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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic or well-known or conventional computer components. That is, other than reciting “online system, user interface, machine based language model”, nothing in these claim limitations precludes the steps from practically being performed in the mind and/or organizing human activity. As a whole, claim 1 pertains to managing a conversation with a user according to their intent and using a script to respond to the topic accordingly, which is a mental process and/or organizing human activity that a human can do. Individually, each of the limitations also pertains to a mental process/organizing human activity, and/or insignificant extra solution activity, for example:
configuring, by an online system, a user interface for performing conversations; (e.g., a human agent using a computer to talk to a user.)
storing metadata describing a plurality of conversation flow types, each conversation flow type comprising a sequence of steps, each step describing a natural language-based interaction with a user; (e.g., using a notebook or binder to store a script for various topics, such as sales, service or returns.)
repeatedly performing: receiving a natural language request from the user via the user interface; (e.g., keeps on listening for audio or pay attention and look at the incoming text from the user.)
generating a prompt for input to a machine learning based language model comprising the natural language request and metadata describing one or more conversation flow types and requesting the machine learning based language model to identify a particular conversation flow type relevant to the natural language request; (e.g., write down the user request and the one or more topic of discussion, and use that to determine a particular script to handle the user request.)
providing the prompt to the machine learning based language model for execution; (e.g., entering the instruction to ChatGPT for instance, or the human processing the information in their brain.)
receiving a response generated by the machine learning based language model based on the prompt, the response identifying a conversation flow type relevant to the natural language request; (e.g., read or look at the response output by the ChatGPT, or use the human brain to identify the conversational flow type most closely associated with the request.)
and for one or more subsequent natural language requests received via the user interface from the user: generating a reply based on steps of the identified conversation flow type, and sending the generated reply for display via the user interface. (e.g., use the identified customer service script to respond to the user accordingly.)
The judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 1 is not patent eligible.
The examiner further notes that the use of claimed generic computer components (“online system, user interface, machine based language model”) to obtain, extract, and/or generate data invokes such generic computer components “merely as a tool to perform an existing process”. MPEP 2106.05(f). MPEP 2106.05(f) further explains:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 1 recites generic computer components (“online system, user interface, machine based language model”), with respect to performing tasks. MPEP 2106.05(d) and (f) further provides examples of court decisions where the courts found generic computing components to be mere instructions to apply a judicial exception, and further explains “increased speed” (e.g., using a computer to increase the speed of an otherwise mental process) does not provide an inventive concept. For example:
A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (emphasis added).
Performing repetitive calculations. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
Claim 8 recites a non-transitory computer-readable storage medium claim that corresponds to the system of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 8 further recites “non-transitory computer-readable storage medium”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 8 is not patent eligible.
Claim 15 recites a computer system claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 15 further recites “one or more processors, and a non-transitory computer readable storage medium storing instructions”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 15 is not patent eligible.
Claims 2-7, 9-14 and 16-20 depend from independent claims 1, 8 and 15 respectively, do not remedy any of the deficiencies of claims 1, 8 and 15, and therefore are rejected on the same grounds as claims 1, 8 and 15 from above.
Claim 2 further recites: wherein a conversation flow type is an onboarding conversation flow used when a user starts a conversation for a first time with the online system. (e.g., using a welcome or greeting script when the user contacts for the first time.)
Claim 3 further recites: determining whether all steps of the identified conversation flow type are processed; responsive to determining whether all steps of the identified conversation flow type are processed, determining a next conversation flow type. (e.g., cross off a checklist, then looks at a training manuscript to see which topic or subject of discussion is the next logical step.)
Claim 4 further recites: wherein the machine learning based language model is a large language model. (e.g., use of a LLM which is considered to be a generic computer component. The human brain can serve similar function.)
Claim 5 further recites: wherein the conversations are associated with an organization, wherein each conversation is performed by the online system with a user of the organization. (e.g., managing or handling a conversation with a user that belong to a church or works for a particular company.)
Claim 6 further recites: wherein a conversation flow type is for advising users of an organization regarding issues encountered in the organization. (e.g., using training manuscript that is designed for a particular organization to handle conversation (issues) dealing with user or member of that organization.)
Claim 7 further recites: tracking a current step of the identified conversation flow type being executed wherein a second prompt generated for a second natural request from the user comprises the identified conversation flow type, the current step of the identified conversation flow type and requesting the machine learning based language model to generate a response to the natural language request; and sending a reply to the user via the user interface, the reply generated based on the response of the natural language request generated by the machine learning based language model. (e.g., keeping track of the conversation, jotting down notes, and looking at the training script, write down the request from the user which contains the type of conversation script, determine which step the conversation is currently on and enter the request to the ChatGPT or using the human brain to process the information and provide the response back to the user.)
Claim 8 further recites: wherein identifying the one or more of the main nodes comprises searching the directed graph using a parametrized number of shortest paths from the main root node of the directed graph to nodes from the main nodes that each has a title that at least partially matches the keyword. (e.g., analyzing the directed graph, compare title with keyword to determine closeness of the match, count the number of edges or connection along each path, and note the shortest path for each match node, rank the shortest path by length, select a number of top ranked paths.)
Claims 9-14 are recited similarly to claims 2-7, therefore the rationale and grounds of rejection applied to claims 2-7 can also be applied to claims 9-14 respectively.
Claims 16-20 are recited similarly to claims 2-5 and 7, therefore the rationale and grounds of rejection applied to claims 2-5, and 7 can also be applied to claims 16-20 respectively.
In sum, claims 2-7, 9-14 and 16-20 depend from claims 1, 8 and 15 respectively, and further recite mental processes as explained above. None of the additional limitations recited in claims 2-7, 9-14 and 16-20 amount to anything more than the same or a similar abstract idea as recited in claims 1, 8 and 15 respectively. Nor do any limitations in claims 2-7, 9-14 and 16-20: (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception because the additional limitations of using generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Claims 2-7, 9-14 and 16-20 are not patent eligible.
Claims 1, 4-8, 11-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mcintyre (US 20250111167), in view Bekal (US 12676166).
Regarding Claim 1, Mcintyre discloses: 1.A computer-implemented method for conversation flow routing in an online conversation, comprising: configuring, by an online system (see fig. 2, system, also see fig. 5), a user interface for performing conversations; (see para 0030, user interface)
([0082] an intent inferred from the contextual information. As another example, the orchestration loop engine 220 generates and sends a second command prompting the LLM 240 to select at least one LM skill of the plurality of candidate LM skills the target LM skill used to service an aspect of the initial prompt. In this example, the LLM 240 sends, to the orchestration loop, the target LM skill and corresponding API input parameters associated with the user input and the corresponding contextual information.) [plurality of candidate LM skills reads on plurality of conversation flow types]
repeatedly performing: receiving a natural language request from the user via the user interface; ([0082] orchestration loop further send communications…prompt with user input…)
generating a prompt for input to a machine learning based language model comprising the natural language request and metadata describing one or more conversation flow types and requesting the machine learning based language model to identify a particular conversation flow type relevant to the natural language request; ([0082] the orchestration loop engine 220 generates and sends a second command prompting the LLM 240 to select at least one LM skill of the plurality of candidate LM skills the target LM skill used to service an aspect of the initial prompt.) [target LM skills reads on identifying a conversation flow type]
providing the prompt to the machine learning based language model for execution; ([0082] In response, the LLM 240 generates and communicates a response (labeled as “target LLM skill DSL or user response” in FIG. 5) to the orchestration loop engine 220.)
receiving a response generated by the machine learning based language model based on the prompt, the response identifying a conversation flow type relevant to the natural language request; ([0082] In response, the LLM 240 generates and communicates a response (labeled as “target LLM skill DSL or user response” in FIG. 5) to the orchestration loop engine 220.)
and for one or more subsequent natural language requests received via the user interface from the user: generating a reply based on steps of the identified conversation flow type, ([0082] the LLM 240 generates and communicates a response (labeled as “target LLM skill DSL or user response” in FIG. 5) to the orchestration loop engine 220. … the target LM skill used to service an aspect of the initial prompt.)
and sending the generated reply for display via the user interface. ([0129] present data to user…a display device)
Although Mcintyre discloses skills which may represent a sequence of steps to manage an interaction or conversation, but it silent on storing metadata regarding conversation flow types.
Bekal in the relate art discloses: storing metadata ([col. 6, lines 1-11] Information about the dialog-driven applications, such as logical descriptors of the applications, program code (e.g., in source code or executable form), configuration settings for various stages or interaction categories of the applications, may be stored in an application metadata repository 224)
Mcintyre and Bekal are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Mcintyre to combine the teaching of Bekal, because storing metadata preserve important information needed to manage the dialog or conversation (Bekal, [col. 6, lines 1-11]).
Regarding Claim 4, Mcintyre in view of Bekal disclose all the element of claim 1,
Mcintyre further discloses: wherein the machine learning based language model is a large language model. ([0082] orchestration loop engine 220 further send communications (illustrated as “prompt with user input, conversation state, accumulated ground data” in FIG. 5) to the LLM 240.)
Regarding Claim 5, Mcintyre in view of Bekal disclose all the elements of claim 1,
Mcintyre further discloses: wherein the conversations are associated with an organization, wherein each conversation is performed by the online system with a user of the organization. ([0047] based on the particular user, user device, or a plurality of users (such as a specific group of people, a group of people sharing a role within an organization, a student, a professor, or faculty), and/or user devices.) Also see para 0046 performing work-related task.
Regarding Claim 6, Mcintyre in view of Bekal disclose all the elements of claim 5,
Mcintyre further discloses: wherein a conversation flow type is for advising users of an organization regarding issues encountered in the organization. ([0047] Some embodiments of context extractor 214 determine, from the monitored user activity data and the initial prompt, intent associated with the initial prompt based on the particular user, user device, or a plurality of users (such as a specific group of people, a group of people sharing a role within an organization, a student, a professor, or faculty), and/or user devices. In some embodiments, an intent determined by context extractor 214 is provided to other components of system 200 or stored in a user profile associated with a user.) Also see para 0046 performing work-related task.
Regarding Claim 7, Mcintyre in view of Bekal disclose all the elements of claim 1,
Mcintyre further discloses: tracking a current step ([0082] the orchestration loop engine manage/tracks conversation state) of the identified conversation flow type being executed wherein a second prompt generated for a second natural request ([0082] the orchestration loop engine generates and sends a second command prompting the LLM to select at least one LM skill of the plurality of candidate LM skills the target LM skill used to service an aspect of the initial prompt.) from the user comprises the identified conversation flow type, the current step of the identified conversation flow type and requesting the machine learning based language model to generate a response to the natural language request; ([0082] sending a prompt with user input, conversation state, and accumulated ground data to the LLM, in response the LLM generates and communicates a response)
and sending a reply to the user via the user interface, ([0082] generating a target skill or user response to be sent back through the loop) the reply generated based on the response of the natural language request generated by the machine learning based language model. ([0082] The LLM generates and communicates a response … to generate a respective output)
Regarding Claim 8, Mcintyre discloses: 8. A non-transitory computer readable storage medium storing instructions that when executed by one or more computer processors cause the one or more computer processors to perform steps comprising: ([0103] In some embodiments, a system, such as the computerized system described in any of the embodiments above, comprises at least one computer processor and computer storage media storing computer-useable instructions that, when used by the at least one computer processor, cause the system to perform operations.)
As for the rest the claim, they recite the elements from the method of claim 1, therefore the rationale and grounds of rejection applied to claim 1 is also applicable to claim 8.
Regarding Claims 11-14, they are CRM claims that corresponds to the method of claims 4-7 respectively and are therefore rejected under the same grounds and rationale as claims 4-7.
Regarding Claim 15, Mcintyre discloses: 15. A computer system comprising: one or more computer processors; ([0103] In some embodiments, a system, such as the computerized system described in any of the embodiments above, comprises at least one computer processor and computer storage media storing computer-useable instructions that, when used by the at least one computer processor, cause the system to perform operations.) and a non-transitory computer readable storage medium storing instructions that when executed by one or more computer processors cause the one or more computer processors to perform steps, comprising: ([0103] In some embodiments, a system, such as the computerized system described in any of the embodiments above, comprises at least one computer processor and computer storage media storing computer-useable instructions that, when used by the at least one computer processor, cause the system to perform operations.)
As for the rest the claim, they recite the elements from the method of claim 1, therefore the rationale and grounds of rejection applied to claim 1 is also applicable to claim 15.
Regarding Claims 18-20, they are system claims that corresponds to the method of claims 4-5 and 7 respectively and are therefore rejected under the same grounds and rationale as claims 4-5 and 7.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mcintyre (US 20250111167), in view Bekal (US 12676166), and further in view of Koohmarey (US 20200311208).
Regarding Claim 2, Mcintyre in view of Bekal disclose all the elements of claim 1,
Mcintyre and Bekal are silent regarding wherein a conversation flow type is an onboarding conversation flow used when a user starts a conversation for a first time with the online system.
Koohmarey in the related art discloses: wherein a conversation flow type is an onboarding conversation flow used when a user starts a conversation for a first time with the online system. ([0164] the particular conversational state may be the initial conversational state at which the particular conversation flow is arranged to initiate by default. In other cases, the particular conversational state may be a conversation state of the particular conversation flow that is different from its initial conversational state. In any case, the particular conversational state may be associated with the particular attribute if the particular conversational state one during which the software application 610 is arranged to receive, provide, record, and/or modify information related the particular attribute, among other options.)
Mcintyre/Bekal/Koohmarey are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Mcintyre and Bekal to combine the teaching of Koohmarey, because initial conversation would enable a better use experience in instance where the user may be new or inexperienced and needed to be guidance initially (Koohmarey, [0164]).
Regarding Claims 9 and 16, they are CRM and system claims that corresponds to the method of claim 2 respectively and are therefore rejected under the same grounds and rationale as claim 2.
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mcintyre (US 20250111167), in view Bekal (US 12676166), and further in view of Chu (US 20200395000).
Regarding Claim 3, Mcintyre in view of Bekal disclose all the elements of claim 1,
Mcintyre and Bekal are silent regarding determining whether all steps of the identified conversation flow type are processed; responsive to determining whether all steps of the identified conversation flow type are processed, determining a next conversation flow type.
Chu in the related art discloses: determining whether all steps of the identified conversation flow type are processed; responsive to determining whether all steps of the identified conversation flow type are processed, determining a next conversation flow type. ([0013] after completing a human-machine dialogue of the initial topic, determining a jump topic for jumping to according to the jump probability of jumping out of the initial topic to the k jump topics at the downstream level so as to conduct a next round of recommendation to the user;[0068] The completion of the human-machine dialogue in the embodiment of the present invention refers to that the user is satisfied with the current human-machine dialogue, which can be determined by selecting the evaluation or answering the question that the robot actively asks whether the dialogue is satisfactory before ending the human-machine dialogue. If the user expresses satisfaction with this human-machine dialogue, it is determined that this human-machine dialogue is completed, and the subsequent steps are performed.)
Mcintyre/Bekal/Chu are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Mcintyre and Bekal to combine the teaching of Chu, because detection of completing the initial discussion and transitioning to next topic of discussion provides user a better interactive experience (Chu, [background]).
Regarding Claims 10 and 17, they are CRM and system claims that corresponds to the method of claim 3 respectively and are therefore rejected under the same grounds and rationale as claim 3.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Koneru US 12130848 – discloses method/system for orchestrating conversation using trained LLM as it relates to use cases. See Abstract, col. 4 and figs 5 and 6 for additional details.
Lee US 20250265422- discloses a system that deals with task-oriented response that keeps tracks and modifies dialog states dynamically. See Abstract, para 0022, 0032-0053 and figs. 2-6 for additional details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip H Lam whose telephone number is (571)272-1721. The examiner can normally be reached 9 AM-3 PM Pacific time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHILIP H LAM/ Examiner, Art Unit 2656
