DETAILED ACTION
Status of Claims
This communication is the final action on the merits in response to the amendments and arguments filed on July 21, 2026. Claims 1-20 were amended. Claims 1-20 are currently pending and have been examined.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-7 are directed to a machine. Claims 8-14 are directed to an article of manufacture. Claims 15-20 are directed to a process. As such, each claim is directed to a statutory category of invention.
Step 2A Prong 1
The examiner has identified independent Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent Claims 8 and 15.
Independent Claim 1 recites the following abstract ideas: “receive, from an interview subject, topic information associated with a topic; determine a classification based on the topic information, wherein the classification is determined based on a keyword associated with the topic information; identify at least one relevant question set based on the classification, wherein the at least one relevant question set comprises a set of questions relevant to the topic; provide the at least one relevant question set to the interview subject; in response to providing the at least one relevant question set to the interview subject: receive feedback from the interview subject, wherein the feedback is associated with relevance or organization of at least one question of the questions in the at least one relevant question set; update a likelihood that a question selection algorithm will identify the at least one relevant question associated with the feedback as relevant to the topic based on the feedback; update, using the updated likelihood of the question selection algorithm , the at least one relevant question set to generate an updated question set; and generate an interview template based on the updated question set; conduct an interview of the interview subject based on the interview template, wherein conducting the interview comprises providing the updated question set to the interview subject and capturing media content of the interview subject; determine, for each question of the updated question set and as the interview is conducted, a question delimiter in the media content identifying that an answer corresponding to a respective question of the updated question set is complete; associate the respective question delimiter to a portion of the media content that contains the respective question and the corresponding answer; demarcate, as the interview is conducted and based on the question delimiter, the media content associated with each question and the corresponding answer of the updated question set into one or more scenes including the respective question and the corresponding answer; generate the demarcated media content, comprises the demarcated media content associated with the one or more scenes, and comprises an introduction that includes a company name and logo and an ending that includes text or graphic information received from the interview subject; provide attributes to the interview subject, wherein the attributes vary depending on a size and an orientation of a screen ; and embed, responsive to receiving input , at least one of the one or more scenes
The limitations, as drafted, are a process that, under its broadest reasonable interpretation, relates to managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions (i.e., receive, from an interview subject, topic information associated with a topic; determine a classification based on the topic information, wherein the classification is determined based on a keyword associated with the topic information; identify at least one relevant question set based on the classification, wherein the at least one relevant question set comprises a set of questions relevant to the topic; provide the at least one relevant question set to the interview subject; in response to providing the at least one relevant question set to the interview subject: receive feedback from the interview subject, wherein the feedback is associated with relevance or organization of at least one question of the questions in the at least one relevant question set; update a likelihood that a question selection algorithm will identify the at least one relevant question associated with the feedback as relevant to the topic based on the feedback; update, using the updated likelihood of the question selection algorithm, the at least one relevant question set to generate an updated question set; and generate an interview template based on the updated question set; conduct an interview of the interview subject based on the interview template, wherein conducting the interview comprises providing the updated question set to the interview subject and capturing media content of the interview subject; determine, for each question of the updated question set and as the interview is conducted, a question delimiter in the media content identifying that an answer corresponding to a respective question of the updated question set is complete; associate the respective question delimiter to a portion of the media content that contains the respective question and the corresponding answer; demarcate, as the interview is conducted and based on the question delimiter, the media content associated with each question and the corresponding answer of the updated question set into one or more scenes including the respective question and the corresponding answer; generate the demarcated media content, comprises the demarcated media content associated with the one or more scenes, and comprises an introduction that includes a company name and logo and an ending that includes text or graphic information received from the interview subject; provide attributes to the interview subject, wherein the attributes vary depending on a size and an orientation of a screen; and embed, responsive to receiving input, at least one of the one or more scenes), but for the recitation of generic computer components (i.e., a device comprising memory storing instructions and one or more processors in communication with the memory, at least one trained machine learning model, generating and distributing a demarcated video, providing the demarcated video for download, a graphical user interface (GUI) including one or more GUI attributes, and embedding scenes on a website). If a claim limitation, under its broadest reasonable interpretation, relates to managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) 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 (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). In particular, the claim recites the additional elements of a device comprising memory storing instructions and one or more processors in communication with the memory, at least one trained machine learning model, generating and distributing a demarcated video, providing the demarcated video for download, a graphical user interface (GUI) including one or more GUI attributes, and embedding scenes on a website (in addition to the non-transitory CRM of Claim 8). The computer hardware is recited at a high level of generality (i.e., generic computers receiving and transmitting information; generic trained machine learning model identifying and transmitting information, and being updated based on feedback; generation, distribution, and download of a generic demarcated video, a generic GUI for receiving and displaying information, and inserting media on a website) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application, since they do not involve improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)), they do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and they do not apply or use the abstract idea in some other meaningful way beyond generally linking its use 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)). Therefore, the claim is directed to an abstract idea without a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of using computer hardware (a device comprising memory storing instructions and one or more processors in communication with the memory, at least one trained machine learning model, generating and distributing a demarcated video, providing the demarcated video for download, a graphical user interface (GUI) including one or more GUI attributes, and embedding scenes on a website (in addition to the non-transitory CRM of Claim 8)) amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claim is not patent-eligible.
Dependent claims 2, 4, 9, 11, 16, and 18 recite providing various items “for download.” Dependent claims 4, 11, and 18 recite “comprising at least one hyperlink.” Dependent claims 5, 12, and 19 recite “generate one or more hyperlinks” and “embed at least one of the one or more hyperlinks on a website.” The additional elements are generic technology (i.e., downloads and hyperlinks, embedding data on a website) used to implement the abstract idea, and they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Dependent claims 3, 6-7, 10, 13-14, 17, and 20 do not include any additional elements beyond those identified above. They further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. As such, they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Therefore, dependent claims 2-7, 9-14, and 16-20 are directed to an abstract idea, and do not include additional elements that integrate the abstract idea into a practical application, or that are sufficient to amount to significantly more than the abstract idea. Thus, the aforementioned claims are not patent-eligible.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101 set forth in this Office action.
Stewart, in combination with the other references relied upon, teaches receiving, from an interview subject, topic information associated with a topic, identifying, using at least one trained machine learning model, at least one relevant question set based on the topic information, wherein the at least one relevant question set comprises a set of questions relevant to the topic, providing the at least one relevant question set to the interview subject, receiving feedback from the interview subject, wherein the feedback is associated with relevance or organization of the questions in the at least one relevant question set, updating the at least one trained machine learning model based on the feedback, updating the at least one relevant question set, generating an interview template based on the at least one relevant question set, conducting an interview based on the interview template, wherein conducting the interview comprises providing the at least one relevant question set to the interview subject and capturing media content of the interview subject, generating a video based on the captured media content, wherein the video comprises an introduction that includes a company name and logo and an ending that includes text or graphic information received from the interview subject, and providing the video for download. However, the combination of references does not teach updating, using the updated at least one trained machine learning model, the at least one relevant question set to generate an updated question set, generating an interview template based on the updated question set, conduct an interview of the interview subject, wherein conducting the interview comprises providing the updated question set, demarcating, as the interview is conducted, the media content associated with each question of the updated question set, generating a demarcated video based on the demarcated media content, wherein the demarcated video comprises question delimiters corresponding to the demarcated media content associated with each question.
The closest NPL, “Automatic Skill-Oriented Question Generation and Recommendation for Intelligent Job Interviews,” teaches a system for automatically generating skill-oriented interview questions using machine learning. However, it does not teach receiving feedback from an interview subject and updating the question set to generate an updated question set, then conducting an interview of the interview subject comprising providing the updated question set, or capturing media content of the interview subject, demarcating the media content, and generating a demarcated video based on the demarcated media content.
Response to Arguments
Applicant’s Argument Regarding 35 USC 101 Rejection of Claims 1-20:
Step 2A, Prong One:
The claimed invention does not recite claim limitations relating to managing personal behavior, relationships, or interactions between people but for generic computer components. Regardless of whether the claimed invention merely involves managing personal behavior, relationships, or interactions, it is more properly understood to recite specific, unconventional, and technical limitations for generating and refining media content that improve the functioning of a computer.
Specifically, the claimed invention includes updating a likelihood that a question selection algorithm of at least one trained machine learning model will identify at least one question as relevant to a topic based on feedback associated with relevance and organization of the at least one question. The claimed invention then updates at least one relevant question set using the updated likelihood of the question selection algorithm. Updating a likelihood that a question selection algorithm of the at least one trained machine learning model will identify the at least one question associated with the feedback as relevant to the topic based on the feedback does not recite managing personal behavior or relationships or interactions between people whatsoever. Instead, the claimed invention performs a specific update to a question selection algorithm that alters a likelihood that a specialized machine learning model will identify at least one question as relevant to a topic. In other words, the claim recites a particular way in which a machine learning model may be refined to generate a question set without claiming the underlying concept of preparing a question set. These operations utilize specific feedback to adapt the logical structure of the machine learning model.
Similarly, amended claim 1 includes determining, for each question of the updated question set and as the interview is conducted, a question delimiter in the captured media content identifying that an answer corresponding to a respective question of the updated question set is complete, associating the question delimiter to the portion of the captured media content that contains the respective question and the corresponding answer, and demarcating, as the interview is conducted and based on the question delimiters, the media content associated with each question and the corresponding answer of the updated question set into one or more scenes including the question and the corresponding answer. These limitations are directed to specific computational operations that improve how the device generates the demarcated video. As provided in the specification, a content generator may associate question delimiters to media (i.e., audiovisual content), where the question delimiters may include edit points for audiovisual media that are used to define sections of media that include one or more interview questions and the associated answers. See the pending Application at paragraph [0031].
Accordingly, the question delimiters are a specific technological improvement, such that the device may demarcate the media content into scenes containing the questions and corresponding answers as an interview is conducted. That is, determining question delimiters and mathematically demarcating digital media content in real time is not directed to certain methods of human activity relating to conducting an interview. Instead, this specific technical configuration performs real-time automated scene segmentation and embeds structural question delimiters directly into the generated video file.
Furthermore, amended claim 1 includes additional limitations reciting specific computational steps. Particularly, amended claim 1 recites determining a classification based on the topic information, wherein the classification is determined based on a keyword associated with the topic information, and identifying, using at least one trained machine learning model, at least one relevant question set based on the classification. These limitations set forth precise data pre-processing for input to the machine learning model. Thus, the limitation recites a specific technical sequence for automated data parsing and classification mapping unrelated to certain methods of organizing human activity. Additionally, amended claim 1 recites providing a graphical user interface (GUI) including one or more GUI attributes to the interview subject, wherein the GUI attributes vary depending on a size and an orientation of a screen on which the GUI is provided, and wherein the interview subject may distribute the demarcated video via the GUI. This limitation defines structural constraints that adapt data presentation based on the physical characteristics of the display hardware, rather than reciting a method of organizing human activity. Programmatically varying interface attributes to account for screen size and orientation recites a specific automated function that alters the visual delivery of data based on the underlying hardware environment-not an abstract idea relating to managing personal behavior, relationships, or interactions between people but for generic computer components.
Moreover, the device of claim 1 embeds, responsive to receiving input at the GUI, at least one of the one or more scenes on a website. Embedding at least one of the scenes on a website involves automated post-processing steps following generation of the demarcated video to convert the video to an embeddable format. For example, this limitation may involve adding HTML markups to the one or more scenes in order to provide a copy-and-paste-ready code version of the one or more scenes that may be inserted in an HTML field on a page of the website.
In sum, the claim recites automated keyword-based classification mapping, a particular way in which a machine learning model may be refined to generate a question set, specific hardware-dependent user interface adaptations, and improvements in generating and embedding a demarcated video in a manner specific to computer technology, while-at most-the claim merely involves concepts relating to conducting an interview. Therefore, the claimed invention is directed to a technological improvement and not any abstract idea underlying it.
Step 2A, Prong Two:
The Claims Provide a Technological Solution to a Technological Problem:
Amended claim 1 provides particular technological solutions for parsing user inputs to extract keyword-based classifications, using those classifications within a machine learning model to optimize question relevance, generating a set of questions relevant to a topic via machine learning and, demarcating media content in real time as an interview is conducted, adapting interface attributes dynamically across diverse hardware environments, and automating structured media distribution.
The at least one trained machine learning model of amended claim 1 reflects the sort of technical improvement that the USPTO Appeals Review Panel identified in Ex Parte Desjardins. In Desjardins, the Panel vacated a 35 U.S.C. § 101 rejection involving machine learning, holding that an improvement to how a machine learning model operates is patent eligible. The Panel cautioned that examiners must not equate any machine learning with an unpatentable algorithm and the remaining additional elements as generic computer components.
The claimed device includes a machine learning model specialized to improve the relevance of questions included in question sets generated by the claimed device. As described in portions of the pending Application relating to the limitations of amended claim 1, the question selection algorithm may recommend question sets based on classification information using a question selection algorithm. See the pending Application at paragraph [0091]. The claimed device may update the question selection algorithm based on training, for example by updating the likelihood that the machine learning model will recommend the question in the future based on similar selection criteria. See the pending Application at paragraph [0091].
Accordingly, the claims do not merely reflect the general use of machine learning to generate a question set. The claim recites a device tailored to solve the problem of generating question sets including questions relevant to a topic by integrating a uniquely structured closed-loop machine learning model. The claims recite receiving specific feedback associated with relevance or organization of at least one question of a question set and updating a likelihood that the question selection algorithm of the at least one trained machine learning model will identify that at least one question associated with the feedback is relevant to a topic. Thus, the device is structured in a particular way to improve machine learning as it applies to a device for generating relevant questions, and not merely the general application of machine learning to content generation. Therefore, the guidance of Desjardins underscores that the device of amended claim 1 constitutes an improvement in technology or a technical field.
Moreover, determining, for each question of the updated question set and as the interview is conducted, a question delimiter in the captured media content identifying that an answer corresponding to a respective question of the updated question set is complete is not an improvement to the abstract idea of certain methods of organizing human activity. On the contrary, determining question delimiters in captured media content and demarcating the media content based on the question delimiters as the interview is conducted represents specific technical improvements to computer technology. That is, determining question delimiters and mathematically demarcating digital media content in real time is not directed to an improvement to human activity relating to conducting an interview. It is a particular way to achieve a desired outcome.
Furthermore, the claimed device provides a hardware-responsive solution to user interface presentation challenges by providing a GUI with attributes that vary depending on a size and an orientation of a screen on which the GUI is provided. Interview subjects may interface with the device across diverse hardware platforms, including smartphones, personal computers, and tablets, and the claimed device responsively varies the GUI attributes depending on screen size and orientation of the hardware platform. Rather than claiming an abstract idea relating to certain methods of organizing human activity, the claim recites a technology-based solutions to technology-rooted problems relating to visual display of GUI attributes. Based on the foregoing, Applicant respectfully submits that the claims provide a technical solution to a technical problem, integrating any judicial exception into a practical application.
The Claimed Limitations Are Not Mere "Apply It" Instructions:
"[A]dditional elements are more than 'apply it' or are not 'mere instructions' when the claim recites a technological solution to a technological problem. MPEP § 2106.05(f)(1). As discussed above, the limitations of amended claim 1 are highly particular. The claim does not merely provide instructions for conducting an interview and generating a demarcated video. Instead, it details a distinct and specialized application of machine learning tailored to improve the identification of questions as relevant to a topic. The claimed invention does not use the at least one trained machine learning model in a generic manner but instead recites an improvement to operation of the at least one trained machine learning model itself. That is, updating a likelihood that a question selection algorithm of the at least one trained machine learning model will identify at least one question associated with feedback as relevant to a topic is a specific improvement to the operation of the at least one trained machine learning model for a particular purpose-not using a machine learning model in its ordinary capacity or training a model in a generic manner.
Additionally, the claimed invention applies a particular solution of determining question delimiters that the system uses to demarcate media content as an interview is conducted. This solution extends well beyond instructions to apply a generic computer to carry out certain methods of organizing human activity. On the contrary, determining the question delimiter in captured media content and associating the question delimiter to the portion of the captured media content that contains the respective question and the corresponding answer represents a specific mechanism with which the claimed device demarcates the media content. That is, determining question delimiters and mathematically demarcating digital media content in real time is a highly specific data processing operation. For at least these reasons, Applicant respectfully submits that the claims are not merely "apply it" instructions but instead include limitations that capture specific solutions to a technical problem.
The Claims Do Not Monopolize an Abstract Idea:
A claim integrates a judicial exception into a practical application by applying the judicial exception in a manner that imposes a meaningful limit on the exception, ensuring that the claim does not monopolize or preempt the abstract idea. See MPEP § 2106.04(d). Amended claim 1 is directed to a device that implements keyword-based classification mapping, integrates machine learning into interview question selection, provides variable GUI attributes depending on size and orientation of a screen on which the GUI is provided, demarcates media content as an interview is conducted, and embeds at least one scene of the one or more scenes of the demarcated media content on a website. The claimed invention applies specific updates to a question selection algorithm of a machine learning model and uses the updated question selection algorithm to generate an updated question set. Thus, the claimed device is intrinsically tied to a specific implementation of an iterative feedback loop configured to improve generation of a question set for an interview via a specialized machine learning model. The claimed invention then uses improvements in computer technology to determine question delimiters and mathematically demarcate digital media content in real-time. Thus, the claimed invention does not preempt or monopolize general concepts related to conducting an interview, the generation of media content associated with questions and corresponding answers, or even the general use of machine learning to generate interview content. On the contrary, the claimed device is limited to a particular and non-preemptive solution for classification mapping based on a keyword from topic information, leveraging machine learning to improve relevance of question set to a particular topic, providing variable GUI attributes to an interview subject, embedding scenes of an interview on a website, and improving computer technology to demarcate media content in real time based on delimiters that indicate the portion of the captured media content that contains the respective question and the corresponding answer.
In conclusion, the claim recites a particular way in which a machine learning model may be refined to generate a question set, specific hardware-dependent user interface adaptations, and improvements in generating a demarcated video in a manner specific to computer technology. These claim limitations are directed to technological improvements and not any abstract idea underlying them. Moreover, the claimed invention integrates any alleged judicial exception into a practical application by reciting a particular technological improvement to a device that performs keyword classification, applies machine learning to interview question selection, adapts GUI attributes dynamically across diverse hardware environments, automates structured media distribution, and demarcates media content in real time as an interview is conducted. The claims detail a distinct and specialized device tailored to update a likelihood that a question selection algorithm identifies at least one question as relevant to a topic based on received feedback. The device determines question delimiters that associate and demarcate captured media content into scenes that include questions and corresponding answers as the interview is conducted. These limitations far exceed merely applying a generic computer and/or generic machine learning model to an abstract idea. On the contrary, the claimed invention includes specific and concrete limitations that represent a technological solution, going beyond any alleged judicial exception to improve question selection of the machine learning model and execute highly specific, real-time demarcation of media content. Therefore, the claims are not directed to a judicial exception under Step 2A, Prong Two, and the rejection under 35 U.S.C. § 101 should be withdrawn.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive.
Step 2A, Prong One:
Updating a likelihood of identifying a question as relevant to a topic based on feedback associated with relevance and organization of the question set, then updating the question set, are steps that are part of the abstract idea itself. The machine learning model used to implement the abstract idea is recited at a high level of generality, as a generic trained machine learning model being updated with feedback to produce better output. Regarding the question selection algorithm, machine learning algorithms are what allow machine learning models to adjust their behavior and become more accurate as they receive more data. The present invention is using machine learning algorithms and machine learning models in their ordinary capacity, and to implement the abstract idea of identifying a question set and updating the question set based on relevance.
The steps of determining, for each question of the updated question set and as the interview is conducted, a question delimiter in the captured media content identifying that an answer corresponding to a respective question of the updated question set is complete, associating the question delimiter to the portion of the captured media content that contains the respective question and the corresponding answer, and demarcating, as the interview is conducted and based on the question delimiters, the media content associated with each question and the corresponding answer of the updated question set into one or more scenes including the question and the corresponding answer, are all part of the abstract idea. The implementation of the steps on a computer is using a generic computer to implement the abstract idea. Further, paragraph [0040] of the specification recites that a user can “at least partially define question delimiters 220 (e.g., when a "next question" command is received via a resource such as playback controls 430, an endpoint and a start point may be defined within the associated media content).” This further puts the question delimiters into the abstract idea, as the question delimiters are simply identifiers of when one question ends and another one begins, and further which a user can identify, rather than the computer or system doing this identification. If the question delimiters provide any improvement, it is an improvement to the abstract idea itself, and not a technical improvement to any technology. The demarcation of content into scenes containing the questions and corresponding answers as an interview is conducted is part of the abstract idea. Again, the device is a generic computer used to implement the abstract idea, without any improvement to the functioning of the computer itself.
The steps of determining a classification based on the topic information, wherein the classification is determined based on a keyword associated with the topic information, and identifying at least one relevant question set based on the classification are all part of the abstract idea. The machine learning model is used as a generic model, receiving input and generating output based on the input, to implement the abstract idea.
Regarding the GUI and GUI attributes, the interface is described as a generic interface used to implement the abstract idea, for displaying and receiving information. As for the GUI attributes, the extent to which the specification describes these is in paragraph [0037], which recites that “the GUI attributes may vary depending on attributes such as screen size, orientation, etc. As another example, GUI attributes may vary depending on platform (e.g., a web-based portal, an application installed on a user device, etc.).” The attributes are generic user interface elements, and their size and orientation vary depending on the screen they are being viewed on, which is analogous to, for example, viewing a website on a smartphone in a vertical orientation, versus viewing the website on the smartphone in a horizontal orientation, versus viewing the website on a laptop screen. The present claims do not provide any technical improvement to interface technology or any other technology. Regarding the interview subject distributing the demarcated video via the GUI, this is also using the GUI as a generic interface for receiving and displaying information, to implement the abstract idea of distributing information, such as demarcated question and answer sets, from one user to other users.
Regarding the embedding of the scenes on a website, this is also using a computer and website in a generic manner, and to implement the abstract idea of distributing information from one user to other users, without any technical improvement to computers or websites. Regarding Applicant’s argument that embedding the scenes on a website “involves automated post-processing steps following generation of the demarcated video to convert the video to an embeddable format,” the specification does not provide any details of how the claimed invention provides any technical improvement to video formatting or embedding technology.
Step 2A, Prong Two:
The present claims are not analogous to Desjardins. The claims of Desjardins provided a technical solution to the technical problem of “catastrophic forgetting” in machine learning, and the specification provided sufficient details of how the claimed invention provided this solution. The present claims do not provide a technical improvement to machine learning models. Rather, the machine learning model is used in a generic manner, as a model trained with training data, receiving input, generating output based on the input, receiving feedback, and updating the output based on the feedback. Further, the specification does not provide any details of how the claimed invention provides any improvement to the functioning of machine learning models.
As previously stated, the steps of determining, for each question of the updated question set and as the interview is conducted, a question delimiter in the captured media content identifying that an answer corresponding to a respective question of the updated question set is complete, and demarcating the media content based on the question delimiters as the interview is conducted, are all part of the abstract idea. The implementation of the steps on a computer is using a generic computer to implement the abstract idea.
Regarding the GUI and GUI attributes, also as previously stated, , the interface is described as a generic interface used to implement the abstract idea, for displaying and receiving information, and the GUI attributes are generic user interface elements, and their size and orientation vary depending on the screen they are being viewed on, which is analogous to, for example, viewing a website on a smartphone in a vertical orientation, versus viewing the website on the smartphone in a horizontal orientation, versus viewing the website on a laptop screen. The present claims do not provide any technical improvement to interface technology, nor does the specification provide any details of how the claimed invention provides any technical improvement to interface technology.
Regarding Applicant’s argument that the claimed invention recites “an improvement to operation of the at least one trained machine learning model itself,” the improvement is an improvement regarding generating updated and more relevant output from the model in response to user feedback, which is the result of using the machine learning model in its ordinary capacity, and not the result of a technical improvement to the functioning of machine learning models. Further, the specification does not provide any details of how the claimed invention provides any technical improvement to the functioning of machine learning models.
The claimed invention does not pertain to an improvement in the functioning of the computer, or machine learning model, or interface, or any other technology or technical field. Thus, the additional elements do not integrate the abstract idea into a practical application.
Conclusion
THIS ACTION IS MADE FINAL. 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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/KARMA A EL-CHANTI/Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
