DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/25/2026 has been entered.
Status of Claims
Claims 4-5, 8, 11-17, and 19-20 are canceled.
Claims 1 and 18 are independent claims.
Claims 1, 3, 6-7, 9, and 18 are amended.
Claims 1-3, 6-7, 9-10 and 18 are pending in this application and are presented for examining on merits.
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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §102 and §103 (or as subject to pre-AIA 35 U.S.C. §102 and §103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Gustman et al., US Pub. No. 2023/0274481 A1 (hereinafter as “Gustman”, in view of YAMAMOTO et al., US Pub. No. 2020/0275062 A1 (hereinafter as “Yamamoto”), and in view of Thirachengode Vajravel et al., US Pub. No. 2025/0117526 A1 (hereinafter as “Vajravel”), and further in view of Fahrendorff et al., US Pub. No. 2021/0056860 A1 (hereinafter as “Fahrendorff”), and Byron et al., US Pub. No. 2018/0033325 A1 (hereinafter as “Byron”).
Regarding claim 1, Gustman teaches: a method, comprising:
acquiring, by a computer system over a computer network (Fig. 2A), a first captured video recording and first metadata (par. [0050]: “In operation, at 302, the rich metadata generation circuit 242 can acquire image data. According to various implementations, the image data can be acquired by enabling a user to upload an image, to capture the image in real-time (e.g., in a live feed or live capture mode), browse for an image, and so forth. In some implementations, the image is extracted, at 304, from a sequence or collection of images, such as a video. Operations 302-314 can be performed in any suitable order. For instance, conversational sessions can be initiated by a user searching for a particular image or feature or by the system by generating, at 306, one or more question prompts regarding the parsed image feature. As another example, a user narrative used to generate rich metadata, at 310, can be captured, at 308, before, during, or after acquiring image data. As yet another example, rich metadata can be generated, at 310, and bound to a particular image feature, at 312, before, during, or after generating an augmented (enriched) image at 314.”; and par. [0058-59] e.g., “…example tools can include units of computer-executable code for editing of time metadata, geo-locator metadata, adding text, recording audio, recording video, transcribing audio and video, adding subtitles, adding closed-captions and/or providing automatic translations”).
acquiring, by the computer system over the computer network (Fig. 2A), a second captured video recording and second metadata (same teaching algorithm in par. [0050] for “second” live video capture/record with rich metadata; and par. [0053] e.g., “the record/review tools (e.g., camera, microphone, etc.), adjust environmental factors (e.g., brightness), zoom level(s), and so on”; and par. [0055] “a video of a user narrating, a computer-generated avatar of the user narrating, and/or a photograph of a user. These elements can be captured using a camera associated with a user device, retrieved from the user device or a social media, archival, or other image retrieval network or system (based on, for example, logged-in user information), and/or generated for the user in static or animated avatar form”);
performing, by the computer system, a first content segmentation action based on the first captured video recording (pars. [0050, 55] and [0058-59] via capture/record video as explained/quoted above), the first metadata, and a content segmentation model to produce first individual data for the first individual (par. [0070] e.g., “the suggested text annotation 712b is dynamically supplemented with tokens (sentence segments) parsed from user responses as the user answers the question prompts”; see claim 2 via “parsing the image from the live feed prior to identifying the image feature” teaches the content segmentation model to approach the user=individual answer/data).
Gustman does not explicitly teach amended claim limitations: “continuously using a field worker camera installed on a helmet of a first individual”, “using a training event camera that is installed in a meeting room and by recording a live training event of a plurality of employees of an organization”, “performing, by the computer system, a second content segmentation action based on the second captured video recording, the second metadata, and video frame processing to produce attendance status data for the live training event; wherein the video frame processing determines whether the first individual attended the live training event based on the first individual appearing in one or more video frames within the second captured video recording; determining, by the computer system and from the first individual data and the attendance status data, a first skillset for the first individual using a natural language processing (NLP) model and a skill description; generating, by the computer system, a content map that associates each skill in the first skillset to at least one content resource of a content repository; and recommending, by the computer system, a first content resource from the content repository for the first individual based on the first skillset and the content map.”
In the same field of endeavor (i.e., data processing), Yamamoto teaches the amended claim feature: “continuously using a field worker camera installed on a helmet of a first individual” (par. [0047] “an external appearance when the video recording device is mounted. In the work support, a worker 400 often wears a helmet, and thus an example is described in which the video recording device 1000 is connected to the helmet”; and par. [0069] “ state in which a worker 400 wearing the helmet 300 provided with the video recording device 1000 is performing a board work”).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of the cited references because the teachings of Yamamoto would have provided Gustman with a field worker camera installed on a helmet for allowing a skill artisan in motivation to perform the video captured using the camera mounts on the field worker’s helmet for acquiring the video recording (Yamamoto: pars. [0047-49, and 0101]).
Gustman and Yamamoto do not explicitly teach amended claim limitations: “using a training event camera that is installed in a meeting room and by recording a live training event of a plurality of employees of an organization”, “performing, by the computer system, a second content segmentation action based on the second captured video recording, the second metadata, and video frame processing to produce attendance status data for the live training event; wherein the video frame processing determines whether the first individual attended the live training event based on the first individual appearing in one or more video frames within the second captured video recording; determining, by the computer system and from the first individual data and the attendance status data, a first skillset for the first individual using a natural language processing (NLP) model and a skill description; generating, by the computer system, a content map that associates each skill in the first skillset to at least one content resource of a content repository; and recommending, by the computer system, a first content resource from the content repository for the first individual based on the first skillset and the content map.”
In the same field of endeavor (i.e., data processing), Vajravel teaches: “using a training event camera that is installed in a meeting room and by recording a live training event of a plurality of employees of an organization” (Fig. 1; and Fig. 5, element 100; and pars. [0034] e.g., “… meetings, broadcasts, training events”, [0047] “receive images obtained with one or more camera(s) located in room 100 and to share those images with remote participants of a remote meeting (e.g., as live-video images)”). Furthermore, Vajravel teaches, discloses, or expressly suggests claim limitations:
performing, by the computer system, a second content segmentation action (par. [0253] via “segmentation”) based on the second captured video recording, the second metadata, and video frame processing (par. [0240] “the capture video frames that occlude the object and/or its contents, prior to transmitting the modified video frames (now stripped of sensitive information) to remote participants”) to produce attendance status data for the live training event (pars. [0034] e.g., “…meetings, broadcasts, training events”, [0047] “receive images obtained with one or more camera(s) located in room 100 and to share those images with remote participants of a remote meeting (e.g., as live-video images)”, [0160] “an attendance policy may determine when (e.g., how often, contextual triggers, etc.) and how (e.g., by ultrasound) to take attendance or otherwise record the presence or absence of participants during a remote meeting”);
wherein the video frame processing determines whether the first individual attended the live training event based on the first individual appearing in one or more video frames within the second captured video recording (pars. [0047] “ receive images obtained with one or more camera(s) located in room 100 and to share those images with remote participants of a remote meeting (e.g., as live-video images)”, and [0160] “an attendance policy may determine when (e.g., how often, contextual triggers, etc.) and how (e.g., by ultrasound) to take attendance or otherwise record the presence or absence of participants during a remote meeting”; and par. [0240] “the capture video frames that occlude the object and/or its contents, prior to transmitting the modified video frames (now stripped of sensitive information) to remote participants”).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of the cited references because the teachings of Vajravel would have provided Gustman and Yamamoto with the above indicated limitation for allowing a skill artisan in motivation to perform monitoring the attendance of participant(s) in the conference room with the remote live broadcast meetings of training events (Vajravel: pars. [0034, 47, 160, 240]).
Gustman and Vajravel, in combination, teach the captured video recording and metadata relate to the participant/individual/attendee in the live training event in the remote live conference room (Gustman: Figs. 1, 3A, and 4A-4B; and pars. [0050 and 53]; and Vajravel: pars. [0034, 47, 160, 240]). However, Gustman, Yamamoto, and Vajravel do not explicitly teach the amended limitations: “determining a first skillset for the first individual using a natural language processing (NLP) model and a skill description; generating, by the computer system, a content map that associates each skill in the first skillset to at least one content resource of a content repository; and recommending, by the computer system, a first content resource from the content repository for the first individual based on the first skillset and the content map.”
In the same field of endeavor (i.e., data processing), Fahrendorff teaches: “determining a first skillset for the first individual using a natural language processing (NLP) model and a skill description” (pars. [0026] via an “NLP module”, [0125] “converts content into actionable items. This can be done by the stakeholders OR automatically using NLP and machine learning”; par. [0209] “Methods of the current embodiment include skillset matching for the purposes of including proper personnel in cross-functional and self-directed environments…”; par. [0211] “When a meeting is proposed, the system refers to a database for the skillset-related keyword matches, i.e., the meeting agenda, title, and skillset arrays based on previous meetings is leveraged.”, and par. [0212]: “If a participant is not available, the system pushes similar skillsets highlighting expertise not previously known, based on user profiles created by users themselves and/or management (résumés, performance reviews, Social Media, etc.).”).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of the cited references because the teachings of Fahrendorff would have provided Gustman, Yamamoto, and Vajravel with the above indicated limitation for allowing a skill artisan in motivation to using NLP model and skill description to determine the set of skills for the user/individual.
Gustman, Yamamoto, Vajravel, Fahrendorff do not explicitly teach: “generating, by the computer system, a content map that associates each skill in the first skillset to at least one content resource of a content repository; and recommending, by the computer system, a first content resource from the content repository for the first individual based on the first skillset and the content map.”
In the same field of endeavor (i.e., data processing), Byron teaches:
generating, by the computer system, a content map that associates each skill in the first skillset to at least one content resource of a content repository (Fig. 3, elements 306, 310, 312, 318, and 250; par. [0050]: “…user skills content 310 is selected is a matter of design choice. The resulting features are then processed with the selected subset of user skills content 310 to generate or update an associated user skills competency profile 314, which in turn is stored in a repository of user skills content 312”; and par. [0051] “user skills competency profile 314 is then retrieved from the repository of user skills content 312 and processed with the previously-generated features to identify matching instructional content 302 stored in, or referenced by, the repository of instructional content 306…” and par. [0054], wherein the matching instructional content is interpreted as the content map; and par. [0056] “the matching instructional content 302 is ranked according to how closely the skills it references matches the user's 308 proficiency or competence in those skills.”); and
recommending, by the computer system, a first content resource from the content repository for the first individual based on the first skillset and the content map (Fig. 3, element 250; par. [0025] “the server performance information 15B may include operational metric data relating to the available processing resources at the question prioritization system 10 and/or QA system 100”; pars. [0054 and 56] teaches the skillset and the content map, and [0058-59] via “recommended instruction content 318” and “the recommendation assessment input 320 relates to an evaluation of whether the recommended instructional content 318 met the user’s 308 expectations”).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of the cited references because the teachings of Byron would have provided Gustman, Yamamoto, Vajravel, Fahrendorff with the above indicated limitation for allowing a skill artisan in motivation to generated content map that associates each skill in the first skillset to at least one content resource of a content repository and recommending the content resource from content repository for the user/individual (Byron: Fig. 3, Figs. 5-7; pars. [0005-6], and [0054-56, and 58-59]).
Regarding claim 2, Gustman and Vajravel, in combination, teach: “wherein the first individual data comprises an application usage of the first individual” (Gustman: par. [0085] “ characteristics of a particular user device and/or video player (e.g., device type, operating system version, browser version, video player version, screen size, application window state (e.g., maximized, minimized)) are considered during the dynamic rendering.”; and Vajravel: par. [0035] discloses specific application usage);
Regarding claim 3, Gustman and Byron, in combination, teach: wherein the first skillset (Byron: Fig. 3, element 310 via user skills content; par. [0041-42]) comprises:
a first weight associated with a first metric (Byron: par. [0032] e.g., “generates a score”; par. [0033]: “The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm. Each resulting score is then weighted against a statistical model.”);
a second metric comprising a description and a value (Gustman: par. [0030] e.g., “key-value pair”; par. [0032] “ summation function which combines the values of all its inputs together”; and Byron: pars.[0025], e.g., “metric data”, [0049] and [0053] teaches the description and attribute values); and
a second weight associated with the second metric (Byron: par. [0032] e.g., “generates a score”; par. [0033]: “The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm. Each resulting score is then weighted against a statistical model.”), and
wherein the first content resource is recommended based on a first score that is a weighted average of the first metric and the second metric based on the first weight and second weight (Byron: Fig. 3, element 250; par. [0025] “the server performance information 15B may include operational metric data relating to the available processing resources at the question prioritization system 10 and/or QA system 100”; par. [0032] e.g., “generates a score”; par. [0033]: “The scores obtained from the various reasoning algorithms… Each resulting score is then weighted against a statistical model.”).
Independent claim 18 is rejected in the analysis of above independent claim 1, and therefore, the claim is rejected on that basis.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Gustman, Yamamoto, Vajravel, Fahrendorff, and Byron, and further in view of Dadkhahnikoo et al., US Pub. No. 2019/0311790 A1(hereinafter as “Dadkhahnikoo”).
Regarding claim 6, the claim is rejected by the same reasons set forth above to claim 1. However, Gustman, Yamamoto, Vajravel, Fahrendorff, and Byron do not teach: “a global vectors for word representation (GloVe) algorithm configured to determine a first word embedding representation and a second word embedding representation”.
In the same field of endeavor (i.e., data processing), Dadkhahnikoo teaches: “a global vectors for word representation (GloVe) algorithm configured to determine a first word embedding representation and a second word embedding representation” (see par. [0031] “…word2vec or GloVe”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of the cited references because the teachings of Dadkhahnikoo would have provided Gustman, Yamamoto, Vajravel, Fahrendorff, and Byron with the above indicated limitation for allowing a skill artisan in motivation to perform embedding word vector(s) in the AI system (Dadkhahnikoo: Figs. 1, 6-7; and pars. [0030-32]).
Regarding claim 7, the claim is rejected by the same reasons set forth above to claims 1 and 6. Furthermore, Byron teaches: “wherein the skill description is a first textual description comprising at least one word, and wherein the first individual data comprises text data comprising at least one word” (pars. [0049] and [0053]: “ the generated features may include the words “prepare,” “pie dough,” and “food processor.” The user's 308 associated user skills competency profile 314 is then retrieved from the repository of user skills content 312 and processed with the previously-generated features to identify matching instructional content 302 stored in, or referenced by, the repository of instructional content 306. To continue the previous example, the features “prepare,” “pie dough,” and “food processor” may be used to retrieve a subset of instructional content 302 that references the preparation of pie dough, the use of a food processor, or any combination thereof”).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Gustman, Yamamoto, Vajravel, Fahrendorff, and Byron, and further in view of Dow et al., US Pub. No. 2013/0041876 (hereinafter as “Dow”).
Regarding claim 9, the claim is rejected by the same reasons set forth above to claim 1. However, Gustman, Yamamoto, Vajravel, Fahrendorff, and Byron do not explicitly teach: “determining a similarity metric using an artificial intelligence (AI) model, wherein the similarity metric is used to recommend the first content resource.”
In the same field of endeavor (i.e., data processing), Dow teaches: “determining a similarity metric using an artificial intelligence (AI) model, wherein the similarity metric is used to recommend the first content resource” (Fig. 1, element 110 for “Recommendation component”; par. [0023] e.g., “Similar links can be recommended to users that have shared a link, wherein various similarity metrics can be utilized to determine similarity between users and links as discussed further below. Various techniques can be utilized with respect to recommending similar links such as user-based top-N (N is a positive integer) recommendation and link-based top-N recommendation”; and par. [0031] e.g., “computing a similarity metric”; and par. [0055] “include or consist of artificial intelligence, machine learning, or knowledge or rule-based components, sub-components, processes, means, methodologies, or mechanisms (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, classifiers . . . )…”).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to combine the teachings of the cited references because the teachings of Dow would have provided Gustman, Yamamoto, Vajravel, Fahrendorff, and Byron with the above indicated limitation for allowing a skill artisan in motivation to facilitate identified links to recommend content/resource to user (Dow: Fig. 1, and pars. [0031 and 55])
Regarding claim 10, the claim is rejected by the same reasons set forth above to claims 1 and 9. Furthermore, Dow teaches: “wherein the AI model is a clustering model” (see again par. [0055] “artificial intelligence…”; and pars. [0035] and [0037] disclose “clustered” and “clusters”/ “clustering” technique/model).
Response to Arguments
Referring to claim rejections under 35 U.S.C. §101 as being abstract ideas,
Applicant's arguments (Remarks, pages 8-14) have been fully considered, and are persuasive in view of newly amended limitations. Thus, the rejections have been removed.
Referring to claim rejections under 35 U.S.C. §103, Applicant’s arguments (Remarks, pages 14-15) with respect to claim 1 (same as to claim 18) have been fully considered but are moot in view of the new grounds of rejection necessitated by applicant's amendment to the claims. Applicant's newly amended features are taught implicitly, expressly, or impliedly by the prior art of record. Please see rejections set forth above for details.
The other claims argued merely because of a dependency on a previously argued claims in the brief presented to the examiner, filed on 06/25/2026, are moot in view of the examiner's interpretation of the claims and art and are still considered rejected based on their respective rejections from at least a prior Office action (part(s) of recited again above).
Prior Arts
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968)); Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jessica N. Le whose telephone number is (571)270-1009. The examiner can normally be reached M-F 9:30 am - 5:30 pm (EST).
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/Jessica N Le/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
