DETAILED ACTION
This communication is a Non-Final Office Action on the merits in response to communications received on 1/8/2026. Claims 25, 29-31, 34-35 have been amended. Therefore, claims 25-36 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/8/2026 has been entered.
Claim Rejections - 35 USC § 112
2. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
3. Claims 25-36 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
With respect to claims 25 and 31:
The applicant’s disclosure fails to comply with the written description requirement, which demands that an applicant’s specification “describes the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the invention had possession of the claimed invention.” In cases involving computer-implemented functional claims, examiners are instructed to “determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed functions in sufficient detail….”
The MPEP explains that “the level of detail required to satisfy the written description requirement varies depending on the nature and scope of the claims and on the complexity and predictability of the relevant technology.”
The Specification in [¶ 0027, 0061-0063] recites:
[0027] In one example, a “Lite” edition (partial functionality) may operate to generate interaction insights (110) using, for example, the following information and techniques: a) sentiment analytics, b) generic AI/ML models on agent behaviors, c) contact metadata (e.g., silence time, agent time, customer time), d) transcription, and e) robust search. Sentiment analytics includes analysis of conversation(s) between a customer and an agent using deep learning and machine learning that are built to understand the overall sentiment of the customer at the end of the conversation. Generic AI/ML models on agent behaviors are deep learning algorithms built based on experience over an extended period of time, e.g., months or years, to analyze the conversations and show insights on the behaviors displayed by the agent (e.g., effective probing, actively listening to customer, showing empathy, setting expectations, etc.). The transcriptions can be stored, for example, in database(s) and come from voice recordings, chat conversations, email conversations, social media conversations, etc. For voice recordings, transcriptions may be generated using an infrastructure built on deep learning and Graphics Processing Unit (GPU) technologies versus conventional processors as they are faster at processing, due to the processing need of online games, for example. For the other text-based communication channels, the unstructured data is cleansed and persisted for further processing. Searches may be performed on the conversations transcriptions, and on metadata for the conversations, using natural language processing (NLP) based techniques that help to filter the data quickly and easily. A typical timeline to enable the “Lite” edition deployment (e.g., 4-6 weeks) includes data acquisition and data integration, inference on one month historical data and categories configuration, and onboarding and training before going live.
[0061] The speech analytics pipeline 510 may include an audio pre-processor 511, a speaker diarization model 512, and a speech to text model 513. Audio recordings 505 are input to the speech analytics pipeline 510, and results of the speech analytics performed using components 511, 512, 513 are output to data collector/shipper 530. In some example embodiments, the output speech analytics results may be stored in a database 515 (e.g., a MONGO DB) and made available to data collector/shipper 530 for retrieval. The speech analytics pipeline 510 may be implemented using DASK, for example, or other known or future developed equivalents.
[0062] The email/chat conversation platform 520 may include various components including but not limited to Sales Force 521, Azure 522, Secure File Transfer Protocol (SFTP) 523, and Chat Dump 524. The email/chat conversation platform 520 can also provide various interaction data to the data collector/shipper 530 from one or more of these components 521, 522, 523, 524.
[0063] The data collector/shipper 530 may include various components associated with the processed audio recordings from the speech analytics pipeline 510 and the email/chat conversations from email/chat conversation platform 520, including but not limited to a sales data shipper 531, a database (e.g., Mongo DB) data shipper 532, an SFTP data shipper 533, a database management system (DBMS) data shipper 534, and a data shipper 535 (e.g., a cloud computing service such as AZURE). The data collector/shipper facilitates the transfer of interaction data from the client to the system (e.g., data pipeline 550 and analytics and scoring engine 560) at regular intervals.
The claims now recite:
“storing, by the computer, the interaction data and metadata in a NoSQL database configured to store data in a Binary JSON format that uses collections and documents”
The original specification filed 11/28/2022 does not disclose a NoSQL database or a Binary JSON format. In the instant case, the specification fails to describe the database and format being claimed in sufficient detail. The written description requirement mandates the specification adequately describes the features of the claimed invention and applicant’s approach to how the claimed steps are performed. Since the original specification fails to clearly outline the features or steps, the support for the amendments to the claim encompasses new matter and is being rejected as failing to comply with the written description requirement.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 25-36 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Under Step 1 of the two-part analysis from Alice Corp, claim 25 recites a process (i.e., an act or step, or a series of acts or steps) and claim 31 recites a machine (i.e., a concrete thing, consisting of parts, or of certain devices and combination of devices). Thus, each of the claims fall within one of the four statutory categories.
6. Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
Claim 25 which is representative of claim 31 recites:
“collecting…interaction data and metadata data from one or more of voice data, chat data, email data, and mobile data;”, “storing…the interaction data and metadata”, “generating…a transcript of the collected interaction data and metadata data;”, “determining…a KPI of the transcript, wherein the KPI is associated with a customer experience outcome.”, and “wherein the metadata is collected from voice data, the metadata comprises one or more of silence time, customer time, and agent time.”
Under the broadest reasonable interpretation, the limitations recite a series a steps for determining key performance indicator(s) from a conversation between an agent and a customer which encompasses concepts that fall within the mental processes (i.e., observations, evaluations, judgements, and opinions) and certain methods of organizing human activities (i.e., marketing or sales activities/business relations) groupings of abstract ideas. See MPEP 2106.04
The Applicant’s Specification in at [0002]Customer support systems can drive differentiated consumer experience and growth in a variety of ways. As consumers make their experience a top reason to choose brands, a consumer-focused, digitally connected and brand consistent consumer care program elevates a given brand’s reputation in the eyes of the consumer, while managing operation efficiency and increasing relevance. By listening intently and learning from each consumer interaction, and delivering delightful experiences and effectively addressing issues, consumer care programs can drive brand loyalty, create vocal champions that can be digitally activated, and drive revenue growth.[0003] Currently, customer support systems rely on operations driven decision making using key performance indicators (KPIs) like average handling time (AHT), provide only market research and consumer feedback survey-based consumer insights, and require human based quality monitoring. One challenge in a customer support program is to proactively identify the resolution rates, customer sentiment, and customer satisfaction (CSAT) scores and/or net promoter scores (NPS) scores.
Consistent with the specification, the limitations recite mental processes for collecting and evaluating known information from interactions between an agent and a customer to derive key performance metric(s). The acts for evaluating the known information involve generating a transcript of the interactions and determining a key performance indicator(s) of the transcript which are limitations can be practically performed in the human mind, with or without the use of a physical aid such as pen and paper. Additionally, the limitations cover commercial interactions, i.e. marketing/sales activities or business relations because they generate and organize key performance indicator(s) of a transcript for agent or manager to track customer satisfaction and/or agent performance metrics. As such, the claim recites an abstract idea.
7. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “using a machine learning model (MLM)” “by a computer”, “training…the MLM”, “one or more of a data processing technique”, “a classification model”, “a trained MLM”, “by the computer”, “a NoSQL database configured to store data in a Binary JSON format that uses collections and documents”, “using the trained MLM”, “a system, “one or more computer processors”, “one or more computer-readable storage media, program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising:” – see claims 25 and 31 are all recited at a high-level of generality in light of the specification. In [Figs 1A-1B, ¶ 0025 - the invention is not limited to any specific hardware or software configuration, but may rather be implemented as computer executable instructions in any computing or processing environment, including, for example, in digital electronic circuitry or in computer hardware, firmware, device driver, or software. The various computing devices involved may include clients, servers, storage devices and databases, personal computers, mobile devices such as smartphones and tablets, or other similar electronic and/or computing devices.] Thus, because the specification describes the additional elements in general terms without describing the particulars, the additional elements may be broadly but reasonably construed as reciting generic computer components performing the judicial exception in light of the applicant’s specification. Therefore, the additional elements merely add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer processor as a tool to perform the abstract idea as discussed in MPEP 2106.05 (f).
The other additional elements of “a method…to determine key performance indicators (KPI) of an interaction between computing devices, comprising” is merely an attempt to limit the claimed invention to a particular field of use or technological environment, 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: “using a machine learning model (MLM)” “by a computer”, “training…the MLM”, “one or more of a data processing technique”, “a classification model”, “a trained MLM”, “by the computer”, “a NoSQL database configured to store data in a Binary JSON format that uses collections and documents”, “using the trained MLM”, “a system, “one or more computer processors”, “one or more computer-readable storage media, program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising:” amounts to no more than mere instructions in which to apply the judicial exception which cannot provide an inventive concept at Step 2B.
9. Claims 26-30 and 32-36 are dependent of claims 25 and 31.
Claims 26 and 32 recite “generating the interaction training data using the data processing technique; wherein the data processing technique comprises one or more of: Term Frequency Inverse Document Frequency (TF-IDF); Word2Vec; Latent Dirichlet Allocation; and Nonnegative Matrix Factorization.” is/are data processing techniques recited at a high-level of generality and amount to nothing more than mere instructions to apply the judicial exception using a computer – see MPEP 2106.05(f), Claims 27 and 33 recite “further comprising: generating the interaction training data using the classification model; wherein the classification model comprises one or more of: Random Forest; Logistic Regression; Support Vector Machines (SVM);Extreme Gradient Boosting (XGBoost); and Neural Networks (NN).” is/are types of classification models recited at a high-level of generality and amount to nothing more than mere instructions to apply the judicial exception using a computer – see MPEP 2106.05(f), Claims 28 and 34 recite “wherein the KPI comprises one or more of: a Customer Dissatisfaction (DSAT) score; a Net Promoter Score (NPS); and a Customer Satisfaction (CSAT) score”, further describes the data or information recited in the abstract idea, but does not make the claim any less abstract. Claims 29 recite “wherein the steps of collecting the interaction data and metadata; storing the interaction data and metadata; generating the transcript; and determining the KPI; are conducted in real-time.” further narrow how the abstract idea may be performed, but does not make the claim any less abstract. The recitation of “real-time” is merely an attempt to limit the claim to a particular technological environment or field of use – see MPEP 2106.05(h) Claims 30 and 35 recite “the step of generating the transcript comprises generating the transcript using a deep learning model and a graphical processor unit.” further narrows how the abstract idea may be performed, but do not make the claim any less abstract. Here, the “deep learning model” and “graphical processor unit” are recited at a high-level of generality and merely being used in their ordinary capacity to organize/arrange information or data recited from the abstract idea – see MPEP 2106.05(f). Claim 36 recites “wherein the program instructions further comprise: program instructions to display the KPI on a graphical user interface (GUI).” which merely adds insignificant extra-solution activity, i.e., data transmission/output, to the judicial exception, but does not add any meaningful limitations to the claim – see MPEP 2106.05(g). Here, the “graphical user interface” is recited at a high-level of generality and merely being used in its ordinary capacity to present information or data from the abstract idea. Thus, the dependent claims when viewed individually and as an ordered combination do not integrate the judicial exception 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) 25, 27-31, 33-36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zarecki in further view of KulKarni (US 2023/0252496 A1).
With respect to claims 25 and 31, Zarecki discloses
a method (Fig. 1: discloses contact center 102) of using a machine learning model (MLM) to determine key performance indicators (KPI) of an interaction between computing devices (col. 15:26-45), comprising:
training, by a computer, the MLM based on interaction training data and one or more of a data processing technique and a classification model to generate a trained MLM (col. 15:26-45: discloses the contact center system 102 can use supervised machine learning to train a machine learning model to predict customer effort metrics.);
collecting, by the computer, interaction data and metadata data from one or more of voice data, chat data, email data, and mobile data (col. 6:39-62: discloses the contact center system can generate and/or store dialogue data 118, telephony data 120, and/or application usage data associated with communication sessions.);
generating, by the computer, a transcript of the collected interaction data and metadata data (col. 6:39-62, col. 19:1-12: discloses the contact center system 102 may use speech recognition systems to generate a text transcript.); and
determining, by the computer, a KPI of the transcript using the trained MLM, wherein the KPI is associated with a customer experience outcome (cols. 9-10:59-62, col. 12:45-61, col. 19:12-15: discloses the contact center system 102 can generate and store key performance indicators (KPIs). The contact center system 102 can be configured to derive one or more KPI’s based on one or more or the dialogue data 118, the telephony data 120, and other data available to the contact center system 102); and
The Zarecki reference does not explicitly disclose the following limitations.
In the same field of endeavor, the Kulkarni reference is related to the field of contact center operations (¶ 0002) and teaches:
storing, by the computer, the interaction data and metadata in a NoSQL database configured to store data in a Binary JSON format that uses collections and documents (¶ 0030, 0032, 0063: discloses the event database may store event data. For example, in various embodiments one or more databases may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology 38 such as those referred to in the art as “NoSQL”. Call events may include, but are not limited to: call dropped (by enterprise), poor voice quality (observed live voice quality, real-time mean object score and audio generation device mean object score), no issues (e.g., successful calls), missed dual-tone multi-frequency signaling or first attempt at recognition (e.g., no match), failed transfer (e.g., interactive voice response [IVR] to agent, agent to agent, etc.), ring-out (e.g., call not answered), afterhours notice (e.g., calling after office hours, needs special way using choice tags), out of date or incorrect prompts (e.g., incorrect IVR prompt), long delay in getting responses (e.g., major timeout failure), incorrect data being readout (e.g., variable data tag, CURRENCY, NUMBER, ALPHANUM, special type of no match), failed authentication, technical difficulties message (e.g., IVR technical difficulty messaging), long wait time, not understanding customer (e.g., no match, three times failure in understanding), and dead air (e.g., silence, could happen during route to the agents as well [no audio heard])), wherein when the metadata is collected from voice data, the metadata comprises one or more of silence time, customer time, and agent time.(¶ 0032)
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 and methods of Zarecki, to include storing, by the computer, the interaction data and metadata in a NoSQL database configured to store data in a Binary JSON format that uses collections and documents, wherein when the metadata is collected from voice data, the metadata comprises one or more of silence time, customer time, and agent time, as disclosed by Kulkarni in order to achieve the claimed invention. As disclosed by Kulkarni, the motivation for the combination would have been to provide advantages for logging and storing event metadata from contact center operations. (¶ 0002, 0005, 0030)
With respect to claims 27 and 33, the combination of Zarecki and Kulkarni discloses the method and system, further comprising:
generating the interaction training data using the classification model (col. 15:26-45 - Zarecki); wherein the classification model comprises one or more of: Random Forest; Logistic Regression; Support Vector Machines (SVM) ;Extreme Gradient Boosting (XGBoost); and Neural Networks (NN). (col. 15:26-45: Zarecki discloses supervised machine learning can train the machine learning model. The supervised machine learning can be based on support vector networks, linear regression, logistic regression, neural networks, and/or other machine learning.)
With respect to claims 28 and 34, the combination of Zarecki and Kulkarni discloses the method and system,
wherein the KPI comprises one or more of: a Customer Satisfaction (CSAT) score (col.10:5-17: Zarecki discloses the KPIs 126 can be customer effort metrics that measure or estimate customer’s perceptions of customer effort associated with communication sessions with the contact center.):
With respect to claims 29, the combination of Zarecki and Kulkarni discloses the method of claim 25,
wherein the steps of collecting the interaction data and metadata (col. 6:45-62, Fig. 8);
storing the interaction data and metadata (col. 6:45-62, Fig. 8: discloses the contact center system 102 can store data about communication sessions that occur between customers and representatives.);
generating the transcript (col. 6:45-62, Fig. 8: discloses the contact center system 102 uses speech recognition systems to generate a text transcript substantially in real-time as a call is occurring between a customer and representative.); and
determining the KPI (cols. 9-10:59-62: discloses the contact center system 102 can automatically generate and store key performance indicators about communication sessions between customers and representatives.); are conducted in real-time (col. abstract, col. 6:45-62, Fig. 8).
With respect to claims 30 and 35, the combination of Zarecki and Kulkarni discloses the method of claim 25,
wherein the step of generating the transcript comprises generating the transcript using a model (col. 6:54-56: Zarecki discloses using speech recognition systems to generate text transcript from the audio recording) and a graphical processor unit. (col. 19:51-63: Zarecki discloses a graphics processor unit.)
However, the Examiner asserts that the data identifying the model as including deep learning is simply a label for the model and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific type of information) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability.
Therefore, it would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention, to have the deep learning be included in the system of Zarecki because the name of the model does not functionally alter or relate to the steps of the method and merely labeling the information differently from that in the prior art does not patentably distinguish the claimed invention.
With respect to claim 36, the combination of Zarecki and KulKarni discloses he system of claim 31,
wherein the program instructions further comprise:
program instructions to display the KPI on a graphical user interface (GUI). (Fig. 8, col. 10:38-62: Zarecki discloses the contact center system 102 can also have a dashboard 128. The dashboard 128 can include a user interface that can display scorecards, trends, statistics, records, and/or other information about or derived from communication sessions. For example, the dashboard 128 can display data associated with KPIs 126.)
13. Claim(s) 26 and 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zarecki in view of KulKarni in further view of Currier (US 2021/0319457 A1).
With respect to claims 26 and 32, the combination of Zarecki and Kulkarni discloses the method and system, further comprising:
generating the interaction training data using the data processing technique (col. 15:26-45);
The Zarecki reference does not explicitly disclose the following limitations. In the same field of endeavor, the Currier reference is related to a data aggregation platform the utilizes models to aggregate data and to identify insights from the aggregated data (¶ 0011) and teaches:
wherein the data processing technique comprises one or more of:
Latent Dirichlet Allocation (¶ 0029, 0104: discloses the data aggregation platform may train a first model with the first structured historical customer data and the structured second historical customer data to generate a trained first model. The first model may include a machine learning model, such as a latent Dirichlet allocation (LDA) model. An LDA model is a type of topic model, such as a model that can be used to identify abstract topics that occur in a collection of documents, a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar, and/or the like. For example, if observations are words provided in documents, the LDA model may determine that each document is associated with a mixture of a quantity of topics and that the presence of each word is attributable to one topic, of the quantity of topics.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Zarecki and Kulkarni, to include wherein the data processing technique comprises Latent Dirichlet Allocation, as disclosed by Currier to achieve the claimed invention. As disclosed by Currier, the motivation for the combination would have been to provide benefits ranging from topic discovery to improved text analysis and information retrieval. (¶ 0029, 0104)
Response to Arguments
Applicant's arguments filed 08 January 2026 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “Claim 25, which is representative of claim 30, is patent eligible, as the claim merely involves an exception. As disclosed in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. See MPEP § 2106.04. However, the Office further clarifies that “Examiners should be careful to distinguish claims that recite an exception (which require further eligibility analysis) from claims that merely involve an exception (which are eligible and do not require further eligibility analysis).” Memorandum, USPTO dated August 4, 2025, at pg. 3. In other words, claims that merely involve a judicial exception are patent eligible.
“Claim 25 recites in part “training . . . the MLM based on interaction training data and one or more of a data processing technique and a classification model to generate a trained MLM[.]” Even though “training the MLM” involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. See id. As such, claim 25 does not recite a judicial exception and merely involves a judicial exception. Given that claim 25 merely involves a judicial exception, the broadest reasonable interpretation of the claim is that it does not set forth or describe an abstract idea and is eligible.” The Examiner respectfully disagrees.
Contrary to the remarks, the claims remain ineligible under Step 2A Prong One of the two-part analysis. Here, the Applicant’s reply does not properly challenge the limitations that were identified as being part of the abstract idea. It is important for applicant to note that steps for analyzing conversational or contact data resulting from an agent interacting with a customer involves commercial interactions and evaluations which are concepts that fall within the mental processes and certain methods of organizing human activity groupings. Also, the “training” step was considered an additional element and addressed under Prong Two of the analysis as nothing more than mere instructions to implement the abstract idea on a computer. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Claim 25 further recites in part “storing, by the computer, the interaction data and metadata in a NoSQL database configured to store data in a Binary JSON format that uses collections and documents[.]” Under its broadest reasonable interpretation, this limitation does not recite mental processes because it cannot be practically performed in the human mind. That is, the human mind is not equipped to store interaction data and metadata in a NoSQL database configured to store data in a Binary JSON format that uses collections and documents. Even more, such steps cannot be performed by a human using pen and pad. See MPEP 2106.04(a)(2).” The Examiner respectfully disagrees.
Contrary to the remarks, the claims remain ineligible under Step 2A Prong One of the analysis. In the instant case, merely adding features describing where the data is being stored and/or describing the type of formatting that may be used at best simply narrows how the abstract idea may be performed but does not make the claimed invention any less abstract. The additional elements of “a NoSQL database” and “a Binary JSON format” may add some specificity to the claim if supported by the disclosure, however, these limitations are recited in a conclusory manner in the original Specification, and therefore do not alter the previous analysis. See BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1286 (Fed. Cir. 2018) ("[C]laims are not saved from abstraction merely because they recite components more specific than a generic computer."). For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Instead, “using a machine learning model (MLM),” “training . . . the MLM,” “collecting . . . interaction data and metadata,” “storing . . . the interaction data and metadata in a NoSQL database configured to store data in a Binary JSON format,” “generating . . . a transcript,” and “determining . . . a KPI of the transcript using the trained MLM”: Implements the purported abstract idea with a computer having a NoSQL database configured to store data in a Binary JSON format that is integral to claim 25. A POSITA would understand that a NoSQL database configured to store data in a Binary JSON format that uses collections and documents (i.e., a MongoDB) goes beyond the functionality of a generic computer having a generic database because compared to a generic database the claimed NoSQL database is a specialized database allowing for faster reads and writes for unstructured or changing data, and storage of nested data without needing multiple tables. Assuming, arguendo, that Claim 25 includes a judicial exception, which Applicant does not agree with, such judicial exception is integrated with storing interaction data and metadata in a NoSQL database configured to store data in a Binary JSON format that uses collections and documents and therefore integrates the judicial exception into a practical application. Given that claim 25 recites additional elements that integrate the judicial exception into a practical application, Applicant respectfully submits that claim 25 is not directed to a judicial exception.” The Examiner respectfully disagrees.
Contrary to the remarks, the claims remain ineligible under Step 2A Prong Two of the two-part analysis. In the instant case, the examiner maintains the ordered combination of additional elements in the claim are merely being used as tool to perform the abstract idea. See MPEP 2106.05(f) It is important to note, the courts have previously held "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Here, the remarks discuss or point to advantages of using a NoSQL database, i.e., binary json format, may provide in a conclusory manner without any support from the Applicant’s own Specification that discusses any of these advantages or how the NoSQL database is being implemented to solve a technological solution to a technological problem. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant’s arguments with respect to claim(s) 25-36 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
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 L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629