DETAILED ACTION
This Final Office Action is in response to the application filed on 12/12/2024 and the Amendment & Remark filed on 04/15/2026.
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 § 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 1-20 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.
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc).
While the Applicant specifies in claims 1, 9 and 17 that “train, using historical claim data, one or more predictive machine learning models for implementing a personalized and automated negotiation process with individual users, including performance of one or more escalation actions to complete the automated negotiation process, the training being based on historical claim data, settlement offers, and payout amounts for claims events of the historical claim data”, there is no written content as to how or what specific training procedures are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to train a predictive machine learning model for automated settlement negotiation. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 2, 10 and 18 that “wherein the predictive machine learning model processes the claim file of the user based on the historical claim data to generate a set of settlement amounts and a personalized negotiation strategy for the user”, there is no written content as to how or what specific predictive machine learning model are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to generate a set of settlement amounts and personalized negotiation strategy. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 4, 12 and 20 that “perform vocal sentiment analysis on the user's voice during the automated negotiation process to dynamically determine a receptiveness level of the user to a currently offered settlement amount”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to dynamically determine a receptiveness level of the user. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 5 and 13 that “wherein the voice-AI engine alters the personalized negotiation strategy based on the dynamically determined receptiveness level of the user to a currently offered settlement amount”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to alter the personalized negotiation strategy based on the receptiveness level of the user. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
The written description requirement can be satisfied if the particular steps, i.e., algorithm, necessary to perform the claimed function were “described in the specification.” In re Hayes Microcomputer Prods, Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, (Fed. Cir. 1992).
As such, claims 1-20 are rejected as failing the written description requirement.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As an initial matter, the claims as a whole are to an apparatus, a process and a manufacture, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
Claim 1 recites:
A computing system comprising:
a network communication interface;
one or more processors; and
a memory storing instructions that, when executed by the one or more processors, cause the computing system to:
train, using historical claim data, one or more predictive machine learning models for implementing a personalized and automated negotiation process with individual users, including performance of one or more escalation actions to complete the automated negotiation process, the training being based on historical claim data, settlement offers, and payout amounts for claims events of the historical claim data;
process claim data that identifies information for a claim event affecting a user;
during a call session with the user, initiate a voice-AI engine that includes an artificial intelligence negotiator to implement an automated negotiation process with the user, including performance of one or more escalation actions to complete the automated negotiation process, the artificial intelligence negotiator using the one or more trained machine learning models to personalize the automated negotiation process for the user in real-time, based at least in part on information identified for the claim event affecting the user.
Claim 9 recites:
A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to:
train, using historical claim data, one or more predictive machine learning models for implementing a personalized and automated negotiation process with individual users, including performance of one or more escalation actions to complete the automated negotiation process, the training being based on historical claim data, settlement offers, and payout amounts for claims events of the historical claim data;
process claim data that identifies information for a claim event affecting a user;
during a call session with the user, initiate a voice-AI engine that includes an artificial intelligence negotiator to implement an automated negotiation process with the user, including performance of one or more escalation actions to complete the automated negotiation process, the artificial intelligence negotiator using the one or more trained machine learning models to personalize the automated negotiation process for the user in real-time, based at least in part on information identified for the claim event affecting the user.
Claim 17 recites:
A computer-implemented method performed by one or more processors, comprising:
train, using historical claim data, one or more predictive machine learning models for implementing a personalized and automated negotiation process with individual users, including performance of one or more escalation actions to complete the automated negotiation process, the training being based on historical claim data, settlement offers, and payout amounts for claims events of the historical claim data;
process claim data that identifies information for a claim event affecting a user;
during a call session with the user, initiate a voice-AI engine that includes an artificial intelligence negotiator to implement an automated negotiation process with the user, including performance of one or more escalation actions to complete the automated negotiation process, the artificial intelligence negotiator using the one or more trained machine learning models to personalize the automated negotiation process for the user in real-time, based at least in part on information identified for the claim event affecting the user.
Claims 2, 10 and 18 recites:
wherein the historical claim data comprises details of a number of claims and their final settlement amount, and wherein the predictive machine learning model processes the claim file of the user based on the historical claim data to generate a set of settlement amounts and a personalized negotiation strategy for the user.
Claims 3, 11 and 19 recites:
wherein the automated negotiation process includes multiple call sessions with the user in which the voice-AI engine utilizes the set of settlement amounts and the personalized negotiation strategy to communicate with the user.
Claims 4, 12 and 20 recites:
perform vocal sentiment analysis on the user's voice during the automated negotiation process to dynamically determine a receptiveness level of the user to a currently offered settlement amount.
Claims 5 and 13 recites:
wherein the voice-AI engine alters the personalized negotiation strategy based on the dynamically determined receptiveness level of the user to a currently offered settlement amount.
Claims 6 and 14 recites:
wherein the claim event corresponds to at least one of a vehicle incident, damage to property of the user, or an injury to the user.
Claims 7, and 15 recites:
wherein the executed instructions perform the one or more escalation actions by offering successive and increasing settlement amounts until the user either accepts a particular settlement amount or the personalized negotiation strategy reaches a threshold settlement amount.
Claims 8, and 16 recites:
wherein the executed instructions cause the voice-AI engine to perform an escalation action that includes escalating the automated negotiation process to a human representative when the threshold settlement amount is reached.
Based on the limitations above, the claims describe a process that covers negotiating insurance settlement. Negotiating insurance settlement is considered to be a commercial interaction, which falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas. As such, the claim(s) recite(s) a Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “processor” as a mere tool to perform the … steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “train, using historical claim data, one or more predictive machine learning models for implementing a personalized and automated negotiation process with individual users, including performance of one or more escalation actions to complete the automated negotiation process, the training being based on historical claim data, settlement offers, and payout amounts for claims events of the historical claim data” encompasses no more than generically invoking a processor to apply the Judicial Exception step of using historical claim data to train negotiator model;
the limitation “process claim data that identifies information for a claim event affecting a user” encompasses no more than generically invoking a processor to apply the Judicial Exception step of processing claim data for a claim event;
the limitation “during a call session with the user, initiate a voice-AI engine that includes an artificial intelligence negotiator to implement an automated negotiation process with the user, including performance of one or more escalation actions to complete the automated negotiation process, the artificial intelligence negotiator using the one or more trained machine learning models to personalize the automated negotiation process for the user in real-time, based at least in part on information identified for the claim event affecting the user” encompasses no more than generically invoking a processor to apply the Judicial Exception step of initiating the negotiator to perform negotiation process including escalation actions to complete the negotiation process;
the limitation “wherein the historical claim data comprises details of a number of claims and their final settlement amount, and wherein the predictive machine learning model processes the claim file of the user based on the historical claim data to generate a set of settlement amounts and a personalized negotiation strategy for the user” encompasses no more than generically invoking a processor to apply the Judicial Exception step of processing the claim file of the user based on historical claim data to generate a set of settlement amounts and a personalized negotiation strategy for the user;
the limitation “wherein the automated negotiation process includes multiple call sessions with the user in which the voice-AI engine utilizes the set of settlement amounts and the personalized negotiation strategy to communicate with the user” encompasses no more than generically invoking a processor to apply the Judicial Exception step of conducting multiple call sessions with the user using the settlement amounts and personalized negotiation strategy;
the limitation “perform vocal sentiment analysis on the user's voice during the automated negotiation process to dynamically determine a receptiveness level of the user to a currently offered settlement amount” encompasses no more than generically invoking a processor to apply the Judicial Exception step of analyzing the user’s voice during the call session to determine a receptiveness level of the user to a offered settlement amount;
the limitation “wherein the voice-AI engine alters the personalized negotiation strategy based on the dynamically determined receptiveness level of the user to a currently offered settlement amount” encompasses no more than generically invoking a processor to apply the Judicial Exception step of dynamically altering the personalized negotiation as the receptiveness level of the user changes to a offered settlement amount;
the limitation “wherein the executed instructions perform the one or more escalation actions by offering successive and increasing settlement amounts until the user either accepts a particular settlement amount or the personalized negotiation strategy reaches a threshold settlement amount” encompasses no more than generically invoking a processor to apply the Judicial Exception step of escalating settlement amount until the user either accepts a particular settlement amount or reaches a threshold settlement amount;
the limitation “wherein the executed instructions cause the voice-AI engine to perform an escalation action that includes escalating the automated negotiation process to a human representative when the threshold settlement amount is reached” encompasses no more than generically invoking a processor to apply the Judicial Exception step of escalating the personalized negotiation strategy to another representative when the threshold settlement amount is reached.
Other than being generally linked to the steps of the Judicial Exception, the additional elements in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically.
The additional element(s) of “memory” and/or “non-transitory storage medium” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “network communication interface” are generically recited to perform communication steps such as receiving and transmitting.
The additional element(s) of “Voice-AI engine” and “artificial intelligence negotiator” are generically recited to perform negotiation steps described only by a result-oriented solution with insufficient detail for how the Voice-AI engine accomplish it.
The additional element(s) of “predictive machine learning model” are generically recited to perform steps of generating settlement amount and strategy described only by a result-oriented solution with insufficient detail for how the model accomplish it.
The examiner further noted generic computer affixes such as “automated” or “artificial intelligence” are appended to abstract elements such as “settlement negotiation” and “negotiator”, but found that to be mere instructions to implement the Judicial Exception idea on a computer.
Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I). Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
The claim 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 element of using a processor and generic machine learning model to facilitate claim settlement negotiation to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Dependent claim 6 and 14 merely limit the abstract idea but do not recite any additional element beyond the cited abstract idea, thus, do not amount to significantly more. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-7, 9-15 and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fields et al. (US 20240291778)
As per claim 1, Fields discloses a system comprising:
train, using historical claim data, one or more predictive machine learning models for implementing a personalized and automated negotiation process with individual users, including performance of one or more escalation actions to complete the automated negotiation process, the training being based on historical claim data, settlement offers, and payout amounts for claims events of the historical claim data; (See Fields Paragraph 0016, 0027, 0034, 0039, 0077 and 0082)
process claim data that identifies information for a claim event affecting a user; (See Fields Paragraph 0027, 0060 and 0075)
during a call session with the user, initiate a voice-AI engine that includes an artificial intelligence negotiator to implement an automated negotiation process with the user, including performance of one or more escalation actions to complete the automated negotiation process, the artificial intelligence negotiator using the one or more trained machine learning models to personalize the automated negotiation process for the user in real-time, based at least in part on information identified for the claim event affecting the user. (See Fields Paragraph 0016, 0027, 0034, 0039, 0077 and 0082)
As per claim 9, Fields discloses:
A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to:
train, using historical claim data, one or more predictive machine learning models for implementing a personalized and automated negotiation process with individual users, including performance of one or more escalation actions to complete the automated negotiation process, the training being based on historical claim data, settlement offers, and payout amounts for claims events of the historical claim data; (See Fields Paragraph 0016, 0027, 0034, 0039, 0077 and 0082)
process claim data that identifies information for a claim event affecting a user; (See Fields Paragraph 0027, 0060 and 0075)
during a call session with the user, initiate a voice-AI engine that includes an artificial intelligence negotiator to implement an automated negotiation process with the user, including performance of one or more escalation actions to complete the automated negotiation process, the artificial intelligence negotiator using the one or more trained machine learning models to personalize the automated negotiation process for the user in real-time, based at least in part on information identified for the claim event affecting the user. (See Fields Paragraph 0016, 0027, 0034, 0039, 0077 and 0082)
As per claim 17, Fields discloses:
A computer-implemented method performed by one or more processors, comprising:
train, using historical claim data, one or more predictive machine learning models for implementing a personalized and automated negotiation process with individual users, including performance of one or more escalation actions to complete the automated negotiation process, the training being based on historical claim data, settlement offers, and payout amounts for claims events of the historical claim data; (See Fields Paragraph 0016, 0027, 0034, 0039, 0077 and 0082)
process claim data that identifies information for a claim event affecting a user; (See Fields Paragraph 0027, 0060 and 0075)
during a call session with the user, initiate a voice-AI engine that includes an artificial intelligence negotiator to implement an automated negotiation process with the user, including performance of one or more escalation actions to complete the automated negotiation process, the artificial intelligence negotiator using the one or more trained machine learning models to personalize the automated negotiation process for the user in real-time, based at least in part on information identified for the claim event affecting the user. (See Fields Paragraph 0016, 0027, 0034, 0039, 0077 and 0082)
As per claims 2, 10 and 18, Fields discloses:
wherein the automated negotiation process includes multiple call sessions with the user in which the voice-AI engine utilizes the set of settlement amounts and the personalized negotiation strategy to communicate with the user. (See Fields Paragraph 0026-0027)
As per claims 3, 11 and 19, Fields discloses:
wherein the automated negotiation process involves multiple voice-AI call sessions with the user in which the voice-AI engine utilizes the set of settlement amounts and the personalized negotiation strategy to communicate with the user. (See Fields Paragraph 0026, 0058 and 0062)
As per claims 4, 12 and 20, Fields discloses:
perform vocal sentiment analysis on the user's voice during the automated negotiation process to dynamically determine a receptiveness level of the user to a currently offered settlement amount. (See Fields Paragraph 0060 and 0077-0078)
As per claims 5 and 13, Fields discloses:
wherein the voice-AI engine alters the personalized negotiation strategy based on the dynamically determined receptiveness level of the user to a currently offered settlement amount. (See Fields Paragraph 0060 and 0077-0078)
As per claims 6 and 14, Fields discloses:
wherein the claim event corresponds to at least one of a vehicle incident, damage to property of the user, or an injury to the user. (See Fields Paragraph 0026-0027)
As per claims 7 and 15, Fields discloses:
wherein the executed instructions perform the one or more escalation actions by offering successive and increasing settlement amounts until the user either accepts a particular settlement amount or the personalized negotiation strategy reaches a threshold settlement amount. (See Fields Paragraph 0090-0094)
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.
Claim(s) 4, 12 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fields et al. (US 20240291778) in view of Chan et al. (US 11593685)
As per claims 4, 12 and 20, Fields teaches:
perform analysis on the user during the automated negotiation process to dynamically determine a receptiveness level of the user to a currently offered settlement amount. (See Fields Paragraph 0060 and 0077-0078, accept or counter)
Fields does teach performing vocal sentiment analysis on the user's voice.
However, Chan teaches performing voice sentiment analysis on the user’s voice during the automated negotiation process. (See Chan Col. 19 Line 23-46)
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the insurance settlement negotiation system taught by Fields with teaching from Chan to perform voice sentiment analysis on a user's voice during negotiation. One of ordinary skill in the art would have been motivated as voice sentiment analysis may reveal valuable indicators, such as whether a counterparty is closed to an agreement.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fields et al. (US 20240291778) in view of Official Notice.
As per claims 8 and 16, Fields does not disclose:
wherein the executed instructions cause the voice-AI engine to escalate the personalized negotiation strategy to a human representative when the threshold settlement amount is reached.
However, Official Notice is taken that it is a common practice in the field of customer service to escalate or direct communication to a human representation when a predetermined threshold for automated communication is reached.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the insurance settlement negotiation system taught by Fields with a common practice in the customer service industry to escalate a case to a human representative when a pre-determined threshold designated for an automated system is reached. One of ordinary skill in the art would have been motivated as a human representative may have higher authorization than the automated system, allowing better customer experience.
Response to Arguments
Applicant's arguments filed 04/15/2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that Specification paragraph 0174-0176 provide adequate support the features rejected under 35 USC 112(a), the examiner respectfully disagrees. Paragraphs 0174-0176 only disclose highly generalized and result-only disclosure such as “the computing system 100 can use a corpus of historical claim data to train a predictive machine-learning model on claim information …”, “can initiate an artificial intelligence negotiator using the claim corpus of a user 194 to perform an automated negotiation process using the voice-AI engine”, “the voice-AI engine 130 can be implemented as an artificial intelligence negotiator, and can determine or calculate one or more settlement offers for the user 194 using user-specific information (e.g., in the claim corpus)”, “voice-AI engine 130 can optionally perform vocal sentiment analysis on the user 194 (1520). For example, the sentiment analysis can be performed to determine whether the user 194 is open to accepting the settlement offer or if the user 194 is likely to reject the settlement offer”, “implementing an artificial intelligence negotiator, determines that the user's voice and manner during a voice-AI call session indicates receptiveness to a current settlement offer, the voice-AI engine 130 can alter the negotiation strategy (e.g., to maintain the current offer)”. Indeed, other than nominally stating AI/ML “can perform” the desired functions, no description can be found on how the AI/ML models technologically perform achieve it. Thus, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
Regarding the applicant’s argument the additional element of training AI/ML models to perform steps the Judicial Exception of negotiating insurance settlement integrate the Judicial Exception into practical application, the examiner respectfully disagrees. The examiner noted that the mere inclusion of machine learning model usage / training does not render an otherwise abstract claim patent eligible under 101. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit held that "patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101." 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025). The court specifically rejected the argument that requiring iterative training of a machine learning model creates patent eligibility, noting that "[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning." Id. at 12. As the patentee in Recentive conceded, "'using a machine learning technique … necessarily includes [an] iterative training step.'" Id. The court further explained that "the requirements that the machine learning model be 'iteratively trained' or dynamically adjusted . . . do not represent a technological improvement" because these features are inherent to the applying of machine learning technology itself. Id. Accordingly, the claimed invention here, which similarly applies conventional machine learning technique[s] of machine learning to automate insurance negotiation, fails to recite patent-eligible subject matter under 101.
Regarding the argument that Fields does not disclose “the one or more trained machine learning models to personalize the automated negotiation process for the user in real time” and “performance of one or more escalation actions to complete the automated negotiation process”, the examiner respectfully disagrees. Fields paragraph 0077 explicitly recites “the server 105 may use an ML chatbot 152 trained (e.g., via ML module 140 and/or MLTM 142) to determine conversation styles based on outputs from the NLP module 148. The same, or different, ML chatbot 152 may subsequently generate a plurality of counter terms based on the conversation style”. ML Chatbot holding chat-based negotiation with conversations styles discloses “the one or more trained machine learning models to personalize the automated negotiation process for the user in real time”. Fields paragraph 0082 explicitly recites “The exchange of terms via output(s) 244 and input(s) 242 may be characterized as a negotiation. The negotiation between the first contracting party 240 and the server 105 on behalf of the second contracting party 220 may continue for any number of cycles, wherein the server 105 may receive input(s) 242 indicating prospective terms and may send output(s) 244 indicating counter terms until a set of terms (e.g., prospective terms, counter terms) acceptable to both parties is finalized by a contractual agreement or the negotiation is abandoned by one or both parties”. The exchanges of terms and counter until acceptance or abandonment which discloses “performance of one or more escalation actions to complete the automated negotiation process.
The applicant traversed the Official Notice but is deemed inadequate because the applicant did not point out any supposed errors in the examiner’s action, stating why the noticed fact (escalate or direct communication to a human representation when a predetermined threshold for automated communication is reached) is not considered to be common knowledge or well-known in the art. To adequately traverse a finding based on official notice, an applicant must specifically point out the supposed errors in the examiner’s action, which would include stating why the noticed fact is not considered to be common knowledge or well-known in the art. (See MPEP 2144.03 (c)) The common knowledge or well-known in the art statement is taken to be admitted prior art because applicant either failed to traverse the examiner’s assertion of official notice or that the traverse was inadequate.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MICHAEL W ANDERSON can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693
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