DETAILED ACTION
This communication is a Non-Final Office Action on the merits in response to communications received on 09/08/2026. Claims 1, 11, and 20 have been amended. Therefore, claims 1-20 are pending and have been addressed below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/08/2026 has been entered.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract without significantly more.
4. Under Step 1 of the two-part analysis from Alice Corp, claim 1 recites a process (i.e., an act or step, or a series of acts or steps), claim 11 recites a machine (i.e., a concrete thing, consisting of parts, or of certain devices and combination of devices), claim 20 recites a manufacture (i.e., an article that is given a new form, quality, property, or combination through man-made or artificial means). Thus, each of the claims fall within one of the four statutory categories.
5. Under Step 2A – [Prong One] of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
6. Claim 1 is representative of claims 11 and 20 recites:
“receiving…a plurality of customer interaction records, each record associated with a channel from the plurality of channels and an identifier of a customer from the plurality of customers, each record including a customer interaction transcript;”, “providing…the plurality of customer interaction transcripts as inputs;”, “executing… the execution resulting in an output including an interaction theme and an interaction summary associated with each one of the plurality of customer interaction transcripts;”, “wherein the executing comprises: supplying each customer interaction transcript to…generate attention weights for one or more words in the transcript that numerically capture relationships among words in the customer interaction transcript; and supplying the attention weights to…select one or more words to include output that form the interaction theme and interaction summary;”, “clustering…the plurality of themes, the clustering resulting in a plurality of clustered themes associated with each one of the plurality of themes;”, “mapping…the pluralities of clustered themes and the plurality of interaction summaries, the mapping resulting in an interaction reason associated with each one of the plurality of customer interaction records;”
The limitations under the broadest reasonable interpretation recite an abstract idea for “combining customer feedback from multiple communications, analyzing transcripts, to produce a short theme and summary that are mapped to an interaction reason which indicates reason for contacting the business” encompasses commercial interactions (i.e. marketing/sales activities or behaviors, business relations) and mental processes, (i.e., observations, evaluations, judgments, and opinions). As such, the limitations cover concepts that fall within the certain methods of organizing human activity and mental processes groupings enumerated in MPEP 2106.04 II
The Applicant’s Specification in at least [pgs. 1-2] the present invention relates to voice-of-the-customer data integration, and more specifically to integration and analysis of voice-of-the-customer data from multiple channels. The voice of the customer (VOC) summarizes customers' expectations, preferences, and concerns. Analyzing VOC data helps a business with identifying any emerging topics in their initial states before they become an issue. Analyzing VOC data also helps a business to be proactive in communicating trends and changes that may impact its customers. Further, VOC data helps a business with tracking customer activities to find potential friction points, such as technical issues or cybercrime. VOC enables a business to determine, for example, how customers engage with the business and what their preferred way of engagement is, where customers are getting frustrated and how they attempt to resolve the issue, what customers are doing before contacting the business, what needs and issue customers discuss the most, and many other questions. Customer feedback, or VOC, is usually received over multiple channels, for example over the phone, online chat, email, virtual assistants, social media, etc. Solutions exist that analyze feedback for a single channel. For example, an existing solution may analyze VOC data from phone conversations. Another existing solution may analyze VOC data from online chats. A third existing solution may analyze VOC data received from social media. However, using different solutions for different interaction channels prevents integration of all VOC data to which a business has access.
Consistent with the disclosure, the ordered combination of limitations that recite “receiving”, “providing”, “executing”, “supplying” “clustering”, “mapping”, in the context of the claim pertain to tasks or activities performed in contact center operations. For example, combining customer feedback from multiple communications, analyzing customer transcripts, and grouping/mapping the customer interactions according to themes that indicate the reason the customer contacted the business are tasks or activities typically performed by employees of contact centers that monitor trends and repeated customer needs across various communications, thus, the ordered combination of limitations recite concepts that may be reasonably characterized as performing commercial interactions such as marketing or sales activities and/or business relations. Also, the limitations of “clustering” and “mapping” in the context of the claim pertain to mental processes for collecting and comparing known information about the customer communications to group/map relevant or important themes/interaction summaries, are evaluations that may be performed in the human mind or by a person using pen and paper. As such, the claim recites an abstract idea.
7. Under Step 2A – [Prong Two] of the two-part analysis from Alice Corp, the judicial exception is not integrated into a practical application because the additional elements of: “by a computer system”, “by the computer system,”, “to a machine learning model”, “the machine learning model”, “a first stacked long short- term memory network (LSTM) serving as an encoder to”, “a second stacked LSTM serving as a decoder to”, “using a multi-level taxonomy”, “a database”, “in a graphical visualization platform” – see claims 1, 11, and 20 are recited at a high-level of generality in light of the specification [i.e., Figs. 1 – i.e., computer system, machine learning model commercially available, Fig. 7 – i.e., encoder, decoder]. Thus, because the specification describes the additional elements in general terms without any of the particulars, the additional elements may be broadly construed as reciting generic computer components and machine learning technology. At best, the additional elements add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f).
The other additional elements of: “storing…the interaction reason associated with each one of the plurality of customer interaction records…” and “displaying…the interaction reasons for the plurality of customer interaction records at different levels…, wherein the different levels are successively more granular, along with the interaction themes determined…” adds insignificant extra-solution activity, [i.e., data storage/ data output] to the judicial exception, as discussed in MPEP 2106.05(g).
The other additional elements of: “a computer-implemented method for integrating customer interaction data from a plurality of channels and for a plurality of customers, the method comprising:” is an attempt to limit the claimed invention to a particular technological environment or field of use, as discussed in MPEP 2106.05(h)
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
8. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “by a computer system”, “by the computer system,”, “to a machine learning model”, “the machine learning model”, “a first stacked long short- term memory network (LSTM) serving as an encoder to”, “a second stacked LSTM serving as a decoder to”, “using a multi-level taxonomy”, “a database”, “in a graphical visualization platform” – see claims 1, 11, and 20 at best amount to no more than mere instructions in which to apply the judicial exception but do not provide an inventive concept at Step 2B.
The other additional elements of: “storing…the interaction reason associated with each one of the plurality of customer interaction records…” and “displaying…the interaction reasons for the plurality of customer interaction records at different levels…, wherein the different levels are successively more granular, along with the interaction themes determined…” were considered to be insignificant extra-solution activity in Step 2A Prong Two, and thus must be re-evaluated in Step 2B to determine if they are more than well-understood, routine, conventional activity. The Versata Dev. Group court and OIP Techs decisions cited in MPEP 2106.05(d)(II) indicate: “storing and retrieving information in memory”, “presenting offers and gathering statistics” and “arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price” is/are computer functions that are considered as well-understood, routine, conventional when claimed in a generic manner. Accordingly, when viewed individually and in combination with the judicial exception the combination of additional elements do not add an inventive concept. At Step 2B the claim(s) are ineligible.
9. Claims 2-10 and 12-19 are dependents of claims 1 and 11.
Claims 2 and 12 recite “wherein the machine learning model is a sequence-to-sequence model.” at a high-level of generality. Merely reciting the type of model that may be used does not preclude the claim limitations from being within the certain methods of organizing human activity grouping or integrate the judicial exception into a practical application. See MPEP 2106.05(f) Claims 3, 4, and 13 recite “wherein the multi-level taxonomy is a hierarchical taxonomy includes at least four levels.” at a high-level of generality. Merely reciting the type of taxonomy and/or number of levels that may be used does not preclude the claim limitations from being within the certain methods of organizing human activity grouping or integrate the judicial exception into a practical application. See MPEP 2106.05(f) Claims 5 and 14, recite further comprising: retrieving, by the computer system, a plurality of data records associated with a query customer identifier from the database, each data record including a query interaction reason; aggregating, by the computer system, the plurality of data records by query interaction reason; and causing, by the computer system, display of the aggregated data which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Here, the computer system and database are being used in their ordinary capacity to store and/or retrieve data. Thus, the additional elements do not integrate the abstract idea into practical application or provide an inventive concept.
Claims 6 and 15 recite “further comprising: retrieving, by the computer system, a plurality of data records associated with a query interaction reason from the database, each data record including a query customer identifier; aggregating, by the computer system, the plurality of data records by query customer identifier; and causing, by the computer system, display of the aggregated data” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Here, the computer system and database are being used in their ordinary capacity to store and/or retrieve data. Thus, the additional elements do not integrate the abstract idea into practical application or provide an inventive concept. Claims 7 and 16 recite “wherein at least one record of the plurality of customer interaction records is associated with a channel different from the channel associated with another record of the plurality of customer interaction records.” which further describes the data or information recited within the abstract idea but does not make the claim any less abstract. Claims 8 and 17 recite “wherein the computer system is configured to execute the method periodically” at a high-level of generality. The limitation adds the words "apply it" (or an equivalent) with the judicial exception and/or represents mere instructions to implement an abstract idea on a computer See MPEP 2106.05(f) Claims 9 and 18 recite “wherein each theme of the plurality of themes includes five words or less.” which further describes rules related to the themes. At best, the limitation narrows the data in the abstract idea but does not make the claim any less abstract. Claims 10 and 19 recite “wherein each summary of the plurality of summaries includes more than five words and less than twenty words.” which further describes rules related to the summaries. At best, the limitation narrows the data in the abstract idea but does not make the claim any less abstract. Accordingly, when considered individually and as a whole the dependent claims merely narrow how the judicial exception may be performed. As shown, the dependent claims do not add any additional elements in combination with the judicial exception that integrate the abstract idea into a practical application or provide an inventive concept.
Claim Rejections - 35 USC § 103
10. 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.
11. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
12. Claim(s) 1-3, 5-8, 11-12, 14-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora (US 12,062,368 B1) in view of Faizakof (US 2016/0012818 A1) in further view of Subramanian (WO 2021/234610 A1).
With respect to claims 1, 11, and 20, Arora discloses
a computer-implemented method, a system, a non-transitory computer-readable medium for integrating customer interaction data from a plurality of channels and for a plurality of customers (col. 29:13-28: discloses contacts analytics service 606 is used to obtain contacts data, i.e., audio data or text based data, and process the data to identify diagnostics, insights, and trends.),
the method comprising:
receiving, by a computer system, a plurality of customer interaction records (col. 13:9-35, col. 26:48-50: discloses an organization’s client computing environment includes computer systems that are used to receive contacts from customers.), each record associated with a channel from the plurality of channels (col. 13:9-35: discloses contacts data may refer to different types of touch points that customers use to contact the organization and may include phone calls, chat messages, e-mails, social media messages, and more. col. 39:1-2: discloses a channel field may be chat, voice call, and more.) and an identifier of a customer from the plurality of customers (col. 39:1-2: discloses an account ID may represent the end customer’s account identifier.), each record including a customer interaction transcript (col. 13:9-35);
providing, by the computer system, the plurality of customer interaction transcripts as inputs to a machine learning model (col. 22:43-47, col. 23:42-67: discloses a machine learning model may receive as an input text-based data, i.e., from a chat log or call transcript, that is organized and determine/identify a call-driver or issue.);
executing, by the computer system, the machine learning model, the execution resulting in a model output including an interaction theme and an interaction summary associated with each one of the plurality of customer interaction transcripts (col. 22:43-47, col. 23:42-67, col. 58:17-34: discloses the system may analyze the transcripts using a natural language service to generate metadata about the calls, such as keyword and phrase matches, entity matches.);
clustering, by the computer system, the plurality of themes, the clustering resulting in a plurality of clustered themes associated with each one of the plurality of themes (col. 24:29-32: discloses a set of customer contacts are analyzed to identify key phases and the key phases for those contacts may be clustered to identify specific themes. col. 27:57-60 and col. 28:1-16: discloses a step to cluster 512 key phases together based on semantic meaning.);
mapping, by the computer system, the pluralities of clustered themes and the plurality of interaction summaries (col. 20:41-57: discloses categorization service may generate a set of results including information on which categories were matched as well as points of interest associated with the categories.),
storing, by the computer system, one of the plurality of customer interaction records in a database (col. 13:52-57: discloses client data store 106 may refer to an electronic data store that an organization uses to store contact data. Contact data may refer to audio recordings, chat logs, video interactions, and more.) and
displaying, by the computer system, in a graphical visualization platform the interaction reasons for the plurality of customer interaction records at different levels (Figs. 13-14, col. 8:50-67, col. 9:1-67: discloses contacts analytics service may be used to perform theme detection by analyzing multiple conversations at once and presenting a set of themes. In some cases, themes are presented in a visual format and surface findings in an easy-to-understand format for supervisors. Theme and/or trend detection may have various use cases and organizations may use theme and/or trend detection to understand top reasons for customer outreach over a period of time and/or for specific products or business workflows. Supervisors can use contact analytics service to quickly identify calls and chats with criteria of interest that they want to track.),
wherein the different levels are successively more granular, along with the interaction themes determined by the machine learning model (Figs. 13-14, col. 3:14-19, col. 4:28-35, col. 45:22-67: discloses the contacts analytics service may analyze customer contacts using machine learning techniques to identify previously unknown themes and trends emerging in incoming customer contacts. The may be a hierarchy displayed on the search page which can be used to navigate to different levels of the search. The user may review common themes presented and select any theme to drill deeper to learn new insights or identify root causes or previously undiscovered issues.)
The Arora reference does not explicitly disclose the following limitations. In the same field of endeavor, the Faizakof reference is related to performing analytics on communications (¶ 0001) and teaches:
using a multi-level taxonomy (¶ 0008-0009, 0056-0058: discloses the discovered and extracted topics can further be clustered into “parent categories”, where each parent category contains one or more base topics thereby creating a hierarchical taxonomy.) the mapping resulting in an interaction reason associated with each one of the plurality of customer interaction records (¶ 0055: discloses categorizing interactions based on the automatically identified categories.); and
storing, by the computer system, the interaction reason.(¶ 0074: discloses the contact center includes one or more mass storage devices for storing different databases relating to interaction data, i.e., details of each interaction with a customer including reason for the interaction, disposition data, and the like.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the contacts analytics service of Arora, to include using a multi-level taxonomy; the mapping resulting in an interaction reason associated with each one of the plurality of customer interaction records; storing, by the computer system, the interaction reason, as disclosed by Faizakof to achieve the claimed invention. As disclosed by Faizakof, the motivation for the combination would have been to allow for easier analysis of current pattens in customer interactions and faster responses to changing circumstances. (¶ 0055-0056)
The combination of Arora and Faizakof does not explicitly disclose the following limitations. In the same field of endeavor, the Subramanian reference is related to a method of and system for training a machine learning algorithm to generate a text summary (i.e., title/abstract, pg. 1:4-7) and teaches:
wherein the executing comprises:
supplying each customer interaction transcript to a first stacked long short-term memory network (LSTM) serving as an encoder (pg. 28:9-27: discloses the encoder 294 comprises a stack of identical layers…the encoder receives an input document 412 comprising a plurality of sentences. The encoder receives as input an extractive summary of the document 416 comprising a set of extracted sentences.)
to generate attention weights for one or more words in the transcript that numerically capture relationships among words in the customer interaction transcript (pg. 28:9-27, pg. 29:20-24: discloses for each input that the encoder 294 reads the attention-mechanism takes into account several other inputs at the same time and decides which ones are important by attributing different weights to those inputs. The encoder 294 will then take as input the encoded sentences and the weights provided by the attention-mechanism.); and
supplying the attention weights (pg. 24:1-8: discloses the attention weights) to a second stacked LSTM serving as a decoder (pg. 5:15, pg. 23:10: discloses the decoder includes a LSTM) to select one or more words to include in the model output that form the interaction theme and interaction summary (pg. 23:10-24, pg. 24:1-8: discloses the decoder outputs a set of positions of the extracted sentences in the plurality of sentences 314 in the document which are used to form the set of extracted sentences and then pg. 30:1-8: discloses the transformer 292 outputs an abstractive summary 452 comprising a set of abstractive sentences 454. The set of abstractive sentences 454 provide a summary of the document 412.);
As can be seen from the teachings in at least [pg. 2:23-27, pg. 3:1-20] of the Subramanian reference, using encoder-decoder architectures were known in the state of the art and have been successful when applied to problems such as machine translation and abstractive summarization in the industry.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/methods of Arora and Faizakof, to include the architecture and techniques using encoder-decoder, as disclosed by Subramanian to achieve the claimed invention. As disclosed by the teachings of Subramanian, the motivation for the combination would have been provide advantages for improved document summarization [pg. 2:23-27, pg. 3:1-20]
With respect to claims 2 and 12, the combination of Arora, Faizakof, and Subramanian discloses the computer-implemented method and system,
wherein the machine learning model is a sequence-to-sequence model. (col. 23:60-67: Arora discloses a deep learning model which represents a sequence-to-sequence model may be trained based on historical contact records where specific turns of customer contacts are labeled to identify specific issues and/or call drivers.)
With respect to claim 3, the combination of Arora, Faizakof, and Subramanian discloses the computer-implemented method of claim 1,
wherein the multi-level taxonomy is a hierarchical taxonomy. (¶ 0008-0009, 0056-0058: Faizakof discloses the discovered and extracted topics can further be clustered into “parent categories”, where each parent category contains one or more base topics thereby creating a hierarchical taxonomy.)
With respect to claims 5 and 14, the combination of Arora, Faizakof, and Subramanian discloses the computer-implemented method and system, further comprising:
retrieving, by the computer system, a plurality of data records associated with a query customer identifier from the database (col. 43:9-39: Arora discloses data generated by contacts analytics service may be indexed and used to identify contacts that meet a particular search query. Contact search page can be scoped to a customer or particular contact identifier.), each data record including a query interaction reason (Fig. 11, col. 43:9-39); aggregating, by the computer system, the plurality of data records by query interaction reason (col. 43:9-39: Arora discloses searching for “account is locked” or “can’t access my account”); and causing, by the computer system, display of the aggregated data. (Figs. 11, 16-17: Arora discloses the contact search result page may include aggregate data.),
With respect to claims 6 and 15, the combination of Arora, Faizakof, and Subramanian discloses the computer-implemented method and system, further comprising:
retrieving, by the computer system, a plurality of data records associated with a query interaction reason from the database (col. 43:9-39: Arora discloses data generated by contacts analytics service may be indexed and used to identify contacts that meet a particular search query.), each data record including a query customer identifier (col. 43:9-39: Arora discloses contact search page can be scoped to a customer or particular contact identifier); aggregating, by the computer system, the plurality of data records by query customer identifier (col. 43:9-39); and causing, by the computer system, display of the aggregated data. (Figs. 11, 16-17: Arora discloses the contact search result page may include aggregate data.)
With respect to claims 7 and 16, the combination of Arora, Faizakof, and Subramanian discloses the computer-implemented method and system,
wherein at least one record of the plurality of customer interaction records is associated with a channel different from the channel associated with another record of the plurality of customer interaction records. (¶ 0050: Arora discloses various call center related activities including active contacts via different modalities, i.e., call, chats, and emails and trend lines that show relative load.)
With respect to claims 8 and 17, the combination of Arora, Faizakof, and Subramanian discloses the computer-implemented method and system,
wherein the computer system is configured to execute the method periodically. (col. 24:11-15: Arora discloses the customer contacts are clustered on a periodic schedule, i.e., daily, weekly, or monthly and used to identify emerging trends in customer contacts.)
12. Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora in view of Faizakof in view of Subramanian in further view of Haikin (US 2025/0258850 A1).
With respect to claims 4 and 13, the combination of Arora and Faizakof discloses the computer-implemented method and system,
wherein the multi-level taxonomy (¶ 0008-0009, 0056-0058: Faizakof discloses the discovered and extracted topics can further be clustered into “parent categories”, where each parent category contains one or more base topics thereby creating a hierarchical taxonomy.)
The combination of Arora, Faizakof, and Subramanian references do not explicitly disclose the following limitations.
In the same field of endeavor, the Haikin reference is related to automated systems and methods that leverage large language models to efficiently generate taxonomy analytics in relation to aspects of customer center interactions (¶ 0001) and teaches:
includes at least four levels.(¶ 0054-0058: discloses the hierarchal taxonomy 300 related to interaction aspects. The hierarchical taxonomy may be configured in accordance with several hierarchical levels, including top aspect categories 305, one or more levels of aspect subcategories 310 related to each of the aspect categories, and a final level, the interactions 315 themselves which are grouped within each of the subcategories. Each level in the hierarchical taxonomy may be sorted by size in descending order which focuses attention on the largest and most impactful categories, i.e., those that include the most interactions.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the hierarchical taxonomy from the combination of Arora, Faizakof, Subramanian, to include at least four levels, as disclosed by Haikin to achieve the claimed invention. As disclosed by Haikin, the motivation for the combination would have been to allow efficient drilling down into narrowing categorical subject matter and focus attention on the largest and most impactful interactions. (¶ 0053-0054, 0057)
13. Claim(s) 9-10 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arora in view of Faizakof in view of Subramanian in further view of Ashok (US 2025/0131245 A1)
With respect to claims 9 and 18, the combination of Arora and Faizakof discloses the computer implemented method and system, however, the combination does not explicitly disclose the following limitations.
The Ashok reference is related to a computing system used to present terms and aspects (¶ 0045) and teaches:
wherein each theme of the plurality of themes includes five words or less. (¶ 0046: discloses commands or rules for generating the topic name. The commands or rules can include a threshold size for the topic name, descriptors for how to determine the topic, instruction on generating a summary, among other types of commands or rules. In some cases, the commands or rules can include: i) find the major topic. ii) the topic name should be less than a number (e.g., 2, 3, 5) words. iii) use the topic and the extracts (e.g., terms) to generate a one-line summary. iv) the summary should be concise. v) return the topic and summary in the format [Topic: topic you find][Summary: summary you generate]. vi) else return [Topic: mixed topic name][Summary: mixed topic name].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Arora, Faizakof, Subramanian, to include wherein each theme of the plurality of themes includes five words or less, as disclosed by Ashok to achieve the claimed invention. As disclosed by Ashok, the motivation for the combination would have been to use a threshold size for the topic name to help focus the analysis on only the most relevant information. (¶ 0046)
With respect to claim 10 and 19, the combination of Arora, Faizakof, Subramanian discloses the computer implemented method and system, however, the combination does not explicitly disclose the following limitations.
wherein each summary of the plurality of summaries includes more than five words and less than twenty words. (¶ 0046: discloses commands or rules for generating the topic name. The commands or rules can include a threshold size for the topic name, descriptors for how to determine the topic, instruction on generating a summary, among other types of commands or rules. In some cases, the commands or rules can include: i) find the major topic. ii) the topic name should be less than a number (e.g., 2, 3, 5) words. iii) use the topic and the extracts (e.g., terms) to generate a one-line summary. iv) the summary should be concise. v) return the topic and summary in the format [Topic: topic you find][Summary: summary you generate]. vi) else return [Topic: mixed topic name][Summary: mixed topic name].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Arora, Faizakof, and Subramanian, to include wherein each summary of the plurality of summaries includes more than five words and less than twenty words, as disclosed by Ashok to achieve the claimed invention. As disclosed by Ashok, the motivation for the combination would have been to use a one-line summary to help focus the analysis on only the most relevant information. (¶ 0046)
Response to Arguments
Applicant's arguments filed 09/08/2026 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “Because amended claim 1 does not fall into any of the enumerated groupings, the claim does not recite any abstract ideas. Thus, under the Step 2A-Prong 1 analysis defined in the 2019 PEG, the pending claims recite patent eligible subject matter and the eligibility analysis should conclude here.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. Here, the remarks do not challenge any of the identified limitations under Step 2A Prong One of the previous rejection. The ordered combination of limitations that recite “receiving”, “providing”, “executing”, “supplying”, “clustering”, “mapping”, in the context of claim 1 pertain to tasks or activities performed in contact center operations to determine interaction reasons for customers. See Applicant’s Specification [pgs. 1-2]. The Applicant’s remarks allege that the step of “displaying” now recited in claim 1 does not fall within the certain methods of organizing human activity or mental process groupings of abstract ideas. The Examiner asserts the limitation of “displaying” was considered an additional element under Step 2A Prong Two of the analysis and maintains the step adds merely insignificant extra- solution activity to the judicial exception. Communicating or transmitting information for display constitutes post-solution activity. See MPEP 2106.05(d) and MPEP2106.05(g); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Assuming arguendo that one or more elements of Applicant's claims are found to recite an abstract idea, Applicant respectfully submits that any such abstract idea is integrated into a practical application to impose a meaningful limit on the abstract idea. Amended claim 1 is directed to applying a customized machine learning algorithm that involves using two stacked long short-term memory networks in a specialized manner to select words and determine word orders in an interaction theme and an interaction summary for customer interaction data. The combination of (i) supplying each customer interaction transcript to a first stacked long short-term memory network (LSTM) serving as an encoder to generate attention weights for one or more words in the transcript that numerically capture relationships among words in the customer interaction transcript; and (ii) supplying the attention weights to a second stacked LSTM serving as a decoder to select one or more words to include in the model output that form the interaction theme and interaction summary, as recited in amended claim 1, integrates any exception into a practical application.”
The Applicant’s arguments are not persuasive. Here, the remarks restate the machine learning techniques recited in claim 1, however, it is important to note the specificity of the presently recited steps does not automatically confer eligibility under Step 2A Prong Two. The Applicant’s Specification, [pgs. 9-10, 17-20] reveals the generic nature of the machine learning techniques and remarks are silent with respect to any technical improvements relating to operations performed the first stacked long short-term memory network (LSTM) and a second stacked LSTM. At best, they are additional elements recited in claim 1 at a high-level of generality merely used aid with performance of the judicial exception and are considered nothing more than mere instructions to be implemented by a computer. See MPEP 2106.05(f) Using result-focused functional claim language is a frequent feature of ineligible claims especially those that claim the use of generic computer and machine learning technology to carry out commercial/business interactions or objectives. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “More specifically, these steps are directed to the application of a particular machine learning algorithm in a specific manner that sufficiently limits the use of any abstract idea (e.g., certain methods of organizing human activity or mental processes) to the practical application of automatic word selection and word ordering in a text summary.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The courts have previously held the utility of the method does not make it eligible. See Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019) (automated data synthesis technology did not make claims non-abstract even if it produced "life altering consequences"); In re Elbaum, No. 2023-1418, 2023 WL 8794636, at *2 (Fed. Cir. Dec. 20, 2023) (an abstract idea's tax benefits and usefulness did not confer eligibility); In re Mahapatra, 842 F. App'x 635, 638 (Fed. Cir. 2021) ("[T]he fact that an abstract idea may have beneficial uses does not mean that claims embodying the abstract idea are
rendered patent eligible.") Same is the case here. Thus, using machine learning techniques to aid with word selection/ordering of a text summary does not automatically confer eligibility. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “In addition, the step of displaying, in a graphical visualization platform, the specific interaction reasons mapped to different levels of a multi-level taxonomy and the interaction themes determined from the machine learning model sufficiently integrates any abstract idea into the practical application of data visualization. In view of the above, the pending claims clearly provide for a practical application of any alleged abstract ideas. As a result, Applicant respectfully submits that the pending claims are patent eligible under Step 2A-Prong 2 of the 2019 PEG analysis.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In the instant case, claim 1 displays information, i.e., interaction reasons, without improving the functioning of computers or any other computing technology. The courts have previously held claims that “merely organize and arrange sets of visual information into layers and then present said layers on a generic display device…did not recite an improvement in any computing technology. See also Int 'l Bus. Machs., 50 F .4th at 1380; Interval Licensing, 896 F. 3d at 1345 The Applicant’s Specification and remarks do not identify any technical problems associated with displaying this information. Communicating or transmitting information for display constitutes insignificant post solution activity. See MPEP 2106.05(g) After further consideration, the claim does not improve the functioning of a computer or make it operate more efficiently or solve any technology problem but only recites an arrangement of generic information to be displayed and used to assist users/employees in understanding information. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant's arguments with respect to claims 2-20 fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EHRIN PRATT whose telephone number is (571)270-3184. The examiner can normally be reached 8-5 EST Monday-Friday.
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, Lynda Jasmin can be reached at 571-272-6782. 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.
EHRIN PRATT
Examiner
Art Unit 3629
/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
Prosecution Insights