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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 12/23/2025 has been entered.
Status of the Claims
Claims 1, 10 and 16 have been amended, claim 23 have been canceled. Claims 1-8, 10-22 are pending.
Claim Rejections - 35 USC § 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.
Claims 1-6, 7-13, 15-19, 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Konig et al. (US 20210203784 in view of Shipper et al. (US 20180011678) and in further view of Hardebeck et al. (US 20210029247).
Regarding claim 1, Konig teaches a method of self-learning actions for an automated co-browse session, the method comprising:
initiating an interaction between a user and a chat bot ([0072], [0078], [0108]);
determining a user intent of the user based on the interaction between the user and the chat bot ([0094]);
routing the interaction to a human contact center agent for a co-browse session ([0072], [0108]) of a webpage to enable parallel ([0113] “established in parallel to the primary communication”) interaction with the webpage ([0044] “customers to initiate, manage, and respond to … web-browsing sessions, and other multi-media transactions”, [0053] “media interactions may be … chat, video, text-messaging, web, social media, co-browsing”, [0057]-[0058] “Customers may browse the web pages and get information about the enterprise's products and services”; “contact center via … web chat”, [0125] “interaction by storing and indexing any messages, documents, files, and other media involved or shared during in the interaction … an image that was shared and annotated by an agent when explaining how to set up a particular piece of equipment”) between the user and the human contact center agent ([0052], [0082], [0108], [0112]);
storing a plurality of actions performed by the human contact center agent ([0060]), including a
performing machine learning to determine an optimal solution ([0195], [0213], [0218], [0264]) for resolving the user intent based on an analysis of the plurality of actions performed by the human contact center agent ([0114], [0216]),
Konig does not explicitly teach, however Shipper discloses storing a plurality of actions performed by the human contact center agent ([0064] “store and reference historical data about previously highlighted UI elements… .may track historical data based on the same section of the user interface where previously highlighted UI elements have assisted other customers, or based on a customer identifier where previously highlighted UI elements have assisted the specific customer”), including a set of document object model elements of the webpage ([0064] “select all DOM tree elements”, [0065], [0089], [0092]) and web actions performed by the human contact center agent on one or more of the document object model elements ([0068], [0072], [0083]), during the co-browse session ([0067] “facilitate collaborative application usage such as full collaborative web browsing or co-browsing, or screen sharing with the customer service representative”) to a data store ([0071] “server receives the collection of UI elements in a JSON object”).
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 Konig to include document object model elements tracked during the co-browse session as disclosed by Shipper. Doing so would help improve customer service performance metrics such as lowering average handle time (Shipper [0042]).
Although Konig teaches using machine learning to interactions between users and customer support and Shipper discloses using DOM to collect such interactions, Konig as modified by Shipper does not explicitly teach, however Hardebeck discloses performing machine learning to determine an optimal solution for resolving the user intent based on an analysis of the plurality of actions performed by the human contact center agent, including the set of document object model elements of the webpage and the web actions performed by the human contact center agent on the one or more of the document object model elements, during the co-browse session ([0095] “the co-browsing system receives DOM updates from each of the website visitors on visualization sessions and provides that information to the machine learning algorithm”).
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 Konig to include document object model elements tracked during the co-browse session as disclosed by Hardebeck. Doing so enables the machine learning algorithm to know exactly what the customers are seeing as they interact with the website and what remedy is required to fix the issue (Hardebeck [0095]).
Claims 10 and 16 recite substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claims 2, 11 and 17, Konig as modified teaches the method, the system and the media, further comprising:
generating an intent configuration file for the optimal solution based on the machine learning (Konig [0095], [0127], [0241]), wherein the intent configuration file defines a sequence of actions to be executed by the chat bot (Konig [0130], [0133], [0135], [0138], [0148], [0160]) in an automated co-browse session between the chat bot and another user to resolve the user intent (Konig [0097], [0108], [0111]-[0112]); and storing the intent configuration file in association with the user intent (Konig [0108], [0125]-[0126]).
Regarding claims 3, 12 and 18, Konig as modified teaches the method, the system and the media, further comprising determining a confidence score indicative of a confidence of the system in the optimal solution based on the machine learning (Konig [0082], [0203[-[0204]); and
wherein generating the intent configuration file for the optimal solution comprises generating the intent configuration file for the optimal solution in response to determining that the confidence score exceeds a threshold confidence level (Konig [0082], [0189], [0212], [0228]).
Regarding claim 4, Konig as modified teaches the method of claim 2, wherein the sequence of actions comprises one or more actions of the plurality of actions performed by the human contact center agent during the co- browse session (Konig [0108], [0235], [0290], Hardebeck [0095], Shipper [0042).
Regarding claims 5, 13 and 19, Konig as modified teaches the method, the system and the media, wherein performing the machine learning comprises analyzing a plurality of sequences of actions performed by human contact center agents during respective co-browse sessions to resolve the user intent (Konig [0096], [0121], [0108], [0235], [0290]).
Regarding claims 7, 15 and 21, Konig as modified teaches the method, the system and the media, wherein the optimal solution is selected from the plurality of sequences of actions performed by the human contact center agents during the corresponding co-browse sessions to resolve the user intent (Konig [0096], [0121], [0108], [0235], [0290], Hardebeck [0042]).
Regarding claim 8, Konig as modified the method of claim 1, wherein the plurality of actions comprises at least one of a mouse movement, mouse interaction, screen pointer, screen change, audio instruction, video instruction, or text entry (Konig [0086], Shipper [0010]).
Regarding claim 9, Konig as modified teaches the method of claim 1, wherein the plurality of actions comprises a plurality of web actions involving interactions with one or more web pages (Konig [0086], Shipper [0068], [0072], [0083]).
Regarding claim 22, Konig as modified teaches the one or more non-transitory machine readable storage media of claim 16, wherein to route the interaction to the human contact center agent for a co-browse session between the user and the human contact center agent comprises to route the interaction to the human contact center agent in response to a determination that the chat bot is unable to resolve the user intent of the user ([0060], Konig [0039] “target the use of human agents for the more difficult customer interactions”, [0052], [0082], [0206], [0208]-[0209]).
NOTE as previously cited Shlomov likewise discloses claim 22 in [0041] (“include an escalated portion that occurs because an automated agent was unable to satisfy and/or understand the human so that the conversation was escalated to a human agent”) and further obviates the teaching of Konig.
Claims 6, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Konig as modified and in further view of Munavalli (US 20210203784).
Regarding claims 6, 14 and 20, Konig as modified does not explicitly teach, however Munavalli discloses the method, the system and the media, wherein analyzing the plurality of sequences of actions performed by the human contact center agents during the corresponding co-browse sessions to resolve the user intent ([0019]) comprises applying a Q-learning reinforcement algorithm ([0022], [0039]) to the plurality of sequences of actions performed by the human contact center agents during the corresponding co-browse sessions to resolve the user intent ([0023]-[0024], [0026]-[0027]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Konig as modified to include Q-learning reinforcement algorithm as disclosed by Munavalli. Doing so would improve the efficiency and efficacy of the chatbot system through the reinforcement learning framework enabling the machine learning model to adapt to changes, such as changes in the manner in which users interact with the chatbot system (Munavalli [0028]).
Claims 2-3, 11-12, 17-18 is/are alternatively or additionally rejected under 35 U.S.C. 103 as being unpatentable over Konig as modified in view of SINGH et al. (US 20210158146).
Regarding claims 2, 11 and 17, SINGH further discloses generating an intent configuration file for the optimal solution based on the machine learning, wherein the intent configuration file defines a sequence of actions ([0012], [0018]-[0019]) to be executed by the chat bot in an automated co-browse session between the chat bot and another user to resolve the user intent; and storing the intent configuration file in association with the user intent ([0011], [0013]-[0014]).
Regarding claims 3, 12 and 18, SINGH further discloses media, further comprising determining a confidence score indicative of a confidence of the system in the optimal solution based on the machine learning ([0017]-[0018]); and wherein generating the intent configuration file for the optimal solution comprises generating the intent configuration file for the optimal solution in response to determining that the confidence score exceeds a threshold confidence level ([0071], [0033], [0021]).
Response to Arguments
Applicant's arguments, filed 12/23/2025, in regard to the presently amended claims have been fully considered and are addressed in the updated rejections to the claims above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 March 8, 2026