encompassed in claim 19, therefore, Examiner relies on the same rationale set forth in claim 19 to reject claim 20.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Neervannan as cited above, in view of Mann et al. (hereinafter “Mann”), U.S. Patent No. 11501255 B2.
Claim 17 depends on claim 1:
Neervannan fails to expressly teach wherein the operations further comprise: generating, via the one or more machine-learned models, a graphical image representing the plurality of items of content; (e.g., graphical image of a report representing a range of documents par. 174; The processes of the deep search system and method described above can be used to generate reports for the user that help the user quickly review a quick “cliff notes” summary of the document or a range of documents because the system as described above can identify relevant sentences within a large document, or many documents, based on the user's custom criteria (e.g. topic, tense, tone, recurring, keyword search, industries, market caps, etc. . . . ), and create the document summary of summary of a range of documents. Par. 176; However, using the processes of the deep search system, the user can have a custom report generated as shown in FIGS. 10A and 10B which is a user-defined summary of what a selection of companies said about a topic of interest to the user. )
and providing a location including the graphical image, the location storing the plurality of items of content and a project file including the summary description. (e.g., providing storage for a report of summaries and search content par. 244; Summaries are loaded into feature storage 1410 from summarization backup 1412 if needed. par. 247; Referring to FIG. 15, summarization generator 1534 hosts the code that generates the summaries and writes them to storage. Par. 256; The documents and/or metadata for public cloud server 1702 are stored, for example, in feature storage datastore 1708. Private cloud server 1710 comprises offline generation 1712, summarization 174, and summarization pre-processing and post-processing 1716. The documents and/or metadata for private cloud server 1710 are stored, for example, in private storage datastore 1718 )
Neervannan fails to expressly teach providing a folder.
However, Mann teaches providing a folder for storage of information; (col. 226; line 29; A folder may refer to a virtual cabinet, tab, or any combination thereof reserved for generating, defining, or storing information.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the storage for content as taught by Neervannan to include folders as taught by Mann to provide benefit of improving the organization of stored data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Soubbotin; DmitriUS 12038958 B
See abstract; A system and methods are provided for achieving multi-document summarization according to the present invention, which sets forth the integrating of multi-document summary into a search result of a search engine, or any other application where the system receives a user natural language/keyword query and generates coherent summarized response(s) based on parts of one or more corresponding source documents.
ABERLE STEVEN THOMAS WO 2024044011 A1
See abstract; The disclosure relates to systems and methods of automatically generating unique content including natural language text based on a corpus of previously generated response documents and discrete requirements defined in a requirements specification. The system may use generative stitching that includes multi-layer processes that execute to influence the generation of unique content including natural language text through an artificial intelligence (AI) language transformer model trained to output the content based on previously written material that is semantically relevant to the discrete requirements and is weighted against labeled attributes.
Sinha; Anoop K et al. US 20240311577 A1
See abstract; Techniques are described herein for personalized multi-response dialog generated using one or more large language models.
Krishnan et al. 20250005050 A1
See abstract; Embodiments of the disclosed technologies include generating a search prompt based on an input portion of an online dialog involving a user of a computing device. The search prompt includes a dialog summarization instruction configured to instruct a generative artificial intelligence model to generate and output a dialog summary.
Conclusion
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/HENRY ORR/ Primary Examiner, Art Unit 2172
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