DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to arguments and amendments entered on February 9, 2026 for the patent application 18/537,809 originally filed on December 13, 2023. Claims 1 and 4 are amended. Claims 2, 3, 5 and 7-18 are cancelled. Claims 19-27 are new. Claims 1, 4, 6 and 19-27 are pending. The first office action of April 25, 2025 is fully incorporated by reference into this Final Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4, 6 and 19-27 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – “Statutory Category Identification”
Claim 1 is directed to “a computerized method” (i.e. “a process”) and claim 19 is directed to “a system” (i.e. “a machine”), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 1 “Abstract Idea Identification”
However, the claims are drawn to the abstract ideas of “automated training content creation,” either in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
Per claim 1:
“automatically ingesting an agent entity's media and instructional content from contact center recordings;
automatically capturing the agent entity's media and instructional content,
converting video content of the agent entity's media and instructional content into a plurality of screen views, and
exporting the plurality of screen views to creator's automatic speech recognition (ASR) tool;
automatically converting dual channel audio files of the agent entity's media and instructional content into computer readable transcriptions using speech-to-text processing with automated personally identifiable information (PII) redaction;
automatically clustering transcripts,
dynamically generate a library of educational exercises and simulations based on all the inputs of data of the agent entity's media and instructional content,
wherein the library of educational exercises and simulations comprises a robust set of tags that are automatically correlated to content,
benchmark, QA, and performance metrics of the inputs of data of the agent entity's media and instructional content using tag-matching algorithms that match QA and performance metrics to simulation tags, and
implementing automated editor operations, scoring operations, and test operations for the simulations, and
wherein the automated generation replaces manual storyboard creation and content development processes; and
integrating with a learning management system (LMS) Experience to automatically;
assign educational exercises,
send reporting data, and
alert managers when action is required.”
Per claim 19:
“automatically ingest an agent entity's media and instructional content from contact center recordings;
automatically capture the agent entity's media and instructional content,
convert video content of the agent entity's media and instructional content into a plurality of screen views, and
export the plurality of screen views to the creator's automatic speech recognition (ASR) tool;
convert dual channel audio files of the agent entity's media and instructional content into computer readable transcriptions using speech-to-text processing with automated personally identifiable
information (PII) redaction;
automatically cluster transcripts,
dynamically generate a library of educational exercises and simulations based on all the inputs of data of the agent entity's media and instructional content, and
wherein the library of educational exercises and simulations comprises a robust set of tags that are automatically correlated to content,
benchmark, QA, and performance metrics of the inputs of data of the agent entity's media and instructional content using tag-matching algorithms that match QA and performance metrics to simulation tags, and
implement automated editor operations, scoring operations, and test operations for the simulations, and
wherein the automated generation replaces manual storyboard creation and content development processes; and
integrate with a learning management system (LMS) Experience to automatically:
assign educational exercises,
send reporting data, and
alert managers when action is required.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 2 – “Practical Application”
Furthermore, the applicants claimed elements of “an automated simulation creator,” “a creator's automatic speech recognition (ASR) tool,” “a generative Al system,” “a learning management system (LMS) Experience API,” “a processor,” and “a memory,” are merely claimed to generally link the use of a judicial exception (e.g., pre-solution activity of data gathering and post-solution activity of presenting data) to (1) a particular technological environment or (2) field of use, per MPEP §2106.05(h); and are applying 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, per MPEP §2106.05(f). In other words, the claimed “automated training content creation,” per claims 1 and 19, are not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Step 2B – “Significantly More”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “an automated simulation creator,” “a creator's automatic speech recognition (ASR) tool,” “a generative Al system,” “a learning management system (LMS) Experience API,” “a processor,” and “a memory,” are claimed, these are generic, well-known, and conventional data gather computing elements. As evidence that these are generic, well-known, and a conventional data gathering computing elements (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known, the Applicant’s specification discloses these in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a). As such, this satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “an automated simulation creator,” “a creator's automatic speech recognition (ASR) tool” and “a generative Al system,” as described in para. [0048] of the Applicant’s written description as originally filed, provides the following: “[0048] For example, simulation creation tool 510 can perform ASR on this output to convert the dual channel audio files into transcriptions. Simulation creation tool 510 uses the transcriptions to generate the educational simulations and screenshots. Simulation creation tool 510 can leverage generative Al models (e.g. ChatGPT®, Bing Chat®, BARD®, etc.) and Al chatbot(s) systems to improve simulations. Additionally, various ML optimization algorithms can be used to further train and improve simulation performance (e.g. see infra).” As such, the Applicant’s claimed “an automated simulation creator,” “a creator's automatic speech recognition (ASR) tool” and “a generative Al system,” are reasonably interpreted as generic, well-known, and conventional data gathering computing elements.
Likewise, the Applicant’s claimed “a learning management system (LMS) Experience API,” as described in para. [0020] of the Applicant’s written description as originally filed, provides the following:
“[0020] Experience API (xAPI) is an e-learning software specification that records and tracks various types of learning experiences for learning systems. Learning experiences are recorded in a Learning Record Store (LRS), which can exist within traditional learning management systems (LMSs). The integration can be through varies methods (API and services, xAPI, etc.).”
As such, the Applicant’s claimed “a learning management system (LMS) Experience API,” is reasonably understood to be generic, well-known, and conventional, as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known. This is evidenced by the fact that the Applicant’s specification discloses this additional element in a manner that indicates that the additional element is so sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). Therefore, this element does not provide anything significantly more.
Finally, the Applicant’s claimed “a processor,” and “a memory,” as described in para. [0049] of the Applicant’s written description as originally filed, provides the following:
[0049] FIG. 6 depicts an exemplary computing system 600 that can be configured to perform any one of the processes provided herein. In this context, computing system 600 may include, for example, a processor, memory, storage, and I/O devices (e.g., monitor, keyboard, disk drive, Internet connection, etc.). However, computing system 600 may include circuitry or other specialized hardware for carrying out some or all aspects of the processes. In some operational settings, computing system 600 may be configured as a system that includes one or more units, each of which is configured to carry out some aspects of the processes either in software, hardware, or some combination thereof.”
As such, the Applicant’s claimed “a processor,” and “a memory,” are reasonably interpreted as generic, well-known, and conventional computing elements commonly available. Therefore, the Applicant’s own specification discloses ubiquitous standard equipment and services within modern computing and does not provide anything significantly more. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 4, 6 and 20-27 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 4, 6 and 20-27 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to claim 1 or 19. Therefore, claims 1, 4, 6 and 19-27 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject-matter.
Response to Arguments
The Applicant’s arguments filed on February 9, 2026 related to claims 1, 4, 6 and 19-27 are fully considered, but are not persuasive.
Claim Rejection - 35 U.S.C. § 101
The Claims Solve a Specific Technological Problem
The Applicant respectfully argues “The specification at paragraph [0002] establishes the technological problem:
"Dedicating personnel to training and coaching contact center representatives is expensive, emotionally draining for training and hiring staff and seldom provides enough experience to make an agent ready for live contact with clients." The claimed invention solves this by automating what paragraph [0030] describes as combining "traditional plan, design, and capture steps into a single automated step" and "skipping traditional storyboard and build steps."
Claim 1 specifically recites that the method "replaces manual training content creation processes" and that the "automated generation replaces manual storyboard creation and content development processes," directly addressing the technological inefficiencies in training content creation.”
The Examiner respectfully disagrees. The Applicant is merely describing a mankind problem/burden and automating tasks to alleviate the burden. As such, the argument is not persuasive.
B. The Claims Provide a Specific Technological Implementation
The Applicant respectfully argues “Under MPEP § 2106.05(a), claims that apply a judicial exception "with, or to, a particular machine or manufacture" provide a practical application. The amended claims recite a specific technological implementation involving:
Multi-API data ingestion: Claim 1 recites "automatically ingesting an agent entity's media and instructional content from contact center recordings via third-party APIs" (supported by specification paragraph [0039])
Sophisticated multi-modal processing: The claim requires "a screen creation tool that implements screen capture methods including full screen selection, specific region selection, menu selection, text recognition with optical character recognition (OCR) operations, and panoramic selection" (supported by specification paragraph [0044])
Advanced speech processing with security features: The claim specifies "converting dual channel audio files of the agent entity's media and instructional content into computer readable transcriptions using speech-to-text processing with automated personally identifiable information (PII) redaction" (supported by specification paragraphs [0048] and [0034])
Al-driven clustering and generation: The claim requires "automatically clustering transcripts based on key metadata" using "a generative Al system configured to leverage generative Al models" with "machine learning optimization algorithms" (supported by specification paragraphs [0035] and [0048])
This technological integration is analogous to Thales Visionix Inc. V. United States, 850 F.3d 1343 (Fed. Cir. 2017), where the Federal Circuit found patent eligibility in a system that used "a particular configuration of inertial sensors and a particular method of utilizing the data generated by the inertial sensors" to solve a technological problem.
These four technological components represent a comprehensive system for automated learning content creation from contact center interactions, demonstrating sophisticated integration of multiple advanced technologies. The multi-API data ingestion capability enables seamless extraction of agent interactions from various contact center platforms through standardized interfaces, eliminating manual data collection bottlenecks. The sophisticated multi-modal processing through screen capture tools provides granular control over content creation, allowing precise selection of visual elements while incorporating OCR technology to extract textual information from any screen region. The advanced speech processing with security features addresses both functional and compliance requirements by converting dual-channel audio recordings into searchable transcripts while automatically identifying and redacting sensitive personally identifiable information to maintain privacy standards.
Finally, the Al-driven clustering and generation component leverages machine learning algorithms to intelligently organize transcribed content based on metadata patterns and utilizes generative AI models to create structured learning materials, transforming raw contact center data into actionable training content through automated analysis and content generation processes.”
The Examiner respectfully disagrees. First, the Applicant is not providing any advancement in technology, but instead using technology to advance the abstract idea of “automated training content creation.” Specifically, the Applicant is merely using a computer as a tool to carry out the abstract idea of “automated training content creation” (see MPEP § 2106.05(f)).
Second, the Applicant’s interpretation of Thales is incorrect. Thales used a particular configuration of specific sensors (i.e. inertia sensors) to overcome a real-world problem “recognizing that conventional solutions for tracking inertial motion of an object on a moving platform were flawed because both object- and platform-based inertial sensors measured motion relative to earth, and the error-correcting sensors on the tracked object measured position relative to the moving platform. Id. at 1:23–42. Attempting to fuse this data produced inconsistent position information when the moving platform accelerated or turned. Id.”
Thales demonstrated a technical solution to this problem: “The inertial sensors disclosed in the ’159 patent do not use the conventional approach of measuring inertial changes with respect to the earth. Id. at 7:12–23. Instead, the platform (e.g., vehicle) inertial sensors directly measure the gravitational field in the platform frame. Id. at 7:12–49, fig. 3D.”
In the Applicant’s case, applying an abstract idea of “automated training content creation,” using existing technology of “speech recognition,” “generative Al,” “a processor,” and “a memory,” fails the subject-matter eligibility analysis. As such, the argument is not persuasive.
C. The Claims Transform One Type of Data Into a Different Type
The Applicant respectfully argues “Under MPEP § 2106.05(c), transforming data can indicate a practical application when the transformation is not merely nominal but produces a different type of information. Here, the claimed invention transforms raw contact center recordings into structured, performance-correlated training simulations through multiple technical processes.
Claim 1 specifically recites transforming video content "into a plurality of screen views," converting "dual channel audio files into computer readable transcriptions," and generating "a library of educational exercises and simulations" with "tags that are automatically correlated to content, benchmark, QA, and performance metrics...using tag-matching algorithms that match QA and performance metrics to simulation tags."
The final output integrates "with a learning management system (LMS) Experience API (xAPI) to automatically: assign educational exercises, send reporting data, and alert managers when action is required," creating an automated workflow system from raw recordings.
This transformation is analogous to that found patent-eligible in SiRF Technology, Inc. V. International Trade Commission, 601 F.3d 1319 (Fed. Cir. 2010), where GPS data was transformed into a more useful form for navigation purposes.
These six technological components represent highly specialized and unconventional arrangements that go far beyond standard contact center or learning management implementations. The multi-API integration for contact center data ingestion requires custom orchestration of disparate third-party systems that typically weren't designed to work together, creating complex data pipelines that most organizations don't attempt. The sophisticated video processing with multiple capture methods combines advanced computer vision technologies like OCR with panoramic selection capabilities in ways that exceed typical screen recording tools, requiring significant technical expertise to implement effectively. The dual-channel audio processing with automated PII redaction presents a particularly complex challenge, as it must simultaneously perform high-quality speech-to-text conversion while intelligently identifying and removing sensitive information in real-time across separate audio channels. The Al-driven clustering and generation using generative AI models represents cutting-edge application of machine learning that requires sophisticated algorithms to automatically organize and create content from unstructured data sources. The automated simulation operations encompassing editor, scoring, and test operations demonstrates a level of automation that most training systems don't achieve, requiring complex workflow orchestration and decision-making capabilities.
Finally, the enterprise system integration with LMS xAPI for automated assignment and reporting creates seamless end-to-end learning workflows that most organizations handle through manual processes, representing a highly sophisticated integration that transforms raw contact center interactions into fully deployed learning experiences without human intervention.”
The Examiner respectfully disagrees. First, the Applicant’s citing of SiRF Technology, Inc. v. International Trade Commission is not on point. Specifically, SiRF Technology, Inc. v. International Trade Commission, 601 F.3d 1319, 1331-33 (Fed. Cir. 2010} is pre-Alice and provided that the machine “must play a significant part in permitting the claimed method to be performed.” Here the machine is generic and merely claimed as “a processor” and “a memory.”
Second, the Applicant’s argument is misguided as to the proper analysis of a “Practical Application” as required under Step 2A, Prong 2. Specifically, the Applicant’s argument appears to describe claimed utility, which is not the test. Instead, the Applicant’s claims are not considered a “Practical Application,” because the claims do not provide any of the following:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
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Furthermore, there are also several factors that reasonably explain that the Applicant’s claims are not indicative of integration into a practical application, which include:
Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
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Here, the Applicant’s claims are not providing any technological advancement as described in the first five bulleted factors and, as described above in the rejection, the Applicant’s claims are merely claimed to use a computer as a tool to perform an abstract idea and to generally link the use of a judicial exception to a particular technological environment or field of use. As such, the argument is not persuasive.
III. THE AMENDED CLAIMS INCLUDE SIGNIFICANTLY MORE (STEP 2B)
A. Unconventional Arrangement of Known Elements
The Applicant respectfully argues “The amended claims recite an unconventional combination of technologies that goes beyond routine data processing. Under MPEP § 2106.05(c), "applying the judicial exception with, or to, an unconventional technological process" can provide significantly more.
Claim 1 recites a specific technical arrangement that includes:
Multi-API integration for contact center data ingestion Sophisticated video processing with multiple capture methods including OCR operations and panoramic selection
Dual-channel audio processing with automated PII redaction for security compliance
Al-driven clustering and generation using generative Al models with machine learning optimization
Automated simulation operations including "editor operations, scoring operations, and test operations"
Enterprise system integration with LMS xAPI for automated assignment and reporting
The specification at paragraph [0048] describes how this system "leverage[s] generative AI models (e.g. ChatGPT, Bing Chat, BARD, etc.) and Al chatbot(s) systems to improve simulations" in the specific context of automated training generation, representing an unconventional application.”
The Examiner respectfully disagrees. The Applicant’s argument is conclusory. The Applicant has failed to provide any evidence supporting either “an unconventional arrangement of known elements” or “an unconventional application.” As such, the argument is not persuasive.
B. Specific Technical Implementations Beyond Generic Computer Functions
The Applicant respectfully argues “The amended claims recite numerous specific technical implementations that exceed generic computer functions under MPEP § 2106.05(f):
Complex multi-modal processing pipeline: Claim 1 requires processing through screen creation tools with specific capture methods, ASR with PII redaction, metadata clustering, and AI generation
Specialized algorithms: The claim specifies "tag-matching algorithms that match QA and performance metrics to simulation tags" and "machine learning optimization algorithms"
Enterprise integrations: Specific API integrations for data ingestion and LMS xAPI integration for automated workflow management with specific functions: "assign educational exercises, send reporting data, and alert managers when action is required"
Security-conscious processing: The claim requires "automated personally identifiable information (PII) redaction" during speech-to-text processing
Automated operations suite: The claim specifies "implementing automated editor operations, scoring operations, and test operations for the simulations"
These limitations are more than "merely using a computer as a tool to perform an abstract idea" because they require specific technical implementations, integrations, and processing methods that solve technological problems in contact center training automation.”
The Examiner respectfully disagrees. Again, the Applicant’s argument is conclusory. The Applicant has failed to provide any evidence supporting either “specific technical implementations beyond generic computer functions,” or that “these limitations are more than "merely using a computer as a tool to perform an abstract idea" because they require specific technical implementations, integrations, and processing methods that solve technological problems in contact center training automation.” As such, the argument is not persuasive.
C. Measurable Improvement in Technology and Specific Industry Solution
The Applicant respectfully argues “The amended claims provide measurable technological improvements in a specific industry context. Under MPEP § 2106.05(a), improvements to technology or technological processes can indicate patent eligibility.
Claim 1 specifically recites that the method is "configured to reduce manual training development time" and that the "automated generation replaces manual storyboard creation and content development processes." The specification indicates this reduces development time "from weeks to hours" while eliminating processes that are "expensive" and "emotionally draining" (paragraphs [0002], [0030]).
The technical integration described in claim 1-from API ingestion through Al generation to LMS integration-represents a complete technological solution that automatically assigns training, sends reporting data, and alerts managers, creating an end-to-end automated workflow.
This is similar to the technological improvement recognized in Ancora Technologies, Inc. V. HTC America, Inc., 908 F.3d 1343 (Fed. Cir. 2018), where the court found patent eligibility in claims that provided a specific technological solution with measurable benefits.”
The Examiner respectfully disagrees. Once again, the Applicant’s argument is conclusory. The Applicant has failed to provide any evidence supporting “reduces development time "from weeks to hours" while eliminating processes that are "expensive" and "emotionally draining" (paragraphs [0002], [0030]).”
Furthermore, it is unclear how “eliminating processes that are "expensive" and "emotionally draining"” is a “measurable improvement in technology.” In other words, machines are not bound by cost and/or emotion.
Also, Ancora Technologies, Inc. V. HTC America, Inc., 908 F.3d 1343 (Fed. Cir. 2018), is not on point. First, the case dealt with storing data in the memory of a computer component that generally stores data” “a structure containing a license record is stored in a particular, modifiable, non-volatile portion of the computer’s BIOS, and the structure in that memory location is used for verification by interacting with the distinct computer memory that contains the program to be verified.” Factually, this is not on point with the Applicant’s current claim set. Second, Ancora Technologies, Inc. V. HTC America, Inc., 908 F.3d 1343 (Fed. Cir. 2018), was reversed at Step One of the subject-matter inquiry. Here, Step 2B is at issue, not Step One. As such, the argument is not persuasive.
D. Specific Technical Integration Requirements
The Applicant respectfully argues “The amended claims require specific technical integrations that constitute significantly more than abstract idea implementation:
API-level integration with third-party systems for automated data ingestion from contact center recordings;
Advanced processing integration combining video capture with OCR, dual-channel speech processing with PII redaction, and metadata clustering;
AI model integration with generative AI platforms and machine learning optimization algorithms; and
Enterprise system integration with LMS xAPI for automated workflow management including exercise assignment, reporting, and management alerts.
Under MPEP § 2106.05(f), these integrations represent "additional elements [that] amount to significantly more than the judicial exception" because they require specific technical implementations that solve technological problems through automated processes.”
The Examiner respectfully disagrees. Collecting and analyzing training data is not a real-world technological problem. The Applicant’s combination of generic hardware and software is not a technical solution. As such, the argument is not persuasive.
IV. DISTINGUISHING CITED CASE LAW
The Applicant respectfully argues “The Examiner's citation to Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) is distinguishable. In Electric Power Group, the claims merely collected and analyzed information without any specific technological implementation or improvement. Here, amended claim 1 recites:
Specific multi-API data ingestion processes from contact center recordings;
Complex multi-modal data processing with security features (automated PII
redaction);
Sophisticated Al-driven clustering and generation using generative AI models
with machine learning optimization;
Automated simulation creation with specific technical operations (editor,
scoring, test operations); and
Enterprise system integrations with automated workflow management for
exercise assignment, reporting, and management alerts.
The claims transform raw contact center data into a functionally different technological product (automated training simulations with LMS integration) through specific technical processes that provide measurable improvements in training development efficiency and automation.”
The Examiner respectfully disagrees. The Applicant’s claims are more focused on the abstract idea of automating training materials by gathering and analyzing data and displaying results, not on any specified technological improvement for performing these functions. The Applicant’s claims are simply automating the data collection and analysis process as it pertains to creating training material and alleviating any burden on mankind by doing so. The Applicant’s argument appears to center around alleviating a human burden of collecting and calculating data while using existing technologies without providing any actual technological advancements of those technologies.
Courts have long held that mathematical algorithms for performing calculations, without more, are patent ineligible under § 101. See, e.g., Parker v. Flook, 437 U.S. 584, 595 (1978) (“[I]f a claim is directed essentially to a method of calculating, using a mathematical formula, even if the solution is for a specific purpose, the claimed method is nonstatutory.” (internal citation omitted)); Gottschalk v. Benson, 409 U.S. 63, 71–72 (1972) (finding claims patent ineligible because they “would wholly pre-empt the mathematical formula and in practical effect would be a patent on the algorithm itself”) (See, e.g., the CAFC’s opinion in In Re: Board Of Trustees Of The Leland Stanford Junior University, slip. op., page 9). Further, the different use of a mathematical calculation, even one that yields different or better results, does not render patent eligible subject matter. (See, e.g., the CAFC’s opinion in In Re: Board Of Trustees Of The Leland Stanford Junior University, slip. op., page 11).
Finally, MPEP 2106.04(d)(1) provides examples of claims that improve technology and are not directed to a judicial exception: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a). Regardless, the Applicant is claiming generic computer components and algorithms at a high-level and provides no details of anything beyond ubiquitous standard equipment commercially available. As such, the argument is not persuasive. Therefore, the rejection of claims 1, 4, 6 and 19-27 under 35 U.S.C. §101 is not withdrawn.
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 extension fee 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 date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT P BULLINGTON whose telephone number is (313)446-4841. The examiner can normally be reached on Mon.-Fri. 8:00-4:00. 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, Peter Vasat, can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Robert P Bullington, Esq./
Primary Examiner, Art Unit 3715