DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims Status
Claims 1-19 are pending and rejected.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 5/10/2024, 6/30/2025 and 12/30/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-17 are directed to a method, which is a process. Claim 18 is directed to a medium, which is an apparatus. Claim 19 is directed to a system, which is a machine. Therefore, claims 1-19 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Claim 1 sets forth the following limitations which recite the abstract idea of providing sales recommendations:
encoding an input query into a numerical representation in a business domain;
retrieving data from a deal knowledge base based on the numerical representation;
generating a prompt based on the encoded input query and data retrieved from the deal knowledge base;
feeding the prompt to a generic-trained language model; and
ranking responses provided by the generic-trained language model, wherein the responses are related to at least a deal pipeline.
The recited limitations as a whole set forth the process for providing sales recommendations. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors).
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106).
Step 2A (Prong 2):
Examiner acknowledges that claim 1 does recite additional elements, such as a knowledge base, a model, etc.
Taken individually and as a whole, claim 1 does not integrate the recited judicial exception into a practical application of the exception. The claim merely includes instruction to implement an abstract idea on a computer, or to merely use a computer as a tool to perform an abstract idea, while the additional elements do no more than generally link the use of a judicial exception to a particular field of technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106).
Step 2B:
When taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer device to perform the receiving and determining steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Certain additional elements also recite well-understood, routine, and conventional activity (See MPEP 2106.05(d)).
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-17 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the process for providing sales recommendations. Thus, each of claims 2-17 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-17 do not add “significantly more” to the abstract idea. The dependent claims recite additional functions that describe the abstract idea and only generally link the abstract idea to a particularly technological environment, and applied on a generic computer. Further, the additional limitations fail to provide an improvement to the functioning of the computer, another technology, or a technical field.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B for at least similar rationale as discussed above regarding claim 1.
The analysis above applies to all statutory categories of invention. Regarding independent claims 18 (medium) and 19 (system), the claim recites substantially similar limitations as set forth in claim 1. As such, claims 18 and 19 are rejected for at least similar rationale as discussed above.
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 of this title, 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 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nahamoo et al. (U.S. Pre-Grant Publication No. 2021/0406735 A1) (“Nahamoo”) in view of Layton et al. (U.S. Pre-Grant Publication No. 2022/0092028 A1) (“Layton”).
Regarding claims 1, 18 and 19, Nahamoo teaches a method (and related medium and system) for streamlining a deal pipeline based on large language models, comprising:
encoding an input query into a numerical representation in a business domain (para [0050], user associated with the customer that provided that document library (e.g., an employee of the customer) can subsequently submit a query; para [0054], data sent to the cloud servers (e.g., to perform the search at a centralized location) may already have been processed into encoded (ingested) content...fine-detail numerical transforms; para [0059], transform converts textual content into a vector of numerical values);
retrieving data from a deal knowledge base based on the numerical representation (para [0062], DOM record can include data items associated with a particular source document or a source document portion);
generating a prompt based on the encoded input query and data retrieved from the deal knowledge base (para [0050]; para [0065], perform the second stage of the search by converting the query to a fine-detail transformed query and searching fine-detail transformed content associated with the search results identified in the first stage of the search process);
feeding the prompt to a generic-trained language model (para [0066]-[0069], upon submission of a query (generally in transformed format computed, for example, according to a coarse-BERT or a fine-BERT type transformation), at least one DOM record/element will be identified); and
ranking responses provided by the generic-trained language model (para [0068], matching of query data to the past questions and associated answers stored in cache is performed by computing a score that is based on the combination of the questions and their answers, and ranking the computed scores to identify one or more likely matching candidates).
However, Nahamoo does not explicitly teach wherein the responses are related to at least a deal pipeline.
In a similar field of endeavor, Layton teaches wherein the responses are related to at least a deal pipeline (para [0005], custom object, in language-independent data form, may be sent to at least one of a user device or one or more services of a multi-service business platform, for use with at least one of a marketing process, a sales process… the sales process, or the customer service process may include services providing at least one of customer relationship management, social media marketing, content management, lead generation).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would have been obvious to one of ordinary skill in the art at the time of filing to include the noted limitations as taught by Layton in the method of Nahamoo, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Namely, improved customer relationship management (CRM) systems that may generally provide ability to manage and analyze interactions with customers for businesses utilizing the benefits of machine learning technologies (see Layton; para [0004]).
Regarding claim 2, Nahamoo and Layton teach the above method of claim 1. Layton also teaches wherein the responses include fully worded messages related to deal stages for different prospects (Fig. 13; para [0148], lead scoring system 214 and the content generation system 216 for identifying intended recipients of messages and generating personalized messages).
Regarding claim 3, Nahamoo and Layton teach the above method of claim 2. Layton also teaches further comprising: generating fully worded messages (Fig. 13; para [0148], lead scoring system 214 and the content generation system 216 for identifying intended recipients of messages and generating personalized messages).
Regarding claim 4, Nahamoo and Layton teach the above method of claim 3. Layton also teaches further comprising:
encoding an input message outline with customer relationship management data (para [0085], the CRM system 158 may support interactions with a customer);
retrieving data from a deal knowledge base based on an encoded input message outline (para [0085], see suggested topics 138 that may be of interest to the customer, such as based on the customer data records);
generating a prompt based on the encoded input message outline and data retrieved from the deal knowledge base (para [0085], see suggested topics 138 that may be of interest to the customer, such as based on the customer data records);
feeding the prompt to a generic-trained language model to generate fully worded messages (para [0085], conversational agent 182 may take suggested topics from the suggestion generator 134 to facilitate initiation of conversations with customers around topics that differentiate the enterprise); and
ranking and displaying the fully worded messages (para [0085], such as topics that are semantically relevant to key phrases found in the primary online content object).
Regarding claim 5, Nahamoo and Layton teach the above method of claim 4. Layton also teaches wherein the deal knowledge base includes information on similar message outlines, similar deals, and previously worded messages to prospects (para [0070], content clusters 130 a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150 within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevant information about it (such as indicators of its similarity or relevancy as described elsewhere herein) and create content, such as web pages, emails, customer chats, and other generated online presence content 160 on behalf of the enterprise.).
Regarding claim 6, Nahamoo and Layton teach the above method of claim 1. Layton also teaches wherein the responses include at least one offer to purchase a product or service tailored for a prospect (para [0108], content for a conversation or chat with a customer (including one that may be managed by the conversational agent 182 or bot), content for a marketing message or offer to the customer).
Regarding claim 7, Nahamoo and Layton teach the above method of claim 6. Layton also teaches further comprising: generating the at least one offer (para [0108], content for a conversation or chat with a customer (including one that may be managed by the conversational agent 182 or bot), content for a marketing message or offer to the customer).
Regarding claim 8, Nahamoo and Layton teach the above method of claim 7. Layton also teaches further comprising:
encoding an input offer request with customer relationship management data (para [0085], the CRM system 158 may support interactions with a customer; para [0108]);
retrieving data from a deal knowledge base based on an encoded offer request (para [0085], see suggested topics 138 that may be of interest to the customer, such as based on the customer data records; para [0108]);
generating a prompt based on an encoded offer request and data retrieved from the deal knowledge base (para [0085], see suggested topics 138 that may be of interest to the customer, such as based on the customer data records; para [0108]);
feeding the prompt to a generic-trained language model to generate offers tailored for specific prospects (para [0085], conversational agent 182 may take suggested topics from the suggestion generator 134 to facilitate initiation of conversations with customers around topics that differentiate the enterprise; para [0108]); and
ranking and displaying the tailored offers (para [0085], such as topics that are semantically relevant to key phrases found in the primary online content object; para [0108]).
Regarding claim 9, Nahamoo and Layton teach the above method of claim 8. Layton also teaches wherein the deal knowledge base includes information on similar pricing models from previous deals, similar offers, previous offers from prospects, and previous conversations with a prospect (para [0070], content clusters 130 a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150 within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevant information about it (such as indicators of its similarity or relevancy as described elsewhere herein) and create content, such as web pages, emails, customer chats, and other generated online presence content 160 on behalf of the enterprise.).
Regarding claim 10, Nahamoo and Layton teach the above method of claim 1. Layton also teaches wherein the responses include answers to questions asked by prospects during a live call (para [0176], variations in the script for a sales call may be developed by the natural language generation system).
Regarding claim 11, Nahamoo and Layton teach the above method of claim 10. Layton also teaches further comprising: generating an answer response during a live call (para [0176], Machine learning, such as trained on outcomes of sales conversations, may be used to develop models and heuristics for guiding conversations, as well as to develop content for scripts.).
Regarding claim 12, Nahamoo and Layton teach the above method of claim 11. Layton also teaches further comprising:
encoding an input question with live call transcripts (para [0085], the CRM system 158 may support interactions with a customer; para [0176]);
retrieving data from a deal knowledge base based on an encoded input question, wherein such retrieved data includes question and answer pairs (para [0085], see suggested topics 138 that may be of interest to the customer, such as based on the customer data records; para [0176]);
generating a prompt based on the encoded input question and data retrieved from the deal knowledge base (para [0085], see suggested topics 138 that may be of interest to the customer, such as based on the customer data records; para [0176]);
feeding the prompt to a generic-trained language model to generate answer responses (para [0085], conversational agent 182 may take suggested topics from the suggestion generator 134 to facilitate initiation of conversations with customers around topics that differentiate the enterprise; para [0176]);
ranking the answer responses (para [0085], such as topics that are semantically relevant to key phrases found in the primary online content object; para [0176]); and
rephrasing and displaying a highest scoring answer response (para [0176], Machine learning, such as trained on outcomes of sales conversations, may be used to develop models and heuristics for guiding conversations, as well as to develop content for scripts.).
Regarding claim 13, Nahamoo and Layton teach the above method of claim 12. Layton also teaches wherein the deal knowledge base includes similar pairs of deal questions and answers, similar prospects, and previously generated answer responses (para [0070], content clusters 130 a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150 within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevant information about it (such as indicators of its similarity or relevancy as described elsewhere herein) and create content, such as web pages, emails, customer chats, and other generated online presence content 160 on behalf of the enterprise.).
Regarding claim 14, Nahamoo and Layton teach the above method of claim 1. Layton also teaches further comprising: generating a next action recommendation to assist with closing a deal based on a stage of a deal, prospect information, and correlating the stage of the deal and prospect information with similar deals at the same stage (para [0171], outcome tracking may include tracking content to determine what events extracted by the information extraction system, when used to generate natural language content for communications, tend to predict deal closure or other favorable outcomes).
Regarding claim 15, Nahamoo and Layton teach the above method of claim 14. Layton also teaches further comprising:
encoding an input question with customer representative management data (para [0085], the CRM system 158 may support interactions with a customer; para [0176]);
retrieving data about similar deals from a deal knowledge base based on an encoded input question (para [0085], see suggested topics 138 that may be of interest to the customer, such as based on the customer data records; para [0176]);
generating a prompt based on the encoded input question and data about similar deals from a deal knowledge base (para [0085], see suggested topics 138 that may be of interest to the customer, such as based on the customer data records; para [0176]);
feeding the prompt to a causal inference model to generate next action recommendations (para [0085], conversational agent 182 may take suggested topics from the suggestion generator 134 to facilitate initiation of conversations with customers around topics that differentiate the enterprise; para [0176]); and
ranking the next action recommendations (para [0085], such as topics that are semantically relevant to key phrases found in the primary online content object; para [0176]).
Regarding claim 16, Nahamoo and Layton teach the above method of claim 15. Layton also teaches wherein the deal knowledge base includes information about similar deals at the same stage, similar prospects, and previously generated actions (para [0070], content clusters 130 a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150 within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevant information about it (such as indicators of its similarity or relevancy as described elsewhere herein) and create content, such as web pages, emails, customer chats, and other generated online presence content 160 on behalf of the enterprise.).
Regarding claim 17, Nahamoo and Layton teach the above method of claim 15. Layton also teaches wherein the causal inference model evaluates potential next actions and determines which next action will increase probability of a deal closing based on stage of a deal, correspondence data, and actions performed in previous deals (para [0172], system 200 may explore how to slice up time periods with respect to particular types of events in order to determine when an event of a given type is likely to have what kind of influence on a particular type of outcome (e.g., a deal closure)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND LOHARIKAR whose telephone number is 571-272-8756. The examiner can normally be reached Monday through Friday, 9am – 5pm.
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689