DETAILED ACTION
Acknowledgement
This final office action is in response to the amendment filed on 07/31/2025.
Status of Claims
Claims 1,6, 11, 16, and 20 have been amended.
Claims 1-20 are now pending.
Response to Arguments
Claims 6 and 16 objections have been withdrawn in light of amendments.
Applicant's arguments filed on 07/31/2025 regarding the 35 U.S.C. 101 and 103 rejections of claims 1-20 have been fully considered. The Applicant argues the following.
(1) As per the 101 rejection, the Applicant argues, in summary, that (i) the representative claim does not describe a mental process or certain method of organizing human activity. The claim contain limitations that cannot practically be performed in the human mind such as using a machine learning model and accessing an application of an online service; and (ii) the claims recite additional elements that integrate the judicial exception into a practical application. The representative claim recites an improvement in the functioning of a computer or an improvement to other technology or technical field. The representative claim utilizes multiple ML models to solve the technical problem of conventional recommendation systems.
The Examiner respectfully disagrees. The Examiner maintains the position that the claims as amended are directed to the abstract groupings of Mental Processes and Certain Methods of Organizing Human Activity. Based on the claim limitations highlighted in Step 2A(1), the claims describe a process of computing distributions that comprises probability values which can practically be performed in the human mind using pen, paper, and mathematical equations, thus reflecting mental processes. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Using these distribution computations to select and display invitations to connect with other users, job postings and applications, and courses for a person to take reflects certain methods of organizing human activity. Certain Methods of Organizing Human Activity encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Per MPEP 2106.04(a), a claim recites a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The Examiner is not stating that the human mind can perform machine learning and access an application of an online service. These limitations are considered additional elements in Steps 2A(2) and 2B. However, the additional elements recited in the claims and listed in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application nor provide significantly more. The additional elements do not improve the functioning of a computer or improve another technology of technical field. The additional elements such as the machine learning models, computer components, etc. are used to perform the abstract idea of computing distributions, extracting feature data, generating a graph data structure, using the distributions and graphs to select specialties, and provide an invitation to connect with others, job postings, or courses. Therefore, the additional elements are viewed as are viewed as mere instructions to implement an abstract idea on a computer and merely indicates a field of use or technological environment in which to apply a judicial exception. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). The Applicant's claims are similar to Eligibility Example 47 claim 2 that was deemed ineligible. The Applicant's claims uses ML to analyze data (e.g. computing distributions) and uses the results in an online application service to assist a user with job applications, online courses, and with connecting with other people to advance their career. These results are not being used to improve a technology or address a technological problem. Providing career recommendations to a user is considered abstract and improving an abstract idea is not considered an improvement in technology (see MPEP 2106.05(a)). Therefore, the 35 U.S.C. 101 rejection is maintained.
(2) As per the 103 rejection, the Applicant argues that Varga does teach the amended claims. Vontobel and Bauer fail to remedy the deficiency of Varga since both references fail to discuss "specialty data" in any context.
The Examiner finds the Applicant’s arguments somewhat persuasive. Therefore, the previous 103 rejections have been withdrawn. However, upon further search and consideration, a new ground of 103 rejection is made. See details below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 20 limitations refer to “a system comprising: means for extracting feature data from a profile…, means for computing a skill-to-specialty distribution…, means for computing a user-to-skill distribution…, means for computing a user-to-specialty distribution…, and means for using the user-to-specialty distribution in an application of the online service”. The Applicant’s specification states that the online service 100 performs the computing functions as highlighted in Figs. 8 and 9 methods. The specification also states that the methods or embodiments disclosed herein may be implemented as a computer system having one or more components implemented in hardware or software. For example, the methods or embodiments disclosed herein may be embodied as instructions stored on a machine-readable medium that, when executed by one or more hardware processors, cause the one or more hardware processors to perform the instructions [0022]. FIG. 11 shows a diagrammatic representation of the machine 1100 in the example form of a computer system, within which instructions 1116 (e.g., software, a program, an application 1110, an applet, an app, or other executable code) for causing the machine 1100 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 1116 may cause the machine 1100 to execute the method 800 of FIG. 8 or the method 9 of FIG. 9 [0091]. Therefore, the means for computing is interpreted as a computer with hardware and software instructions.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 11, and 20 recites the limitations of “for each skill in the plurality of skills, computing a user-to-skill distribution for the plurality of skills based on the feature data of the first user of the online service using a second machine learning model when the field for storing specialty data is empty, the user-to-skill distribution comprising a user-to-skill probability value for each skill in the plurality of skills given the first user;”. Support for this limitation was not found in the Applicant’s Specification. Paragraphs [0019], [0033], and [0034] state the following:
“As a result of the user lacking the knowledge of what specialties should be added to the profile of the user or the online service not providing the functionality to allow the user to add specialties to the profile of the user, the online service may be deprived of specialty data for the user, thereby limiting the accuracy and effectiveness of the online service in performing its functions. However, in accordance with example embodiments of the present disclosure, a computer system may be configured to use machine learning model machine learning models along with the law of total probability to predict which specialties apply to the user or to some other type of entity (e.g., which specialties apply to a job posting).” [0019]
“In some example embodiments, the artificial intelligence component 114 is configured to, for each skill in the plurality of skills, compute a user-to-skill distribution for the plurality of skills based on feature data of a first user of the online service using a second machine learning model ”, [0033] and
“In some example embodiments, the artificial intelligence component 114 is configured to compute a user-to-specialty distribution for the plurality of specialties based on the skill-to-specialty distribution and the user-to-skill distribution .” [0034]
The Examiner notes that the Applicant’s specification does not explicitly state that computing a user-to-skill distribution involves the use of specialty data and second, the specification does not explicitly state that in the absence of specialty data that a second machine learning model is used to compute a user-to-skill distribution. Therefore, claims 1, 11, and 20 contain new matter and are rejected under 35 U.S.C. 112(a). Dependent claims 2-10 and 12-19 are also rejected under 35 U.S.C. 112(a).
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 3-4 and 13-14 recite the limitation of “a profile of the first user”. There is insufficient antecedent basis for this limitation because “a profile of the first user” was first introduced in claims 1 and 11 in which claims 3-4 and 13-14 depend upon, respectively. Therefore, claims 3-4 and 13-14 are considered indefinite and are rejected under 35 U.S.C. 112(b). For examination purposes, “a profile of the first user” will be interpreted as “the profile of the first user”.
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, “Machine Learning Model For Specialty Knowledge Base”, is directed to an abstract idea, specifically Mental Processes and Certain Methods of Organizing Human Activity, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1-20 are directed to a statutory category, namely a process (claims 1-10) and a machine (claims 11-20).
Step 2A (1): Claims 1-9 and 11-20 are directed to an abstract idea of Mental Processes and Certain Methods of Organizing Human Activity, based on the following claim limitations:
Mental Processes
“ for each skill in a plurality of skills, computing a skill-to-specialty distribution for a plurality of specialties…, the skill-to-specialty distribution comprising a skill-to-specialty probability value for each specialty in the plurality of specialties given the skill in the plurality of skills; for each skill in the plurality of skills; extracting feature data from a profile of a first user …; for each skill in the plurality of skills, computing a user-to-skill distribution for the plurality of skills based on the feature data of the first user …, the user-to-skill distribution comprising a user-to-skill probability value for each skill in the plurality of skills given the first user; computing a user-to-specialty distribution for the plurality of specialties based on the skill-to-specialty distribution and the user-to-skill distribution, the user-to-specialty distribution comprising a user-to-specialty probability value for each specialty in the plurality of specialties given the first user; and using the user-to-specialty distribution….” (claims 1, 11, and 20)
“generating a graph data structure using the user-to-specialty distribution, the generated graph data structure comprising a plurality of entity nodes and a plurality of specialty nodes, each one of the plurality of entity nodes corresponding to a different entity, each one of the plurality of specialty nodes corresponding to a different specialty, each one of the plurality of specialty nodes being connected to each one of the plurality of specialty nodes by an edge that indicates a probability value of the specialty corresponding to the specialty node given the entity corresponding to the entity node, wherein the using the user-to-specialty distribution…comprises using the graph data structure… ” (claims 2 and 12)
“wherein the using the user- to-specialty distribution…comprises: selecting one or more specialties from the plurality of specialties based on the user-to-specialty probability value for each one of the one or more specialties…” (claims 3 and 13)
“wherein the feature data of the first user comprises profile data extracted from a profile of the first user” (claims 4 and 14)
“wherein the feature data of the first user comprises interaction data indicating online content with which the first user has interacted with…” (claims 5 and 15)
“for each skill in the plurality of skills, computing an entity-to-skill distribution for the plurality of skills based on feature data of an entity …, the entity-to-skill distribution comprising an entity-to-skill probability value for each skill in the plurality of skills given the entity; and computing an entity-to-specialty probability distribution for the plurality of specialties based on the skill-to-specialty distribution and the entity-to-skill distribution, the entity-to-specialty probability distribution comprising an entity-to-specialty probability value for each specialty in the plurality of specialties given the entity, wherein the using the user-to-specialty distribution … comprises using the user-to-specialty distribution and the entity-to-specialty probability distribution...” (claims 6 and 16)
Certain Methods of Organizing Human Activity:
“wherein the entity comprises a second user different from the first user, and using the user-to-specialty distribution and the entity-to-specialty probability distribution… comprises: based on the user-to-specialty distribution and the entity-to-specialty probability distribution, displaying…an indication of the second user…a transmission of an invitation to connect from the first user to the second user.” (claims 7 and 17)
“wherein the entity comprises an…job posting, and using the user-to-specialty distribution and the entity-to-specialty probability distribution …comprises: based on the user-to-specialty distribution and the entity-to-specialty probability distribution, displaying …an indication of the… job posting,… or initiate an…application process for the … job posting….” (claims 8 and 18)
wherein the entity comprises an…course, and using the user-to-specialty distribution and the entity- to-specialty probability distribution …comprises: based on the user-to-specialty distribution and the entity-to-specialty probability distribution, displaying … an indication of the … course…” (claims 9 and 19)
These claims describes a process of computing a skill-to-specialty distribution, a user-to-skill distribution, a user-to-specialty distribution, an entity-to-skill distribution, and an entity-to-specialty probability distribution that comprises probability values using models. These computations can be practically performed in the human mind using pen, paper and mathematical models, thus reflecting mental processes. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Using these distribution computations to select and display invitations to connect with other users, job postings and applications, and courses for a person to take reflects certain methods of organizing human activity. Certain Methods of Organizing Human Activity encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes and Certain Methods of Organizing Human Activity. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Therefore, claims 1-20 are directed to an abstract idea and are not patent eligible.
Step 2A (2): This judicial exception is not integrated into a practical application. In particular, claims 1-20 recite additional elements of “A computer-implemented method performed by a computer system having a memory and at least one hardware processor; using a first machine learning model; stored in a data structure of a database for an online service, the data structure comprising a field for storing skill data and a field for storing specialty data associated with the first user; using a second machine learning model when the field for storing specialty data is empty; using…in an application of the online service; displaying a selectable user interface element for each one of the selected one or more specialties on a computing device of the first user, the selectable user interface element being configured to trigger storing of the specialty as part of a profile of the first user in response to a selection of the selectable user interface element, the profile being stored on the online service; using a third machine learning model; displaying a selectable user interface element in association with an indication of the second user on a computing device of the first user, the selectable user interface element being configured to trigger, in response to its selection, a transmission of an invitation to connect from the first user to the second user; displaying a selectable user interface element in association with an indication of the online job posting on a computing device of the first user, the selectable user interface element being configured to, in response to its selection, trigger a display of the online job posting on the computing device of the first user or initiate an online application process for the online job posting on the computing device of the first user; displaying a selectable user interface element in association with an indication of the online course on a computing device of the first user, the selectable user interface element being configured to, in response to its selection, trigger an online process for playing the online course on the computing device of the first user; training the first machine learning model using a supervised machine learning algorithm; a system comprising: at least one hardware processor; and a non-transitory machine-readable medium embodying a set of instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations; and a system comprising: means for computing ”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing and display devices that are used to perform the abstract processes indicated in Step 2A(1). Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Limitations that amount to merely indicating a field of use (e.g. job/people/course matching) or technological environment (e.g. online, machine learning, etc.) in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)). Therefore, claims 1-20 do not include individual or a combination of additional elements that integrate the judicial exception into a practical application and thus are not patent eligible.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-20 recite additional elements as listed in Step 2A(2). The use of machine learning and trained models/algorithms are considered instructions to apply or implement a model on a computer. Training involves fitting a particular model to a dataset and is an integral part of the machine learning process. Therefore, the additional elements are viewed as mere instructions to implement an abstract idea on a computer and merely indicates a field of use or technological environment in which to apply a judicial exception. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-20 do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the judicial exception and thus are not patent eligible.
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 1-6, 8-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Varga et al. (US 2020/0302564 A1) in view of Vontobel et al. (US 2022/0327487 A1), in further view of Merhav et al. (US 2017/0371957 A1), and in further view of Bauer et al. (US 2018/0121823 A1).
As per claims 1 and 11 (Currently Amended), Varga teaches a computer-implemented method performed by a computer system having a memory and at least one hardware processor, the computer-implemented method comprising (Varga e.g. FIG. 6a depicts a flow diagram of an embodiment of a computerized method for dynamically customizing career and educational counseling [0013]. The drawings of FIGS. 6a-6b represent embodiments of an algorithm 600 for dynamically providing customized career and educational counseling as described in FIGS. 1-5b using one or more computer systems as defined generically by computer system 700 of FIG. 7 [0108].) and a system comprising: at least one hardware processor; and a non-transitory machine-readable medium embodying a set of instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations, the operations comprising (Varga e.g. A computer system comprising: a processor(s) set; a storage medium; and computer code stored on storage medium, with the computer code including instruction and data for causing the processor(s) set to perform at least the following operations [0006]. FIGS.1-5b depict diagrams of a computing environment 100, 200, 400 capable of generating customized vocational actions, including career and educational guidance which may provide, present and/or recommend career, vocational and educational options to a user based on each user's interests, activities, preferences, and behaviors [0028].):
Varga teaches generating an ontology of careers, vocations and educational opportunities that associates skills to a plurality of specialties (e.g. career or education) using machine learning models (Varga e.g. In the exemplary embodiment of FIG. 1, the server computer 101 may be loaded with a vocational application 103 that provides services comprising the generation of customized education and career guidance, recommendations and information to users based on individual user profiles and presents a generated report or interactive report via a user interface, to each client device(s) 110 accessing the vocational application 103 [0055]. Embodiments of the vocational application 103 may include a plurality of components, modules, engine and/or programs, which may be part of or linked to the program code of the vocational application 103. Examples of these components, modules, engines and programs include a data gathering module 105, user profile module 107, knowledge base management module 109 (abbreviated "knowledge base mgmt 109" in the drawings), ranking module 111, analysis engine 113, and a reporting engine 115 [0057]. In some embodiments, the user profile module 107 may match each user to user profiles associated with particular careers, occupations, vocations and/or educational opportunities using an ontology of careers, vocations and educational opportunities created by vocational application 103 [0075]. The ontology created by the vocational application 103 may comprise the relationships and properties of all the existing and known vocational actions available to a user, including but not limited to careers, vocations, educational courses, educational institutions, licensing requirements, internships/volunteering etc. [0075]. The ontology may be represented by a graph, and each node of the graph may contain a list of properties, skills, interests, etc. (i.e. as parameter values or parameter value ranges associated with a vocational action), that a user may have to be considered interested or associated with the vocational action (such as a career, vocation or educational opportunity) that may be associated with the node of the graph [0075]. For example, nodes of the ontology could be associated with any field, course of study, interest, hobby, educational level, etc., such as math, logic, digital circuits, animal study, biology, chemistry, robotics, or any other field [0075]. Embodiments of the ontology may be maintained as part of the knowledge base 117. An ontology may refer to a representation of knowledge by a set of concepts within a domain and the relationships between those concepts in order to model the domain of knowledge being represented [0075]. The ontology may be created from the data collected by the knowledge base 117 (described below) using automated techniques such as text mining, machine learning, data clustering, etc. to create the nodes of the ontology's graphical representation [0076]. The knowledge base 117 may be a dynamic resource having the cognitive capacity for self-learning, using one or more data modeling techniques and/or by working in conjunction with one or more machine learning programs and predictive modeling algorithms to improve the accuracy of predicting and presenting vocational actions to the user based on user profiles, vocational data being stored by the knowledge base 117 and the ontology modeled using the collected data [0082]. In the exemplary embodiment, a support-vector machine (SVM) model algorithm may be used. SVM models may use classification and/or regression analysis to create correlations between careers, vocations and educational opportunities and weights for the different types/values of gathered and stored by the knowledge base 117 [0092]. Based on the feedback data, one or more models, including the ontology can be updated based on the feedback from the user. More weight may be given to nodes of the ontology that are related to the careers, vocations and educational options that received positive feedback data from the user (both implicitly and explicitly) and/or increase the weight a node may receive when being ranked by the ranking module 111 [0106].)
Varga teaches extracting feature data from a profile of a first user stored in a data structure of a database for an online service, the data structure comprising a field for storing skill data and a field for storing specialty data associated with the first user (Varga e.g. The collected user data and user-defined parameters define a user profile, providing insight into career or educational options best suited to the user (Abstract). The user data set may include information indicative of the identity of the user and a plurality of pieces of information relating to the user. From the user data set, a plurality of user parameter values can be generated that may describe the characteristics, interests, features, strengths and personality of the user [0019]. Based on the correlation between the current user data set being analyzed and historical data, a user profile may be defined, which may include a plurality of user parameter values describing the user and the user data set [0022]. Embodiments of the disclosure may further use the defined user profile in conjunction with a knowledge base of historical data to further identify one or more vocations, educational programs or career paths that may be most interesting to the user based on the defined user profile [0022]. In the exemplary embodiment of FIG. 1, the server computer 101 may be loaded with a vocational application 103 that provides services comprising the generation of customized education and career guidance, recommendations and information to users based on individual user profiles and presents a generated report or interactive report via a user interface, to each client device(s) 110 accessing the vocational application 103 [0055]. The first function of the data gathering module 105 may be to gather information and user data 130 describing a user for the purpose of defining a user profile. Embodiments of the user profile may categorize and/or identify characteristics of the user that may be relevant for providing one or more vocational action to the user, including characterization of the user based on the user's interests, activity, habits, personality, experiences, preferences, geographic location, etc. [0058]. Embodiments of the user profile module 107 may analyze the set of user data 130 extracted from each user-accessed data source 128 and client device 110, characterize each piece of user data 130 as applying to one or more characteristics or features that may be used to define the user profile and/or apply one or more user parameter values, tags or keywords to each piece of user data collected [0071]. Embodiments of the user profile module 107 may include a profile database or type of repository where the gathered user data 130 can be stored, including data describing the user's activity in both a real or virtual space, along with the user parameter values corresponding to the collected set of user data 130 [0071]. Embodiments of the user profile module 107 may structure the data being stored within a database in a parameter-value tuple. The analyzed user data 130 may be used to create, modify and update the user profile by filling in one or more fields of the user profile [0073]. The traits of the user as described by the user data 130 and user-defined parameters, can be further matched to historical user profiles maintained by the user profile module 107 and/or the knowledge base 117 to further determine which vocational actions, including which careers, occupations, vocations and/or educational opportunities are most closely aligned with the user profile have been considered interesting by historical users of the historical user profiles [0074]. Wherein, the more closely a user profile resembles one or more historical user profiles, the higher the probability that the vocational application 103 would recommend one or more of the vocational action previously recommended to a historical user profile to the current user seeking guidance from the vocational application 103 [0074]. In some embodiments, the user profile module 107 may match each user to user profiles associated with particular careers, occupations, vocations and/or educational opportunities using an ontology of careers, vocations and educational opportunities created by vocational application 103 [0075]. Embodiments of the ontology may be maintained as part of the knowledge base 117. An ontology may refer to a representation of knowledge by a set of concepts within a domain and the relationships between those concepts in order to model the domain of knowledge being represented [0075]. The ontology created by the vocational application 103 may comprise the relationships and properties of all the existing and known vocational actions available to a user, including but not limited to careers, vocations, educational courses, educational institutions, licensing requirements, internships/volunteering etc. [0075]. The ontology may be represented by a graph, and each node of the graph may contain a list of properties, skills, interests, etc. (i.e. as parameter values or parameter value ranges associated with a vocational action), that a user may have to be considered interested or associated with the vocational action (such as a career, vocation or educational opportunity) that may be associated with the node of the graph [0075]. the knowledge base 117 may be a dynamic resource having the cognitive capacity for self-learning, using one or more data modeling techniques and/or by working in conjunction with one or more machine learning programs and predictive modeling algorithms to improve the accuracy of predicting and presenting vocational actions to the user based on user profiles, vocational data being stored by the knowledge base 117 and the ontology modeled using the collected data [0082]. Embodiments of the knowledge base 117 may determine which vocational action to present to each through the use of one or more machine learning techniques. The machine learning techniques may be used to analyze user profiles, user-data 130, vocational data and user-defined parameters to arrive at the vocational action that will be presented to the user and may include supervised learning unsupervised learning and/or semi-supervised learning techniques [0088]. In the exemplary embodiment, a support-vector machine (SVM) model algorithm may be used. SVM models may use classification and/or regression analysis to create correlations between careers, vocations and educational opportunities and weights for the different types/values of gathered and stored by the knowledge base 117. For example, in some embodiments, the correlation between the available vocational actions and the user may be based on identifying which vocational actions of the knowledge base 117 has a parameter value range that matches the user parameter values of the user's profile identified via the collected user data 130 [0092].)
Varga teaches for each skill in the plurality of skills, computing a user-to-skill distribution for the plurality of skills based on the feature data of the first user of the online service using a second machine learning model,…, the user-to-skill distribution comprising a user-to-skill…value for each skill in the plurality of skills given the first user; (Varga e.g. The collected user data and user-defined parameters define a user profile, providing insight into career or educational options best suited to the user (Abstract). The user data set may include information indicative of the identity of the user and a plurality of pieces of information relating to the user. From the user data set, a plurality of user parameter values can be generated that may describe the characteristics, interests, features, strengths and personality of the user [0019]. Based on the correlation between the current user data set being analyzed and historical data, a user profile may be defined, which may include a plurality of user parameter values describing the user and the user data set [0022]. Embodiments of the user profile module 107 may analyze the set of user data 130 extracted from each user-accessed data source 128 and client device 110, characterize each piece of user data 130 as applying to one or more characteristics or features that may be used to define the user profile and/or apply one or more user parameter values, tags or keywords to each piece of user data collected [0071]. Embodiments of the user profile module 107 may structure the data being stored within a database in a parameter-value tuple [0073]. In some embodiments, the user profile module 107 may match each user to user profiles associated with particular careers, occupations, vocations and/or educational opportunities using an ontology of careers, vocations and educational opportunities created by vocational application 103 [0075]. Embodiments of the ontology may be maintained as part of the knowledge base 117. An ontology may refer to a representation of knowledge by a set of concepts within a domain and the relationships between those concepts in order to model the domain of knowledge being represented [0075]. The knowledge base 117 may be a dynamic resource having the cognitive capacity for self-learning, using one or more data modeling techniques and/or by working in conjunction with one or more machine learning programs and predictive modeling algorithms to improve the accuracy of predicting and presenting vocational actions to the user based on user profiles, vocational data being stored by the knowledge base 117 and the ontology modeled using the collected data [0082].)
Varga teaches computing a user-to-specialty distribution for the plurality of specialties based on the skill-to-specialty distribution and the user-to-skill distribution, the user-to-specialty distribution comprising a user-to-specialty probability value for each specialty in the plurality of specialties given the first user; and (Varga e.g. Embodiments of the present disclosure may further gather educational information for presentation to a user, which may be relevant to a career or vocation that that aligns within an identified user's profile [0023]. Embodiments of the present disclosure may apply predictive modeling techniques to rank vocational actions that may be pursued by the user, including career, vocational, educational, volunteer, geographical, and social options for the user based on the predicted likelihood that a recommended career, vocational and/or educational option (referred to herein collectively as "vocational action") would be found interesting to the user receiving the recommendations and vocational data [0024]. The traits of the user as described by the user data 130 and user-defined parameters, can be further matched to historical user profiles maintained by the user profile module 107 and/or the knowledge base 117 to further determine which vocational actions, including which careers, occupations, vocations and/or educational opportunities are most closely aligned with the user profile have been considered interesting by historical users of the historical user profiles [0074]. In some embodiments, the user profile module 107 may match each user to user profiles associated with particular careers, occupations, vocations and/or educational opportunities using an ontology of careers, vocations and educational opportunities created by vocational application 103 [0075]. Embodiments of the computing environment 100, 200, 400 may comprise a ranking module 111. Embodiments of the ranking module 111 may be integrated with the knowledge base 117 in some embodiments [0092]. The ranking module 111 may rank the vocational actions from the options most probable to be considered interesting or suited for the user to the least likely the least likely option, based on the user profile, user-defined parameters, user parameter values associated with the set of user data, the vocational data and user data 130 [0092]. Embodiments of the ranking module 111 may be performed using machine learning models that may be supervised, unsupervised or semi-supervised as described below [0092]. Based on the analysis of the vocational actions gathered from the data gathering module 105, user-defined parameters and in light of the defined user profile, the user parameter values of the user profile, and/or the parameter value ranges of each vocational action, the ranking module 111 may grade each option being considered for presentation to the user using a grading mechanism such as a point system, probability score, compatibility score or other sorting mechanism which may prioritize each career, vocation or educational option based on the compatibility of the available options with the defined user profile, user-defined parameters and/or an interest, hobby, experience, activity, user personality, etc., of the user [0093]. Each of the options being ranked may be scored or have a probability assigned [0113].)
Varga teaches using the user-to-specialty distribution in an application of the online service. (Varga e.g. FIG. 1 depicts an exemplary embodiment of computing environment 100, which may comprise a server computer 101 and client device 110 connected to a network 150 as shown [0057]. Embodiments of the server computer 101 may provide educational and career guidance services as part of the vocational application 103 to each of the client devices 110 connecting to and requesting the services from the vocational application 103 [0057]. The traits of the user as described by the user data 130 and user-defined parameters, can be further matched to historical user profiles maintained by the user profile module 107 and/or the knowledge base 117 to further determine which vocational actions, including which careers, occupations, vocations and/or educational opportunities are most closely aligned with the user profile have been considered interesting by historical users of the historical user profiles [0074]. Wherein, the more closely a user profile resembles one or more historical user profiles, the higher the probability that the vocational application 103 would recommend one or more of the vocational action previously recommended to a historical user profile to the current user seeking guidance from the vocational application 103 [0074]. Embodiments of the knowledge base 117 may determine which vocational action to present to each through the use of one or more machine learning techniques. The machine learning techniques may be used to analyze user profiles, user-data 130, vocational data and user-defined parameters to arrive at the vocational action that will be presented to the user [0088]. The ranking module 111 may organize the ranking of the vocational action from the highest scoring or most probable to interest or be suitable to the user to the least scoring or most probable option to suit the interests of the user [0113]. The ranked vocational action, along with corresponding vocational data may be compiled into a report and/or presented to the user using an interactive user interface [0024].)
Varga does not explicitly teach the following limitations:
(1) Varga does not explicitly teach, for each skill in a plurality of skills, computing a skill-to-specialty distribution for a plurality of specialties using a first machine learning model, the skill-to-specialty distribution comprising a skill-to-specialty probability value for each specialty in the plurality of specialties given the skill in the plurality of skills; and
(2) for each skill in the plurality of skills, computing a user-to-skill distribution for the plurality of skills…using a second machine learning model, when the field for storing specialty data is empty; the user-to-skill distribution comprising a user-to-skill probability value for each skill in the plurality of skills given the first user;
However, Vontobel teaches an ontology that for each skill in a plurality of skills, computing a skill-to-specialty distribution for a plurality of specialties using a machine learning model, the skill-to-specialty distribution comprising a skill-to-specialty score for each specialty in the plurality of specialties given the skill in the plurality of skills (Vontobel e.g. The systems and methods described herein relate to creation and management of an ontology-based technology platform for jobs and skills, including a data structure of job titles and associated skills, and various technology-based processes for map