Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
2. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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 when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitations are:
a product/service module in claims 1-5;
a request module in claim 1;
a natural language processing module and transmission module in claim 2-4;
a natural language semantic construction module in claim 2;
a natural language receiving module in claim 3;
a content conversion module in claim 3;
a semantic determination and feedback module in claims 3 and 12
a semantic overlay module in claim 4;
a language vocabulary conversion module in claim 4;
a requester classification and labeling module in claim 5;
a requester classification and labeling module in claim 5;
an emotion sensing and computing module in claim 6;
an emotion sensing module in claim 6;
a transaction completion feedback module in claim 7;
a user transaction feedback and comment module in claim 7;
a recommendation ranking module in claim 8;
an advertising module in claim 9;
an advertising setting module in claim 9;
an advertising management and evaluation module in claim 9;
an external platform integration module in claim 10;
a semantic-oriented conversation stabilization module in claim 11.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
3. 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 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claims recite:
a request module, provided for a user to make a request in a natural language; a natural language communication system, for receiving the request of the user and then converting and analyzing the request to obtain an analysis result; and a product/service module, for providing a corresponding product or service according to the analysis result.
Following is the subject matter eligibility test for products and processes (MPEP 2106 Patent Subject Matter Eligibility):
The independent claims 1 and 13 fall under the four categories of subject matter eligible for patent protection: process, machines, manufactures, and compositions of matter (Step 1: YES).
This judicial exception is not integrated into a practical application because the combination of elements in the claim appear to be capable of being performed by a human as a mental process. Viewed as a whole, these additional claim elements such as a request module, and a product/service module, could be performed by a human. A human could receive a request from another human/user either virtually or in person, and perform mental analysis of the user’s request and provide a desired product or service to the user essentially amounting to basic customer service (Step 2A – YES).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the generic use of a request module, a natural language communication system and a product/service module could all be performed by a human as a mental task, i.e., receiving/understanding user input (either through text or speech) and performing a task to complete customer service for the user (i.e., providing a desired physical or digital record of data) (Step 2B – NO).
Regarding dependent claims 2-12 and 14-24: Dependent claims 2-12 and 14-24 provide an inventive concept by articulating in detail how a transmission module is used to transmit messages between a request module and a product/service module and a specific language model related to that request in the natural language is chosen in order to fulfill the request, therefore providing a useful tool to automate the capabilities of multiple digital agents providing efficient and fast responses to user requests, thereby integrating the abstract idea into a practical application.
Claim Rejections - 35 USC § 102
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1-5, 8-10, 13-17 and 20-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu (US 2019/0325081).
Regarding Claim 1:
Liu discloses a service platform using natural language for communication (Liu: ¶[[0033] and Fig. 1 disclose a platform for communication with a user), comprising:
a request module, provided for a user to make a request in a natural language (Liu: ¶[0038] and Fig. 2 disclose client system, messaging platform and assistant bots for gathering user requests);
a natural language communication system, for receiving the request of the user and then converting and analyzing the request to obtain an analysis result (Liu: ¶[0038]-[0039] and Fig. 2 disclose receiving user input through speech recognition or text, the ‘xbot’ 215 may receive the input, wherein it is then sent to a natural language understanding (NLU) unit to obtain an interpretation of the input);
and a product/service module, for providing a corresponding product or service according to the analysis result (Liu: ¶[0040] the intent and domain can be sent to a dialog engine, which determines slots it needs to resolve, ¶[0041] discloses the dialog engine may communicate with other agents based on the identified intent and domain and resolved entities in order to broker across a plurality of content providers many of which may provide services (music streaming, ticket sales etc.)).
Regarding Claim 2:
Liu further discloses the platform using natural language for communication according to claim 1, wherein the natural language communication system comprises a natural language processing and transmission module and a natural language semantic construction module, the natural language processing and transmission module is provided for transmitting messages between the request module and the product/service module, and the natural language semantic construction module is provided for retrieving a language model through the request in the natural language (Liu: ¶[0038]-[0039] discloses the xbot (transmission model) for receiving and transferring to NLU units (natural language processing module), which then transfer to the dialog engine, which links to service providers (i.e., transmits messages between request modules and service provides)).
Regarding Claim 3:
Liu further discloses the service platform using natural language for communication according to claim 2,
wherein the natural language processing and transmission module comprises a natural language receiving module (Liu: ¶[0040] discloses the xbot may receive a natural language user input),
a conversion content conversion module (Liu: ¶[0038]-[0039] converts user input into text, ¶[0042] discloses obtaining objects from the user input to make the text an understandable, executable tasks by the third party agents) and a semantic determination and feedback module (Liu: ¶[0039] NLU unit contains access to a semantic information aggregator that provides feedback to accurately understand the user, additionally, the assistant system may first identify the semantic intents and then process them by calling multiple agents in parallel, a ranker model processes the results from the agents, the output of the ranker model may include ranked dialog intents with confidence scores, this is feedback), the natural language receiving module is provided for receiving the request (Liu: ¶[0040] discloses the xbot may receive a natural language user input request),
the conversion content conversion module is provided for performing a specific conversion of the request based on an AI language model (Liu: ¶[0042] discloses obtaining objects from the user input to make the text an understandable, executable tasks by the third party agents. Specifically, the dialogue unit has to find ways to take user text and parse important pieces of it in order to give proper instruction to the agents it works through),
and the semantic determination and feedback module is provided for determining whether or not the converted request needs supplementary information and feeds it back to the request module or the product/service module (Liu: ¶[0039] Explicitly discloses the assistant system may first identify the semantic intents and then process them by calling multiple agents in parallel, a ranker model processes the results from the agents, the output of the ranker model may include ranked dialog intents with confidence scores, a multitude of supplementary information is stored about the user (what ¶[0062] refers to as ‘context information’ in conjunction with these scores to determine the correct action also see Fig. 5 steps 530-550).
Regarding Claim 4:
Liu further discloses the service platform using natural language for communication according to claim 3, wherein the natural language processing and transmission module further comprises a semantic overlay module, a language vocabulary conversion module and a transmission model, the semantic overlay module is provided for performing a semantic overlay of the request, the language vocabulary conversion module is provided for converting the user’s intention, request, feature and fragment of text into the natural language text, which is completed by a bot in any form that can read the natural language, and the transmission model is provided for transmitting the analysis result obtained after the conversion and analysis of the request to the request module or the product/service module (Liu: ¶[0038] with respect to the language vocabulary conversion module, Liu discloses converting multimodal inputs into natural language via the messaging platform and/or ASR module, ¶[0039] with respect to the semantic overlay module, Liu discloses semantically enriching/annotating the user input using the semantic information aggregator and user context engine to enable context aware understanding, the assistant xbot sends textual input to the NLU module and the NLU module may get information from a semantic information aggregator 230 to accurately understand the user input, where the semantic information aggregator provides ontology data associated with domains, intents and slots and further annotates n-grams of user input, ranks the n-grams with confidence scores, formulates the ranked n gram into features that can be used by the NLU module after which the NLU module identifies a domain/intent/slot. With respect to the transmission model, ¶[0040]-[0042] of Liu discloses sending the analyzed interpretation outputs to the dialog engine and onward for fulfillment and the engine may communicate with difference agents based on the identified intent and domain and the resolved entities, where agents retrieve information/services from the first-party/third-party providers).
Regarding Claim 5:
Liu further discloses the service platform using natural language for communication according to claim 4, wherein the natural language semantic construction module comprises a requester classification and labeling module, a product/service provider classification and labeling module and a special term lexicon language model, the requester classification and labeling module is provided for classifying and labeling the information obtained by the request module, the product/service provider classification and labeling module is provided for classifying and labeling the product/service provided by the product/service module, and the special term lexicon language model is provided for supplementing and interpreting the specialized terminology used in the product/service provided by the product/service module (Liu: ¶[0039] explicitly classifies/labels the user request via NLU outputs (domain/intent/slots) and semantic aggregator-derived ranked annotated features, the methods of the “requester classification and labeling module” is accomplished similarly through the NLU module and the semantic information aggregator).
Regarding Claim 8:
Liu further discloses the service platform using natural language for communication according to claim 5, wherein the natural language communication system comprises a recommendation ranking module, and the recommendation ranking module further comprises a recommendation ranking algorithm and a semantic knowledge graph module (Liu: ¶[0036]-[0040] discloses relevance and ranking engine as part of the system modules, this module necessarily performs a recommendation algorithm, additionally explicitly uses a knowledge graph which may comprise a plurality of entities, values, probabilities and semantic weights).
Regarding Claim 9:
Liu further discloses the service platform using natural language for communication according to claim 5, wherein the natural language communication system comprises an advertising module, and the advertising module further comprises an advertising setting module and an advertising management and evaluation module (Liu: ¶[0036] discloses a social networking system may include advertisement/targeting module which may base the advertisements on suitable information to provide relevant ads to the user).
Regarding Claim 10:
Liu discloses the service platform using natural language for communication according to claim 5, wherein the natural language communication system comprises an external platform integration module, and the external platform integration module is provided to be integrated with an external system (Liu: ¶[0031]-[0032] and ¶[0041]-[0042] discloses integration of third party/external systems via networks and APIs).
Regarding Claim 13:
Claim 13 has been analyzed with regards to claim 1 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 14:
Claim 14 has been analyzed with regards to claim 2 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 15:
Claim 15 has been analyzed with regards to claim 3 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 16:
Claim 16 has been analyzed with regards to claim 4 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 17:
Claim 17 has been analyzed with regards to claim 5 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 20:
Claim 20 has been analyzed with regards to claim 8 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 21:
Claim 21 has been analyzed with regards to claim 9 (see rejection above) and is rejected for the same reasons of anticipation used above.
Regarding Claim 22:
Claim 22 has been analyzed with regards to claim 10 (see rejection above) and is rejected for the same reasons of anticipation used above.
Claim Rejections - 35 USC § 103
6. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claims 6-7 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (US 2019/0325081) in view of Herzig (US 2019/0188261).
Regarding Claim 6:
Liu further discloses the service platform using natural language for communication according to claim 5, except wherein the natural language communication system comprises an emotion sensing and computing module, and the emotion sensing and computing module further comprises an emotion sensing module and an emotion index algorithm.
However, Herzig wherein the natural language communication system comprises an emotion sensing and computing module (Herzig: ¶[0036]-[0039] discloses a dialog emotion detector, and is the emotion sensing portion, it is used to determine emotion levels based on dialog node intents and emotion probability vectors), and the emotion sensing and computing module further comprises an emotion sensing module and an emotion index algorithm (Herzig: ¶[0030] and ¶[0038]-[0039] explicitly teaches computing node emotion level via formulas and probability vector processing this is the emotion index algorithm).
Liu teaches a natural language assistant dialog system that receives user input, performs NLU and manages conversation flow. Claim 6 adds an emotion sensing computing capability, which Herzig expressly teaches through a dialog emotion detector that computes emotion level for dialog elements. These disclosures relate to the same field of endeavor, i.e., both disclose dialogue systems for providing user services. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose an emotion sensing computation into Liu’s system. The suggestion/motivation for doing so is “ Dialog systems, in combination with the automated conversation agents guided by the dialog systems, serve to reduce frustration in answering simple questions by human agents, who would hold the conversation in place of the automated conversation agents, and the number of repetitive questions human agents have to answer in these situations” as disclosed by Herzig in ¶[0010].
Regarding Claim 7:
Liu further discloses the service platform using natural language for communication according to claim 5, wherein the natural language communication system comprises a transaction completion feedback module (Liu: ¶[0037] discloses assisting a user to obtain information or services through conversations, i.e., the exact environment where a transaction/service can complete and feedback about the user can be captured and stored), and the transaction completion feedback module further comprises a user transaction feedback and comment module and a conversion semantic analysis (Liu: ¶[0035]-[0039] expressly teaches supporting user comments and message and posts as user generated content communicated through the platform, also discloses semantic analysis of user input via the NLU modules) (Herzig: ¶[0030] and ¶[0038]-[0039] uses emotional algorithms).
Liu does not explicitly disclose and emotion evaluation algorithm.
However, Herzig discloses and emotion evaluation algorithm (Herzig: ¶[0030] and ¶[0038]-[0039] uses emotional algorithms). (Herzig: ¶[0030] and ¶[0038]-[0039] uses emotional algorithms).
Liu teaches a natural language assistant dialog system that receives user input, performs NLU and manages conversation flow. Claim 7 adds an emotion sensing computing capability, which Herzig expressly teaches through a dialog emotion detector that computes emotion level for dialog elements. These disclosures relate to the same field of endeavor, i.e., both disclose dialogue systems for providing user services. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose an emotion sensing computation into Liu’s system. The suggestion/motivation for doing so is “ Dialog systems, in combination with the automated conversation agents guided by the dialog systems, serve to reduce frustration in answering simple questions by human agents, who would hold the conversation in place of the automated conversation agents, and the number of repetitive questions human agents have to answer in these situations” as disclosed by Herzig in ¶[0010].
Regarding Claim 18:
Claim 18 has been analyzed with regards to claim 6 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 19:
Claim 19 has been analyzed with regards to claim 7 (see rejection above) and is rejected for the same reasons of obviousness used above.
Claims 11-12 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Ross (US 2022/0101833).
Recording Claim 11:
Liu further discloses the service platform using natural language for communication according to claim 5, wherein the natural language communication system comprises a semantic-oriented conversation stabilization module(Liu: ¶[0040]-[0043] discloses dialog-state control and coordinating multiple agents supports, because the dialog engine is the common controller over the multi-agent behavior)
the semantic-oriented conversation stabilization module comprises (Liu: ¶[0037] discloses assisting the user by summarizing the information, supporting a summary guidance system, summarization is used to help manage/redirect interaction and guide or clarify the next step, ¶[0040] also teaches storing prior conversation, which enables generating summaries of multi-turn content, which can then be used for guidance and correction).
However, Ross discloses:
wherein the natural language communication system comprises a semantic-oriented conversation stabilization module, the semantic-oriented conversation stabilization module is provided for ensuring the bot-to-bot consistency on topics understood by natural language and detecting/identifying whether the conversation begins to deviate from the preset topic, and providing immediate correction and guidance (Ross: ¶[0069] discloses a system that explicitly constrains/organizes permissible topics and how they relate is a functional conversation stabilization component in the natural language communication system, also discloses defining allowed topics/relations and provides a stable semantic frame, promoting consistent topic handling across multiple dialog paths, ¶[0070]-[0072] discloses ‘deviation’ corresponding to the user conversational content that doesn’t fit the domain/goal/rule structure. The rule framework therefore supports detection/identification of drift away from the preset topic, when this is identified the system immediately guides the user back by asking targeted questions);
the semantic-oriented conversation stabilization module comprises a conversation deviation sensing system (Ross: ¶[0069]-[0070] supports deviation sensing through topic constraints and rule/goal applicability, this is because the system can use these rules to sense deviation as failure to satisfy rule conditions/mismatch to domain goals).
Liu and Ross are combinable because they are from the same field of endeavor. Liu discloses a system that manages dialog state, and can summarize the information for the user while storing prior conversations, Ross teaches a system for domain specification that describes the topics that may be discussed, and forms a rules and goals for determining dialog control where unmet conditions become subgoals that prompt user questions supporting detection of deviation and guided correction. Liu does not disclose a conversation deviation system like Ross does. It would have been obvious to one of ordinary skill in the art to disclose conversation deviation detection and alleviation. The suggestion/motivation for doing so is “This has an advantage of allowing the improved conversational agent to be more efficient and begin a next conversation within a shorter period of time, potentially allowing a single improved conversational agent to process more orders and generate greater sales for a sales entity using the improved conversational agent” as disclosed in ¶[0030] of Ross.
Regarding Claim 12:
The proposed combination of Liu and Ross further discloses the service platform using natural language for communication according to claim 11, wherein the conversation deviation sensing system of the semantic-oriented conversation stabilization module cooperates with the semantic determination and feedback module and the special term lexicon language model (Ross: ¶[0069] discloses tracking movement among nodes/intents allows sensing whether the conversation is deviating from the previously followed intent pathway (the main axis)) to sense the change in intention of the conversation and whether it begins to deviate from the original main axis of the conversation during the process of analyzing the semantics and understanding the intents of the conversation content for each time, and for conversations where the intent changes, the conversation summary guidance system is triggered to process the conversation and return to the original intent of the conversation (Ross: ¶[0069]-[0072] teaches deviation from the preset topic/main axis by disclosing a deviation from the domain specification, when intent drifts away from domain topics/goals it is detected as deviation from the preset topic Additionally discloses triggers to correct behavior when conditions for goal achievement aren’t met. A dialog engine managing flow can implement “return to original intent” logic by selecting prompts and next actions that realign the dialog to the prior intent);
the conversation summary guidance system is provided for summarizing the entire conversation content that is noticed by the conversation deviation sensing system to be a conversation with a change of intent (Liu ¶[0037] and ¶[0040] directly supports summarization, and supports multi-turn summarization via stored prior conversations. These two together support summarizing multi-turn conversation content (entire conversation content relevant to the detected drift/intent change)),
and then the semantic determination and feedback module analyzes the semantics and understands the intents again (Liu: ¶[0039], and ¶[0051] the summarizer can be retrieved from in later semantic determination for the Assistant XBot and NLU),
and the transmission module transmits the intent which is converted into natural language according to the confirmed conversation intent to the requester, and the product/service provider, so as to complete a conversation intent correction (Liu: ¶[0037] and ¶[0041]-[0042] teaches generating natural language responses and sending them to the user and interacting with agents/providers to obtain services).
Liu and Ross are combinable because they are from the same field of endeavor. Liu discloses a system that manages dialog state, and can summarize the information for the user while storing prior conversations, Ross teaches a system for domain specification that describes the topics that may be discussed, and forms a rules and goals for determining dialog control where unmet conditions become subgoals that prompt user questions supporting detection of deviation and guided correction. Liu does not disclose a conversation deviation system like Ross does. It would have been obvious to one of ordinary skill in the art to disclose conversation deviation detection and alleviation. The suggestion/motivation for doing so is “This has an advantage of allowing the improved conversational agent to be more efficient and begin a next conversation within a shorter period of time, potentially allowing a single improved conversational agent to process more orders and generate greater sales for a sales entity using the improved conversational agent” as disclosed in ¶[0030] of Ross.
Regarding Claim 23:
Claim 23 has been analyzed with regards to claim 11 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 24:
Claim 24 has been analyzed with regards to claim 12 (see rejection above) and is rejected for the same reasons of obviousness used above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IAN SCOTT MCLEAN whose telephone number is (703)756-4599. The examiner can normally be reached "Monday - Friday 8:00-5:00 EST, off Every 2nd Friday".
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/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654