DETAILED ACTION
This is in reference to communication received 05 May 2026. 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 10, representative of claims 1 and 15, 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 prioritizing customer interactions by comparing keywords in an interaction with a list of keywords with importance assigned by entity, prioritized the keywords based upon assigned importance value, and append an alerting flag to the customer interaction (e.g., red, yellow and green (aka, traffic light colors), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to customer-support or sales-support activities or behaviors but for the recitation of generic computer components. Accordingly, the claim recites an abstract idea.
These limitations describe customer-support or sales-support activities. Identifying a keyword in a customer interaction (e.g., communication), checking whether an identified keyword has been recorded in a database with some assigned valve (e.g., alert value, importance value, etc.), calculating a score of the customer communication based upon the identified keyword(s) and their associated importance value, and categorizing the customer interaction as high, medium and low priorities, which an administrative of any entity (a human) can reference and respond accordingly. Causing presentation of customer interaction record with assigned alert-value/importance-value as an appended flag-indicator/alert-indicator in appropriate color for enabling a support team member decide which customer-interaction should get a priority for resolving the customer-issue. Displaying of a color-coded list of customer communication based upon detected keywords in the associated customer-interaction would be a support-team (or person) providing priority level based color-coded customer-interaction log to the customer-support-staff of an entity.
The independent claims further recite the additional functional element of using AI model for identifying keywords in a customer interaction and comparing them to a list of stored keywords for determining their importance. 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 1 and 16, 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 10), 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 19).
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 – 9, 12 – 15 and 17 - 20, these claims recite limitations that further define the same abstract idea reusing the claimed limitation of additional customer, defining values for priorities, color assigned to priorities, defining that historical information will be used, what values will be compared to the historical data, what values will be considered for calculating scores, 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.
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 - 6, 8 - 13, 15 - 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kuppulal et al. US Publication 2025/0080654 in view of Tatiana Poly published article “How To Use AI To Drastically Improve Contact Center Script Adherence” hereinafter to as Poly, Kate Eby published article “Comprehensive Guide to Understanding and Using Priority Matrices” hereinafter referred to as Eby and Infowise published YouTube Video “Customer Support System and SharePoint” hereinafter referred to as Infowise.
Regarding claim 10 and representative claims 1 and 16, Kuppulal teaches for agent interaction analysis using artificial intelligence. Kuppulal teaches system and method comprising:
a processor (Kuppulal, a system for agent interaction analysis using artificial intelligence may include at least one processor and at least one memory comprising a plurality of instructions stored thereon) [Kuppulal, 0013] and
a processor and
a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations (Kuppulal, a system for agent interaction analysis using artificial intelligence may include at least one processor and at least one memory comprising a plurality of instructions stored thereon) [Kuppulal, 0013];
receiving a transcript of a first customer interaction in an agent inbox (Kuppulal, receiving, by a computing system, a transcript for a real-time agent interaction between a contact center agent and a contact center client, processing, by the computing system, the real-time agent interaction using at least one artificial intelligence model to determine a call adherence score; in addition, Kuppulal teaches the computing system retrieves a plurality of transcripts associated with historical agent interactions between contact center agents and contact center clients) [Kuppulal, 0003, 0084];
Kuppulal does not explicitly teach extracting keywords from transcript. However, Poly teaches Automated Quality Management software utilizes machine learning and Natural Language Processing (NLP) to transcribe call recordings into text that can then be parsed by AI to automatically detect keywords or phrases from a predefined list [Poly, page 3].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kuppulal by adopting teachings of Poly implement capability for increasing adherence to call scripts.
Kuppulal in view of Poly teaches system and method further comprising:
extracting, in real-time, keywords from the transcript (Poly, In addition to simply checking for the mention of key phrases, AI can assign predefined weights. For example, an agent saying their name and thanking the customer for calling might score 10 points, whereas “I will take care of that for you" or "Let's get that resolved for you now!" can score 20 or even 30 points. Even negative scores can be assigned for deteriorating statements, rude language, lack of empathy, and so on.) [Poly, page 4];
comparing, in real-time by an artificial intelligence (AI) model, the extracted keywords to keywords in a customized historical database (Poly, Automated Quality Management software utilizes machine learning and Natural Language Processing (NLP) to transcribe call recordings into text that can then be parsed by AI to automatically detect keywords or phrases from a predefined list) [Poly, page 3];
Kuppulal in view of Poly does not explicitly teach calculating priority score. However, Poly, AI can assign predefined weights. For example, an agent saying their name and thanking the customer for calling might score 10 points, whereas “I will take care of that for you" or "Let's get that resolved for you now!" can score 20 or even 30 points. Even negative scores can be assigned for deteriorating statements, rude language, lack of empathy, and so on.) [Poly, page 4]. However, Eby teaches A priority matrix, also called a prioritization matrix, is a customizable tool used to identify critical tasks or projects. A priority matrix can be a simple chart that compares urgency and importance or a complex grid that analyzes many criteria [Eby, page 2]. Eby further teaches A priority matrix is most helpful when used to rank a list of potential upcoming projects or tasks in order of importance. By setting your own criteria, you can create and use these matrices to aid in a project selection process that fits the needs of your organization [Eby, page 2].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kuppulal in view Poly by adopting teachings of Eby by assigning priorities in project management to facilitate the selection of potential projects. In addition, project managers can use matrices to help delegate tasks to their team, create project plans, or map out their daily assignments [Eby, page 4].
Kuppulal in view of Poly and Eby teaches system and method further comprising:
calculating, in real-time by the AI model, a priority score of the first customer interaction based on the comparison (Eby by assigning priorities in project management to facilitate the selection of potential projects. In addition, project managers can use matrices to help delegate tasks to their team, create project plans, or map out their daily assignments)
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[Eby, page 3, 4];
Kuppulal in view of Poly and Eby does not explicitly teach applying, in real-time, a visual indicator on the first customer interaction in the agent inbox, wherein the visual indicator corresponds to the assigned priority of the first customer interaction. However, Infowise teaches assigning visual indicator corresponding to assigned priority.
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[Infowise, page 5].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kuppulal in view of Poly and Eby to help personnel to prioritize emerging issues and taking actions as appropriate.
Kuppulal in view of Poly, Eby and Infowise teaches system and method further comprising:
assigning, in real-time by the AI model, a priority to the first customer interaction based on the calculated priority score
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[Eby, page 3, 4]; and
applying, in real-time, a visual indicator on the first customer interaction in the agent inbox, wherein the visual indicator corresponds to the assigned priority of the first customer interaction
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[Infowise, page 5].
Regarding claim 2, as combined and under the same rationale as above, Kuppulal in view of Poly, Eby and Infowise teaches system and method, wherein the assigned priority of the first customer interaction is low, medium, or high and the visual indicator is colored based on the assigned priority, and the visual indicator comprises a color matrix representation
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[Infowise, page 5; also see Eby, page 5].
Regarding claim 3, as combined and under the same rationale as above, Kuppulal in view of Poly, Eby and Infowise teaches system and method, wherein the visual indicator is colored and a green visual indicator is applied to a low priority customer interaction, a yellow visual indicator is applied to a medium priority customer interaction, and a red visual indicator is applied to a high priority customer interaction (as responded to above, see at least Eby, page 5].
Regarding claim 11 and representative claim 4, as combined and under the same rationale as above, Kuppulal in view of Poly, Eby and Infowise teaches system and method, wherein the keywords in the customized historical database are categorized as low priority, medium priority, or high priority (Poly, Screenshot of the section in the MiaRec Admin panel that allows you to easily and quickly set up keywords and key phrases for a particular topic and assign positive or negative point values)
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[Poly, page 5].
Regarding claim 12 and representative claims 5 and 17, as combined and under the same rationale as above, Kuppulal in view of Poly, Eby and Infowise teaches system and method, wherein the customized historical database further comprises historical attributes of interactions that are categorized as low priority, medium priority, or high priority (Kuppulal, In some embodiments, retrieving the agent-specific content may include retrieving an excerpt from a positive historical interaction with the contact center agent, and transmitting the agent-specific content to the agent device may include transmitting the excerpt from the positive historical interaction to the agent device for display in conjunction with the real-time agent interaction.) [Kuppulal, 0006].
Regarding claim 13 and representative claims 6 and 18, as combined and under the same rationale as above, Kuppulal in view of Poly, Eby and Infowise teaches system and method comprises comparing attributes of the first customer interaction to the historical attributes in the customized historical database, wherein the priority score of the first customer interaction is further based on the comparison of the attributes to the historical attributes (Poly, In addition to simply checking for the mention of key phrases, AI can assign predefined weights. For example, an agent saying their name and thanking the customer for calling might score 10 points, whereas “I will take care of that for you" or "Let's get that resolved for you now!" can score 20 or even 30 points. Even negative scores can be assigned for deteriorating statements, rude language, lack of empathy, and so on.) [Poly, page 4].
Regarding claim 15 and representative claims 8, 9 and 20, as combined and under the same rationale as above, Kuppulal in view of Poly, Eby and Infowise teaches system and method, wherein:
calculating the priority score of the first customer interaction comprises determining a historical relevance score, a key phrase relevance score, and a context relevance score (Kuppulal, In some embodiments, retrieving the agent-specific content may include retrieving an excerpt from a positive historical interaction with the contact center agent, and transmitting the agent-specific content to the agent device may include transmitting the excerpt from the positive historical interaction to the agent device for display in conjunction with the real-time agent interaction.) [Kuppulal, 0006],
determining the historical relevance score comprises comparing the extracted keywords to the keywords in the customized historical database (Poly, Automated Quality Management software utilizes machine learning and Natural Language Processing (NLP) to transcribe call recordings into text that can then be parsed by AI to automatically detect keywords or phrases from a predefined list) [Poly, page 3],
determining the key phrase relevance score comprises scoring the extracted keywords based on relevance and importance of the extracted keywords in the transcript (Poly, In addition to simply checking for the mention of key phrases, AI can assign predefined weights. For example, an agent saying their name and thanking the customer for calling might score 10 points, whereas “I will take care of that for you" or "Let's get that resolved for you now!" can score 20 or even 30 points. Even negative scores can be assigned for deteriorating statements, rude language, lack of empathy, and so on.) [Poly, page 4], and
determining the context relevance score comprises extracting an urgency associated with the first customer interaction, a sentiment associated with the first customer interaction, a customer type of a customer associated with the first customer interaction, or a combination thereof (Poly, Screenshot of the section in the MiaRec Admin panel that allows you to easily and quickly set up keywords and key phrases for a particular topic and assign positive or negative point values) [see at least list of customer interaction and sentiments) [Poly, page 5].
Claims 14, 7 and 19 rejected under 35 U.S.C. 103 as being unpatentable over Kuppulal et al. US Publication 2025/0080654 in view of Tatiana Poly published article “How To Use AI To Drastically Improve Contact Center Script Adherence” hereinafter to as Poly, Infowise published YouTube Video “Customer Support System and SharePoint” hereinafter referred to as Infowise and iTech Studies published YouTube video “SharePoint | Sort a List” hereinafter referred to as iTech-Studies.
Regarding claim 14 and representative claims 7 and 19, as combined and under the same rationale as above, Kuppulal in view of Poly, Eby and Infowise teaches system and method, which further comprises:
receiving a transcript of a second customer interaction in the agent inbox (Kuppulal, receiving, by a computing system, a transcript for a real-time agent interaction between a contact center agent and a contact center client, processing, by the computing system, the real-time agent interaction using at least one artificial intelligence model to determine a call adherence score; in addition, Kuppulal teaches the computing system retrieves a plurality of transcripts associated with historical agent interactions between contact center agents and contact center clients) [Kuppulal, 0003, 0084];
extracting, in real-time, keywords from the transcript of the second customer interaction (Poly, In addition to simply checking for the mention of key phrases, AI can assign predefined weights. For example, an agent saying their name and thanking the customer for calling might score 10 points, whereas “I will take care of that for you" or "Let's get that resolved for you now!" can score 20 or even 30 points. Even negative scores can be assigned for deteriorating statements, rude language, lack of empathy, and so on.) [Poly, page 4];
comparing, in real-time, the extracted keywords from the transcript of the second customer interaction to the keywords in the customized historical database (Poly, Automated Quality Management software utilizes machine learning and Natural Language Processing (NLP) to transcribe call recordings into text that can then be parsed by AI to automatically detect keywords or phrases from a predefined list) [Poly, page 3];
calculating, in real-time, a priority score of the second customer interaction based on the comparison (Poly, AI can assign predefined weights. For example, an agent saying their name and thanking the customer for calling might score 10 points, whereas “I will take care of that for you" or "Let's get that resolved for you now!" can score 20 or even 30 points. Even negative scores can be assigned for deteriorating statements, rude language, lack of empathy, and so on.) [Poly, page 4];
assigning, in real-time, a priority to the second customer interaction based on the calculated priority score (as responded to above in response to claims 10, 1 and 16) [Poly, page 6, 7, 17;
applying, in real-time, a visual indicator on the second customer interaction in the agent inbox, wherein the visual indicator corresponds to the assigned priority of the second customer interaction (as responded to above in response to claims 10, 1 and 16) [Poly, page 5, 6, 7, 12, 17]; and
Kuppulal in view of Poly, Eby and Infowise does not explicitly sorting of customer interaction list. However, iTech-Studies teaches that SharePoint list can be sorted in ascending or descending order and you can save the list with the sorting order for later user [iTech-Studies, page 2, 3, 5].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Kuppulal in view of Poly, Eby and Infowise by adopting teachings of iTech-Studies and keep the list on sorted order display open customer interactions as a prioritized sorted list.
Kuppulal in view of Poly, Eby, Infowise and iTech-Studies teaches system and method further comprising:
sorting the first customer interaction and the second customer interaction in the agent inbox based on the assigned priority, wherein a customer interaction with a higher assigned priority is placed closer to a top of the agent inbox than a customer interaction with a lower assigned priority [as responded to earlier iTech-Studies, page 2,3 and 5].
Response to Arguments
Applicant's argument that pending claimed invention is eligible for patent under 35 USC 101 because the claimed invention is not directed to marketing, advertising, or sales activities, and, claimed features are significantly more than an abstract idea is acknowledged and considered.
However, upon further review, it is deemed that the claimed invention is directed to assisting customer-support or sales-support team for displaying to them or providing to them in the inbox of the support-personnel, list of customer-interaction log with an associated color-coded indicator. The claimed invention would be a support-team (or person) providing priority level based color-coded customer-interaction log to the customer-support-staff of an entity. Therefore, the claimed invention is not eligible for patent under 35 USC 101.
Applicant's argument that pending claimed amended invention is eligible for patent because combination of cited prior art does not teach calculated of a priority score. However, cited prior art Kuppulal teaches that customer-support person can assign a higher priority of customer-interaction to resolve the issue sooner (Kuppulal, see at least Fig. 9 and associated disclosure]. While performing an updated search, a new prior art was found that teaches using priority matrix and color code the task (e.g., handling of customer-interaction) to indicate their importance level for handling the issue. Therefore, applicant’s arguments 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
June 30, 2026
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