DETAILED ACTION
This communication is a Final Office Action on the merits in response to communications received on 02/24/2026. 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 .
Claim Rejections - 35 USC § 101
1. 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.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract without significantly more.
3. 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.
4. Under Step 2A – [Prong One] of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
5. Claim 1 which 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;” and “storing…the 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”, “storing” 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.
6. 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”, – 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: “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.
7. 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”, – 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.
8. 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
9. 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.
10. 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.
11. 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.)
The Arora reference does not explicitly discloses 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 02/24/2026 have been fully considered but they are not persuasive.
With Respect to the Rejections Under 35 USC 101
Applicant argues “For Prong 1 of Step 2A, a determination is made regarding whether the claim recites an abstract idea, law of nature, or natural phenomenon. To recite an abstract idea, the claim must include elements that fall into one of three specifically enumerated subject matter groupings- mathematical concepts, certain methods of organizing human activity, or mental processes. There is no recitation of mathematical concepts in amended claim 1 because the claim does not explicitly recite an equation, formula, calculation or relationship. There is no recitation of methods of organizing human activity in amended claim 1 because the claim involves executing a machine learning algorithm by applying at least two stacked long short-term memory networks to determine words in an interaction theme and interaction summary of an input text. These technical concepts do not involve financial, legal or personal relationships. There is no mental processes in amended claim 1 as the claim is directed to detailed execution of a machine learning algorithm that cannot be performed in the human mind or with pen and paper. 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. The Examiner asserts the previous Non-Final Office Action mailed did not indicate under Step 2A Prong One that claim 1 recites mathematical concepts, thus, the remarks directed towards mathematical concepts here are moot. Next, the remarks rely upon additional elements recited by claim 1 (such as “a machine learning model”, “at least two stacked long short-term memory networks”) which were limitations considered under Step 2A Prong Two of the analysis. It is important to note adding these additional elements does not preclude the identified limitations that recite the judicial exception in claim 1 from falling within the certain methods of organizing human activity grouping.
In regards to mental process remarks, the MPEP 2106.04(a)(2)(III)(c) section provides guidance and several examples indicating how claims can recite a mental process even if the claims are being performed by a computer. The claimed methods are not rendered non-abstract by the fact that using existing machine learning technology they perform a task previously undertaken by humans with greater speed and efficiency than could be previously achieved. The courts have consistently held that such claims are not made patent eligible simply because they speed up human activity. See, e.g., Trinity Info Media,LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) (rejecting argument that “humans could not mentally engage in the ‘same claimed process’ because they could not perform ‘nanosecond comparisons’ and aggregate ‘result values with huge numbers of polls and members’”) Thus, the identified limitations under Step 2A Prong One in claim 1 can be directed to a mental process even if the claims require generic computer components or require operations that a human could not perform as quickly as a computer. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “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. 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. Here, the reply merely restates the ordered combination of steps in claim 1. The specificity of the presently recited techniques does not automatically confer eligibility. Under Step 2A Prong Two, the additional elements of claim 1 were considered such as [“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”]. The additional elements are recited at a high level of generality as generic computer components and machine learning techniques that behave in their expected or normal capacity as tools to perform the generic functions of the abstract and apply it in a particular technological environment. The description of the additional elements from the Specification [i.e., Figs. 1 – i.e., computer system, machine learning model commercially available, Fig. 7 – i.e., encoder, decoder], provides findings or evidence that these are generic elements that are used as tools to perform the abstract idea. The remarks and Specification fail to provide any technical details for the computer components and/or machine learning model, but instead predominately describes the system and methods in purely functional terms. Concerning the recitations of "a machine learning model, a first stacked LSTM serving as an encoder, a second stacked LSTM serving as a decoder" in claim 1, it is well settled that "requiring the use of a 'software' 'brain' 'tasked with tailoring information and providing it to the user provides no additional limitation beyond applying an abstract idea, restricted to the Internet, on a generic computer." Intellectual Ventures I LLC V. Capital One Bank (USA), 792 F.3d 1363, at 1370 (Fed. Cir. 2015). Thus, the additional elements in combination with the judicial exception as recited in claim 1 do not integrate the judicial exception a practical application or provide an inventive concept. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant’s amendments and arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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