DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 26 June 2026. Claims 1-20 are pending and have been examined. Hence, this action has been made FINAL.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The IDS dated 08 April 2026 has been considered and placed in the application file.
The IDS dated 23 June 2026 has been considered and placed in the application file.
Response to Arguments
The reply filed on 26 June 2026 has been entered. Applicant’s arguments with respect to claims 1-20 have been considered but are not persuasive/moot in view of new ground(s) of rejection caused by the amendments.
With respect to the applicant’s arguments to claim rejections under 35 U.S.C § 101, Applicant has amended each of the independent claims and asserts that “While a human might mentally resolve a pronoun in a simple conversation, a human mind cannot implement a "first language model" and a "second language mode" (such as complex neural networks, e.g., LLMs with billions of parameters as described in the specification) that are structurally decoupled to process data streams independently.” The examiner respectfully disagrees with these assertions. The limitations of a “language model” are recited at a high level of generality (¶ [0061]) and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). Accordingly, the stated example of “complex neural networks, e.g., LLMs with billions of parameters” cannot be construed or inferred from the claim limitations without reading the specification into the claims. The limitations of a “first language model” and “second language model” therefore do not constitute patentable subject matter under the broadest reasonable interpretation.
Applicant further asserts that “the claimed operations provide a specific architectural solution for how to address context-overload in natural language processing systems, improving the functioning of the computing system.” The examiner respectfully disagrees with these assertions. The idea of separating contextual understanding from downstream processing tasks is not inherent only to the field of computer processing; a human could easily separate the task of understanding a question from the task of answering it. Thus, separating contextual understanding from downstream processing tasks is considered an improvement in an abstract idea, which cannot be considered a practical improvement in computer technology. See MPEP 2106.05(a), “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Applicant further asserts that “Similarly to the table at issue in Enfish, claim 1 recites specific features of a computing architecture (decoupled first and second language models processing data independently) that improve the speed, computational efficiency, and accuracy (by reducing hallucinations) of the system, providing significantly more than any alleged abstract idea.” The examiner respectfully disagrees with these assertions. Enfish found “that the claims at issue ... are not directed to an abstract idea within the meaning of Alice. Rather, they are directed to a specific improvement to the way computers operate, embodied in the self-referential table." (Enfish, 822 F.3D 1327, 1336 (2016)). In other words, Enfish relies on the specific improvement made to computer operation technology, e.g. a self-referential table. This self-referential table is considered a specific improvement because it intrinsically modifies the way the computer works, e.g. by having the computer interact with, manage, and reference the self-referential table for processing. The independent claims of the instant application by contrast make no such specific improvement, instead merely reciting the use of “language models” at a high level of generality to perform a mental process, as described further below with respect to claim rejections under 35 U.S.C. 101.
Applicant further asserts that “improved computational efficiency, reduced hallucinations, and enhanced system modularity, as provided by the claimed invention, are improvements to the functioning of a computing system.” The examiner respectfully disagrees with these assertions. Merely claiming that the invention enables specific improvements cannot constitute valid justification without further explaining how the claim limitations are directly involved in enabling such improvements. See MPEP 2106.04(d)(1), “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”
Applicant further asserts that “By ignoring the specific technical implementation of the decoupled dual-model architecture, the Examiner has failed to evaluate the claims as a whole. When evaluated at the proper level of specificity required by Desjardins, the claims provide a practical application that improves the functioning of the computing system itself, rendering them patent-eligible.” The examiner respectfully disagrees with these assertions. The Appeals Review Panel (ARP) decision highlights in Ex Parte Desjardins pg. 9, paragraph 1 that “When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation." In other words, an improvement must be made to the machine learning model itself in order to constitute integration into practical application. Simply using a machine learning model (e.g. “first language model,” “second language model”) to fulfill a purpose without improving the machine learning model itself merely equates to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). As amended, there is no language in the independent claims that provides an improvement to a machine learning model itself.
Applicant further asserts that “the claims recite inventive concepts, providing significantly more than any alleged abstract idea. As explained above, the claims as amended recite detailed, new, and improved techniques, with multiple additional elements beyond just generic hardware. … the specific deployment of a first language model trained for contextual understanding that outputs a self-contained query to a decoupled second language model trained for question answering that processes the query independently of the conversation history are all specific, technological steps for processing data elements that fall far outside of a mere mental process.” The examiner respectfully disagrees with these assertions. Any human would be able to specialize in contextual understanding, as would any human be able to separate the task of contextual understanding from the task of answering a question. The examiner fails to see what “specific, technological steps” the Applicant is referring to that would amount to “significantly more” than the judicial exception. As amended, there is no language in the independent claims that would prevent a human from performing these steps, as addressed in further detail below with respect to claim rejections under 35 USC § 101.
With respect to the applicant’s arguments to claim rejections under 35 U.S.C § 102, the Applicant has amended each of the independent claims as asserts that “Even assuming, for the sake of argument, that the Examiner is correct in asserting that Ge's query answering machine constitutes a decoupled second language model, Ge fundamentally fails to teach that this downstream model processes the query independently of the conversation history.” The examiner respectfully disagrees with these assertions. Paragraph [0057] of Ge et al. discloses "Since the rewritten utterance is fully resolved, with any entities mentioned in an unambiguous way, the rewritten utterance can be processed by the one or more query answering machines without needing to access context from the computer-accessible conversation history." In other words, Ge et al.’s query answering machines are able to process modified utterances without needing access to conversation history, i.e., are able to process queries independently of conversation history.
The remaining applicant arguments with respect to claims 1-20 have been considered but are moot in view of new ground(s) of rejection caused by the amendments.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
The following terms in the claims have been given the following interpretations in light of the specification:
Anaphora: paragraph [0089], “In some instances, the ambiguity in the first user utterance includes an anaphora. An anaphora is a word referring to or replacing another word, such as a pronoun or noun phrase. For example, in the conversation snippet “I had lunch already”, “It was delicious,” “it” is an anaphora referring to the noun “lunch.” In the sentence “How do I enable the last one?”, the noun phrase “last one” is a noun phrase referring to previous conversation history.”
Thus, an anaphora is any word used to refer to or replace a noun or pronoun phrase. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
Should applicant wish different definitions, Applicant should point to the portions of the specification that clearly show a different definition.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. All of the claims are method claims (1-7), apparatus/machine claims (8-20) or manufacture claim under (Step 1), but under Step 2A all of these claims recite abstract ideas and specifically mental processes. These mental processes are more particularly recited in claims 1, 8, and 15 as:
[identifying] an ambiguity in the first user utterance and one or more terms in the conversation history to resolve the ambiguity…
[modifying] the first user utterance to include the one or more terms identified to resolve the ambiguity to generate a modified utterance…
providing, by the computing system, the modified utterance as input to a second language model…
[performing] a natural language processing task based on the input modified utterance…
Under Step 2A Prong One, claims 1, 8, and 15 are directed to an abstract idea and specifically a mental process. As detailed above, the steps of identifying, modifying, providing, performing, etc. may be practically performed in the human mind with the use of a physical aid such as a pen and paper. For example, a human receptionist could be found in conversation with a human client, wherein the client asks the receptionist a question. The receptionist could identify an anaphora in the client’s question, reference the conversation they’ve had thus far with the client to identify the term that the anaphora is referring to, mentally replace the anaphora in the client’s query with the remembered term from the conversation history in order to generate a modified query, and then transmit the modified query to a human expert. The human expert could then provide an answer to the modified query, and the moderator could transmit the expert answer to the client. It is noted that the human receptionist’s contextual understanding of the client’s question is decoupled and separate from the expert’s processing and answering of said question, and that the expert can be wholly unaware of the conversation history between the human receptionist and the human client.
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because claims 1-20 do not recite additional elements that integrate the exception into a practical application. In particular, claims 1, 8, and 15 recite the additional elements of a processor (¶ [0155]), memory storing instructions (¶ [0161]), a computing system (¶ [0051]), and a language model (¶ [0061]). These additional elements are recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under Step 2B, the claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is noted as a general computer {processor (¶ [0155]); memory storing instructions (¶ [0161]); computing system (¶ [0051])}. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claims 2, 9, and 16, the claim relates to processing a second user utterance that is free from ambiguity. This relates to a human client posing a question with no ambiguity, and in response, a human receptionist passing the question to a human expert without modification. The limitation of “computing system” is recited at a high level of generality (¶ [0051]) and merely equates to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 3, 10, and 17, the claim relates to the modified utterance comprising a query, and the result comprising an answer. This relates to a human client posing a question, and in response, a human expert responding with an answer. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 4-7, 11-14, and 18-20, the claim relates to processing various types of ambiguities. This relates to a human client posing a question, and in response, a human receptionist flexibly correcting any ambiguities (e.g. anaphors, missing words, missing intents, or conversation misunderstandings) before transmitting the query to the human expert. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
For all of the above reasons, taken alone or in combination, claims 1-20 recite a non-statutory mental process.
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 1-20 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 20200380077 A1 (Ge et al.) in view of “Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the Art” (Chai et al.).
Claim 1
Regarding claim 1, Ge et al. disclose a computer-implemented method comprising:
inputting, by a computing system, a first user utterance (Ge et al. ¶ [0014], "In some examples, a user utterance may define a query by including a query intent and one or more query entities. ") and a conversation history (Ge et al. ¶ [0015], "The user utterance may be contextually related to other utterances in a computer-accessible conversation history of the multi-turn dialogue, e.g., previous utterances by the user(s) and/or by the automated agent.") to a first language model (Ge et al. ¶ [0044], "At 302, method 300 includes using a predefined language model (e.g., any suitable predefined language model as described above) to recognize an ambiguous entity in an unresolved user utterance from a multi-turn dialogue.") trained for contextual understanding (Ge et al. ¶ [0073], "method 500 includes using a predefined language model to recognize a suggested intent in an unresolved user utterance from the multi-turn dialogue. As with detection of suggested entities, any suitable predefined language model may be used to detect the suggested intent based on any suitable criteria, for example by detecting a phrase such as “what about?” or “how about?”." A language model trained to recognize a suggested intent is considered analogous to a first language model trained for contextual understanding);
wherein the first language model identifies an ambiguity in the first user utterance (Ge et al. ¶ [0044], "At 302, method 300 includes using a predefined language model (e.g., any suitable predefined language model as described above) to recognize an ambiguous entity in an unresolved user utterance from a multi-turn dialogue." The predefined language model is considered analogous to a first language model) and one or more terms in the conversation history to resolve the ambiguity (Ge et al. ¶ [0047], "At 306, method 300 further includes, in a computer-accessible conversation history of the multi-turn dialogue, searching a set of previously-resolved entities for a candidate entity having entity properties with a highest confidence correspondence to the entity constraints of the ambiguous entity."), modifies the first user utterance to include the one or more terms identified to resolve the ambiguity to generate a modified utterance (Ge et al. ¶ [0040], "coreference resolution machine 102 is configured to pre-process the user utterance 106 to resolve any potentially ambiguous coreferences in the user utterance, to produce a rewritten utterance.") comprising a self-contained query (Ge et al. ¶ [0056], "FIGS. 2C-2F show rewritten user utterances 218 corresponding to the unresolved user utterances 216, with ambiguous entities replaced by selected candidate entities from the computer-accessible conversation history." See Figures 2C-2F, which illustrate modified utterances (e.g. "rewritten utterances") comprising a self-contained query (e.g. "What is Bill Gates's net worth?")), and outputs the modified utterance (Ge et al. ¶ [0040], "The rewritten utterance is self-contained for detection of speech acts by speech act classifier machine 110, and/or further processing by downstream query answering machines and the like.");
providing, by the computing system, the modified utterance as input to a second language model (Ge et al. ¶ [0027], "dataflow architecture 100 may be configured to provide multiple different possible rewritten queries to the one or more query answering machines, and to receive answers for each of the different possible rewritten queries." Query answering machines are considered analogous to a second language model), wherein the second language model is [trained] configured for question answering (Ge et al. ¶ [0022], " The one or more query answering machines may return a plurality of different answers to a given query."), [is decoupled from the first language model to separate the contextual understanding from a downstream natural language processing task], and performs the natural language processing task based on the input modified utterance being processed independently of the conversation history (Ge et al. ¶ [0057], "Since the rewritten utterance is fully resolved, with any entities mentioned in an unambiguous way, the rewritten utterance can be processed by the one or more query answering machines without needing to access context from the computer-accessible conversation history.") and outputs a result (Ge et al. ¶ [0033], "the query answering machine may be configured to recognize entities from an utterance, and configured to output each entity that occurs in the utterance." Entity recognition is considered analogous to a natural language processing task); and
outputting, by the computing system, a response to the first user utterance based on the result (Ge et al. ¶ [0027], "dataflow architecture 100 may be configured to provide multiple different possible rewritten queries to the one or more query answering machines, and to receive answers for each of the different possible rewritten queries.").
Ge et al. do not explicitly disclose all of decoupling a second language model from a first language model.
However, Chai et al. disclose wherein a first language model identifies an ambiguity in [the first user utterance] a corpus and one or more terms [in the conversation history] to resolve the ambiguity (Chai et al. pg. 64, Section 5, Paragraph 3, "deep-coref (Lee et al., 2018) is a neural model that first extracts candidate mentions using syntactic information. For each candidate mention, it scores all preceding mentions to select the best scoring one as the antecedent. It also includes a dummy antecedent to determine nonanaphoric mentions, i.e., if the dummy antecedent has the highest score, the mention is non-anaphoric."), modifies the [first user utterance] corpus to include the one or more terms identified to resolve the ambiguity to generate a modified [utterance] corpus (Ge et al. ¶ [0040], "coreference resolution machine 102 is configured to pre-process the user utterance 106 to resolve any potentially ambiguous coreferences in the user utterance, to produce a rewritten utterance.") (Chai et al. pg. 63, Section 3.3, Paragraph 2, "We first apply the coreference resolver on all candidate answers and get the resulting coreference chains.") [comprising a self-contained query], and outputs the modified [utterance] corpus (Chai et al. pg. 63, Figure 1, "The red line indicates decontextualizing sentences using coreference information in the data, while the blue line shows the original data." See Figure 1, which illustrates coreference resolvers (e.g. e2e-coref) outputting modified utterances (e.g. decontextualized sentences) to answer selection models); and
providing, by the computing system, the modified [utterance] corpus as input to a second language model (Chai et al. pg. 63, Figure 1 illustrates coreference resolvers (e.g. e2e-coref) outputting modified corpora (e.g. decontextualized sentences) to answer selection models. Answer selection models are considered analogous to a second language model), wherein the second language model is trained for question answering (Chai et al. pg. 62, Section 3.1, Paragraph 1, "We use Sentence-BERT (Reimers and Gurevych, 2019) as an unsupervised baseline for answer selection." Answer selection is considered analogous to question answering), is decoupled from the first language model to separate the contextual understanding from a downstream natural language processing task (Chai et al. pg. 63, Section 3.3, Paragraph 1, "To benefit from coreference information in downstream tasks, one can either incorporate coreference relations in the model ..., or in its input data..., from which we use the second approach." Only incorporating coreference relations in input data is considered analogous to decoupling second language models from first language models. Figure 1 illustrates second language models (e.g. Answer Selection Models) being decoupled from first language models (e.g. coreference resolvers)), and performs the natural language processing task based on the input modified [utterance] corpus being processed independently of the conversation history (Chai et al. pg. 62, Section 2, Paragraph 1, "Given a question, the task of answer selection is to find the correct answer among the set of candidate answers." pg. 62, Section 3.1, Paragraph 1, "We use Sentence-BERT (Reimers and Gurevych, 2019) as an unsupervised baseline for answer selection. ... we select the candidate answer with the highest cosine similarity to the question." It is noted that at no point does an answer selection model interact with conversation history. See Figure 1) and outputs a result (Chai et al. pg. 62, Section 3.1, Paragraph 1, "We use Sentence-BERT (Reimers and Gurevych, 2019) as an unsupervised baseline for answer selection. ... we select the candidate answer with the highest cosine similarity to the question.")….
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Ge et al.’s query disambiguation to incorporate Chai et al.’s separate coreference resolvers.
The suggestion/motivation for doing so would have been that, “using coreference resolvers for decontextualizing input sentences has the following benefits: (1) a single coreference annotated dataset can be used for evaluating various answer selection models, and (2) it does not require developing specialized coreference-aware models for the application,” as noted by the Chai et al. disclosure in pg. 63, Section 3.3, Paragraph 1.
Claim 2
Regarding claim 2, the rejection of claim 1 is incorporated.
Ge et al. further disclose providing, by the computing system to the first language model, a second user utterance (Ge et al. ¶ [0037], "In some examples, as shown in FIG. 2B, an unresolved user utterance 216B is a simple “thanks” which does not actually include any references to entities." See Fig. 2B, which illustrates the user's second utterance in a multi-turn conversation being "thanks"),
wherein the first language model determines that no ambiguity meriting resolution exists in the second user utterance and outputs an indication thereof (Ge et al. ¶ [0037], "Accordingly, coreference resolution machine 102 detects that the unresolved user utterance 216B can be sent straight to the speech act classifier machine 110, which in turn detects an acknowledgment speech act." ¶ [0020], "speech act classifier machine 110 may use a predefined language model to detect predefined speech acts including ... acknowledgement" Detecting an acknowledgement speech act in order to correctly route the user utterance is considered analogous to indicating no ambiguity) ; and
based on the indication, passing, by the computing system, the second user utterance to the second language model without modification (Ge et al. ¶ [0021], "Responsive to detecting [acknowledgement] speech acts, the speech act classifier machine 110 may delegate handling of the user utterance 106 to a greeting/acknowledgment/rejection response machine 112" ¶ [0037], “In addition to politely handling user utterances such as “thanks” or “hello,” the greeting/acknowledgment/rejection response machine 112 can issue similarly polite responses to other user utterances such as affirming a proposed course of action, rejecting a proposed course of action, etc.”) .
Claim 3
Regarding claim 3, the rejection of claim 1 is incorporated.
Ge et al. further disclose wherein the modified utterance comprises a query, and wherein the result is an answer to the query (Ge et al. ¶ [0016], "after resolving ambiguities in the user utterance (e.g., via coreference resolution, resolving an ambiguous suggestion to discuss a new topic, or asking a clarifying question) a rewritten utterance may be output to one or more downstream query answering machines for further processing").
Claim 4
Regarding claim 4, the rejection of claim 1 is incorporated.
Ge et al. further disclose wherein the ambiguity in the first user utterance comprises an anaphora (Ge et al. ¶ [0042], "“FIGS. 2D and 2E show further examples of multi-turn dialogues in which an ambiguous entity from an unresolved user utterance 216 is resolved to refer to “Bill Gates.” FIG. 2D features a coreference to “Bill Gates” in the form of a reference to “the first one” from the ordered listing of “Bill Gates and Paul Allen” in agent utterance 206D. FIG. 2E features a coreference to “Bill Gates” in the form of a pronoun, “he.”" Replacing "Bill Gates" with the terms "he" or "the first one" is considered analogous to an anaphora).
Claim 5
Regarding claim 5, the rejection of claim 1 is incorporated.
Ge et al. further disclose wherein the ambiguity in the first user utterance comprises one or more missing words (Ge et al. ¶ [0039], "As shown in FIG. 2C, the methods of the present disclosure (namely, method 300 which will be discussed below with regard to FIG. 3) may be used to deduce that the partial name “Bill” refers to the entity “Bill Gates” tracked in the entities 212C from the history 202C.").
Claim 6
Regarding claim 6, the rejection of claim 1 is incorporated.
Ge et al. further disclose wherein the ambiguity in the first user utterance comprises a reference to an intent from the conversation history (Ge et al. ¶ [0059], "the rewritten utterance may still require context from the computer-accessible conversation history 202 for query answering. ... For example, an utterance may be an entity suggestion speech act or an entity correction speech act, intended to change the conversation topic to discuss a previously-established intent with regard to a different entity, and accordingly the utterance may be processed by an entity suggestion/correction resolver machine 118 of FIG. 1" A previously-established intent is considered analogous to a reference to an intent from conversation history).
Claim 6
Regarding claim 6, the rejection of claim 1 is incorporated.
Ge et al. further disclose wherein the ambiguity in the first user utterance comprises a correction to one or more words in the conversation history (Ge et al. ¶ [0084]-[0085], "The unresolved user utterance 216J uses the pronoun “she” and is associated with an entity who founded a company. However, the “female” gender of the “she” pronoun does not match the gender property for any of the entities 212J from conversation history 202J, namely Bill Gates or Paul Allen. Since “she” cannot be resolved to a single distinct entity, the detected speech act 220J is “ambiguity.” ... The user clarifies that they actually wanted to ask about “Melinda Gates,” even though she was not yet tracked in the entities 212M from the history. ... After resolving the disambiguated entity, the original, ambiguous user utterance (e.g., 208M in FIG. 2M) can be rewritten to replace the ambiguous entity (e.g., “she”) with the disambiguated entity (e.g., “Melinda Gates”) as in possible rewritten utterance 218M." Correcting "she" to "Melinda Gates" is considered analogous to a correction to one or more words in conversation history).
Claim 8
Regarding claim 8, Ge et al. disclose a system comprising:
one or more processors (Ge et al. ¶ [0104], "The logic subsystem may include one or more hardware processors configured to execute software instructions."); and
one or more computer-readable media storing instructions (Ge et al. ¶ [0105], " Storage subsystem 704 includes one or more physical devices configured to temporarily and/or permanently hold computer information such as data and instructions executable by the logic subsystem.").
The remaining limitations of claim 8 are similar to that of claim 1 and therefore are rejected for similar reasons as described above.
Claim 9
Regarding claim 9, the rejection of claim 8 is incorporated. The limitations of claim 9 are similar to that of claim 2 and therefore are rejected for similar reasons as described above.
Claim 10
Regarding claim 10, the rejection of claim 8 is incorporated. The limitations of claim 10 are similar to that of claim 3 and therefore are rejected for similar reasons as described above.
Claim 11
Regarding claim 11, the rejection of claim 8 is incorporated. The limitations of claim 11 are similar to that of claim 4 and therefore are rejected for similar reasons as described above.
Claim 12
Regarding claim 12, the rejection of claim 8 is incorporated. The limitations of claim 12 are similar to that of claim 5 and therefore are rejected for similar reasons as described above.
Claim 13
Regarding claim 13, the rejection of claim 8 is incorporated. The limitations of claim 13 are similar to that of claim 6 and therefore are rejected for similar reasons as described above.
Claim 14
Regarding claim 14, the rejection of claim 8 is incorporated. The limitations of claim 14 are similar to that of claim 7 and therefore are rejected for similar reasons as described above.
Claim 15
Regarding claim 15, Ge et al. disclose one or more non-transitory computer-readable media storing instructions (Ge et al. ¶ [0105], " Storage subsystem 704 includes one or more physical devices configured to temporarily and/or permanently hold computer information such as data and instructions executable by the logic subsystem.").
The remaining limitations of claim 15 are similar to that of claim 1 and therefore are rejected for similar reasons as described above.
Claim 16
Regarding claim 16, the rejection of claim 8 is incorporated. The limitations of claim 16 are similar to that of claim 2 and therefore are rejected for similar reasons as described above.
Claim 17
Regarding claim 17, the rejection of claim 8 is incorporated. The limitations of claim 17 are similar to that of claim 3 and therefore are rejected for similar reasons as described above.
Claim 18
Regarding claim 18, the rejection of claim 8 is incorporated. The limitations of claim 18 are similar to that of claim 4 and therefore are rejected for similar reasons as described above.
Claim 19
Regarding claim 19, the rejection of claim 8 is incorporated. The limitations of claim 19 are similar to that of claim 5 and therefore are rejected for similar reasons as described above.
Claim 20
Regarding claim 20, the rejection of claim 8 is incorporated. The limitations of claim 20 are similar to that of claim 6 and therefore are rejected for similar reasons as described above.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB B VOGT whose telephone number is (571)272-7028. The examiner can normally be reached Monday - Friday, 11am - 8pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PARAS D SHAH can be reached at (571)270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JACOB B VOGT/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
09/09/2026
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