DETAILED ACTION
This is in response to the reply filed on 04/27/2026. Claims 1-24 are pending in this Action.
Remark
In the response filed 04/27/2026, claims 1, 3-7, 9, 11-15, 17, and 19-23 have been amended, no claim has been cancelled, and no new claim has been added.
The Applicant’s Interview Summary is acknowledged and it is OK.
Response to Arguments
Applicant's arguments filed 04/27/2026 have been fully considered but they are not persuasive.
With respect to 35 USC 101 rejection:
The applicant alleges that:
representative claim 1, as a whole, integrates the alleged abstract idea into a practical application because it is directed to improvements in the technical field of information retrieval and management of conversational data. Paragraph 74 of the specification states that "[p]resent invention embodiments intelligently organize and categorize conversation topics to enable rapid searching and identification of precise locations in the conversation, and automatically summarize content to reduce amounts of data transferred. The conversations may use a topic-based index to provide locations, or content, for topics. This index enables rapid searching of the conversation. Further, the index may apply across multiple conversations to enable rapid searching of content from across those conversations. For example, a topic may rapidly provide content associated with the same or similar topic from plural different conversations. Conversations may be filtered by data topic, thereby eliminating unnecessary and irrelevant content" (emphasis added).
Thus, the specification explicitly discusses a technical solution that enables faster information retrieval and improves efficiency of data transfer. And, the claims themselves reflect the disclosed improvement.
The Examiner respectfully disagrees.
In response to the applicant’s allegation that "[p]resent invention embodiments intelligently organize and categorize conversation topics to enable rapid searching and identification of precise locations in the conversation, and automatically summarize content to reduce amounts of data transferred… filtering by data topics, thereby eliminating unnecessary and irrelevant content”, the Examiner holds that:
first, improvement in quality and organization of information [into topics] is not is not equivalent to an improvement in a database, storage, or a computer functionality. The courts have provided examples that may not be sufficient to show an improvement in computer-functionality, such as providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018). See MPEP 2106.05 (a)(I).
secondly, using a list or index to locate information at a high level of generality could be a mental process that practically be performed in the human mind; or arguendo, at best it could be adding an extra-solution activity which is still not sufficient to improve the functioning of a computer or technology.
thirdly, the alleged improved features of “automatically summarize content to reduce amounts of data transferred” and “filtering by data topics, thereby eliminating unnecessary and irrelevant content” are not required by the claim. Said features are not reflected in the claimed invention.
Additionally, the alleged speed in search of conversion comes solely from the capabilities of a general-purpose computer. "Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Therefore, the features of claimed invention as drafted by independent claims 1, 9, and 17 individually or as a whole fail to improve the functionality of a computer or technology and do not integrate the recited abstract idea into a practical application. Thus, the 35 USC 101 rejection of claims 1-24 for being directed to abstract
idea is maintained.
With respect to 35 USC 103 rejection:
Applicant's arguments with respect to newly amended claims 1, 9, and 17 that the prior art of the record fails to disclose or suggest the amended limitation of “updating, via the at least one processor, a data structure to indicate that the segment is associated with the particular data topic, wherein the data structure contains a listing of data topics and corresponding content of associated segments” have been considered but are moot in view of the new ground(s) of rejection over the new references, Marsh, US 2017/0060917.
The new combination of Kim, Szymanski, and Marsh discloses the limitations of amended claims 1, 9, and 17. See below for details. Therefore, the 35 USC 103 rejection of claims 1-24 is maintained.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas.
Step 1:
Claims 1-24 are directed to a method/device/storage which is one of the statutory categories of invention.
Step 2A:
Prong 1:
Claims 1, 9, and 17 are directed to an abstract idea without significantly more.
The claims recite the steps of:
monitoring a conversation; [recited at a high level of generality, and based on broadest and reasonable interpretation of the claim (BRI), it constitutes concepts of observation and evaluation which could be practically performed in the human mind]
extracting, based on monitoring the conversation, a set of entities from the conversation; [recited at a high level of generality, and based on BRI, it constitutes a concept which could be practically performed in the human mind. A person could mentally determine and extracting terms relating to entities from the conversation]
converting the set of entities to a textual embedding; [recited at a high level of generality, and based on BRI, it constitutes a mathematical concept which could be practically performed in the human mind. A person could mentally and manually transform the terms/words into a vector of numerical representation using a pen and paper]
partitioning, in real-time, a portion of the conversation into a segment based on associating the portion of the conversation with a particular data topic [recited at a high level of generality, and based on BRI, it constitutes a concept which could be practically performed in the human mind. A person could mentally and manually divide a conversation into at least a segment using data topics]
updating, via the at least one processor, a data structure to indicate that the segment is associated with the particular data topic, wherein the data structure contains a listing of data topics and corresponding content of associated segments [recited at a high level of generality, and based on BRI, it constitutes a concept which could be practically performed in the human mind. A person could mentally and manually update a list of topics]
The above-mentioned steps are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Prong 2:
This judicial exception is not integrated into a practical application. Claims 1, 9,
and 17 recite the additional steps of “processing, via the at least one processor and in response to an interaction with a user interface, a query including a topic corresponding to the particular data topic; retrieving the segment from the data structure based on processing the query; and displaying, via the user interface and based on retrieving the segment from the data structure, information associated with the topic” at a high level of generality are considered to be insignificant extra solution activities of searching, retrieving, and displaying data. See MPEP 2106.04(d) and 2106.05(g).
Furthermore, the processor, memory, and/or storage medium are recited so generically (no details whatsoever are provided other than that they are a memory, display and processor) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See MPEP 2106.04(d) and 2106.05(g).
Step 2B:
The claims do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. Claims 1, 9, and 17 recite the additional steps of “processing, via the at least one processor and in response to an interaction with a user interface, a query including a topic corresponding to the particular data topic; retrieving the segment from the data structure based on processing the query; and displaying, via the user interface and based on retrieving the segment from the data structure, information associated with the topic” at a high level of generality are considered as well-understood, conventional, and routine activities of searching, retrieving, and displaying data. See MPEP 2106.04(d) and 2106.05(g).
Furthermore, the recited generic computer components (e.g., “a processor”, “a memory” and/or “a storage medium”) to implement the steps of the invention. Said generic computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and considered to be well-understood, conventional, and routine activities. Therefore, the claims are not patent eligible.
Regarding dependent claims 2, 10, and 18,
the dependent claims also lack additional elements that sufficient to integrate the judicial exception into a practical application or amount to significantly more than abstract idea found in the independent claims. The dependent claims additional limitation of using “an artificial intelligence” to generate response is recited at a high level of generality.
The claims do no more than describe desired function or outcome, without providing limiting details that confine the claimed to a practical solution to an identified problem. The feature of using an artificial intelligence (i.e., a machine learning model) to generate response is extra-solution activities the central idea of claims. An invocation to use such an old technology in the manner it is intended to be used for its ordinary purpose is both generic and well-understood and conventional activity. It does not describe any particular improvement in the manner of computer functions. See MPEP 2106.04(d) and 2106.05(g).
Moreover, the feature of using a machine learning (i.e., artificial intelligence)
function to process data is a conventional and well-understood function in the art (See for example Koudas et al., US 2009/0319518, paragraph 130) which is simply appending well-understood, routine, conventional activities previously known to the industry, specified at high level of generality to the general exception (See MPEP 2106.05(d)). Thus, the claimed additional elements individually and in combination do not amount significantly more than abstract idea.
Regarding dependent claims 3-8, 11-16, and 19-24,
the dependent claims also lack additional elements that sufficient to integrate the judicial exception into a practical application or amount to significantly more than abstract idea found in the independent claims.
The dependent claims further recite the additional steps for determining, extracting, generating a textual embedding, assigning, and arranging that could be performed mentally failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea. The dependent claims (e.g., claims 6-8) further include additional steps of presenting and retrieving data which are considered to be generic computer functions. They are considered to be insignificant extra solution and/or well-understood routine computer routines of outputting and retrieving data that fail to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 5, 7-10, 12, 13, 15-18, 20, 21, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al., KR 20190109614 A (Kim, hereafter) in view of Szymanski et al., US 2021/0027783 (Szymanski, hereafter) and further in view of Marsh, US 2017/0060917.
Regarding claim 1,
Kim discloses a method of searching conversations for desired content comprising:
monitoring a conversation; extracting, via the at least one processor and based on monitoring the conversation, a set of entities from the conversation (See Kim: at least abstract and highlighted sections in pages 2-3, checking and processing a conversation, and extracting entities from the conversation); and
converting, via the at least one processor, the set of entities to a textual embedding (See Kim: at least abstract and highlighted sections in page 3, based on
extracted entities, generating at least a vector using word embedding);
processing, via the at least one processor and in response to an interaction with a user interface, a query including a topic corresponding to the particular data topic (See Kim: at least abstract and pages 1-3 of the translation, processing a query in response to receiving a user query in a user device (e.g., in form of utterance) the query could include user intention and query situation).
Kim further discloses processing data in “real time”; however, Kim does not explicitly teach partitioning a portion of the conversation into a segment based on associating the portion of the conversation with a particular data topic.
On the other hand, Szymanski discloses dividing a conversation into different
stages and segments based on social actions and topics (See Szymanski: at least Fig. 3-6 and para 27 and 47). Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of Kim with Szymanski’s teaching in order to partition, via the at least one processor and in real-time, a portion of the conversation into a segment based on associating the portion of the conversation with a particular data topic, with reasonable expectation of success. The motivation for doing so would have been to improve functionality of the method by classifying the conversation/chat utilizing K-means features.
The combination of Kim and Szymanski discloses the limitations as stated above. However, it does not explicitly teach updating, via the at least one processor, a data structure to indicate that the segment is associated with the particular data topic, wherein the data structure contains a listing of data topics and corresponding content of associated segments; retrieving the segment from the data structure based on processing the query; and displaying, via the user interface and based on retrieving the segment from the data structure, information associated with the topic.
On the other hand, Marsh discloses:
updating, via the at least one processor, a data structure to indicate that the segment is associated with the particular data topic, wherein the data structure contains a listing of data topics and corresponding content of associated segments (See Marsh: at least Fig. 3A-3B, Fig. 5, and para 34, 38, and 55, updating a topic index such by adding an index to the topic index, wherein the topic index includes a list of topics associated with content segments);
retrieving the segment from the data structure based on processing the query; and displaying, via the user interface and based on retrieving the segment from the data structure, information associated with the topic (See Marsh: at least Fig. 5-6, and para 38, 39, 55, and 66 “the topics and content segments may be associated with other data, such as an identity (e.g., username) of a speaker/writer who discussed the topic, a date/time at which the topic was discussed (e.g., a time at which discussion of the topic began, a relevant time duration in which the topic was discussed, etc.), sentiment data related to the topic (e.g., approval/disapproval by participants in the conversational event), an identity (e.g., username) of those expressing sentiment, or other related data. For example, a search may be performed to identify content
segments related to topic T for which user A expressed negative sentiment.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the
time the invention was effectively filed to modify the teachings of the combination of Kim and Szymanski with Marsh’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to improve functionality of the method by generating a topic index associated with a conversation to find those segments that are desired by a user.
Regarding claim 2,
the combination of combination of Kim, Szymanski, and Marsh discloses wherein the conversation is generated by artificial intelligence (See Szymanski: at least Fig. 5, para 10-11 and 41).
Regarding claim 4,
the combination of Kim, Szymanski, and Marsh discloses wherein partitioning the portion of the conversation into the segment comprises: assigning the segment to the particular data topic based on determining that the segment is associate with the particular data topic, wherein the particular data topic is an existing topic (See Szymanski: at least Fig. 3-6 and para 27 and 47 and Marsh: at least Fig. 3A-B, Fig. 7, para 37, 35, and 61).
Regarding claim 5,
the combination of Kim, Szymanski, and Marsh discloses wherein partitioning the portion of the conversation into the segment comprises: assigning the segment to the particular data topic based on determining that the segment is associate with the particular data topic, wherein the particular data topic is a new topic (See Szymanski: at least Fig. 3-6 and para 27 and 47 and Marsh: at least Fig. 3A-B, Fig. 7, para 37, 35, and 61).
Regarding claim 7,
the combination of Kim, Szymanski, and Marsh discloses presenting, via the at least one processor, a change from the particular data topic to a different topic adjacent to one or more segments of the conversation corresponding to the change (See Marsh: at least Fig. 5-6, and para 38, 39, 55, and 66).
Regarding claim 8,
the combination of Kim, Szymanski, and Marsh discloses further comprising: monitoring a plurality of conversations, and retrieving segments, including the segment, of the plurality of conversations pertaining to the topic of the query (See Kim: at least abstract and highlighted sections in pages 2-3 and Marsh: at least Fig. 5-6, and para 38, 39, 55, and 66).
Regarding claims 9, 10, 12, 13, 15, and 16,
the scopes of the claims are substantially the same as claims 1, 2, 4, 5, 7, and 8 respectively, and are rejected on the same basis as set forth for the rejections of claims 1, 2, 4, 5, 7, and 8, respectively.
Regarding claims 17, 18, 20, 21, 23, and 24,
the scopes of the claims are substantially the same as claims 1, 2, 4, 5, 7, and 8
respectively, and are rejected on the same basis as set forth for the rejections of claims 1, 2, 4, 5, 7, and 8, respectively.
Claims 3, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al., KR 20190109614 A in view of Szymanski et al., US 2021/0027783 further in view of Marsh et al., US 2017/0060917 and further in view of Nedivi, US
2021/0233423.
Regarding claim 3,
the combination of Kim, Szymanski, and Marsh discloses extracting an other set of entities from an inquiry of a user during an other conversation wherein the “question data 50 is expressed as a digitized vector representing question through text processing, object extraction, and feature vector generation” (See Kim: at least the highlighted section in page 3) and determining topic changes in the dialogs (Marsh: at least Fig. 3A-B, Fig. 7, para 37, 35, and 61). However, it does not explicitly teach generate a textual embedding of the other set of entities; and determine the presence of a change from the particular data topic based on a distance between the textual embedding of the other set of entities and the textual embedding of a set of entities.
On the other hand, Nedivi discloses creating word embeddings and detecting topic changes based on similarity metrics between the word (i.e., textual) embedding
and previous one (See Nedivi: at least para 38-39).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was effectively filed to modify the teachings of Kim, Szymanski, and Marsh with Nedivi’s teaching in order to determine a presence of change from the particular data topic to a different data topic, wherein determining the precise of change comprises: extracting an other set of entities from an inquiry of a user during an other conversation; generate a textual embedding of the other set of entities; and determine the presence of a change from the particular data topic to the different data topic based on a distance between the textual embedding of the other set of entities and the textual embedding of a set of entities, implement above function with reasonable expectation of success. The motivation for doing so would have been to improve functionality of the method by using word/textual embedding to detect topic changes.
Regarding claims 11 and 19,
the scopes of the claims are substantially the same as claim 3, and are rejected
on the same basis as set forth for the rejection of claim 3.
Claims 6, 14, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al., KR 20190109614 A in view of Szymanski et al., US 2021/0027783 further in view of Marsh et al., US 2017/0060917 and further in view of Breedvelt-Schouten et al., US 2017/0277801 (Breedvelt, hereafter).
Regarding claim 6,
the combination of Kim, Szymanski, and Marsh discloses partitioning the one or more conversations and determining topics for the conversation. However, it does not explicitly teach determining one or more topics related to the particular data topic based on an ontology; arranging the particular topic and the one or more related topics according to relationships from the ontology; and presenting the particular data topic and one or more related topics arranged according to the relationships.
On the other hand, Breedvelt discloses determining related topics to a main topic
using an ontology engine, ranking the topic lists, and display topics and the contents to a user (See Breedvelt: at least Fig. 2-3E, para 20, 31, and 33).
Therefore, it would have been obvious to one of ordinary skill in the art before the
time the invention was effectively filed to modify the teachings of Kim, Szymanski, and Marsh with Breevelt’s teaching in order to wherein partitioning the potion of the conversations further comprises: determining one or more topics related to the particular data topic based on an ontology; arranging the particular data topic and the one or more related topics according to relationships from the ontology; and presenting the particular data topic and one or more related topics arranged according to the relationships adjacent to a corresponding segment of a conversation, implement above function with reasonable expectation of success. The motivation for doing so would have been to improve functionality of the method to generate insight by determining additional topics relevant to the user interest and inquiry.
Regarding claims 14 and 22,
the scopes of the claims are substantially the same as claim 6, and are rejected on the same basis as set forth for the rejection of claim 6.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Points of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARES JAMI whose telephone number is (571)270-1291. The examiner can normally be reached M-F 9:00a-5:00p.
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/Hares Jami/ Primary Examiner, Art Unit 2164
07/08/2026
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