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 .
Status of Claims
This action is in reply to the claims filed on 02/28/2025.
Claims 1-20 are currently pending and have been examined.
Claim Rejections- 35 U.S.C. § 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.
Under Step 1 of the subject matter eligibility (SME) analysis described in MPEP 2106.03, the instant claims fall within the four statutory categories of invention identified by 35 U.S.C. 101. In the instant case, claims 1-7 are directed to a method, claims 8-14 are directed to a manufacture, and claims 15-20 is directed to a system. Claims 1, 8, and 15 are parallel in nature, therefore, the analysis will use claim 1 as the representative claim.
In Step 2A Prong One, it must be considered whether the claims recite a judicial exception. Claim 1, as exemplary, recites abstract concepts including: receiving ... in real-time or near real-time, a transcript of an ongoing conversation between a first user and an individual via a first user device; generating ... real-time or near real-time feedback to the first user by: ... using pairs of historic conversations and corresponding success states to determine that the individual conveyed a message to the first user that includes an objection and generate a proposed response to address the objection based on a context of the ongoing conversation; and causing ... display of the proposed response in real-time or near real-time via the first user device.
These identified limitations recite the abstract idea of “proposing a response to an ongoing conversation”, which falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas as it relates to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). In this case, the claims set forth a method for recommending a response during a conversation which is a fundamental social activity. Accordingly, claims 1, 8, and 15 recite an abstract idea. See MPEP 2106.04.
In Step 2A Prong Two, examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Instant claims 1, 8, and 15 recite additional elements including: a computing system; a first user device; interfacing with a large language model. The computing system, device, and large language model are each recited at a high-level of generality and are invoked as tools used in their ordinary capacity to transmit data in real-time (computer/device) and generate a response after receiving input text (LLM). The combination of these elements amount to no more than mere instruction to implement the abstract idea of proposing responses using a generic computer system. Implementing an abstract idea on a generic computer or use of computer or other machinery in its ordinary capacity for economic of other tasks does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). Accordingly, claims 1, 8, and 15 are directed to the abstract idea.
Under Step 2B of the SME analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) individually and in combination are merely being used to apply the abstract idea to a general computer components. For the same reason, the elements are not sufficient to provide an inventive concept. As explained in MPEP 2106.05(f), implementing an abstract idea with a generic computer does not add significantly more in Step 2B. The claims in this case specify what computer data “it is desirable to gather, analyze, and display, including in ‘real time’; but they do not include any requirement for performing the claimed functions of gathering, analyzing, and displaying in real time by use of anything but entirely conventional, generic technology” (Electric Power Group, LLC v. ALSTOM SA, 830 F. 3d 1350). See Specification ¶ [0023] “Network 105 may be of any suitable type”; ¶ [0024] “Network 105 may include any type of computer networking arrangement used to exchange data”; ¶ [0025] “User device may be representative of a mobile device ... or any computing system having the capabilities described herein”; ¶ [0032] “large language model 120 may be representative of one or more third party large language models, such as, but not limited to ChatGPT commercially available from Open AI”. The additional elements and combination thereof are no more than well-understood activities known in the computer arts recited at a high level of generality and do not amount to significantly more than the abstract idea itself.
Dependent claim(s) 2-6, 9-13, 16-18 and 20 do not aid in the eligibility of the independent claims. These claims merely further define the abstract idea without reciting any further additional elements. Thus dependent claims 2-6, 9-13, 16-18 and 20 are also ineligible.
Dependent claims 7, 14, and 19 recite additional elements including: wherein the further communications ... are used to train or fine-tune the large language model. Similar to the additional elements identified above, the training/fine-tuning of the model are described in general terms which do not distinguish from existing training/fine-turning methods. Simply training or fine-tuning an LLM does not add meaningful limits to practicing the abstract idea and instead merely generally links the abstract idea to LLM technology. Accordingly, claim(s) 7, 14, and 19 are ineligible.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Palmer et al. (US 11,140,265 B1) in view of Smith Lewis et al. (US 2024/0289863 A1).
Claim 1
Palmer discloses a method, comprising:
receiving, by a computing system, in real-time or near real-time, a transcript of an ongoing conversation between a first user and an individual via a first user device (Col 6, ll. 40-50 “Referring now to an instantiation of a real-time directive providing system 101 residing on a computing device that a party uses to make audio calls (e.g., a personal computer or smart phone), a call monitoring module 313 automatically monitors audio calls made by the user, in real-time while the calls are occurring. This monitoring of calls can take the form of performing speech recognition on the audio calls (e.g., converting speech of the monitored calls to text)”);
generating, by the computing system, real-time or near real-time feedback to the first user (Col 7, ll. 5-10) by:
interfacing with a ... model fine-tuned using pairs of historic conversations and corresponding success states (Col 7, ll. 55-60 “the effectiveness of different directives 303 can be tracked over time ... and the directives 303 can be redeployed, edited, tweaked ... as desired”; Col 8, ll. 35-50 “Results and tracked information can also be input into a machine learning module 309 ... The machine learning can be used in this context perform actions such as creating new triggers, editing existing triggers”) to determine that the individual conveyed a message to the first user that includes an objection (Col 6, ll. 5-10 “Specific circumstances detected as occurring during monitored audio calls can also be factored into triggers 301, such as ... the raising of an objection”) and generate a proposed response to address the objection based on a context of the ongoing conversation (Col 6, ll. 20-25 “The content of directives 303 are variable design choices, but can be in the form of instructions to a caller, for example providing a script for responding to a given objection raised by a customer”; Col 6, ll. 30-40 “A receiving module 307 of the real-time directive providing system 101 receives triggers 301 and associated directives 303 ... from a machine learning module 309”); and
causing, by the computing system, display of the proposed response in real-time or near real-time via the first user device (Col 7, ll. 5-10 “In response to detecting the occurrence of a specific trigger 301, a directive displaying module 317 of the real-time directive providing system 101 automatically outputs a specific corresponding directive 303 to a party to the audio call, on a screen of the calling device being operated by the party, in real-time as the call is occurring”).
While Palmer discloses interfacing with a machine learning module, Palmer does not disclose interfacing with a large language model, specifically. However Smith Lewis et al. –which is also directed to providing recommendations to conversation agents—teaches interfacing with a large language model (¶ [0041] “ In some examples, conversational AI system 100 may employ one or more language models with which one or more of the other components interface, such as to provide input to or obtain output from the language model. In some examples, the language model may be a large language model 160, examples of which may include, but are not limited to GPT-4, Claude 2, GPT-3, BERT, BLOOM, etc. In some embodiments, one or more of the other components may interface with the language model, as shown”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the machine learning module of Palmer to include the large language model taught by Smith Lewis in order to, for example, optimize and continually improve the system (Smith Lewis ¶ [0095]).
Claim 2 – The combination of Palmer in view of Smith Lewis teaches the method of claim 1. Palmer further discloses,
receiving, by the computing system, in real-time or near real-time, a further transcript of the ongoing conversation between the first user and the individual via the first user device (Col 6, ll. 40-50 “a call monitoring module 313 automatically monitors audio calls made by the user, in real-time while the calls are occurring”. Examiner notes monitoring calls in real time while they occur requires receiving a further transcript as claimed.);
generating, by the computing system, further real-time or near real-time feedback to the first user by:
interfacing with the large language model to determine that the individual conveyed a further message to the first user that includes a further objection and generate a further proposed response to address the further objection based on a continued context of the ongoing conversation (See previous citations for claim 1. Examiner notes the triggers which include objections are monitored for in real-time during an audio call and can include more than 1); and
causing, by the computing system, further display of the further proposed response in real- time or near real-time via the first user device (See previous citations for claim 1. Examiner notes the directives/proposals of Palmer are delivered automatically, in real-time during an ongoing call and therefore include a further proposed response as recited).
Claim 3 – The combination of Palmer in view of Smith Lewis teaches the method of claim 1. Palmer further discloses, further comprising:
receiving, by the computing system, in real-time or near real-time, a further transcript of the ongoing conversation between the first user and the individual via the first user device (Col 6, ll. 40-50 “a call monitoring module 313 automatically monitors audio calls made by the user, in real-time while the calls are occurring”. Examiner notes monitoring calls in real time while they occur requires receiving a further transcript as claimed.);
generating, by the computing system, further real-time or near real-time feedback to the first user by:
interfacing with the ... model to identify a continued context of the ongoing conversation and generate a proposed tip for the first user to advance the ongoing conversation (Col 6, ll. 30-35 “A receiving module 307 of the real-time directive providing system 101 receives triggers 301 and associated directives 303, from the defining module 305 described above, and/or from a machine learning module 309; Col 6, ll. 60-65 “A trigger detecting module 315 of the real-time directive providing system 101 automatically detects the occurrence of specific triggers during monitored audio calls, in real-time as the calls are occurring. The determination process can take into account context of the audio call”); and
causing, by the computing system, further display of the proposed tip in real-time or near real-time via the first user device (See previous citations for claim 1. Examiner notes the directives/proposals of Palmer are delivered automatically, in real-time during an ongoing call and therefore include a further proposed response as recited).
Palmer does not disclose a large language model, however and as explained above, the combination of Palmer in view Smith Lewis teaches wherein a machine learning module can be a large language model.
Claim 4 – The combination of Palmer in view of Smith Lewis teaches the method of claim 1. Palmer further discloses, further comprising:
receiving, by the computing system, in real-time or near real-time, a further transcript of the ongoing conversation between the first user and the individual via the first user device (Col 6, ll. 40-50 “a call monitoring module 313 automatically monitors audio calls made by the user, in real-time while the calls are occurring”. Examiner notes monitoring calls in real time while they occur requires receiving a further transcript as claimed.); and
generating, by the computing system, further real-time or near real-time feedback to the first user by:
interfacing with the ... model to identify a continued context of the ongoing conversation and identify a deficient process performed by the first user in the ongoing conversation (Col 6, ll. 30-35 “machine learning module 309”; Col 6, ll. 1-10 “Specific circumstances detected as occurring during monitored audio calls can also be factored into triggers 301, such as the resolution (or non-resolution of an issue) during a call, the raising of an objection, the utterance of an apology...etc.”).
Palmer does not disclose a large language model, however and as explained above, the combination of Palmer in view Smith Lewis teaches wherein a machine learning module can be a large language model.
Claim 5 – The combination of Palmer in view of Smith Lewis teaches the method of claim 1. Palmer further discloses, wherein the ongoing conversation is a sales process, the first user is a sales person, and the individual is a customer (Col 6, ll. 67 “sales call”).
Claim 6 – The combination of Palmer in view of Smith Lewis teaches the method of claim 1. Palmer further discloses, wherein the proposed response is a pre-generated response that maps to the objection based on a context of the objection (Col 7, ll. 25-30 “directives 303 can be mapped to triggers at any level of granularity, taking into account monitored content of the call, the parties involved, circumstances concerning the call, other contextual information, etc”).
Claim 7 – The combination of Palmer in view of Smith Lewis teaches the method of claim 1. Palmer further discloses, wherein further communications between the first user and the individual are used to further train or fine-tune the large language model (Col 8, ll. 35-35 “Results and tracked information can also be input into a machine learning module 309 of the real-time directive providing system 101. The machine learning module 309 can apply machine learning techniques to the tracked occurrences of triggers 301, corresponding outputting of directives 303, and corresponding results. The machine learning can be used in this context to perform actions such as creating new triggers 301, editing existing triggers 301, creating new directives 303 corresponding to specific triggers 301, and editing existing directives 303, at any level of granularity”).
As explained above, the combination of Palmer in view Smith Lewis teaches wherein a machine learning module can be a large language model. Smith Lewis also teaches using further communications to further train the large language model in ¶ [0086] “In some embodiments, adaptive curation module 130 may dynamically refine user preference encoding and item attribute encoding neural network layers based on collected recommendation feedback data indicating user actions on recommended items, in addition to full retraining on datasets obtained in ways described herein” and ¶ [0102].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the machine learning module of Palmer to include further training the large language model, as taught by Smith Lewis, in order to optimize and continually improve the system (Smith Lewis ¶ [0095]).
Claims 8-14, which are directed to a non-transitory computer readable medium, recite limitations that are parallel in nature as those addressed above for method claims 1-7. Claim(s) 8-14 are therefore rejected for the same reasons as set forth above for claims 1-7, respectively.
Claims 15-20, which are directed to a system, recite limitations that are parallel to those addressed above for method claims 1-4 and 6-7. Claims 15-20 are therefore rejected for the same reasons as set forth above for claims 1-4 and 6-7, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Aviv, Aviram, et al. (NPL Reference U) proposes a methodology for the development of an assisting agent that provides online advice to operators while they attend clients.
Brown et al. (US 10,440,181 B1) is directed to a conversational system that may selectively control the conversations when the conversations become non-compliant, deviate from best practices, or can be controlled to more effectively reach a positive disposition than when allowing a telephone agent to independently control the conversation.
Das et al. (US 10,965,812 B1) relates to methods and apparatuses, including computer program products, for analysis and classification of unstructured computer text for generation of a recommended conversation topic flow.
Wooters (US 2014/0067375 A1) describes techniques for analyzing individual conversation records to characterize the conversations according to multiple metrics.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNEDY A GIBSON-WYNN whose telephone number is (571)272-8305. The examiner can normally be reached M-F 8:30-5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.G.W./Examiner, Art Unit 3688
/KELLY S. CAMPEN/Primary Examiner, Art Unit 3691
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