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 .
Application 19/056,353 is a continuation of Application 18/135,183 (now U.S. Patent No. 12,248,518 B2), which in turn is a continuation of Application 17/127,700 (now U.S. Patent No. 11657096 B2), filed 12/18/2020.
Applicant’s preliminary amendment dated 04/28/2025 has been received and made of record.
Claim 1 has been canceled. New claims 2-21 have been added.
Claims 2-21 are pending in Application 19/056,353.
Double Patenting
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.
Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,248,518 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the differences amount to matters of drafting choice and/or would have been obvious (e.g., explicitly rather than implicitly generating code for dialog flows) to a person having ordinary skill in the art prior to the effective filing date of the invention. See also the table below.
Patent US 12,248,518 B2
Instant Application 19/056,353
1. A system, comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: receiving an input from a user device in a conversational artificial intelligence (AI) interface, the user device being associated with a user; determining one or more of a plurality of identifiers corresponding to the input based on a language processor associated with the conversational AI interface; mapping the input to one or more of a plurality of objects based on the one or more of the plurality of identifiers corresponding to the input, wherein each of the plurality of objects corresponds to one of the plurality of identifiers and is associated with an action and a parameter for performing the action; predicting an initial intent of the user based on the one or more of the plurality of identifiers, wherein the initial intent is associated with the action and the parameter for each of the one or more of the plurality of objects for the one or more of the plurality of identifiers; generating an initial dialogue flow from the input and the initial intent; determining a subsequent intent of the user based on a subsequent input and the plurality of identifiers; and generating a dynamic dialogue flow for the conversational AI interface based on the initial dialogue flow, the subsequent input, and the subsequent intent.
2. The system of claim 1, wherein, prior to the determining the initial intent of the user, the operations further comprise: identifying contextual information for the user, wherein the determining the initial intent of the user is further determined based on the contextual information for the user.
3. The system of claim 2, wherein the contextual information includes one or more historical interactions of the user with the conversational AI interface and one or more prior intents of the user interacting with the conversational AI interface.
4. The system of claim 1, wherein, prior to the determining the one or more of the plurality of identifiers, the operations further comprise: selecting an object-entity model based on the input and the language processor, the object-entity model containing the plurality of objects for elements and functions tagged with the plurality of identifiers mapped to intents available to users of the conversational AI interface.
5. The system of claim 4, wherein, prior to the determining the one or more of the plurality of identifiers, the operations further comprise: determining a plurality of historical actions taken by a set of users on the plurality of objects of the object-entity model; and identifying contextual information for the user based on the plurality of historical actions wherein the determining the initial intent is further based on the contextual information.
6. The system of claim 4, wherein the object-entity model is associated with annotations, the annotations including tagged identifiers of the plurality of identifiers and metadata for dialogue flows associated with each object, and wherein generating the initial dialogue flow further comprises: parsing the annotations of an object for the metadata defining a dialogue flow generation for user interactions with elements and functions associated with the object; and generating the initial dialogue flow for the object based on the metadata, the initial intent, and the input.
7. The system of claim 4, wherein, prior to the generating the dynamic dialogue flow, the operations further comprise: determining the initial intent corresponds to two or more elements or functions of the object-entity model; and determining the subsequent intent as an intent associated with a specified element or function of the two or more elements or functions of the object-entity model, wherein the generating the dynamic dialogue flow is further based on the specified element or function.
8. A method, comprising: receiving an input from a user device in a conversational artificial intelligence (AI) interface; determining one or more of a plurality of identifiers corresponding to the input based on a language processor associated with the conversational AI interface; mapping the input to a set of objects from an object-entity model based on the one or more of the plurality of identifiers corresponding to the input, wherein each of the set of objects corresponds to one of the plurality of identifiers and is associated with metadata for an action and a parameter for performing the action; predicting an initial intent of the user based on the one or more of the plurality of identifiers, wherein the initial intent is associated with the action and the parameter for each of the set of objects corresponding to the one or more of the plurality of identifiers; and generating a dynamic dialogue flow for the conversational AI interface based on the input, the initial intent, and annotations associated with the set of objects.
9. The method of claim 8, wherein the object-entity model contains a plurality of objects corresponding to the set of objects for elements and functions tagged with the plurality of identifiers mapped to intents available to users of the conversational AI interface, and wherein the annotations include tagged identifiers of the plurality of identifiers and metadata for dialogue flows associated with each object.
10. The method of claim 9, wherein the intent of the user is further based on the input and the set of objects, the method further comprising: determining a subsequent intent of the user based on a subsequent input; determining a subset of objects from the set of objects; and generating an updated dynamic dialogue flow for the conversational AI interface based on the initial intent, the subsequent intent, and the subset of objects.
11. The method of claim 9, generating the dynamic dialogue flow further comprises: parsing the annotations of an object for the metadata defining a dialogue flow generation for user interactions with elements and functions associated with the object; and generating the dynamic dialogue flow for the object based on the metadata, the intent, and the input.
12. The method of claim 8, wherein, prior to the determining the initial intent of the user, the method further comprises: identifying contextual information for the user wherein the determining the initial intent of the user is further determined based on the contextual information for the user.
13. The method of claim 8, wherein, prior to the determining the one or more of the plurality of identifiers, the method further comprises: determining a plurality of historical actions taken by a set of users on objects of the object-entity model; and identifying contextual information for the user based on the plurality of historical actions wherein the determining the initial intent is further based on the contextual information.
14. A non-transitory machine-readable medium having instructions stored thereon that are executed by a computer system to perform operations comprising: initializing a chat session from a user device through a conversational artificial intelligence (AI) interface; in response to initializing the chat session, identifying contextual information for the user; receiving an input from the user device through the conversational AI interface; determining one or more of a plurality of identifiers corresponding to the input based on a language processor associated with the conversational AI interface; mapping the input to one or more of a plurality of objects based on the one or more of the plurality of identifiers corresponding to the input, wherein each of the plurality of objects corresponds to one of the plurality of identifiers and is associated with metadata for an action and a parameter for performing the action; predicting an initial intent of the user based on the contextual information and the one or more of the plurality of identifiers, wherein the initial intent is associated with the action and the parameter for each of the one or more of the plurality of objects corresponding to the one or more of the plurality of identifiers; generating an initial dialogue flow from the input and the initial intent; determining a subsequent intent of the user based on a subsequent input and the plurality of identifiers; and generating a dynamic dialogue flow for the conversational AI interface based on the initial dialogue flow, the subsequent input, and the subsequent intent.
15. The non-transitory machine-readable medium of claim 14, wherein the contextual information includes one or more historical interactions of the user with the conversational AI interface and one or more prior intents of the user interacting with the conversational AI interface.
16. The non-transitory machine-readable medium of claim 14, wherein, prior to the determining the one or more of the plurality of identifiers, the operations further comprise: selecting an object-entity model based on the input and the language processor, the object-entity model containing the plurality of objects for elements and functions tagged with the plurality of identifiers mapped to intents available to users of the conversational AI interface.
17. The non-transitory machine-readable medium of claim 16, wherein contextual information includes a plurality of actions taken by a set of users on objects of the object-entity model.
18. The non-transitory machine-readable medium of claim 16, wherein the object-entity model is associated with annotations, the annotations including tagged identifiers of the plurality of identifiers and metadata for dialogue flows associated with each object, and wherein generating the initial dialogue flow further comprises: parsing the annotations of an object for the metadata defining a dialogue flow generation for user interactions with elements and functions associated with the object; and generating the initial dialogue flow for the object based on the metadata, the initial intent, and the input.
19. The non-transitory machine-readable medium of claim 16, wherein, prior to the generating the dynamic dialogue flow, the operations further comprise: determining the initial intent corresponds to two or more elements or functions of the object-entity model; and determining the subsequent intent as an intent associated with a specified element or function of the two or more elements or functions of the object-entity model, wherein the generating the dynamic dialogue flow is further based on the specified element or function.
20. The non-transitory machine-readable medium of claim 16, wherein the object-entity model is associated with annotations, the annotations including tagged identifiers of the plurality of identifiers and metadata for dialogue flows associated with each object, and wherein the operations further comprise: detecting an updated object-entity model, the updated object-entity model modifying one or more object, element, function, or annotation associated with the object-entity model; and generating one or more of an updated initial dialogue flow and an updated dynamic dialogue flow based on the updated object-entity model.
2. A system, comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: mapping, based on an input associated with an intent of a user provided by the user via a user device, the input to a plurality of objects usable by a conversational artificial intelligence (AI) to respond to the user, wherein the plurality of objects are each associated with an action and a parameter for performing the action, and wherein the predicted intent is associated with an identifier associated with one or more of the plurality of objects; predicting a dialogue flow by the conversational AI that is responsive to the input from the user based at least on the predicted intent and the identifier, wherein the dialogue flow comprises a first set of the plurality of objects associated with assisting the user with a computing service; generating source code for executing the dialogue flow by the conversational AI using the first set of the plurality of objects and a plurality of annotations to the first set that indicate how each object in the first set is presented for user interaction in dialogue flows; outputting, via the user device, the dialogue flow to the user via the conversational AI using the source code; and adjusting, based on a subsequent input by the user via the user device associated with a subsequent intent of the user, the dialogue flow to include a second set of the plurality of objects associated with the subsequent intent, wherein the adjusting uses additional generated source code associated with the second set of the plurality of objects.
3. The system of claim 2, wherein, prior to the adjusting the dialogue flow, the operations further comprise: prompting the user for at least one response during the dialogue flow that indicates one of a change to the intent or the subsequent intent of the user.
4. The system of claim 2, wherein, prior to the predicting the dialogue flow, the operations further comprise: identifying contextual information for the user, wherein the contextual information includes a past interaction of the user with the conversational AI and a prior intent of the user during the past interaction with the conversational AI, wherein the dialogue flow is further generated based on the contextual information.
5. The system of claim 2, wherein, prior to the mapping the input, the operations further comprise: selecting an object-entity model based on the input and a language processor, wherein the language processor is used to perform the mapping based on the intent determined by the language processor, and wherein the object-entity model includes the plurality of objects tagged with the identifier and a plurality of additional identifiers associated with different intents determinable by the language processor, wherein the mapping is further based on the object-entity model.
6. The system of claim 2, wherein, prior to the predicting the dialogue flow, the operations further comprise: determining a plurality of historical actions taken by a set of users with the plurality of objects; and identifying contextual information associated with the input by the user based at least in part on the plurality of historical actions taken by the set of users, wherein the predicting the dialogue flow is further based on the contextual information.
7. The system of claim 2, wherein the generating the source code for executing the dialogue flow at least in part includes: parsing the plurality of annotations for metadata associated with a dialogue flow generation based on interactions between the plurality of objects, wherein the dialogue flow is further generated based on the metadata.
8. The system of claim 2, wherein at least one of the plurality of annotations indicates that an additional input is to be requested from the user during the dialogue flow for an execution of a function corresponding to one of the plurality of objects, and wherein the generating the source code includes configuring the function of one of the first set of the plurality of objects to request the additional input from the user at a place in the dialogue flow.
9. A method, comprising: determining a set of objects from a plurality of objects available via an object-entity model based on a set of identifiers from a plurality of identifiers for the plurality of objects, wherein the set of identifiers are associated with one or more previous inputs by a user, wherein the plurality of identifiers are usable to associate the plurality of objects with different intents determined from inputs to a conversational artificial intelligence (AI), and wherein each object in the set of objects is associated with a function usable by the conversational AI for chat dialogues; generating a dialogue flow of a chat dialogue for use by the conversational AI with the user based on the set of objects and chat information associated with the chat dialogue; generating source code for the dialogue flow using at least one function associated with the set of objects and at least one annotation indicating at least one interaction between objects in the set of objects; and executing the source code using the conversational AI during the chat dialogue with the user.
10. The method of claim 9, further comprising: outputting the dialogue flow to the user via the conversational AI based on the executing the source code; and adjusting, based on a subsequent input by the user, the dialogue flow to include an additional set from the plurality of objects associated with the subsequent intent.
11. The method of claim 10, wherein the adjusting uses additional generated source code associated with the additional set of the plurality of objects.
12. The method of claim 10, wherein, prior to the adjusting the dialogue flow, the method further comprises: prompting the user for at least one response during the dialogue flow that changes the dialogue flow to using the additional set of object to respond to the user.
13. The method of claim 9, further comprising: identifying a past interaction of the user with the conversational AI, wherein the dialogue flow is further generated based on the past interaction.
14. The method of claim 9, further comprising: selecting an object-entity model based on the one or more previous inputs by the user and a language processor, wherein the dialogue flow is further generated based on the object-entity model.
15. The method of claim 9, further comprising: parsing the at least one annotation for metadata associated with generating dialogue flows based on interactions between the plurality of objects, wherein the dialogue flow is further generated based on the metadata.
16. A non-transitory machine-readable medium having instructions stored thereon that are executed by a computer system to perform operations comprising: receiving an input from a user during a chat dialogue with a conversational artificial intelligence (AI); determining an identifier associated with the input based on an intent of the user predicted by a natural language processor and contextual information for the user comprising at least one or more previous inputs by the user, wherein the identifier is usable to associate one or more of a plurality of objects with different intents predicted from inputs to the conversational AI, and wherein each of the plurality of objects is associated with a function usable by the conversational AI for chat dialogues; determining an object from the plurality of objects based on the identifier and the intent of the user; generating a dialogue flow for the conversational AI to respond to the input from the user in the chat dialogue based at least on the object and at least one of the one or more previous inputs or an additional input by the user; generating source code for the dialogue flow using at least the function associated with the object and an annotation indicating a use of the function in the chat dialogues; and executing the source code using the conversational AI during the chat dialogue with the user.
17. The non-transitory machine-readable medium of claim 16, wherein the operations further comprise: outputting the dialogue flow to the user via the conversational AI using the executed source code.
18. The non-transitory machine-readable medium of claim 16, wherein the operations further comprise: receiving a subsequent input to the outputted dialogue flow from the user, wherein the subsequent input redirects the dialogue flow to a different identifier associated with the a different object; and adjusting the dialogue flow to include the different object usable by the conversational AI to respond to the subsequent input.
19. The non-transitory machine-readable medium of claim 16, wherein the adjusting comprises generating additional source code that includes the different object with the source code for the dialogue flow.
20. The non-transitory machine-readable medium of claim 16, wherein the operations further comprise: selecting an object-entity model based on the one or more previous inputs by the user and a language processor, wherein the dialogue flow is further generated based on the object-entity model.
21. The non-transitory machine-readable medium of claim 16, parsing the annotation for metadata associated with generating dialogue flows based on interactions between the plurality of objects, wherein the dialogue flow is further generated based on the metadata.
Claim Objections
Claim 18 is objected to because of the following informalities: “the a” should read “a”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Radebaugh (US 2018/0196683 A1), Kalns (US 2014/0310001 A1), and Hirzel (US 2019/0066694 A1).
Regarding claims 2,9, and 16, Radebaugh discloses 2. A system/method/non-transitory machine-readable medium (Radebaugh: Claim 1, Claim 7, and Claim 8, “electronic device”, “non-transitory computer-readable storage medium”, “method”), comprising:
a non-transitory memory (Radebaugh: Paragraph [0060], “a non-transitory computer-readable storage medium of memory 202”);
and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations (Radebaugh: Paragraph [0060], “for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions”, and Claim 1) comprising:
predicting a dialogue flow by the conversational AI that is responsive to the input from the user based at least on the predicted intent and the identifier, wherein the dialogue flow comprises a first set of the plurality of objects associated with assisting the user with a computing service (Radebaugh: Paragraphs [0285] and [0314], “candidate parameters”, “task flow”, and Figures 10A-10C);
and adjusting, based on a subsequent input by the user via the user device associated with a subsequent intent of the user, the dialogue flow to include a second set of the plurality of objects associated with the subsequent intent, wherein the adjusting uses additional generated source code associated with the second set of the plurality of objects (Radebaugh: Paragraph [0285], “Candidate parameters 1004 provided in this manner may be provided by the software application in some examples. The user device may select one of the candidate parameters by providing a touch-input and/or by providing a natural-language user input to the user device, and in response, the user device may provide the selected candidate parameter to the software application”; also Paragraph [0258, “Identifying parameters may include identifying portions of the natural-language input that specify a manner in which a task corresponding to the intent is to be performed. Parameters may, for instance, specify locations (e.g., addresses or places of interest), times, dates, contacts, types, text (e.g., to be inserted into an email or message), quantities (e.g., distance, money) and, in some instances, names of software applications to perform the task”).
Radebaugh does not explicitly disclose mapping, based on an input associated with an intent of a user provided by the user via a user device, the input to a plurality of objects usable by a conversational artificial intelligence (AI) to respond to the user, wherein the plurality of objects are each associated with an action and a parameter for performing the action, and wherein the predicted intent is associated with an identifier associated with one or more of the plurality of objects.
However, Kalns teaches mapping, based on an input associated with an intent of a user provided by the user via a user device, the input to a plurality of objects usable by a conversational artificial intelligence (AI) to respond to the user, wherein the plurality of objects are each associated with an action and a parameter for performing the action, and wherein the predicted intent is associated with an identifier associated with one or more of the plurality of objects (Kalns: Paragraph [0074], “the VPA 110 understands from information in the VPA model 154 that "thriller" is a movie genre and "Hitchcock" is the name of a person. Accordingly, the VPA 110 creates a new current input intent 214, "unspecified request," fills the "genre" field with "thriller," and fills the "person" field with "Hitchcock." Referring to the intent mapping 222, the intent merger 220 knows that the "person" field of the "unspecified request" intent and the "director" field of the "search movie" intent are of the same type, "person type." Further, the intent mapping 222 reveals that both the "search movie" intent and the "unspecified request" intent contain the "genre" field. Accordingly, the intent merger 220 inserts "thriller" into the "genre" field of the "search movie" intent and inserts "Hitchcock" into the "director" field of the "search movie" intent, and the VPA 110 can execute the desired search based on the new version of the "search movie" intent without asking the user any further questions”, and Paragraph [0062], “create and maintain the dialog context 212, which includes a history of the intents 136, 142 that are instantiated during the current dialog session (e.g., a session-specific intent history). Alternatively or in addition, the dialog context 212 may include other contextual indicators, such as information that may be contained in or derived from the various multi-modal inputs”).
Radebaugh and Kalns are analogous art in the same field of endeavor as the instant invention as all are drawn to conversational AIs/chatbots/assistants. 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; that is, it would have been obvious to incorporate Kaln’s contextual elements into the system of Radebaugh to improve automation, efficiency, and accuracy.
Radebaugh-Kalns does not explicitly disclose:
generating source code for executing the dialogue flow by the conversational AI using the first set of the plurality of objects and a plurality of annotations to the first set that indicate how each object in the first set is presented for user interaction in dialogue flows;
outputting, via the user device, the dialogue flow to the user via the conversational AI using the source code.
However, Hirzel teaches
generating source code for executing the dialogue flow by the conversational AI using the first set of the plurality of objects and a plurality of annotations to the first set that indicate how each object in the first set is presented for user interaction in dialogue flows (Hirzel: Paragraph [0064], “For instance, the natural language expression is transformed into symbols, and the symbols are transformed into instructions for generating a natural language prompt to the user and/or executable computer code for invoking the API call”);
outputting, via the user device, the dialogue flow to the user via the conversational AI using the source code (Hirzel: Paragraph [0065], “the natural language prompt or response is generated based on the instructions”).
Radebaugh-Kalns and Hirzel are analogous art in the same field of endeavor as the instant invention as all are drawn to interactive dialog flow systems. 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; that is, it would have been obvious to incorporate Hirzel’s automatic code generation into the system of Radebaugh-Kalns to allow for increased automation and ease of real-world deployment.
Radebaugh-Kalns-Hirzel teaches 3. The system of claim 2, wherein, prior to the adjusting the dialogue flow, the operations further comprise:
prompting the user for at least one response during the dialogue flow that indicates one of a change to the intent or the subsequent intent of the user (Radebaugh: Paragraph [0285], “the software application may request additional input from the user” and Figures 10A-10C).
Radebaugh-Kalns-Hirzel teaches 4. The system of claim 2, wherein, prior to the predicting the dialogue flow, the operations further comprise:
identifying contextual information for the user, wherein the contextual information includes a past interaction of the user with the conversational AI and a prior intent of the user during the past interaction with the conversational AI (Radebaugh: Paragraph [0222], “obtain contextual information associated with the user input from the user device, along with or shortly after the receipt of the user input. The contextual information can include user-specific data, vocabulary, and/or preferences relevant to the user input. In some examples, the contextual information also includes software and hardware states of the user device at the time the user request is received, and/or information related to the surrounding environment of the user at the time that the user request was received”), wherein the dialogue flow is further generated based on the contextual information (Radebaugh: Paragraph [0253], “In the example, “Order my usual from Domino's,” the natural-language user input may include a request for a user device to order food from the pizza chain Domino's. “my usual” may further specify, contextually, what food is to be ordered.”).
Radebaugh-Kalns-Hirzel teaches 5. The system of claim 2, wherein, prior to the mapping the input, the operations further comprise:
selecting an object-entity model based on the input and a language processor, wherein the language processor is used to perform the mapping based on the intent determined by the language processor, and wherein the object-entity model includes the plurality of objects tagged with the identifier and a plurality of additional identifiers associated with different intents determinable by the language processor, wherein the mapping is further based on the object-entity model (Kalns: Paragraph [0032], “a set of intents may include intents or sets of intents that are instantiated by the VPA 110 or by other VPAs during natural language dialog interactions and/or dialog sessions between the VPA 110 or other VPAs and other users. For example, in some embodiments, the set of intents may include intents that are instantiated by other executing instances of the VPA 110 during dialog sessions with users who have previously been identified to the VPA 110 as family, friends, or others having a "connection" or relationship to the user (e.g., people who are connected with the user via a social media platform such as FACEBOOK, LINKEDIN, and/or others). In some embodiments, the set of intents may include intents that are instantiated by other instances of the VPA 110 during dialog sessions with users who do not have a relationship or connection with the user, other than the fact that they have also used the same VPA application and/or other VPA applications. In this way, some embodiments of the VPA 110 can tailor the user's dialog experience with the VPA 110 based on information that the VPA 110 learns from its or other VPAs' dialog interactions with other users”, and Claim 23).
Radebaugh-Kalns-Hirzel teaches 6. The system of claim 2, wherein, prior to the predicting the dialogue flow, the operations further comprise:
determining a plurality of historical actions taken by a set of users with the plurality of object (Kalns: Paragraph [0032], “a set of intents may include intents or sets of intents that are instantiated by the VPA 110 or by other VPAs during natural language dialog interactions and/or dialog sessions between the VPA 110 or other VPAs and other users. For example, in some embodiments, the set of intents may include intents that are instantiated by other executing instances of the VPA 110 during dialog sessions with users who have previously been identified to the VPA 110 as family, friends, or others having a "connection" or relationship to the user (e.g., people who are connected with the user via a social media platform such as FACEBOOK, LINKEDIN, and/or others). In some embodiments, the set of intents may include intents that are instantiated by other instances of the VPA 110 during dialog sessions with users who do not have a relationship or connection with the user, other than the fact that they have also used the same VPA application and/or other VPA applications. In this way, some embodiments of the VPA 110 can tailor the user's dialog experience with the VPA 110 based on information that the VPA 110 learns from its or other VPAs' dialog interactions with other users”, and Claim 23);
and identifying contextual information associated with the input by the user based at least in part on the plurality of historical actions taken by the set of users (Kalns: Paragraph [0032], Paragraph [0048], and Claim 23), wherein the predicting the dialogue flow is further based on the contextual information (Kalns: Paragraph [0032], Paragraph [0078], and Claim 23; and Radebaugh: Paragraphs [0285] and [0314], “candidate parameters”, “task flow”, and Figures 10A-10C).
Radebaugh-Kalns-Hirzel teaches 7. The system of claim 2, wherein the generating the source code for executing the dialogue flow at least in part includes:
parsing the plurality of annotations for metadata associated with a dialogue flow generation based on interactions between the plurality of objects (Kalns: incorporated by reference US 2014/00337266 A1 Paragraph [0043]: “user intent interpreter 216 may apply syntactic, grammatical, and/or semantic rules to the NL dialog input, in order to parse and/or annotate the input to better understand the user's intended meaning and/or to distill the natural language input to its significant words (e.g., by removing grammatical articles or other superfluous language)”), wherein the dialogue flow is further generated based on the metadata (Kalns: Paragraph [0032], Paragraph [0078], and Claim 23; and Radebaugh: Paragraphs [0285] and [0314], “candidate parameters”, “task flow”, and Figures 10A-10C).
Radebaugh-Kalns-Hirzel teaches 8. The system of claim 2, wherein at least one of the plurality of annotations indicates that an additional input is to be requested from the user during the dialogue flow for an execution of a function corresponding to one of the plurality of objects (Radebaugh: Paragraph [0285], “the software application may request additional input from the user” and Figures 10A-10C), and wherein the generating the source code includes configuring the function of one of the first set of the plurality of objects to request the additional input from the user at a place in the dialogue flow (Radebaugh: Paragraph [0285], “Candidate parameters 1004 provided in this manner may be provided by the software application in some examples. The user device may select one of the candidate parameters by providing a touch-input and/or by providing a natural-language user input to the user device, and in response, the user device may provide the selected candidate parameter to the software application”; also Paragraph [0258, “Identifying parameters may include identifying portions of the natural-language input that specify a manner in which a task corresponding to the intent is to be performed. Parameters may, for instance, specify locations (e.g., addresses or places of interest), times, dates, contacts, types, text (e.g., to be inserted into an email or message), quantities (e.g., distance, money) and, in some instances, names of software applications to perform the task”; Hirzel: Paragraph [0064], “For instance, the natural language expression is transformed into symbols, and the symbols are transformed into instructions for generating a natural language prompt to the user and/or executable computer code for invoking the API call”).
Radebaugh-Kalns-Hirzel teaches 10. The method of claim 9, further comprising:
outputting the dialogue flow to the user via the conversational AI based on the executing the source code (Hirzel: Paragraph [0065], “the natural language prompt or response is generated based on the instructions”);
and adjusting, based on a subsequent input by the user, the dialogue flow to include an additional set from the plurality of objects associated with the subsequent intent (Radebaugh: Paragraph [0285], “Candidate parameters 1004 provided in this manner may be provided by the software application in some examples. The user device may select one of the candidate parameters by providing a touch-input and/or by providing a natural-language user input to the user device, and in response, the user device may provide the selected candidate parameter to the software application”; also Paragraph [0258, “Identifying parameters may include identifying portions of the natural-language input that specify a manner in which a task corresponding to the intent is to be performed. Parameters may, for instance, specify locations (e.g., addresses or places of interest), times, dates, contacts, types, text (e.g., to be inserted into an email or message), quantities (e.g., distance, money) and, in some instances, names of software applications to perform the task”).
Radebaugh-Kalns-Hirzel teaches 11. The method of claim 10, wherein the adjusting uses additional generated source code associated with the additional set of the plurality of objects (Hirzel: Paragraph [0065], “the natural language prompt or response is generated based on the instructions”; Radebaugh: Paragraph [0285], “Candidate parameters 1004 provided in this manner may be provided by the software application in some examples. The user device may select one of the candidate parameters by providing a touch-input and/or by providing a natural-language user input to the user device, and in response, the user device may provide the selected candidate parameter to the software application”; also Paragraph [0258, “Identifying parameters may include identifying portions of the natural-language input that specify a manner in which a task corresponding to the intent is to be performed. Parameters may, for instance, specify locations (e.g., addresses or places of interest), times, dates, contacts, types, text (e.g., to be inserted into an email or message), quantities (e.g., distance, money) and, in some instances, names of software applications to perform the task”).
Radebaugh-Kalns-Hirzel teaches 12. The method of claim 10, wherein, prior to the adjusting the dialogue flow, the method further comprises:
prompting the user for at least one response during the dialogue flow that changes the dialogue flow to using the additional set of object to respond to the user (Radebaugh: Paragraph [0285], “the software application may request additional input from the user” and Figures 10A-10C).
Radebaugh-Kalns-Hirzel teaches 13. The method of claim 9, further comprising:
identifying a past interaction of the user with the conversational AI, wherein the dialogue flow is further generated based on the past interaction (Radebaugh: Paragraph [0222], “obtain contextual information associated with the user input from the user device, along with or shortly after the receipt of the user input. The contextual information can include user-specific data, vocabulary, and/or preferences relevant to the user input. In some examples, the contextual information also includes software and hardware states of the user device at the time the user request is received, and/or information related to the surrounding environment of the user at the time that the user request was received”).
Radebaugh-Kalns-Hirzel teaches 14. The method of claim 9, further comprising:
selecting an object-entity model based on the one or more previous inputs by the user and a language processor, wherein the dialogue flow is further generated based on the object-entity model (Kalns: Paragraph [0032], “a set of intents may include intents or sets of intents that are instantiated by the VPA 110 or by other VPAs during natural language dialog interactions and/or dialog sessions between the VPA 110 or other VPAs and other users. For example, in some embodiments, the set of intents may include intents that are instantiated by other executing instances of the VPA 110 during dialog sessions with users who have previously been identified to the VPA 110 as family, friends, or others having a "connection" or relationship to the user (e.g., people who are connected with the user via a social media platform such as FACEBOOK, LINKEDIN, and/or others). In some embodiments, the set of intents may include intents that are instantiated by other instances of the VPA 110 during dialog sessions with users who do not have a relationship or connection with the user, other than the fact that they have also used the same VPA application and/or other VPA applications. In this way, some embodiments of the VPA 110 can tailor the user's dialog experience with the VPA 110 based on information that the VPA 110 learns from its or other VPAs' dialog interactions with other users”, and Claim 23).
Radebaugh-Kalns-Hirzel teaches 15. The method of claim 9, further comprising:
parsing the at least one annotation for metadata associated with generating dialogue flows based on interactions between the plurality of objects (Kalns: incorporated by reference US 2014/00337266 A1 Paragraph [0043]: “user intent interpreter 216 may apply syntactic, grammatical, and/or semantic rules to the NL dialog input, in order to parse and/or annotate the input to better understand the user's intended meaning and/or to distill the natural language input to its significant words (e.g., by removing grammatical articles or other superfluous language)”), wherein the dialogue flow is further generated based on the metadata (Kalns: Paragraph [0032], Paragraph [0078], and Claim 23; and Radebaugh: Paragraphs [0285] and [0314], “candidate parameters”, “task flow”, and Figures 10A-10C).
Radebaugh-Kalns-Hirzel teaches 17. The non-transitory machine-readable medium of claim 16, wherein the operations further comprise:
outputting the dialogue flow to the user via the conversational AI using the executed source code (Hirzel: Paragraph [0065], “the natural language prompt or response is generated based on the instructions”).
Radebaugh-Kalns-Hirzel teaches 18. The non-transitory machine-readable medium of claim 16, wherein the operations further comprise:
receiving a subsequent input to the outputted dialogue flow from the user, wherein the subsequent input redirects the dialogue flow to a different identifier associated with a different object (Radebaugh: Paragraph [0285], “Candidate parameters 1004 provided in this manner may be provided by the software application in some examples. The user device may select one of the candidate parameters by providing a touch-input and/or by providing a natural-language user input to the user device, and in response, the user device may provide the selected candidate parameter to the software application”; also Paragraph [0258, “Identifying parameters may include identifying portions of the natural-language input that specify a manner in which a task corresponding to the intent is to be performed. Parameters may, for instance, specify locations (e.g., addresses or places of interest), times, dates, contacts, types, text (e.g., to be inserted into an email or message), quantities (e.g., distance, money) and, in some instances, names of software applications to perform the task”);
and adjusting the dialogue flow to include the different object usable by the conversational AI to respond to the subsequent input (Radebaugh: Paragraph [0285], “Candidate parameters 1004 provided in this manner may be provided by the software application in some examples. The user device may select one of the candidate parameters by providing a touch-input and/or by providing a natural-language user input to the user device, and in response, the user device may provide the selected candidate parameter to the software application”; also Paragraph [0258, “Identifying parameters may include identifying portions of the natural-language input that specify a manner in which a task corresponding to the intent is to be performed. Parameters may, for instance, specify locations (e.g., addresses or places of interest), times, dates, contacts, types, text (e.g., to be inserted into an email or message), quantities (e.g., distance, money) and, in some instances, names of software applications to perform the task”).
Radebaugh-Kalns-Hirzel teaches 19. The non-transitory machine-readable medium of claim 16, wherein the adjusting comprises generating additional source code that includes the different object with the source code for the dialogue flow (Radebaugh: Paragraph [0285], “Candidate parameters 1004 provided in this manner may be provided by the software application in some examples. The user device may select one of the candidate parameters by providing a touch-input and/or by providing a natural-language user input to the user device, and in response, the user device may provide the selected candidate parameter to the software application”; also Paragraph [0258, “Identifying parameters may include identifying portions of the natural-language input that specify a manner in which a task corresponding to the intent is to be performed. Parameters may, for instance, specify locations (e.g., addresses or places of interest), times, dates, contacts, types, text (e.g., to be inserted into an email or message), quantities (e.g., distance, money) and, in some instances, names of software applications to perform the task”).
Radebaugh-Kalns-Hirzel teaches 20. The non-transitory machine-readable medium of claim 16, wherein the operations further comprise:
selecting an object-entity model based on the one or more previous inputs by the user and a language processor, wherein the dialogue flow is further generated based on the object-entity model (Kalns: Paragraph [0032], “a set of intents may include intents or sets of intents that are instantiated by the VPA 110 or by other VPAs during natural language dialog interactions and/or dialog sessions between the VPA 110 or other VPAs and other users. For example, in some embodiments, the set of intents may include intents that are instantiated by other executing instances of the VPA 110 during dialog sessions with users who have previously been identified to the VPA 110 as family, friends, or others having a "connection" or relationship to the user (e.g., people who are connected with the user via a social media platform such as FACEBOOK, LINKEDIN, and/or others). In some embodiments, the set of intents may include intents that are instantiated by other instances of the VPA 110 during dialog sessions with users who do not have a relationship or connection with the user, other than the fact that they have also used the same VPA application and/or other VPA applications. In this way, some embodiments of the VPA 110 can tailor the user's dialog experience with the VPA 110 based on information that the VPA 110 learns from its or other VPAs' dialog interactions with other users”, and Claim 23).
Radebaugh-Kalns-Hirzel teaches 21. The non-transitory machine-readable medium of claim 16, parsing the annotation for metadata associated with generating dialogue flows based on interactions between the plurality of objects (Kalns: incorporated by reference US 2014/00337266 A1 Paragraph [0043]: “user intent interpreter 216 may apply syntactic, grammatical, and/or semantic rules to the NL dialog input, in order to parse and/or annotate the input to better understand the user's intended meaning and/or to distill the natural language input to its significant words (e.g., by removing grammatical articles or other superfluous language)”), wherein the dialogue flow is further generated based on the metadata (Kalns: Paragraph [0032], Paragraph [0078], and Claim 23; and Radebaugh: Paragraphs [0285] and [0314], “candidate parameters”, “task flow”, and Figures 10A-10C).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. John (US 2020/0374244 A1) describes a system for integrating chatbots and dynamic dialog flows into application services.
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/IMAD HUSSAIN/Primary Examiner, Art Unit 2453
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