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 .
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 October 28, 2025 has been entered.
Status of Claims
Claims 1-20 have been amended.
Claims 1-20 are currently pending and have been examined.
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. Claims 1, 8 and 15 recite “train a machine learning model using sets of training data to create a trained machine learning model, the sets of training data comprising: a first set of training data comprising a plurality of languages to train the machine learning model to recognize each of the plurality of languages from natural language inputs.” Examiner is unable to find support for this claim. Applicant directs the Examiner to the specification at paragraph [0095] however the broadly stated “linguistically compatible with a detected language” is not the same as disclosing the steps “a first set of training data comprising a plurality of languages to train the machine learning model to recognize each of the plurality of languages from natural language inputs.” The dependent claims do not cure the deficiencies above.
Additionally, Claim 3, 10 and 17 recite “a second chat interface in a second format different than the first format,” Examiner is unable to find support for this claim. There nothing in the specification that address the format of the interface. The supporting claims do not cure the deficiencies.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “an appropriate human support agent” in claim 1, 3, 4, 6-8, 10, 11, 13, 14 15 17, 18 and 20 is a relative term which renders the claim indefinite. See MPEP 2173.05 (b) IV Relative Terminology. The word “appropriate” requires the exercise of subjective judgment without restriction which renders the claim indefinite. Applicant can overcome this rejection by removing the word “appropriate.”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 15-20 are drawn to methods while claim(s) 1-14 is/are drawn to an apparatus. As such, claims 1-20 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
Claim 15 (representative of independent claim(s) 1 and 8 recites the following steps:
training, a learning model using sets of training data to create a trained learning model, the sets of training data comprising a first set of training data comprising a plurality of languages to train the learning model to recognize each of the plurality of languages from natural language inputs;
generating and displaying, a first chat that facilitates a chat communication session between the user and support agent of the financial institution;
generating and displaying, one or more probing questions from the support agent;
receiving, responsive to a service request in a service request in a first natural language input
detecting a language used by the user executing natural language processing of the first natural language input;
executing, human support agent analysis of the service request, using the trained learning model, based on stored human support agent data associated with a plurality oidentify an appropriate human support agent to automatically route the chat communication session, based at least on a linguistic compatibility with the detected language
automatically routing, in response to executing he human support agent analysis, the chat communication session from the identified support agent to the appropriate human support agent.
These steps, under its broadest reasonable interpretation, describe or set-forth delivering financial services by facilitating chat support, which amounts to a “managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)”. These limitations therefore fall within the "certain methods of organizing human activity" subject matter grouping of abstract ideas.
Alternatively, these steps, under its broadest reasonable interpretation, encompass a learning model which is interpreted to be a mathematical relationship. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
Alternatively, these steps, under its broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) engaging with a user, detecting a linguistic compatibility and routing to a qualified agent (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the "mental processes" subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 15 recites an abstract idea (Step 2A - Prong One: YES).
Independent claim(s) 1 and 8 are determined to recite an abstract idea under the same analysis.
Step 2A - Prong Two:
This judicial exception is not integrated into a practical application. The claim(s) recite the additional elements/limitations of:
by one or more financial institution servers of a server computer system associated with a financial institution,
machine learning model
by the one or more financial institution servers on a client device
a first chat interface
a virtual chat communication session
an automated support agent
by the one or more financial institution servers via the first chat interface,
by the one or more financial institution servers via the first chat interface,
executing, by the one or more financial institution servers
A server computer system comprising: one or more processors; and a non-transitory memory coupled to the one or more processors, the non-transitory memory including a set of instructions of computer-executable program code, which when executed by the one or more processors, (Claim 1)
A computer program product comprising at least one non-transitory computer readable medium having with a set of instructions of computer- executable program code, which when executed by one or more processors of a server computer system, (Claim 8)
The requirement to execute the claimed steps/functions listed above is equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. This/these limitation(s) do/does not impose any meaningful limits on producing the abstract idea and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Additionally, “Step 2A - Prong 2”, the recited additional element(s) of "during execution of a software application associated with the financial institution by a client device of a user over a communication network" serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A -Prong Two: NO).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above in "Step 2A - Prong 2", the requirement to execute the claimed steps/functions listed above is equivalent to adding the words "apply it" on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as "significantly more" (see MPEP 2106.05 (f)).
As discussed above in “Step 2A - Prong 2”, the recited additional element(s) of "during execution of a software application associated with the financial institution by a client device of a user over a communication network" serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more5' (see MPEP 2106.05(g, h)).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Regarding Dependent Claims:
Dependent claims 2-7, 9-15 and 16-20 include additional limitations that are part of the abstract idea except for:
Machine learning model
virtual chat communication session
automated support agent
client device
server computer system
The additional elements of the dependent claims are equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
The additional elements of the dependent claims of “displaying, by the one or more financial institution servers, a widget superimposed on the first chat interface,” and “displaying on the client device a second chat interface in a second format different than the first format. the second format comprising a floating action chat icon superimposed on the first chat interface and connects the client device to the identified appropriate human support agent” and “wherein the second chat interface comprises a chat bubble that is superimposed on the first chat interface the chat bubble comprising a drag-and-drop chat bubble” serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more5' (see MPEP 2106.05(g, h)).
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cummins (2019/0087707) in view of Can (2024/0073321) and How Conversation AI Facilitates Smart Routing ([date: July 23, 2021 Retrieved from the Internet from << https://www.ringcentral.com/gb/en/blog/conversational-ai-smart-routing/#:~:text=Conversational%20AI%20can%20help%20with%20smart%20routing,Response%20(IVR)%20*%20Show%20emotion%20and%20accents>> on November 12, 2025 herein referred to as Yan).
Claims 1, 8 and 15
Cummins discloses a server computer system, comprising:
one or more processors (Cummins [0074]); See the ELectronic DEVice (ELDEV) “includes one or more processors 610 and a memory 612 coupled to processor(s) 610.”
a non-transitory memory coupled to the one or more processors, the non-transitory memory including a set of instructions of computer-executable program code, which when executed by the one or more processors, cause the server computer system (Cummins [0011][0070]); See “First and second primary content servers 590A and 590B may also host for example other Internet services such as a search engine, financial services, third party applications and other Internet based services.”
A computer program product comprising at least one non-transitory computer readable medium having with a set of instructions of computer-executable program code, which when executed by one or more processors of server computer system, (Cummins [0011]);
generating and display, on a client device of a user, a first chat interface that facilitates a virtual chat communication session between the user and an automated support agent of a financial institution (Cummin Figure 8B, 824]); See also [0093] where the Artificial Conversational Entity (ACESAP) message is the automated support agent. See also [0010] “providing a conversation interface within a graphical user interface of an electronic device…”
generating and display, via the first chat interface, one or more probing questions from the automated support agent; receive, via the first chat interface, first natural language input by the user responsive to the one or more probing questions (Cummins [0094]); See at least “Accordingly, the ACESAP provides second ACESAP message 826 which is a repeat of second ACESAP output 730 in flow 700 in FIG. 7 indicating the selected product and seeking the user's next input through a directed question.”
train a machine learning model using sets of training data to create a trained machine learning model, the sets of training data comprising: a first set of training data comprising a plurality of languages to train the machine learning model to recognize each of the plurality of languages from natural language inputs; and during execution of a software application by a client device of a user over a communication network: (Cummins [0153]) Which teaches the training of conversation.
Cummin does not explicitly disclose routing based on human support agent datal. Can teaches:
execute human support agent analysis of the service request, using the trained machine learning model, based on stored human support agent data associated with a plurality oSee at least “Automated System Assistance system 122 may, based on an extracted preference of "anger",… the customer call router may route this customer to a more experienced agent or a call agent that has a high satisfaction rating [stored human support agent data].” See also [0134] “Machine-learning engine 302 processes this training set to recognize call agent interactions with similar customers.” Where the learning of past call agent interactions is the stored human support agent data.
automatically route, via the automated support agent in response to executing the human support agent analysis, the virtual chat communication session from the automated support agent to the identified appropriate human support agent (Can [0107]) See at least “Automated System Assistance system 122 may… route this customer to a more experienced agent or a call agent that has a high satisfaction rating [identified appropriate human support agent].”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a chat support platform that facilitates enhanced chat routing, as taught by Cummins, using human support agent data to route communication, as taught by Can, to present a custom and personalized experience instead of a generic experience where customers do not feel special or valued (Can [0019]).
Neither of the reference specifically teach detecting language. Yan teaches:
detect a language used by the user by executing natural language processing of the first natural language input (Yan [Page 1]); See “Conversational AI works by bringing together artificial intelligence (AI), natural language processing (NLP) and conversational user interfaces - enabling digital communication in contact centers to closely resemble human engagement. These AI technologies can recognize different languages as well as intent, text semantics, message types (public or private), email metadata, and other information to deliver a seamless and smart routing call experience for your customers.”
identify an appropriate human support agent to automatically route the virtual chat communication session based at least on a linguistic compatibility with the detected language used by the user (Yan [Page 1]); See “Conversational AI works by bringing together artificial intelligence (AI), natural language processing (NLP) and conversational user interfaces - enabling digital communication in contact centers to closely resemble human engagement. These AI technologies can recognize different languages as well as intent, text semantics, message types (public or private), email metadata, and other information to deliver a seamless and smart routing call experience for your customers.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of chat communications, as taught by Cummins and Can, the detection of language used by the user, as taught by Yan, to better engage and respond to customers (Yan [Page1]).
Claims 2, 9 and 16
Modified Cummins and Can discloses the limitation above. Can further teaches:
wherein executing human support agent analysis comprises applying the trained machine learning model to the human support agent data that comprises calendar data, scheduling data, educational data specific to each individual human support agent of the plurality of human support agents, and professional experience data that are specific to each individual human support agent of the plurality of human support agents (Can [0144][0153]). See at least [0153] “A machine learning engine 302 trains a routing predictive model 322 to r… to infer (predict) a relevant call agent (or call agent group tasked with working with a specific category ( e.g., credit card issues)). See Claim 5 where the agent availability is disclosed.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a chat support platform that facilitates enhanced chat routing and an enhanced user chat experience, as taught by Cummins, characteristics of the human agent that inquiries should be routed to, as taught by Can, to present a custom and personalized experience instead of a generic experience where customers do not feel special or valued (Can [0019]).
Claims 3, 10 and 17
Modified Cummins discloses the following:
wherein the virtual chat communication session is automatically routed from the automated support agent to the identified appropriate human support agent by generating and displaying on the client device a second chat interface in a second format different than the first format, the second format comprising a floating action chat icon superimposed on the first chat interface that connects the client device to the identified appropriate human support agent. (Cummins [0067][0155][Claim 1][Figure 8E; 820E]). See [0097] “the Artificial Conversational Entity (ACESAP) has notified the user that they can be connected with a customer representative but that there is a wait or alternatively they can leave their details and the customer representative will call them back. In this instance the ACESAP defaults to the enquiry form to be displayed but if the user elected to be connected to the customer representative then the user may be provided with a VOIP interface such as Google™ Hangouts, Skype™ etc.” Where the second format is 820E in Figure 8E.
Claims 4 and 11, 18
Modified Cummins discloses the following:
wherein the appropriate human support agent is identified based on a current availability of the appropriate human support agent and the appropriate human support agent not having been previously engaged in a predetermined number of virtual chat communication sessions during a current business day (Cummins [0132]). Where the reference teaches analyzing agent call time and resolution success. Cummins does not explicitly disclose current availability of the human support agent. Cummins does teach the support agent history of interactions including call duration. It would have been obvious to a person having ordinary skill in the art at the time the invention was filed that the availability would be considered because it would affect wait times.
Claims 5, 12 and 19
Modified Cummins and Can discloses the limitation above. Can further teaches:
wherein training the machine learning model using sets of training data comprises iteratively training the machine learning model based on sets of training data that include: the first set of training data; a second set of training data comprising a minimum amount of experience needed for a support agent to respond to specific service requests; a third set of training data comprising sample requests for different types ofSee at least “Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.”
Although the limitation has been addressed in view of prior art, the Examiner notes that the particular type of training sets (i.e. “second set; third set; fourth set” as claimed) is considered non-functional descriptive material, of which does not explicitly alter or impact the steps of the method in such a way as to establish a new and unobvious functional relationship with the method as claimed. As such, the non-functional descriptive material limitation can be given little to no patentable weight. See MPEP 2111.05. The functional limitation is sets of training data. The reference cited teaches this.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a chat support platform that facilitates enhanced chat routing and an enhanced user chat experience, as taught by Cummins, multiple data sets to train the system, as taught by Can, to present a custom and personalized experience instead of a generic experience where customers do not feel special or valued (Can [0019]).
Claims 6, 13 and 18
Modified Cummins and Can discloses the limitation above. Can further teaches:
wherein the human support agent analysis, using the trained machine learning model, is further configured to identify the appropriate human support agent having the minimum amount of experience needed and specialized training in a specific type ofSee “the current caller represents a long average call length and may require a higher level ( e.g., more experienced) call agent or one who has a strong history of shorter resolutions. In addition, this information may also allow strategic decisions to be made such as predicting how many calls can be completed within the next hour depending on the incoming call volume, allocating more resources dynamically.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a chat support platform that facilitates enhanced chat routing and an enhanced user chat experience, as taught by Cummins, multiple tiers of service, as taught by Can, to provide the best assistance to a customer (Can [0021]).
Claims 7, 14 and 20
Modified Cummins discloses the following:
wherein the set of instructions, when executed by the one or more processors, cause the server computer system to generate and display a widget superimposed on the first chat interface, the widget including a professional profile of the appropriate human support agent, wherein the professional profile comprises: name, job title, location, and contact information. (Cummins [Figure 8F, 870]). Where the profile image of the human agent is displayed.
Although the limitation has been addressed in view of prior art, the Examiner notes that the particular type of profile data (i.e. “name, job title, location, and contact information” as claimed) is considered non-functional descriptive material, of which does not explicitly alter or impact the steps of the method in such a way as to establish a new and unobvious functional relationship with the method as claimed. As such, the non-functional descriptive material limitation can be given little to no patentable weight. See MPEP 2111.05. The functional limitation is displaying a widget of the customer service representative. The reference cited teaches this.
Response to Arguments
Applicant's arguments with respect to the rejection under 35 USC 112 have been fully considered but they are not persuasive.
Applicant Argues: Applicant, however, describes that handover of the virtual chat communication session is made to "a human support agent that is linguistically compatible with a detected language used by the user."
Examiner respectfully disagrees. While there is no requirement that the Applicant recite the claimed limitation verbatim within the Specification, the broadly stated “based on a linguistic compatibility with the user” could encompass a plurality of different steps to achieve the result. Applicant’s failure to introduce the embodiments of the detection step are considered new matter when firstly introduced in the amended claims. First, “"Linguistically compatible" means that two or more people or texts use language in a way that is consistent, similar, and understandable to each other, enabling effective communication.” This does not only mean language as defined as French or Mandarin, this also covers regional dialect of the same language. So the detection of linguistically compatible language could be a word specific to a geographic location as well and detection would not have to occur by machine learning models or algorithms. Furthermore, there is nothing found in the specification that limits the detection to a machine learning model that trains data sets of languages. Detected language could be determined by detecting keywords or phrases in the chat bot by any number of means. The rejection is maintained.
Applicant's arguments with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant Argues: When considered as an ordered combination, the generation of a first chat interface and a second chat interface in a second format (a floating action chat icon) different than the first format should similarly demonstrate an improvement to a computer's basic ability to display information and interact with the user.
Examiner respectfully disagrees. Applicant’s claims of improving the display do not represent an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. The interface is not improved, and there is not support for the improvement to the computer other than a general allegation that the claims define a patentable invention. The rejection is maintained.
Applicant Argues: Bascom
Examiner respectfully disagrees. the Examiner notes that the fact pattern present in the Bascom v. AT&T case is entirely different than the fact pattern of the instant application, and thus cannot be relied upon as a prima facie basis for patent eligibility simply because Applicant purports their invention is an improvement in the relevant technology.
Moreover, the Examiner notes the following excerpt from Bascom (pages 12 and 14-17 of Opinion):
“We agree with the district court that filtering content is an abstract idea because it is a longstanding, well-known method of organizing human behavior, similar to concepts previously found to be abstract. See Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015) (holding that "tracking financial transactions to determine whether they exceed a pre-set spending limit (i.e., budgeting)" is an abstract idea that "is not meaningfully different from the ideas found to be abstract in other cases...involving methods of organizing human activity"); see also Content Extraction, 776 F.3d at 1347 (finding that "1) collecting data, 2) recognizing certain data within the collected data set, and 3) storing that recognized data in a memory" was an abstract idea because "data collection, recognition, and storage is undisputedly well-known" and "humans have always performed these functions"); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350 (Fed. Cir. 2014) (finding that "a process of organizing information through mathematical correlations" is an abstract idea). An abstract idea on "an Internet computer network" or on a generic computer is still an abstract idea. See Intellectual Ventures I, 792 F.3d at 1368 n.2 (collecting cases).
“We agree with the district court that the limitations of the claims, taken individually, recite generic computer, network and Internet components, none of which is inventive by itself[…]However, we disagree with the district court's analysis of the ordered combination of limitations[…] the claims may be read to “improve[] an existing technological process.” Id. at 2358 (discussing the claims in Diehr, 450 U.S. 175).”
Based on the decision detailed above, it is clear that the claims in Bascom were deemed eligible exclusively on the fact that the claimed solution was the combination of elements, and it was how those elements were all used together in combination relative to the state of the prior art as of the filing date that sufficiently moved the claims beyond an abstract idea itself or merely applying the abstract idea (i.e. filtering content) on a computer.
Furthermore, any general allegation of patent eligibility because the instant claims may contain individual elements present in Bascom (e.g.. filtering, profile, ISP server) would be non-persuasive and insufficient to constitute eligible subject matter, as the Court was clear the individual elements were routine and conventional and thus not inventive, and it was the combination of those elements that was the deciding factor on eligibility.
Moreover, it is clear from the Bascom decision that the apparent improvement was not merely directed to the abstract idea itself (i.e. filtering content), but to the actual technology. However, such an improvement is not readily apparent in the instant case. In fact, the instant application does not parallel the fact patterns in Bascom at all, and more importantly Applicant has failed to provide evidence on how the instant claims, and particularly the combination of the instant claimed elements, provide an improvement or solution to an existing technological process that can be considered some more than routine or conventional.
Finally, generally speaking as it relates to Bascom, the Examiner notes that the ‘606 Patent of Bascom was directed towards technological processes of the late 1990's, and while the "inventive concept" was deemed patent eligible based on improving existing technological processes (as of the filing date), such an old and established improvement could now very well be considered routine and conventional relative to the current state of the art. Therefore, even if the instant case had an improvement paralleling that of Bascom (which the Examiner does not concede), the instant claims could still be deemed ineligible.
Applicant Argues: The present claims integrate the claimed steps… which is a practical application of the various steps, even if alone each would be considered organization of human activity.
Examiner respectfully disagrees and maintains the previous response. Applicant’s alleged improvement is not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. A showing that a claim is directed to any improvement does not automatically mean a claim is patent eligible (e.g., an improved business function or an improved idea itself is not patent eligible). In this case, routing a customer to an experienced customer service representative is an abstract idea, and an “improved” way of routing a customer to an experienced customer service representative is, if anything, an improvement to the idea itself. Examiner respectfully disagrees. Additionally, the Specification fails to clearly evidence how the use of a machine learning model being trained with experienced representative data is an actual technological improvement over, or differs from, the expected general concept of applying the ML model. It is unclear how the ML models are being integrated in any specialized manner that serves any specialized technical purpose/solution. The claims are not rooted in machine learning technology, and the claims do not solve a technical problem that only arises in AI or machine learning. MPEP § 2106.05(a).
The amended limitations being referred to simply apply data analysis to train the generic ML models and do nothing more than use computational instructions to be implemented in a computer processing environment, simply to "apply it" without any improvement to the computer functionality or technology itself.
Applicant’s arguments, with respect to the rejection under 35 USC 103 have been fully considered and the rejections, including newly cited prior art to address the amended claims are above.
Applicant Argues: Cummins does not further elaborate on what demographics may include - therefore, it is not reasonable to assume that it includes the specific language used by the user. Can appears to suggest that a call may be routed to an agent that can handle a perceived preference of a caller, there is no hint or suggestion, let alone an explicit description of "the human support agent selected being linguistically compatible with a detected language used by the user," as required by the claims.
Examiner respectfully agrees. however the claims recite a training of a machine learning model to recognize each of the plurality of languages from natural language inputs;, which is not supported by the specification. Please see the 112 rejection above for more information. The claims reciting a linguistic compatibility is taught by (Yan [Page 1]); See “Conversational AI works by bringing together artificial intelligence (AI), natural language processing (NLP) and conversational user interfaces - enabling digital communication in contact centers to closely resemble human engagement. These AI technologies can recognize different languages as well as intent, text semantics, message types (public or private), email metadata, and other information to deliver a seamless and smart routing call experience for your customers.”
Applicant Argues: Claims 3, 10, and 17
Examiner respectfully disagrees. First there is no support for a second format. See the rejection under USC 112 above. Also, (Cummins [Figure 8E; 820E]) teaches a first and second chat interface.
Applicant Argues: Claims 4, 11, and 18
Examiner respectfully disagrees. Cummins [0132] teaches analyzing agent call time and resolution success. Cummins does not explicitly disclose current availability of the human support agent. Cummins does teach the support agent history of interactions including call duration. It would have been obvious to a person having ordinary skill in the art at the time the invention was filed that the availability would be considered because it would affect wait times.
Applicant Argues: Claims 5, 12, and 19
Examiner respectfully disagrees and maintains the rejection and rationale above. The functional limitation of the claims is the number data sets used to train a model. The reference cited teaches the limitation. The specific details of what type of information does not effect the functioning of the system, only the information produced as a result.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHIDA R SHORTER whose telephone number is (571)272-9345. The examiner can normally be reached Monday- Friday from 9am- 530pm.
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/RASHIDA R SHORTER/Primary Examiner, Art Unit 3626
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