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 .
Acknowledgments
Claims 2 and 12 are cancelled.
Claims 21 and 22 are new claims.
Applicant did not provide information disclosure statement.
Claims 1, 3-11, and 13-22 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/22/2026 has been entered.
Response to Arguments
35 USC 101
Applicant's arguments filed 7/22/2026 with respect to 35 USC 101 have been fully considered but they are not persuasive.
Applicant Argues on page 12-13
performed using less computing resource-intensive processes in earlier stages and more accurate but more computing resource-intensive processes in later stages in order to conserve resources by applying more resource-intensive filtering to only a limited subset of the message data." These limitations set forth in Applicant's claims are directed toward specific technological improvements and therefore are far more than mere abstract ideas.
Examiner respectfully Disagrees
The claim is merely automating a manual process of filtering messages, which does not correspond to a technical improvement. Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality include mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential). In addition, the claims are not solving a technical problem but a business problem. Applicant’s specification in 002 state the business problem of following up with customers and filtering messages.
Applicant argues on page 13
It would be highly improbable, for example, for a human to identify a sequence of messages with a high rate from a set of filtered messages. In fact, as the set of filtered messages can be extremely vast, a claimed trained rate-based machine learning model is used to process data and identify the sequences of messages from the set of filtered messages. Accordingly, the independent claim language includes significantly more than merely generic computer elements.
Examiner respectfully disagrees.
The claims do not state that the set of filtered messages are extremely vast. The claims make no mention of the size of message sets. In addition, the machine learning model is considered an additional element. Additional elements are not part of Step 2A prong 1. The machine learning model is merely an additional element that carries out the abstract idea step of identifying messages.
Applicant argues on page 13
Applicant submits that the claimed trained rate-based machine learning model and clustering machine learning model are crucial limitations of the independent claim language. Applicant submits that all claim language must be given patentable weight and that the pending rejection is incomplete as the rejection omits this language of the claims. Should the rejection be maintained, finality of the Office Action is precluded as the rejection would constitute a new ground of rejection not facilitated by an amendment.
Examiner respectfully disagrees.
The machine learning model and clustering machine learning model are merely additional limitations that carryout abstract idea steps. For example, the clustering machine learning model merely carries out the step of clustering data. A computer is not needed to cluster data. The clustering machine learning model is merely a black box which shows an output but no details on how the clustering actually occurs.
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, 3-11, and 13-22 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 than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1, 3-11, and 13-22 are directed to non-transitory computer readable medium, system, and method.
Regarding step 2A-1, Claims 1, 3-11, and 13-20 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 10 and 11 recite the limitations of
Determining…whether messages of a plurality of messages are eligible for response…in response to determining the eligibility, generating a first set of filtered messages by filtering ineligible messages from the plurality of messages…identifying…a sequence of messages from the first set of filtered messages…that has potentially redundant messages assigning…messages of the first set of filtered messages to a plurality of clusters based on contents of the first set of filtered messages…determining one or more redundant tasks in at least a cluster of the plurality of clusters using the sequence of messages; generating a second set of filtered messages by filtering, from the first set of filtered messages, messages associated with the one or more redundant tasks…wherein generating the first set of filtered messages…and generating at least one call-to-action based on the second set of filtered messages
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts determining, filtering, assigning, identifying, and generating. The claim limitations fall under the abstract idea grouping of a mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and non-transitory computer-readable medium, the claim language encompasses determining whether messages are eligible or ineligible for a response, creating sets of messages by filtering, assigning sets of messages for clustering, determining a redundant task in the clusters, identifying sequences of messages, and generating a call to action based on a set of filtered messages. These steps are mere data manipulation steps that do not require a computer. A user is able to determine what messages are eligible for a response and filter those messages. A user is also able to identify a task in the messages as well as make clusters for the messages. A user is able to generate and carry out an action based on the sets of messages.
The claims recite Determining…whether messages of a plurality of messages are eligible for response, this is with respect to interactions with representatives and customers as seen in para 002 of the Specification (See para 002- Customer service representatives, account executives, sales development representatives, customer success managers, and other business-to-customer (B2C) and business-to-business (B2B) representatives rely on following up with customers or potential customers in order to achieve their goals.). In addition, para 0023 teaches conversation data with respect to customers. In addition, the instant application also teaches managing and filtering an employee’s messages as seen here generating a first set of filtered messages by filtering ineligible messages from the plurality of messages. These make the claims fall into the abstract idea category of certain methods of organizing human activities (business relations, interactions between people).
It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components do not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional of elements of non-transitory computer readable medium, processing devices, system, computer resources, trained rate-based machine learning model, clustering machine learning model, and memory.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, 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.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe marking tasks as completed. The dependent claims further recite populating a template with plurality of messages. The dependent claims further recite determining redundant tasks based on scores between pairs of messages.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however a method is not considered an additional element
Claim 1 further recites trained rate-based machine learning model, clustering machine learning model, processing devices, computer resources.
Claim 6 and 16 recite computer resources.
Claim 10 recites trained rate-based machine learning model, clustering machine learning model, processing devices, non-transitory computer readable medium, and computing resources.
Claim 11 recites trained rate-based machine learning model, clustering machine learning model, system, processing device, and memory.
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states general purpose microprocessors in para 0075
When looking at the additional elements in combination, the Applicant’s specification merely states general purpose microprocessors as seen in para 0075. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1, 3-11, and 13-22 are rejected under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Adington (20200219031) Discloses embodiments that provide a computer-implemented technique for filtering electronic communication based on the current task of a user. The current task of a user is derived using a workflow segmentation model.
Delaney (20160350696) Discloses determining resource utilization in an organization, a portion describing an interaction between a first participant and a second participant is identified in a human-readable content of a message. Using NLP, the portion is analyzed to extract a verb and a subject corresponding to the interaction.
Punera (20150134389) Discloses a method and apparatus for the automatic suggestion generation in a relationship management system is described.
Uppala (20190036849) Discloses a system that is configured to replay a selected conversation with a selected agent. The selected conversation may be selected from a plurality of previously conducted conversations with other agents.
Sheng (10657158) Discloses techniques for automatically generating data extraction templates for structured documents (e.g., B2C emails, invoices, bills, invitations, etc.), and for assigning classifications to those data extraction templates to streamline data extraction from subsequent structured documents.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
Prosecution Insights