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
This communication is a Final Office Action in response to communications received on 7/2/26.
Claims 1- 20 have been amended.
Therefore, Claims 1-20 are now pending and have been addressed below.
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 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, 11 and 16 recites new limitation “ determining, a current user proficiency rating for the specific user based on the user statement, the user responses, the historical average user proficiency rating, the most recent user proficiency rating, the support contact history for the current problem, and the aggregated support analytics for the other users”. Examiner could not find support for this limitation i.e. specification is silent regarding determining a current user proficiency rating based on all of the factors listed in claim. Specification at [0040] the AI virtual support agent 204 of system 200 calculates a current Customer Proficiency Rating based on customer data and analytics, such as one or more of a.) Experience with the product, including length of time (e.g., days, weeks, months, years) the customer has been using the product or service for the contact of customer support; b.) Frequency of prior support communication for current product/service; c.) Frequency of prior support communication for the current problem or issue; d.) Average Length of Time spent to solve the customer problems for this product in the past; e.) Historical average Customer Proficiency Rating (for the specific customer; and/or f.) Most recent Customer Proficiency Rating recorded for this customer. [0054] the AI virtual support agent 204 calculates a Customer Proficiency Rating based on customer Data and Analytics to identify a customer skill level for the current problem, where the customer Data and Analytics includes such as, customer experience with the product including a length of time (e.g., days, weeks, months, years) the customer has been using the product/service of the customer request for customer support. Specification does not provide support for “determining, a current user proficiency rating for the specific user based on the user statement, the user responses, the historical average user proficiency rating, the most recent user proficiency rating, the support contact history for the current problem, and the aggregated support analytics for the other users”.
Further, amended claims 1, 11 and 16 recite new limitation “generating, by the AI virtual support agent using one or more machine learning models trained on historical support data and analytical data, a set of questions as an adaptive question sequence in which a next question is selected based on a prior user response and the current problem, the set of questions being generated based on the user statement, the support contact history for the specific user, and the aggregated support analytics for the other users, to obtain user responses. Specification is silent regarding “an adaptive question sequence in which a next question is selected based on a prior user response and the current problem, the set of questions being generated based the aggregated support analytics for the other users”. [0038] The AI virtual support agent 204 is trained, based on historical customer data and analytical data to identify and present questions to the customer [0053] FIG. 3A, when a customer data file does not exist, system 200 obtains a customer statement of understanding for the current problem, and obtains customer responses to a set of questions that are based on the customer statement and the current problem.
Thus the claims merely recite a description of the end desired result ("problem to be solved") and the scope of claims encompasses all techniques to attain that result ("all solutions") without describing how the terms are functionally related. A description that merely renders the invention obvious does not satisfy the requirement, Lockwood v. Am. Airlines, 107 F.3d 1565, 1571-72 (Fed. Cir. 1997). Dependent claims are rejected due to their dependence from independent claims.
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 (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1-10 are directed to a method, claims 16-20 are directed to a non-transitory medium and claims 11-15 are directed to a system. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 11 and 16 recite methods for performing customer support operation that includes receiving an electronic request for user support for a current problem of a product or service area, wherein the electronic request comprises digital identification data to identify a specific user for the electronic request; accessing, based on the digital identification data, a user proficiency data set storing a historical average user proficiency rating, a most recent user proficiency rating, support contact history for the current problem, an average duration of prior support contacts, and prior levels of support used to resolve prior problems; accessing, aggregated support analytics for other users of the product or service area for problems corresponding to the current problem; obtaining a user statement of understanding for the current problem; generating, a set of questions as an adaptive question sequence in which a next question is selected based on a prior user response and the current problem, the set of questions being generated based on the user statement, the support contact history for the specific user, and the aggregated support analytics for the other users, to obtain user responses; determining, a current user proficiency rating for the specific user based on the user statement, the user responses, the historical average user proficiency rating, the most recent user proficiency rating, the support contact history for the current problem, and the aggregated support analytics for the other users selecting, a support agent to reduce automated re-routing of the electronic request by matching the current user proficiency rating, a difficulty of the current problem, and a specialized area of the support agent and automatically routing the specific user to the selected support agent; and upon resolving the current problem, updating, the user proficiency data set for the specific user for subsequent electronic support requests from the specific user to reduce processing required to reassess the specific user during a subsequent support session
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), including a person’s interaction with computer) and mathematical calculations (calculating customer proficiency rating), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as executing an Artificial Intelligence (AI) virtual support agent within a system, the executing the AI virtual support agent performing automated electronic user routing, by the AI virtual support agent using one or more machine learning models trained on historical support data and analytical data, system data storage (Claim 1, 11 , 16), one or more computer processors, a memory (Claim 11), a computer storage medium (Claim 16)), the claims are directed to providing customer support by routing customer to agent based on proficiency rating. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing routing based on proficiency rating. In particular, the claims only recites the additional element – executing an Artificial Intelligence (AI) virtual support agent within a system, the executing the AI virtual support agent performing automated electronic user routing, by the AI virtual support agent using one or more machine learning models trained on historical support data and analytical data, system data storage (Claims 1, 11, 16), one or more computer processors, a memory (Claim 11), a computer storage medium (Claim 16)). The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component and merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Further, the limitation of “using one or more machine learning model trained..” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In addition, limitations reciting data gathering such as “receiving an electronic request..“ is insignificant pre-solution activity that merely gather data and, therefore, do not integrate the exception into a practical application for that additional reason. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en bane), aff’d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371-72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)).
The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; providing customer support by routing customer to agent based on proficiency rating. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to executing an Artificial Intelligence (AI) virtual support agent within a system, the executing the AI virtual support agent performing automated electronic user routing, by the AI virtual support agent using one or more machine learning models trained on historical support data and analytical data, system data storage (Claims 1, 11, 16), one or more computer processors, a memory (Claim 11), a computer storage medium (Claim 16)), these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0022] details “ The processor 102 may be implemented as microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions.[0035] System includes one or more processors used with AI virtual support agent, [0036] The system 100 may also include a memory 204 coupled to the processor 102.The memory 204 may include any non-transitory computer-readable medium including volatile memory (e.g., RAM), and/or non-volatile memory (e.g., EPROM, flash memory, Memristor, etc.). ” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data. As discussed in Step 2A, Prong Two above, the recitations of “receiving steps” amount to receiving data over a network and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself.
Dependent claims 2-10, 12-15, and 17-20 add additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as representative claims 1, 11 and 16.
Claims 2-4, 12-13 and 17-18 recites wherein receiving the electronic request for customer support further comprises receiving the electronic request from one of a virtual phone agent, a virtual video-conferencing agent, a virtual text messaging agent, a virtual email agent, a support case bot, an interactive web form, or an online chatbot; accessing the user proficiency data set further comprises accessing, by the AI virtual support agent, based on the digital identification data, one or more historical support records for the specific user to identify one or more of the historical average user proficiency rating, the most recent user proficiency rating, the support contact history for the current problem, the average duration of prior support contacts, and the prior levels of support used to resolve prior problems; comprising updating one or more of the historical average user proficiency rating and the most recent user proficiency rating in the user proficiency data set based on the current user proficiency rating determined for the current problem. The claims are directed to the same abstract idea as independent claims and simply provide further details to limit the abstract idea. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Regarding claims 5-7, 14 and 19, The method of claim 1, wherein providing the set of questions further comprises identifying, via the AI virtual support agent, based on the customer responses, a proposed solution for the current problem, and providing the proposed solution to the customer; providing documentation to the customer that is related to the proposed solution, and answering customer questions; providing the set of questions further comprises receiving , via the AI virtual support agent, a customer question and answering the customer question, wherein answering the customer question further comprises providing at least one of questions related to the customer question, or documentation related to the customer question. The claims are directed to the same abstract idea as independent claims and simply provide further details to limit the abstract idea. The limitation of identifying via AI virtual support agent merely adds the words apply it (or an equivalent) with the judicial exception , or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Regarding claims 8-10, 15 and 20, The method of claim 1, updating the user proficiency data set further comprises, based on receiving a user response that the current problem is resolved, storing, in the user proficiency data set, statistics related to the user support, wherein the statistics comprise the current user proficiency rating, the difficulty of the current problem, the level of support used to resolve the current problem, and the time spent to resolve the current problem; wherein selecting the support agent further comprises identifying, based on the current problem and the product or service area, one or more of a product or service area of the support agent, an expertise level of the support agent, and an experience of the support agent for the current problem wherein providing the set of questions further comprises providing, via the AI virtual support agent, a plurality of interactive user prompts , based on one or more user The claims are directed to the same abstract idea as independent claims and simply provide further details to limit the abstract idea. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do 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 elements are merely used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
Subject Matter distinguished from prior art
Regarding claims 1, 11 and 16, Sait et al. (US 2023/0089757 A1)discloses the computer-implemented method/system/medium comprising:
Sait teaches executing an Artificial Intelligence (AI) virtual support agent within a system, the executing the AI virtual support agent performing automated electronic user routing support operations ([0010] A customer support center is generally a location where virtual support agents and human support agents answer telephone calls or respond to text messages from users seeking support. When a user contacts a customer support center, the first interaction may take place with a virtual support agent, hereinafter referred to as a virtual agent [0014] an interactive voice response (IVR) may provide the set of questions to the user. An automated conversation tool, such as a chatbot, may provide the set of questions, [0016] the call from a user categorized as belonging to the first category may be routed to the virtual agent as the user of the first category may have a higher technical skill level than average and may be able to resolve the query with the guidance received from the virtual agent) comprising
Sait discloses receiving, by the AI virtual support agent, an electronic request for customer support for a current problem of a product or service area ([0010] A customer support center is generally a location where virtual support agents and human support agents answer telephone calls or respond to text messages from users seeking support (electronic request). When a user contacts a customer support center, the first interaction may take place with a virtual support agent, hereinafter referred to as a virtual agent [0067] method 500, at block 502, a call for technical support is received from a user of a user device. The user device may be, for example, the user device 200. In an example, the user may call for technical support for resolving queries about products/services of interest, working of a product, and the like. The call may be in any format, such as a voice call, a text message, or a chat message.), wherein the customer request comprises customer data ([0033]the system 100 may be connected to a user device 200 through a communication network 202, Fig 2 # 200 user device/user identity)
Sait discloses accessing, by the AI virtual support agent, a user proficiency data set; ([0025] The processor 102 may monitor the responses received from the user. The processor 102 may execute instructions 106 to categorize the user in a category from among a plurality of categories based on the responses. In one example, the plurality of categories may correspond to the technical skill level and the type of support to be provided to the user. [0056]FIG. 3, responses 302 to a series of questions may be received, for example, through an IVR, from a user seeking resolution to a query, speech to text conversion 304 may be performed where applicable, and the responses may be analyzed by the first machine learning model 206 to categorize the user (proficiency data). Categorization of users based on technical skills may be performed by the first machine learning model 206 using word embeddings 306 and customer classifier 308 .[0082] determine a technical skill level of the user based on responses received to a set of questions from the user, in response to a query received from the user over an incoming call)
Sait discloses obtaining a customer statement of understanding for the current problem ([0012] a virtual agent may first provide a series of instructions in response to a query posed by the user. [0014] when a user calls a customer support center to seek a resolution for product or service issues Fig 5 #502 receive query from user, [0058] the user may state ‘printer is not working’ in the form of unstructured text. Further, in response to a question, the user may mention that ‘paper is jammed’.)
Sait discloses generating, a set of questions as an adaptive question sequence in which a next question is selected based on a prior user response and the current problem, the set of questions being generated based on the user statement, ([0024] a chatbot may provide the set of questions (prompt). In various examples, the questions may be provided in audio format or as speech converted to text or in text., [0025] the set of questions may be provided in a series, with a next question being provided based on the response received for a previous question. (analytical data). The user may provide responses to the set of questions as text or speech, which may be converted into text., [0050] the response provided by a user for a question posed to the user, such as “have you performed troubleshooting before?”. In an example, other responses such as “yes” and “of course” may have a similar context as the word “okay”. [0053] the user may call the customer support center regarding an issue such as a printer related issue. A respondent, such as an interactive voice response (IVR) or a chatbot or a human agent, may provide a set of questions related to the printer issue. The set of questions may be provided in series, such as ‘what troubleshooting steps have you performed?’, ‘were any software driver or firmware changes made on the printer?’, and the like, Fig5 #504 providing a series a questions to user, [0014] when a user calls a customer support center to seek a resolution for product or service issues or for enquires, the user may be provided with a set of questions. In an example, an interactive voice response (IVR) may provide the set of questions to the user. In another example, a human agent may provide the set of questions. In yet another example, an automated conversation tool, such as a chatbot, may provide the set of questions., [0068] At block 504, a series of questions may be provided to the user to assess a technical skill level of the user. The series of questions may be provided by a respondent. In an example, an interactive voice response (IVR) may be used as the respondent to provide the series of questions to the user.);
Sait discloses determining, by the AI virtual support agent, a current customer proficiency rating for the specific user based on the user statement (Fig 5 #504-506 assigning the technical skill level of the user based on responses [0069]At block 506, the technical skill level (proficiency rating) of the user may be assessed based on the responses received to the series of questions, [0024] The query may be related to, for example, products/services of interest, the working of a product, and the like. In response to the call, the user may be provided with a set of questions.[0025] Processor 106 to categorize the user in a category from among a plurality of categories based on the responses. In one example, the plurality of categories may correspond to the technical skill level (proficiency level) and the type of support to be provided to the user [0026]categorize a user based on a probability of the user being technically skilled as determined from the responses of the user. In one example, each category may be associated with a probability range of a user being technically skilled and the user may be classified into one of the categories based on the probability determined for the user. [0028] categorize users based on their technical skill levels. [0029] Users categorized in the first category may be those who have a higher than average technical skill level and users categorized in the second category may be those who have an average or lower than average technical skill level. [0082]. to determine the technical skill level of the user, the user may be categorized into a category from among a plurality of categories based on the responses. In an example, the processor 102 may use the first machine learning model 206 to categorize the user based on the technical skill level of the user. [0070] At block 508, the call of the user may be routed to one of a human agent and a virtual agent based on the assessment. In an example, the call may be routed to the virtual agent when the user is categorized in the first category as having a technical skill level above an average technical skill level usable to resolve the query. In an example, the call may be routed to the human agent when the user is categorized in the second category as having a technical skill level below the average technical skill level usable to resolve the query, [0084] a decision engine 406 may be used to decide to route the call to the human agent or the virtual agent. In an example, the decision engine may use decision rules related to the technical skill level of the user and the technical complexity of the query for routing the call to the human agent or the virtual agent as has been explained earlier.); updating the current proficiency rating ([0044] at the end of a call, a human agent or the user may provide a feedback to the system 100 to indicate if the call may have been handled by a virtual agent (update rating based on feedback) or to indicate whether any problems arose during the call, for better categorization of users, classification of queries, and decision making. The feedback may be used to update the decision rules and the machine learning models., [0055] if the user had indicated in one of the responses that they have recently installed an update to a printer driver, the agent may take this into account while providing the resolution steps.) and
Sait discloses selecting, a support agent for electronic request by matching the current user proficiency rating, a difficulty of the current problem, ([0071] call routing based on technical skills of users and technical complexity of a query (difficulty of problem), [0075] technical complexity of the query of the user may be assessed. In an example, a second machine learning model, such as the second machine learning model 208, may be used to classify the issue or query based on its technical complexity. In an example, the query may be classified into a class from among a plurality of classes based on a technical complexity of the query. For example, the query may be classified into a first class when the technical complexity of the query is assessed as being greater than a predefined threshold technical complexity., [0076] at block 610, the call of the user may be routed to one of a human agent and a virtual agent based on the technical skill level of the user and the technical complexity of the query.); and automatically routing the specific user to the selected support agent.([0076] at block 610, the call of the user may be routed to one of a human agent and a virtual agent based on the technical skill level of the user and the technical complexity of the query. In an example, the virtual agent may be the virtual agent 210. In an example, a decision engine, such as the decision engine 406, may provide a decision to route the call to the human agent or the virtual agent based on decision rules. [0077]if the user is categorized in either the first category or the second category and the query from the user is complex, calls from the user may be routed to the human agent, to receive guidance from the human agent.)
Sait discloses upon resolving the current problem, ending communication with the customer and storing the feedback ([0062] the decision rules used by the decision engine 406 may be predefined initially and updated based on feedback received from human agents and users after a call is concluded.)
Sait does not specifically teach the electronic request comprises digital identification data to identify a specific user for the electronic request; accessing, by the AI virtual support agent, based on the digital identification data, a user proficiency data set storing a historical average user proficiency rating, a most recent user proficiency rating, support contact history for the current problem, an average duration of prior support contacts, and prior levels of support used to resolve prior problems; accessing, aggregated support analytics for other users of the product or service area for problems corresponding to the current problem; the set of questions being generated based on the support contact history for the specific user, and the aggregated support analytics for the other users, to obtain user responses; determining, a current user proficiency rating for the specific user based on the user statement, the user responses, the historical average user proficiency rating, the most recent user proficiency rating, the support contact history for the current problem, and the aggregated support analytics for the other users; selecting, a support agent to reduce automated re-routing of the electronic request by matching a specialized area of the support agent; upon resolving the current problem, updating, the user proficiency data set for the specific user for subsequent electronic support requests from the specific user to reduce processing required to reassess the specific user during a subsequent support session . Sait, however, teaches categorize the user in a category from among a plurality of categories based on the responses. In one example, the plurality of categories may correspond to the technical skill level and the type of support to be provided to the user. ([0025])
Daianu (US 11,270,235 B1), teaches receiving an electronic request for customer support for a current problem of a product or service area (Fig 5 #502 receive a query and a personal identification from user); wherein the electronic request comprises digital identification data to identify a specific customer for the customer request (Fig 1 # 110 user profile, Col 1 lines 57-62 retrieving, based on the personal ID, a user profile associated with the user, wherein the user profile comprises: user attribute data, a clickstream history of the user, and a product SKU of the product. Col 8 lines 66-67 a featurization system 306 can extract syntax and semantic data 308 from each query. The extracted syntax and semantic data 308 can include an intent of the query and entity information regarding context of the query. Fig 5 #502 and Col 11 lines 39-47 At step 502, a query and a personal ID is received from a user of a product. In some implementations, the query can be associated with a product or service offered an organization. In other implementations, the personal ID is associated with the user and can include Social Security number, serial number of product, service number, a random string of digits assigned to the user by the organization, and other forms of identifiers associated with the user for user identification.); accessing, by the AI virtual support agent, based on the digital identification data, historical analytical data (Col 1 lines 58-62 retrieving, based on the personal ID, a user profile associated with the user, wherein the user profile comprises: user attribute data, a clickstream history of the user (historical data), and a product SKU of the product. Col 3 lines 59-67, Col 4 lines 1-9 The user profile data may include user attribute data such as address, geographic location information, marital status, phone number, e-mail address, employment information, employment history, number of dependents, financial and tax history, current tax year information, prior tax year information, medical history, education, demographic information, and other information that describes features or characteristics of the user. In some cases, the user profile data can include product or service specific information, such as a clickstream history (analytical data) of the user or a product SKU of the user's product. For example, the user profile of a user of a software product can include a clickstream history of the user's session(s) with the software product. The clickstream history of the user is a navigation path of the user such as the various tabs, pages, sections, subsections, etc. of the software product that the user has visited in one or more previous sessions (historical data) the user has used the software product.); obtaining a customer statement of understanding for the current problem, based on identified historical data and analytical data (Col 3 lines 10-14 a user may have a query regarding how to access a specific feature of a tax preparation software product, such as retrieving the previous year's tax return information., Col 3 lines 29-32 a user may have a query regarding how to determine the number of dependents to claim when using a tax preparation software product to prepare tax documents.); updating support contact history for the customer comprising a frequency of customer calls and an average duration of the customers call (Col 8 lines 26-42 The user-agent interaction database 304 can store recordings of previous user-agent interactions that can provide information related to the content of user-agent interaction and user sentiment. In some cases, the user-agent interaction database 304 can store chat logs, email chains, and other forms of communications (frequency of customer contact) regarding the interaction between the user and the agent. Each user-agent interaction stored in the user-agent interaction database 304 (e.g., can be converted to score(s), such as a NET PROMOTER SCORE®, a sentiment score, or other metrics that indicates a measurement of user satisfaction, an effectiveness of user-agent interaction, or a measurement associated with the user-agent interaction (e.g., time elapsed during the user-agent interaction) (duration); selecting the support agent based on a specialized area of the support agent for the current problem; automatically routing the customer to the selected support agent (Col 2 lines 2-11 generating, based on the user attribute data, the processed user data, and the agent profile data for each agent in the set of available agents, a predicted quality score for each agent in the set of available agents. The method further includes determining a qualified agent with a highest predicted quality score from the set of available agents, wherein the agent profile data of the agent corresponds to the query. The method further includes routing the user, based on the product SKU, to the agent with the highest predicted quality score.); storing the difficulty of the current problem and a time spent to resolve the current problem for the customer (Col 8 lines 26-42 The user-agent interaction database 304 can store recordings of previous user-agent interactions that can provide information related to the content of user-agent interaction and user sentiment. Each user-agent interaction stored in the user-agent interaction database 304 (e.g., can be converted to score(s), such as a NET PROMOTER SCORE®, a sentiment score, or other metrics that indicates a measurement of user satisfaction, an effectiveness of user-agent interaction, or a measurement associated with the user-agent interaction (e.g., time elapsed during the user-agent interaction) (time spent) Col 10 lines 59-62a featurization system 306 can extract syntax and semantic data 308 from each query. The extracted syntax and semantic data 308 can include an intent of the query and entity information regarding context of the query.)
Khudia (US 2018/01365027 A1) teaches updating the customer proficiency rating, based on support contact history for the customer ([0032] determined that a maximum number of attempts (frequency) has not been reached (at 328), the support process 300 includes determining whether a new request from the requester is needed at 332. The support process 300 returns to selecting the starting point of the support path at 318, and the support process 300 may reevaluate at least one of the issue-related information (determined at 304) or the requester information (determined at 308) in order to select a new starting point for attempting to resolve the issue indicated by the assistance request. The support process 300 may select a different starting point in the same channel 164 that was previously selected, such as a starting point that is more suitable for a requester that has less experience or less proficiency (updated proficiency)with the software application than was previously assumed in one or more prior unsuccessful instances of the process 300, [0033] if it is determined that a new request from the requester is needed (at 332), then the support process 300 includes prompting the requester for a new assistance request at 334. For example, in at least some implementations, the prompting of the requester (at 334) may include querying the requester for a new or differently-worded description of the issue, or querying the requester for additional details regarding the issue. [0032] after prompting the requester for a new assistance request (at 334), the support process 300 returns to receiving the assistance request (at 302), and the above-described operations 302 through 334 may be repeated (updating user information including proficiency/experience/skill) indefinitely until the issue is resolved (at 322), or until the maximum number of attempts has been reached (at 328). comprising a frequency of customer calls for the current problem ([0041] select the starting point (at 410) may be based on a variety of requester information, including age, experience (e.g. experience with software application, experience with client device 110, experience with computers or electronic devices in general, etc.), technical skills of the particular requester (e.g. degree, certification, credential, training, etc.), or other characteristics of the particular requester (e.g. previous instances or interactions with support system 150 (frequency), demographic information, one or more other software applications operated by the particular requester, responses or inputs by the particular requester that indicate proficiency or lack thereof, etc.).
Ruano (US8,155,948 B2) teaches updating the current customer proficiency rating (Col 8 lines 7-14 USD module 230 associates a default USS with the user, until such time as USD module 230 can determine a more accurate skill level (and USS) for the user. In an alternate embodiment, USD module 230 stores USS information associated with each user, according to a unique user identifier associated with the user. In such embodiments, the default USS is the stored USS for the user. Col 8 lines 17-24 USD module 230 revises the stored USS for each user based on newly received user input. Thus, in one embodiment, USD module 230 adaptively configures the USS based on the user's current actual expertise, adjusting the historically-oriented default value to account for changes in the user's skill level (user proficiency rating). For example, as a novice user progresses, the user's skill level typically increases., Col 11 lines 45-51 at block 465, the SDA module adjusts the USS based on the SS of each selected SWP. In one embodiment, USD module 230 adjusts the USS based on the SS of each selected SWP.); storing statistics/data related to the customer in the customer proficiency data set (Col 8 lines 7-14 USD module 230 associates a default USS with the user, until such time as USD module 230 can determine a more accurate skill level (and USS) for the user. In an alternate embodiment, USD module 230 stores USS information associated with each user, according to a unique user identifier associated with the user. In such embodiments, the default USS is the stored USS for the user. Col 8 lines 17-24 USD module 230 revises the stored USS for each user based on newly received user input. Thus, in one embodiment, USD module 230 adaptively configures the USS based on the user's current actual expertise, adjusting the historically-oriented default value to account for changes in the user's skill level (user proficiency rating). For example, as a novice user progresses, the user's skill level typically increases., Col 11 lines 45-51 at block 465, the SDA module adjusts the USS based on the SS of each selected SWP. In one embodiment, USD module 230 adjusts the USS based on the SS of each selected SWP.)
Jungmeisteris et al. (US 2022/0374956 A1) teaches based on receiving a customer response that the current problem is resolved; via the AI virtual support agent, ending the customer support ([0062] FIGS. 4A and 4B each depict a user interface (400 and 410, respectively) which inquire whether the user's problem was solved. In FIG. 4A, the user's problem was resolved, and a progression of screens is displayed in which system 110 requests additional information from the user (402), takes in additional input from the user (404), requests free-form text input with user feedback (406), and end the interaction (408)., Fig 4A #400 user selects ‘yes’ problem solved, 408 disconnect/end interaction)
Wadhwa (US 7,526,722) teaches updating the customer proficiency rating, based on support contact history for the customer comprising a frequency of customer current problem (Col 4 lines 44-55the system may determine a user's proficiency category based on the number of times the user has used the system.(contact history) The user history 112 may indicate the number of times the user has logged in. The more times the user logs in, the more advanced the user may be deemed to be. After a predetermined number of log-ins, the system may update the user's proficiency category to a more advanced level. For example, after a single log-in, the user's proficiency category may be determined to be "beginner," and may be increased to "intermediate" after 10 log-ins. In one example embodiment, the date and/or time of the user's last log-in may be recorded in the user history 112.), and an average duration (Col 5 lines 1-10 The user history 112 may also indicate for a previously encountered event, an amount of time that has passed since occurrence of the event, a number of log-ins after occurrence of the event, and/or a number of times the user has revisited the point in the sequence of executed instructions at which the event had previously occurred without recurrence of the event. As time passes, the number of log-ins increases, and/or the number times the user has revisited the point in the execution sequence increases without recurrence of the event, the user's proficiency category may be advanced.)
Kumar (US10,382,626) discusses the call handling platform computes an experience score for the caller using measurements of a subset of data points based on an interaction of the caller with an interactive voice response (IVR) module during the call. The experience score reflects a numerical measure of a level of satisfaction of the caller in interacting with the IVR module.
However, the prior art fails to teach or suggest at least “accessing, by the AI virtual support agent, based on the digital identification data, a user proficiency data set storing a historical average user proficiency rating, a most recent user proficiency rating, support contact history for the current problem, an average duration of prior support contacts, and prior levels of support used to resolve prior problems; accessing, aggregated support analytics for other users of the product or service area for problems corresponding to the current problem; the set of questions being generated based on the support contact history for the specific user, and the aggregated support analytics for the other users, to obtain user responses; determining, a current user proficiency rating for the specific user based on the user statement, the user responses, the historical average user proficiency rating, the most recent user proficiency rating, the support contact history for the current problem, and the aggregated support analytics for the other users; selecting a support agent to reduce automated re-routing of the electronic request by matching the current user proficiency rating….”. The prior art teachings as recited above fail to set forth any sufficient rationale for combining or otherwise modifying any of the relevant prior art to arrive at the claimed invention, as a whole. To arrive at the claimed invention with the precise combination of claimed features would not have been obvious to one of ordinary skill in the art without relying on improper hindsight to substantially reconstruct Applicant's claimed invention.
Thus, the aforementioned combination of features claimed, as a whole, are not anticipated nor rendered obvious for any sufficient rationale by any of the prior art teachings. Furthermore, the prior art of record does not anticipate nor render obvious the combination of limitations for the dependent claims due to their respective dependencies to the independent claims 1 and 13.
Response to Arguments
Applicant's arguments filed 7/2/26 have been fully considered but they are not persuasive. Regarding 101 rejection, examiner has considered all arguments and respectfully disagrees. New limitations have been addressed in rejection above. The current claims are directed to abstract idea of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), including a person’s interaction with computer) and mathematical calculations (calculating customer proficiency rating), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as an Artificial Intelligence (AI) virtual support agent (Claim 1, 11 , 16), one or more computer processors, a memory (Claim 11), a computer storage medium (Claim 16)), the claims are directed to providing customer support by routing customer to agent based on proficiency rating. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea. The judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing routing based on proficiency rating. In particular, the claims only recites the additional element – an Artificial Intelligence (AI) virtual support agent (Claim 1), one or more computer processors, a memory (Claim 11), a computer storage medium (Claim 16)). The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component and merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding improvement, Examiner respectfully disagrees. While the Applicant’s specification may disclose alleged improvements to processor efficiency, the specification merely recites the alleged improvements ([0013] with no further detail to how the claim set achieves such an improvement. MPEP 2106.05(a) recites “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. Examiner notes neither specification nor claims recite how the improvement/processor efficiency is achieved. The instant claims are directed to an abstract idea, and does not integrate the abstract idea into a practical application. The additional elements recited in the instant claims are only to generic computing components that implement the abstract idea on a computing environment. As such, it can be interpreted that the instant claims only make the abstract idea more efficient, and there are no actual changes/improvements to any computing components.
35U.S.C 103 rejection is withdrawn in view of claim amendments and applicant remarks on pages 21-22.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rath (US 2021/014136) discloses a system receives support tickets and trains a machine-learning model to identify support agents who have experience resolving support tickets of multiple complexities. The system receives a support ticket, identifies a topic of the support ticket, and estimates a complexity of the support ticket.
Gray (US 9,218,410) discloses the adaptive module 430 tracks the behavior of the user (stored in the user information database 440 as engagement performance) and adapts the engagement to adjust to the user's skill level.
Dwane (US 20200364758A1) discloses the CSat prediction data 402 may include a customer support issue category 404, a customer sentiment score 406, an issue complexity rating 408, a classification confidence rating 410, and a customer context score 412,
Kannan (US11,080721) discloses the ASL engine 203 uses a customer experience score to measure, compare, and improve models. FIG. 9 is a block schematic diagram that depicts a model for customer experience score according to the invention. In FIG. 9, an example model for a customer experience score 402 incorporates measures of customer effort involved in an engagement 400, time spent on the engagement 404, and outcome of the engagement 406. The consumer experience score is developed as a statistical model that is a function of these there parameters, where the customer effort score is measures as a function of how long resolution took, how many channels, and how many contacts it took for resolution. (Fig 6)
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 SANGEETA BAHL whose telephone number is (571)270-7779. The examiner can normally be reached 7:30 - 4PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at 571-270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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