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 .
Response to Amendment
The amendments filed 01/26/2026 have been entered.
Claims 1-3, 6-11 and 14-23 remain pending within the application.
The amendments filed 01/26/2026 are sufficient to overcome each and every objection previously set forth in the Non-Final Office Action mailed 10/24/2025. The objections have been withdrawn.
Claim Objections
Claim 8 is objected to because of the following informalities: “ to receive…comprises to receive…” should be “…receiving… comprises receiving…”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The disclosure does not provide adequate structure to perform “wherein updating the knowledge base model based on the evaluation of the data indicative of behaviors of the agents with respect to the at least one suggestion using machine learning comprises ranking each of the at least one suggestion as a function of the corresponding usage score” in claims 7 and 15. Specification ¶[0089] discloses generating suggestion usage scores, but does not describe a function of a usage score. It is not apparent how the function of a usage score is calculated nor how it used to rank suggestions. For examination purposes, the examiner is interpreting “wherein updating the knowledge base model based on the evaluation of the data indicative of behaviors of the agents with respect to the at least one suggestion using machine learning comprises ranking each of the at least one suggestion as a function of the corresponding usage score” under its broadest reasonable interpretation.
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.
The claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim directed to a process, machine, manufacture, or composition of matter?
Claims 1-8 are directed to a system, hence fall within the statutory category of a machine.
Claims 9-16 are directed to a method, hence fall within the statutory category of a process.
Claims 17-29 are directed to a non-transitory machine readable storage media, hence fall within the statutory category of a machine.
Thus, each of the claims fall within one of the four statutory categories.
Claim 1 includes the steps of:
A system for providing implicit feedback using multi-factor behavior monitoring, the system comprising: a computing system comprising at least one first processor and at least one first memory having a first plurality of instructions stored thereon that, in response to execution by the at least one first processor, causes the computing system to:
receive a transcript of a conversation between an agent and a user;
provide at least one suggestion to the agent via an agent application based on the transcript of the conversation between the agent and the user;
evaluate data indicative of behaviors of the agent with respect to the at least one suggestion using learning,
including evaluation of at least one interaction of the agent with at least one graphical user interface element of the agent application that displays the at least one suggestion to determine a usage score for the corresponding suggestion;
update a knowledge base model based on the evaluation of the data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The broadest reasonable interpretation of the following limitations falls within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. The claim(s) recite(s) in part:
“provide at least one suggestion to the agent via an agent application based on the transcript of the conversation between the agent and the user”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because providing a suggestion based on a conversation encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“evaluate data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because evaluating data indicative of behaviors of the agent with respect to the at least one suggestion encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components, such as machine learning. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“including evaluation of at least one interaction of the agent with at least one graphical user interface element of the agent application that displays the at least one suggestion to determine a usage score for the corresponding suggestion”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because evaluating interactions and determining usage scores encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“update a knowledge base model based on the evaluation of the data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because updating a knowledge base model based on the evaluation of the data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning, with models and machine learning recited at a high level of generality encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The judicial exception is not integrated into a practical application. In particular, The claim(s) recite(s) in part:
“A system for providing implicit feedback using multi-factor behavior monitoring, the system comprising: a computing system comprising at least one first processor and at least one first memory having a first plurality of instructions stored thereon that, in response to execution by the at least one first processor, causes the computing system to: ”. As drafted and under its broadest reasonable interpretation, this limitation recites additional elements which amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
“receive a transcript of a conversation between an agent and a user”. As drafted and under its broadest reasonable interpretation, this limitation recites receiving input data, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
“including evaluation of at least one interaction of the agent with at least one graphical user interface element of the agent application that displays the at least one suggestion to determine a usage score for the corresponding suggestion”. As drafted and under its broadest reasonable interpretation, this limitation recites displaying suggestions, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application. Therefore, no meaningful claim limits are imposed practicing the abstract idea. Accordingly, at Step 2A, prong two, the additional elements do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed, the claim limitations reciting generic computer elements amounts to no more than mere instructions to apply the exception using a generic computer. The claim reciting the additional elements of “display[ing]” amount to outputting information. The claim reciting the additional elements of “receiving” and/or “transmitting” amount to receiving/transmitting information.
“Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)) MPEP § 2106.05(d)(II)(i).
The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. Thus, the claim does not provide an inventive concept.
The claim is ineligible.
Claim 2, which depends upon claim 1 (and thus the rejection of claim 1 is incorporated), recite(s) in part:
“analyze the transcript of the conversation between the agent and the user to find suggestion content related to the transcript”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because analyzing the transcript of the conversation between the agent and the user to find suggestion content related to the transcript encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
“wherein to provide the at least one suggestion to the agent comprises to provide at least one suggestion to the agent that references the suggestion content related to the transcript.”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because providing at least one suggestion to the agent that references the suggestion content related to the transcript encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
The claim does not integrate the judicial exception into practical application.
The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 3, which depends upon claim 2 (and thus the rejection of claim 2 is incorporated), recite(s) in part:
“further comprising an agent device having a display, at least one second processor, and at least one second memory having a second plurality of instructions stored thereon that, in response to execution by the at least one second processor, causes the agent device to execute the agent application to present the at least one suggestion to the agent on the display via a graphical user interface”. As drafted and under its broadest reasonable interpretation, this limitation recites additional elements which amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
“present the at least one suggestion to the agent on the display via a graphical user interface”. As drafted and under its broadest reasonable interpretation, this limitation recites presenting output data, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The claim reciting the additional elements of generic computers and mere data gathering do not integrate the judicial exception into practical application. The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application.
The claim limitations reciting generic computer elements amounts to no more than mere instructions to apply the exception using a generic computer. The claim reciting the additional elements of “providing” amount to outputting information. The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 6, which depends upon claim 1 (and thus the rejection of claim 1 is incorporated), recite(s) in part:
“wherein evaluating the data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning comprises evaluating times at which the at least one suggestion were displayed and times of the agent interactions with the agent application”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because evaluating times at which the at least one suggestion were displayed and times of the agent interactions with the agent application encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
The claim does not integrate the judicial exception into practical application.
The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 7, which depends upon claim 1 (and thus the rejection of claim 1 is incorporated), recite(s) in part:
“wherein updating the knowledge base model based on the evaluation of the data indicative of behaviors of the agents with respect to the at least one suggestion using machine learning comprises ranking each of the at least one suggestion as a function of the corresponding usage score”. As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because ranking each of the at least one suggestion encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B.
The claim does not integrate the judicial exception into practical application.
The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 8, which depends upon claim 1 (and thus the rejection of claim 1 is incorporated), recite(s) in part:
“wherein to receive the transcript of the conversation between the agent and the user comprises to receive transcribed messages of the conversation between the agent and the user in real time”. As drafted and under its broadest reasonable interpretation, this limitation recites receiving data, which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The claim reciting the additional elements of mere data gathering do not integrate the judicial exception into practical application. The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application.
The claim reciting the additional elements of “receiving” amount to receiving input information. The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claim 21, which depends upon claim 1 (and thus the rejection of claim 1 is incorporated), recite(s) in part:
“evaluate the at least one interaction of the agent with the at least one graphical user interface element of the agent application that displays the at least one suggestion through evaluation of one or more of cursor position, time focusing on each graphical user interface element, cursor events, mouse over, one or more clicks to open articles, copying and pasting of content, or selecting or highlighting text.” As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of a mental process because evaluating interactions such as various cursor positions and activity, or time spent on various activities, encompasses mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. Even if most humans would use a physical aid, like a pen and paper, to make such evaluations, the use of a physical aid would not negate the mental nature of this limitation. See MPEP 2106.04(a)(2), subsection III.B. For example, a human could reasonably evaluate how long an interaction takes place or various details relating to the interaction, such as making note of clicks and positions and etc.
The claim does not integrate the judicial exception into practical application.
The claim limitations do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim is ineligible.
Claims 9-16 are substantially similar to claims 1-8, and thus are rejected on the same basis as claims 1-8.
Claims 17, 18, 19, and 20 are substantially similar to claims 1, 2, 6, and 8 respectively and thus are rejected on the same basis as claims 1, 2, 6, and 8 respectively.
Claims 22 and 23 are substantially similar to claim 21 and thus are rejected on the same basis as claim 21.
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.
Claims 1-3, 6, 8-11, 14, 16-23 are rejected under 35 U.S.C. 103 as being unpatentable over Clodore et al. (WO 2021/173611 A1), as cited in the IDS dated 06/26/2024, hereafter Clodore, in view of Zhakov et al. (US 2017/0272574 A1), hereafter Zhakov, in further view of Wasulkar et al. (US 2023/0269330 A1), hereafter Wasulkar.
Regarding claim 1, Clodore discloses:
A system for providing implicit feedback using multi-factor behavior monitoring, the system comprising: a computing system comprising at least one first processor and at least one first memory having a first plurality of instructions stored thereon that, in response to execution by the at least one first processor, causes the computing system to (Clodore, Fig. 15, Fig. 1, and ¶[0125]),
receive a transcript of a conversation between an agent and a user (Clodore, Fig. 4 and ¶[0076] teaches receiving conversation transcripts between agents and clients as a transcript of a conversation between an agent and a user),
provide at least one suggestion to the agent via an agent application based on the transcript of the conversation between the agent and the user (Clodore, Fig. 4, ¶[0076] and ¶[0089] teaches providing recommendations as suggestions to the agent via an agent application, i.e. agent training system, based on the transcript of the conversation between the agent and the user),
evaluate data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning (Clodore, ¶[0090]) teaches evaluating data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning),
update … based on the evaluation of the data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning (Clodore, Fig. 5 and ¶[0094] teaches generating predictions as updates based on the evaluation of the data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning).
While Clodore teaches update … based on the evaluation of the data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning, they do not explicitly teach updating a knowledge base model.
Zhakov teaches:
update a knowledge base model (Fig. 3 element 110 and ¶[0049] teaches updating a knowledge base model).
Clodore and Zhakov are analogous art because they are from the same field of endeavor, contact centers and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Clodore to include updating a knowledge base mode, based on the teachings of Zhakov. One of ordinary skill in the art would have been motivated to make this modification in order to leverage acquired knowledge to optimize performance and solve problems, as suggested by Zhakov (¶[0035]).
Clodore, in view of Zhakov, does not disclose evaluation of at least one interaction of the agent with at least one graphical user interface element of the agent application that displays the at least one suggestion to determine a usage score for the corresponding suggestion.
Wasulkar discloses:
evaluation of at least one interaction of the agent with at least one graphical user interface element of the agent application that displays the at least one suggestion to determine a usage score for the corresponding suggestion (Wasulkar, Figure 5, ¶[0010-0013] discloses displaying at least one suggestion, in elements 520 and 540, to determine a usage score, i.e. ASSI score, for the corresponding suggestion).
Clodore, Zhakov, and Wasulkar are analogous art because they are from the same field of endeavor, contact centers and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Clodore, in view of Zhakov, in further view of Wasulkar, to include evaluation of at least one interaction of the agent with at least one graphical user interface element of the agent application that displays the at least one suggestion to determine a usage score for the corresponding suggestion, based on the teachings of Wasulkar. One of ordinary skill in the art would have been motivated to make this modification in order to improve coaching of agents, as suggested by Wasulkar (¶[0070]).
Regarding claim 2, Clodore, in view of Zhakov, in further view of Wasulkar, discloses the system of claim 1 (and thus the rejection of claim 1 is incorporated). Clodore further discloses:
wherein the first plurality of instructions further causes the computing system to analyze the transcript of the conversation between the agent and the user to find suggestion content related to the transcript (Clodore, Fig. 4, ¶[0086] and ¶[0089] teaches analyzing transcripts to find suggestion content related to the transcript),
wherein to provide the at least one suggestion to the agent comprises to provide at least one suggestion to the agent that references the suggestion content related to the transcript (Clodore, Fig. 4 and ¶[0089] teaches providing recommendations to the agent that references the suggestion content related to the transcript).
Regarding claim 3, Clodore, in view of Zhakov, in further view of Wasulkar, discloses the system of claim 2 (and thus the rejection of claim 2 is incorporated). Clodore further discloses:
an agent device having a display, at least one second processor, and at least one second memory having a second plurality of instructions stored thereon that, in response to execution by the at least one second processor, causes the agent device to execute the agent application to present the at least one suggestion to the agent on the display via a graphical user interface (Clodore, Fig. 1, Fig. 2, ¶[0027] and ¶[0169] teaches multiple devices, including agent terminal devices and presenting suggestions to the agent via the device displays).
Regarding claim 6, Clodore, in view of Zhakov, in further view of Wasulkar, discloses the system of claim 1 (and thus the rejection of claim 1 is incorporated). Clodore further discloses:
wherein evaluating the data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning comprises evaluating times at which the at least one suggestion were displayed and times of the agent interactions with the agent application (Clodore, ¶[0041] teaches evaluating times at which the at least one suggestion were displayed and times of the agent interactions with the agent application by analyzing communication sessions or transcripts to check whether the agent has followed the suggestion).
Regarding claim 8, Clodore, in view of Zhakov, in further view of Wasulkar, discloses the system of claim 1 (and thus the rejection of claim 1 is incorporated). Clodore further discloses:
wherein to receive the transcript of the conversation between the agent and the user comprises to receive transcribed messages of the conversation between the agent and the user in real time (Clodore, ¶[0041] and ¶[0076] teaches receiving transcribed messages of the conversation between the agent and the user in real time).
Regarding claim 21, Clodore, in view of Zhakov, in further view of Wasulkar, discloses the system of claim 1 (and thus the rejection of claim 1 is incorporated). Clodore further discloses:
evaluate the at least one interaction of the agent with the at least one graphical user interface element of the agent application that displays the at least one suggestion through evaluation of one or more of cursor position, time focusing on each graphical user interface element, cursor events, mouse over, one or more clicks to open articles, copying and pasting of content, or selecting or highlighting text (Clodore, ¶[0108] discloses evaluation of cursor events).
Claims 9, 10, 11, 14, 16, and 22 are substantially similar to claims 1, 2, 3, 6, 8, and 21 respectively, and thus are rejected on the same basis as claims 1, 2, 3, 6, 8, and 21.
Claims 17, 18, 19, 20, and 23 are substantially similar to claims 1, 2, 6, 8, and 21 respectively and thus are rejected on the same basis as claims 1, 2, 6, 8, and 21 respectively.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Clodore et al. (WO 2021/173611 A1), as cited in the IDS dated 06/26/2024, hereafter Clodore, in view of Zhakov et al. (US 2017/0272574 A1), hereafter Zhakov, in further view of Wasulkar et al. (US 2023/0269330 A1), hereafter Wasulkar, in further view of Kirk et al. (US 2017/0168690 A1), hereafter Kirk.
Regarding claim 7, Clodore, in view of Zhakov, in further view of Wasulkar, discloses the system of claim 1 (and thus the rejection of claim 1 is incorporated).
While Clodore discloses updating the knowledge base model based on the evaluation of the data indicative of behaviors of the agents with respect to the at least one suggestion using machine learning in claim 1, and Clodore, in view of Zhakov, in further view of Wasulkar, discloses usage scores for suggestions, they do not disclose ranking each of the at least one suggestion as a function of the corresponding usage score.
Kirk discloses:
ranking each of the at least one suggestion as a function of the corresponding usage score (Kirk, ¶[0051] teaches ranking suggestions as a function of corresponding usage, i.e. recommendation, score).
Clodore, Zhakov, Wasulkar, and Kirk are analogous art because they are from the same field of endeavor, customer interaction and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Clodore, in view of Zhakov, in further view of Wasulkar, to include ranking each of the at least one suggestion as a function of the corresponding usage score, based on the teachings of Kirk. One of ordinary skill in the art would have been motivated to make this modification in order to provide a holistic customer experience by leveraging real-time customer care collaboration and big data analytics, as suggested by Kirk (¶[0021]).
Claim 15 is substantially similar to claim 7 and thus is rejected on the same basis as claim 7.
Response to Arguments
Applicant's arguments filed 01/26/2026 have been fully considered with regards to the 35 U.S.C. 101 rejection, but they are not persuasive.
The applicant asserts on pages 10-11 of the remarks that the claims recite machine learning and thus cannot be directed to a mental evaluation. The Examiner respectfully disagrees, as machine learning is recited generically and thus, is at best directed to additional elements which amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
The applicant asserts on page 13 of the remarks “claim 1 expressly recites specific details on how the system evaluates data indicative of behaviors of the agent with respect to the at least one suggestion using machine learning, to enable updates to the knowledge base model. Specifically, claim 1 recites that the system performs an evaluation of at least one interaction of the agent with at least one graphical user interface element of the agent application that displays the at least one suggestion to determine a usage score for the corresponding suggestion”. The MPEP 2106.04(d)(1) discloses the evaluation of claimed improvements in the functioning of a computer or improvement to a technical field in step 2A prong two. The MPEP section discloses — "if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification ... ". The claim does not adequately reflect an improvement as there are no steps detailing how this usage score is determined, how machine learning is used, or any other steps required to reflect a disclosed improvement. Furthermore, page 14 of the remarks recite a passage from the specification to set forth an improvement, which discloses improvement in a conclusory manner, without the detail necessary to be apparent to a person of ordinary skill in the art. Thus, the claims do integrate the abstract idea into a practical application.
The applicant asserts on page 15 of the remarks “The prior art of record does not teach or suggest each of the features recited in any of the pending claims, as described in more detail with reference to the rejections under § 103. The Applicant further submits that the amended features of the claims likewise recite "additional features" that are not taught by the prior art of record when each claim is interpreted as a whole. The Applicant respectfully submits that at least those features not taught, when read in view of the claim as a whole, constitute significantly more than the alleged abstract idea.”. The Examiner respectfully disagrees, as Step 2B does not look to whether prior art teaches the claim, but rather whether the claim includes additional elements that are sufficient to amount to significantly more that the judicial exception. In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed in the 101 rejection above, the claim limitations reciting generic computer elements amounts to no more than mere instructions to apply the exception using a generic computer. The claim reciting the additional elements of “display[ing]” amount to outputting information. The claim reciting the additional elements of “receiving” amount to receiving information.
Applicant's arguments filed 01/26/2026 have been fully considered with regards to the 35 U.S.C. 102/103 rejection.
Applicant’s arguments with respect to claim(s) 1-3,6-11 and 14-23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
U.S. Pub No. 20200394590 A1: Kaimal et al. teaches machine learning, contact centers and ranking.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/H.Z.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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