DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 20 December 2024, 15 September 2025, and 19 November 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-14 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite steps for providing and processing conversation. The limitations of claims 1-14, as drafted, are a computer program product or system that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “interface”, “computer readable storage medium”, “program instructions” “computer”, “processor”, and “memory” nothing in the claim element precludes the steps from practically being performed in the mind and/or with pen and paper calculations. Under the BRI, a person (i.e., agent) could generate and provide an utterance to another person (i.e., user) based on that person’s conversation history. Accordingly, the steps of the claims are directed to organizing human interactions and/or a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claims 1-14 only recite the additional elements “computer readable storage medium” and “memory” to perform the aforementioned steps. The processor and other hardware are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function for transliterating text such that they amount to no more than mere instructions to apply the exception using generic computer components.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional hardware elements to perform both the aforementioned steps amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
A similar analysis applies to the dependent claims. The summarizing may involve nothing more than the first person (i.e., agent) writing down a recap of the conversation with the user, wherein the paper with the written summary serves as memory. Furthermore, there appear to be no technical specifics about how the steps of the claims are performed.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4, and 14 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20230281466, hereinafter referred to as Bendale et al.
Regarding claim 1, Bendale et al. discloses a conversation provision method, comprising:
forming a conversation session between an agent and a user (Bendale et al., fig. 5 – agent-user conversation.);
generating an utterance of the agent using the user's history related to a previous conversation session having been formed prior to the conversation session (“Even though all users might start from similar conversational models, the digital agent may be highly personalized with respective interaction histories over a period,” Bendale et al. And, Bendale et al., fig. 5, shows conversation exchange between agent and user based on historical conversation summaries.); and
performing a conversation with the user by providing the utterance of the agent to the user (Bendale et al., fig. 5, shows conversation exchange between agent and user.).
As to claim 14, CRM claim 24 and method claim 1 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 14 is similarly rejected under the same rationale as applied above with respect to method claim. And, Bendale et al., para [0068], teaches CRM for storing a program.
Regarding claim 4, Bendale et al. discloses the conversation provision method of claim 1, wherein summary content exists in the user's history for each of a plurality of different categories (“The machine-learning-based context engine 120 may extract high level features from raw conversational data to understand the category and topics of the conversation. Salient features/topics of the conversation may be stored in memory in form of interaction summary 530A. The interaction summary may be referred to as a conversation summary,” Bendale et al., para [0037]. The examiner notes that the claim does not specify that a separate summary content is one-to-one paired with a category.).
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(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230281466, hereinafter referred to as Bendale et al.
Regarding claim 2, Bendale et al. discloses the conversation provision method according to claim 1, wherein the user's history is configured of summary content summarizing the utterance of the user from the previous conversation session formed between the agent and the user prior to the conversation session (Bendale et al., fig. 5, shows conversation exchange between agent and user based on historical conversation summaries.), and the summary content includes content related to the user's state or situation (Another embodiment of Bendale et al. teaches extracting and analyzing content related to the user's state or situation, such as a user’s sentiment. “FIG. 6A illustrates an example sentiment analysis process. During a particular conversation session between a user and agent, the user might be talking about visit to national park, during another session the user might be talking about travel arrangements, yet in another session the user might be talking with agent about purchasing of shoes and buying an electric car and so on. The user input 6101 in various forms may be delivered to the machine-learning-based context engine 120 through the API interface 110. From these conversations, the machine-learning-based context engine 120 may generate sentiment template 6110, insight template 6120 and global interest map 6130. Aggregating this information over successive session may help understand overall intent and behavior of the user, which can further help the machine-learning-based context engine 120. The aggregated information may be passed to the content generation engine 130,” Bendale et al., para [0041].). The embodiment of Bendale et al., fig. 6A, benefits the embodiment of Bendale et al., fig. 5, by allowing a user’s state or situation (such as sentiment or intent) to be incorporated into the conversation summary generation, thereby allowing the agent to be more helpful to the user in future conversations. Therefore, it would be obvious for one skilled in the art to combine the embodiments of Bendale et al. to arrive at the applicant’s claim.
Regarding claim 3, Bendale et al. discloses the conversation provision method of claim 2, wherein, when a plurality of summary content corresponding each to a plurality of different categories related to the user's state or situation exist in the user's history, in the generating of the utterance of the agent, the utterance of the agent is generated using one summary content of the plurality of summary content that corresponds to context of the conversation of the conversation session, on the basis of the context of the conversation in the conversation session (Another embodiment of Bendale et al. teaches extracting and analyzing content related to the user's state or situation, such as a user’s sentiment. “FIG. 6A illustrates an example sentiment analysis process. During a particular conversation session between a user and agent, the user might be talking about visit to national park, during another session the user might be talking about travel arrangements, yet in another session the user might be talking with agent about purchasing of shoes and buying an electric car and so on. The user input 6101 in various forms may be delivered to the machine-learning-based context engine 120 through the API interface 110. From these conversations, the machine-learning-based context engine 120 may generate sentiment template 6110, insight template 6120 and global interest map 6130. Aggregating this information over successive session may help understand overall intent and behavior of the user, which can further help the machine-learning-based context engine 120. The aggregated information may be passed to the content generation engine 130,” Bendale et al., para [0041].). The embodiment of Bendale et al., fig. 6A, benefits the embodiment of Bendale et al., fig. 5, by allowing a user’s state or situation (such as sentiment or intent) to be incorporated into the conversation summary generation, thereby allowing the agent to be more helpful to the user in future conversations. Therefore, it would be obvious for one skilled in the art to combine the embodiments of Bendale et al. to arrive at the applicant’s claim.
Claim(s) 5-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230281466, hereinafter referred to as Bendale et al., in view of US 20230244855, hereinafter referred to as Attwater et al.
Regarding claim 5, Bendale et al. discloses the conversation provision method of claim 1, further comprising:
delivering, when the conversation session ends, the conversation of the conversation session to a summarizer (“At the end of session 1, the machine-learning-based context engine 120 may generate an interaction summary 530A. The machine-learning-based context engine 120 may extract high level features from raw conversational data to understand the category and topics of the conversation. Salient features/topics of the conversation may be stored in memory in form of interaction summary 530A. The interaction summary may be referred to as a conversation summary,” Bendale et al., para [0037]. Interaction summary 530A is interpreted as the summarizer.).
Bendale et al., though, does not disclose summarizing, in the summarizer, a specific utterance of the utterance of the user included in the conversation of the conversation session and corresponding to a preset category in the form of a sentence.
Attwater et al. is cited to disclose summarizing, in the summarizer, a specific utterance of the utterance of the user included in the conversation of the conversation session and corresponding to a preset category in the form of a sentence (“Summary features may be generated automatically in different ways. In one embodiment the system could generate one summary feature for specific conversational features. For example generate a summary description of a specific agent or client goal or intent from the training data for client goals 2560′, or agent intents 2560″. One or more representative sentences that are closest to the centroid of the relevant cluster(s) can then be automatically summarized using one of the known techniques for summarizing text in a manner similar to the generation of the short labels for each cluster, for example using models such as the example process 2900 shown in FIG. 29, suitable for execution by a computer processor, for creating summary features from clusters of intent utterances is shown as follows: a. build 2902 a vector for each utterance in the group of utterances from the conversational database 2901, such as by using a deep learning model trained for generating semantic sentence vectors,” Attwater et al., para [0160]-[0161].). Attwater et al. benefits Bendale et al. by allowing a specific utterance of the user included in the conversation session and corresponding to a preset category in the form of a sentence to be summarized. Therefore, it would be obvious for one skilled in the art to combine the teachings of Bendale et al. with those of Attwater et al. to allow the summarizer of Bendale et al. to succinctly extract and summarize a user’s intent.
Regarding claim 6, Bendale et al., as modified by Attwater et al., discloses the conversation provision method of claim 5, wherein the summarizer is trained to generate summary content only for content of the utterance of the user included in the conversation of the conversation session that corresponds to the preset category (“g. generate 2908 from the short text multiple summary sentences, such as by using with a beam search decoding strategy of a deep learning generative model trained with datasets of long texts and summaries, and optionally including one or more narrative structures 2912; h. identify 2910 from the multiple summary sentences the one summary sentence that follows a best or a preferred summary format (e.g., for client intents, summaries that describe the action relative to the client may be preferred, “The client has requested a new card”),” Attwater et al., para [0167]-[0168]. In this example the preset category is “client intent”.).
Regarding claim 7, Bendale et al., as modified by Attwater et al., discloses the conversation provision method of claim 6, further comprising:
obtaining an output value corresponding to a specific operation corresponding to one of different operations for a memory, with respect to a pair of summary content summarized from the conversation of the conversation session and summary content included in the user's history stored in the memory (“The machine-learning-based context engine 120 may update the context model with the queried information 540B at step 505. The updated context model 520B may be used for generating a response corresponding to the third input from the user,” Bendale et al., para [0037]. The updated context model 520B is an output value corresponding to an updating operation in memory, the model update based on conversation summary content and the user’s history stored in memory. See Bendale et al., fig. 5.); and
updating the user's history stored in the memory according to the output value (Bendale et al., fig. 5.).
Regarding claim 8, as modified by Attwater et al., discloses the conversation provision method of claim 7, wherein a first operation of the different operations is an operation that maintains the storage of the summary content corresponding to the user's history from the pair of summary content in the memory, but does not store the summary content summarized from the conversation of the conversation session in the memory (The option to save or not save summary information in memory is a matter of program design choice.),
a second operation of the different operations is an operation that maintains the storage of the summary content corresponding to the user's history from the pair of summary content from the memory, while also storing the summary content summarized from the conversation of the conversation session in the memory (The option to save or not save summary information in memory is a matter of program design choice.),
a third operation of the different operations is an operation that deletes the summary content corresponding to the user's history from the pair of summary content from the memory and stores the summary content summarized from the conversation of the conversation session in the memory (The option to save or not save summary information in memory is a matter of program design choice.), and
a fourth operation of the different operations is an operation that deletes the summary content corresponding to the user's history from the pair of summary content from the memory and does not also store the summary content summarized from the conversation of the conversation session in the memory (The option to save or not save summary information in memory is a matter of program design choice.).
Regarding claim 9, Bendale et al. discloses a conversation processing system, comprising:
a memory configured to store a user's history related to a past conversation session (“When a second input from the user arrives, the machine-learning-based context engine 120 may query information 540A relevant to the second input from the stored interaction summary. The machine-learning-based context engine 120 may update the context model with the queried information 540A at step 503,” Bendale et al., para [0037]. See Bendale et al., fig. 5(540A).); and
a memory operator configured to specify an operation for the memory using summary information summarized by the summarizer and the user's history (“When a second input from the user arrives, the machine-learning-based context engine 120 may query information 540A relevant to the second input from the stored interaction summary. The machine-learning-based context engine 120 may update the context model with the queried information 540A at step 503,” Bendale et al., para [0037]. See Bendale et al., fig. 5(503). Here, the push model update 503 is an operation for the memory using the summary information based on user’s history.).
Bendale et al., though, does not specifically disclose a summarizer configured to receive a conversation of a current conversation session formed between an agent and a user, and to summarize at least part of an utterance of the user included in the conversation in the form of a sentence.
Attwater is cited to disclose a summarizer configured to receive a conversation of a current conversation session formed between an agent and a user, and to summarize at least part of an utterance of the user included in the conversation in the form of a sentence (Attwater et al., fig. 29(2910).). Attwater et al. benefits Bendale et al. by allowing a specific utterance of the user included in the conversation session and corresponding to a preset category in the form of a sentence to be summarized. Therefore, it would be obvious for one skilled in the art to combine the teachings of Bendale et al. with those of Attwater et al. to allow the summarizer of Bendale et al. to succinctly extract and summarize a user’s intent.
Regarding claim 10, Bendale et al., as modified by Attwater et al., discloses the conversation processing system of claim 9, wherein the summary information and the user's history include content summarized by the summarizer (Bendale et al., fig. 5.), and
wherein the memory operator specifies an operation for the memory using a pair of specific content included in the summary information and specific content included in the user's history (Bendale et al., fig. 5.).
Regarding claim 11, Bendale et al., as modified by Attwater et al., discloses the conversation processing system of claim 10, wherein the pair of content are content corresponding to a same category (Bendale et al., fig. 5.).
Regarding claim 12, Bendale et al., as modified by Attwater et al., discloses the conversation processing system of claim 11, wherein the memory is updated so that the user's history reflects the conversation of the current conversation session on the basis of the operation for the memory (“When a second input from the user arrives, the machine-learning-based context engine 120 may query information 540A relevant to the second input from the stored interaction summary. The machine-learning-based context engine 120 may update the context model with the queried information 540A at step 503,” Bendale et al., para [0037]. See Bendale et al., fig. 5(503). Here, the push model update 503 is an operation for the memory using the summary information based on user’s history.).
Regarding claim 13, Bendale et al., as modified by Attwater et al., discloses the conversation processing system of claim 12, further comprising:
a generator configured to generate an utterance of the agent (Bendale et al., fig. 1(110).),
wherein the generator generates the utterance of the agent in a newly formed conversation session between the agent and the user, after the current conversation session ends, using the updated user's history (Bendale et al., fig. 1(130).).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892. In particular, the examiner notes JP2020118842, KR20200072315, and JP6882975 as teaching, alone or in combination, the applicant’s claims.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE L THOMAS-HOMESCU whose telephone number is (571)272-0899. The examiner can normally be reached Mon-Fri 8-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh M Mehta can be reached on 5712727453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANNE L THOMAS-HOMESCU/Primary Examiner, Art Unit 2656
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