DETAILED ACTION
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 .
Double Patenting
1. Claims 1-20 of this application is patentably indistinct from Claims 1-20 of Application No. 18,047,154. Pursuant to 37 CFR 1.78(f), when two or more applications filed by the same applicant or assignee contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
2. Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of copending Application No. 18,047,154. Although the claims at issue are not identical, they are not patentably distinct from each other.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
19,008,899
18,047,154
Similarities
Regarding claim 1; A computer-implemented method of generating a sequence of actions in response to natural language dialogue, comprising: identifying, by a computing system, a dialogue message originating from a user account; identifying, by the computing system using natural language processing, a user goal associated with the user account based on the dialogue message; generating, by the computing system, a sequence of actions to address the user goal using a machine learning model to identify the sequence of actions based on the dialogue message; and executing, by the computing system, at least one action of the sequence of actions.
Regarding claim 1; A computer-implemented method of generating an actionable plan in response to natural language dialogue, comprising: identifying, by a computing system, a dialogue message originating from a user account; identifying, by the computing system using natural language processing, a user goal associated with the user account based on the dialogue message; generating, by the computing system, a plan comprising a sequence of actions to address the user goal using a machine learning model trained to identify the sequence of actions based on the dialogue message; and executing, by the computing system, at least one action of the sequence of actions according to the plan.
Similar recitation of the 18,008,899 Application
Regarding claim 11; A system comprising: a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising: identifying, by a computing system, a dialogue message originating from a user account; identifying, by the computing system using natural language processing, a user goal associated with the user account based on the dialogue message; generating, by the computing system, a sequence of actions to address the user goal using a machine learning model to identify the sequence of actions based on the dialogue message; and executing, by the computing system, at least one action of the sequence of actions.
Regarding claim 15; A system, comprising: a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising: identifying a dialogue message originating from a user account; identifying, using natural language processing, a user goal associated with the user account based on the dialogue message; generating a plan comprising a plurality of actions to address the user goal using a machine learning model trained to identify the plurality of actions based on the dialogue message; and executing at least one action of the plurality of actions according to the plan.
Similar recitation of the 18,008,899 Application
Regarding claim 20; A non-transitory computer readable medium comprising one or more sequence of instructions, which, when executed by a processor, causes a computing system to perform operations comprising: identifying, by the computing system, a dialogue message originating from a user client; identifying, by the computing system using one or more machine learning models, a user goal based on the dialogue message; generating, by the computing system, a sequence of actions to address the user goal using the one or more machine learning models to identify the sequence of actions based on the dialogue message; and executing, by the computing system, at least one action of the sequence of actions.
Regarding claim 8; A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by one or more processors, causes a computing system to perform operations comprising: identifying, by the computing system, a dialogue message originating from a user account; identifying, by the computing system using natural language processing, a user goal associated with the user account based on the dialogue message; generating, by the computing system, a plan comprising at least one action to address the user goal using a machine learning model trained to identify the at least one action based on the dialogue message; and executing, by the computing system, the at least one action according to the plan.
Similar recitation of the 18,008,899 Application
Claim Rejections - 35 USC § 103
1. 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.
2. 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.
3. Claims 1-5, 10-15, 19 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zoller et al. (US 20180261203 A1 hereinafter, Zoller ‘203) in view of Liang et al. (US 20180261205 A1 hereinafter, Liang ‘205).
Regarding claim 11; Zoller ‘203 discloses a system (Fig. 1, System 100)
comprising:
a processor (Fig. 2, Processor 210);
and a memory (Fig. 2, Memory 230) having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations (i.e. The system may include one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method to provide automated natural language dialogue. Paragraph 0006)
comprising:
identifying, by a computing system, a dialogue message originating from a user account (i.e. The system may execute the instructions to generate a first event to be placed in an event queue in response to receiving an incoming customer dialogue message, where the event queue is monitored by a dialogue management device that includes a rules-based platform, a trained machine learning module, and a customer context. Paragraph 0017)
identifying, by the computing system using natural language processing, a user goal associated with the user account based on the dialogue message (i.e. The system may then process the message to understand its meaning (e.g., via NLP server 124) and make a determination about how to respond. In the process of making the determination about how to respond, the system may consider the customer context of the user. For example, the system may analyze all of the currently known data about the customer, such as the customer's account information, all of the previous interactions with the customer, the customer's goals, the customer's social media presence, and an estimation of the customer's emotional state to make a determination about how to respond. Paragraph 0092)
generating, by the computing system, a sequence of actions to address the user goal using a machine learning model to identify the sequence of actions based on the dialogue message (i.e. For example, the system may analyze all of the currently known data about the customer, such as the customer's account information, all of the previous interactions with the customer, the customer's goals, the customer's social media presence, and an estimation of the customer's emotional state to make a determination about how to respond. Paragraph 0092)
and executing, by the computing system, at least one action of the sequence of actions (i.e. The incoming dialogue message may be received by a device of organization 108. An event may be generated by interfacing with the receiving device. After the event is created, it may be placed in the event queue 260. An event queue 260 may be configured to temporarily store a plurality of events. The events are placed in the event queue in a first-in first-out (FIFO) manner, such that the events will be executed in the order that they were received. Paragraph 0059)
Examiner reasonably believes that Zoller ‘203 discloses a sequence of actions for a dialogue message as expressed above. However, Examiner cites Liang ‘205 to cure any deficiencies of Zoller ‘203.
Liang ‘205 discloses a sequence of actions for a dialogue message (i.e. A dialogue consists of a sequence of turns, where each turn consists of who is talking (e.g., an agent or an administrator), an utterance, and a sequence of actions. Paragraph 0046)
Zoller ‘203 and Liang ‘205 are combinable because they are from same field of endeavor of speech systems (Liang ‘205 at “Background”).
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the speech system as taught by Zoller ‘203 by adding a sequence of actions for a dialogue message as taught by Liang ‘205. The motivation for doing so would have been advantageous to expand the capability of the system to improve a dialogue system that does not require significant engineering resources. Therefore, it would have been obvious to combine Zoller ‘203 with Liang ‘205 to obtain the invention as specified.
Regarding claim 12; Zoller ‘203 discloses wherein identifying the user goal comprises: analyzing, by the computing system, the dialogue message to identify one or more intents (i.e. NLP device 124 may determine the meaning of an incoming dialogue message by utilizing one or more of the following artificial intelligence techniques: intent classification, named entity recognition, sentiment analysis, relation extraction, semantic role labeling, question analysis, rule extraction and discovery, and story understanding. Intent classification may include mapping text, audio, video, or other media into an intent chosen from a set of intents, which represent what a customer is stating, requesting, commanding, asking, or promising, in for example an incoming customer dialogue message. Paragraph 0062)
and categorizing, by the computing system, the one or more intents as at least one of an express goal or an implied goal (i.e. Intent classification may include mapping text, audio, video, or other media into an intent chosen from a set of intents, which represent what a customer is stating, requesting, commanding, asking, or promising, in for example an incoming customer dialogue message. Paragraph 0062)
Regarding claim 13; Zoller ‘203 discloses further comprising: determining, by the computing system, a sentiment of a user associated with the user account based on the dialogue message (i.e. The customer information may include one or more of account types, account statuses, transaction history, conversation history, people models, an estimate of customer sentiment, customer goals, and customer social media information. Paragraph 0060)
Regarding claim 14; Zoller ‘203 discloses wherein generating the sequence of actions comprises: tailoring, by the computing system, at least one action in the sequence of actions based on the determined sentiment (i.e. The customer information may include one or more of account types, account statuses, transaction history, conversation history, people models, an estimate of customer sentiment, customer goals, and customer social media information. The customer context may allow system 100 to adapt and tailor its responses to a particular customer based on the customer context. Paragraph 0060).
Regarding claim 15; Zoller ‘203 discloses generating, by the computing system, a verification message that includes a summary of the sequence of actions; (i.e. an event may represent the occurrence of some action, such as the receipt of a message, receipt of a request, retrieval of customer information, acknowledgement of a change to a customer's account, verification or denial of a customer's credentials, or any other such information that may be used to maintain and administer customer accounts. Paragraph 0074)
and transmitting, by the computing system, the verification message to a client device associated with the user account (i.e. Following method 400, the system may generate a response dialogue message that may be transmitted for display at, for example, user device 102. Paragraph 0058)
Regarding claim 19; Zoller ‘203 discloses wherein the machine learning model is trained using historical dialogue messages and corresponding sequences of actions (i.e. the customer information may include one or more of account types, account statuses, transaction history, conversation history, people models, an estimate of customer sentiment, customer goals, and customer social media information. The customer context may allow system 100 to adapt and tailor its responses to a particular customer based on the customer context. Paragraph 0060)
Regarding claim 20; Claim 20 contains substantially the same subject matter as claim 11. Therefore, claim 20 is rejected on the same grounds as claim 11. However, claim 2 further discloses a non-transitory computer readable medium comprising one or more sequence of instructions, which, when executed by a processor, causes a computing system to perform operations. Paragraph 0043 of Zoller ‘203 discloses wherein the disclosed embodiments also relate to tangible and non-transitory computer readable media that include program instructions or program code that, when executed by one or more processors, perform one or more computer-implemented operations.
Regarding claim 1; Claim 1 contains substantially the same subject matter as claim 11. Therefore, claim 1 is rejected on the same grounds as claim 11.
Regarding claim 2; Claim 2 contains substantially the same subject matter as claim 12. Therefore, claim 2 is rejected on the same grounds as claim 12.
Regarding claim 3; Claim 3 contains substantially the same subject matter as claim 13. Therefore, claim 3 is rejected on the same grounds as claim 13.
Regarding claim 4; Claim 4 contains substantially the same subject matter as claim 14. Therefore, claim 4 is rejected on the same grounds as claim 14.
Regarding claim 5; Claim 15 contains substantially the same subject matter as claim 15. Therefore, claim 15 is rejected on the same grounds as claim 15.
Regarding claim 10; Claim 10 contains substantially the same subject matter as claim 19. Therefore, claim 10 is rejected on the same grounds as claim 19.
Allowable Subject Matter
Claims 6-9 & 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Examiners Statement of Reasons for Allowance
The cited reference (Zoller ‘203) teaches wherein a system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained-machine learning model.
The cited reference (Liang ‘205) teaches wherein a system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.
The cited references fail to disclose receiving, by the computing system, a confirmation message from the client device; and updating, by the computing system, an event queue with instructions to execute the sequence of actions in response to receiving the confirmation message; wherein generating the sequence of actions comprises: cross-referencing, by the computing system, the sequence of actions against one or more policies to ensure the sequence of actions does not violate any of the one or more policies; querying, by the computing system, a database to identify one or more pre-existing plans associated with the user account; and cross-referencing, by the computing system, the sequence of actions against the one or more pre-existing plans to ensure the sequence of actions does not violate any of the one or more pre-existing plans; and determining, by the computing system, that additional information is needed to address the user goal; generating, by the computing system, a clarification message requesting the additional information; and transmitting, by the computing system, the clarification message to a client device associated with the user account. As a result, and for these reasons, Examiner indicates Claims 6-9 & 16-18 as allowable subject matter.
Relevant Prior Art References Not Relied Upon
1. Coman et al. (US 20190272547 A1) - Consistent with the disclosed embodiments, systems and methods are provided herein for autonomously generating a thoughtful gesture for a customer. In one example implementation of the disclosed technology, a method is provided that includes receiving incoming customer dialogue and determining, based on the customer dialogue, customer information including one or more of: customer preferences, customer biographical information, and customer current life circumstances. The method also includes generating, based on the customer information, gesture-specific information-eliciting utterances for additional dialogue with the customer and identifying one or more response opportunities based on additional incoming customer dialogue responsive to sending the gesture-specific information-eliciting utterances to the customer. Further, the method includes generating a thoughtful gesture based on the identified one or more response opportunities and outputting, for presentation to the customer, the thoughtful gesture.
2. Zoller et al. (US 20210248993 A1) - A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCUS T. RILEY, ESQ. whose telephone number is (571)270-1581. The examiner can normally be reached 9-5 M-F.
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MARCUS T. RILEY, ESQ.
Primary Examiner
Art Unit 2654
/MARCUS T RILEY/Primary Examiner, Art Unit 2654
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