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 is a nonfinal rejection in response to claims filed on 05/30/2025. Claims 1-20 are pending and are examined herein.
Priority
The present application has provisional application #63/654,265 filed on 05/31/2024, which is the effective filing date of the present disclosure.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/30/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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: Is the claim to a Process, Machine, Manufacture, or Composition of Matter?
Claims 1-6, 18: A video-assisted networking platform for networking by users with accounts on the platform, comprising: a host network platform server; a host networking platform software installed on the server and configured for access by each of the users;
Claims 7-12, 19: A video-assisted networking platform for candidates and recruiters, each with accounts on the platform, comprising: a host network platform server; a host networking platform software installed on the server and configured for access by each of the candidates and recruiters;
Claims 13-17, 20: A method of networking using a video-assisted networking platform, the networking platform for networking by candidates and recruiters, each with accounts on the platform, comprising: accessing the networking platform by use of one of the accounts of the recruiters on the platform, the platform comprising a host networking platform software installed on a host network platform server;
Claims 1-6, & 18 and Claims 7-12, & 19 are directed to a video-assisted networking platform installed on a host network platform server, which in [0038] of the specification is recited as any suitable device, suitable for “running continuously and reliably with standard power(e.g., 120V AC), therefore it falls under at least one potentially eligible subject matter category, particularly “machine,” or “manufacture.” Claims 13-17 & 20 recite a method which falls within “process.” Therefore, the claims are to be further analyzed under Step 2.
Step 2a Prong 1: Is the claim reciting a Judicial Exception(A Law of Nature, a Natural Phenomenon (Product of Nature), or An Abstract Idea?)
Representative claims 1, 7, and 13 are analyzed for consideration as an abstract idea and have been marked up accordingly, wherein additional elements are italicized and the abstract idea is in bold:
Claim 1: A video-assisted networking platform for networking by users with accounts on the platform, comprising:
a host network platform server;
a host networking platform software installed on the server and configured for access by each of the users; and
a plurality of video resumes stored on the server, each video resume authored by one of the users and comprising searchable user-authored content in a video resume format specified by the platform software,
wherein the host networking platform software is configured such that each video resume can be generated and modified by one of the users,
wherein the host networking platform software is configured such that one or more of the users can evaluate each of the plurality of video resumes based at least in part on the searchable user-authored content of each of the video resumes,
wherein the searchable user-authored content is derived from raw user-authored video content,
wherein the host networking platform software is configured such that each of the plurality of video resumes can be authenticated by one of the users, and
further wherein the host networking platform software is configured with a conversion component capable of converting the raw user-authored video content into the searchable user- authored content.
Claim 7: A video-assisted networking platform for candidates and recruiters, each with accounts on the platform, comprising:
a host network platform server;
a host networking platform software installed on the server and configured for access by each of the candidates and recruiters; and
a plurality of video resumes stored on the server, each video resume authored by one of the candidates and comprising searchable user-authored content in a video resume format specified by the platform software, the searchable content comprising textual content,
wherein the host networking platform software is configured such that each video resume can be generated and modified by one or more of the candidates,
wherein the host networking platform software is configured such that each of the plurality of video resumes can be authenticated by one of the candidates,
wherein the host networking platform software is configured such that one of the recruiters associated with an open position can evaluate each of the plurality of video resumes against the open position based at least in part on the searchable user-authored content of each of the plurality of video resumes,
wherein the textual content comprises a plurality of resume topic headings configured to organize the searchable user-authored content, and
further wherein the resume topic headings can be based on input from the one of the recruiters.
Claim 13: A method of networking using a video-assisted networking platform, the networking platform for networking by candidates and recruiters, each with accounts on the platform, comprising:
accessing the networking platform by use of one of the accounts of the recruiters on the platform, the platform comprising a host networking platform software installed on a host network platform server;
designating a video resume format on the platform software based at least in part on an open position, the designating conducted during the step of accessing the networking platform by use of one of the accounts of the recruiters;
accessing the networking platform by use of one of the accounts of the candidates;
recording a video resume using the host networking platform software for storage on the host network platform server, the recording conducted during the step of accessing the networking platform by use of one of the accounts of the candidates;
evaluating the video resume against the open position using the host networking software and by accessing the networking platform by use of the one of the accounts of the recruiters; and
authenticating the video resume using the host networking platform software, the authenticating conducted during the step of accessing the networking platform by use of one of the accounts of the candidate,
wherein the designating a video resume format step is conducted before the recording a video resume step,
wherein the video resume comprises searchable user-authored content derived from raw user-authored video content in a video resume format provided by the host networking platform software, and
further wherein the evaluating the video resume step is conducted based at least in part on the searchable user-authored content of the video resume.
When analyzing the limitations in bold under their broadest reasonable interpretation in light of the specification, the limitations are merely recitations of a judicial exception under the abstract idea category of certain methods of organizing human activity, particularly managing personal behavior or relationships or interactions between people. According to MPEP 2106.04(a)(2)(II)(C), "managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions. As claimed, the limitations recite steps that consist of social networking on a video-assisted networking platform for candidates and recruiters. Each of the steps in bold merely recite activities that fall within “social activities, teaching, and following rules or instructions.” For example, in claim 1, allowing users to generate, store, and modify video content, even when in a specific video resume format, is merely a facilitation of interactions between people. Furthermore, the steps of “evaluating” the video resumes, and “authenticating” the video resumes are recited at such a high level of generality that they encompass any manner of performing the evaluation and authentication, including as mere instructions to manage personal behavior. Users can evaluate the video resume against the open position, and authenticate the video, using manual techniques that are longstanding in the field of job recruiting. Furthermore, deriving searchable user-authored content from the raw user-authored video content is recited at a high level of breadth, such that its broadest reasonable interpretation (BRI) includes any manner of deriving searchable information.
The examiner further notes that whether these interactions are directly between people or are conveyed through a computer does not impact their categorization under “certain methods of organizing human activity.” MPEP 2106.04(a)(2)(II) states, “ Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping.” Therefore, a system such as the claimed invention which recites interactions between a user and a system, wherein the user inputs video resumes, evaluates video resumes against open positions, authenticates video resumes, and derives searchable user-authored content still falls under “certain methods of organizing human activity.”
Therefore, all of the recitations of an abstract idea within the claims above have been identified in bold, along with an explanation for why they fall under the “certain methods of organizing human activity” category. Therefore, the claims at least recite one abstract idea and are to be further analyzed under Step 2A Prong 2 and Step 2B.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The abstract idea is not integrated into a practical application due to the following additional elements:
- host networking platform software (claims 1, 7, 13)
- host network platform server; (claims 1, 7, 13)
The additional elements listed above, when considered individually and in combination with the claim as a whole, no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on generic computing components as outlined in MPEP 2106.05(f). These additional elements are not considered to have integrated the abstract idea into a practical application because they merely recite components being used as a tool to execute the abstract idea.
More specifically, the abstract idea of “social networking on a video-assisted networking platform for candidates and recruiters” are merely instructed to be implemented as software steps on generic computing components such as “software” and “server. ” Furthermore, merely specifying the transmitting step to be done electronically, restricting the abstract idea to be performed “automatically” (for example, but not limited to “auto-populating”) or limiting the abstract idea to be performed on an application program are all equivalents of “apply it” or mere instructions to perform the abstract idea on a generic computer or on a technological environment. Please see MPEP 2106.05(f) for more guidance.
Finally, even when considering all of the additional elements individually or as an ordered combination, the claims still do not integrate the abstract idea into a practical application. Even when considering all the computing components in combination, there is no improved computing infrastructure or improvement to computer functionality beyond what is already capable of being performed by a generic computer. MPEP 2106.05(a)(I) states, “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool.” Therefore, the addition of general purpose computers added post-hoc to the abstract idea, which reflects the present claim language, does not qualify as a “specific implementation of a solution to a problem in the software arts.”
Therefore, representative claims 1, 7, and 13 are directed to an abstract idea because the additional elements fail to integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The additional elements of the claim have not been found to be significantly more than the abstract idea for the same reasons set forth in Step 2A Prong 2. The additional elements are repeated:
- host networking platform software (claims 1, 7, 13)
- host network platform server; (claims 1, 7, 13)
The additional elements, for the same reasons as stated in Prong 2, also fail to provide significantly more, whether considered individually or as an ordered combination. More specifically because merely using generic computing components such as “software” and “server” to perform the abstract idea of “social networking on a video-assisted networking platform for candidates and recruiters” does not provide significantly more than mere instructions to implement an abstract idea or other exception on generic computing components.
Furthermore, the claims also do not recite improvements to other technology or technical field because they do not recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. See MPEP § 2106.05(a). Even when viewed as a whole, nothing in the claims meaningfully limits the abstract idea such that it is significantly more (an inventive concept). Even when viewed as a whole, all of the functionalities are either part of the abstract idea, or are merely instructed to be performed on a generic computer, and fail to expand computer functionality or provide any improvements to a particular technological environment or technical field.
Therefore representative claims 1, 7, and 13 are directed to an abstract idea without significantly more, and are thereby patent ineligible.
Dependent Claims 2-6, 8-12, and 14-20 are also given the full two part analysis individually, and in combination with the claims they depend upon in the following analysis.
-Claims 2-5, 9, 10, and 14-16 merely further limit the abstract idea by further limiting the searchable user-authored content to comprise textual content, such as “resume topic headings (claim 3 & 15)”, “textual passages (claims 5 & 10),” and “textual content and processed video content (claims 2, 4, 9, 14, and 16). These are still more of the same abstract idea because it merely describes the processing of “transcribing” or even merely organizing the video data into textual content manually. In this case, the claims still fall within “managing personal behavior, or interactions, or relationships” between people, particularly since they are merely instructions or a set of rules to manage personal behavior. Furthermore, there are no further additional elements to consider, and even when considering these additional limitations with the previous additional elements, they are still an example of “apply it” or mere instructions to perform the abstract idea on a generic computer (MPEP 2106.05(f)). Even when viewed as a whole, nothing in the claims meaningfully limit the abstract idea such that it is directed to significantly more.
-Claims 6, 12, and 17 further limit the abstract idea by evaluating the raw-video content with a machine learning component against a communication-oriented criterion. Evaluating the video content under a communication-oriented criterion is merely managing personal behavior or interactions between people, because it is merely instructing users to analyze the behavior. The additional element of using a “machine learning component” to perform this task is merely an “apply it” level element because it merely instructs the task to be performed using machine learning without specifically limiting how machine learning is used to accomplished this task. This is equivalent to reciting the idea of a solution or outcome without the necessary steps or mechanism to arrive at the solution(MPEP 2106.05(f)). Furthermore, it is also a general link to the technological field of machine learning as it merely invokes machine learning by name without meaningfully limiting its use on the abstract idea. Whether considering this additional element individually or as an ordered combination, the claims still fail to integrate the abstract idea to a practical application because using generic machine learning on a general purpose computer to perform the abstract idea is equivalent to “apply it.” Even when viewed as a whole, nothing meaningfully limits the use of the additional elements on the abstract idea such that it is significantly more.
-Claims 8 further limits claim 7 by converting the raw user-authored video content into the searchable user-authored content using a “conversion component.” However, this is “conversion component” is interpreted to be part of the abstract idea because it is not limited to computer functionality or a particular structure, therefore, it is merely a part of the workflow of the platform instructing the conversion of data. Therefore, it is no more than “managing personal behavior, interactions, or relationships between people,” since the conversion of the information can be inputted manually as instructions to a user to perform the conversion. Furthermore, there are no further additional elements to consider, and even when considering these additional limitations with the previous additional elements, they are still an example of “apply it” or mere instructions to perform the abstract idea on a generic computer (MPEP 2106.05(f)). Even when viewed as a whole, nothing in the claims meaningfully limit the abstract idea such that it is directed to significantly more.
-Claim 11 further limits the abstract idea by adding “digital resumes” corresponding to the video resumes, which can be evaluated against the open position. Other than reciting that the resumes are “digital” this limitation merely recites the use of traditional, paper-based resumes to analyze against the open positions, therefore it is more of the same abstract idea of “managing personal behavior, or interactions, or relationships between people.” The additional element of the resumes being “digital” is merely equivalent to “apply it” as it is an indication that the abstract idea is performed on generic computing infrastructure. Whether considering this additional element individually or as an ordered combination, the claims still fail to integrate the abstract idea to a practical application because using generic machine learning on a general purpose computer to perform the abstract idea is equivalent to “apply it.” Even when viewed as a whole, nothing meaningfully limits the use of the additional elements on the abstract idea such that it is significantly more.
-Claims 18, 19, and 20 merely limits the authentication of the video content to be performed by ensuring that the video content is generated in one take. The concept of recording in one take is merely part of the abstract idea as it is merely a rule for how to “manage personal behavior, or interactions between people.” The “one take” limitation is claimed in a manner such that it encapsulates any manner of recording in one take, not a specific additional element that technologically adds computer functionality to ensure that the video is recorded in one take. Thus, there are no further additional elements to consider, and even when considering these additional limitations with the previous additional elements, they are still an example of “apply it” or mere instructions to perform the abstract idea on a generic computer (MPEP 2106.05(f)). Even when viewed as a whole, nothing in the claims meaningfully limit the abstract idea such that it is directed to significantly more.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Stewart et al. (US 11663397 B1) hereinafter Stewart, in view of Bilodeau et al. (US 20140317009 A1) hereinafter Bilodeau.
Regarding Claim 1:
Stewart teaches:
A video-assisted networking platform for networking by users with accounts on the platform, comprising: (Stewart [Col. 9 Lines 4-13] With continued reference to FIG. 1, computing device 104 is configured to receive a posting datum 112. For the purpose of this disclosure, “posting datum” is information related to an available and/or open job position. For the purposes of this disclosure, a “job position” (also referred to in this disclosure as a “job”) is a paid occupation with designated tasks associated therewith. A job position may include an employment with an employer, such as work as an employee (part-time or full-time), worker, contractor, self-employed, and the like. [Col. 9 Lines 51-54] In other embodiments, posting datum 112 may be provided to computing device 104 by a database over a network from, for example, a network-based platform.)
a host network platform server; (Stewart [Col. 9 Lines 57-61] In other embodiments, posting datum 112 may be communicated from a third-party application, such as from a third-party application on a third-party server, using a network. For example, posting datum 112 may be downloaded from a hosting website for job listings.)
a host networking platform software installed on the server and configured for access by each of the users; and(Stewart [Col. 10 Lines 15-30]In another example, and without limitation, database 132 may be remote to computing device 104 and communicative with computing device 104 by way of one or more networks. A network may include, but is not limited to, a cloud network, a mesh network, and the like. By way of example, a “cloud-based” system can refer to a system which includes software and/or data which is stored, managed, and/or processed on a network of remote servers hosted in the “cloud,” e.g., via the Internet, rather than on local severs or personal computers. A “mesh network” as used in this disclosure is a local network topology in which the infrastructure computing device 104 connect directly, dynamically, and non-hierarchically to as many other computing devices as possible. A “network topology” as used in this disclosure is an arrangement of elements of a communication network. )
a plurality of video resumes stored on the server, each video resume authored by one of the users and (Stewart [Col. 11 Lines 9-18] With continued reference to FIG. 1, computing device 104 is further configured to receive a user datum 108, as previously mentioned. For the purposes of this disclosure, “user datum” is personal user information and/or attributes relevant to a job position of a posting. User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, [Col. 14 Lines 19-31] In one or more embodiments, record recommendation 116 may include a video component, audio components, text components, and combination thereof, and the like. For instance, and without limitation, customized record may include a video resume. As used in this disclosure, a “video resume” is a video in visual and/or audio form to provide a recording promoting a jobseeker for employment, such as for a particular job position. In some cases, video resume may include content that is representative or communicative of an at least attribute of a subject, such as a user. As used in this disclosure, a “subject” is a person such as, for example a jobseeker.)
- (each video resume) comprising searchable user-authored content in a video resume format specified by the platform software,(Stewart [Col. 12 Lines 39-45 ]A target video resume may be representative of generic information related to posting data. For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. As used in this disclosure, “credentials” are any piece of information that indicates an individual's qualification to perform a certain task or job. [Col. 14 Lines 34-49] As used in this disclosure, an “image component” may be a visual representation of information, such as a plurality of temporally sequential frames and/or pictures, related to video resume and target video resume. For example, image component may include animations, still imagery, recorded video, and the like. Attributes may include subject's skills, competencies, credentials, talents, and the like. In some cases, attributes may be explicitly conveyed within video resume. Alternatively, or additionally, in some cases, attributes may be conveyed implicitly with video resume. Video resume may include a digital video. Digital video may be compressed to optimize speed and/or cost of transmission of video. Videos may be compressed according to a video compression coding format (i.e., codec). Exemplary video compression codecs include H.26x codecs, MPEG formats, VVC, SVT-AV1, and the like. [Col. 12 Lines 10-14] For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like.) Labels, attributes, and identifying indicia are all examples of searchable user-authored content.
wherein the host networking platform software is configured such that each video resume can be generated and modified by one of the users, (Stewart [Col. 18-24] Digital posting match recommendation also maximizes efficiency of the job application process by allowing a user to rapidly customize their resume based on requirements and qualifications posted by for the employment position as well as optimizing a jobseeker's performance during various stages of the job application process, such as during a job interview. [Col. 11 Lines 63-64] In one or more embodiments, record recommendation 116 may include suggested recommendations for a video resume.)
wherein the searchable user-authored content is derived from raw user-authored video content,(Stewart [Col. 14 Line 55 – Col. 15 Line 17] In some cases, computing device 104 may include audiovisual speech recognition (AVSR) processes to recognize verbal content in a video resume. For example, computing device 104 may use image content to aid in recognition of audible verbal content such as viewing user move their lips to speak on video to process the audio content of video resume. AVSR may use image component to aid the overall translation of the audio verbal content of video resumes. In some embodiments, AVSR may include techniques employing image processing capabilities in lip reading to aid speech recognition processes. In some cases, AVSR may be used to decode (i.e. recognize) indeterministic phonemes or help in forming a preponderance among probabilistic candidates. In some cases, AVSR may include an audio-based automatic speech recognition process and an image-based automatic speech recognition process. AVSR may combine results from both processes with feature fusion. Audio-based speech recognition process may analysis audio according to any method described herein, for instance using a Mel frequency cepstral coefficients (MFCCs) and/or log-Mel spectrogram derived from raw audio samples. Image-based speech recognition may perform feature recognition to yield an image vector. In some cases, feature recognition may include any feature recognition process described in this disclosure, for example a variant of a convolutional neural network. In some cases, AVSR employs both an audio datum and an image datum to recognize verbal content. For instance, audio vector and image vector may each be concatenated and used to predict speech made by a user, who is “on camera.”) This audio-based and image-based speech recognition technique satisfies the limitation because it turns the video data into verbal/textual information.
- wherein the host networking platform software is configured such that each of the plurality of video resumes can be authenticated by one of the users, and(Stewart [Col. 12 Lines 6-14] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. [Col. 24 Lines 43-55] For example, and without limitation, a plurality of resumes of a user may be preserved in an order that the resumes were submitted by the user or generated by processor 140 upon a query or request by user. Temporally sequential listing may be accessible at any of various security settings. For instance, and without limitation, temporally sequential listing may be readable and modifiable publicly, may be publicly readable but writable only by entities and/or devices having access privileges established by password protection, confidence level, or any device authentication procedure or facilities described herein, or may be readable and/or writable only by entities and/or devices having such access privileges.)
further wherein the host networking platform software is configured with a conversion component capable of converting the raw user-authored video content into the searchable user- authored content. (Stewart [Col. 15 Lines 18-27] In some cases, computing device 104 may be configured to recognize at least a keyword as a function of visual verbal content. In some cases, recognizing at least keyword may include an optical character recognition (OCR). In some cases, computing device 104 may transcribe much or even substantially all verbal content from target resume video. Similarly for textual resumes, such as written resumes, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. [Col. 14 Lines 55-57] In some cases, computing device 104 may include audiovisual speech recognition (AVSR) processes to recognize verbal content in a video resume. [Col. 14 Lines 39-43] Attributes may include subject's skills, competencies, credentials, talents, and the like. In some cases, attributes may be explicitly conveyed within video resume. Alternatively, or additionally, in some cases, attributes may be conveyed implicitly with video resume.) The AVSR and transcription capabilities satisfy the limitation “conversion component capable of...” as it turns the raw audio-visual content into textual information such as attributes.
However, Stewart fails to teach or suggest:
- wherein the host networking platform software is configured such that one or more of the users can evaluate each of the plurality of video resumes based at least in part on the searchable user-authored content of each of the video resumes,(In Stewart, the video resume is automatically evaluated using the machine learning classifiers, however, there is no recited step of recruiters evaluating the video resumes or their content.)
Alternatively, Bilodeau teaches:
- wherein the host networking platform software is configured such that one or more of the users can evaluate each of the plurality of video resumes (Bilodeau [0047] Process 200D begins at step 201D and immediately proceeds to step 202D. In step 202D, user 101a creates a profile and chooses various facets such as, but not limited to, employment field of interest, geographic region of interest, relevant skills possessed, prior work history, resume, video profile, video interview, social media content, or any other facet useful in creating a profile. Process 200D then proceeds to step 203D where user 101a may elect to show or hide multiple versions of any of the facets such as, for example, only allowing certain users to view a particular resume or set of responses to a video interview. [0048] Process 200D then proceeds to step 204D when a user submits a request to system 100 by, for example, using a mouse to click on the user's profile. System 100 then identifies the type of user requesting to view the profile of user 101a and proceeds to step 205D where system 100 filters the facets to only display the appropriate facets based on the user attempting to access the profile.) In Bilodeau, other users are able to access and evaluate the user profile (which includes a resume and video profile).
- based at least in part on the searchable user-authored content of each of the video resumes,(Bilodeau [0009] In an aspect, systems, methods, and computer program products for facilitating the job interview process are disclosed that includes a job database, an interview database, and a candidate database that users, such as job seekers and employers, can use to search for other users. For example, a first user may be a job seeker searching for a job and a second user may be an employer searching for a prospective employee. [0078] Referring now to FIG. 2N, a flow chart of an exemplary process 200N for allowing recruiters to search candidates based on the transcription of audio or video recordings, or social media posts, according to an aspect of the present disclosure, is shown. [0079] Process 200N begins at step 201N and immediately proceeds to step 202N. In step 202N, user 101a provides a post on a social media site or update feed, or uploads an audio or video recording such as a video interview. Then, in step 203N, system 100 transcribes the audio and video files into searchable text documents. The transcribing of the documents can be done manually or using an automated method supported by system 100. Then, in step 204N, system 100 compiles and stores the transcribed documents and the social media posts in candidate database 105, which in step 205N, user 101b is now able to search while attempting to find potential candidates for a particular job posting. Process 200N then terminates at step 206N.) Since the video posts/video interviews are transcribed into searchable text documents, then the combination satisfies the limitation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart’s system by adding the features of allowing other users to evaluate the video resumes of users based on the searchable user-authored content of the video resumes as taught by Bilodeau. By simply substituting Stewart’s video resumes for the video interviews/video profiles in Bilodeau, one would have arrived at the combined teachings above. One of ordinary skill in the art would have been motivated to perform this combination by Bilodeau’s benefits as followed: (Bilodeau [0010] In an aspect, the system would provide the first user and the second user the ability to fully perform the interview process by, for example, testing the first user's qualifications; interviewing the first user, including the ability to match automated questions to interview subjects; grading the qualification tests; transcribing audio and video; analyzing non-verbal cues; and enabling game-like scoring for these activities.)
Regarding Claim 2:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 1,
Furthermore, Stewart teaches:
-wherein the searchable user-authored content comprises textual content.(Stewart [Col. 15 Lines 18-48] In some cases, computing device 104 may be configured to recognize at least a keyword as a function of visual verbal content. In some cases, recognizing at least keyword may include an optical character recognition (OCR). In some cases, computing device 104 may transcribe much or even substantially all verbal content from target resume video. Similarly for textual resumes, such as written resumes, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine-learning processes... A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters.)
Regarding Claim 3:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 2,
Furthermore, Stewart teaches:
- wherein the textual content comprises a plurality of resume topic headings configured to organize the searchable user-authored content. (Stewart [Col. 12 Lines 41-43] For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. [Col. 14 Lines 7-13] For example, a format of a resume may vary depending on the field of the job position. For example, and without limitation, categorization, syntax, grammar, section titles, headers, font, margins, and the like may vary depending on the field or area of the job position, such as in the field of engineering, advertisement, medicine, acting, and the like. [Col. 14 Lines 39-40] Attributes may include subject's skills, competencies, credentials, talents, and the like. [Col. 18 Lines 46-49] As a non-limiting example, training data classifier 216 may classify elements of training data to according to fields of job description for instance, title, role, organization, requisite experience, requisite credentials, and the like. [Col. 11 Lines 11-23] For the purposes of this disclosure, “user datum” is personal user information and/or attributes relevant to a job position of a posting. User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, personal address, social security number, phone number, employment history, experience level, education, certification, acquired skills, geographical location, expected compensation, job performance acknowledgements (e.g., awards, honors, distinguishments), photograph of user, sample work product, and the like.) The broadest reasonable interpretation (BRI) of resume topic headings in view of the the instant specification [0046] covers “Such resume topic headings 72 can include, for example, a statement, education, work experience, interests, and/or headings suitable to organize a resume, profile or curriculum vitae.” These align with the attributes, categories, and fields, determined by the classifier in Stewart.
Regarding Claim 4:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 3,
Furthermore, Stewart teaches:
- wherein the searchable user-authored content consists of the textual content and processed video content derived from the raw user-authored video content. (Stewart [Col. 11 Lines 13-24] User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, personal address, social security number, phone number, employment history, experience level, education, certification, acquired skills, geographical location, expected compensation, job performance acknowledgements (e.g., awards, honors, distinguishments), photograph of user, sample work product, and the like. [Col. 12 Lines 6-17] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes.)
Regarding Claim 5:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 3,
Stewart further teaches:
- wherein the textual content further comprises processed textual passages derived from the raw user-authored video content, and (Stewart [Col. 15 Lines 18-30] In some cases, computing device 104 may be configured to recognize at least a keyword as a function of visual verbal content. In some cases, recognizing at least keyword may include an optical character recognition (OCR). In some cases, computing device 104 may transcribe much or even substantially all verbal content from target resume video. Similarly for textual resumes, such as written resumes, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine-learning processes.) A transcription of the video resume is an example of “processed textual passages.”
- wherein the searchable user-authored content consists of the textual content. (Stewart [Col. 15 Lines 31-38] Still referring to FIG. 1, in some cases, OCR may include post-processing. For example, OCR accuracy may be increased, in some cases, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters.)
Regarding Claim 6:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 1,
Furthermore, Stewart teaches:
- evaluate the raw user-authored video content of each of the plurality of video resumes with a machine learning component against a criterion.(Stewart [Col. 12 Lines 14-45] Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes. In some embodiments, computing device 104 may utilize a candidate classifier, which may include any classifier used throughout this disclosure, to run an initial pass over the video elements of video resumes, break down and categorizes such elements before comparing it to target video resume. A “classifier,” as used in this disclosure is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like. As used in this disclosure, a “candidate classifier” is a classifier that classifies users to a target resume or a job position description. In some cases, candidate classifier may include a trained machine-learning model, which is trained using candidate training data. As used in this disclosure, “candidate training data” is a training data that correlates one or more of users and user datum to one or more job descriptions, description-specific data, and posting data. A target video resume may be representative of generic information related to posting data. For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. As used in this disclosure, “credentials” are any piece of information that indicates an individual's qualification to perform a certain task or job.)
However, Stewart fails to teach:
- wherein the host networking platform software is further configured such that a user can evaluate (Stewart does not teach that the user performs the evaluation with a machine learning component)
- the evaluation is against a communication-oriented criterion.
Bilodeau teaches or suggests:
- wherein the host networking platform software is further configured such that a user can evaluate(Bilodeau [0024] factor(s) as will be apparent to those skilled in the relevant art(s) after reading the description herein; uploading new job postings; taking tests; interviewing; watching interviews; rating interviews; [0082] In step 204O, the metrics are then used as input into an algorithm that calculates the likelihood that user 101a possesses certain personality traits such as, but not limited to, sincerity, confidence, introversion, or any other trait that may be associated with the non-verbal cues. Then in step 205O, the algorithm produces scores along each trait which indicate how much of each trait that user 101a is estimated to possess based on the analysis of the non-verbal cues. In step 206O, system 100 compiles the calculated scores and produces a report which, in step 207O, is provided to user 101a and user 101b.) In this case, the user is directly involved with the evaluation of the video resume.
- the evaluation is against a communication-oriented criterion. (Bilodeau [0080] Referring now to FIG. 2O, a flow chart of an exemplary process 200O for analyzing video of a user answering interview questions to determine non-verbal communication cues, according to an aspect of the present disclosure, is shown. [0081] Process 200O begins at step 201O and immediately proceeds to step 202O. In step 202O, user 101a uploads an audio or video file, participates in a video interview posted by user 101b or performs any other action that results in an audio or video file being available for system 100 to analyze. In step 203O, system 100 analyzes the audio file, video file, or any combination of the two, to determine non-verbal cues such as, but not limited to, speech timber, pitch, posture, gestures, eye contact, eye direction or any other non-verbal cues that may be useful in hiring determination metrics.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart’s machine learning classifier, to additional perform the features of Bilodeau to be based on non-verbal communication criterion using machine learning. It would have been obvious to one of ordinary skill in the art to predictably arrive at the claimed limitation as machine learning classifiers are capable of identifying speech, timber, pitch, posture, eye contact, and other visual stimuli. One would have been motivated to combine because of the benefit of Bilodeau’s system of expanding the types of traits that can be analyzed for compatibility in hiring. (Bilodeau [0082] In step 204O, the metrics are then used as input into an algorithm that calculates the likelihood that user 101a possesses certain personality traits such as, but not limited to, sincerity, confidence, introversion, or any other trait that may be associated with the non-verbal cues. Then in step 205O, the algorithm produces scores along each trait which indicate how much of each trait that user 101a is estimated to possess based on the analysis of the non-verbal cues.)
Regarding Claim 7:
Stewart teaches:
A video-assisted networking platform for candidates and recruiters, each with accounts on the platform, comprising: (Stewart [Col. 9 Lines 4-13] With continued reference to FIG. 1, computing device 104 is configured to receive a posting datum 112. For the purpose of this disclosure, “posting datum” is information related to an available and/or open job position. For the purposes of this disclosure, a “job position” (also referred to in this disclosure as a “job”) is a paid occupation with designated tasks associated therewith. A job position may include an employment with an employer, such as work as an employee (part-time or full-time), worker, contractor, self-employed, and the like. [Col. 9 Lines 51-54] In other embodiments, posting datum 112 may be provided to computing device 104 by a database over a network from, for example, a network-based platform.)
a host network platform server; (Stewart [Col. 9 Lines 57-61] In other embodiments, posting datum 112 may be communicated from a third-party application, such as from a third-party application on a third-party server, using a network. For example, posting datum 112 may be downloaded from a hosting website for job listings.)
a host networking platform software installed on the server and configured for access by each of the candidates and recruiters; and(Stewart [Col. 10 Lines 15-30]In another example, and without limitation, database 132 may be remote to computing device 104 and communicative with computing device 104 by way of one or more networks. A network may include, but is not limited to, a cloud network, a mesh network, and the like. By way of example, a “cloud-based” system can refer to a system which includes software and/or data which is stored, managed, and/or processed on a network of remote servers hosted in the “cloud,” e.g., via the Internet, rather than on local severs or personal computers. A “mesh network” as used in this disclosure is a local network topology in which the infrastructure computing device 104 connect directly, dynamically, and non-hierarchically to as many other computing devices as possible. A “network topology” as used in this disclosure is an arrangement of elements of a communication network. [Col. 9 Lines 7-21] For the purposes of this disclosure, a “job position” (also referred to in this disclosure as a “job”) is a paid occupation with designated tasks associated therewith. A job position may include an employment with an employer, such as work as an employee (part-time or full-time), worker, contractor, self-employed, and the like. For example, and without limitation, posting datum 112 may include information and/or data from a job posting and/or listing that describes an open job position. Posting datum 112 may include a job position title, qualifications and/or requirements for the job position, expected responsibilities associated with the job position, benefits with the job position, compensation, geographical location, employer information, and the like.)
a plurality of video resumes stored on the server, each video resume authored by one of the candidates and (Stewart [Col. 11 Lines 9-18] With continued reference to FIG. 1, computing device 104 is further configured to receive a user datum 108, as previously mentioned. For the purposes of this disclosure, “user datum” is personal user information and/or attributes relevant to a job position of a posting. User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, [Col. 14 Lines 19-31] In one or more embodiments, record recommendation 116 may include a video component, audio components, text components, and combination thereof, and the like. For instance, and without limitation, customized record may include a video resume. As used in this disclosure, a “video resume” is a video in visual and/or audio form to provide a recording promoting a jobseeker for employment, such as for a particular job position. In some cases, video resume may include content that is representative or communicative of an at least attribute of a subject, such as a user. As used in this disclosure, a “subject” is a person such as, for example a jobseeker.)
- comprising searchable user-authored content in a video resume format specified by the platform software, the searchable content comprising textual content, (Stewart [Col. 12 Lines 39-45 ]A target video resume may be representative of generic information related to posting data. For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. As used in this disclosure, “credentials” are any piece of information that indicates an individual's qualification to perform a certain task or job. [Col. 14 Lines 34-49] As used in this disclosure, an “image component” may be a visual representation of information, such as a plurality of temporally sequential frames and/or pictures, related to video resume and target video resume. For example, image component may include animations, still imagery, recorded video, and the like. Attributes may include subject's skills, competencies, credentials, talents, and the like. In some cases, attributes may be explicitly conveyed within video resume. Alternatively, or additionally, in some cases, attributes may be conveyed implicitly with video resume. Video resume may include a digital video. Digital video may be compressed to optimize speed and/or cost of transmission of video. Videos may be compressed according to a video compression coding format (i.e., codec). Exemplary video compression codecs include H.26x codecs, MPEG formats, VVC, SVT-AV1, and the like. [Col. 12 Lines 10-14] For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like.) Labels, attributes, and identifying indicia are all examples of searchable user-authored content.
wherein the host networking platform software is configured such that each video resume can be generated and modified by one or more of the candidates, (Stewart [Col. 18-24] Digital posting match recommendation also maximizes efficiency of the job application process by allowing a user to rapidly customize their resume based on requirements and qualifications posted by for the employment position as well as optimizing a jobseeker's performance during various stages of the job application process, such as during a job interview. [Col. 11 Lines 63-64] In one or more embodiments, record recommendation 116 may include suggested recommendations for a video resume.)
wherein the host networking platform software is configured such that each of the plurality of video resumes can be authenticated by one of the candidates, (Stewart [Col. 12 Lines 6-14] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. [Col. 24 Lines 43-55] For example, and without limitation, a plurality of resumes of a user may be preserved in an order that the resumes were submitted by the user or generated by processor 140 upon a query or request by user. Temporally sequential listing may be accessible at any of various security settings. For instance, and without limitation, temporally sequential listing may be readable and modifiable publicly, may be publicly readable but writable only by entities and/or devices having access privileges established by password protection, confidence level, or any device authentication procedure or facilities described herein, or may be readable and/or writable only by entities and/or devices having such access privileges.)
wherein the textual content comprises a plurality of resume topic headings configured to organize the searchable user-authored content, and(Stewart [Col. 12 Lines 41-43] For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. [Col. 14 Lines 7-13] For example, a format of a resume may vary depending on the field of the job position. For example, and without limitation, categorization, syntax, grammar, section titles, headers, font, margins, and the like may vary depending on the field or area of the job position, such as in the field of engineering, advertisement, medicine, acting, and the like. [Col. 14 Lines 39-40] Attributes may include subject's skills, competencies, credentials, talents, and the like. [Col. 18 Lines 46-49] As a non-limiting example, training data classifier 216 may classify elements of training data to according to fields of job description for instance, title, role, organization, requisite experience, requisite credentials, and the like. [Col. 11 Lines 11-23] For the purposes of this disclosure, “user datum” is personal user information and/or attributes relevant to a job position of a posting. User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, personal address, social security number, phone number, employment history, experience level, education, certification, acquired skills, geographical location, expected compensation, job performance acknowledgements (e.g., awards, honors, distinguishments), photograph of user, sample work product, and the like.) The broadest reasonable interpretation (BRI) of resume topic headings in view of the the instant specification [0046] covers “Such resume topic headings 72 can include, for example, a statement, education, work experience, interests, and/or headings suitable to organize a resume, profile or curriculum vitae.” These align with the attributes, categories, and fields, determined by the classifier in Stewart.
However, Stewart fails to teach:
- wherein the host networking platform software is configured such that one of the recruiters associated with an open position can evaluate each of the plurality of video resumes against the open position
- based at least in part on the searchable user-authored content of each of the plurality of video resumes,
- further wherein the resume topic headings can be based on input from the one of the recruiters.
Alternatively, Bilodeau teaches:
- wherein the host networking platform software is configured such that one of the recruiters associated with an open position can evaluate each of the plurality of video resumes against the open position (Bilodeau [0047] Process 200D begins at step 201D and immediately proceeds to step 202D. In step 202D, user 101a creates a profile and chooses various facets such as, but not limited to, employment field of interest, geographic region of interest, relevant skills possessed, prior work history, resume, video profile, video interview, social media content, or any other facet useful in creating a profile. Process 200D then proceeds to step 203D where user 101a may elect to show or hide multiple versions of any of the facets such as, for example, only allowing certain users to view a particular resume or set of responses to a video interview. [0048] Process 200D then proceeds to step 204D when a user submits a request to system 100 by, for example, using a mouse to click on the user's profile. System 100 then identifies the type of user requesting to view the profile of user 101a and proceeds to step 205D where system 100 filters the facets to only display the appropriate facets based on the user attempting to access the profile. [0052] In step 204E, system 100 compares the broadcasted information of all users and matches the qualifications of the job-seeking users with the requirements of the job-posting users in order to "match" them to one another. System 100 may then record the matches across multiple physical encounters to build a history of the people or entities that each user has been matched with over time.) In Bilodeau, other users are able to access and evaluate the user profile (which includes a resume and video profile).
- based at least in part on the searchable user-authored content of each of the plurality of video resumes, (Bilodeau [0009] In an aspect, systems, methods, and computer program products for facilitating the job interview process are disclosed that includes a job database, an interview database, and a candidate database that users, such as job seekers and employers, can use to search for other users. For example, a first user may be a job seeker searching for a job and a second user may be an employer searching for a prospective employee. [0078] Referring now to FIG. 2N, a flow chart of an exemplary process 200N for allowing recruiters to search candidates based on the transcription of audio or video recordings, or social media posts, according to an aspect of the present disclosure, is shown. [0079] Process 200N begins at step 201N and immediately proceeds to step 202N. In step 202N, user 101a provides a post on a social media site or update feed, or uploads an audio or video recording such as a video interview. Then, in step 203N, system 100 transcribes the audio and video files into searchable text documents. The transcribing of the documents can be done manually or using an automated method supported by system 100. Then, in step 204N, system 100 compiles and stores the transcribed documents and the social media posts in candidate database 105, which in step 205N, user 101b is now able to search while attempting to find potential candidates for a particular job posting. Process 200N then terminates at step 206N.) Since the video posts/video interviews are transcribed into searchable text documents, then the combination satisfies the limitation.
- further wherein the resume topic headings can be based on input from the one of the recruiters.(Bilodeau[0060] Process 200G begins at step 201G and immediately proceeds to step 202G. In step 202G, user 101b creates a job posting to be stored in job database 103. Then in step 203G, user 101b associates "tags" to the job posting which are designed to indicate that the job posting is a job posting for, for example, a particular industry or requires particular skills. Alternatively, or in addition to the tags associated with the job posting by user 101b, system 100 inspects the text and video content of the job posting to determine the appropriate tags to associate to the job posting.) Bilodeau’s manual selection of tags can be applied to Stewart’s headings.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart’s system by adding the features of allowing other users to evaluate the video resumes of users against the open positions based on the searchable user-authored content of the video resumes as taught by Bilodeau, and allow the users the select the resume topic headings. By simply substituting Stewart’s video resumes for the video interviews/video profiles in Bilodeau, one would have arrived at the combined teachings above. Similarly, by substituting the ability to select a tag in Bilodeau, with the ability to select a topic heading in Stewart, one would have arrived at the claimed limitation. One of ordinary skill in the art would have been motivated to perform this combination by Bilodeau’s benefits as followed: (Bilodeau [0010] In an aspect, the system would provide the first user and the second user the ability to fully perform the interview process by, for example, testing the first user's qualifications; interviewing the first user, including the ability to match automated questions to interview subjects; grading the qualification tests; transcribing audio and video; analyzing non-verbal cues; and enabling game-like scoring for these activities.)
Regarding Claim 8:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 7,
- wherein the searchable user-authored content is derived from raw user-authored video content, and (Stewart [Col. 11 Lines 13-24] User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, personal address, social security number, phone number, employment history, experience level, education, certification, acquired skills, geographical location, expected compensation, job performance acknowledgements (e.g., awards, honors, distinguishments), photograph of user, sample work product, and the like. [Col. 12 Lines 6-17] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes.)
- further wherein the host networking platform software is configured with a conversion component capable of converting the raw user-authored video content into the searchable user-authored content. (Stewart [Col. 15 Lines 18-27] In some cases, computing device 104 may be configured to recognize at least a keyword as a function of visual verbal content. In some cases, recognizing at least keyword may include an optical character recognition (OCR). In some cases, computing device 104 may transcribe much or even substantially all verbal content from target resume video. Similarly for textual resumes, such as written resumes, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. [Col. 14 Lines 55-57] In some cases, computing device 104 may include audiovisual speech recognition (AVSR) processes to recognize verbal content in a video resume. [Col. 14 Lines 39-43] Attributes may include subject's skills, competencies, credentials, talents, and the like. In some cases, attributes may be explicitly conveyed within video resume. Alternatively, or additionally, in some cases, attributes may be conveyed implicitly with video resume.) The AVSR and transcription capabilities satisfy the limitation “conversion component capable of...” as it turns the raw audio-visual content into textual information such as attributes.
Regarding Claim 9:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 8,
- wherein the searchable user-authored content consists of the textual content and processed video content derived from the raw user-authored video content. (Stewart [Col. 11 Lines 13-24] User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, personal address, social security number, phone number, employment history, experience level, education, certification, acquired skills, geographical location, expected compensation, job performance acknowledgements (e.g., awards, honors, distinguishments), photograph of user, sample work product, and the like. [Col. 12 Lines 6-17] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes.)
Regarding Claim 10:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 8,
- wherein the textual content further comprises processed textual passages derived from the raw user-authored video content, and (Stewart [Col. 15 Lines 18-30] In some cases, computing device 104 may be configured to recognize at least a keyword as a function of visual verbal content. In some cases, recognizing at least keyword may include an optical character recognition (OCR). In some cases, computing device 104 may transcribe much or even substantially all verbal content from target resume video. Similarly for textual resumes, such as written resumes, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine-learning processes.) A transcription of the video resume is an example of “processed textual passages.”
- wherein the searchable user-authored content consists of the textual content. (Stewart [Col. 15 Lines 31-38] Still referring to FIG. 1, in some cases, OCR may include post-processing. For example, OCR accuracy may be increased, in some cases, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters.)
Regarding Claim 11:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 7, further comprising:
Furthermore, Stewart teaches:
- a plurality of digital resumes stored on the server, (Stewart [Col. 15 Line 63 – Col. 16 Line 5] Memory component 140 may be configured to store information and datum related to posting match recommendation. For example, memory component 140 may store previously prepared records (e.g., draft resumes), customized records generated by computing device 104, posting datum 112, user datum 108, interaction preparation 120, record recommendation 116, and the like. In one or more embodiments, memory component may include a storage device, as described further in this disclosure below.)
However, Stewart fails to teach:
- each digital resume corresponding to one of the plurality of video resumes,
- wherein the host networking platform software is further configured such that the one of the recruiters can evaluate each of the plurality of digital resumes against the open position.
Furthermore, Bilodeau teaches:
- each digital resume corresponding to one of the plurality of video resumes, (Bilodeau [0047] Process 200D begins at step 201D and immediately proceeds to step 202D. In step 202D, user 101a creates a profile and chooses various facets such as, but not limited to, employment field of interest, geographic region of interest, relevant skills possessed, prior work history, resume, video profile, video interview, social media content, or any other facet useful in creating a profile. Process 200D then proceeds to step 203D where user 101a may elect to show or hide multiple versions of any of the facets such as, for example, only allowing certain users to view a particular resume or set of responses to a video interview.)
- wherein the host networking platform software is further configured such that the one of the recruiters can evaluate each of the plurality of digital resumes against the open position. (Bilodeau [0048] Process 200D then proceeds to step 204D when a user submits a request to system 100 by, for example, using a mouse to click on the user's profile. System 100 then identifies the type of user requesting to view the profile of user 101a and proceeds to step 205D where system 100 filters the facets to only display the appropriate facets based on the user attempting to access the profile. [0052] In step 204E, system 100 compares the broadcasted information of all users and matches the qualifications of the job-seeking users with the requirements of the job-posting users in order to "match" them to one another. System 100 may then record the matches across multiple physical encounters to build a history of the people or entities that each user has been matched with over time.) [0067] In step 205I, user 101a, using a social media site or update feed, posts relevant content such as, but not limited to, a change in their work experience, volunteer activity, education, interests, skills, qualifications or any other information in their profile. Then, in step 206I, system 100 receives push updates from the social media site and update feeds, and updates the profile and resume of user 101a within candidate database 105. Process 200I then terminates at step 207I.)The use of broadcasted information with the requirements of the job-posting satisfies the limitation because the digital resume is one of the broadcasted information sources.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart’s system by adding the features of allowing other users to evaluate the digital resumes of users against the open positions based on the searchable user-authored content of the digital resumes as taught by Bilodeau. One of ordinary skill in the art would have been motivated to perform this combination by Bilodeau’s benefits as followed: (Bilodeau [0010] In an aspect, the system would provide the first user and the second user the ability to fully perform the interview process by, for example, testing the first user's qualifications; interviewing the first user, including the ability to match automated questions to interview subjects; grading the qualification tests; transcribing audio and video; analyzing non-verbal cues; and enabling game-like scoring for these activities.)
Regarding Claim 12:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 7,
Furthermore, Stewart teaches:
- wherein the searchable user-authored content is derived from raw user-authored video content, and (Stewart [Col. 11 Lines 13-24] User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, personal address, social security number, phone number, employment history, experience level, education, certification, acquired skills, geographical location, expected compensation, job performance acknowledgements (e.g., awards, honors, distinguishments), photograph of user, sample work product, and the like. [Col. 12 Lines 6-17] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes.)
evaluate the raw user-authored video content of each of the plurality of video resumes with a machine learning component (Stewart [Col. 12 Lines 14-45] Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes. In some embodiments, computing device 104 may utilize a candidate classifier, which may include any classifier used throughout this disclosure, to run an initial pass over the video elements of video resumes, break down and categorizes such elements before comparing it to target video resume. A “classifier,” as used in this disclosure is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like. As used in this disclosure, a “candidate classifier” is a classifier that classifies users to a target resume or a job position description. In some cases, candidate classifier may include a trained machine-learning model, which is trained using candidate training data. As used in this disclosure, “candidate training data” is a training data that correlates one or more of users and user datum to one or more job descriptions, description-specific data, and posting data. A target video resume may be representative of generic information related to posting data. For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. As used in this disclosure, “credentials” are any piece of information that indicates an individual's qualification to perform a certain task or job.)
However, Stewart fails to teach:
- wherein the networking platform software is further configured such that one of the recruiters can evaluate the raw user-authored video content(Stewart does not involve the recruiters in the evaluation process.)
- against a communication-oriented criterion.
Alternatively, Bilodeau teaches: - evaluating the raw user-authored video content of the video resume with a machine learning component of the host networking software against a communication-oriented criterion. (Bilodeau [0024] factor(s) as will be apparent to those skilled in the relevant art(s) after reading the description herein; uploading new job postings; taking tests; interviewing; watching interviews; rating interviews; [0080] Referring now to FIG. 2O, a flow chart of an exemplary process 200O for analyzing video of a user answering interview questions to determine non-verbal communication cues, according to an aspect of the present disclosure, is shown. [0081] Process 200O begins at step 201O and immediately proceeds to step 202O. In step 202O, user 101a uploads an audio or video file, participates in a video interview posted by user 101b or performs any other action that results in an audio or video file being available for system 100 to analyze. In step 203O, system 100 analyzes the audio file, video file, or any combination of the two, to determine non-verbal cues such as, but not limited to, speech timber, pitch, posture, gestures, eye contact, eye direction or any other non-verbal cues that may be useful in hiring determination metrics. [0082] In step 204O, the metrics are then used as input into an algorithm that calculates the likelihood that user 101a possesses certain personality traits such as, but not limited to, sincerity, confidence, introversion, or any other trait that may be associated with the non-verbal cues. Then in step 205O, the algorithm produces scores along each trait which indicate how much of each trait that user 101a is estimated to possess based on the analysis of the non-verbal cues. In step 206O, system 100 compiles the calculated scores and produces a report which, in step 207O, is provided to user 101a and user 101b)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart’s machine learning classifier, to additional perform the features of Bilodeau to be based on non-verbal communication criterion using machine learning. It would have been obvious to one of ordinary skill in the art to predictably arrive at the claimed limitation as machine learning classifiers are capable of identifying speech, timber, pitch, posture, eye contact, and other visual stimuli. One would have been motivated to combine because of the benefit of Bilodeau’s system of expanding the types of traits that can be analyzed for compatibility in hiring. (Bilodeau [0082] In step 204O, the metrics are then used as input into an algorithm that calculates the likelihood that user 101a possesses certain personality traits such as, but not limited to, sincerity, confidence, introversion, or any other trait that may be associated with the non-verbal cues. Then in step 205O, the algorithm produces scores along each trait which indicate how much of each trait that user 101a is estimated to possess based on the analysis of the non-verbal cues.)
Regarding Claim 13:
Stewart teaches:
- A method of networking using a video-assisted networking platform, the networking platform for networking by candidates and recruiters, each with accounts on the platform, comprising: (Stewart [Col. 9 Lines 4-13] With continued reference to FIG. 1, computing device 104 is configured to receive a posting datum 112. For the purpose of this disclosure, “posting datum” is information related to an available and/or open job position. For the purposes of this disclosure, a “job position” (also referred to in this disclosure as a “job”) is a paid occupation with designated tasks associated therewith. A job position may include an employment with an employer, such as work as an employee (part-time or full-time), worker, contractor, self-employed, and the like. [Col. 9 Lines 51-54] In other embodiments, posting datum 112 may be provided to computing device 104 by a database over a network from, for example, a network-based platform.)
- the platform comprising a host networking platform software installed on a host network platform server; (Stewart [Col. 9 Lines 57-61] In other embodiments, posting datum 112 may be communicated from a third-party application, such as from a third-party application on a third-party server, using a network. For example, posting datum 112 may be downloaded from a hosting website for job listings. [Col. 10 Lines 15-30]In another example, and without limitation, database 132 may be remote to computing device 104 and communicative with computing device 104 by way of one or more networks. A network may include, but is not limited to, a cloud network, a mesh network, and the like. By way of example, a “cloud-based” system can refer to a system which includes software and/or data which is stored, managed, and/or processed on a network of remote servers hosted in the “cloud,” e.g., via the Internet, rather than on local severs or personal computers. A “mesh network” as used in this disclosure is a local network topology in which the infrastructure computing device 104 connect directly, dynamically, and non-hierarchically to as many other computing devices as possible. A “network topology” as used in this disclosure is an arrangement of elements of a communication network.)
- designating a video resume format on the platform software based at least in part on an open position, [Col. 14 Lines 4-27] In one or more embodiments, record recommendation 116 may include information from posting datum 112 and user datum 108 to insert into a new customized record. Record recommendation 116 may include a formatted record. For example, a format of a resume may vary depending on the field of the job position. For example, and without limitation, categorization, syntax, grammar, section titles, headers, font, margins, and the like may vary depending on the field or area of the job position, such as in the field of engineering, advertisement, medicine, acting, and the like. Computing device 104 may determine the proper formatting for a customized record or record suggestion using posting datum 112 and/or or a database that includes generalize resume information and etiquette. Record recommendation 116 may provide a new customized resume in the format related to the job position associated with the posting datum 112. In one or more embodiments, record recommendation 116 may include a video component, audio components, text components, and combination thereof, and the like. For instance, and without limitation, customized record may include a video resume. As used in this disclosure, a “video resume” is a video in visual and/or audio form to provide a recording promoting a jobseeker for employment, such as for a particular job position.) The format of a resume varying based on the job position also applies to video resumes, therefore, the limitation is satisfied since it is based on the open job position field.
- accessing the networking platform by use of one of the accounts of the candidates;(Stewart [Col. 9 Lines 37-48] In one or more embodiments, posting datum 112 may be provided to or received by computing device 104 using various means. In one or more embodiments, posting datum 112 may be provided to computing device 104 by a user, such as a jobseeker or potential job candidate that is interested in being a candidate or considered for a job position by the employer of the job position. A user may manually input posting datum 112 into computing device using, for example, a graphic user interface and/or an input device. For example, and without limitation, a user may use a peripheral input device to navigate graphic user interface and provide posting datum 112 to computing device 104.)
- evaluating the video resume against the open position using the host networking software(Stewart [Col. 12 Lines 7-39] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes. In some embodiments, computing device 104 may utilize a candidate classifier, which may include any classifier used throughout this disclosure, to run an initial pass over the video elements of video resumes, break down and categorizes such elements before comparing it to target video resume. A “classifier,” as used in this disclosure is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like. As used in this disclosure, a “candidate classifier” is a classifier that classifies users to a target resume or a job position description. In some cases, candidate classifier may include a trained machine-learning model, which is trained using candidate training data. As used in this disclosure, “candidate training data” is a training data that correlates one or more of users and user datum to one or more job descriptions, description-specific data, and posting data.)
- authenticating the video resume using the host networking platform software, the authenticating conducted during the step of accessing the networking platform by use of one of the accounts of the candidate, (Stewart [Col. 12 Lines 6-14] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. [Col. 24 Lines 43-55] For example, and without limitation, a plurality of resumes of a user may be preserved in an order that the resumes were submitted by the user or generated by processor 140 upon a query or request by user. Temporally sequential listing may be accessible at any of various security settings. For instance, and without limitation, temporally sequential listing may be readable and modifiable publicly, may be publicly readable but writable only by entities and/or devices having access privileges established by password protection, confidence level, or any device authentication procedure or facilities described herein, or may be readable and/or writable only by entities and/or devices having such access privileges.)
- wherein the video resume comprises searchable user-authored content derived from raw user-authored video content in a video resume format provided by the host networking platform software, and(Stewart [Col. 15 Lines 18-27] In some cases, computing device 104 may be configured to recognize at least a keyword as a function of visual verbal content. In some cases, recognizing at least keyword may include an optical character recognition (OCR). In some cases, computing device 104 may transcribe much or even substantially all verbal content from target resume video. Similarly for textual resumes, such as written resumes, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. [Col. 14 Lines 55-57] In some cases, computing device 104 may include audiovisual speech recognition (AVSR) processes to recognize verbal content in a video resume. [Col. 14 Lines 39-43] Attributes may include subject's skills, competencies, credentials, talents, and the like. In some cases, attributes may be explicitly conveyed within video resume. Alternatively, or additionally, in some cases, attributes may be conveyed implicitly with video resume.) The AVSR and transcription capabilities satisfy the limitation “conversion component capable of...” as it turns the raw audio-visual content into textual information such as attributes.
- further wherein the evaluating the video resume step is conducted based at least in part on the searchable user-authored content of the video resume.(Bilodeau [Col. 12 Lines 28-45] A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like. As used in this disclosure, a “candidate classifier” is a classifier that classifies users to a target resume or a job position description. In some cases, candidate classifier may include a trained machine-learning model, which is trained using candidate training data. As used in this disclosure, “candidate training data” is a training data that correlates one or more of users and user datum to one or more job descriptions, description-specific data, and posting data. A target video resume may be representative of generic information related to posting data. For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. As used in this disclosure, “credentials” are any piece of information that indicates an individual's qualification to perform a certain task or job.)
However, Stewart fails to teach:
- accessing the networking platform by use of one of the accounts of the recruiters on the platform,
- designating a video resume format on the platform software based at least in part on an open position, the designating conducted during the step of accessing the networking platform by use of one of the accounts of the recruiters;
- recording a video resume using the host networking platform software for storage on the host network platform server, the recording conducted during the step of accessing the networking platform by use of one of the accounts of the candidates;
-that the evaluating the video resume step is also done and by accessing the networking platform by use of the one of the accounts of the recruiters; and
- wherein the designating a video resume format step is conducted before the recording a video resume step,
Alternatively, Bilodeau teaches: - accessing the networking platform by use of one of the accounts of the recruiters on the platform, (Bilodeau [0068] Referring now to FIG. 2J, a flow chart of an exemplary process 200J for allowing recruiters to simultaneously search multiple candidate sources including an internal candidate database, according to an aspect of the present disclosure, is shown.)
- the designating conducted during the step of accessing the networking platform by use of one of the accounts of the recruiters;(Bilodeau [0032] Process 200A begins at step 201A and immediately proceeds to step 202A. In step 202A, system 100 populates test questions. These questions may be in written form, video form, audio form, or any other format appropriate for utilization in process 200A, and may be specified by user 101b or they may be predetermined by system 100 based on the "job type" of the posting as identified by either user 101b or through an internal set of algorithms. Upon completion of the question population, process 200A then proceeds to step 203A.) Bilodeau allowing user 101b (which refers to the recruiters) to select questions, falls within the scope of the limitation because specifying the questions is an example of designating a resume format.
- recording a video resume using the host networking platform software for storage on the host network platform server, the recording conducted during the step of accessing the networking platform by use of one of the accounts of the candidates;(Bilodeau [0054] Referring now to FIG. 2F, a flow chart of an exemplary process 200F for allowing a user to interview remotely using motion, audio, and video capture hardware from a gaming system, according to an aspect of the present disclosure, is shown. [0055] Process 200F begins at step 201F and immediately proceeds to step 202F. In step 202F, user 101a connects to portal 111 using a gaming console as a computing device 102, such as Microsoft Kinect.RTM. (available from Microsoft Corp. of Redmond, Wash.), with motion capture, audio capture, video capture or any combination of the three. Process 200F then proceeds to step 203F when user 101a selects a job posting or interview they are interested in completing before proceeding to step 204F. Using the gaming console, user 101a can read questions, listen to questions, or watch questions and then respond to the questions using text, audio, video or any combination of the three.) Recording responses to the questions falls within the scope of “recording a video resume,” especially when considered in combination with Stewart’s video resume. In Bilodeau, the word “video profile,” maps to video resume.
-that the evaluating the video resume step is also done and by accessing the networking platform by use of the one of the accounts of the recruiters; and(Bilodeau [0024] In another aspect of the present disclosure, user 101a and user 101b may interact with job database 103... watching interviews; rating interviews; creating a profile; controlling facets of a profile; searching for potential employees; or any other process(es), as will be apparent to those skilled in the relevant art(s) after reading the description herein. In such aspects, profiles stored in candidate database 105 may provide search results when searched on external search results provider 109 (e.g., an Internet search engine). [0047] Process 200D begins at step 201D and immediately proceeds to step 202D. In step 202D, user 101a creates a profile and chooses various facets such as, but not limited to, employment field of interest, geographic region of interest, relevant skills possessed, prior work history, resume, video profile, video interview, social media content, or any other facet useful in creating a profile. Process 200D then proceeds to step 203D where user 101a may elect to show or hide multiple versions of any of the facets such as, for example, only allowing certain users to view a particular resume or set of responses to a video interview.)
- wherein the designating a video resume format step is conducted before the recording a video resume step,(Bilodeau [0055] Process 200F begins at step 201F and immediately proceeds to step 202F. In step 202F, user 101a connects to portal 111 using a gaming console as a computing device 102, such as Microsoft Kinect.RTM. (available from Microsoft Corp. of Redmond, Wash.), with motion capture, audio capture, video capture or any combination of the three. Process 200F then proceeds to step 203F when user 101a selects a job posting or interview they are interested in completing before proceeding to step 204F. Using the gaming console, user 101a can read questions, listen to questions, or watch questions and then respond to the questions using text, audio, video or any combination of the three.) Step 200F is when a user selects a job positing or interview they are interested in completing, (the format is already designated in this step because the questions have been chosen). Then in step 204F, the questions are provided then answered.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart’s system by adding the features of allowing recruiters to evaluate the video resumes of users, and designate the format of the video resume before the candidate user records their responses taught by Bilodeau. By simply substituting Stewart’s video resumes for the video interviews/video profiles in Bilodeau, one would have arrived at the combined teachings above. One of ordinary skill in the art would have been motivated to perform this combination by Bilodeau’s benefits as followed: (Bilodeau [0010] In an aspect, the system would provide the first user and the second user the ability to fully perform the interview process by, for example, testing the first user's qualifications; interviewing the first user, including the ability to match automated questions to interview subjects; grading the qualification tests; transcribing audio and video; analyzing non-verbal cues; and enabling game-like scoring for these activities.)
Regarding Claim 14:
The combination of Stewart and Bilodeau teach or suggest The method of networking of claim 13,
- wherein the searchable user-authored content comprises textual content. (Stewart [Col. 15 Lines 18-48] In some cases, computing device 104 may be configured to recognize at least a keyword as a function of visual verbal content. In some cases, recognizing at least keyword may include an optical character recognition (OCR). In some cases, computing device 104 may transcribe much or even substantially all verbal content from target resume video. Similarly for textual resumes, such as written resumes, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine-learning processes... A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters.)
Regarding Claim 15:
The combination of Stewart and Bilodeau teach or suggest The method of networking of claim 14,
Furthermore, Stewart teaches:
- wherein the textual content comprises a plurality of resume topic headings configured to organize the searchable user-authored content, and (Stewart [Col. 12 Lines 41-43] For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. [Col. 14 Lines 7-13] For example, a format of a resume may vary depending on the field of the job position. For example, and without limitation, categorization, syntax, grammar, section titles, headers, font, margins, and the like may vary depending on the field or area of the job position, such as in the field of engineering, advertisement, medicine, acting, and the like. [Col. 14 Lines 39-40] Attributes may include subject's skills, competencies, credentials, talents, and the like. [Col. 18 Lines 46-49] As a non-limiting example, training data classifier 216 may classify elements of training data to according to fields of job description for instance, title, role, organization, requisite experience, requisite credentials, and the like. [Col. 11 Lines 11-23] For the purposes of this disclosure, “user datum” is personal user information and/or attributes relevant to a job position of a posting. User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, personal address, social security number, phone number, employment history, experience level, education, certification, acquired skills, geographical location, expected compensation, job performance acknowledgements (e.g., awards, honors, distinguishments), photograph of user, sample work product, and the like.) The broadest reasonable interpretation (BRI) of resume topic headings in view of the the instant specification [0046] covers “Such resume topic headings 72 can include, for example, a statement, education, work experience, interests, and/or headings suitable to organize a resume, profile or curriculum vitae.” These align with the attributes, categories, and fields, determined by the classifier in Stewart.
- further wherein the resume topic headings are based on the open position.(Stewart [Col. 14 Lines 6-19] For example, a format of a resume may vary depending on the field of the job position. For example, and without limitation, categorization, syntax, grammar, section titles, headers, font, margins, and the like may vary depending on the field or area of the job position, such as in the field of engineering, advertisement, medicine, acting, and the like. Computing device 104 may determine the proper formatting for a customized record or record suggestion using posting datum 112 and/or or a database that includes generalize resume information and etiquette. Record recommendation 116 may provide a new customized resume in the format related to the job position associated with the posting datum 112.)
Regarding Claim 16:
The combination of Stewart and Bilodeau teach or suggest The method of networking of claim 15,
Furthermore, Stewart teaches:
- wherein the searchable user-authored content consists of the textual content and processed video content derived from the raw user-authored video content. (Stewart [Col. 11 Lines 13-24] User datum 108 may be audio and/or visual information related to the user's personal information, attributes, and/or credentials. For example, user datum may be a video, audio file, text, and the like. User datum may include a user's prior record, such as a draft resume, personal address, social security number, phone number, employment history, experience level, education, certification, acquired skills, geographical location, expected compensation, job performance acknowledgements (e.g., awards, honors, distinguishments), photograph of user, sample work product, and the like. [Col. 12 Lines 6-17] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes.)
Regarding Claim 17:
The combination of Stewart and Bilodeau teach or suggest The method of networking of claim 16,
Furthermore, Stewart teaches:
- wherein the evaluating step further comprises evaluating the raw user-authored video content of the video resume with a machine learning component of the host networking software (Stewart [Col. 12 Lines 14-45] Comparison result may contain a comparison score that represents a degree of similarity between target video resume and existing video resume of the plurality of existing video resumes. In some embodiments, computing device 104 may utilize a candidate classifier, which may include any classifier used throughout this disclosure, to run an initial pass over the video elements of video resumes, break down and categorizes such elements before comparing it to target video resume. A “classifier,” as used in this disclosure is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like. As used in this disclosure, a “candidate classifier” is a classifier that classifies users to a target resume or a job position description. In some cases, candidate classifier may include a trained machine-learning model, which is trained using candidate training data. As used in this disclosure, “candidate training data” is a training data that correlates one or more of users and user datum to one or more job descriptions, description-specific data, and posting data. A target video resume may be representative of generic information related to posting data. For example, in the initial pass, video resume may be categorized based on user datum, such as attributes or credentials of user. As used in this disclosure, “credentials” are any piece of information that indicates an individual's qualification to perform a certain task or job.)
However, Stewart fails to teach:
- the evaluation is against a communication-oriented criterion.
Bilodeau teaches or suggests:
- evaluating the raw user-authored video content of the video resume with a machine learning component of the host networking software against a communication-oriented criterion. (Bilodeau [0024] factor(s) as will be apparent to those skilled in the relevant art(s) after reading the description herein; uploading new job postings; taking tests; interviewing; watching interviews; rating interviews; [0080] Referring now to FIG. 2O, a flow chart of an exemplary process 200O for analyzing video of a user answering interview questions to determine non-verbal communication cues, according to an aspect of the present disclosure, is shown. [0081] Process 200O begins at step 201O and immediately proceeds to step 202O. In step 202O, user 101a uploads an audio or video file, participates in a video interview posted by user 101b or performs any other action that results in an audio or video file being available for system 100 to analyze. In step 203O, system 100 analyzes the audio file, video file, or any combination of the two, to determine non-verbal cues such as, but not limited to, speech timber, pitch, posture, gestures, eye contact, eye direction or any other non-verbal cues that may be useful in hiring determination metrics. [0082] In step 204O, the metrics are then used as input into an algorithm that calculates the likelihood that user 101a possesses certain personality traits such as, but not limited to, sincerity, confidence, introversion, or any other trait that may be associated with the non-verbal cues. Then in step 205O, the algorithm produces scores along each trait which indicate how much of each trait that user 101a is estimated to possess based on the analysis of the non-verbal cues. In step 206O, system 100 compiles the calculated scores and produces a report which, in step 207O, is provided to user 101a and user 101b)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart’s machine learning classifier, to additional perform the features of Bilodeau to be based on non-verbal communication criterion using machine learning. It would have been obvious to one of ordinary skill in the art to predictably arrive at the claimed limitation as machine learning classifiers are capable of identifying speech, timber, pitch, posture, eye contact, and other visual stimuli. One would have been motivated to combine because of the benefit of Bilodeau’s system of expanding the types of traits that can be analyzed for compatibility in hiring. (Bilodeau [0082] In step 204O, the metrics are then used as input into an algorithm that calculates the likelihood that user 101a possesses certain personality traits such as, but not limited to, sincerity, confidence, introversion, or any other trait that may be associated with the non-verbal cues. Then in step 205O, the algorithm produces scores along each trait which indicate how much of each trait that user 101a is estimated to possess based on the analysis of the non-verbal cues.)
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stewart (US 11663397 B1), in view of Bilodeau (US 20140317009 A1), further in view of Giraldo et al. (US 20050283717 A1) hereinafter Giraldo.
Regarding Claims 18 and 19:
The combination of Stewart and Bilodeau teach or suggest The networking platform of claim 1/7,
Furthermore, Stewart teaches:
- wherein the host networking platform software is configured such that each of the plurality of video resumes can be authenticated by one of the users. (Stewart [Col. 12 Lines 6-14] For example, the initial pass may include classifying the plurality of existing video resumes based on an image component, an audio component, user datum, or at least identifying user indica. For example, identifying indica could include personal information of user such as a name of user or subject, account number, social security number, telephone number, address, and the like. [Col. 24 Lines 43-55] For example, and without limitation, a plurality of resumes of a user may be preserved in an order that the resumes were submitted by the user or generated by processor 140 upon a query or request by user. Temporally sequential listing may be accessible at any of various security settings. For instance, and without limitation, temporally sequential listing may be readable and modifiable publicly, may be publicly readable but writable only by entities and/or devices having access privileges established by password protection, confidence level, or any device authentication procedure or facilities described herein, or may be readable and/or writable only by entities and/or devices having such access privileges.)
However, neither Stewart nor Bilodeau teach or suggest that the host networking platform software is configured such that each of the plurality of video resumes can be authenticated by one of the users:
- by limiting the raw user-authored video content to generation in one take.
Alternatively, Giraldo discloses an apparatus for creating, capturing, and distributing video resumes directly from the user’s desktop. Giraldo teaches:
- by limiting the raw user-authored video content to generation in one take. (Giraldo [0034] It will be obvious based on the above disclosure that there are additional ways, within the scope of the disclosed invention, to further enhance the audiovisual presentation under the disclosed system and method. For example, per the preferred embodiment for video resumes, the video resume may show, in addition to the job candidate, other speakers such as the candidate's reference named on the textual resume. The reference could speak about his/her experience with the job candidate. The recording of the reference segment of the video resume may be done at the job candidate's desktop as part of a single continuous audiovisual presentation. Alternatively, the reference may independently record the audiovisual presentation at his/her desktop using the disclosed Application Tool. The presentation recorded and captured by the reference could then be electronically disseminated to the job candidate who will upload it to his account, and enhance it as appropriate using the Application Tool. [0026] m. The user is prompted to review the video resume before deciding whether or not to publish it.[0027] n. If the user decides to not publish the audiovisual presentation, he/she is prompted by the application to re-record the audiovisual presentation. If the user chooses to not re-record at this time, then the just-recorded or uploaded presentation is stored on the database server of the disclosed system for the user's later access and retrieval via his/her account.) A “single continuous” audiovisual presentation is synonymous with “one take.” Therefore, it would have been obvious to add this feature as a way to authenticate the user to ensure that the video is genuine.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart by adding the steps of Giraldo, particularly, the recording of video content in one take (single continuous audiovisual presentation). One of ordinary skill in the art would have been motivated to add this feature by Giraldo’s benefit of enabling anyone with a computer to readily create personal audiovisual presentations. (Giraldo [0033] In summary, the disclosed apparatus, method, and system enable anyone with a computer to readily, quickly, frequently, and inexpensively create, manage, and distribute on-line, directly from his/her desktop, a custom, personal audiovisual presentation authored for various purposes.)
Regarding Claim 20:
The combination of Stewart and Bilodeau teach or suggest The method of networking of claim 13,
However, neither Stewart nor Bilodeau teach:
- wherein the authenticating is conducted such that the raw user-authored video content is generated in one take.
Alternatively, Giraldo teaches:
- wherein the authenticating is conducted such that the raw user-authored video content is generated in one take. (Giraldo [0034] It will be obvious based on the above disclosure that there are additional ways, within the scope of the disclosed invention, to further enhance the audiovisual presentation under the disclosed system and method. For example, per the preferred embodiment for video resumes, the video resume may show, in addition to the job candidate, other speakers such as the candidate's reference named on the textual resume. The reference could speak about his/her experience with the job candidate. The recording of the reference segment of the video resume may be done at the job candidate's desktop as part of a single continuous audiovisual presentation. Alternatively, the reference may independently record the audiovisual presentation at his/her desktop using the disclosed Application Tool. The presentation recorded and captured by the reference could then be electronically disseminated to the job candidate who will upload it to his account, and enhance it as appropriate using the Application Tool. [0026] m. The user is prompted to review the video resume before deciding whether or not to publish it.[0027] n. If the user decides to not publish the audiovisual presentation, he/she is prompted by the application to re-record the audiovisual presentation. If the user chooses to not re-record at this time, then the just-recorded or uploaded presentation is stored on the database server of the disclosed system for the user's later access and retrieval via his/her account.) A “single continuous” audiovisual presentation is synonymous with “one take.” Therefore, it would have been obvious to add this feature as a way to authenticate the user to ensure that the video is genuine.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Stewart by adding the steps of Giraldo, particularly, the recording of video content in one take (single continuous audiovisual presentation). One of ordinary skill in the art would have been motivated to add this feature by Giraldo’s benefit of enabling anyone with a computer to readily create personal audiovisual presentations. (Giraldo [0033] In summary, the disclosed apparatus, method, and system enable anyone with a computer to readily, quickly, frequently, and inexpensively create, manage, and distribute on-line, directly from his/her desktop, a custom, personal audiovisual presentation authored for various purposes.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Glase et al. (US 20220374592 A1) discloses a “video about me” feature to enable a user to redirect the reviewer to a video resume.
Arran Stewart (US 11538462 B1) discloses a system for querying and transcribing video resumes in order to classify the user inputs to the job postings. Paul Deuchar (US 20220343285 A1) discloses a job seeker interface and a recruiter interface which allows job seekers to edit and construct their video resume for display to recruiters.
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/NICO L PADUA/Junior Patent Examiner, Art Unit 3626
/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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