Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/377,645, filed on October 6, 2023. In response to Examiner’s Non-Final Office Action of May 20, 2025, Applicant, on October 20, 2025, amended claim 1, 11-12 and 18. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amended claims have been considered
and are insufficient to overcome the rejection. Please refer to the 35 U.S.C. § 101 rejection for further explanation and rationale.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed October 25, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed October 20, 2025.
On Pgs. 10-12, regarding the 35 U.S.C. § 101 rejection, Applicant states (1) the claims recite a particular machine and a non-conventional arrangement of components that is integral to the solution; (2) improve the functioning of the computer system itself and another technology; (3) apply any alleged abstract concept in a way that effects a practical real- world application and changes the state of the system beyond mere "displaying" or "storing" results; (4) operations cannot be practically performed in the human mind and are not mere data manipulation untethered from technology; (5) the blockchain and smart-contract recitations are not drafted at a results-oriented, black-box level; (6) the continuous monitoring and deviation-triggered updates demonstrate that the claims are directed to an active control loop implemented by a specific machine, not passive information collection; (7) the claim set as a whole avoids preemption . In response, the claims recite the additional element of using computer components to perform each step. The “processor”, “recommendation server (RS) node”, “recommendation server (RS) “, “sale lead entity node”, “CRM entity node”, “machine learning (ML) module “, “network”, “memory”, “database” and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
Examiner finds the present claims improve an existing business process of matching analysis and there are currently no functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing computer structure and technology to match data are all, both individually and in combination, computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) 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).
On Pgs. 13-15, regarding the 35 U.S.C. § 103 rejection, Applicant states any combination of Miller and He to arrive at claims 1, 12, and 18 would require hindsight and a change in principle of operation of Miller and He. Because Miller and He neither teach nor suggest the permissioned-blockchain smart- contract scheduler with consensus-gated retrieval nor the LLM language-indicator parsing of multi-modal lead data linked to automatic CRM schedule changes. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). New ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Padmanabhan is now applied for Claims 1, 12 and 18 to support use of blockchain ledger Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to recommendations based on leads.
Claim 1 recites a system for recommendations based on leads, Claim 12 recites a method for recommendations based on leads and Claim 18 recites an article of manufacture for recommendations based on leads, which include acquiring sales lead data comprising data entered into a sales form by a customer; deriving a language indicator from the sales lead data; parse the sales lead data based on the language indicator to derive a plurality of features; querying a local customers' database to retrieve local historical customers'- related data related to previous customers' engagements associated with previous lead data based on the plurality of features and query a remote customers' database or a replicated dataset maintained on a permissioned blockchain ledger; generating at least one feature vector based on the plurality of features and the local historical customers'-related data; and automatically initiate, by the RS node, a scheduling action with the at least one CRM entity node based on the recommendation lead response parameter and record, by execution of a smart contract, the recommendation lead response parameter and an associated schedule entry .
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing interactions. The recitation of “processor”, “recommendation server (RS) node”, “recommendation server (RS) “, “sale lead entity node”, “CRM entity node”, “network”, “memory”, “database” and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing personal behavior. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “processor”, “recommendation server (RS) node”, “recommendation server (RS) “, “sale lead entity node”, “CRM entity node”, “network”, “memory”, “database” and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 12 and claim 18 recite using one or more machine learning/ LLM analysis techniques. The specification discloses the machine learning/ LLM analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning/ LLM analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning processing is solely used a tool to perform the instructions of the abstract idea. Furthermore, claim 1, claim 12 and claim 18 recite using blockchain ledger- the “blockchain” amounts no more than mere instructions to apply the exception. See MPEP 2106.05(f).. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail 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, and/or an additional element applies or uses 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. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in sales analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not 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 elements of “processor”, “recommendation server (RS) node”, “recommendation server (RS) “, “sale lead entity node”, “CRM entity node”, “machine learning (ML) module “, “network”, “memory”, “database” and “computer readable medium” is insufficient to amount to significantly more. (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 a generic computer component cannot provide an inventive concept.
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. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) 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). With regards to step 2B and “machine learning/ LLM”- the machine learning is used as tool to perform the abstract idea. Regarding the blockchain ledger and Step 2B- it is tool to perform the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-11, 13-17and 19-20 recite generate at least one sales lead response parameter associated with the sales lead data for setting an interaction with a CRM specialist associated with the at least one CRM entity node based on the at least one lead response recommendation parameter; retrieve remote historical customers'-related data from at least one remote customers' database based on the local historical customers'-related data, wherein the remote historical customers'-related data is collected at locations associated with a plurality of CRM entities affiliated with service or sales facilities; generate the at least one feature vector based on the plurality of features, the local historical customers'-related data combined with the remote historical customers'-related data; parse the sales lead data comprising audio interactions between the customer and a bot associated with the at least one CRM entity node; generate the plurality of features based on sales lead-related data collected and recorded by the bot; continuously monitor incoming sales lead data to determine if at least one value of the incoming sales lead data deviates from a value of previous customers'-related data by a margin exceeding a pre-set threshold value; responsive to the at least one value of the incoming sale lead data deviating from the value of previous customers'-related data by the margin exceeding the pre-set threshold value, generate an updated feature vector based on the incoming sales lead data and generate the customer-related recommendation based on the at least one recommendation lead response parameter produced by the predictive model in response to the updated feature vector; record the at least one recommendation lead response parameter on a blockchain ledger along with the features retrieved from the sales lead data; retrieve the at least one recommendation lead response parameter from the blockchain responsive to a consensus among the RS node and the at least one CRM entity node; execute a smart contract to record data reflecting scheduling of a sale lead response interaction associated with the customer and the at least one CRM entity node on the blockchain for future audits; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 12 and 18. Regarding Claims, 2-11, 13, and 19-20, and the additional elements of “processor”, “RS node”, “CRM entity node” and “database” -it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) 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). Regarding claims 9-11, 17 and the additional element of “block chain”- it is MPEP 2106.05(f). Regarding claims 5-6 and “bot” -it is MPEP 2106.05(h) field of use.
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 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.
Claims 1-7 , 9-10, and 12-15 and 17- 19 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al., US Publication No. 20230316186 A1, [hereinafter Miller], in view of He et al., US Publication No. 20240362286 A1, [hereinafter He], and in further view of Padmanabhan, US Publication No. 20200250747 A1, [hereinafter Padmanabhan].
Regarding Claim 1,
Miller teaches
A system for generation of automated recommendations based on a sales lead, comprising: a processor of a recommendation server (RS) node configured to host a machine learning (ML) module … and connected to a sale lead entity node and to at least one CRM entity node over a network (Miller Par. 92-93-“ In example embodiments, a method and system for creating custom objects may be offered for addressing need for customizability with CRM systems and other-related systems for marketing and sales activities. For example, a multi-service business platform (e.g., framework) may include a customization system that may be used to create custom objects. The multi-service business platform may be configured to provide processes related to marketing, sales, and/or customer service. The multi-service business platform may include a database structure that already has preset or fixed core objects “; Par. 225; Par. 295-296-“ In embodiments, the analytics module 1914 may analyze one or more aspects of the data collected by the system 1900. In embodiments, the analytics module 1914 calculates a contact score for a contact that is indicative of a value of the contact to the client. …, a net promoter score (e.g., feedback given by the contact indicating how likely he or she is to recommend the client's product or products to someone else) and the like. In embodiments, the contact score may be based on feedback received by the feedback module 1916. ….”; Par. 458; Par. 338; Par. 392);
and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire sales lead data from the sales lead entity node comprising data entered into a sales form by a customer via audio captured during a bot interaction associated with the at least one CRM entity node (Miller Par. 92-93; Par. 186-“ in embodiments, determining which recipients (referred to in some cases as “leads”) should receive a communication may be based on one or more events extracted by the information extraction system 204. Examples of these types of events may include, but are not limited to, job postings, changes in revenue, legal events, changes in management, mergers, acquisitions, corporate restructuring, and many others. For a given recipient, the lead scoring system 214 may seek to match attributes of an individual (such as a person associated with a company) to an extracted event.”; Par. 255- “In embodiments, the feedback system 1610 is configured to receive and/or extract feedback regarding interactions with a contact. The feedback system 1610 may receive feedback in any suitable manner. For example, a client-specific service system may present a contact with questions relating to a ticket (e.g., “was this issue resolved to your liking?” or “on a scale of 1-10 how helpful was this article?”) in a survey, during a chat bot communication, or during a conversation with a customer service specialist. A contact can respond to the question and the feedback system 1610 can update a contact's records with the response and/or pass the feedback along to the machine learning system 1608. In some embodiments, the feedback system 1610 may send surveys to contacts and may receive the feedback from the contacts in responsive surveys.; Par. 285-“The chat bot 1908 may receive communication from the contact (e.g., via text or audio) and may process the communication. For example, the chat bot 1908 may perform natural language processing to understand the response of the user. In embodiments, the chat bot 1908 may utilize a rules-based approach and/or a machine learning approach to determine the appropriate response.”);
generate at least one feature vector based on the plurality of features and the local historical customers'-related data (Miller Par. 293-“ In embodiments, the machine learning module 1912 can train a sentiment model using training data that is derived from transcripts of conversations. The transcripts may be labeled (e.g., by a human) to indicate the sentiment of the contact during the conversation. For example, each transcript may include a label that indicates whether a contact was satisfied, upset, happy, confused, or the like. The label may be provided by an expert or provided by the contact (e.g., using a survey). In embodiments, the machine learning module 1912 may parse a transcript to extract a set of features from each transcript. The features may be structured in a feature vector, which is combined with the label to form a training data pair. The machine learning module 1912 may train and reinforce a sentiment model based on the training data pairs. As the client-specific service system 1900 records new transcripts, the machine learning module 1912 may reinforce the sentiment model based on the new transcripts and respective labels that have been assigned thereto.”; Par. 383);
provide the at least one feature vector to the ML module for generating a predictive model configured to produce at least one recommendation lead response parameter for generation of a lead-related recommendation for the at least one CRM entity node. (Miller Par. 186; Par. 293-295-“ In embodiments, the analytics module 1914 may analyze one or more aspects of the data collected by the system 1900. In embodiments, the analytics module 1914 calculates a contact score for a contact that is indicative of a value of the contact to the client. The contact score may be based on a number of different variables. For example, the contact score may be based on a number of tickets that the user has initiated, an average amount of time between tickets, the sentiment of contact when interacting with the system 1900, an amount of revenue resulting from the relationship with the contact (or the entity with which the contact is affiliated), a number of purchases made by the contact (or an affiliated entity), the most recent purchase made by the contact, the date of the most recent purchase, a net promoter score (e.g., feedback given by the contact indicating how likely he or she is to recommend the client's product or products to someone else) and the like. In embodiments, the contact score may be based on feedback received by the feedback module 1916. The contact score may be stored in the contacts data record, the knowledge graph 1622, and/or provided to another component of the system (e.g., a chat bot 1608 or the service specialist portal 1910).
Miller teaches customer analysis and the feature is expounded upon by He:
…including a pre-trained large language model configured to derive a language indicator and to parse multi-modal lead data…(He Par. 42-“ The method may further include sending a natural language generation (NLG) request to a generative artificial intelligence (AI) model. The generative AI model may comprise a machine learning model that implements a large language model (LLM) to support natural language processing (NLP) operations, such as natural language understanding (NLU), natural language generation (NLG), and other NLP operations.”; Par. 115; Par. 121-“ For example, the generative AI model 728 may implement a language model such as a generative pre-trained transformer (GPT) language model, among others.”; Par. 145; Par. 152)
derive a language indicator from the sales lead data using the large language model (He Par. 38-“ While semantic searching provides clear technical advantages over lexical searches, semantic search by itself may not provide a user, such as a legal representative or business person, with a clear understanding of the entire context of the information for which they are searching. Consequently, as an addition or alternative, the AI/ML techniques are designed to implement a generative artificial intelligence (AI) platform that uses a large language module (LLM) to assist in contract management. A combination of both semantic search capabilities with a short summary of the relevant information based on a search query provides an optimal solution. This combination provides an overview of the information and highlights it in the agreement to make sure none of the details are missed. A user may use the semantic search capability to quickly locate relevant information and then use the summarization to get a clear understanding of the details.”; Par. 40; Par. 43; Par. 48-“ For example, the system 100 may be implemented as an online signature system, online document creation and management system, an online workflow management system, a multi-party communication and interaction platform, a social networking system, a marketplace and financial transaction management system, a customer record management system, and other digital transaction management platforms. Embodiments are not limited in this context.”);
parse the sales lead data based on the language indicator to derive a plurality of features(He Par. 37-38-“ Semantic searching is a process of searching for information by understanding the meaning behind the search query and the content being searched. It involves analyzing the context, relationships, and connections between words and concepts to provide more accurate and relevant search results. Unlike lexical searching, which relies on exact matches of search terms, semantic searching takes into account the overall meaning and intent of the query, as well as the meaning and relationships between words and phrases within the content being searched…. Consequently, as an addition or alternative, the AI/ML techniques are designed to implement a generative artificial intelligence (AI) platform that uses a large language module (LLM) to assist in contract management. A combination of both semantic search capabilities with a short summary of the relevant information based on a search query provides an optimal solution. This combination provides an overview of the information and highlights it in the agreement to make sure none of the details are missed. A user may use the semantic search capability to quickly locate relevant information and then use the summarization to get a clear understanding of the details.”; Par. 40; Par. 48; Par. 82);
query a local customers' database to retrieve local historical customers'- related data related to previous customers' engagements associated with previous lead data based on the plurality of features … (He Par. 87-“ The data sources 302 may source difference types of data 304. For instance, the data 304 may comprise structured data from relational databases, such as customer profiles, transaction histories, or product inventories. The data 304 may comprise unstructured data from websites such as customer reviews, news articles, social media posts, or product specifications.”; Par. 135- “Additionally or alternatively, the search query 144 may be modified or expanded using context information 734. The context information 734 may be any information that provides some context for the search query 144. For example, the context information 734 may comprise a previous search query 144 by the same user, a search query 144 submitted by other users, or prior search results 146 from a previous search query 144. The context information 734 may allow the user to build search queries in an iterative manner, drilling down on more specific search questions in follow-up to reviewing previous search results 146. The context information 734 may also comprise metadata for the electronic document 706 (e.g., signatures, STME, marker elements, document length, document type, etc.), the user generating the search query 144 (e.g., demographics, location, interests, business entity, etc.), a device used to generate the search query 144 (e.g., capabilities, compute resources, memory resources, I/O devices, screen size, interfaces, etc.), sensors (e.g., temperature, accelerometers, altitude, proximity, etc.), and any other context information 734 that may be suitable for further refining the search query 144 (e.g., using search term expansion techniques).”);
Miller and He are directed to integrated client management systems utilizing sematic analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller, as taught by He, by utilizing additional language model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller with the motivation of analyzing the context, relationships, and connections between words and concepts to provide more accurate and relevant search results (He Par. 37).
Miller in view of He teaches customer analysis and the feature is expounded upon by Padmanabhan:
…and query a remote customers' database or a replicated dataset maintained on a permissioned blockchain ledger(Padmanabhan Par. 74-76-“ Certain requests 115 received at the host organization may be directed toward a blockchain for which the blockchain services interface 190 of the host organization 110 operates as an intermediary. The query interface 180 is capable of receiving and executing requested queries against the databases and storage components of the database system 130 and returning a result set, response, or other requested data in furtherance of the methodologies described. The query interface 180 additionally provides functionality to pass queries from web-server 175 into the database system 130 for execution against the databases 155 for processing search queries, or into the other available data stores of the host organization's computing environment 111. In one embodiment, the query interface 180 implements an Application Programming Interface (API) through which queries may be executed against the databases 155 or the other data stores. Additionally, the query interface 180 provides interoperability with the blockchain services interface 190, thus permitting the host organization 110 to conduct transactions with either the database system 130 via the query interface 180 or to transact blockchain transactions onto a connected blockchain for which the host organization 110 is a participating node or is in communication with the participating nodes 133, or the host organization 110 may conduct transactions involving both data persisted by the database system 130 (accessible via the query interface 180) and involving data persisted by a connected blockchain (e.g., accessible from a participating node 133 or from a connected blockchain directly, where the host organization operates a participating node on such a blockchain).; Par. 90”)
and automatically initiate, by the RS node, a scheduling action with the at least one CRM entity node based on the recommendation lead response parameter (Padmanabhan Par. 478-“ According to such embodiments, any platform, including those utilizing AI based models or utilizing deep learning, etc., will ultimately make recommendations, predications, decisions, and take actions, which if adopted and accepted by the business using such platforms, generates data which is potentially important to capture and immutably record via the EinsChain service in a distributed ledger, such as the blockchain upon which the businesses are participating nodes. Notably, the information could be written to a blockchain for which the business is not a participating node, so long as the blockchain services interface of the host organization has access to the blockchain due to the host org operating a participating node, however, it is generally contemplated that the blockchain upon which the business is already a participating node is a suitable blockchain to which to immutably persist such audit and tracking information on behalf of the business.”)
and record, by execution of a smart contract, the recommendation lead response parameter and an associated schedule entry on the permissioned blockchain as a time-stamped, cryptographically signed, immutable audit record retrievable through consensus of permissioned nodes (Padmanabhan Fig 4A; Fig 4B; FIG. 4A depicts another exemplary architecture, with additional detail of a blockchain implemented smart contract created utilizing a smartflow contract engine, in accordance with described embodiments.; Par. 81; Par. 85; Par. 88; Par. 90; Par. 124).
Miller, He and Padmanabhan are directed to integrated client management system analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller in view He, as taught by Padmanabhan, by utilizing blockchain analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller in view of He with the motivation of improved data management for records and information (Padmanabhan Par. 200).
Regarding Claim 2,
The system of claim 1, wherein the instructions further cause the processor to generate at least one sales lead response parameter associated with the sales lead data for setting an interaction with a CRM specialist associated with the at least one CRM entity node based on the at least one lead response recommendation parameter. (Miller Par. 214-“ In embodiments, the system 200 may provide email recommendations and content for service professionals. For example, when a customer submits a support case, or has a question, the system may use events about their account (such as what have the customer has done before, product usage data, how much the customer is paying, and the like), such as based on data maintained in a service support database (which may be integrated with a sales and marketing database as described herein), in order to provide recommendations about what the service professional should write in an email (such as by suggesting templates and by generating customized language for the emails, as described herein). Over time, service outcomes, such as ratings, user feedback, measures of time to complete service, measures of whether a service ticket was opened, and others may be used to train the system 200 to select appropriate content, to generate appropriate language and the, in various ways described throughout this disclosure. Outcomes may further include one or more indicators of solving a customer's problem, such as the number of responses required (usually seeking to keep them low); presence or absence of ticket deflection (i.e., avoiding unnecessary opening of service tickets by providing the right answer up front); the time elapsed before solution or resolution of a problem; user feedback and ratings; the customers net promotor score for the vendor before and after service was provided; one or more indices of satisfaction or dissatisfaction; and the like.”; Par. 228)
Regarding Claim 3 and Claim 13, Miller in view of He teach The system of claim 1, wherein the instructions further cause the processor to … and The method of claim 12, further comprising…
Miller teaches customer analysis and the feature is expounded upon by He:
… retrieve remote historical customers'-related data from at least one remote customers' database based on the local historical customers'-related data, wherein the remote historical customers'-related data is collected at locations associated with a plurality of CRM entities affiliated with service or sales facilities (He Par. 52-“ The server device 102 may be coupled to a local or remote data store 126 to store document records 138. It may be appreciated that the system 100 may have more or less devices than shown in FIG. 1 with a different network topology as needed for a given implementation. “;Par. 87-“ The data sources 302 may source difference types of data 304. For instance, the data 304 may comprise structured data from relational databases, such as customer profiles, transaction histories, or product inventories. The data 304 may comprise unstructured data from websites such as customer reviews, news articles, social media posts, or product specifications.”; )
Miller and He are directed to integrated client management systems utilizing sematic analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller, as taught by He, by utilizing additional language model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller with the motivation of analyzing the context, relationships, and connections between words and concepts to provide more accurate and relevant search results (He Par. 37).
Regarding Claim 4 and Claim 14, Miller in view of He teach The system of claim 3, wherein the instructions further cause the processor to … and The method of claim 13, further comprising…
Miller teaches customer analysis and the feature is expounded upon by He:
… generate the at least one feature vector based on the plurality of features, the local historical customers'-related data combined with the remote historical customers'-related data (He Par. 41-“ The search query may comprise any free form text in a natural language representation of a human language. The method may generate a contextualized embedding for the natural language query request to form a search vector. A contextualized embedding may comprise a vector representation of a sequence of words in the search query that includes contextual information for the sequence of words. The method may include searching a document index of contextualized embeddings for the electronic document with the search vector, where each contextualized embedding comprises a vector representation of a sequence of words in the electronic document that includes contextual information for the sequence of words. The search results may include a set of candidate document vectors that are semantically similar to the search vector.“; Par. 71; Par. 87-“ The data sources 302 may source difference types of data 304. For instance, the data 304 may comprise structured data from relational databases, such as customer profiles, transaction histories, or product inventories. The data 304 may comprise unstructured data from websites such as customer reviews, news articles, social media posts, or product specifications.”; Par. 89; Par. 116)
Miller and He are directed to integrated client management systems utilizing sematic analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller, as taught by He, by utilizing additional language model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller with the motivation of analyzing the context, relationships, and connections between words and concepts to provide more accurate and relevant search results (He Par. 37).
Regarding Claim 5,
The system of claim 1, wherein the instructions further cause the processor to parse the sales lead data comprising audio interactions between the customer and a bot associated with the at least one CRM entity node. (Miller Par. 139-“ Thus, integration of the content development and management platform 100 with the CRM system 158 may produce appropriate topics within the historical context of the customer and the customer's engagement with the enterprise. For example, in embodiments, tickets or tasks may be opened in a CRM system 158, such as prompting creation of content, such as based on customer-relevant suggestions, via the content development and management application 150, such as content for a conversation or chat with a customer (including one that may be managed by a conversational agent 182 or bot), content for a marketing message or offer to the customer, content to drive customer interest in a web page, or the like. In embodiments, a customer conversation or customer chat 184 may be managed through the content development and management application 150, such as by having the chat occur within the user interface 152, such that an agent of the enterprise, like an inside sales person, can engage in the chat by writing content, while seeing suggested topics 138, indicators of relevance or similarity 154 and the like.”)
Regarding Claim 6,
The system of claim 5, wherein the instructions further cause the processor to generate the plurality of features based on sales lead-related data collected and recorded by the bot. (Miller Par. 139; Par. 211-“ In embodiments, the system 200 may be used to generate a smart reply, such as for an automated agent or bot that supports a chat function, such as a chat function that serves as an agent for sales, marketing or service. For example, if representatives typically send out a link or reference in response to a given type of question from a customer within a chat, the system 200 can learn to surface the link or reference to a service person during a chat, to facilitate more rapidly getting relevant information to the customer. Thus, the system 200 may learn to select from a corpus a relevant document, video, link or the like that has been used in the past to resolve a given question or issue.; Par. 235”)
Regarding Claim 7, Claim 15 and Claim 19,
Miller in view of He teach The system of claim 1, wherein the instructions further cause the processor to …, The method of claim 12, further comprising … and The non-transitory computer readable medium of claim 18, further comprising instructions, that when read by the processor, cause the processor to …
… continuously monitor incoming sales lead data to determine if at least one value of the incoming sales lead data deviates from a value of previous customers'-related data by a margin exceeding a pre-set threshold value. (Miller Par. 4; Par. 186-“ In embodiments, determining which recipients (referred to in some cases as “leads”) should receive a communication may be based on one or more events extracted by the information extraction system 204. Examples of these types of events may include, but are not limited to, job postings, changes in revenue, legal events, changes in management, mergers, acquisitions, corporate restructuring, and many others. For a given recipient, the lead scoring system 214 may seek to match attributes of an individual (such as a person associated with a company) to an extracted event. For example, in response to an event where Company A has acquired Company B, the attributes of an individual that may match to the event may be “works for Company B,” “Is a C-level or VP level executive,” and “Lives in New York City.” In this example, a person having these attributes may be receptive to a personalized message 218 from an executive headhunter. In this way, the lead scoring system 214 may generate the list of intended users based on the matches of attributes of the individual to the extracted event. In some embodiments, the lead scoring system 214 may generate the list of the intended users based on the matches of attributes of the individual to the extracted event given the subject matter and/or objective of the message. “; Par. 188; Par. 248)
Regarding Claim 9, and Claim 17,
Miller in view of He teach The system of claim 1, wherein the instructions further cause the processor to,…, and The method of claim 12, further comprising,, …
Miller in view of He teach customer recommendation analysis and the feature is expounded upon by Padmanabhan:
… record the at least one recommendation lead response parameter on a blockchain ledger along with the features retrieved from the sales lead data. (Padmanabhan Par. 492- Sometimes these alternative workflows 894 are produced and generated by the AI of the Einstein cloud platform 888 for customer review and acceptance, in which an affirmative action to accept or reject the recommended alternative workflow 894 is required before the alternative workflow 894 is transacted onto the blockchain. “; Par. 498)
Miller, He and Padmanabhan are directed to integrated client management system analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller in view He, as taught by Padmanabhan, by utilizing blockchain analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller in view of He with the motivation of improved data management for records and information (Padmanabhan Par. 200).
Regarding Claim 10,
Miller in view of He in further view of Padmanabhan in further view of Jakobsson teach The system of claim 9, wherein the instructions further cause the processor to,…,
Miller in view of He teach customer recommendation analysis and the feature is expounded upon by Padmanabhan:
… retrieve the at least one recommendation lead response parameter from the blockchain responsive to a consensus among the RS node and the at least one CRM entity node.. (Padmanabhan Abstract Systems, methods, and apparatuses for dynamically assigning nodes to a group within blockchains based on transaction type and node intelligence using Distributed Ledger Technology (DLT) in conjunction with a cloud based computing environment. For example, according to one embodiment there is a system having at least a processor and a memory therein executing within a host organization, in which such a system includes means for operating a blockchain interface to the blockchain on behalf of a plurality of tenants of the host organization, in which each one of the plurality of tenants operate as a participating node with access to the blockchain; creating a consensus group on the blockchain and associating the consensus group with a specific transaction type for transactions to be processed via the blockchain; assigning a subset of the participating nodes to the consensus group; granting increased weight consensus voting rights to any participating nodes assigned to the consensus group; receiving a transaction at the blockchain having a transaction type matching the specific transaction type associated with the consensus group; and determining consensus for the transaction based on the consensus votes of the participating nodes assigned to the consensus group. Other related embodiments are disclosed.”)
Miller, He and Padmanabhan are directed to integrated client management system analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller in view He, as taught by Padmanabhan, by utilizing blockchain analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller in view of He with the motivation of improved data management for records and information (Padmanabhan Par. 200).
Regarding Claim 12,
Miller teaches
A method for generation of automated recommendations based on a sales lead, comprising: acquiring, by a recommendation server (RS), sales lead data from the sales lead entity node comprising data entered into a sales form by a customer and audio captured during a bot interaction (Miller Par. 92-93; Par. 186-“ in embodiments, determining which recipients (referred to in some cases as “leads”) should receive a communication may be based on one or more events extracted by the information extraction system 204. Examples of these types of events may include, but are not limited to, job postings, changes in revenue, legal events, changes in management, mergers, acquisitions, corporate restructuring, and many others. For a given recipient, the lead scoring system 214 may seek to match attributes of an individual (such as a person associated with a company) to an extracted event.”; Par. 255- “In embodiments, the feedback system 1610 is configured to receive and/or extract feedback regarding interactions with a contact. The feedback system 1610 may receive feedback in any suitable manner. For example, a client-specific service system may present a contact with questions relating to a ticket (e.g., “was this issue resolved to your liking?” or “on a scale of 1-10 how helpful was this article?”) in a survey, during a chat bot communication, or during a conversation with a customer service specialist. A contact can respond to the question and the feedback system 1610 can update a contact's records with the response and/or pass the feedback along to the machine learning system 1608. In some embodiments, the feedback system 1610 may send surveys to contacts and may receive the feedback from the contacts in responsive surveys.; Par. 285-“The chat bot 1908 may receive communication from the contact (e.g., via text or audio) and may process the communication. For example, the chat bot 1908 may perform natural language processing to understand the response of the user. In embodiments, the chat bot 1908 may utilize a rules-based approach and/or a machine learning approach to determine the appropriate response.”););
generating, by the RS, at least one feature vector based on the plurality of features and the local historical customers'-related data (Miller Par. 293-“ In embodiments, the machine learning module 1912 can train a sentiment model using training data that is derived from transcripts of conversations. The transcripts may be labeled (e.g., by a human) to indicate the sentiment of the contact during the conversation. For example, each transcript may include a label that indicates whether a contact was satisfied, upset, happy, confused, or the like. The label may be provided by an expert or provided by the contact (e.g., using a survey). In embodiments, the machine learning module 1912 may parse a transcript to extract a set of features from each transcript. The features may be structured in a feature vector, which is combined with the label to form a training data pair. The machine learning module 1912 may train and reinforce a sentiment model based on the training data pairs. As the client-specific service system 1900 records new transcripts, the machine learning module 1912 may reinforce the sentiment model based on the new transcripts and respective labels that have been assigned thereto.”; Par. 383);
providing, by the RS, the at least one feature vector to the ML module for generating a predictive model configured to produce at least one recommendation lead response parameter for generation of a lead-related recommendation for the at least one CRM entity node. (Miller Par. 186; Par. 293-295-“ In embodiments, the analytics module 1914 may analyze one or more aspects of the data collected by the system 1900. In embodiments, the analytics module 1914 calculates a contact score for a contact that is indicative of a value of the contact to the client. The contact score may be based on a number of different variables. For example, the contact score may be based on a number of tickets that the user has initiated, an average amount of time between tickets, the sentiment of contact when interacting with the system 1900, an amount of revenue resulting from the relationship with the contact (or the entity with which the contact is affiliated), a number of purchases made by the contact (or an affiliated entity), the most recent purchase made by the contact, the date of the most recent purchase, a net promoter score (e.g., feedback given by the contact indicating how likely he or she is to recommend the client's product or products to someone else) and the like. In embodiments, the contact score may be based on feedback received by the feedback module 1916. The contact score may be stored in the contacts data record, the knowledge graph 1622, and/or provided to another component of the system (e.g., a chat bot 1608 or the service specialist portal 1910).
Miller teaches customer analysis and the feature is expounded upon by He:
deriving, by the RS, a language indicator from the sales lead data using a pre-trained large language model; (He Par. 38-“ While semantic searching provides clear technical advantages over lexical searches, semantic search by itself may not provide a user, such as a legal representative or business person, with a clear understanding of the entire context of the information for which they are searching. Consequently, as an addition or alternative, the AI/ML techniques are designed to implement a generative artificial intelligence (AI) platform that uses a large language module (LLM) to assist in contract management. A combination of both semantic search capabilities with a short summary of the relevant information based on a search query provides an optimal solution. This combination provides an overview of the information and highlights it in the agreement to make sure none of the details are missed. A user may use the semantic search capability to quickly locate relevant information and then use the summarization to get a clear understanding of the details.”; Par. 40; Par. 48-“ For example, the system 100 may be implemented as an online signature system, online document creation and management system, an online workflow management system, a multi-party communication and interaction platform, a social networking system, a marketplace and financial transaction management system, a customer record management system, and other digital transaction management platforms. Embodiments are not limited in this context.”; (He Par. 42-“ The method may further include sending a natural language generation (NLG) request to a generative artificial intelligence (AI) model. The generative AI model may comprise a machine learning model that implements a large language model (LLM) to support natural language processing (NLP) operations, such as natural language understanding (NLU), natural language generation (NLG), and other NLP operations.”; Par. 115; Par. 121-“ For example, the generative AI model 728 may implement a language model such as a generative pre-trained transformer (GPT) language model, among others.”; Par. 145; Par. 152);
parsing, by the RS, the sales lead data based on the language indicator to derive a plurality of features (He Par. 37-38-“ Semantic searching is a process of searching for information by understanding the meaning behind the search query and the content being searched. It involves analyzing the context, relationships, and connections between words and concepts to provide more accurate and relevant search results. Unlike lexical searching, which relies on exact matches of search terms, semantic searching takes into account the overall meaning and intent of the query, as well as the meaning and relationships between words and phrases within the content being searched…. Consequently, as an addition or alternative, the AI/ML techniques are designed to implement a generative artificial intelligence (AI) platform that uses a large language module (LLM) to assist in contract management. A combination of both semantic search capabilities with a short summary of the relevant information based on a search query provides an optimal solution. This combination provides an overview of the information and highlights it in the agreement to make sure none of the details are missed. A user may use the semantic search capability to quickly locate relevant information and then use the summarization to get a clear understanding of the details.”; Par. 40; Par. 48; Par. 82);
querying, by the RS, a local customers' database to retrieve local historical customers'-related data related to previous customers' engagements associated with previous lead data based on the plurality of features… (He Par. 87-“ The data sources 302 may source difference types of data 304. For instance, the data 304 may comprise structured data from relational databases, such as customer profiles, transaction histories, or product inventories. The data 304 may comprise unstructured data from websites such as customer reviews, news articles, social media posts, or product specifications.”; Par. 135- “Additionally or alternatively, the search query 144 may be modified or expanded using context information 734. The context information 734 may be any information that provides some context for the search query 144. For example, the context information 734 may comprise a previous search query 144 by the same user, a search query 144 submitted by other users, or prior search results 146 from a previous search query 144. The context information 734 may allow the user to build search queries in an iterative manner, drilling down on more specific search questions in follow-up to reviewing previous search results 146. The context information 734 may also comprise metadata for the electronic document 706 (e.g., signatures, STME, marker elements, document length, document type, etc.), the user generating the search query 144 (e.g., demographics, location, interests, business entity, etc.), a device used to generate the search query 144 (e.g., capabilities, compute resources, memory resources, I/O devices, screen size, interfaces, etc.), sensors (e.g., temperature, accelerometers, altitude, proximity, etc.), and any other context information 734 that may be suitable for further refining the search query 144 (e.g., using search term expansion techniques).”);
Miller and He are directed to integrated client management systems utilizing sematic analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller, as taught by He, by utilizing additional language model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller with the motivation of analyzing the context, relationships, and connections between words and concepts to provide more accurate and relevant search results (He Par. 37).
Miller in view of He teaches customer analysis and the feature is expounded upon by Padmanabhan:
…and further including querying a remote customers' database or a replicated dataset maintained on a permissioned blockchain ledger(Padmanabhan Par. 74-76-“ Certain requests 115 received at the host organization may be directed toward a blockchain for which the blockchain services interface 190 of the host organization 110 operates as an intermediary. The query interface 180 is capable of receiving and executing requested queries against the databases and storage components of the database system 130 and returning a result set, response, or other requested data in furtherance of the methodologies described. The query interface 180 additionally provides functionality to pass queries from web-server 175 into the database system 130 for execution against the databases 155 for processing search queries, or into the other available data stores of the host organization's computing environment 111. In one embodiment, the query interface 180 implements an Application Programming Interface (API) through which queries may be executed against the databases 155 or the other data stores. Additionally, the query interface 180 provides interoperability with the blockchain services interface 190, thus permitting the host organization 110 to conduct transactions with either the database system 130 via the query interface 180 or to transact blockchain transactions onto a connected blockchain for which the host organization 110 is a participating node or is in communication with the participating nodes 133, or the host organization 110 may conduct transactions involving both data persisted by the database system 130 (accessible via the query interface 180) and involving data persisted by a connected blockchain (e.g., accessible from a participating node 133 or from a connected blockchain directly, where the host organization operates a participating node on such a blockchain).; Par. 90”)
and initiating, by the RS node, a scheduling action with the at least one CRM entity node based on the recommendation lead response parameter (Padmanabhan Par. 478-“ According to such embodiments, any platform, including those utilizing AI based models or utilizing deep learning, etc., will ultimately make recommendations, predications, decisions, and take actions, which if adopted and accepted by the business using such platforms, generates data which is potentially important to capture and immutably record via the EinsChain service in a distributed ledger, such as the blockchain upon which the businesses are participating nodes. Notably, the information could be written to a blockchain for which the business is not a participating node, so long as the blockchain services interface of the host organization has access to the blockchain due to the host org operating a participating node, however, it is generally contemplated that the blockchain upon which the business is already a participating node is a suitable blockchain to which to immutably persist such audit and tracking information on behalf of the business.”)
and recording, by execution of a smart contract, the recommendation lead response parameter and an associated schedule entry on a permissioned blockchain for audit and later retrieval by consensus. (Padmanabhan Fig 4A; Fig 4B; FIG. 4A depicts another exemplary architecture, with additional detail of a blockchain implemented smart contract created utilizing a smartflow contract engine, in accordance with described embodiments.; Par. 81; Par. 85; Par. 88; Par. 90; Par. 124).
Miller, He and Padmanabhan are directed to integrated client management system analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller in view He, as taught by Padmanabhan, by utilizing blockchain analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller in view of He with the motivation of improved data management for records and information (Padmanabhan Par. 200).
Regarding Claim 18,
Miller teaches
A non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform: acquiring sales lead data from the sales lead entity node comprising data entered into a sales form by a customer and audio captured during a bot interaction (Miller Par. 8-9; Par. 92-93; Par. 186-“ in embodiments, determining which recipients (referred to in some cases as “leads”) should receive a communication may be based on one or more events extracted by the information extraction system 204. Examples of these types of events may include, but are not limited to, job postings, changes in revenue, legal events, changes in management, mergers, acquisitions, corporate restructuring, and many others. For a given recipient, the lead scoring system 214 may seek to match attributes of an individual (such as a person associated with a company) to an extracted event.”; Par. 255- “In embodiments, the feedback system 1610 is configured to receive and/or extract feedback regarding interactions with a contact. The feedback system 1610 may receive feedback in any suitable manner. For example, a client-specific service system may present a contact with questions relating to a ticket (e.g., “was this issue resolved to your liking?” or “on a scale of 1-10 how helpful was this article?”) in a survey, during a chat bot communication, or during a conversation with a customer service specialist. A contact can respond to the question and the feedback system 1610 can update a contact's records with the response and/or pass the feedback along to the machine learning system 1608. In some embodiments, the feedback system 1610 may send surveys to contacts and may receive the feedback from the contacts in responsive surveys.; Par. 285-“The chat bot 1908 may receive communication from the contact (e.g., via text or audio) and may process the communication. For example, the chat bot 1908 may perform natural language processing to understand the response of the user. In embodiments, the chat bot 1908 may utilize a rules-based approach and/or a machine learning approach to determine the appropriate response.”););
generating at least one feature vector based on the plurality of features and the local historical customers'-related data (Miller Par. 293-“ In embodiments, the machine learning module 1912 can train a sentiment model using training data that is derived from transcripts of conversations. The transcripts may be labeled (e.g., by a human) to indicate the sentiment of the contact during the conversation. For example, each transcript may include a label that indicates whether a contact was satisfied, upset, happy, confused, or the like. The label may be provided by an expert or provided by the contact (e.g., using a survey). In embodiments, the machine learning module 1912 may parse a transcript to extract a set of features from each transcript. The features may be structured in a feature vector, which is combined with the label to form a training data pair. The machine learning module 1912 may train and reinforce a sentiment model based on the training data pairs. As the client-specific service system 1900 records new transcripts, the machine learning module 1912 may reinforce the sentiment model based on the new transcripts and respective labels that have been assigned thereto.”; Par. 383);
and providing the at least one feature vector to the ML module for generating a predictive model configured to produce at least one recommendation lead response parameter for generation of a lead-related recommendation for the at least one CRM entity node. (Miller Par. 186; Par. 293-295-“ In embodiments, the analytics module 1914 may analyze one or more aspects of the data collected by the system 1900. In embodiments, the analytics module 1914 calculates a contact score for a contact that is indicative of a value of the contact to the client. The contact score may be based on a number of different variables. For example, the contact score may be based on a number of tickets that the user has initiated, an average amount of time between tickets, the sentiment of contact when interacting with the system 1900, an amount of revenue resulting from the relationship with the contact (or the entity with which the contact is affiliated), a number of purchases made by the contact (or an affiliated entity), the most recent purchase made by the contact, the date of the most recent purchase, a net promoter score (e.g., feedback given by the contact indicating how likely he or she is to recommend the client's product or products to someone else) and the like. In embodiments, the contact score may be based on feedback received by the feedback module 1916. The contact score may be stored in the contacts data record, the knowledge graph 1622, and/or provided to another component of the system (e.g., a chat bot 1608 or the service specialist portal 1910).
Miller teaches customer analysis and the feature is expounded upon by He:
deriving a language indicator from the sales lead data using a pre-trained large language model (He Par. 38-“ While semantic searching provides clear technical advantages over lexical searches, semantic search by itself may not provide a user, such as a legal representative or business person, with a clear understanding of the entire context of the information for which they are searching. Consequently, as an addition or alternative, the AI/ML techniques are designed to implement a generative artificial intelligence (AI) platform that uses a large language module (LLM) to assist in contract management. A combination of both semantic search capabilities with a short summary of the relevant information based on a search query provides an optimal solution. This combination provides an overview of the information and highlights it in the agreement to make sure none of the details are missed. A user may use the semantic search capability to quickly locate relevant information and then use the summarization to get a clear understanding of the details.”; Par. 40; Par. 48-“ For example, the system 100 may be implemented as an online signature system, online document creation and management system, an online workflow management system, a multi-party communication and interaction platform, a social networking system, a marketplace and financial transaction management system, a customer record management system, and other digital transaction management platforms. Embodiments are not limited in this context.; (He Par. 42-“ The method may further include sending a natural language generation (NLG) request to a generative artificial intelligence (AI) model. The generative AI model may comprise a machine learning model that implements a large language model (LLM) to support natural language processing (NLP) operations, such as natural language understanding (NLU), natural language generation (NLG), and other NLP operations.”; Par. 115; Par. 121-“ For example, the generative AI model 728 may implement a language model such as a generative pre-trained transformer (GPT) language model, among others.”; Par. 145; Par. 152”);
parsing the sales lead data based on the language indicator to derive a plurality of feature (He Par. 37-38-“ Semantic searching is a process of searching for information by understanding the meaning behind the search query and the content being searched. It involves analyzing the context, relationships, and connections between words and concepts to provide more accurate and relevant search results. Unlike lexical searching, which relies on exact matches of search terms, semantic searching takes into account the overall meaning and intent of the query, as well as the meaning and relationships between words and phrases within the content being searched…. Consequently, as an addition or alternative, the AI/ML techniques are designed to implement a generative artificial intelligence (AI) platform that uses a large language module (LLM) to assist in contract management. A combination of both semantic search capabilities with a short summary of the relevant information based on a search query provides an optimal solution. This combination provides an overview of the information and highlights it in the agreement to make sure none of the details are missed. A user may use the semantic search capability to quickly locate relevant information and then use the summarization to get a clear understanding of the details.”; Par. 40; Par. 48; Par. 82);
querying a local customers' database to retrieve local historical customers'- related data related to previous customers' engagements associated with previous lead data based on the plurality of features …(He Par. 87-“ The data sources 302 may source difference types of data 304. For instance, the data 304 may comprise structured data from relational databases, such as customer profiles, transaction histories, or product inventories. The data 304 may comprise unstructured data from websites such as customer reviews, news articles, social media posts, or product specifications.”; Par. 135- “Additionally or alternatively, the search query 144 may be modified or expanded using context information 734. The context information 734 may be any information that provides some context for the search query 144. For example, the context information 734 may comprise a previous search query 144 by the same user, a search query 144 submitted by other users, or prior search results 146 from a previous search query 144. The context information 734 may allow the user to build search queries in an iterative manner, drilling down on more specific search questions in follow-up to reviewing previous search results 146. The context information 734 may also comprise metadata for the electronic document 706 (e.g., signatures, STME, marker elements, document length, document type, etc.), the user generating the search query 144 (e.g., demographics, location, interests, business entity, etc.), a device used to generate the search query 144 (e.g., capabilities, compute resources, memory resources, I/O devices, screen size, interfaces, etc.), sensors (e.g., temperature, accelerometers, altitude, proximity, etc.), and any other context information 734 that may be suitable for further refining the search query 144 (e.g., using search term expansion techniques).”);
Miller and He are directed to integrated client management systems utilizing sematic analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller, as taught by He, by utilizing additional language model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller with the motivation of analyzing the context, relationships, and connections between words and concepts to provide more accurate and relevant search results (He Par. 37).
Miller in view of He teaches customer analysis and the feature is expounded upon by Padmanabhan:
…and further including querying a remote customers' database or a replicated dataset maintained on a permissioned blockchain ledger(Padmanabhan Par. 74-76-“ Certain requests 115 received at the host organization may be directed toward a blockchain for which the blockchain services interface 190 of the host organization 110 operates as an intermediary. The query interface 180 is capable of receiving and executing requested queries against the databases and storage components of the database system 130 and returning a result set, response, or other requested data in furtherance of the methodologies described. The query interface 180 additionally provides functionality to pass queries from web-server 175 into the database system 130 for execution against the databases 155 for processing search queries, or into the other available data stores of the host organization's computing environment 111. In one embodiment, the query interface 180 implements an Application Programming Interface (API) through which queries may be executed against the databases 155 or the other data stores. Additionally, the query interface 180 provides interoperability with the blockchain services interface 190, thus permitting the host organization 110 to conduct transactions with either the database system 130 via the query interface 180 or to transact blockchain transactions onto a connected blockchain for which the host organization 110 is a participating node or is in communication with the participating nodes 133, or the host organization 110 may conduct transactions involving both data persisted by the database system 130 (accessible via the query interface 180) and involving data persisted by a connected blockchain (e.g., accessible from a participating node 133 or from a connected blockchain directly, where the host organization operates a participating node on such a blockchain).; Par. 90”)
And executing a smart contract to record the recommendation lead response parameter and an associated schedule entry on a permissioned blockchain and to initiate a scheduling action with the at least one CRM entity node (Padmanabhan Fig 4A; Fig 4B; FIG. 4A depicts another exemplary architecture, with additional detail of a blockchain implemented smart contract created utilizing a smartflow contract engine, in accordance with described embodiments.; Par. 81; Par. 85; Par. 88; Par. 90; Par. 124; Par. 478-“ According to such embodiments, any platform, including those utilizing AI based models or utilizing deep learning, etc., will ultimately make recommendations, predications, decisions, and take actions, which if adopted and accepted by the business using such platforms, generates data which is potentially important to capture and immutably record via the EinsChain service in a distributed ledger, such as the blockchain upon which the businesses are participating nodes. Notably, the information could be written to a blockchain for which the business is not a participating node, so long as the blockchain services interface of the host organization has access to the blockchain due to the host org operating a participating node, however, it is generally contemplated that the blockchain upon which the business is already a participating node is a suitable blockchain to which to immutably persist such audit and tracking information on behalf of the business.”)
Miller, He and Padmanabhan are directed to integrated client management system analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller in view He, as taught by Padmanabhan, by utilizing blockchain analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller in view of He with the motivation of improved data management for records and information (Padmanabhan Par. 200).
Claims 8, 11, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al., US Publication No. 20230316186 A1, [hereinafter Miller], in view of He et al., US Publication No. 20240362286 A1, [hereinafter He] , in further view of Padmanabhan, US Publication No. 20200250747 A1, [hereinafter Padmanabhan] , and in further view of Pingali et al., US Publication No. 20210264332 A1, [hereinafter Pingali].
Regarding Claim 8, Claim 16 and Claim 20,
Miller in view of He teach The system of claim 7, wherein the instructions further cause the processor to,…, The method of claim 15, further comprising, … and The non-transitory computer readable medium of claim 19, further comprising instructions, that when read by the processor, cause the processor to, …
… responsive to the at least one value of the incoming sale lead data deviating from the value of previous customers'-related data by the margin exceeding the pre-set threshold value, (Miller Par. 4; Par. 186-“ In embodiments, determining which recipients (referred to in some cases as “leads”) should receive a communication may be based on one or more events extracted by the information extraction system 204. Examples of these types of events may include, but are not limited to, job postings, changes in revenue, legal events, changes in management, mergers, acquisitions, corporate restructuring, and many others. For a given recipient, the lead scoring system 214 may seek to match attributes of an individual (such as a person associated with a company) to an extracted event. For example, in response to an event where Company A has acquired Company B, the attributes of an individual that may match to the event may be “works for Company B,” “Is a C-level or VP level executive,” and “Lives in New York City.” In this example, a person having these attributes may be receptive to a personalized message 218 from an executive headhunter. In this way, the lead scoring system 214 may generate the list of intended users based on the matches of attributes of the individual to the extracted event. In some embodiments, the lead scoring system 214 may generate the list of the intended users based on the matches of attributes of the individual to the extracted event given the subject matter and/or objective of the message. “; Par. 188; Par. 248)
Miller in view of He in further view of Padmanabhan teach customer analysis and the feature is expounded upon by Pingali:
… generate an updated feature vector based on the incoming sales lead data and generate the customer-related recommendation based on the at least one recommendation lead response parameter produced by the predictive model in response to the updated feature vector. (Pingali Par. 248-250-“ For example, the ML model may determine a feature vector (or embedding) based on features of activity logs 815. The feature vector (or embedding) may be a mathematical, multi-dimensional representation generated based on the activity logs 815. Different states of an enterprise process may have different feature vectors, based on respective states corresponding to the activity logs. The predicted states 830 generated by ML model 825 are provided to feedback generator 840. Feedback generator 840 is also provided with the realized states 820 corresponding to the enterprise process. Feedback 850 is generated by feedback generator 840 based on a comparison of the predicted states with the realized states. For example, the loss value is utilized to adjust one or more parameters of the ML model.”)
Miller, He and Padmanabhan are directed to integrated client management systems analysis. Pingali improves upon the CRM system analysis .It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller in view of He in further view of Padmanabhan, as taught by Pingali, by utilizing additional machine learning model analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller in view of He in further view of Padmanabhan with the motivation of enabling decision making to improve an enterprise process (end-to-end enterprise system). The decision making may be fully or partially automated and integrated into a process to make itself healing and/or provide decision support to a decision maker. (Pingali Par. 40).
Regarding Claim 11, Miller in view of He in further view of Padmanabhan in further view of Pingali teach The system of claim 8, wherein the instructions further cause the processor to,…,
Miller fails to teach the following feature taught by He:
… execute a smart contract to record data reflecting scheduling of a sale lead response interaction associated with the customer and the at least one CRM entity node ... for future audits(He Par. 48 and related text-“ FIG. 1 illustrates an embodiment of a system 100. The system 100 may be suitable for implementing one or more embodiments as described herein. In one embodiment, for example, the system 100 may comprise an electronic document management platform (EDMP) suitable for managing a collection of electronic documents. An example of an EDMP includes a product or technology offered by DocuSign®, Inc., located in San Francisco, California (“DocuSign”). DocuSign is a company that provides electronic signature technology and digital transaction management services for facilitating electronic exchanges of contracts and signed documents. An example of a DocuSign product is a DocuSign Agreement Cloud that is a framework for generating, managing, signing and storing electronic documents on different devices. It may be appreciated that the system 100 may be implemented using other EDMA, technologies and products as well. For example, the system 100 may be implemented as an online signature system, online document creation and management system, an online workflow management system, a multi-party communication and interaction platform, a social networking system, a marketplace and financial transaction management system, a customer record management system, and other digital transaction management platforms.”)
Miller in view of He teaches customer analysis and the feature is expounded upon by Padmanabhan:
…on the blockchain…(Padmanabhan Fig 4A; Fig 4B; FIG. 4A depicts another exemplary architecture, with additional detail of a blockchain implemented smart contract created utilizing a smartflow contract engine, in accordance with described embodiments.; Par. 81; Par. 85; Par. 88; Par. 90; Par. 124).
Miller, He and Padmanabhan are directed to integrated client management system analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Miller in view He, as taught by Padmanabhan, by utilizing blockchain analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Miller in view of He with the motivation of improved data management for records and information (Padmanabhan Par. 200).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure US Publication No. 20220405775A1 to Siebel et al.- Abstract-“ A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.”
THIS ACTION IS MADE FINAL. 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 extension fee 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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Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624