DETAILED ACTION
This is in response to a Request for Continued Examination (RCE) filed on 10/14/2025. Claims 1-24 are pending in this Office Action.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/14/2025 has been entered.
Remark
In the response to final Office action mailed 08/01/2025, claims 1, 3, 6, 7, 9, 11, 14, 15, 17, 19, 22, and 23 have been amended, no claim has been cancelled, and no new claim has been added.
The Applicant's Interview Summary is acknowledged by the Examiner and it is OK.
The Applicant’s amendments regarding 35 USC 112(a) and (b) rejections are accepted by the Examiner. Therefore, prior 35 USC 112(a) and (b) rejections are withdrawn.
Response to Arguments
With respect to 35 USC 101 rejection:
The applicant did not provide any argument with respect to 35 USC 101 rejection of the claims for being directed to abstract idea.
The Examiner respectfully disagrees that the amendments to claims 1, 9, and 17 would overcome the 35 USC 101 rejection of the claims for being directed to abstract idea. The limitations of the claims are recited at a high level of generality and they are either mental processes under Step 2A, Prong 1 or extra-solution activities and/or well-understood, conventional, and routine computer activities, under Step 2A, Prong 2, failing to integrate the recited abstract idea into a practical application and amount to more than abstract idea. See below for details.
Therefore, 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 “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 context” have been considered but are moot in view of the new ground(s) of rejection over the new references, Szymanski et al., US 2021/0027783.
The new combination of Kim, Szymanski, and Pathak 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; [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; [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; [constitutes a mathematical concept which could be practically performed in the human mind. A person could mentally transform the terms/words into a vector of numerical representation]
partitioning, in real-time, a portion of the conversation into a segment based on associating the portion of the conversation with a particular data context [constitutes a concept which could be practically performed in the human mind. A person could mentally divide a conversation into at least a segment using data context]
The above-mentioned steps are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in a human mind or with pen and paper. 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 context; retrieving the segment based on processing the query; and displaying, via the user interface and based on retrieving the segment, information associated with the topic” which 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 context; retrieving the segment based on processing the query; and displaying, via the user interface and based on retrieving the segment, information associated with the topic” which 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 Pathak et al., US 2024/0394477 (Pathak, hereafter).
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 context (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 context.
On the other hand, Szymanski discloses dividing a conversation into different
stages and segments based on social actions and topics (i.e., data context) (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 context, 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.
Although, the combination of Kim and Szymanski discloses portioning a conversation into segments/clusters, receiving and processing a query and conversation topic, it does not explicitly teach retrieving the segment based on processing the query; and displaying, via the user interface and based on retrieving the segment, information associated with the topic.
On the other hand, Pathak discloses processing a query pertaining to particular topic, and answering the query by selecting only parts or segments of a context information or a dialogue that pertains to the particular topic. Pathak further discloses retrieving answer based on input query and outputting the response to the user (See Pathak: at least Fig. 7, Fig. 15-6, para 43, 64, 86, 90, 127-128, and 146). 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 Pathak’s teaching in order to implement above function with reasonable expectation of success. The motivation for doing so would have been to only process the parts that pertains to the query, reducing number of resources consumed by the system in processing the query, and reducing a latency for providing the response.
Regarding claim 2,
the combination of combination of Kim, Szymanski, and Pathak discloses wherein the conversation is generated by artificial intelligence (See Szymanski: at least Fig. 5, para 10-11 and 41 and Pathak: at least Fig. 7, Fig. 15, para 64, 86, 90, 127, and 146).
Regarding claim 4,
the combination of Kim, Szymanski, and Pathak discloses wherein partitioning the portion of the conversation into the segment comprises: assigning the segment to the topic based on determining that the segment is associate with the topic, wherein the topic is an existing topic (See Szymanski: at least Fig. 3-6 and para 27 and 47 and Pathak: at least Fig. 3, Fig. 7, para 65, 68, 80-81 and 96).
Regarding claim 5,
the combination of Kim, Szymanski, and Pathak discloses wherein partitioning the portion of the conversation into the segment comprises: assigning the segment to the topic based on determining that the segment is associate with the topic, wherein the topic is a new topic (See Szymanski: at least Fig. 3-6 and para 27 and 47 and Pathak: at least Fig. 3, Fig. 7, para 65, 68, 80-81 and 96).
Regarding claim 7,
the combination of Kim, Szymanski, and Pathak discloses presenting, via the at least one processor, changes in topics for the conversation adjacent segments of the conversation corresponding to the changes in topics (See Pathak: at least Fig.
3, Fig. 7, para 65, 68, 80-81 and 96).
Regarding claim 8,
the combination of Kim, Szymanski, and Pathak 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 Pathak: at least Fig. 7, Fig. 15-16, para 64, 86, 90, 127, and 146).
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 Pathak et al., US 2024/0394477 and further in view of Nedivi, US 2021/0233423.
Regarding claim 3,
the combination of Kim, Szymanski, and Pathak 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 (See Pathak: at least Fig. 3, Fig. 7, para 65, 68, 80-81 and 96). 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 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 Pathak with Nedivi’s teaching in order to determine a presence of change from the topic to a different 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 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 Pathak et al., US 2024/0394477 and further in view of Breedvelt-Schouten et al., US 2017/0277801 (Breedvelt, hereafter).
Regarding claim 6,
the combination of Kim, Szymanski, and Pathak 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 topic based on an ontology; arranging the topic and the one or more related topics according to relationships from the ontology; and presenting the 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 Pathak with Breevelt’s teaching in order to wherein partitioning the potion of the conversations further comprises: determining one or more topics related to the topic based on an ontology; arranging the topic and the one or more related topics according to relationships from the ontology; and presenting the 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Molina et al., US 9,449,050 disclosing a metho for identifying a relevant conversation including identifying a conversation graph including identifiers of messages posted by a set of authoring accounts; identifying a relevance score for each authoring account; applying, by a computer processor, a first relevance filter to the conversation graph in order to modify the set of relevance scores; applying, by the computer processor and after applying the first relevance filter, a second relevance filter to the conversation graph in order to further modify the set of relevance scores; selecting, based on the set of relevance scores and after applying the second relevance filter, a subset of messages of the conversation graph for inclusion in the relevant conversation; and providing at least a portion of the relevant conversation to a client device.
Kodish-Wachs, US 11,062,704 disclosing a method for retrieving, from the one or more computer storage memory, dialogue data associated with a plurality of utterances from two or more entities. A target concept in one or more dialogue segments of the transcribed dialogue data can be determined, where the one or more dialogue
segments comprising a first dialogue segment associated with a first utterance by a first entity, and where the first dialogue segment includes the target concept.
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/Hares Jami/ Primary Examiner, Art Unit 2164
01/22/2026