Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is a Final Office Action in response to communications received on 10/2/25.
Claims 1-4, 7-13, 15-20 have been amended.
Therefore, Claims 1-20 are now pending and have been addressed below.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1-9 are directed to a method, claims 18-20 are directed to a non-transitory medium and claims 10-17 are directed to a system. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 10 and 18 recite methods that access data of a customer, wherein the customer is an entity providing a product or service to one or more accounts, with each account associated with one or more users of the product or service; for each account, obtain data representing usage of the product or service by the account or by the one or more users of the product or service; generate a Unified Feature Representation (UFR) for the accessed data of the customer and the obtained data representing usage of the product or service by the account or by the one or more users of the product or service; identify a business goal of the customer; for the identified business goal of the customer, predict or infer one or more of account or user behavior that is expected to assist in achieving the business goal of the customer; generate a proposed set of actions for an account or user to take to assist in achieving the business goal of the customer; present the proposed set of actions to the customer; and provide display to enable the customer to interact with and manage the proposed set of actions; receiving a selection from the entity of one of the proposed set of actions; and implementing the selected one of the proposed set of actions by presenting one or more
of information, an incentive, or a recommended approach to using the product or service to a specific account or to one or more users of the product or service associated with the specific account.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as one or more datastores, one or more displays or user interface, at least one electronic processor, an electronic non-transitory data storage, computer readable media), the claims are directed to generating proposed action to assist in achieving a business goal. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing proposed action. In particular, the claims only recites the additional element – one or more datastores, one or more displays or user interface, at least one electronic processor, an electronic non-transitory data storage, computer readable media. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional elements merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; generating a proposed actions to assist in achieving a business goal. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to the one or more datastores, one or more displays or user interface, at least one electronic processor, an electronic non-transitory data storage, computer readable media, these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0027] details “ The system may include a set of computer-executable instructions, a memory or data storage element containing the set of instructions (such as a non-transitory computer-readable medium), and an electronic processor or co-processors. .” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data. Furthermore, the use of such generic computers to receive or transmit data over a network See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself.
Dependent claims 2-9, 11-17, and 19-20 add additional limitations, for example but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as representative claims 1, 10 and 18.
Claims 2, 11 and 19 recites wherein the one or more datastores include customer relationship management (CRM) data, the CRM data including a list of accounts to which the customer provides the product or service is simply data storage, database is recited at high level of generality. Claims 3 and 12 recites wherein the data representing usage of the product or service by the account or a user or users of the product or service associated with the account further comprises one or more of keystrokes, entered data, entered alphanumeric strings, signals generated by a software application, an instruction or command executed by a software application or service, an event generated in response to a signal, or a user input is simply data gathering. Claims 4-5 and 13-14 recites wherein generating the Unified Feature Representation (UFR) for the accessed data of the customer and the obtained data representing usage of the product or service by the account or by the one or more users of the product or service further comprises: determining one or more features of the accessed or obtained data; and representing the Unified Feature Representation as a feature vector in a multi-dimensional vector space corresponding to the one or more features; determining one or more features of the accessed or obtained data further comprises evaluating each feature of the feature vector for its predictive importance or deriving a new attribute from the one or more features and representing the new attribute in a format corresponding to the UFR is data analysis recited at apply it level. Claims 7, 15 and 20 recites wherein the business goal of the customer is one or more of reducing churn, increasing productivity of uses of the product or service provided by the customer, increased revenue, increased profit, increased deal flow, improved employee retention, or decreased requests for support assistance for the product or service is simply data gathering. Claims 6, 8 and 16 recites wherein evaluating each feature of the feature vector for its predictive importance further comprises using one or more of a trained machine learning model, regression, classification, or correlation analysis; .wherein predicting or inferring one or more of account or user behavior that is expected to assist in achieving the business goal of the customer further comprises using one or more of a trained machine learning model to generate an expected result of a specific account or user action or behavior, A/B testing, constructing a simulation, or applying a causal inference technique to actions previously applied to an account or user. The trained model is applied at high level of generality. Claims 9 and 17 recites wherein generating a proposed set of actions for an account or user to take to assist in achieving the business goal of the customer further comprises using a trained machine learning model to generate one or more actions to propose to an account or user, wherein the one or more actions are expected to assist in achieving the business goal based on the expected result of the account or user action or behavior is simply displaying result. These limitation of merely adds the words apply it (or an equivalent) with the judicial exception , or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The receiving function is similar to a data gathering function. The displaying/transmitting function is a generic computer function and is also considered as an insignificant post solution activity. The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing system is merely being used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 7-12, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Siebel (US 2022/0405775 A1) in view of Nathenson et al. (US 2017/0236131 A1)
Regarding Claims 1, 10 and 18, Seibel discloses the method/system/medium, comprising:
Seibel discloses accessing data from one or more datastores of an entity (Fig 1 #116a-n extraprise system, [0099] the information used by the application server 106 and/or stored in the database 110 may be received over at least one additional network 114 from one or more extraprise systems 116a-116n. For example, the network 114 may represent a public data network (such as the Internet) or other network that allows the one or more extraprise systems 116a-116n to provide information to and receive information from a company.), that provide a product or service to one or more accounts, with each account associated with one or more users of the product or service ([0007] One or more of the data objects may be associated with at least one of: one or more customers, one or more companies, one or more accounts, one or more products, one or more employees, one or more suppliers, one or more opportunities, one or more contracts, one or more locations, and one or more digital portals. [0010] CRM application may include an AI customer satisfaction application that scores features of the one or more data models, and the features may be associated with at least one of: marketing data, account data, stakeholder data, competitor data, and contextual data. [0099] the one or more extraprise systems 116a-116n may be used by the application server 106 or the database server 108 to obtain information such as financial indices, commodity prices, equity prices, credit ratings, news volumes and sentiments, social media content, and business performance indicators like stock prices and analyst ratings);
Seibel discloses for each account, obtaining data representing interactions with the product or service by the account or by the one or more users of the product or service ([0101] Precision product forecasting generally involves predicting sales volumes or other transaction volumes for one or more specific products or services within a given timeframe. Precision product forecasting may also provide a demand forecast (usage) for one or more products that are to be sold to customers, which can be used to help ensure that product inventory is prepared for delivery of a product after the conclusion of a transaction., [0125] Hidden in the interrelationships of these big data sets are insights that can improve understandings of customer behaviors, customer interactions, and ways to optimize company operations. Identifying these insights may involve the use of advanced machine learning data processing tools to help companies and representatives discover, analyze, and understand the relationships [0186] data from a wide variety of data sources 302 may be obtained and used by the architecture 300, such as when the data is obtained and curated using the data handler function 240. In this example, the data sources 302 include one or more enterprise CRM data sources (such as a history of opportunities and a history of sales or other transactions recorded in a company's internal system or systems) and one or more extraprise data sources providing macroeconomic financial data (such as stock prices, stock indices, and commodity prices). The data sources 302 also include one or more extraprise news sources, such as news related to a specific customer or more generally to a relevant country, region, or industry. [0268] the information from the data sources 902 may be generally combined into two sets of data, namely information identifying customer characteristics and information identifying customer behaviors., [0356] involve unifying product configuration systems, historical orders, sales or other transaction systems, and external data (such as demographics, news, and social media content) [0357]one or more machine learning models to manage customers' journey orchestration via Internet self-service. This may involve unifying website traffic and history, sales or other transaction systems, service systems, and external data (such as demographics, news, and social media content));
Seibel discloses generating a Unified Feature Representation (UFR) for the accessed data of the entity ([0125] Hidden in the interrelationships of these big data sets are insights that can improve understandings of customer behaviors, customer interactions, and ways to optimize company operations. Identifying these insights may involve the use of advanced machine learning data processing tools to help companies and representatives discover, analyze, and understand the relationships [0189] data from a wide variety of data sources 302 may be obtained and used by the architecture 300, such as when the data is obtained and curated using the data handler function 240. In this example, the data sources 302 include one or more enterprise CRM data sources (such as a history of opportunities and a history of sales or other transactions recorded in a company's internal system or systems) and one or more extraprise data sources providing macroeconomic financial data (such as stock prices, stock indices, and commodity prices). The data sources 302 also include one or more extraprise news sources, such as news related to a specific customer or more generally to a relevant country, region, or industry., [0187] This information is processed using an opportunity scoring (OS) machine learning model 304. The OS machine learning model 304 uses this information to evaluate individual opportunities and to provide, for each opportunity, a probability that a representative can win the opportunity within a given timeframe. The OS machine learning model 304 may receive a snapshot of the opportunities in a pipeline and generate the predictions 306 daily, although other time intervals may also be used. Fig 32 shows UFR for customer manufacturer) and the obtained data representing interactions with the product or service by the account or by the one or more users of the product or service ([0189] data from a wide variety of data sources 302 may be obtained and used by the architecture 300, such as when the data is obtained and curated using the data handler function 240. In this example, the data sources 302 include one or more enterprise CRM data sources (such as a history of opportunities and a history of sales or other transactions recorded in a company's internal system or systems) and one or more extraprise data sources providing macroeconomic financial data (such as stock prices, stock indices, and commodity prices [0125] Hidden in the interrelationships of these big data sets are insights that can improve understandings of customer behaviors, customer interactions, and ways to optimize company operations. Identifying these insights may involve the use of advanced machine learning data processing tools to help companies and representatives discover, analyze, and understand the relationships [0357]one or more machine learning models to manage customers' journey orchestration via Internet self-service. This may involve unifying website traffic and history, sales or other transaction systems, service systems, and external data (such as demographics, news, and social media content)).;
Seibel discloses identifying a business goal of the entity ([0088] CRM systems can be used to help decision-makers increase revenue, maximize profits, increase customer satisfaction, increase customer retention, increase market share, and increase sales and service effectiveness within an organization. [0091] focus their efforts on specific factors to influence desired outcomes in the CRM process (such as when choosing among options to increase revenue, profitability, or customer engagement/satisfaction or reduce customer churn, [0274] Companies routinely attempt to identify which of its existing customers are likely to chum (goal) or to cease having relationships (partially or completely) with the companies within a given timeframe. Early identification of such risks can allow the companies to attempt to remediate potential issues and maintain revenue flows.);
Seibel discloses for the identified business goal of the entity, predicting or inferring one or more action/recommendation that is expected to assist in achieving the business goal of the customer ([0054] CRM function may include performing a recommendation function by identifying sales or service actions in order to achieve one or more specified objectives [0115] create actionable recommendations for representatives to improve their machine learning scores to thereby improve their chances of achieving specified objectives (such as closing an opportunity or mitigating churn risk). [0275] Churn management generally applies machine learning techniques to generate likelihood scores of existing customers completely or partially changing their customer relationships (such as changing suppliers or adding competitive suppliers) within a given timeframe. The machine learning techniques may also identify one or more possible ways to overcome the potential loss of each customer in order to maintain the company's relationships with those customers. a recommendation system can be used to identify potential courses of actions to take to retain at-risk customers);
Seibel discloses generating a proposed set of actions for an account or user to take to assist in achieving the business goal of the entity ([0115] create actionable recommendations for representatives to improve their machine learning scores to thereby improve their chances of achieving specified objectives (such as closing an opportunity or mitigating churn risk). [0285] The churn prevention recommendation 1024 identifies a proposed course of action that might be used to prevent churn for the particular product(s) or service(s). The AI evidence package 1026 can provide explanation(s) for the churn likelihood 1022 or the churn prevention recommendation 1024. For instance, the AI evidence package 1026 may identify the largest contributors to the churn likelihood 1022 or the churn prevention recommendation 1024 (both positive and negative), such as when the AI evidence package 1026 identifies which features have the largest impact on that churn likelihood 1022 or the churn prevention recommendation 1024., [0293] The opportunity contact entry point recommendation 1108 represents a recommendation on how the representative of the company may interact with the officer or employee of the customer. The relationship strength 1110 identifies an estimated strength of any relationship between the two contacts. [0355] applying a machine learning model for customer segmentation in order to predict changes in customer loyalty. This may be done to identify customers at risk of churning and to identify engagement recommendations and offers in order to maintain or increase loyalty. This approach may be used in conjunction with next best offer and customer churn management functionality. );
Seibel discloses providing the proposed set of actions to the customer ([0102] estimating a propensity or likelihood of a new or existing customer to purchase or otherwise obtain one or more specific products or services, which can be used to make a first or subsequent offer to the customer or otherwise identify at least one action (presenting action) that be taken with respect to the customer. Churn management generally involves predicting whether particular customers of the company will remain customers of the company (either entirely or for one or more specific products/services) and generating recommendations (proposed set of actions) for ensuring customer retention., [0275] a recommendation system can be used to identify potential courses of actions to take to retain at-risk customers. Thus, representatives can identify customers at risk of churning and see clear explanations about key risk factors and access recommendations to retain them so that preventative actions may be taken.); and
Seibel discloses providing one or more displays or user interface elements to enable the customer to interact with and manage the proposed set of actions ([0414] The user interface 2600 may allow a user to understand the connection activity and strength of connection between a company and a customer and to identify the best path through a network to a given contact. The user interface 2600 may also provide (such as through related parties) next best contact recommendations to help opportunities progress., [0416] the user interface 2700 includes a churn alert section 2710, which can identify the open chum alerts for customers. Each churn alert here can be identified by the customer having the chum alert, a chum risk (chum probability) associated with the customer, a current balance associated with the customer, and a recent change in the balance associated with the customer (which may form at least part of the basis for the chum alert). Each chum alert also has an associated status and length of time (such as number of days or other length of time) since the chum alert was issued. Each chum alert may further have an indicator identifying whether any type of remediating action or other action has been undertaken. A user may select a particular chum alert (such as by clicking on the customer name or other portion of the chum alert) in order to view more information about a particular chum alert, such as one or more chum prevention recommendations or relationship intelligence graphics.).
Seibel discloses receiving a selection from the entity of one of the proposed set of actions ([0260] after one or more NBO recommendations are made here, one or more representatives may provide feedback accepting, rejecting, or modifying the recommendations. [0416] A user may select a particular chum alert (such as by clicking on the customer name or other portion of the chum alert) in order to view more information about a particular chum alert, such as one or more chum prevention recommendations or relationship intelligence graphics); and
Seibel discloses implementing the selected one of the proposed set of actions by presenting one or more of information, an incentive, or a recommended approach to using the product or service to a specific account or to one or more users of the product or service associated with the specific account. ([0275] a recommendation system can be used to identify potential courses of actions to take to retain at-risk customers. Thus, representatives can identify customers at risk of churning and see clear explanations about key risk factors and access recommendations to retain them so that preventative actions may be taken. [0284] The churn prevention recommendation 1018 identifies a proposed course of action that might be used to prevent churn by the particular customer. The AI evidence package 1020 can provide explanation(s) for the churn likelihood 1016 or the churn prevention recommendation 1018. For instance, the AI evidence package 1020 may identify the largest contributors to the churn likelihood 1016 or the churn prevention recommendation 1018 (both positive and negative), such as when the AI evidence package 1020 identifies which features have the largest impact on that churn likelihood 1016 or the churn prevention recommendation 1018., Fig 17B # 1718 opportunities to escalate [0325] information about products or services previously provided to a customer and knowledge of current or future upgrades to those products or services. This information is also processed using a next best product replacement function 1506, which may implement the “next best” functionality described above (while limiting the potential products or services recommended to upgrades or replacements). Fig 29 Next best offer (recommended offer for user account);
Siebel does not specifically teach for the identified business goal of the customer, predicting or inferring one or more of account or user behavior that is expected to assist in achieving the business goal of the customer
Nathenson teaches for the identified business goal of the customer, predicting or inferring one or more of account or user behavior that is expected to assist in achieving the business goal of the customer ([0008] Given a set of possible customer behaviors to encourage (such as purchase of a sale item, a purchase of a more expensive item, a purchase that might encourage further purchases, a purchase that might assist the customer or the company in reaching a desired goal, etc.), it may be beneficial to a company to identify the most desired customer behavior based upon consideration of the company's inventory, sales, revenue, or other relevant data. [0120] The types of customer behaviors that can be encouraged (and are likely to be successfully encouraged) may be determined using one or more of clustering, segmentation, sentiment analysis, or other predictive analytics techniques that are applied to data regarding customer actions and responsiveness to information. Automatically ranks possible suggestions (e.g., based on the outcome of the decision or analytical techniques applied to the data) across the set of predicted outcomes, and as a result to deliver one or more reliable recommendations to a sales associate., [0129]-[0131] recommendations based on customers similar to this customer who purchased certain items [0141] ranks possible suggestions by taking into account the predicted outcomes and ordering them according to a rule or heuristic (such as by the likelihood of success in producing a desired outcome).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included for the identified business goal of the customer, predicting or inferring one or more of account or user behavior that is expected to assist in achieving the business goal of the customer, as disclosed by Nathenson in the system disclosed by Siebel, for the motivation of providing a method of applying data mining techniques, statistical analysis, supervised or unsupervised machine learning techniques to transform the data into a process for generating actionable recommendations for companies, customer service representatives, and customers. ([0018] Nathenson).
Claim 10. Siebel discloses the system, comprising: at least one electronic processor ([0463] CRM forecasting method may be implemented using at least one processor); an electronic non-transitory data storage element ([0464] computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive (HDD), a compact disc (CD)) including a set of computer-executable instructions that, when executed by the electronic processor, cause the system to
Claim 18. Siebel discloses the one or more non-transitory computer-readable media ([0464] computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive (HDD), a compact disc (CD)) comprising a set of computer-executable instructions that when executed by one or more programmed electronic processors.
Regarding Claims 2, 11 and 19, Siebel as modified by Nathenson teaches the method/system/medium of claim 1, 11 and 18,
Siebel teaches wherein the one or more datastores include customer relationship management (CRM) data ([0098] the database server 108 may store various information related to sales opportunities and other sales- or transaction-related information that may be used during performance of one or more CRM functions.), the CRM data including a list of accounts to which the entity provides the product or service.([0099] information may be stored in the database 110 and used by the application server 106 to perform one or more CRM functions. The database 110 may be used to store a wide range of enterprise data, such as histories, sales orders or other transactions, and inventory information., Fig 20A #2002 shows CRM data/accounts)
Regarding Claims 3 and 12, Siebel as modified by Nathenson teaches the method of claim 1,
Siebel teaches wherein the data representing interaction with the product or service by the account or a user or users of the product or service associated with the account further comprises one or more of keystrokes, entered data, entered alphanumeric strings, signals generated by a software application, an instruction or command executed by a software application or service, an event generated in response to a signal, or a user input. ([0122]a feature related to GDP growth with a high importance score but no recommendation may be deprioritized for a feature related to a customer recommendation. As a particular example, the customer recommendation may be prioritized for display on a dashboard, report, action plan, etc. to aid a specified outcome over the GDP growth feature with a higher importance score. Action logic can be triggered by an AI evidence package to automate using the predicted probability in order to apply a use case insight that optimizes one or more specific objectives (signals generated by a software application, an instruction or command executed by a software application or service). For instance, the prioritized customer recommendation can be used to trigger actionability logic that causes an automated electronic communication to optimize customer loyalty or achieve some other result. [0125] Hidden in the interrelationships of these big data sets are insights that can improve understandings of customer behaviors, customer interactions, and ways to optimize company operations. Identifying these insights may involve the use of advanced machine learning data processing tools to help companies and representatives discover, analyze, and understand the relationships)
Regarding Claims 7, 15 and 20, Siebel as modified by Nathenson teaches the method of claim 1,
Siebel teaches wherein the business goal of the entity is one or more of reducing churn, increasing productivity of uses of the product or service provided by the customer, increased revenue from the product or service, increased profit from product or service, increased deal flow, improved employee retention from users of product or service, or decreased requests for support assistance for the product or service. ([0115] the model orchestrator function 242 may create actionable recommendations for representatives to improve their machine learning scores to thereby improve their chances of achieving specified objectives (such as closing an opportunity or mitigating churn risk). (reducing churn), [0118] the CRM engine function 244 may process information obtained by the data handler function 240 using one or more machine learning models to generate probabilities for insights with respect to one or more specified objectives associated with an AI-based function), [0180] these functions support the ability to track opportunities within an organization, which can be used to help predict revenue, increase or maximize profits and efficiencies, generate new insights for sales or other transactions, and manage workflows associated with sales or other transactions.)
Regarding Claims 8 and 16, Siebel as modified by Nathenson teaches the method of claim 1, wherein predicting or inferring one or more of account or user behavior that is expected to assist in achieving the business goal of the entity
Siebel teaches further comprises using one or more of a trained machine learning model to generate an expected result of a specific account or user action or behavior, A/B testing, constructing a simulation, or applying a causal inference technique to actions previously applied to an account or user. ([0104] AI recommendation generally involves using machine learning to identify what action or actions (user behavior) users can take to achieve desired outcomes (in some cases, this functionality may also be referred to as or performed as a part of next best action). AI evidence package functionality generally involves identifying top contributing factors to CRM-related outputs generated using a machine learning algorithm, meaning the identification of reasons why a machine learning model makes a particular prediction and the impact of individual reasons on that particular prediction., [0112] The AI-based CRM functions 222 represent or involve the use of trained machine learning models and other AI-based functionality to implement AI-based CRM. For example, the AI-based CRM functions 222 here include an AI-based revenue forecasting function 226 (feature), an AI-based pricing optimization function 228, an AI-based next best offer/product/action (NBO/NBP/NBA) function 230. One or more trained machine learning models to generate insights with respect to one or more specified objectives, [0115] the model orchestrator function 242 may create actionable recommendations for representatives to improve their machine learning scores to thereby improve their chances of achieving specified objectives (such as closing an opportunity or mitigating churn risk). [0122] For instance, the prioritized customer recommendation can be used to trigger actionability logic that causes an automated electronic communication to optimize customer loyalty or achieve some other result.)
Nathenson also teaches using one or more of a trained machine learning model to generate an expected result of a specific account or user action or behavior, A/B testing, constructing a simulation, or applying a causal inference technique to actions previously applied to an account or user. ([0143] combine access to data at the company level (i.e., vendor, merchant, platform-tenant or account, etc.) and at the customer level (i.e., the end user of an eCommerce platform, a vendor's customers, etc.) with one or more of configured rules or heuristics, data mining techniques, statistical analysis techniques, machine learning techniques, or other relevant analytical methods to process that data and determine actionable recommendations for companies, customer service representatives, and customers. [0120] The types of customer behaviors that can be encouraged (and are likely to be successfully encouraged) may be determined using one or more of clustering, segmentation, sentiment analysis, or other predictive analytics techniques that are applied to data regarding customer actions and responsiveness to information Claim 4)
Regarding Claims 9 and 17, Siebel as modified by Nathenson teaches the method of claim 1,
Siebel teaches wherein generating a proposed set of actions for an account or user to take to assist in achieving the business goal of the customer further comprises using a trained machine learning model to generate one or more actions to propose to an account or user ([0122] a feature related to GDP growth with a high importance score but no recommendation may be deprioritized for a feature related to a customer recommendation. As a particular example, the customer recommendation may be prioritized for display on a dashboard, report, action plan, etc. to aid a specified outcome over the GDP growth feature with a higher importance score For instance, the prioritized customer recommendation can be used to trigger actionability logic that causes an automated electronic communication to optimize customer loyalty or achieve some other result.), wherein the one or more actions are expected to assist in achieving the business goal based on the expected result of the account or user action or behavior. ([0102] estimating a propensity or likelihood of a new or existing customer to purchase or otherwise obtain one or more specific products or services, which can be used to make a first or subsequent offer to the customer or otherwise identify at least one action (presenting action) that be taken with respect to the customer. Churn management generally involves predicting whether particular customers of the company will remain customers of the company (either entirely or for one or more specific products/services) and generating recommendations (proposed set of actions) for ensuring customer retention., [0275] a recommendation system can be used to identify potential courses of actions to take to retain at-risk customers. Thus, representatives can identify customers at risk of churning and see clear explanations about key risk factors and access recommendations to retain them so that preventative actions may be taken.);
Claims 4-6, 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Siebel (US 2022/0405775 A1) in view of Nathenson et al. (US 2017/0236131 A1) as applied to claims 1, 10, further in view of Inzelberg et al. (US 2023/0334378 A1)
Regarding Claims 4 and 13, Siebel as modified by Nathenson teaches the method of claim 1,
Siebel teaches wherein generating the Unified Feature Representation (UFR) for the accessed data of the customer and the obtained data representing interactions with the product or service by the account or by the one or more users of the product or service further comprises: determining one or more features of the accessed or obtained data ([0126] machine learning processing algorithms can involve various types of operations or functions, such as one or more of basic statistics, dimensionality reduction, classification and regression, optimization, recommendations, clustering, and feature selection. [0122] a feature related to GDP growth with a high importance score but no recommendation may be deprioritized for a feature related to a customer recommendation. As a particular example, the customer recommendation may be prioritized for display on a dashboard, report, action plan, etc. to aid a specified outcome over the GDP growth feature with a higher importance score. For instance, the prioritized customer recommendation can be used to trigger actionability logic that causes an automated electronic communication to optimize customer loyalty or achieve some other result.; and
Siebel/Nathenson do not specifically teach representing the Unified Feature Representation as a feature vector in a multi-dimensional vector space corresponding to the one or more features.
Inzelberg teaches a feature vector in a multi-dimensional vector space corresponding to the one or more features.([0070] a vector from the multi-dimensional space (e.g., representations 404 and 414) as input features for performing the task., [0078] the features corresponding to the different data sources into vectors within a multi-dimensional space. For example, the transaction processing module 132 may generate an encoder for each of the data sources 254 and 256. Each of the encoders may be configured to encode the respective features into a set of representations of the features within a multi-dimensional space.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included representing the Unified Feature Representation as a feature vector in a multi-dimensional vector space corresponding to the one or more features., as disclosed by Inzelberg in the system disclosed by Siebel/Nathenson, for the motivation of providing a method of comparing features against each other more accurately when the features are represented within the same multi-dimensional space ([0078] Inzelberg)
Regarding Claims 5 and 14, Siebel as modified by Nathenson/Inzelberg teaches the method of claim 4,
Siebel teaches wherein determining one or more features of the accessed or obtained data further comprises evaluating each feature of the feature vector for its predictive importance or deriving a new attribute from the one or more features and representing the new attribute in a format corresponding to the UFR. ([0126] machine learning processing algorithms can involve various types of operations or functions, such as one or more of basic statistics, dimensionality reduction, classification and regression, optimization, recommendations, clustering, and feature selection. [0122] a feature related to GDP growth with a high importance score but no recommendation may be deprioritized for a feature related to a customer recommendation. As a particular example, the customer recommendation may be prioritized for display on a dashboard, report, action plan, etc. to aid a specified outcome over the GDP growth feature with a higher importance score. [0189] the AI-based evidence package module function 246. This allows for the identification of the features that are contributors (positive and negative) to the predicted probabilities, optionally along with the identification of an extent that each identified feature contributes to an associated predicted probability. These contributing features can be ranked (predictive importance), such as with top features identified for both positive and negative probabilities, and exposed to the representative to aid in decision-making.
Regarding Claim 6. Siebel as modified by Nathenson teaches the method of claim 5,
Siebel teaches wherein evaluating each feature of the feature vector for its predictive importance further comprises using one or more of a trained machine learning model, regression, classification, or correlation analysis. ( [0112] The AI-based CRM functions 222 represent or involve the use of trained machine learning models and other AI-based functionality to implement AI-based CRM. For example, the AI-based CRM functions 222 here include an AI-based revenue forecasting function 226 (feature), an AI-based pricing optimization function 228, an AI-based next best offer/product/action (NBO/NBP/NBA) function 230 [0104] AI recommendation generally involves using machine learning to identify what action or actions users can take to achieve desired outcomes (in some cases, this functionality may also be referred to as or performed as a part of next best action). AI evidence package functionality generally involves identifying top contributing factors to CRM-related outputs generated using a machine learning algorithm, meaning the identification of reasons why a machine learning model makes a particular prediction and the impact of individual reasons on that particular prediction. [0356] one or more machine learning models to predict customers' desired product configurations and streamline sales/other transactions or an onboarding process. This may involve unifying product configuration systems, historical orders, sales or other transaction systems, and external data (such as demographics, news, and social media content) and applying a machine learning model for customer segmentation in order to predict customer preferences for product configurations and bundles)
Response to Arguments
Applicant's arguments filed 10/2/25 have been fully considered but they are not persuasive. Regarding 101 rejection, applicant on pages 10-12 state that claims recite patentable subject matter including collecting, processing and interpreting signals or indications of customer account to determine how best to achieve the goals of entity. Examiner has considered all arguments and respectfully disagrees. The claim limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as one or more datastores, one or more displays or user interface, at least one electronic processor, an electronic non-transitory data storage, computer readable media), the claims are directed to generating proposed action to assist in achieving a business goal. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional elements merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Regarding 103 rejection, applicant states that Siebel does not disclose product usage data. New limitations have been addressed in claim rejection above. Siebel teaches user interaction data and identifying customers who have products or services that can be upgraded([0125] Hidden in the interrelationships of these big data sets are insights that can improve understandings of customer behaviors, customer interactions, and ways to optimize company operations. Identifying these insights may involve the use of advanced machine learning data processing tools to help companies and representatives discover, analyze, and understand the relationships, [0326])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Estes et al. (US 11,847,671 B2) discloses automatically creating a dynamically configured personalized communication for a campaign with autopilot features.
Duncan (US 2017/0220943 A1) providing a methods knowledge base comprising rules which map data types and/or analysis goals to analysis tools; an inference engine; and a user interface module. The method further comprises receiving, by the user interface module, input relating to one or more user-defined analysis goals; determining, by the inference engine, one or more required data sets based on the one or more user-defined analysis goals
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626