DETAILED ACTION
This non-final rejection is responsive to the Request for Continued Examination (RCE) filed August 27, 2026. Claims 1, 14, 15 and 20 are currently amended. Claims 4-5, 7-9, 18 and 19 are canceled. Claims 1-3, 6, 10-17, and 20 are pending in this application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6, 10-17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 15, and 20 recite the following limitations directed to an abstract idea because the broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III:
assign the plurality of messages to one or more conversations based on at least one of: a time of posting, a user identifier, or message content– (mental process – a user can mentally assign messages to conversations by making observation and evaluation based on time, user identifier, or content);
generate one or more conversation documents corresponding to the one or more conversations wherein the one or more conversation documents each comprises one or more identifiers of messages belonging to a corresponding conversation, content associated with the messages belonging to the corresponding conversation, and metadata identifying a corresponding communication channel of the plurality of communication channels associated with each message of the corresponding conversation - (mental process – user can manually generate conversation documents with the aid of pen and paper based on mentally analyzing conversations from multiple communication channels);
identify a referenced document associated with a first conversation of the one or more conversations - (mental process – user can mentally identify a references document by making an evaluation); and
generate summary text based on content of the referenced document - (mental process – user can mentally (and with the aid of pen and paper) summarize a document).
This judicial exception is not integrated into a practical application because the following additional elements are not indicative of integration into practical application:
a hardware storage device configured to store a search index for groups of communications - (mere instructions to apply the exception using a generic computer component);
one or more processors in communication with the storage device – (mere instructions to apply the exception using a generic computer component);
acquire/receive a plurality of messages from a plurality of different communication channels (Mere data gathering or Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
generating conversation documents each comprises a machine-readable data structure (mere instructions to apply the exception using a generic computer component);
using a generative artificial intelligence model that produces new summary text based on content of the referenced document to generate a summary (mere instructions to apply the exception using a generic computer component);
store the summary of the referenced document in a first conversation document of the one or more conversation documents storing the first conversation; wherein the first conversation document comprises a machine-readable data structure that embeds the summary of the referenced document together with the content associated with the messages belonging to the first conversation, such that the summary of the referenced document is searchable as part of the first conversation document (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
store the one or more conversation documents as searchable content within the search index (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
receive a search query associated with at least one of the one or more conversations (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); and
display, via a graphical user interface, a ranked list of documents relevant to the search query, the ranked list comprising at least one of the one or more conversation documents (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the following additional limitations are not indicative of an inventive concept (i.e. “significantly more”):
a hardware storage device configured to store a search index for groups of communications - (mere instructions to apply the exception using a generic computer component);
one or more processors in communication with the storage device – (mere instructions to apply the exception using a generic computer component);
acquire/receive a plurality of messages from a plurality of different communication channels (MPEP 2106.05(d)(II) indicates that merely “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim));
generating conversation documents each comprises a machine-readable data structure (mere instructions to apply the exception using a generic computer component);
using a generative artificial intelligence model that produces new summary text based on content of the referenced document to generate a summary (mere instructions to apply the exception using a generic computer component);
store/incorporate the summary of the referenced document in a first conversation document of the one or more conversation documents storing the first conversation; wherein the first conversation document comprises a machine-readable data structure that embeds the summary of the referenced document together with the content associated with the messages belonging to the first conversation, such that the summary of the referenced document is searchable as part of the first conversation document (MPEP 2106.05(d)(II) indicates that merely “storing and retrieving information in memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim));
store/incorporate the one or more conversation documents as searchable content within the search index (MPEP 2106.05(d)(II) indicates that merely “storing and retrieving information in memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim));
receive a search query associated with at least one of the one or more conversations (MPEP 2106.05(d)(II) indicates that merely “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim)); and
display, via a graphical user interface, a ranked list of documents relevant to the search query, the ranked list comprising at least one of the one or more conversation documents (MPEP 2106.05(d)(II) indicates that merely “receiving or transmitting data over a network” or “presenting offers and gathering statistics” is well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim)).
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 2 recites the following limitations directed to a mental process “detect a conversation boundary between a first group of messages corresponding to the first conversation and a second group of messages corresponding to a second conversation.” This judicial exception is not integrated into a practical application because the additional elements of one or more processors represents mere instructions to apply the exception using a generic computer component and thus are not indicative of integration into practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more processors represents mere instructions to apply the exception using a generic computer component and thus is not indicative of an inventive concept.
Claim 3 recites the following limitations directed to a mental process “detect the conversation boundary between the first group of messages and the second group of messages.” This judicial exception is not integrated into a practical application because the additional elements of one or more processors and using a machine learning model to detect the conversation boundary represents adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and thus are not indicative of integration into practical application. The examiner notes that the additional element of a machine learning model is a high level application of a previously trained model to detect conversation boundaries. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more processors and using a machine learning model to detect the conversation boundary represents adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and thus are not indicative of an inventive concept.
Claim 6 recites the following limitations directed to mental processes “identify a first subject matter classification for the first group of messages and a second subject matter classification for a particular message; and detect that the particular message should be assigned to the second group of messages different from the first group of messages based on the first subject matter classification for the first group of messages and the second subject matter classification for the particular message.” This judicial exception is not integrated into a practical application because the additional elements of one or more processors represents mere instructions to apply the exception using a generic computer component and thus are not indicative of integration into practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more processors represents mere instructions to apply the exception using a generic computer component and thus is not indicative of an inventive concept.
Claims 10-12 recite limitations that further describe the acquired/received messages and/or source of the messages. As such, these limitations do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. MPEP 2106.05(d)(II) indicates that merely “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claims).
Claim 13 recites the following limitation directed to a mental process “detect that the first group of messages has exceeded a maximum number of messages per grouping and detect that the second message should be assigned to the second group of messages different from the first group of messages based on detection that the first group of messages has exceeded the maximum number of messages per grouping”. This judicial exception is not integrated into a practical application because the additional elements of one or more processors represents mere instructions to apply the exception using a generic computer component and thus are not indicative of integration into practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more processors represents mere instructions to apply the exception using a generic computer component and thus is not indicative of an inventive concept.
Claim 14 recites: determine a first time that a first message of the plurality of messages was posted to a chat channel and a second time that a second message of the plurality of messages was posted to the chat channel; determine a first user identifier associated with the first message and a second user identifier associated with the second message; and detect that the second message should be assigned to a different conversation than the first message based on detection that more than a threshold amount of time has passed between the first time and the second time or based on detection that the second user identifier corresponds with a new username that has not been used during a previous set of messages. These limitations fall under the mental process grouping of abstract ideas because a user can mentally determine times messages are posted and user identifiers associated with messages. Further, a user can mentally detect a message should be assigned to a new group based on a threshold amount of time or new user identifier. The judicial exception is not integrated into a practical application because the additional elements of one or more processors represents mere instructions to apply the exception using a generic computer component and thus are not indicative of integration into practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more processors represents mere instructions to apply the exception using a generic computer component and thus is not indicative of an inventive concept.
Claim 16 recites the following limitation: wherein assigning the plurality of messages to the one or more conversations comprises applying a machine learning model to classify messages into one of the one or more conversations. This judicial exception is not integrated into a practical application because the additional element of using a machine learning model to classify messages represents adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and thus is not indicative of integration into practical application. The examiner notes that the additional element of a machine learning model is a high level application of a trained model to assign a second message to the second group of messages. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element of using a machine learning model to assign the plurality of messages to conversations represents adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and thus are not indicative of an inventive concept.
Claim 17 recites the following limitations directed to mental processes “determining a first subject matter classification associated with contents of the first message; determining a second subject matter classification associated with contents of the second message; and detecting that the second message should be assigned a conversation of the one or more conversations based on the first subject matter classification and the second subject matter classification.” This judicial exception is not integrated into a practical application because there are no additional elements. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
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.
Claim 1, 14, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Menon et al. (US 2020/0311151 A1) (‘Menon’) in view of Liu et al. (US 2021/0152506 A1) (‘Liu’), further in view of Russell et al. (US 2015/0156154 A1) (‘Russell’), and further in view of Matson et al. (US 2024/0419710 A1) (‘Matson’).
With respect to claim 1, Menon teaches a system, comprising:
a hardware storage device configured to store a search index for groups of communications (paragraphs 23-24); and
one or more processors (paragraph 61) in communication with the storage device configured to:
acquire a plurality of messages from a plurality of different communication channels (paragraphs 17 and 18);
assign the plurality of messages to one or more conversations based on at least one of: a time of posting, a user identifier, or message content (defines a conversation as a series (e.g., series 112-114) of messages exchanged between or among two or more participants…defines each conversation based on a conversation title, group name, time period spanning messages in the conversation) (paragraph 18);
generate one or more conversation document corresponding to the one or more conversations, wherein the one or more conversation documents each comprises a machine-readable data structure comprising one or more identifiers of messages belonging to a corresponding conversation, content associated with the messages belonging to the corresponding conversation, and metadata associated with each message of the corresponding conversation (paragraphs 18-20and 57);
store a summary (i.e. message content) in a first conversation document of the one or more conversation documents storing the first conversation (paragraphs 19 and 21);
store the one or more conversation documents as searchable content within the search index (paragraphs 13, 22, and 24);
receive a search query associated with at least one of the one or more conversations (paragraphs 22 and 28-29); and
display, via a graphical user interface, a list of documents relevant to the search query, the list comprising at least one of the one or more conversation documents (paragraph 29).
Menon does not explicitly teach wherein conversation documents are configured to include metadata identifying a corresponding communication channel of the plurality of communication channels associated with each message of the corresponding conversation.
Liu teaches wherein conversation documents are configured to include metadata identifying a corresponding communication channel (i.e. source) of the plurality of communication channels associated with each message of the corresponding conversation (paragraphs 36-38).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon to include conversation documents from multiple communication channels as taught by Liu to enable all related messages, regardless of platform, to be viewed/stored together (Liu, abstract and paragraph 38). Menon teaches that a conversation of messages can include an email thread, a series of chats exchanged within a group or channel, and/or a string of responses and/or comments to a post, article, and/or other content, and thus it would have been obvious to incorporating messages from those different places into a single view/document. Therefore, the modification would entail combining known prior art elements (i.e. messages from different communication channels) to achieve predictable results (i.e. a conversation with messages from different communication channels.)
Further regarding claim 1, Menon in view of Liu does not explicitly teach identify a referenced document associated with a first conversation of the one or more conversations; generate new summary text based on content of the referenced document; store the summary of the referenced document in a first conversation document of one or more conversation documents storing the first conversation; wherein the first conversation document comprises a machine-readable data structure that embeds the summary of the referenced document together with the content associated with the messages belonging to the first conversation, such that the summary of the referenced document is searchable as part of the first conversation document.
Russell teaches identify a referenced document associated with a first conversation of the one or more conversations (302 in Fig. 3; paragraphs 10 and 47);
generate new summary text based on content of the referenced document (304 in Fig 3; paragraphs 10 and 48);
store the summary of the referenced document in a first conversation document of one or more conversation documents storing the first conversation (306 in Fig. 3; paragraphs 10 and 52-53);
wherein the first conversation document comprises a machine-readable data structure that embeds the summary of the referenced document together with the content associated with the messages belonging to the first conversation (Fig. 5; paragraphs 10, 59, 61, and 63), such that the summary of the referenced document is searchable as part of the first conversation document (404 in Fig. 4; paragraphs 10 and 56).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon to identify and summarize a reference document associated with a conversation as taught by Russell to enable generation of a summary representation of referenced content of a message to be used in storing and retrieving messages and related content of messages (Russell, paragraph 3), thereby improving search capabilities of all message related content. Further, including the summarized content would improve searching of Menon by allowing all aspects of a conversation to be recorded and searched.
Further regarding claim 1, Menon in view of Liu and Russell does not explicitly teach using a generative artificial intelligence model that produces new summary text based on content of the referenced document to generate the summary; or ranking a list of documents.
Matson teaches using a generative artificial intelligence model that produces new summary text based on content of the referenced document to generate the summary (paragraphs 29, 60, and 112), wherein the summary is searchable and stored in association with content (paragraphs 77, 112, and 118); and
ranking a list of documents (paragraphs 93, 99, and 103).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon in view of Russell to use a generative AI model to generate summaries and to rank documents as taught by Matson to enable a comprehensive search to efficiently and effectively surface search results using a combination of technical approaches such as text summarization, semantic search, and/or lexical search. This results in more relevant or valuable search results presented to a user in a more efficient manner, and oftentimes view search results that would otherwise not be returned via a conventional search approach. Further, ranking indicates an extent of relevance or relatedness to the search query (Matson, paragraphs 3 and 93).
With respect to claim 14, Menon in view of Liu, Russell, and Matson teaches:
determine a first time that a first message of the plurality of messages was posted to a chat channel and a second time that a second message of the plurality of messages was posted to the chat channel (Menon, paragraphs 18 and 20);
determine a first user identifier associated with the first message and a second user identifier associated with the second message (Menon, paragraph 20); and
detect that the second message should be assigned to a different conversation than the first message based on detection that more than a threshold amount of time has passed between the first time and the second time (time period spanning messages in the conversation (e.g., a certain number of days, weeks, months, etc. over which the messages were transmitted)) or based on detection that the second user identifier corresponds with a new username that has not been used during a previous set of messages (Menon, paragraph 18).
With respect to claim 15, Menon teaches a method for operating a search system, comprising:
receiving a plurality of messages from a plurality of different communication channels (paragraphs 17 and 18);
assigning the plurality of messages to one or more conversations based on at least one of: a time of posting, a user identifier, or message content (defines a conversation as a series (e.g., series 112-114) of messages exchanged between or among two or more participants…defines each conversation based on a conversation title, group name, time period spanning messages in the conversation) (paragraph 18);
generating one or more conversation document corresponding to the one or more conversations, wherein the one or more conversation documents each comprises a machine-readable data structure comprising one or more identifiers of messages belonging to a corresponding conversation, content associated with the messages belonging to the corresponding conversation, and metadata associated with each message of the corresponding conversation (paragraphs 18-20and 57);
storing a summary (i.e. message content) in a first conversation document of the one or more conversation documents storing the first conversation (paragraphs 19 and 21);
storing the one or more conversation documents as searchable content within the search index (paragraphs 13, 22, and 24);
receiving a search query associated with at least one of the one or more conversations (paragraphs 22 and 28-29); and
displaying, via a graphical user interface, a list of documents relevant to the search query, the list comprising at least one of the one or more conversation documents (paragraph 29).
Menon does not explicitly teach wherein conversation documents are configured to include metadata identifying a corresponding communication channel of the plurality of communication channels associated with each message of the corresponding conversation.
Liu teaches wherein conversation documents are configured to include metadata identifying a corresponding communication channel (i.e. source) of the plurality of communication channels associated with each message of the corresponding conversation (paragraphs 36-38).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon to include conversation documents from multiple communication channels as taught by Liu to enable all related messages, regardless of platform, to be viewed/stored together (Liu, abstract and paragraph 38). Menon teaches that a conversation of messages can include an email thread, a series of chats exchanged within a group or channel, and/or a string of responses and/or comments to a post, article, and/or other content, and thus it would have been obvious to incorporating messages from those different places into a single view/document. Therefore, the modification would entail combining known prior art elements (i.e. messages from different communication channels) to achieve predictable results (i.e. a conversation with messages from different communication channels.)
Further regarding claim 15, Menon in view of Liu does not explicitly teach identify a referenced document associated with a first conversation of the one or more conversations; generate new summary text based on content of the referenced document; store the summary of the referenced document in a first conversation document of one or more conversation documents storing the first conversation; wherein the first conversation document comprises a machine-readable data structure that embeds the summary of the referenced document together with the content associated with the messages belonging to the first conversation, such that the summary of the referenced document is searchable as part of the first conversation document.
Russell teaches identify a referenced document associated with a first conversation of the one or more conversations (302 in Fig. 3; paragraphs 10 and 47);
generate new summary text based on content of the referenced document (304 in Fig 3; paragraphs 10 and 48);
store the summary of the referenced document in a first conversation document of one or more conversation documents storing the first conversation (306 in Fig. 3; paragraphs 10 and 52-53);
wherein the first conversation document comprises a machine-readable data structure that embeds the summary of the referenced document together with the content associated with the messages belonging to the first conversation (Fig. 5; paragraphs 10, 59, 61, and 63), such that the summary of the referenced document is searchable as part of the first conversation document (404 in Fig. 4; paragraphs 10 and 56).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon to identify and summarize a reference document associated with a conversation as taught by Russell to enable generation of a summary representation of referenced content of a message to be used in storing and retrieving messages and related content of messages (Russell, paragraph 3), thereby improving search capabilities of all message related content. Further, including the summarized content would improve searching of Menon by allowing all aspects of a conversation to be recorded and searched.
Further regarding claim 15, Menon in view of Liu and Russell does not explicitly teach using a generative artificial intelligence model that produces new summary text based on content of the referenced document to generate the summary; or ranking a list of documents.
Matson teaches using a generative artificial intelligence model that produces new summary text based on content of the referenced document to generate the summary (paragraphs 29, 60, and 112), wherein the summary is searchable and stored in association with content (paragraphs 77, 112, and 118); and
ranking a list of documents (paragraphs 93, 99, and 103).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon in view of Russell to use a generative AI model to generate summaries and to rank documents as taught by Matson to enable a comprehensive search to efficiently and effectively surface search results using a combination of technical approaches such as text summarization, semantic search, and/or lexical search. This results in more relevant or valuable search results presented to a user in a more efficient manner, and oftentimes view search results that would otherwise not be returned via a conventional search approach. Further, ranking indicates an extent of relevance or relatedness to the search query (Matson, paragraphs 3 and 93).
With respect to claim 20, Menon teaches one or more non-transitory storage devices containing processor readable code for configuring one or more processors to perform a method for operating a search system, wherein the processor readable code configures the one or more processors to:
acquire a plurality of messages from a plurality of different communication channels (paragraphs 17 and 18);
assign the plurality of messages to one or more conversations based on at least one of: a time of posting, a user identifier, or message content (defines a conversation as a series (e.g., series 112-114) of messages exchanged between or among two or more participants…defines each conversation based on a conversation title, group name, time period spanning messages in the conversation) (paragraph 18);
generate one or more conversation documents corresponding to the one or more conversations (paragraphs 18-19 and 57);
incorporate a summary (i.e. message content) in a first conversation document of the one or more conversation documents storing the first conversation (paragraphs 19 and 21);
incorporate the one or more conversation documents as searchable content within the search index (paragraphs 13, 22, and 24);
receiving a search query associated with at least one of the one or more conversations (paragraphs 22 and 28-29); and
displaying, via a graphical user interface, a list of documents relevant to the search query, the list comprising at least one of the one or more conversation documents (paragraph 29).
Menon does not explicitly teach wherein conversation documents are configured to include messages originating from multiple communication channels.
Liu teaches wherein conversation documents are configured to include messages originating from multiple communication channels (paragraphs 36 and 38).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon to include conversation documents from multiple communication channels as taught by Liu to enable all related messages, regardless of platform, to be viewed/stored together (Liu, abstract and paragraph 38). Menon teaches that a conversation of messages can include an email thread, a series of chats exchanged within a group or channel, and/or a string of responses and/or comments to a post, article, and/or other content, and thus it would have been obvious to incorporating messages from those different places into a single view/document. Therefore, the modification would entail combining known prior art elements (i.e. messages from different communication channels) to achieve predictable results (i.e. a conversation with messages from different communication channels.)
Further regarding claim 20, Menon in view of Liu does not explicitly teach identify a referenced document associated with a first conversation of the one or more conversations; generate new summary text based on content of the referenced document; store the summary of the referenced document in a first conversation document of one or more conversation documents storing the first conversation; wherein the first conversation document comprises a machine-readable data structure that embeds the summary of the referenced document together with the content associated with the messages belonging to the first conversation, such that the summary of the referenced document is searchable as part of the first conversation document.
Russell teaches identify a referenced document associated with a first conversation of the one or more conversations (302 in Fig. 3; paragraphs 10 and 47);
generate new summary text based on content of the referenced document (304 in Fig 3; paragraphs 10 and 48);
store the summary of the referenced document in a first conversation document of one or more conversation documents storing the first conversation (306 in Fig. 3; paragraphs 10 and 52-53);
wherein the first conversation document comprises a machine-readable data structure that embeds the summary of the referenced document together with the content associated with the messages belonging to the first conversation (Fig. 5; paragraphs 10, 59, 61, and 63), such that the summary of the referenced document is searchable as part of the first conversation document (404 in Fig. 4; paragraphs 10 and 56).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon to identify and summarize a reference document associated with a conversation as taught by Russell to enable generation of a summary representation of referenced content of a message to be used in storing and retrieving messages and related content of messages (Russell, paragraph 3), thereby improving search capabilities of all message related content. Further, including the summarized content would improve searching of Menon by allowing all aspects of a conversation to be recorded and searched.
Further regarding claim 20, Menon in view of Liu and Russell does not explicitly teach using a generative artificial intelligence model that produces new summary text based on content of the referenced document to generate the summary; or ranking a list of documents.
Matson teaches using a generative artificial intelligence model that produces new summary text based on content of the referenced document to generate the summary (paragraphs 29, 60, and 112), wherein the summary is searchable and stored in association with content (paragraphs 77, 112, and 118); and
ranking a list of documents (paragraphs 93, 99, and 103).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon in view of Russell to use a generative AI model to generate summaries and to rank documents as taught by Matson to enable a comprehensive search to efficiently and effectively surface search results using a combination of technical approaches such as text summarization, semantic search, and/or lexical search. This results in more relevant or valuable search results presented to a user in a more efficient manner, and oftentimes view search results that would otherwise not be returned via a conventional search approach. Further, ranking indicates an extent of relevance or relatedness to the search query (Matson, paragraphs 3 and 93).
Claims 2, 3, 10, 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Menon in view of Liu, Russell and Matson, as applied to claims 1 and 15 above, and further in view of Brunn et al. (US 2020/0042595 A1) (‘Brunn’).
With respect to claim 2, Menon in view of Liu, Russell, and Matson teaches system of claim 1, wherein the one or more processors are configured to detect that the second message should be assigned to the second group of messages different from the first group of messages (Menon, paragraph 18).
Menon in view of Liu, Russell, and Matson does not explicitly teach detecting a conversation boundary between a first group of messages corresponding to the first conversation and a second group of messages corresponding to a second conversation.
Brunn teaches detecting a conversation boundary between a first group of messages corresponding to the first conversation and a second group of messages corresponding to a second conversation (paragraphs 23, 38, and 67).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified the assigning of messages to groups in Menon to detect a conversation boundary between and first and second group of messages as taught by Brunn to enable more efficient and precise determination of conversations based on conversation boundaries. A person having ordinary skill in the art would have been motivated to make the modification because it is obvious that Menon determines conversation boundaries when he groups messages into conversations, and thus the combination represents combining known elements to achieve predictable results.
With respect to claim 3, Menon in view of Liu, Russell, Matson and Brunn teaches the system of claim 2, wherein: the one or more processors are configured to detect the conversation boundary between the first group of messages and the second group of messages using a machine learning model (Brunn, paragraphs 35 and 41).
With respect to claim 10, Menon in view of Liu, Russell, Matson and Brunn teaches the system of claim 2, wherein: the first group of messages comprises a set of contiguous messages within a messaging application (Menon, paragraphs 17-18 and 21).
With respect to claim 12, Menon in view of Liu, Russell, Matson and Brunn teaches the system of claim 1, wherein: the first group of messages comprises messages from a first application; and the second group of messages comprises messages from a second application (a conversation can include an email thread, a series of chats exchanged within a group or channel, and/or a string of responses and/or comments to a post, article, and/or other content) (Menon, paragraph 18).
With respect to claim 16, Menon in view of Liu, Russell and Matson teaches assigning the plurality of messages to the one or more conversations (Menon, paragraph 18).
Menon in view of Liu, Russell and Matson does not explicitly teach wherein assigning the plurality of messages to the one or more conversations comprises applying a machine learning model to classify messages into one of the one or more conversations.
Brunn teaches wherein assigning the plurality of messages to the one or more conversations comprises applying a machine learning model to classify messages into one of the one or more conversation (paragraphs 23, 35, 41 and 67).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified the assigning of messages to groups in Menon to be based on applying a machine learning model to classify messages into one of the one or more conversations as taught by Brunn to enable more efficient and precise determination of conversations based on conversation boundaries and to enable a smart way of determining message boundaries. A person having ordinary skill in the art would have been motivated to make the modification because it is obvious that Menon determines conversation boundaries when he groups messages into conversations, and thus the combination represents combining known elements to achieve predictable results.
Claims 6 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Menon in view of Liu, Russell, Matson and Brunn, as applied to claim 2 above, and further in view of Hassan et al. (US 2022/0385606 A1) (‘Hassan’).
With respect to claim 6, Menon in view of Liu, Russell, Matson and Brunn teaches system of claim 2, wherein: the one or more processors are configured to identify a first tag/label/title for the first group of messages and a second tag/label/title for a particular message (Menon, paragraphs 11, 18, 19 and 31); and
the one or more processors are configured to detect that the particular message should be assigned to the second group of messages different from the first group of messages based on the first tag/label/title for the first group of messages and the second tag/label/title for the particular message (Menon, paragraphs 11, 18, 19 and 31).
Although Menon in view of Liu teaches identifying tags, label and title for groups of messages and assigning messages to groups based on tags, label or title, Menon does not explicitly teach the one or more processors are configured to identify a first subject matter classification for the first group of messages and a second subject matter classification for a particular message; and the one or more processors are configured to detect that the particular message should be assigned to the second group of messages different from the first group of messages based on the first subject matter classification for the first group of messages and the second subject matter classification for the particular message.
Hassan teaches identify a first subject matter classification for the first group of messages and a second subject matter classification for the particular message; detect that the particular message should be assigned to the second group of messages different from the first group of messages based on the first subject matter classification for the first group of messages and the second subject matter classification for the particular message (Fig. 2, paragraphs 30, 32, 35 and 69).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified the grouping of messages of Menon to be based on subject matter classification as taught by Hassan to enable an additional way to group messages based on subject matter classification, thereby improving capabilities of Menon’s message group and allowing conversations to be determined based on users. A person having ordinary skill in the art would have been motivated to make the combination because Menon teaches defining conversation/grouping messages based on tags/label/title and thus detecting groups based on subject matter classification would only entails swapping the determination based on tag, label or title to be based on subject matter classification.
With respect to claim 11, Menon in view of Liu, Russell, Matson, Brunn, and Hassan teaches system of claim 1, wherein: the particular message comprises a root message for the second group of messages (Menon, paragraphs 54 and 58).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Menon in view of Liu, Russell, Matson and Brunn, as applied to claim 2 above, and further in view of Dunne et al. (US 2020/0057808 A1) (‘Dunne’).
With respect to claim 13, Menon in view of Liu, Russell, Matson and Brunn teaches the system of claim 2.
Menon in view of Liu, Russell, Matson and Brunn does not explicitly teach detect that the first group of messages has exceeded a maximum number of messages per grouping and detect that the second message should be assigned to the second group of messages different from the first group of messages based on detection that the first group of messages has exceeded the maximum number of messages per grouping.
Dunne teaches detect that the first group of messages has exceeded a maximum number of messages per grouping and detect that the second message should be assigned to the second group of messages different from the first group of messages based on detection that the first group of messages has exceeded the maximum number of messages per grouping (paragraphs 4, 57 and 59-60).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Menon to assign a second message to a second group based on number of messages exceeding a threshold as taught by Dunne to improve grouping of messages by determining a divergent topic based on number of messages, topic and time (Dunne, abstract, paragraph 57). A person having ordinary skill in the art would have been motivated to make the combination because Menon teaches several methods for defining conversation/grouping messages and thus detecting groups based on number of messages represents a combination of known group detection elements to achieve predictable results of messages grouped into conversations.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Menon in view of Liu, Russell and Matson, as applied to claim 15 above, and further in view of Hassan et al. (US 2022/0385606 A1) (‘Hassan’).
With respect to claim 17, Menon in view of Liu, Russell and Matson teaches the method of claim 15, further comprising determining a first tag/label/title for the first group of messages and a second tag/label/title for the second message (Menon, paragraphs 11, 18, 19 and 31); and
detecting that the second message should be assigned to a conversation of the one or more conversations based on the first tag/label/title for the first group of messages and the second tag/label/title for the second message (Menon, paragraphs 11, 18, 19 and 31).
Although Menon in view of Liu, Russell and Matson teaches identifying tags, label and title for groups of messages and assigning messages to groups based on tags, label or title, Menon does not explicitly teach determining a first subject matter classification associated with contents of the first message; determining a second subject matter classification associated with contents of the second message; and detecting that the second message should be assigned to a conversation of the one or more conversations based on the first subject matter classification and the second subject matter classification.
Hassan determining a first subject matter classification associated with contents of the first message; determining a second subject matter classification associated with contents of the second message; and detecting that the second message should be assigned to a conversation of the one or more conversations based on the first subject matter classification and the second subject matter classification (Fig. 2, paragraphs 30, 32, 35 and 69).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified the grouping of messages of Menon to be based on subject matter classification as taught by Hassan to enable an additional way to group messages based on subject matter classification, thereby improving capabilities of Menon’s message group and allowing conversations to be determined based on users. A person having ordinary skill in the art would have been motivated to make the combination because Menon teaches defining conversation/grouping messages based on tags/label/title and thus detecting groups based on subject matter classification would only entails swapping the determination based on tag, label or title to be based on subject matter classification.
Response to Arguments
Applicant's arguments filed August 27, 2026 have been fully considered but they are not persuasive. Applicant argues that the claims recite a specific search-index enrichment pipeline, in which the ordered combination reflects a concrete improvement to search technology because it changes the searchable data artifact itself. The examiner disagrees. The steps in said ordered combination recite limitations that are merely applying the abstract idea with a computer or adding insignificant extra-solution activity that is well-understood, routine and conventional. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Applicant argues that the machine-readable conversation document is a searchable data artifact that allows external-document context to be indexed and retrieved with the conversation document. However, the claimed machine-readable conversation document does not appear to be a new data structure that is different from any other machine-readable, searchable document. The specification does not describe a new data structure, but instead only describes embedding data into an electronic document. All electronic documents are machine-readable. Further, MPEP 2106.05(d)(II) indicates that merely “receiving or transmitting data over a network” and “storing and retrieving information from memory” are a well‐understood, routine, conventional function when claimed in a merely generic manner.
Applicant argues that the claims are analogous to USPTO Example 47 because the claims use a generative AI model to generate summaries and deliver ranked search results. Applicant argues that these limitations provide an improvement to quality and relevance of search results. The examiner disagrees. Generating a summary and ranking search results are concepts that can be performed mentally. The use of generative AI does not provide any improvement, but instead merely applies the concepts that can be applied mentally using a computer. If anything, the computer is used as a tool to provide an alleged improvement.
Applicant argues that the specification emphasizes the technical improvement (i.e. improved quality and relevance of search results) at various places. However, applying an abstract idea with a computer does not provide a technical improvement. Similarly, adding limitations directed to insignificant extra-solution activity that are well-understood, routine and conventional does not provide a technical improvement.
Applicant argues that the display of ranked search results via a graphical user interface is not extra-solution activity, but rather practical output of the claimed invention. Applicant argues that the claims transform data through AI-generated enrichment to deliver improved search results. The examiner disagrees. Ranking search results and summarizing documents are steps that can be performed mentally. MPEP 2106.05 (d)(II) recites that “receiving and transmitting data over a network” and “presenting offers” are both well-understood, routine and conventional. Displaying an outcome of data that has been mentally processed is just post-solution activity. Further, using generative AI to perform summary and displaying results with a GUI also represents mere instructions to apply he abstract idea with a computer. There is no data transformation or enrichment to the data. There is no improvement in displaying ranked search results, as claimed.
Applicant further argues that claimed machine-readable data structure therefore represents a concrete technical artifact that improves search functionality by allowing users to search for and retrieve conversation documents based on referenced-document content that was not previously part of the searchable conversation data. However, incorporating additional data that was not previously part of searchable conversation data does not represent a technical improvement. It is merely providing data that can be searched, which is not a technical improvement or an improvement to search functionality.
Applicant further argues that the examiner has not provided any of the forms of evidence required under MPEP 2106.07(a), such as a citation to an express statement in the specification, a court decision, a publication, or a statement of official notice, to establish that this specific combination is well-understood, routine, or conventional. The examiner disagrees. The examiner points to MPEP 2106.05(d)(II) as evidence that certain steps are well-understood, routine, or conventional. The examiner also notes that even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. As such, the examiner has met the burden of establishing that the claimed combination is well-understood, routine, and conventional.
Applicant’s prior art arguments with respect to claims 1-3, 6, 10-17, and 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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/ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156 September 30, 2026
