DETAILED ACTION
This Final Office Action is in response Applicant communication filed on
10/13/2025. In Applicant’s amendment, claims 1, 3, 6-7, 21, 23, 26-27, 29, and 32 were amended. Claims 1-8 and 21-32 are currently pending and have been rejected as follows.
Response to Amendments
Rejections under 35 USC 101 are maintained. Rejections under 35 USC 102 are withdrawn. Applicant’s amendments necessitated new grounds of rejection under 35 USC 103.
Response to Arguments
Applicant’s 35 USC 101 rebuttal arguments and amendments have been fully considered but they are not persuasive to overcome the rejection.
Applicant argues on p. 10-13 that the claims are not directed to certain methods of organizing human activity and mental processes at Step 2A, Prong 1 because the claims recite a technological process involving specific computing components and technical improvements.Examiner respectfully disagrees. Under Step 2A, Prong 1, examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas. If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. The claim then requires further analysis in Step 2A Prong Two, to determine whether any additional elements in the claim integrate the abstract idea into a practical application. Incorporating the use of specific computing components does not preclude the claim from the realm of abstract ideas. The claimed computing components are considered individually and in combination with the limitations directed to the abstract idea at Step 2A, Prong 2. Under Step 2A, Prong 1, the claim is directed to the abstract idea of certain methods of organizing human activity like advertising, marketing or sales activities or behaviors; business relations, and additionally mental processes like concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the focus of the claim is to generate a pitch, identify potentially receptive recipients, determine target recipients, and send the target recipients the pitch. The generative AI and predictive AI modules are functionally labeled software modules. Relying on the trained AI models, claimed at such a high level, to generate a pitch and to predict potential recipients merely amounts to invoking computer components to implement the abstract idea.
Applicant argues on p. 13-16 that the claims are eligible at Step 2A, Prong 2 because the claims integrate any alleged abstract idea into a practical application reciting a concrete data architecture that is utilized to implement AI modules in a non-generic manner to transform data for a specific technical outcome and relying on the specification’s discussion of improved reliability, scalability, performance, and data integrity.
Examiner respectfully disagrees. Under Step 2A, Prong 2, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
Here, the claimed additional elements such as the one or more processors; a memory storing processor-executable instructions; a computing device comprising a data layer; one or more flat file systems, a data lakehouse, one or more relational databases, and cache; a generative Al module; a predictive Al module are merely employed in the claims to receive data, store data, access modules, retrieve from cache, and facilitate transmission. The additional elements are recited at a high level to perform their ordinary function. The claims do not recite an improvement to the function of a computer, or an improvement to other technology or technical field. The claims merely use computers as tools to perform the abstract idea. The claimed improvements of reliability, scalability, performance, and data integrity in the specification are intended results, not technical improvements.
Applicant argues on p. 16-17 that the claims are patent eligible for their similarity to Example 34 by the implementation of a computing device comprising a data layer that comprises one or more flat file systems for storing information associated with the content item, a data lakehouse for accessing the AI modules, relational databases for structured profile data, and cache for low-latency retrieval of generated pitch communications. In addition, the claims require accessing a generative AI module and a predictive AI module via the data lakehouse, wherein these modules are utilized to perform algorithmic transformations of data to generate, and facilitate the transmission of, the pitch communication.Examiner respectfully disagrees. The claims do not recite any non-conventional arrangement of generic components like Example 34’s internet filtering system located remote from users on an ISP server while still enabling individual filtering schemes for users and claiming how the non-conventional arrangement of generic components enable this improvement. In Example 34, the non-conventional arrangement was a particular network architecture that solved a specific technical problem outlined in the second paragraph of the Example 34 Background. In contrast, the present claims employs generic components in a conventional manner to improve public relations activity workflows, which is an improvement to the abstract idea rather than an improvement to the function of a computer, or an improvement to other technology or technical field.
Applicant argues on p. 17-19 that the claims are eligible at Step 2B because the claims introduce a solution to issues associated with the inability of conventional content delivery systems to efficiently and adequately generate targeted content and identifying the content to appropriate recipients, and customizing communications is time-consuming since it requires collecting and analyzing recipient data. Examiner respectfully disagrees. Providing a solution to public relations activity workflows is merely providing an improvement to the abstract idea. Under Step 2B, examiners should evaluate whether the claim recites additional elements that amount to significantly more than the judicial exception. Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should:
• Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two;
• Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h):
• Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and
• Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d).
Here, the claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps of “receiving” information, “storing” the information, “accessing” generative and predictive AI modules, “generating” a pitch, “storing” the pitch, “analyzing” potential recipients, “determining” target recipients, and “facilitating” transmission of the pitch to the target recipients (Example Claim 1).
Applicant's prior art arguments have been fully considered but they are not persuasive to overcome the rejection.
Applicant’s argument A on p. 20 is moot in light of the newly cited Scott reference.
Applicant argues on p. 21 that Kilchenko does not disclose “determining, based on evaluating the alignment between the pitch communication and the one or more potential recipients, one or more target recipients of the one or more potential recipients predicted to be most receptive to the pitch communication.” Examiner respectfully submits that Lagi was previously cited to disclose this feature.
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 1-8 and 21-32 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, apparatus, and non-transitory computer readable media). Claims 1-8 and 21-32 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 integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1-8 are directed toward the statutory category of a process (reciting a “method”). Claims 21-26 are directed toward the statutory category of a machine (reciting an “apparatus”). Claims 27-32 are directed toward the statutory category of an article of manufacturer (reciting a “non-transitory computer readable media”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 21 and 27 are directed to an abstract idea by reciting receiving … information associated with a content item … generating … based on the information, a pitch communication … analyzing … based on historical receptive data and thematic preferences … a plurality of recipient profiles … to identify one or more potential recipients; evaluating, based on utilizing predictive analytics … an alignment between the pitch communication and the one or more potential recipients determining, based on evaluating the alignment between the pitch communication and the one or more potential recipients, one or more target recipients of the one or more potential recipients predicted to be most receptive to the pitch communication; and facilitating … transmission of the pitch communication to the one or more target recipients (Example Claim 1).
The claims are considered abstract because these steps recite certain methods of organizing human activity like advertising, marketing or sales activities or behaviors; business relations, and additionally mental processes like concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The claims recite steps to generate targeted communication, determine recipients, and transmit the targeted communication to the recipients. Applicant’s disclosure suggests that the claimed steps aim to improve generation of customized by using machine learning models to generate customized content based on user inputs (Applicant’s Specification, [0024]). By this evidence, the claims recite a type of certain methods of organizing human activity like advertising, marketing or sales activities or behaviors; business relations, and additionally mental processes like concepts performed in the human mind (including an observation, evaluation, judgment, opinion) common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., an “apparatus” for generating customized content).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as one or more processors; a memory storing processor-executable instructions; a computing device comprising a data layer; wherein the data layer comprises one or more flat file systems, a data lakehouse, one or more relational databases, and cache; storing the information associated with the content item in a flat file system of the one or more flat file systems; accessing, via the data lakehouse, a generative Al module and a predictive Al module; storing the pitch communication in the cache; retrieving the pitch communication from the cache) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply 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 claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)).
Dependent claims 2-8, 22-26, and 28-32 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP 2106.05(f).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps of “receiving” information, “storing” the information, “accessing” generative and predictive AI modules, “generating” a pitch, “storing” the pitch, “analyzing” potential recipients, “determining” target recipients, and “facilitating” transmission of the pitch to the target recipients (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1-8 and 21-32 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
Claim Rejections - 35 USC § 103
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 1-8 and 21-32 are rejected under 35 USC 103 as being unpatentable over the teachings of
Kilchenko et al., US 20190171351 A1, cite no. 1 on IDS filed 4/3/2025, hereinafter Kilchenko, in view of
Scott et al., US 20200210391 A1, hereinafter Scott, in view of
Lagi et al., US 20220222703 A1, cite no. 2 on IDS filed 4/3/2025, hereinafter Lagi. As per,
Claims 1, 21, 27
Kilchenko teaches
A method for utilizing generative artificial intelligence (AI) to facilitate public relations (PR) activities, the method comprising: /
An apparatus for utilizing generative artificial intelligence (AI) to facilitate public relations (PR) activities, the apparatus comprising: one or more processors; a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to: /
One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: (Kilchenko fig. 1; [0059]-[0061])
receiving, by a computing device comprising a data layer, information associated with a content item, wherein the data layer comprises one or more flat file systems, a data lakehouse, one or more relational databases, and cache; (Kilchenko [0021] “the method begins with receiving information associated with a first party. The first party in this example is an end user of a system such as wireless device. The information includes demographic information of the end user and/or behavioral information of the end user … The behavioral information includes metadata associated with the end user collected from various sources including the web page document of the web retailer and other metadata collected from other sources” note the web page document metadata corresponding to the content item; [0117] “The system memory 1006 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 1010 and/or cache memory 1012” note the cache)
[…]
accessing, […], a generative Al module and a predictive Al module; (Kilchenko [0055] “The present invention is customer friendly and provides real-time campaign management and reporting for publishers. Powered by a self-learning artificial intelligence engine” corresponding to a generative AI module; [0081] “advertising server 732 which includes the predictive model 728” corresponding to a predictive AI module)
generating, by the generative Al module accessed via the data lakehouse, based on the information, a pitch communication; (Kilchenko [0054]-[0055]; [0067] “Once a request is received from chat 308, the process continues with determining the type of response … if the request is a sales pitch request, e.g. is it a sales pitch 314 then select and output sales pitch 316 based on preferences setup in TSA management console.”)
[…];
analyzing, by the predictive Al module accessed via the data lakehouse, based on historical receptive data and thematic preferences accessed via the […], a plurality of recipient profiles, accessed via the […], to identify one or more potential recipients; (Kilchenko [0083] “The basic operation of the advertisement system 720 provides for the selection of advertising targeted to the end-user on an affiliate website 724. The predictive model 728 processes all of the informational context in the database 730 and selects a single direct advertisement from a database of available advertisements 734, or a ranked order of direct advertisements to advertising server 732, to which the end-user is most likely to respond” note the predictive model determining which advertisement should be used for users; [0155] “When a tracker records websites visited and end user search queries, instant messages or emails using web-based systems, comments, etc.—it can quickly assemble a very thorough and accurate profile for each end user”)
evaluating, based on utilizing predictive analytics via the data lakehouse, an alignment between the pitch communication and the one or more potential recipients; (Kilchenko [0088] “The chat engine in chat server 752 using a predictive model 758 processes the informational context in the database 760 and selects a single direct response from a database of available responses 760 to the end-user.”)
[…];
facilitating, by the computing device […], transmission of the pitch communication to the one or more target recipients. (Kilchenko fig. 1; [0067] “Once a request is received from chat 308, the process continues with determining the type of response, e.g. is it a greeting 310, then select and output greeting 312 based on preferences setup in TSA management console. Likewise, if the request is a sales pitch request, e.g. is it a sales pitch 314 then select and output sales pitch 316 based on preferences setup in TSA management console.” Note the response facilitated by the TSA)
Kilchenko does not explicitly disclose, Scott however in the analogous art of business analytics teaches
storing the information associated with the content item in a flat file system of the one or more flat file systems; (Scott fig. 2 noting the flat file database 202; [0025] “the data stores 110, 115, and 120 can be part of a variety of different databases within an enterprise. For example, the data store 110 is part of an operational system database (e.g., a marketing or finance database) that stores operational data” noting the operational data corresponding to the content item information; [0078] “when data is moved (via ETL) between various data stores, changing formats from one data store (e.g., the flat file database 202 of FIG. 2)” note the storing of incoming data in a flat file system)
[…] via the data lakehouse […]; (Scott [0025] “the data store 120 is part of a data warehouse or data lake (which stores raw data in any native format). The data stores can be part of other systems, such as Business Intelligence (BI) systems, analysis systems, machine learning (ML) or artificial intelligence (AI) systems;” claim 16 “wherein the source data store is part of a data warehouse for an enterprise, wherein the data warehouse includes a data lake, and wherein the target data store is part of a business intelligence system for the enterprise” noting the data warehouse including a data lake)
[…] one or more relational databases […] one or more relational databases […]; (Scott [0043] “The data then moved, via ETL processes, from the Hadoop system 204, to various relational databases” note the relational databases)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Kilchenko’s generative chat agent to include a data lakehouse, a flat file system, and relational databases in view of Scott in an effort to provide improve data accuracy for business intelligence analytics (see Scott ¶ [0016] & MPEP 2143G).
Kilchenko / Scott do not explicitly disclose, Lagi however in the analogous art of business analytics teaches
storing the pitch communication in the cache; (Lagi [0158] “Upon generating a personalized message, the content generation system 216 may then output the personalized message. Outputting a personalized message may include … storing the personalized message in a data store” the personalized message/pitch communication can be stored before being retrieved later for transmission; [0218] “cache memory”)
determining, based on evaluating the alignment between the pitch communication and the one or more potential recipients, one or more target recipients of the one or more potential recipients predicted to be most receptive to the pitch communication; and (Lagi [0157] “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”)
[…] based on retrieving the pitch communication from the cache […]. (Lagi [0152] “A message template may be created by the user (e.g., using the content management system disclosed elsewhere herein) or may be retrieved by the user and sent from the client device. A message template may include fields to be filled by the content generation system 216 with information, including content that is generated by the content generation system 216;” [0158] “providing the personalized message for display to the user, where the user can edit and/or approve the personalized message before transmission to the intended recipient” note the personalized generated content retrieved and edited before transmission)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Kilchenko’s generative chat agent and Scott’s data architecture to include determining target recipients of the generated content in view of Lagi in an effort to improve filtering of recipients for targeting messages (see Lagi ¶ [0036] & MPEP 2143G).
Claims 2, 22, 28
Kilchenko / Scott do not explicitly disclose, Lagi however in the analogous art of business analytics teaches
wherein the content item comprises one or more of a media article, a blog post, or a user document. (Lagi [0126] “Of particular interest to users of the directed content platform 200 disclosed herein, such as marketers and salespeople, are documents that contain information about events that indicate the direction or intent of a company and/or direction or intent of an individual. These documents may include, among many others, blog posts from or about a company”)
The motivations/rationales to combine Kilchenko / Scott with Lagi persists.
Claims 3, 23, 29
Kilchenko teaches
wherein generating, by the generative Al module accessed via the data lakehouse, based on the information, the pitch communication comprises: analyzing, by the generative Al module, the information to identify thematic elements; (Kilchenko [0125] “A first test in step 1334 is determined if user input or message is received. If message is received in step 1336, one or more keywords in the message are identified.”)
determining a narrative encompassing the identified thematic elements; and generating, based on the narrative, the pitch communication. (Kilchenko [0125] “A response is selected using a combination of demographic information 1306 and behavioral information 1316 to send to the end user based on the keywords identified in the message. The selection of the response in step 1336 includes using history and machine learning algorithms, such as Bayesian algorithms and neural networks;” [0126] “The message or response 1338 is sent from chatbot to user in step 1338. In one example, the message includes an advertisement, such a sales pitch”)
Claims 4, 24, 30
Kilchenko teaches
further comprising: generating, by the generative Al module, based on one or more content items associated with each target recipient of the one or more target recipients and based on the pitch communication, one or more tailored pitch emails; (Kilchenko [0054]-[0055]; [0067]; [0093] “The direct advertisement that is selected is dynamically delivered through to the end-user for him or her to view through email … The end-user may then interact with the direct advertisement.)
receiving one or more inputs associated with each tailored pitch email of the one or more tailored pitch emails; and (Kilchenko [0083] “The basic operation of the advertisement system 720 provides for the selection of advertising targeted to the end-user on an affiliate website 724.”)
facilitating, by the computing device, based on the one or more inputs associated with each tailored pitch email, transmission of the one or more tailored pitch emails to the one or more target recipients. (Kilchenko [0093] “The direct advertisement that is selected is dynamically delivered through to the end-user for him or her to view through email”)
Claims 5, 25, 31
Kilchenko teaches
further comprising determining the one or more target recipients based on one or more filter parameters, wherein the one or more filter parameters comprise one or more of a location or a source. (Kilchenko fig. 13; [0121] “FIG. 13 is an example over-all flow 1300 of information being used for selection of the chatbot … Demographic information includes location of end user”)
Claims 6, 26, 32
Kilchenko / Scott do not explicitly disclose, Lagi however in the analogous art of business analytics teaches
wherein determining based on evaluating the alignment between the pitch communication and the one or more potential recipients, the one or more target recipients of the one or more potential recipients predicted to be most receptive to the pitch communication comprises: determining, by the predictive AI module, based on evaluating the alignment between the pitch communication and the one or more potential recipients, recipient receptiveness associated with the one or more potential recipients; and (Lagi [0157] “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” note the scoring of receptiveness based the objective of the pitch and attributes of individuals)
determining, based on the recipient receptiveness associated with the one or more potential recipients, the one or more target recipients of the one or more potential recipients predicted to be most receptive to the pitch communication. (Lagi [0156] “The lead scoring system 214 may score each person in the recipient list. The lead scoring system 214 may then rank and/or filter the recipient list based, at least in part, on the lead scores of each respective person in the list. For example, the lead scoring system 214 may exclude any people having lead scores falling below a threshold from the recipient list;” [0157] “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” note the lead score as a measure of predicted receptivity to the pitch and the ranking/filtering based on that score and outputting of the list corresponding to the target recipients predicted to be the most receptive to the pitch)
The motivations/rationales to combine Kilchenko / Scott with Lagi persists.
Claim 7
Kilchenko / Lagi do not explicitly disclose, Scott however in the analogous art of business analytics teaches
wherein the information associated with the content item is stored in the flat file system according to one or more formats. (Scott fig. 2 noting the flat file database 202; [0025] “the data stores 110, 115, and 120 can be part of a variety of different databases within an enterprise. For example, the data store 110 is part of an operational system database (e.g., a marketing or finance database) that stores operational data” noting the operational data corresponding to the content item information; [0078] “when data is moved (via ETL) between various data stores, changing formats from one data store (e.g., the flat file database 202 of FIG. 2)” note the storing of incoming data in a flat file system)
The motivations/rationales to combine Kilchenko / Lagi with Scott persists.
Claim 8
Kilchenko teaches
further comprising: receiving one or more user inputs associated with the pitch communication; (Kilchenko [0083] “The basic operation of the advertisement system 720 provides for the selection of advertising targeted to the end-user on an affiliate website 724.”)
generating, by the generative Al module, based on the one or more user inputs and the pitch communication, […]; (Kilchenko [0054]-[0055]; [0067])
determining, by the predictive Al module, […], one or more second target recipients; and (Kilchenko [0083])
facilitating, by the computing device, […] to the one or more second target recipients. (Kilchenko fig. 1; [0067])
Kilchenko / Scott do not explicitly disclose, Lagi however in the analogous art of business analytics teaches
[…] a press release communication; […] based on the press release communication […]; […] transmission of the press release communication […].(Lagi [0128] “ the information extraction system 204 may rely on a combination of documents to extract entities, events, and relationships. For example, in response to the combination of i) a publicly available resume of Person X indicating that Person X works at Company C; and ii) a press release issued by Company B;” [0150] “This includes publicly available information about recipients that may serve to personalize or customize the email in a way that makes the message more likely to have a positive outcome”)
The motivations/rationales to combine Kilchenko / Scott with Lagi persists.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20230351404 A1; WO 2021243382 A1; Todorova et al., Smart Marketing Solutions: Applications with Artificial Intelligence to Increase the Effectiveness of Marketing Operations, 2023.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624