DETAILED ACTION
This communication is a Final Office Action on the merits in response to communications received on 01/13/2026. Claims 1-25 have been canceled. Claims 26-45 have been newly added. Therefore, claims 26-45 are pending and have been addressed below. 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 § 101
1. 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.
2. Claims 26-45 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the two-part analysis from Alice Corp, claim 26 recites a machine (i.e., a concrete thing, consisting of parts, or of certain devices and combination of devices), and claim 38 recites a process (i.e., an act or step, or a series of acts or steps). Thus, each of the claims fall within one of the four statutory categories.
Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
Claims 26 and 38 recite:
“capturing…an audio sample of the voice of the user, the captured audio sample to detect a language communicated by the user, and generating a response to be communicated to the user…in the detected language.”
The limitations as drafted are processes under their broadest reasonable interpretation cover steps for capturing and analyzing audio sample from a user to detect the language of user and creating a response message according the user’s language encompasses commercial interactions (i.e., marketing or sales activities or behaviors; business relations), managing personal behavior or interactions between users (i.e., social activities, teaching, and following rules or instructions), mental processes (i.e., observations, evaluations, judgments, and opinions) which includes subject matter that falls within the certain methods of organizing human activity and mental processes groupings enumerated in MPEP 2106.05(a)(2)
Applicant’s Specification emphasizes in at least [0002] Service providers such as financial institutions providing financial services are increasingly providing a greater number of client services. In order to alleviate call and chat density from customers over a network environment, such client services include automated virtual support agents or chatbots who supplement human virtual support agents by directly interacting with customers via text chat, phone, instant messaging, etc. [0003] At the commencement of or even during a chat communication session between a user and an automated virtual support agent/chatbot, the user may feel more comfortable communicating using a specific language (e.g., a non-English language), and in turn, may desire to have the entire chat communication session conducted using a specific language. The user may have feelings of anger and frustration should there be an inability of the support agent to communicate using the desired language, or to timely handover the chat communication session to a support agent who speaks the language spoken by the user. This failure causes operational inefficiencies in the chat platform that causes increases in the overall length of the chat communication session and bottlenecks in the que for chat assistance. This results in a diminishment in the user's confidence of the ability of the service provider to provide quality services to its customers.
The steps of “capturing”, “detect” and “generating” in the context of the claim describe commercial interactions and managing personal behavior because the steps involve customer service tasks an enterprise or business performs to fulfill the user’s request. Also, the series of steps in the context of the claim recite mental processes for collecting information and recognizing certain information within the information, i.e., detecting the language of the customer, which are acts that can be practically performed in the human mind with and/or without pen and paper. Accordingly, the claim recites an abstract idea.
3. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “a system”, “a machine learning (ML) module”, “a non-transitory memory coupled to”, “one or more processors”, “causing the ML module to” “from a plurality of sources”, “a server computing system”, “the server computing system”, “training, via a neural network, a ML algorithm of the ML module as a trained ML algorithm”, “initiating, via an enterprise mobile application or an enterprise desktop application executing on an authenticated client device, an active chat session”, “a chatbot”, “generating and displaying a graphical user interface (GUI)”, “a chat interface on a user interface (UI) of the authenticated client device”, “from the authenticated client device during the active chat session”, “applying the trained ML algorithm to”, “by the chatbot” – see claims 26 and 38, are recited at a high-level of generality in light of the specification. Thus, the specification describes the additional elements in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting generic computer components and functionalities in light of the disclosure. These claimed additional elements merely recite the words "apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f)
The other additional elements of: “acquire language data… including publicly available data, active chat sessions and chat transcripts from previously-conducted chat sessions conducted” and “using the captured language data as training data”, merely adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05 (f).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “a system”, “a machine learning (ML) module”, “a non-transitory memory coupled to”, “one or more processors”, “causing the ML module to” “from a plurality of sources”, “a server computing system”, “the server computing system”, “training, via a neural network, a ML algorithm of the ML module as a trained ML algorithm”, “initiating, via an enterprise mobile application or an enterprise desktop application executing on an authenticated client device, an active chat session”, “a chatbot”, “generating and displaying a graphical user interface (GUI)”, “a chat interface on a user interface (UI) of the authenticated client device”, “from the authenticated client device during the active chat session”, “applying the trained ML algorithm to”, “by the chatbot” – see claims 26 and 38 are at best the equivalent of merely adding the words “apply it” to the judicial exception. Mere instructions to apply the judicial exception does not provide an inventive concept at Step 2B.
The other additional elements of: “acquire language data… including publicly available data, active chat sessions and chat transcripts from previously-conducted chat sessions conducted” and “using the captured language data as training data” were considered insignificant extra-solution activity under Step 2A Prong Two, and thus must be re-evaluated at Step 2B to determine whether the additional elements are well-understood, routine, and/or conventional.
For example, the MPEP 2106.05(d)(II) cites the Symantec, TLI Communications, OIP Techs court decisions which indicate: “receiving or transmitting data over a networks” and “electronic recordkeeping” is/are computer functions that are well-understood, routine, conventional activity when they are claimed in a generic manner. Therefore, when viewed individually and in combination with the claimed invention, the additional elements do not provide an inventive concept.
4. Claims 27-37 and 39-45 are the dependent claims.
Claims 27 and 39 recite “wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including: detecting current geographic location data of the authenticated client device, and capturing geographic location data of a plurality of human chat support agents” which adds insignificant extra solution activity, as discussed in MPEP 2106.05(g). Here, both steps perform mere data gathering that is necessary for use of the judicial exception and are recited at a high level of generality. Claims 28 and 40 recite “wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including comparing the captured geographic location data with the detected current geographic location data.” which further narrows how the abstract idea may be performed, but does not make the claimed invention any less abstract. Claims 29 and 41 recite “wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including identifying a plurality of human chat support agents having a geographic location within a predetermined threshold distance from the authenticated client device.” which further narrows how the abstract idea may be performed, but does not make the claimed invention any less abstract. Claims 30 and 42 recite “wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including identifying a human chat support agent among the plurality of human chat support agents based on a determination that the identified human virtual chat support agent is linguistically conversant in the detected language.” which further narrows how the abstract idea may be performed, but does not make the claimed invention any less abstract. Claims 31 and 43 recite “wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including automatically routing, by the chatbot, the active chat communication session to the identified human chat support agent.” which further narrows how the abstract idea may be performed, but does not make the claimed invention any less abstract. Claims 32 and 44 recite “wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including storing the captured audio sample in a storage location as a stored audio sample.” which adds insignificant extra solution activity, as discussed in MPEP 2106.05(g). Here, the step performs mere data gathering and/or storage that is necessary for use of the judicial exception and is recited at a high level of generality. Claims 33 and 45 recites “wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including training the ML algorithm based on the stored audio sample” which amounts to generic data processing and mere instructions to implement the judicial exception on a computer, as discussed in MPEP 2106.05(f). Here, the step updates data that is necessary for use of the judicial exception and is recited at a high level of generality. Claim 34 recites “wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including transmitting an electronic notification of the response via chat message in the chat interface.” which adds insignificant extra-solution activity, as discussed in MPEP 2106.05(g). Here, the step performs mere data transmission and/or output that is necessary for use of the judicial exception and is recited at a high level of generality. Claim 35 recites “wherein the electronic notification includes a user-engageable link that facilitates automatic routing of the active chat session to a human agent.” which adds generic computer components to aid in performing the judicial exception, as discussed in MPEP 2106.05(f). Here, the user-engageable link provided in the notification operates in its ordinary or normal capacity to redirect the user to join a human agent which does not integrate the judicial exception into a practical application. Claim 36 recites “wherein the ML algorithm comprises a linear regression algorithm.” at best further describes the type of algorithm that may be used and does not lead towards eligibility. Claim 37 recites “wherein the ML algorithm comprises a logical regression algorithm. at best further describes the type of algorithm that may be used and does not lead towards eligibility. Therefore, with respect to the dependent claims when viewed separately and in combination with the judicial exception, the recited limitations as whole fail to integrate the judicial exception into a practical application or provide an inventive concept.
Claim Rejections - 35 USC § 103
5. 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.
6. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claim(s) 26, 32-34, 38, 44, and 45 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandey (US 2021/0390268 A1) in further view of Ramsay (US 2023/0419287 A1)
With respect to claims 26 and 38, Pandey discloses
a system and method (abstract: discloses method and systems are presented for providing chat assistance in multiple languages.), comprising:
a non-transitory memory (¶ 0077) coupled to one or more processors (¶ 0075: discloses processor 814), 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 one or more processors to perform operations including:
initiating, via an enterprise mobile application or an enterprise desktop application executing on an authenticated client device of a user, an active chat session between the user and a chatbot (¶ 0036, 0042: a user 140 may initiate a chat session 250 with the chat robot 210 through the chat client 170.) by generating and displaying a graphical user interface (GUI) as a chat interface on a user interface (UI) of the authenticated client device (¶ 0026, 0055: discloses the user device 110 may include a chat client 170 for facilitating online chat sessions with a chat robot at the service provider server 130.),
capturing, from the authenticated client device during the active chat session, an audio sample of the voice of the user (¶ 0026-0027, 0030, 0052, 0055: discloses during an online chat session the chat client 170 may present a chat interface that enables the user to input data, i.e., audio data, for transmitting to the chat robot. The process begins by receiving a user query in the primary language from a user device. For example, a user 140 may speak the query into the user device 110 in the primary language using chat client 170.),
applying…to the captured audio sample to detect a language communicated by the user (¶ 0036, 0044, 0053, 0060: discloses the process may transmit the user query to an AI system 180 for chat robots that support natural language processing. The AI system may analyze the user’s query.), and
generating a response to be communicated to the user by the chatbot in the detected language. (¶ 0019, 0036, 0054, 0057: discloses the online chat system may then provide the response in the first language to the user device, i.e., as text or audio) via the chat robot. The response message 538 may then displayed in the chat presentation portion 512 of the chat interface 502 on the user device 110.)
The Pandey reference does not explicitly disclose the following limitations.
In the same field of endeavor, the Ramsay reference is related to providing a communications session between a user and an automated bot (abstract) and teaches:
a machine learning (ML) module (¶ 0077: discloses a machine learning module 220 that includes a machine learning algorithm or artificial intelligence.); and
causing the ML module (¶ 0077: discloses a machine learning module 220) to acquire language data from a plurality of sources including publicly available data (¶ 0078), active chat sessions conducted on a server computing system (¶ 0077, 0095: discloses communications may be recorded in the communications sessions datastore in real-time as the communications are exchanged.), and chat transcripts from previously-conducted chat sessions conducted on the server computing system (¶ 0078: discloses historical communications),
training, via a neural network using the captured language data as training data, a ML algorithm of the ML module as a trained ML algorithm (¶ 0077-0078, 0111: discloses a machine learning data analysis algorithm may also be trained using sample or live data to identify potential correlations. Such algorithms may include neural networks.),
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 modified the online chat system and methods of Pandey, to include a machine learning (ML) module; causing the ML module to acquire language data from a plurality of sources including publicly available data, active chat sessions conducted on a server computing system, and chat transcripts from previously-conducted chat sessions conducted on the server computing system, training, via a neural network using the captured language data as training data, a ML algorithm of the ML module as a trained ML algorithm, as disclosed by Ramsay to achieve the claimed invention. As disclosed by Ramsay, the motivation for the combination would have been to provide advantages in determining the expected intent from the communications and increase the likelihood of the machine learning algorithm generating the desired results. (¶ 0078)
With respect to claims 32 and 44, the combination of Pandey and Ramsay discloses the system and method, wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including storing the captured audio sample in a storage location as a stored audio sample. (¶ 0036: Pandey discloses the chat session manager of the service provider may store the chat flow in a storage such that the chat flow may be accessed even after the online chat session is terminated.)
With respect to claims 33 and 45, the combination of Pandey and Ramsay discloses the system and method, wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including training the ML algorithm based on the stored audio sample. (¶ 0077-0078, 0111: Ramsay discloses a machine learning data analysis algorithm may also be trained using sample or live data to identify potential correlations. Such algorithms may include neural networks.)
With respect to claim 34, the combination of Pandey and Ramsay discloses the system of claim 26, wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including transmitting an electronic notification of the response via chat message in the chat interface. (¶ 0019, 0026, 0057: Pandey discloses the chat interface may also present messages that are received from the chat robot. The response message 538 may then be displayed in the chat interface on the user device.)
8. Claim(s) 27, 28, 29, 39, 40, 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandey in view of Ramsay in further view of Sridharan (US 9,047,631 B2).
With respect to claims 27, 28, 29, 39, 40, 41, the combination of Pandey and Ramsay discloses the system, wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors (¶ 0075 – see Pandey) to perform
The combination of Pandey and Ramsay does not explicitly disclose the following limitations. In the same field of endeavor, the Sridharan reference is related to a method and system for providing location based assistance (abstract) and teaches:
further operations including: detecting current geographic location data of the authenticated client device (col. 7:1-67, col.18:31-34: discloses the system retrieves the customer’s current location. This can be done through automated means, i.e., automatic network location and/or GPS location, etc.), and capturing geographic location data of a plurality of human chat support agents (abstract, col. 6:47-67, col. 8:4-16),
further operations including comparing the captured geographic location data with the detected current geographic location data (abstract, col. 6:47-67, col. 8:4-16: discloses the host system server is able to distribute the call to one of a plurality of customer service agents 220 geographically distributed in independent locations. The agent may be located in the same service establishment location as the caller or may be at another sales location such as an outlet, service department, call center, etc. The system uses the customer’s location to identify the proper sales associate)
further operations including identifying a plurality of human chat support agents having a geographic location within a predetermined threshold distance from the authenticated client device. (abstract, col. 18:49-65: discloses the system determines the location of the customer and determines the availability of at least one sale associate within a predetermined location threshold of the location of the customer to service the customer assistance request.)
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 modified the combination of Pandey and Ramsay to include further operations including: detecting current geographic location data of the authenticated client device, and capturing geographic location data of a plurality of human chat support agents, further operations including comparing the captured geographic location data with the detected current geographic location data, further operations including identifying a plurality of human chat support agents having a geographic location within a predetermined threshold distance from the authenticated client device, as disclosed by Sridharan to achieve the claimed invention. As disclosed by Sridharan, the motivation for the combination would have been for improving customer experience and increasing the likelihood the customer will receive sufficient service. (col. 1:23-56 and col. 7:21-34)
9. Claim(s) 30, 31, 42, and 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandey in view of Ramsay in view of Sridharan in further view of Brunet (US 2019/0065458 A1).
With respect to claims 30, 31, 42, and 43, the combination of Pandey, Ramsay, and Sridharan discloses the system and method, wherein the set of instructions, which when executed by the one or more processors, cause the one or more processors to perform further operations including identifying a human chat support agent among the plurality of human chat support agents (abstract, col. 6:47-67, col. 8:4-16: Sridharan discloses the host system server is able to distribute the call to one of a plurality of customer service agents 220 geographically distributed in independent locations. The agent may be located in the same service establishment location as the caller or may be at another sales location such as an outlet, service department, call center, etc. The system uses the customer’s location to identify the proper sales associate)
The combination of Pandey, Ramsay, and Sridharan does not explicitly disclose the following limitations. In the same field of endeavor, Brunet is related to a computer-implemented method is provided for providing customer care to a user of an electronic device (abstract) and teaches:
based on a determination that the identified human virtual chat support agent is linguistically conversant in the detected language and automatically routing, by the chatbot, the active chat communication session to the identified human chat support agent. (¶ 0022-0024, 0074, 0080-0081: discloses there may be a database that lists all the customer care agents, their availability, and their skills in terms of solving specific problems with specific devices, their language skills, preferred medium of communications, case load i.e. how busy a customer care agent is etc. The customer care agent matching rules are used to match a customer care agent to a case 603. For example these rules take into account the information that has been machine read from the device along with the information that the user may have provided for example asked a specific question, user preferences in terms of language, time zone, medium of communications etc. and finds the best fit in terms of a customer care agent who may be available in the same time zone as the customer, speaks the language that the customer prefers, has skills in tackling and solving the specific problem that the customer is facing, on the specific device make and model that the customer has etc. The case is routed based on these rules to the customer care agent 604. In one embodiment when a match between a customer care agent and the case is found, the case is routed to the customer care agent.)
Therefore, it would have been obvious to one of having ordinary skill in the art before the effective filing date of the claimed invention, to have modified the combination of Pandey, Ramsay, and Sridharan, to include rules for based on a determination that the identified human virtual chat support agent is linguistically conversant in the detected language and automatically routing, by the chatbot, the active chat communication session to the identified human chat support agent, as disclosed by Brunet to achieve the claimed invention. As disclosed by Brunet, the motivation for the combination would have been advantages mapping a customer care agent best suited to tackle this issue and then the customer session is routed to the appropriate customer care resource using a channel of communication that is preferred by the customer. (¶ 0009)
10. Claim(s) 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandey in view of Ramsay in further view of Deegan (US 11,005,997 B1)
With respect to claim 35, the combination of Pandey and Ramsay discloses the system of claim 34, wherein the electronic notification includes (¶ 0019, 0026, 0057: Pandey discloses the chat interface may also present messages that are received from the chat robot. The response message 538 may then be displayed in the chat interface on the user device.)
The combination of Pandey and Ramsay does not explicitly disclose the following limitations. In the same field of endeavor, the Deegan reference is related to method and systems which provide for customer chatbots that detect a customer handoff condition and in response, transferring the customer to a communication session with a live agent (abstract) and teaches:
a user-engageable link that facilitates automatic routing of the active chat session to a human agent. (col. 3:41-55, col. 4:25-30, col. 6:16-28, col. 11:22-28, col. 11:59-61: discloses the chat service 1050 may send a response 2090 with a link. The link may be to one of many chat pages that are associated with an agent service.)
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 modified the response message in the combination of Pandey and Ramsay, to include a user-engageable link that facilitates automatic routing of the active chat session to a human agent, as disclosed by Deegan to achieve the claimed invention. As disclosed by Deegan, the motivation for the combination would have been to prevent the user from having to disconnect from the chatbot and provide an alternative method of contacting the company. (col. 2:10-34)
11. Claim(s) 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandey in view of Ramsay in further view of Rathaur (US 2022/0366915 A1)
With respect to claim 36, the combination of Pandey and Ramsay discloses the system,
The combination of Pandey and Ramsay does not explicitly disclose the following limitations.
In the same field of endeavor, the Rathaur reference is related to an intelligent IVR system that uses machine learning to understand voice input and interpret the intent of a user (¶ 0001-0002) and teaches:
wherein the ML algorithm comprises a linear regression algorithm or
wherein the ML algorithm comprises a logical regression algorithm (¶ 0021-0025, 0038-0039, 0043, 0073: discloses the intelligent interactive voice recognition computing platform 110 may be configured to provide intelligent dynamic voice recognition processing for users speaking a variety of languages, dialects, or the like. For instance, a user may call into an enterprise organization service center and provide natural language or voice input via an interactive voice recognition system of the enterprise organization. The voice data may be processed via a series of modules and machine learning may be used to interpret voice data, evaluate context, determine relevant meaning and the like. Various machine learning machine learning algorithms may be used such as regression algorithms, i.e., linear regression, logistic regression, and the like. In some examples, users having various speech patterns, i.e., different dialects, speech, may rely on the system for customer service. The machine learning aspects may enable more accurate interpretation of natural language, i.e., voice data, received from users having various speech patterns by performing a multistep analysis.)
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 combined system and methods of Pandey and Ramsay, the one or more ML algorithms comprises a linear regression algorithm or a logical regression algorithm, as disclosed by Rathaur to achieve the claimed invention. As disclosed by the teachings of Rathaur, the motivation for the combination would have been to provide advantages to the enterprise organization for more accurately interpreting speech from customers (¶ 0018, 0023)
Response to Arguments
12. Applicant's arguments filed 01/13/2026 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “Applicant respectfully contends that representative independent claim 26 does not fall under "certain methods of organizing human activity and mental processes groupings." Instead, representative independent claim 26 provides a technical solution to a technical problem faced by existing systems for conducting chat sessions. Indeed, representative independent claim 26 is unrelated to any overly broad abstract notion of "certain methods of organizing human activity and mental processes groupings." It provides a specific technical platform for efficiently conducting chat communications between a user and a chat support agent (chatbot or human support agent) based on language data acquired through a machine learning (ML) module. See, U.S. Pat. App. Pub. No. 20240330946 @ [0100].” The Examiner respectfully disagrees.
Contrary to the remarks, the claimed invention remain ineligible under Step 2A Prong One of the two-part analysis. The original specification [¶ 0001-0003] indicates the focus of the claimed invention is related to delivering digital financial services and chat routing. The claims recite interactions and observations that take place between a user and an enterprise that manages a chatbot which is subject matter that may be reasonably characterized as falling within the certain methods of organizing human activity and mental processes groupings of abstract ideas. In the instant case, the additional elements recited in the claim, [i.e., ML module used to analyze the user’s query, chatbot used provide a response to the user in their detected language] do not alter the analysis. The remarks simply describe the computer components and machine learning technology in a generic manner and, accordingly, are not considered a solution to a “technological problem” as was the case in Diamond v Diehr, 450 U.S. 175 (1981). Nor do the claims attempt to solve a ‘challenge particular to the Internet.’ DDR Holdings, LLC v Hotels.com, L.P., 773 F.3d 1245, 1256 – 57 (Fed. Cir. 2014). In other words, vague functional descriptions of technical solution and platforms are insufficient to transform the abstract idea into a patent-eligible invention. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The Court in Enfish, LLC v. Microsoft Corporation held that the claims of the underlying patents U.S. Patent Nos. 6,151,604 and 6,163,775 (hereinafter "the Enfish patents") met the statutory and judicial requirements of patent-eligibility. The Enfish patents claimed an improvement that provided for "faster searching of data than with the relational model," "more efficient storage" of certain types of data, and "more flexibility in configuring the database." The Court further concluded that that "not all improvements in computer-related technology are inherently abstract." Here, it is pertinent to note that where the claims are directed to an improvement to computer functionality, they are not abstract under the first step of Alice test. Thus, it is not necessary to then apply the second step of this test. Here, Applicant was confronted with the operational inefficiencies of preexisting chat platforms that cause increases in the overall length of the chat communication session and bottlenecks in the queue for chat assistance. This results in a diminishment in the user's confidence of the ability of the service provider to provide quality services to its customers.” The Examiner respectfully disagrees.
Contrary to the remarks, the claimed invention remains ineligible under Step 2A Prong One of the two-part analysis. Unlike the claims in Enfish, the asserted claims do not recite a comparable technological improvement. The benefits alleged by applicant with regards to operational inefficiencies have been considered, however, these benefits are not technical improvements to the functioning of a computer or any other technology. Also, the courts have previously held "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Accordingly, representative independent claim 26 includes a system comprising one or more processors operable to perform an ordered combination of operations that facilitate efficient chat sessions, particularly when the user speaks a non-native language (i.e., a language not in English). The ordered combination of operations performed by the one or more processors include causing a ML module to acquire language data from active chat sessions conducted on a server computing system and chat transcripts from previously-conducted chat sessions conducted on the server computing system. In essence, a ML module of a server computing system is operable to capture language data from a variety of different resources of different formats and data types, including, but not limited to, publicly available data, language data supplied by users during an active virtual communication session, and chat transcripts from previously-conducted virtual communication sessions.”
“Here, the ML module is operable to efficiently collect and process huge amounts of data from a variety of data sources that would otherwise take additional time and computing resources to manage. The claimed ML module is to then train a ML algorithm in order to quickly detect a language communicated by the user using a captured audio file of the user. Upon detection of the user's language, the system causes the generation a response to be communicated to the user by the chatbot in the detected language. Use of a ML module to conduct order of operations that add more than generally linking the use of the abstract idea (the general concept of data gathering) over the Internet, because they solve a technical problem faced by existing systems for conducting chat sessions in a non-native language using a technical solution that is rooted in machine learning.”
Accordingly, Applicant respectfully submits that the newly added claims reside firmly outside the scope of what the USPTO considers organizing human activity. The claims include technical features that encompasses significantly more than the alleged judicial exception itself, and thus, include patent-eligible subject matter. Accordingly, reconsideration of the claims and withdrawal of the rejection is respectfully requested.” The Examiner respectfully disagrees.
Contrary to the remarks, the claimed invention remains ineligible under Step 2A Prong Two of the two-part analysis. It is important for Applicant to note a claim for a new abstract idea is still an abstract idea, thus the remarks regarding prior art systems to not alter the analysis. The ML module was considered an additional element under Prong Two in the claim as being used in the claim(s) to aid in performing the judicial exception. See MPEP 2106.05(f) The Specification in [¶ 0100-0101] describes the ML module as shown below:
The ML module 250 may include one or more ML algorithms to train one or more machine learning models of the one or more financial institution servers 200 based on data and/or information resided in the memory 220. The ML algorithms may include one or more of a linear regression algorithm, a logical regression algorithm, or a combination of different algorithms. A neural network may also be used to train the system based on the received data. The ML module 250 may analyze the received data and/or information, and transform the data and/or information in a manner which provides enhanced communication between the client device 100 and the one or more financial institution servers 200, while also enhancing user access and management of the one or more financial accounts. The data and/or information may also be up-linked to other systems and modules in the one or more financial institution servers 200 for further processing to discover additional information that may be used to enhance the understanding of the information. In accordance with one or more embodiments, the ML module 250 may comprise one or more processors, and one or more data stores (e.g., non-volatile memory/NVM and/or volatile memory) containing a set of instructions, which when executed by the one or more processors, cause the ML module 250 to acquire data and information from a plurality of resources, including, but not limited to, publicly available data and information, language data supplied by users during an active virtual communication session, chat transcripts from previously-conducted virtual communication sessions, the one or more processors 210, the one or more data stores 221, the sensor module 240, and any other input/output sources, and process the acquired information to, inter alia, cause implementation of the protocol for conducting and/or routing a virtual communication session. Embodiments, however, are not limited thereto, and thus, the ML module 250 may process the acquired information to execute
other aspects related to conducting a virtual communication session. The ML module 250 may analyze the captured language/linguistic information or data in order to enhance operation of one or more of the mobile financial institution application mobile 223, the automated support agent module 224, and the human support agent module 225. The ML module 250 may train one or more ML algorithms to detect a plurality of languages, including but not limited to, English, Spanish, French, German, Portuguese, Italian, Hindi, Dutch, Danish, Swedish, Finnish, etc.
As such, the Specification discusses the features or tasks, [i.e., types of algorithms/models, training], of the ML module at a high-level of generality and in a results-oriented manner. There are no technical details relating to how the machine learning model(s) are trained and the support from the disclosure does not discuss any improvements in machine learning technology or in the manner machine learning models operate. Thus, the Applicant cannot rely upon the ML module alone to provide their inventive concept. Considered as an ordered combination, the recited additional elements, i.e., computer components, do not add anything that is not already present when the steps are considered separately. The sequence of data reception-analysis-update processing is equally generic and conventional. See Ultramercial, Inc. v .Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited an abstraction); Inventor Holdings, LLC v. Bed Bath &Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission); Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). Therefore the ordering of the steps is therefore ordinary and conventional as recited in the claim(s). For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 103
Applicant’s arguments with respect to claim(s) 26-45 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
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.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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