DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
This office action is in response to the most recent filings filed by applicant on 01/19/25.
No claims are amended
No claims are cancelled
No claims are added
Claims 1-14 are pending
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 1-14 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-12 is/are directed to a system which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 13 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 14 is/are directed to a non-transitory computer readable medium which is a statutory category.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1, 13 and 14, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Method Claim 13 is directed to an abstract idea, as evidenced by claim limitations “receiving internal data associated with a customer, wherein the customer is a service provider; searching, in an ongoing manner, one or more external data sources that are external from the customer to obtain external data; and sending machine learning output to the customer that presents a proposal of a customer action to address a predicted service degradation of the service provider based on the input of the internal data and the external data to based on historical data of the customer.”
Applicants originally submitted specification recites [0003]: Machine learning (ML) and artificial intelligence (Al) based systems may be used for various applications. The model may be trained for a particular purpose or application. Then, input information may be input to the trained model to generate an output. The results of the output may be further provided as feedback to further train the model. In some aspects, the model may be a machine learning model that uses statistical algorithms to perform tasks or provide predictive analytics. Among other examples, AI/ML models may be used for search engines, recommendation systems, and/or creative tools.
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing performing tasks, recommendations and creative tools to predict service degradation for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 1 and 14 is/are recite substantially similar limitations to independent claim 13 and is/are rejected under 2A for similar reasons to claim 13 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “An asynchronous artificial intelligence (AAI) system comprising: memory; and one or more processors configured to: of the AAI system, an Al or machine learning (AI/ML) model that is trained; A method of providing a proposed customer action via an asynchronous artificial intelligence (AAI) system comprising; A non-transitory computer-readable medium storing computer storing computer executable code for providing a proposed customer action via an asynchronous artificial intelligence (AAI) system, the code when executed by one or more processor causes the one or more processor to:” at a high level of generality such that it amounts to no more than: adding 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). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea with no significantly more elements.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 13 and 14 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0017]-[0018], [0023]-[0029]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers").
The additional elements of a “machine learning model”, “asynchronous artificial intelligence (AAI) system” and “Al or machine learning (AI/ML) model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Similarly dependent claims 2-12 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “wherein the proposal includes one or more of an identification of a cause of the predicted service degradation or an indication or one or more service parameters that are predicted to lead to improvement of the service”. Dependent claims 7 recite “wherein the external data includes one or more of internet search results, one or more geographical maps, one or more satellite images, demographic data for a geographic region, statistical data for the geographic region, weather information, a news event, or a news article”. Dependent claims 8 recite “wherein the internal data includes information obtained at the customer for one or more of capacity for the service, one or more services of the customer, or payment information”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 2 recites “wherein the one or more processors are further configured to: create the AI/ML model based on the historical data of the customer.” Dependent claims 3 recites “wherein the AI/ML model is created based on one or more of a target to be achieved, a data relation, a parameter of the AI/ML model and/or weights of the AI/ML model”. Dependent claims 5 recites “wherein the one or more processors are further configured to: find relation in a combination of the internal data and the external data based on the AI/ML model to output the proposal of the customer action.” Dependent claims 6 recites “wherein the one or more processors are further configured to: receive feedback on an outcome at the customer based on the customer action output as a proposal; and refine the AI/ML model based on the feedback.”.
In these claims, “AI/ML mode” is an additional element, but it is still being recited such that it amounts to no more than: adding 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). As a result, Examiner asserts that dependent claims, such as dependent claims 2-12 are also directed to the abstract idea identified above.
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “An asynchronous artificial intelligence (AAI) system comprising: memory; and one or more processors configured to: of the AAI system, an Al or machine learning (AI/ML) model that is trained; A method of providing a proposed customer action via an asynchronous artificial intelligence (AAI) system comprising; A non-transitory computer-readable medium storing computer storing computer executable code for providing a proposed customer action via an asynchronous artificial intelligence (AAI) system, the code when executed by one or more processor causes the one or more processor to:” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0017]-[0018], [0023]-[0029]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 1 and 14 is/are recite substantially similar limitations to independent claim 13 and is/are rejected under 2B for similar reasons to claim 13 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 13 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-12 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 13 and 14. As a result, Examiner asserts that dependent claims, such as dependent claims 2-12 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharya et al. (US 2023/0245022 A1), and further in view of Pirat et al. (US 2018/0084111 A1).
As per claims 1, 13 and 14: Regarding the claim limitations below, Reference Bhattacharya shows:
An asynchronous artificial intelligence (AAI) system comprising (Reference Bhattacharya shows: [0029] In particular embodiments, a client system 106 may include a web browser 112, such as MICROSOFT EDGE, GOOGLE CHROME, MOZILLA FIREFOX, or APPLE SAFARI, and web browser 112 may have one or more add-ons, plug-ins, or other extensions. A user at a client system 106 may enter a Uniform Resource Locator (URL) or other address directing web browser 112 at client system 106 to a particular server (such as a server associated with customer-intelligence system 102), and web browser 112 may generate a Hyper Text Transfer Protocol (HTTP) request and communicate the HTTP request to that server. The server may accept the HTTP request and communicate to client system 106 one or more Hyper Text Markup Language (HTML) files responsive to the HTTP request. Client system 106 may render a webpage based on the HTML files from the server for presentation to the user. This disclosure contemplates any suitable webpage or other source files. As an example and not by way of limitation, webpages may render from HTML files, Extensible Hyper Text Markup Language (XHTML) files, or Extensible Markup Language (XML) files, according to particular needs. Such pages may also execute scripts such as, for example and without limitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations of markup language and scripts such as AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein, reference to a webpage encompasses one or more corresponding webpage or other source files (which a web browser 112 may use to render the webpage) and vice versa, where appropriate. [0036] In particular embodiments, customer-intelligence dashboard 302 is an artificial intelligence (AI)-driven early-warning dashboard that helps businesses reduce churn and accelerate revenue growth, unlocking predictive customer intelligence from existing customer data. More information on the use of AI in customer intelligence may be found in U.S. patent application Ser. No. 17/515,314, filed 29 Oct. 2021 and entitled Named Entity Recognition System for Sentiment Labeling, which is incorporated herein by reference in its entirety. In particular embodiments, AI automatically picks up on trends and patterns as the business gains more customers. The more data and feedback the AI receives, the better its predictions become. In particular embodiments, the customer-intelligence dashboard unifies KPIs for customer experience from product usage, support, and communication signals from data sources 104. These signals then go through particular algorithms to predict top churn risks, revenue expansion opportunities, and tasks to improve overall customer experience.):
Regarding the claim limitations below, Reference Bhattacharya shows:
Memory (Reference Bhattacharya shows: [0099] In particular embodiments, memory 1604 includes main memory for storing instructions for processor 1602 to execute or data for processor 1602 to operate on. As an example and not by way of limitation, computer system 1600 may load instructions from storage 1606 or another source (such as, for example, another computer system 1600) to memory 1604. Processor 1602 may then load the instructions from memory 1604 to an internal register or internal cache. To execute the instructions, processor 1602 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 1602 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 1602 may then write one or more of those results to memory 1604. In particular embodiments, processor 1602 executes only instructions in one or more internal registers or internal caches or in memory 1604 (as opposed to storage 1606 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 1604 (as opposed to storage 1606 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor 1602 to memory 1604. Bus 1616 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 1602 and memory 1604 and facilitate accesses to memory 1604 requested by processor 1602. In particular embodiments, memory 1604 includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory 1604 may include one or more memories 1604, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory); and
Regarding the claim limitations below, Reference Bhattacharya shows:
one or more processors configured to (Reference Bhattacharya shows: [0099] In particular embodiments, memory 1604 includes main memory for storing instructions for processor 1602 to execute or data for processor 1602 to operate on. As an example and not by way of limitation, computer system 1600 may load instructions from storage 1606 or another source (such as, for example, another computer system 1600) to memory 1604. Processor 1602 may then load the instructions from memory 1604 to an internal register or internal cache. To execute the instructions, processor 1602 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 1602 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 1602 may then write one or more of those results to memory 1604. In particular embodiments, processor 1602 executes only instructions in one or more internal registers or internal caches or in memory 1604 (as opposed to storage 1606 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 1604 (as opposed to storage 1606 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor 1602 to memory 1604. Bus 1616 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 1602 and memory 1604 and facilitate accesses to memory 1604 requested by processor 1602. In particular embodiments, memory 1604 includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory 1604 may include one or more memories 1604, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory):
Regarding the claim limitations below, Reference Bhattacharya shows:
A method of providing a proposed customer action via an asynchronous artificial intelligence (AAI) system comprising (Reference Bhattacharya shows: [0029] In particular embodiments, a client system 106 may include a web browser 112, such as MICROSOFT EDGE, GOOGLE CHROME, MOZILLA FIREFOX, or APPLE SAFARI, and web browser 112 may have one or more add-ons, plug-ins, or other extensions. A user at a client system 106 may enter a Uniform Resource Locator (URL) or other address directing web browser 112 at client system 106 to a particular server (such as a server associated with customer-intelligence system 102), and web browser 112 may generate a Hyper Text Transfer Protocol (HTTP) request and communicate the HTTP request to that server. The server may accept the HTTP request and communicate to client system 106 one or more Hyper Text Markup Language (HTML) files responsive to the HTTP request. Client system 106 may render a webpage based on the HTML files from the server for presentation to the user. This disclosure contemplates any suitable webpage or other source files. As an example and not by way of limitation, webpages may render from HTML files, Extensible Hyper Text Markup Language (XHTML) files, or Extensible Markup Language (XML) files, according to particular needs. Such pages may also execute scripts such as, for example and without limitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations of markup language and scripts such as AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein, reference to a webpage encompasses one or more corresponding webpage or other source files (which a web browser 112 may use to render the webpage) and vice versa, where appropriate. [0036] In particular embodiments, customer-intelligence dashboard 302 is an artificial intelligence (AI)-driven early-warning dashboard that helps businesses reduce churn and accelerate revenue growth, unlocking predictive customer intelligence from existing customer data. More information on the use of AI in customer intelligence may be found in U.S. patent application Ser. No. 17/515,314, filed 29 Oct. 2021 and entitled Named Entity Recognition System for Sentiment Labeling, which is incorporated herein by reference in its entirety. In particular embodiments, AI automatically picks up on trends and patterns as the business gains more customers. The more data and feedback the AI receives, the better its predictions become. In particular embodiments, the customer-intelligence dashboard unifies KPIs for customer experience from product usage, support, and communication signals from data sources 104. These signals then go through particular algorithms to predict top churn risks, revenue expansion opportunities, and tasks to improve overall customer experience.):
Regarding the claim limitations below, Reference Bhattacharya shows:
A non-transitory computer-readable medium storing computer storing computer executable code for providing a proposed customer action via an asynchronous artificial intelligence (AAI) system, the code when executed by one or more processor causes the one or more processor to (Reference Bhattacharya shows: [0029] In particular embodiments, a client system 106 may include a web browser 112, such as MICROSOFT EDGE, GOOGLE CHROME, MOZILLA FIREFOX, or APPLE SAFARI, and web browser 112 may have one or more add-ons, plug-ins, or other extensions. A user at a client system 106 may enter a Uniform Resource Locator (URL) or other address directing web browser 112 at client system 106 to a particular server (such as a server associated with customer-intelligence system 102), and web browser 112 may generate a Hyper Text Transfer Protocol (HTTP) request and communicate the HTTP request to that server. The server may accept the HTTP request and communicate to client system 106 one or more Hyper Text Markup Language (HTML) files responsive to the HTTP request. Client system 106 may render a webpage based on the HTML files from the server for presentation to the user. This disclosure contemplates any suitable webpage or other source files. As an example and not by way of limitation, webpages may render from HTML files, Extensible Hyper Text Markup Language (XHTML) files, or Extensible Markup Language (XML) files, according to particular needs. Such pages may also execute scripts such as, for example and without limitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations of markup language and scripts such as AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein, reference to a webpage encompasses one or more corresponding webpage or other source files (which a web browser 112 may use to render the webpage) and vice versa, where appropriate. [0036] In particular embodiments, customer-intelligence dashboard 302 is an artificial intelligence (AI)-driven early-warning dashboard that helps businesses reduce churn and accelerate revenue growth, unlocking predictive customer intelligence from existing customer data. More information on the use of AI in customer intelligence may be found in U.S. patent application Ser. No. 17/515,314, filed 29 Oct. 2021 and entitled Named Entity Recognition System for Sentiment Labeling, which is incorporated herein by reference in its entirety. In particular embodiments, AI automatically picks up on trends and patterns as the business gains more customers. The more data and feedback the AI receives, the better its predictions become. In particular embodiments, the customer-intelligence dashboard unifies KPIs for customer experience from product usage, support, and communication signals from data sources 104. These signals then go through particular algorithms to predict top churn risks, revenue expansion opportunities, and tasks to improve overall customer experience. [0105] Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate):
Regarding the claim limitations below, Reference Bhattacharya shows:
receive internal data associated with a customer of the AAI system, wherein the customer is a service provider (Reference Bhattacharya shows: [0006] Particular embodiments may help a business entity to see the big picture of its customer accounts and growth opportunities. In particular embodiments, a user may receive insights on customer accounts and products so the user may focus on planning growth strategies while customer owners (or customer-success managers (CSMs)) focus on fighting churn and turning at-risk customers into more loyal customers. Particular embodiments handle tedious, manual back-end tasks to reduce time requirements associated with those tasks and to help generate more accurate, reliable insights. Particular embodiments capture and analyze both quantitative and qualitative information to obtain more accurate predictions and develop more accurate customer profiles. Particular embodiments provide prescriptive insights and predictions facilitating progress toward a business's growth goals. [0007] Particular embodiments facilitate customer intelligence by automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users. Particular embodiments provide intuitive presentation of complex information to users who need to understand the data to gain actionable insights into customer experience and customer needs. [0037] In particular embodiments, customer-intelligence dashboard 302 is intuitive, automated, actionable, and AI-driven and facilitates the automatic capture and analysis of customer data for insights to predict potential customer issues and better understand customer-growth potential. In particular embodiments, to generate these predictions, customer-intelligence system 102 integrates with over 176 data sources 104 used by clients. Through these integrations, customer-intelligence system 102 receives access to customer data in data sources 104. While this data may vary depending on the business model, particular embodiments assess and analyze particular categories and KPIs to create more comprehensive customer profiles and provide more accurate churn prediction. In particular embodiments, one or more of the following categories are assessed and analyzed: customer name; customer owner (or CSM); product(s); revenue (e.g. annual recurring revenue (ARR)); or renewal date. In particular embodiments, one or more of the following KPIs are assessed and analyzed: product usage (which may be the frequency at which the customer uses or purchase the product(s)); interaction frequency (which may be the frequency of interaction between the customer and the business and may be measured in e-mail, telephone calls, and visits); NPS/CSAT (which may be the level of customer satisfaction); number of support tickets (which may be the number of support requests submitted by the customer); severity of support tickets (which may be the severity of the issues in the support tickets); customer sentiment (which may be an analysis of written interaction between the business and customer); customer-owner pulse (which may be an assessments of the business's relationship with the customer (or “customer health”) by the business's customer owner (or customer-success manager (CSM) for the customer); up-sells/down-sells (which may be an increase or decrease in revenue generated from the customer); or customer maturity (which may be where the customer is in a customer lifecycle with respect to the business). Although particular categories and particular KPIs are described and illustrated, this disclosure contemplates any suitable categories and any suitable particular KPIs.);
Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
search, in an ongoing manner, one or more external data sources that are external from the customer to obtain external data
Regarding the claim limitations above, Reference Bhattacharya shows “search, in an ongoing manner, one or more … data sources …” :[0006] Particular embodiments may help a business entity to see the big picture of its customer accounts and growth opportunities. In particular embodiments, a user may receive insights on customer accounts and products so the user may focus on planning growth strategies while customer owners (or customer-success managers (CSMs)) focus on fighting churn and turning at-risk customers into more loyal customers. Particular embodiments handle tedious, manual back-end tasks to reduce time requirements associated with those tasks and to help generate more accurate, reliable insights. Particular embodiments capture and analyze both quantitative and qualitative information to obtain more accurate predictions and develop more accurate customer profiles. Particular embodiments provide prescriptive insights and predictions facilitating progress toward a business's growth goals. [0007] Particular embodiments facilitate customer intelligence by automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users. Particular embodiments provide intuitive presentation of complex information to users who need to understand the data to gain actionable insights into customer experience and customer needs. [0037] In particular embodiments, customer-intelligence dashboard 302 is intuitive, automated, actionable, and AI-driven and facilitates the automatic capture and analysis of customer data for insights to predict potential customer issues and better understand customer-growth potential. In particular embodiments, to generate these predictions, customer-intelligence system 102 integrates with over 176 data sources 104 used by clients. Through these integrations, customer-intelligence system 102 receives access to customer data in data sources 104. While this data may vary depending on the business model, particular embodiments assess and analyze particular categories and KPIs to create more comprehensive customer profiles and provide more accurate churn prediction. In particular embodiments, one or more of the following categories are assessed and analyzed: customer name; customer owner (or CSM); product(s); revenue (e.g. annual recurring revenue (ARR)); or renewal date. In particular embodiments, one or more of the following KPIs are assessed and analyzed: product usage (which may be the frequency at which the customer uses or purchase the product(s)); interaction frequency (which may be the frequency of interaction between the customer and the business and may be measured in e-mail, telephone calls, and visits); NPS/CSAT (which may be the level of customer satisfaction); number of support tickets (which may be the number of support requests submitted by the customer); severity of support tickets (which may be the severity of the issues in the support tickets); customer sentiment (which may be an analysis of written interaction between the business and customer); customer-owner pulse (which may be an assessments of the business's relationship with the customer (or “customer health”) by the business's customer owner (or customer-success manager (CSM) for the customer); up-sells/down-sells (which may be an increase or decrease in revenue generated from the customer); or customer maturity (which may be where the customer is in a customer lifecycle with respect to the business). Although particular categories and particular KPIs are described and illustrated, this disclosure contemplates any suitable categories and any suitable particular KPIs.).
Even though, it is reasonably understood that the system above is searching external and internal data sources ([0007] and [0037]). Bhattacharya does not explicitly use the term “external” for the data source. As such, Reference Pirat shows “external” in the claim limitations above at least in [0182]. According to one embodiment, the predictive analytics module 260 is configured to collect data from various external and internal sources, and generate a multimodal predictive model for determining, for example, an optimal communication channel to engage with the customer 210. According to one embodiment, an optimal channel is one that is determined, based on the predictive model, to render an optimal reward for the contact center, customer, or a combination of both. For example, the optimal channel may be one that is predicted to optimize business objectives, goals, rewards, or payoffs (collectively referred to as a “reward.”). An exemplary business objective that may be optimized may be a key performance indicator (KPI) such as, for example, conversion rate, time to resolution, and the like. [0184] The external sources providing data to the predictive analytics module 260 may include, for example, contact activity of the customer 210, social media data, interaction disposition data, and/or the like. The internal sources of data may include, for example, interaction data, customer profile, customer context, agent disposition, call center capabilities, call center load, CRM data, and the like. [0185] FIG. 10 is a flow diagram of a process for generating and updating a multimodal predictive model according to an example embodiment of the present invention. In action 1210, the predictive analytics module 260 aggregates data from various external and internal sources 1209a-1209c. The data may include, for example, contact activity of the customer, social media data, disposition data, interaction data, customer profile, customer context, customer preferences, agent preferences, contact center capabilities, contact center load, statistics, historical data for specific interaction types, and/or the like.
Reference Bhattacharya and Reference Pirat are analogous prior art to the claimed invention because the references generally relate to field of providing support services for businesses. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Pirat, particularly the ability to search external data sources (see Pirat [0182]-[0185]), in the disclosure of Reference Bhattacharya, particularly in the automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users (see Bhattacharya [0007]), in order to provide for a system that allows collection of data from various external and internal sources, and generate a multimodal predictive model for determining, for example, an optimal communication channel to engage with the customer 210 as taught by Reference Pirat (see at least in [0182]), where upon the execution of the method and system of Reference Pirat for generating and updating a multimodal predictive model where the predictive analytics module 260 aggregates data from various external and internal sources 1209a-1209c. The data may include, for example, contact activity of the customer, social media data, disposition data, interaction data, customer profile, customer context, customer preferences, agent preferences, contact center capabilities, contact center load, statistics, historical data for specific interaction types (Pirat: [0185]) and/or the like so that the process of providing support services for businesses can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar providing support services for businesses field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Bhattacharya in view of Reference Pirat, the results of the combination were predictable (MPEP 2143 A); and
Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
send machine learning output to the customer that presents a proposal of a customer action to address a predicted service degradation of the service provider based on the input of the internal data and the external data to an Al or machine learning (AI/ML) model that is trained based on historical data of the customer
Both Reference Bhattacharya and Reference Pirat show the above limitations. Reference Bhattacharya shows: [0090] FIG. 12 illustrates example presentation of example information cards on an example customer on an example customer-intelligence dashboard 302. In particular embodiments, the user may create information cards with action items and delegations to colleagues or the user based on the predictions and recommendations provided by customer-intelligence system. This may serve as a single location where the user and the user's colleagues may see unified customer data for one or more customers, along with predictions, recommendations, and action items.
Reference Pirat also shows: [0205] FIG. 12 is a functional layout diagram of the predictive analytics module 260 according to an exemplary embodiment of the invention. According to this exemplary embodiment, the problem of interaction medium selection/recommendation for long term reward maximization may be formulated as a reinforcement learning problem such as, for example, a “contextual bandits” problem, or more specifically, a k-armed contextual bandit problem known by those of skill in the art. The context or observation includes information on customers, agents, and interactions; the action is the selection of an interaction medium that is recommended to be used in handling the interaction; and the reward is feedback from the environment on completion of the interaction (e.g. value of an achieved goal).
As per claim 2: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the one or more processors are further configured to:
create the AI/ML model based on the historical data of the customer.
Reference Bhattacharya shows [0088] FIG. 10 illustrates example presentation of example customer history for an example customer on an example customer-intelligence dashboard 302. In particular embodiments, a history window provides access to all interaction information associated with the customer. This may include chronological information of updates to the KPIs week over week or month over month, depending on how often the dashboard is refreshed. It may also include e-mail content and e-mail sentiment (e.g. whether the e-mail is positive (high), negative (low), or neutral (medium)). This scoring may be based on sentiment analysis of each e-mail and particular pre-determined keywords. This may help the user access historical data and, using AI, assess that data to determine next steps.
As per claim 3: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the AI/ML model is created based on one or more of a target to be achieved, a data relation, a parameter of the AI/ML model and/or weights of the AI/ML model.
Reference Bhattacharya shows in [0044] In particular embodiments, to generate a customer-health score, customer-intelligence system 102 may use clustering module 204 to segment customer data into component parts by analyzing similarities among the feature space. Herein, reference to a feature may encompass a KPI, and vice versa, where appropriate. This may provide users with a deeper understanding of the data-normalization-and-inference process without requiring users to be well versed in machine learning (ML) or statistics. Particular embodiments provide a renewal-probability score for each customer based on a seven-feature or nine-feature model. In particular embodiments, clustering module 204 uses SHapley Additive exPlanations (SHAP) via Shapley values to represent feature weightages. Clustering module 204 may also incorporate segmentation using a K-Means Clustering Algorithm with Elbow Method to determine K. Clustering module 204 may also review which of all possible features are available for a particular business. In particular embodiments, use of clustering module 204 in customer-intelligence system 102 facilitates scaling the clustering and benchmarking process and clustering module 204 provides deep context on the feature space to help users contextualize the renewal probability outputs and understand customer segmentation in a comprehensive user interface (UI). [0045] FIG. 5 illustrates example KPI weighting in example customer-health score generation. Particular embodiments combine feature existence, weightage analysis, clustering, and benchmarking in a comprehensive UI for customer-health-score understanding and validation. In the example of FIG. 5, four KPIs are used to generate a customer-health score (product usage, NPS/CSAT, interaction frequency, and customer-owner pulse), with other KPIs being unavailable and weighted at zero. In particular embodiments, dynamic-clustering component parts are available on customer-intelligence dashboard 302. The data will be refreshed on a weekly basis, generating new segments, existence analysis, and feature weightages to be displayed in the Clustering tab of the product. [0046] Particular embodiments facilitate better understanding of the relevance of each KPI and provide information describing the relative weightage of each KPI based on SHAP. The KPIs may be color coded and are represented in a pie chart. The outcome is a visual representation of the relative weightages of each feature component of the health-score algorithm executed by health-score module 206. In particular embodiments, the health-score model takes normalized numeric X={0, 1, 2} features and returns the numeric prediction Y={0, . . . ,99}. To achieve the 0, 1, or 2 normalized values over the KPIs, particular embodiments use the following function:
As per claim 4: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the proposal includes one or more of an identification of a cause of the predicted service degradation or an indication or one or more service parameters that are predicted to lead to improvement of the service.
Reference Bhattacharya shows [0032] relevant logins by the customer; relevant sessions by the customer; relevant session times for the customer; relevant API calls by the customer; relevant API throttle limits for the customer; relevant API usage cyclicity by the customer; relevant report downloads by the customer; relevant page views by the customer; transactions between the business and customer; billable actions by the business for the customer; non-billable actions by the business for the customer; value-added actions by the business for the customer; relevant product add-ons requested by the customer; relevant time on product by the customer; relevant quote-to-order conversion for the customer; invoices generated by the business for the customer; order volume for the customer; a maturity level of the customer with respect to the business; identification of the business's customer owner (or CSM) for the customer; relevant revenue (e.g. annual recurring revenue (ARR)) attributable to the customer; relevant renewal dates for the customer with respect to the business. Although particular customer data in particular data sources is described and illustrated, this disclosure contemplates any suitable customer data in any suitable data sources. Herein, reference to a product provided by a business may encompass a service provided by the business, and vice versa, where appropriate. [0033] Customer-intelligence system 102 may be a network-addressable computing system that can host an online customer-intelligence platform providing one or more customer-intelligence web-based applications. Customer-intelligence system 102 may integrate with data sources 104 and, through those integrations, access customer data stored at data sources 104. Customer-intelligence system 102 may analyze the customer data and, based on the analysis, generate predictions about customer churn and opportunities for expanding revenue and identify tasks to improve overall customer experience. Customer-intelligence system 102 may organize that information for presentation to users at client systems 106, who may then act on that information. [0036] In particular embodiments, customer-intelligence dashboard 302 is an artificial intelligence (AI)-driven early-warning dashboard that helps businesses reduce churn and accelerate revenue growth, unlocking predictive customer intelligence from existing customer data. More information on the use of AI in customer intelligence may be found in U.S. patent application Ser. No. 17/515,314, filed 29 Oct. 2021 and entitled Named Entity Recognition System for Sentiment Labeling, which is incorporated herein by reference in its entirety. In particular embodiments, AI automatically picks up on trends and patterns as the business gains more customers. The more data and feedback the AI receives, the better its predictions become. In particular embodiments, the customer-intelligence dashboard unifies KPIs for customer experience from product usage, support, and communication signals from data sources 104. These signals then go through particular algorithms to predict top churn risks, revenue expansion opportunities, and tasks to improve overall customer experience. [0039] In particular embodiments, each KPI measures a different aspect of a customer. As an example, product usage may indicate how often the customer is using a particular product, which may indicate how important that product is to the customer. As another example, interaction frequency between the customer and the business (whether e-mails, calls, or visits) may indicate the customer's commitment or loyalty to the business. However, interaction frequency alone may be insufficient to fully indicate the customer's commitment or loyalty to the business. The content of those interactions (what the customer writes or says in those e-mails, calls, and visits) is important. This is represented by renewal sentiment. When dealing with such complex customer data, particular embodiments use AI to calculate the business's unique KPIs and automatically compares them to industry best practices, which results in more suitable or reasonable value for the business's unique situations.
As per claim 5: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the one or more processors are further configured to:
find relation in a combination of the internal data and the external data based on the AI/ML model to output the proposal of the customer action.
Regarding the claim limitations above, Reference Bhattacharya shows “wherein the one or more processors are further configured to: find relation in a combination of the internal data and the … data based on the AI/ML model to output the proposal of the customer action.” :[0006] Particular embodiments may help a business entity to see the big picture of its customer accounts and growth opportunities. In particular embodiments, a user may receive insights on customer accounts and products so the user may focus on planning growth strategies while customer owners (or customer-success managers (CSMs)) focus on fighting churn and turning at-risk customers into more loyal customers. Particular embodiments handle tedious, manual back-end tasks to reduce time requirements associated with those tasks and to help generate more accurate, reliable insights. Particular embodiments capture and analyze both quantitative and qualitative information to obtain more accurate predictions and develop more accurate customer profiles. Particular embodiments provide prescriptive insights and predictions facilitating progress toward a business's growth goals. [0007] Particular embodiments facilitate customer intelligence by automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users. Particular embodiments provide intuitive presentation of complex information to users who need to understand the data to gain actionable insights into customer experience and customer needs. [0037] In particular embodiments, customer-intelligence dashboard 302 is intuitive, automated, actionable, and AI-driven and facilitates the automatic capture and analysis of customer data for insights to predict potential customer issues and better understand customer-growth potential. In particular embodiments, to generate these predictions, customer-intelligence system 102 integrates with over 176 data sources 104 used by clients. Through these integrations, customer-intelligence system 102 receives access to customer data in data sources 104. While this data may vary depending on the business model, particular embodiments assess and analyze particular categories and KPIs to create more comprehensive customer profiles and provide more accurate churn prediction. In particular embodiments, one or more of the following categories are assessed and analyzed: customer name; customer owner (or CSM); product(s); revenue (e.g. annual recurring revenue (ARR)); or renewal date. In particular embodiments, one or more of the following KPIs are assessed and analyzed: product usage (which may be the frequency at which the customer uses or purchase the product(s)); interaction frequency (which may be the frequency of interaction between the customer and the business and may be measured in e-mail, telephone calls, and visits); NPS/CSAT (which may be the level of customer satisfaction); number of support tickets (which may be the number of support requests submitted by the customer); severity of support tickets (which may be the severity of the issues in the support tickets); customer sentiment (which may be an analysis of written interaction between the business and customer); customer-owner pulse (which may be an assessments of the business's relationship with the customer (or “customer health”) by the business's customer owner (or customer-success manager (CSM) for the customer); up-sells/down-sells (which may be an increase or decrease in revenue generated from the customer); or customer maturity (which may be where the customer is in a customer lifecycle with respect to the business). Although particular categories and particular KPIs are described and illustrated, this disclosure contemplates any suitable categories and any suitable particular KPIs.).
Even though, it is reasonably understood that the system above is searching external and internal data sources ([0007] and [0037]). Bhattacharya does not explicitly use the term “external” for the data source. As such, Reference Pirat shows “external” in the claim limitations above at least in [0182]. According to one embodiment, the predictive analytics module 260 is configured to collect data from various external and internal sources, and generate a multimodal predictive model for determining, for example, an optimal communication channel to engage with the customer 210. According to one embodiment, an optimal channel is one that is determined, based on the predictive model, to render an optimal reward for the contact center, customer, or a combination of both. For example, the optimal channel may be one that is predicted to optimize business objectives, goals, rewards, or payoffs (collectively referred to as a “reward.”). An exemplary business objective that may be optimized may be a key performance indicator (KPI) such as, for example, conversion rate, time to resolution, and the like. [0184] The external sources providing data to the predictive analytics module 260 may include, for example, contact activity of the customer 210, social media data, interaction disposition data, and/or the like. The internal sources of data may include, for example, interaction data, customer profile, customer context, agent disposition, call center capabilities, call center load, CRM data, and the like. [0185] FIG. 10 is a flow diagram of a process for generating and updating a multimodal predictive model according to an example embodiment of the present invention. In action 1210, the predictive analytics module 260 aggregates data from various external and internal sources 1209a-1209c. The data may include, for example, contact activity of the customer, social media data, disposition data, interaction data, customer profile, customer context, customer preferences, agent preferences, contact center capabilities, contact center load, statistics, historical data for specific interaction types, and/or the like.
Reference Bhattacharya and Reference Pirat are analogous prior art to the claimed invention because the references generally relate to field of providing support services for businesses. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Pirat, particularly the ability to search external data sources (see Pirat [0182]-[0185]), in the disclosure of Reference Bhattacharya, particularly in the automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users (see Bhattacharya [0007]), in order to provide for a system that allows collection of data from various external and internal sources, and generate a multimodal predictive model for determining, for example, an optimal communication channel to engage with the customer 210 as taught by Reference Pirat (see at least in [0182]), where upon the execution of the method and system of Reference Pirat for generating and updating a multimodal predictive model where the predictive analytics module 260 aggregates data from various external and internal sources 1209a-1209c. The data may include, for example, contact activity of the customer, social media data, disposition data, interaction data, customer profile, customer context, customer preferences, agent preferences, contact center capabilities, contact center load, statistics, historical data for specific interaction types (Pirat: [0185]) and/or the like so that the process of providing support services for businesses can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar providing support services for businesses field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Bhattacharya in view of Reference Pirat, the results of the combination were predictable (MPEP 2143 A).
As per claim 6: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the one or more processors are further configured to:
receive feedback on an outcome at the customer based on the customer action output as a proposal; and
refine the AI/ML model based on the feedback.
Reference Bhattacharya shows in [0036] In particular embodiments, customer-intelligence dashboard 302 is an artificial intelligence (AI)-driven early-warning dashboard that helps businesses reduce churn and accelerate revenue growth, unlocking predictive customer intelligence from existing customer data. More information on the use of AI in customer intelligence may be found in U.S. patent application Ser. No. 17/515,314, filed 29 Oct. 2021 and entitled Named Entity Recognition System for Sentiment Labeling, which is incorporated herein by reference in its entirety. In particular embodiments, AI automatically picks up on trends and patterns as the business gains more customers. The more data and feedback the AI receives, the better its predictions become. In particular embodiments, the customer-intelligence dashboard unifies KPIs for customer experience from product usage, support, and communication signals from data sources 104. These signals then go through particular algorithms to predict top churn risks, revenue expansion opportunities, and tasks to improve overall customer experience. [0091] FIG. 13 illustrates an example window for example prediction feedback on an example customer-intelligence dashboard 302. In particular embodiments, customer-intelligence dashboard 302 enables the user to provide feedback and establish a continuous feedback loop if the user wants to add additional context or other information to predictions or recommendations being generated by customer-intelligence system 102, which may enable continual training of the AI of customer-intelligence system 102 based on varying business models. [0092] FIG. 14 illustrates example window 1400 presenting KPIs and their contributions to a customer-health score. In the example of FIG. 14, a user may select an icon (not illustrated in FIG. 14) on customer-intelligence dashboard 302 and, in response, be directed to window 1400. In window 1400, a user may select a segment and view a description and breakdown of the segment. In addition, the user may view the benchmarking used with each KPI to determine the scores (high, medium, and low) that are being presented for the KPIs and a breakdown of the data for each KPI.
As per claim 7: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the external data includes one or more of internet search results, one or more geographical maps, one or more satellite images, demographic data for a geographic region, statistical data for the geographic region, weather information, a news event, or a news article.
Reference Bhattacharya shows [0043] Returning to FIG. 3, customer-intelligence dashboard 302 also presents a customer-health score (shown on the right side of customer-intelligence dashboard 302) for each customer, along with a change (positive or negative) since the last health score. In particular embodiments, a health score for a customer indicates a likelihood that the customer will churn, renew, or upsell. Generally, the higher the health score, the stronger the relationship with the customer and the greater the possibility of up-selling the customer. Similarly, the lower the health score, the weaker the relationship with the customer and the greater the possibility of churn. Generally, the more neutral the health score, the more neutral the relationship with the customer and the greater the possibility of the customer renewing without up-selling. In particular embodiments, a customer with a health score that has fallen below a threshold value may represent a high probability of churn. The business may want to take preventative measures with that customer before churn occurs. A higher customer-health score may indicate that the customer has been using the business's product(s) frequently and is satisfied and turning into a loyal customer. A loyal customer may be a good candidate for upsell or cross-sell by the business. [0044] In particular embodiments, to generate a customer-health score, customer-intelligence system 102 may use clustering module 204 to segment customer data into component parts by analyzing similarities among the feature space. Herein, reference to a feature may encompass a KPI, and vice versa, where appropriate. This may provide users with a deeper understanding of the data-normalization-and-inference process without requiring users to be well versed in machine learning (ML) or statistics. Particular embodiments provide a renewal-probability score for each customer based on a seven-feature or nine-feature model. In particular embodiments, clustering module 204 uses SHapley Additive exPlanations (SHAP) via Shapley values to represent feature weightages. Clustering module 204 may also incorporate segmentation using a K-Means Clustering Algorithm with Elbow Method to determine K. Clustering module 204 may also review which of all possible features are available for a particular business. In particular embodiments, use of clustering module 204 in customer-intelligence system 102 facilitates scaling the clustering and benchmarking process and clustering module 204 provides deep context on the feature space to help users contextualize the renewal probability outputs and understand customer segmentation in a comprehensive user interface (UI).
As per claim 8: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the internal data includes information obtained at the customer for one or more of capacity for the service, one or more services of the customer, or payment information.
Reference Bhattacharya shows [0069] Description [0070] Number of customers per cluster [0071] Average revenue per customer per cluster [0072] Number of customers per cluster with an active subscription to the product or service [0073] Average amount of money active customers spend on the product or service [0074] Number of customers per cluster that terminated their subscription to the product or service [0075] Average amount of money churned customers spent on the product or service when they were active [0076] Active customers by name [0077] Churned customers by name [0078] Benchmarks [0079] Name of metric (i.e.: product usage, interaction frequency, etc.) [0080] Items [0081] Name of metric per KPI (i.e.: Interaction Frequency: E-mails, Interaction Frequency: Calls) [0082] High From: Minimum of the metric's raw values (e.g.: For this cluster, the smallest number of e-mails that may be exchanged for a company to still be considered to have overall high interaction frequency) To: Maximum of the metric's raw values (e.g.: For this cluster, this will be the highest number of e-mails exchanged across the dataset) [0083] Medium From: Minimum of the metric's raw values (e.g.: For this cluster, the smallest number of e-mails that may be exchanged for a company to still be considered to have overall medium interaction frequency) To: Maximum of the metric's raw values (e.g.: For this cluster, the largest number of e-mails that may be exchanged for a company to still be considered to have overall medium interaction frequency) [0084] Low From: Minimum of the metric's raw values (e.g.: For this cluster, this will be the lowest number of e-mails exchanged across the dataset) To: Maximum of the metric's raw values (e.g.: For this cluster, the largest number of e-mails that may be exchanged for a company to still be considered to have overall low interaction frequency).
As per claim 9: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the customer is a broadband service operator, and wherein the proposal is to offer an adjusted service to a particular customer.
Regarding the claims above, Reference Bhattacharya does not show the above limitation. However, Reference Pirat shows in [0122] In some embodiments, the user may be having issues with, for example, his or her internet service, and the user may be provided with a link with instructions with step by step fault diagnosis, while the orchestration module invokes a backend system (e.g., a business support system 245) to run diagnosis to check the connection to the user's modem. [0065] The call controller 118 may be configured to process PSTN calls, VoIP calls, and the like. For example, the communication server 118 may be configured with computer-telephony integration (CTI) software for interfacing with the switch/media gateway and contact center equipment. In one embodiment, the call controller 118 may include a session initiation protocol (SIP) server for processing SIP calls. According to some exemplary embodiments, the call controller 118 may, for example, extract data about the customer interaction such as the caller's telephone number, often known as the automatic number identification (ANI) number, or the customer's internet protocol (IP) address, or email address, and communicate with other CC components in processing the interaction. [0082] In some embodiments, the rerouting service 131 may be invoked by a current routing strategy for determining whether an interaction invoked via a current media channel should be rerouted to a second (different) media channel. The second media channel may be one determined to have a waiting time that satisfies a threshold waiting time. According to one embodiment, the rerouting service 131 (or orchestration module 230) may be configured to reserve a resource (e.g., a live agent) at the second media channel upon determining that the rerouting is needed or desired. [0083] In some embodiments, the universal menus module 129 may be invoked for dynamically generating an appropriate self-service menu based on the detected modality of a current interaction. According to one embodiment, the various self-service menus are generated from the single server without the need to invoke separate self-service menu servers that would depend on the modality that is invoked.
Reference Bhattacharya and Reference Pirat are analogous prior art to the claimed invention because the references generally relate to field of providing support services for businesses. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Pirat, particularly the ability to search external data sources (see Pirat [0182]-[0185]), in the disclosure of Reference Bhattacharya, particularly in the automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users (see Bhattacharya [0007]), in order to provide for a system that allows collection of data from various external and internal sources, and generate a multimodal predictive model for determining, for example, an optimal communication channel to engage with the customer 210 as taught by Reference Pirat (see at least in [0182]), where upon the execution of the method and system of Reference Pirat for generating and updating a multimodal predictive model where the predictive analytics module 260 aggregates data from various external and internal sources 1209a-1209c. The data may include, for example, contact activity of the customer, social media data, disposition data, interaction data, customer profile, customer context, customer preferences, agent preferences, contact center capabilities, contact center load, statistics, historical data for specific interaction types (Pirat: [0185]) and/or the like so that the process of providing support services for businesses can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar providing support services for businesses field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Bhattacharya in view of Reference Pirat, the results of the combination were predictable (MPEP 2143 A).
As per claim 10: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the customer is a broadband service operator, and wherein the proposal is to install added service points based on a temporary increase in service need based on an event.
Regarding the claims above, Reference Bhattacharya does not show the above limitation. However, Reference Pirat shows in [0122] In some embodiments, the user may be having issues with, for example, his or her internet service, and the user may be provided with a link with instructions with step by step fault diagnosis, while the orchestration module invokes a backend system (e.g., a business support system 245) to run diagnosis to check the connection to the user's modem. [0065] The call controller 118 may be configured to process PSTN calls, VoIP calls, and the like. For example, the communication server 118 may be configured with computer-telephony integration (CTI) software for interfacing with the switch/media gateway and contact center equipment. In one embodiment, the call controller 118 may include a session initiation protocol (SIP) server for processing SIP calls. According to some exemplary embodiments, the call controller 118 may, for example, extract data about the customer interaction such as the caller's telephone number, often known as the automatic number identification (ANI) number, or the customer's internet protocol (IP) address, or email address, and communicate with other CC components in processing the interaction. [0082] In some embodiments, the rerouting service 131 may be invoked by a current routing strategy for determining whether an interaction invoked via a current media channel should be rerouted to a second (different) media channel. The second media channel may be one determined to have a waiting time that satisfies a threshold waiting time. According to one embodiment, the rerouting service 131 (or orchestration module 230) may be configured to reserve a resource (e.g., a live agent) at the second media channel upon determining that the rerouting is needed or desired. [0083] In some embodiments, the universal menus module 129 may be invoked for dynamically generating an appropriate self-service menu based on the detected modality of a current interaction. According to one embodiment, the various self-service menus are generated from the single server without the need to invoke separate self-service menu servers that would depend on the modality that is invoked.
Reference Bhattacharya and Reference Pirat are analogous prior art to the claimed invention because the references generally relate to field of providing support services for businesses. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Pirat, particularly the ability to search external data sources (see Pirat [0182]-[0185]), in the disclosure of Reference Bhattacharya, particularly in the automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users (see Bhattacharya [0007]), in order to provide for a system that allows collection of data from various external and internal sources, and generate a multimodal predictive model for determining, for example, an optimal communication channel to engage with the customer 210 as taught by Reference Pirat (see at least in [0182]), where upon the execution of the method and system of Reference Pirat for generating and updating a multimodal predictive model where the predictive analytics module 260 aggregates data from various external and internal sources 1209a-1209c. The data may include, for example, contact activity of the customer, social media data, disposition data, interaction data, customer profile, customer context, customer preferences, agent preferences, contact center capabilities, contact center load, statistics, historical data for specific interaction types (Pirat: [0185]) and/or the like so that the process of providing support services for businesses can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar providing support services for businesses field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Bhattacharya in view of Reference Pirat, the results of the combination were predictable (MPEP 2143 A).
As per claim 11: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the customer is a broadband service operator, and wherein the proposal is to for one or measures to protect the service during a predicted weather event.
Regarding the claims above, Reference Bhattacharya does not show the above limitation. However, Reference Pirat shows in [0059] FIG. 1A is a schematic block diagram of a system for supporting a contact center in providing contact center services according to one exemplary embodiment of the invention. The contact center may be an in-house facility to a business or enterprise for serving the enterprise in performing the functions of sales and service relative to the products and services available through the enterprise. In another aspect, the contact center may be operated by a third-party service provider. According to some embodiments, the contact center may operate as a hybrid system in which some components of the contact center system are hosted at the contact center premise and other components are hosted remotely (e.g., in a cloud-based environment). The contact center may be deployed in equipment dedicated to the enterprise or third-party service provider, and/or deployed in a remote computing environment such as, for example, a private or public cloud environment with infrastructure for supporting multiple contact centers for multiple enterprises. The various components of the contact center system may also be distributed across various geographic locations and computing environments and not necessarily contained in a single location, computing environment, or even computing device. [0198] At block 1720, the predictive analytics module 260 selects a possible subset of communication channels that may be recommended based on, for example, service rules, agreements, and/or other constraints (e.g., device type, screen size, geographical location, observed bandwidth or capacity of connection or device, network connection quality, user interaction preferences, and the like). [0225] In some embodiments a single agent may serve various channel mix. For example, the single agent may interact with the customer via both voice and chat. In some embodiments, multiple agents could each serve different channels for a single customer or interaction. For example, one agent handling a voice channel, and another agent handling a chat channel may be joined in a multimodal session to serve a single customer or related interactions. In this case the agents may be kept in sync to be informed of the customer's actions on any of the channels, for example, by real-time sharing of transcripts of their respective channels. In some cases, the agents may even be located at different geographical locations to handle their respective channels to serve the customer, and may be kept in sync by sharing information from their respective channels. [0239] The various servers may be located on a computing device on-site at the same physical location as the agents of the contact center or may be located off-site (or in the cloud) in a geographically different location, e.g., in a remote data center, connected to the contact center via a network such as the Internet. [0122] In some embodiments, the user may be having issues with, for example, his or her internet service, and the user may be provided with a link with instructions with step by step fault diagnosis, while the orchestration module invokes a backend system (e.g., a business support system 245) to run diagnosis to check the connection to the user's modem. [0065] The call controller 118 may be configured to process PSTN calls, VoIP calls, and the like. For example, the communication server 118 may be configured with computer-telephony integration (CTI) software for interfacing with the switch/media gateway and contact center equipment. In one embodiment, the call controller 118 may include a session initiation protocol (SIP) server for processing SIP calls. According to some exemplary embodiments, the call controller 118 may, for example, extract data about the customer interaction such as the caller's telephone number, often known as the automatic number identification (ANI) number, or the customer's internet protocol (IP) address, or email address, and communicate with other CC components in processing the interaction. [0082] In some embodiments, the rerouting service 131 may be invoked by a current routing strategy for determining whether an interaction invoked via a current media channel should be rerouted to a second (different) media channel. The second media channel may be one determined to have a waiting time that satisfies a threshold waiting time. According to one embodiment, the rerouting service 131 (or orchestration module 230) may be configured to reserve a resource (e.g., a live agent) at the second media channel upon determining that the rerouting is needed or desired. [0083] In some embodiments, the universal menus module 129 may be invoked for dynamically generating an appropriate self-service menu based on the detected modality of a current interaction. According to one embodiment, the various self-service menus are generated from the single server without the need to invoke separate self-service menu servers that would depend on the modality that is invoked.
Reference Bhattacharya and Reference Pirat are analogous prior art to the claimed invention because the references generally relate to field of providing support services for businesses. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Pirat, particularly the ability to search external data sources (see Pirat [0182]-[0185]), in the disclosure of Reference Bhattacharya, particularly in the automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users (see Bhattacharya [0007]), in order to provide for a system that allows collection of data from various external and internal sources, and generate a multimodal predictive model for determining, for example, an optimal communication channel to engage with the customer 210 as taught by Reference Pirat (see at least in [0182]), where upon the execution of the method and system of Reference Pirat for generating and updating a multimodal predictive model where the predictive analytics module 260 aggregates data from various external and internal sources 1209a-1209c. The data may include, for example, contact activity of the customer, social media data, disposition data, interaction data, customer profile, customer context, customer preferences, agent preferences, contact center capabilities, contact center load, statistics, historical data for specific interaction types (Pirat: [0185]) and/or the like so that the process of providing support services for businesses can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar providing support services for businesses field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Bhattacharya in view of Reference Pirat, the results of the combination were predictable (MPEP 2143 A).
As per claim 12: Regarding the claim limitations below, Reference Bhattacharya in view of Pirat shows:
wherein the customer is a broadband service operator, and wherein the proposal is to offer service to one or more potential clients based on a prediction of available capacity in a geographic region.
Regarding the claims above, Reference Bhattacharya does not show the above limitation. However, Reference Pirat shows in [0122] In some embodiments, the user may be having issues with, for example, his or her internet service, and the user may be provided with a link with instructions with step by step fault diagnosis, while the orchestration module invokes a backend system (e.g., a business support system 245) to run diagnosis to check the connection to the user's modem. [0065] The call controller 118 may be configured to process PSTN calls, VoIP calls, and the like. For example, the communication server 118 may be configured with computer-telephony integration (CTI) software for interfacing with the switch/media gateway and contact center equipment. In one embodiment, the call controller 118 may include a session initiation protocol (SIP) server for processing SIP calls. According to some exemplary embodiments, the call controller 118 may, for example, extract data about the customer interaction such as the caller's telephone number, often known as the automatic number identification (ANI) number, or the customer's internet protocol (IP) address, or email address, and communicate with other CC components in processing the interaction. [0082] In some embodiments, the rerouting service 131 may be invoked by a current routing strategy for determining whether an interaction invoked via a current media channel should be rerouted to a second (different) media channel. The second media channel may be one determined to have a waiting time that satisfies a threshold waiting time. According to one embodiment, the rerouting service 131 (or orchestration module 230) may be configured to reserve a resource (e.g., a live agent) at the second media channel upon determining that the rerouting is needed or desired. [0083] In some embodiments, the universal menus module 129 may be invoked for dynamically generating an appropriate self-service menu based on the detected modality of a current interaction. According to one embodiment, the various self-service menus are generated from the single server without the need to invoke separate self-service menu servers that would depend on the modality that is invoked.
Reference Bhattacharya and Reference Pirat are analogous prior art to the claimed invention because the references generally relate to field of providing support services for businesses. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Pirat, particularly the ability to search external data sources (see Pirat [0182]-[0185]), in the disclosure of Reference Bhattacharya, particularly in the automatically collecting, unifying, and carrying out complex analyses of vast amounts of disparate and dispersed quantitative and qualitative customer data for users (see Bhattacharya [0007]), in order to provide for a system that allows collection of data from various external and internal sources, and generate a multimodal predictive model for determining, for example, an optimal communication channel to engage with the customer 210 as taught by Reference Pirat (see at least in [0182]), where upon the execution of the method and system of Reference Pirat for generating and updating a multimodal predictive model where the predictive analytics module 260 aggregates data from various external and internal sources 1209a-1209c. The data may include, for example, contact activity of the customer, social media data, disposition data, interaction data, customer profile, customer context, customer preferences, agent preferences, contact center capabilities, contact center load, statistics, historical data for specific interaction types (Pirat: [0185]) and/or the like so that the process of providing support services for businesses can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar providing support services for businesses field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Bhattacharya in view of Reference Pirat, the results of the combination were predictable (MPEP 2143 A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
M. Tabiaa and A. Madani, "The deployment of Machine Learning in eBanking: A Survey," 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 2019, pp. 1-7, doi: 10.1109/ICDS47004.2019.8942379.keywords: {Machine learning;Machine learning algorithms;Task analysis;Online banking;Training;Learning (artificial intelligence);Artificial neural networks;Machine Learning;e-Banking;algorithms},
Thanks to the machine learning algorithms revolution, several organizations will be able to transform their services, automate functions and predict their customer’s behaviors. The fact that digital has begun a huge change in the world of finance such as the traditional bank with its physical breaches and advisers; the digital bank is one of the financial organizations integrates machine learning into its services. In order to properly guide future research and development, it will be extremely beneficial to carry out an integral and up-to-date study, focusing on banking fields integrating (or able to integrate) the machine learning techniques. This paper will allow managers and developers to carefully evaluate and embrace machine learning techniques to solve e-baking issues.
This reference discloses thanks to the machine learning algorithms revolution, several organizations will be able to transform their services, automate functions and predict their customer’s behaviors. The fact that digital has begun a huge change in the world of finance such as the traditional bank with its physical breaches and advisers; the digital bank is one of the financial organizations integrates machine learning into its services. In order to properly guide future research and development, it will be extremely beneficial to carry out an integral and up-to-date study, focusing on banking fields integrating (or able to integrate) the machine learning techniques. This paper will allow managers and developers to carefully evaluate and embrace machine learning techniques to solve e-baking issues.
Foreign Reference:
(IE 87441 B1) Darrel et al. Machine Learning Techniques for Predictive Prioritization
This reference discloses methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive prioritization. Certain embodiments utilize systems, methods, and computer program products that perform predictive prioritization using a combination of supervised machine learning models and unsupervised machine learning models that are in turn used to generate target features for a resultant prioritization machine learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY PRASAD whose telephone number is (571)270-3265. The examiner can normally be reached M-F: 8:00 AM - 4:30 PM EST.
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/N.N.P/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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