DETAILED ACTION
This is in reference to communication received 18 November 2025. Claims 1 – 20 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1, representative of claims 15 and 20, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed to detecting that a user has interacted with an advertising-content (e.g., a vehicle is the advertised product in the advertising-content), and user-interaction with the advertising-content is stored (e.g., creation of historical records). User interaction attributes are retrieved from the stored user-interaction, and analyzed to determine a lead score indicative of a likelihood that each user-interaction will result in a conversion. If determination is made that the determined lead score is above certain threshold value, a set of communication parameters to communicate with the first-user (e.g., potential-buyer) is determined and presented to a second-user (e.g., vehicle dealership) instructing them how then can communicate with the first-user.
These limitations describe marketing/sales/advertising activities. Maintaining a log of user activities with an advertising content marketing a product (e.g. a vehicle), analyzing the maintained log of user activities to determine that the likelihood is high that the first-user may purchase a vehicle, determine how the first-user can be connected to a second-user (e.g. vehicle-dealership) and giving a lead to the second-user that the first-user may be purchasing a vehicle, and how the second-user can contact the first-user. Causing presentation of the determined lead (e.g. first user may be purchasing a vehicle) and presenting the lead information to the second-user (e.g., vehicle-dealership) describe marketing/sales/advertising activities.
the independent claims further recite the additional functional element of training a machine-learning model using the historical data, use the machine-learned modes, if the results are below certain threshold value, retrain the machine-learning model with updated historical file, and use the updated machine-learned model. Not only do these features fail to integrate the abstract idea into a practical application, but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claims 15 and 18, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 18), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 15).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium 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. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2 – 3, 6 – 9, 11 – 12 and 15 – 18, these claims recite limitations that further define the same abstract idea of determination of the relationship between the first-merchant and the second-merchant will be based upon the location characteristics and product characteristics associated with the first-merchant (preference of the customer) and the second-merchant, defining what customer data will be considered for generation of place-graph, and what information will be identified on the place-graph, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components. Accordingly, the claim recites an abstract idea.
As for dependent claims 2 – 14, 16 – 17 and 19 – 20 dependent on the aforementioned independent claims, and include all the limitations contained therein. These claims recite limitations that further define the same abstract idea with details regarding descriptions of various data, what mode of communication did the users respond to, what will be the output of the machine-learning-models; what training technology will be used to train the machine-learning-models; defining communication mode and talking-points types that will be suggested to the second-user. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1 – 20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
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 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kamma US Publication 2019/0236645 in view of Barak et al. US Publication 2017/0046739, MailChimp.com published article ‘Identify Inactive Subscribers” and Walden-University published article “8 Effective Ways to Communicate With Customers”.
Regarding claim 1 and represented claims 15 and 18, Kamma teaches system and method for determining user interest in a vehicle on a user interface of an online system for offer by the online system (Kamma, using models based on machine learning and client feedback to determine when a digital media communication (e.g., post, comment, thread, reply, tweet, image, meme, etc.) from a user indicates that the user is interested in a product (e.g., a vehicle) and/or service (e.g. vehicle service) offered by a client. The present invention allows digital advertisers to know whether a user has decided to make a purchase related to a product or a service offering). Kamma further recites The user associated with the automotive dealership may give input that indicates that an author of the additional post has a low likelihood of intent to buy from the automotive dealership. [Kamma, 0025, 0067, 0089], the method comprising:
a processor [Kamma, 0089], and
a non-transitory computer readable medium configured to store computer-executable instructions [Kamma, 0024].
detecting a collection of user interactions between a first user and information presented by the online system in connection with a given vehicle for offer by the online system, the first user being one of a plurality of users that interact with the online system(Kamma, using models based on machine learning and client feedback to determine when a digital media communication (e.g., post, comment, thread, reply, tweet, image, meme, etc.) from a user indicates that the user is interested in a product (e.g., a vehicle) and/or service (e.g. vehicle service) offered by a client. The present invention allows digital advertisers to know whether a user has decided to make a purchase related to a product or a service offering) [Kamma, 0025, 0089];
Kamma does not explicitly teach storing of user interactions. However, Barak teaches system and method to provide a marketing activity support system ("MASS") configured to generate lead intelligence, for example for sales, marketing, product support, client identification, research purposes, and the like. Lead intelligence is based on an aggregation of marketing activity information and user (e.g., customer) information [Barak, 0007].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kamma by adopting teachings of Barak to store user interactions to used the aggregated user interaction data to retrain their machine learning model.
Kamma in view of Barak teaches system and method further comprising:
as each respective user interaction of the collection of user interactions is detected, storing the respective user interaction in a user profile, each of the plurality of users having their user interactions stored in respective user profiles (Barak, By aggregating data obtained from various internal and external sources, the lead aggregator 108 can create a relatively complete profile of the customer 132, even though the customer 132 may have only provided minimal information about himself. The profile may include personal information about the customer 132, as well as information about the activities of the customer 132 with respect to the Web site 126 and/or the received message [Barak, 0017];
retrieving a set of attributes of the collection of user interactions between the first user and the online system from the user profile (Barak, In at least some embodiments the MASS automatically learns user viewing preferences with respect to lead presentation, and can dynamically adjust the presentation of leads by, for example, adjusting lead scoring rules or filtering criteria.) [Barak, 0045];
for each of the plurality of users, periodically determining, using a first trained machine learning (ML) model, based on the respective user profiles of the plurality of users, a lead score indicative of a likelihood that each user interaction will result in a conversion (Kamma, models based on machine learning and client feedback to determine when a digital media communication (e.g., post, comment, thread, reply, tweet, image, meme, etc.) from a user indicates that the user is interested in a product and/or service offered by a client) [kamma, 0009], wherein the first ML model is trained using a training set having historical examples of profile data as labeled with whether a lead was converted or was abandoned (Kamma, training a model to identify a digital media communication that expresses an intent to buy a product and/or service. The model may be trained on a training set of digital media communications. The training set may be provided to a client. Input from the client may be used to retrain (e.g., recalibrate, etc.) the model.) [Kamma, 0010];
Kamma in view of Barak does not explicitly teach removing of a user for future polling. However, Barak teaches In at least some embodiments the MASS automatically learns user viewing preferences with respect to lead presentation, and can dynamically adjust the presentation of leads by, for example, adjusting lead scoring rules or filtering criteria [Barak, 0045]. MailChimp teaches It's helpful to identify and segment your inactive subscribers, so you can send a re-engagement campaign to win back their interest [MailChimp, page 2]. MailChimp further recites After you identify and segment your inactive subscribers, plan a re-engagement strategy so you can win back their interest. If subscribers don't open or click your re-engagement campaigns, you might want to archive or unsubscribe them (e.g., remove them) [MailChimp, page 6].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kamma in view of Barak by adopting teachings of MailChimp to reduce marketing costs and increase ROI by only targeting advertising to potential future customers.
Kamma in view of Barak and MailChimp teaches system and method further comprising:
responsive to the lead score determined using the first trained ML model being below an abandonment threshold for a given user of the plurality of users:
removing the given user from the plurality of users to cause the given user to no longer be polled using the first ML model during future periodic determinations (MailChimp, After you identify and segment your inactive subscribers, plan a re-engagement strategy so you can win back their interest. If subscribers don't open or click your re-engagement campaigns, you might want to archive or unsubscribe them (e.g., remove them) [MailChimp, page 6], and
retraining the first ML model by generating a given training example having given profile data of the given user as labeled with an indication of abandonment, thereby causing the first ML model to output a lower lead score than it would have prior to the retraining for a different user having the same characteristics as the given user and reducing a likelihood that the different user would receive a targeted communication (kamma, The model may be trained on a training set of digital media communications. The training set may be provided to a client. Input from the client may be used to retrain (e.g., recalibrate, etc.) the model.) [Kamma, 0010];
Kamma in view of Barak and MailChimp does not explicitly teach suggesting communication parameters. However, Barak teaches, In at least some embodiments the MASS automatically learns user viewing preferences with respect to lead presentation, and can dynamically adjust the presentation of leads by, for example, adjusting lead scoring rules or filtering criteria) [Barak, 0045; also see Kamma, 0010]. Walden-University teaches that there a Not so long ago, if you wanted to directly communicate with your customers, there were only three ways to go about it: by phone, by mail, or face-to-face. But the explosion of new technologies has dramatically expanded business communications. Now, you can reach your customers—and your customers can reach you—on a variety of platforms. While some may seem basic, each serves an important purpose in a company’s overall communication strategy, and teaches eight of the most effective ways to communicate with customers [Walden-University, page 1].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kamma in view of Barak and MailChimp by adopting teachings of Walden-University to connect with potential customers by considering associated recommendations taught by Walden-University.
Kamma in view of Barak, MailChimp and Walden-University teaches system and method further comprising:
for the first user, responsive to the lead score being higher than a threshold value, the lead score being based on the set of attributes of the collection of user interactions between the first user and the online system from the user profile (Kamma, The user may be provided scored digital media posts only if the posts are scored as having a high likelihood that a potential customer will purchase an offering. Kamma further teaches At 416, certain scored digital media posts may be provided to the user. For example, the scoring system 102 in FIG. 1 may provide certain scored digital media posts to the user. In one non-limiting example, the user may be provided scored digital media posts only if the posts are scored as having a high likelihood that a potential customer will purchase an offering.) [Kamma, 0029, 0082], determining using a second trained ML model, based at least in part on the user profile information for the first user, a set of communication parameters to communicate with the first user, wherein the second trained ML model is trained to select the set of communication parameters to increase a likelihood of conversion of the first user (as responded to above) [Walden-University]; and
presenting the set of communication parameters to a second user of the online system to instruct the second user to communicate with the first user based on the set of communication parameters (Kamma, A new digital media communication may be selected by the model as indicating that a user intends to buy a product and/or service. The new digital media communication may be provided to the client as part of a feedback loop (which can be communicated to the user using the communication parameter taught by Walden-University) [Kamma, 0010].
Regarding claim 2 and represented claims 16 and 19, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the training set comprises a set of dynamic features describing a set of communications associated with each past user interaction, and a set of static features describing the set of attributes of each past user interaction (Kamma, The posts may form a training dataset. A first post of the training dataset, for example, may read: "I'm looking for a baseball card dealership in San Antonio." A second post of the training dataset, for example, may read: "Can someone recommend a dealership?". A third post of the training dataset, for example, may read: "Thanks to Jessica for recommending Pegassi of San Andreas. Best experience with a car dealership.) [Kamma, 0062].
Regarding claim 3, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the set of static features includes information about the first user associated with the past user interaction (Kamma, training a model to identify a digital media communication that expresses an intent to buy a product and/or service. The model may be trained on a training set of digital media communications) [Kamma, 0010; also see LeadLocate, page 2].
Regarding claim 4, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the set of static features includes information about one or more vehicles associated with the past user interaction (Kamma, training a model to identify a digital media communication that expresses an intent to buy a product and/or service. The model may be trained on a training set of digital media communications) [Kamma, 0010; also see LeadLocate, page 2].
Regarding claim 5, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the set of dynamic features describing the set of communications associated with a past user interaction comprises at least one of information about a channel of each communication of the set of communications, a sentiment score of each communication of the set of communications, and a frequency of communications of the set of communications (Kamma, The sentiment model may assign a score to a digital media communication based on a sentiment about a product and/or service.) [Kamma, 0057].
Regarding claim 6 and represented claims 17 and 20, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the second trained ML model is trained to select the set of communication parameters to maximize a likelihood of conversion with a least number of communications with the first user associated with the user interaction (Kamma, the user associated with the automotive dealership may receive the training dataset at the feedback device 104. The scoring system 102 may receive feedback from the feedback device 104 via the feedback device interface 214, the user associated with the automotive dealership may give feedback indicating that the second post indicates a high likelihood of intent to buy from the automotive dealership but the third post indicates a low likelihood of intent to buy from the automotive dealership) [Kamma, 0064].
Regarding claim 7, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the second trained ML model uses a recurrent network with reinforcement learning (Kamma, the user associated with the automotive dealership may give feedback indicating that the second post indicates a high likelihood of intent to buy from the automotive dealership but the third post indicates a low likelihood of intent to buy from the automotive dealership) [Kamma, 0064].
Regarding claim 8, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the set of communication parameters determined by the second trained ML model includes a communication channel (Kamma, The digital acquisition system 106 may retrieve posts from a digital social media platform from an API associated with the digital social media platform. The posts may form a training dataset) [Kamma, 0064].
Regarding claim 9, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the communication channel is one of an email, a text message, or a phone call (Walden-University, if you wanted to directly communicate with your customers, there were only three ways to go about it: by phone, by mail, or face-to-face. But the explosion of new technologies has dramatically expanded business communications. Now, you can reach your customers—and your customers can reach you—on a variety of platforms) [Walden-University].
Regarding claim 10, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the set of communication parameters determined by the second trained ML model includes a frequency of communications with the first user associated with the user interaction (Kamma, At 416, certain scored digital media posts may be provided to the user. For example, the scoring system 102 in FIG. 1 may provide certain scored digital media posts to the user. In one non-limiting example, the user may be provided scored digital media posts only if the posts are scored as having a high likelihood that a potential customer will purchase an offering.) [Kamma, 0082].
Regarding claim 11, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method further comprising:
responsive to receiving an indication of a new communication with the first user associated with the user interaction, determining, using a third trained ML model based on a transcript of the new communication, a sentiment score for the new communication (Kamma, The NLP model may assign a score based on text in a digital media communication. The assigned score may indicate an intent of an author of the digital media communication to buy a product and/or service. The image handler 212 may assign a score based on a combination of the image relevance and text in the digital media communication) [Kamma, 0055, 0056].
Regarding claim 12, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, wherein the transcript of the new communication comprises one of a transcript of a phone call with the first user associated with the user interaction, a text of an email thread with the first user associated with the user interaction, or a text of a text message chain with the first user associated with the user interaction (Barak, additional broadcast messaging services may be utilized, including social network status updates (e.g., a Facebook status update), news feeds (e.g., RSS), Internet news (e.g., NNTP), Web pages, blogs and the like. In other embodiments, other or additional direct messaging services may be utilized, including email, instant messaging, text messaging (e.g., SMS, MMS), and the like) [Barak, 0033; also see Walden-University].
Regarding claim 13, as combined and under the same rationale as above, Kamma in view of Barak, MailChimp and Walden-University teaches system and method, further comprising updating the lead score based on the sentiment score for the new communication (Kamma, The scoring system 102 may receive feedback from the feedback device 104 regarding the provided additional posts. The rescoring engine 210 may use the feedback to rescore the additional posts. The deep learning model 208 may be updated based on the rescoring of the additional posts.) [Kamma, 0067].
Claims 1 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kamma US Publication 2019/0236645 in view of Barak et al. US Publication 2017/0046739, MailChimp.com published article ‘Identify Inactive Subscribers” and Walden-University published article “8 Effective Ways to Communicate With Customers” and Microsoft published article “Dynamics 365 AI for Sales is now available”
Regarding claim 14, Kamma in view of Barak, MailChimp and Walden-University does not teach determining set of talking points based on information about the user. However, Microsoft teaches the To empower sellers and sales managers to focus on the best opportunities and move relationships forward, we are excited to announce that Dynamics 365 AI for Sales is now generally available [Microsoft, page 2]. AI for Sales help sales team personalize engagement by Empowering sellers to deliver personalized and relevant interactions based on embedded insights that recommend talking points and next actions to take with the customer [Microsoft, page 3].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kamma in view of Barak, MailChimp and Walden-University by adopting teachings of Microsoft to increase sales personalizing engagement between the sales-person and the customer.
as combined and under the same rationale as above, Kamma in view of Barak, MailChimp, Walden-University and Microsoft teaches system and method further comprising:
determining, using a fourth trained ML model, a set of talking points based on information about the first user associated with the user interaction (Microsoft, AI for Sales help sales team personalize engagement by Empowering sellers to deliver personalized and relevant interactions based on embedded insights that recommend talking points and next actions to take with the customer [Microsoft, page 3]
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because amended claimed invention integrates the subject matter into a practical application in that the claimed subject matter is trains the machine-learning model, determines whether the results are above some threshold value provide the lead with communication parameters to second-user, otherwise, update the historical data, retrain the machine-learning model with updated historical data and start using the update machine-learning model is acknowledged and considered.
However, upon further review of the amended claimed invention, the pending amended claims does not meet the requirements to be eligible for patent under 35 USC 101 and have been responded to in rejection under 35 USC 101.
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because amended claimed invention integrates the subject matter into a practical application in that the claimed subject matter is confined to filtering out myriad profiles from computationally-intense analysis based on abandonment status on future periodic runs, and further ensuring that the second ML model only acts on active leads further narrowed to those having an above-threshold likelihood of conversion is acknowledged and considered.
However, upon further review of the amended claimed invention, the pending amended claims does not meet the requirements to be eligible for patent under 35 USC 101 and have been responded to in rejection under 35 USC 101.
Applicant's argument that pending amended claimed amended invention is eligible for patent because cited prior art does not teach have been acknowledged.
However, applicant’s arguments are for amended claimed invention which are moot under new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
March 21, 2026