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 Claim
This action is in reply to the election of restriction filed on 22 of October 2025.
The following is a first action on the merits. In response to Examiner's restriction requirement, on 09/11/2025. Applicant elected group II, claims 18-38 and withdrew from consideration groups I, III, and IV claims 1-17, 39, and 40. Claims 1-17, 39, and 40 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant's election without traverse of the non-elected Claims in the reply filed on 04/21/2025 is acknowledged. Of the pending claims, claims 18-38 are examined and rejected below.
Claim Rejections - 35 USC § 112
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.
The term “an embedding dimension of an embedding mechanism” in claim 18 is a relative term which renders the claim indefinite. The term “an embedding dimension of an embedding mechanism” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The examiner notes that various passages in the specification disclose a contextualizer that may be configured to apply the embedding mechanism, see at least Fig. 1 and 22. Under the broadest reasonable interpretation, terms are being interpreted as being components of a machine learning applied to a recruiting system. These components can take shape of a vector, data points, outputs such as a job match, etc.
Dependent claims 19-33 do not cure this deficiency and are rejected for the same reasons as stated above with respect to claim 18.
Claim Rejections - 35 USC § 101
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 therefore, subject to the conditions and requirements of this title.
Claims 18-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machines, article of manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception.
The claims are then analyzed to determine whether the claims are directed to a judicial exception. MPEP §2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
With respect to 2A Prong 1, claim 18 recites “identify a current location of a learner relative to a plurality of job nodes located in a job space, wherein the plurality of job nodes are contextualized within the job space according to an embedding dimension of an embedding mechanism; calculating a first path in the job space from the current location of the learner toward a target job node of the plurality of job nodes; transforming the first path in the job space into a second path in a concept space comprising a plurality of concept nodes, wherein the plurality of concept nodes are contextualized within the concept space according to the embedding dimension of the embedding mechanism; identifying a first concept node of the plurality of concept nodes based on a distance between a first point on the second path and the first concept node; and reporting content corresponding to the first concept node to a user of the computer system”, and therefore recites an abstract idea.
With respect to 2A Prong 1, claim 34 recites “one or more processors; and a memory storing instructions that, when executed by the one or more processors, configure the learner evaluation system to: receive learner data of a learner; receive a plurality of jobs of an organization; generate a job space corresponding to the plurality of jobs of the organization using the plurality of jobs of the organization; identify a path of the learner in a concept space for a content library of the organization by applying the learner data to the concept space; determine a path of the learner in the job space based on the path of the learner in the concept space; evaluate an affinity of the learner for a first job of the plurality of jobs based on the path of the learner in the job space; and report the affinity of the learner for the first job on a display of the computing apparatus”, and therefore recites an abstract idea.
More specifically, claims 18 and 34 are directed to “Mathematical Concepts” in particular “mathematical calculations” and “mathematical relationships” such as scoring a driver or route as discussed in MPEP §2106.04(a)(2), and in the 2019-01-08 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claims recite an abstract idea.
Dependent claims 19-33 and 35-38 further recite abstract idea(s) contained within the independent claims to include “mathematical formulas”, and do not contribute to significant more or enable practical application. Thus, the dependent claims are rejected under 101 based on the same rationale as the independent claims.
Under Prong Two of Step 2A of the Alice/Mayo test, the examiner acknowledges that Claims 18, 30, and 34 recite additional elements yet the additional elements do not integrate the abstract idea into a practical application. In order for the judicial exception to be “integrated into a practical application”, an additional element or a combination of additional elements in the claim “will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” PEG, 84 Fed. Reg. 54 (Jan. 7, 2019). The courts have identified examples in which a judicial exception has not been integrated into a practical application when “an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.” PEG, 84 Fed. Reg. 55 (Jan. 7, 2019); MPEP § 2106.05(h). The claims are directed to an abstract idea.
In particular, claims 18 and 34 recite additional elements boldened and underlined above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Further, the remaining additional element italicized above reflect insignificant extra solution activities to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Dependent claim 30 recites additional element “user interface for the computing system”. This is a generic computer component recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process.
With respect to step 2B, claims 1, 30, and 34 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claim recites the additional elements described above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process, as evidenced by at least in ¶86-89 “The computing system 600 includes a bus 610 (e.g., an address bus and a data bus) or other communication mechanism for communicating information, which interconnects subsystems and devices, such as one or more processors 608, a memory 602 (e.g., random access memory (RAM)), a static storage 604 (e.g., read only memory (ROM)), a dynamic storage 606 (e.g., magnetic or optical), a communications interface 616 (e.g., a modem, an Ethernet card, a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network, a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a Wi-Fi network, etc.), and/or an input/output (I/O) interface 620 (e.g., a keyboard, a keypad, a mouse, and/or a microphone, etc.). In particular embodiments, a computing system 600 may include one or more of any such components. The communications interface 616 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between the computing system 600 and one or more other computer systems or one or more networks. One or more memory buses (which may each include an address bus and a data bus) may couple the one or more processors 608 to the memory 602. The bus 610 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between the one or more processors 608 and the memory 602 and facilitate accesses to the memory 602 as requested by the one or more processors 608. In particular embodiments, the bus 610 includes hardware, software, or both coupling components of the computing system 600 to each other”.
Claims 19-29, 31-33, and 35-38 do not disclose additional elements, further narrowing the abstract ideas of the independent claims and thus not practically integrated under prong 2A as part of a practical application or under 2B not significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 18-36 are rejected under 35 U.S.C. 103 as being obvious by the combination of US 20160132831 to Sharma et. al. (hereinafter referred to as “Sharma”) in further view of US 20250005346 to Lo et. al. (hereinafter referred to as “Lo”).
(A) As per Claim 18:
Sharma expressly discloses:
calculating a first path in the job space from the current location of the learner toward a target job node of the plurality of job nodes; (Sharma ¶37 the application server 102 may determine a first distance between a first set of nodes and a second node based on the graph matrix. The first set of nodes may correspond to the one or more candidates and the second node may correspond to a job opening under consideration).
transforming the first path in the job space into a second path in a concept space comprising a plurality of concept nodes, wherein the plurality of concept nodes are contextualized within the concept space according to the embedding dimension of the embedding mechanism; (Sharma ¶38 the application server 102 may determine a second score for each of the one or more sets of candidates based on the second distance. Based on the second score, the application server 102 may rank the one or more sets of candidates).
identifying a first concept node of the plurality of concept nodes based on a distance between a first point on the second path and the first concept node; reporting content corresponding to the first concept node to a user of the computer system; (Sharma ¶38-39 based on the ranking, the application server 102 may select the set of candidates from the one or more sets of candidates for the team. In an alternate embodiment, the application server 102 may present a list of ranked one or more sets of candidates to the hiring manager over a display associated with the hiring manager-computing device 104).
Although Sharma teaches methods and systems for human resource management in an organization, it doesn’t expressly disclose current location of a job seeker relative to multiple jobs, however Lo teaches:
identify a current location of a learner relative to a plurality of job nodes located in a job space, wherein the plurality of job nodes are contextualized within the job space according to an embedding dimension of an embedding mechanism; (Lo ¶55 if it is observed that a job seeker in the IT field connects with a recruiter in the IT field, the relationship and distance between the embedding of the job seeker and the embedding of the recruiter in the n-dimensional space can be used to identify candidates for a recruiter in a different field, such as in the medical field).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Sharma’s application server and have a job seeker in the IT field connect with a recruiter in the IT field of Lo as both are analogous art which teach solutions to determining a first distance between a first set of nodes and a second node based on the graph matrix as taught in Sharma and have the relationship and distance between the embedding of the job seeker and the embedding of the recruiter in the n-dimensional space be used to identify candidates for a recruiter in a different field as taught in Lo.
(B) As per Claim 19:
Sharma expressly discloses:
wherein the transforming the first path into the second path comprises multiplying a first vector representing a first location in the job space that is on the first path by a transform matrix L' to calculate a second vector representing a second location in the concept space that is on the second path; (Sharma ¶62-63 at step 304, the graph is transformed to generate a graph matrix. In an embodiment, the processor 202 transforms the graph. The graph matrix may be representative of a mapping of the one or more nodes in a predetermined dimensional space. Since the graph matrix is so generated that each node in the graph is represented in the graph matrix based on the K neighboring nodes. The graph matrix may be utilized to identify relationships that are not directly represented in the graph. For example, if there exists a job description 1 (represented as a node in the graph) that is directly connected to job description 2 (represented as a node in the graph) through an edge in the graph. The job description 2 is further connected to job description 3. If such a graph is transformed to generate the graph matrix, the job description 1 will be shown as related to the job description 3 in the graph matrix).
(C) As per Claim 20:
Sharma expressly discloses:
wherein the transform matrix L' equals J ●KT, where: J is a job matrix representing a first arrangement of the plurality of job nodes within the job space according to the embedding dimension; K is a content matrix representing a second arrangement of the plurality of concept nodes within the concept space according to the embedding dimension; (Sharma ¶66 at step 306, the arithmetic logic unit 210 in the processor 202, determines the first distance between the first set of nodes and the second node based on the graph matrix. The first set of nodes may correspond to the one or more candidates and the second node may correspond to the job opening (information pertaining to the job opening is received as the input from the hiring manager through the hiring manager-computing device 104). In an embodiment, the processor 202 may determine the distance between the job opening and each of the one or more candidates. In an embodiment, the first distance may correspond to a Euclidean distance in the predetermined dimensional space).
(D) As per Claim 21:
Sharma expressly discloses:
calculating the concept matrix K; calculating the job matrix J; calculating the transform matrix L'; (Sharma ¶63 the graph matrix may be representative of a mapping of the one or more nodes in a predetermined dimensional space. Since the graph matrix is so generated that each node in the graph is represented in the graph matrix based on the K neighboring nodes. The graph matrix may be utilized to identify relationships that are not directly represented in the graph. For example, if there exists a job description 1 (represented as a node in the graph) that is directly connected to job description 2 (represented as a node in the graph) through an edge in the graph. The job description 2 is further connected to job description 3. If such a graph is transformed to generate the graph matrix, the job description 1 will be shown as related to the job description 3 in the graph matrix).
(E) As per Claim 22:
Sharma expressly discloses:
the first vector comprises a first plurality of values corresponding to the plurality of job nodes that indicate first affinities for the plurality of job nodes at the first location in the job space; the second vector comprises a second plurality of values corresponding to the plurality of concept nodes that indicate second affinities of the plurality of concept nodes at the second location in the concept space; (Sharma ¶74 as discussed above in conjunction with the FIG. 3, the relationships between the one or more candidates and the one or more job descriptions associated with the one or more job openings are determined. Based on the relationships, the processor 202 may connect the nodes corresponding to the one or more candidates with the nodes corresponding to the one or more job descriptions associated with the one or more job openings. For example, the node 402 c (representing a job description associated with a job opening) is connected to the node 406 a (representing a candidate) through the edge 408. Similarly, the nodes for the one or more job descriptions associated with the one or more job openings may be connected to the nodes for the one or more candidates).
(F) As per Claim 23:
Sharma expressly discloses:
wherein the first concept node of the plurality of concept nodes is identified for being a closest concept node to the second path of the plurality of concept nodes; (Sharma ¶73 the job description node 402 a is connected to the job description node 402 b through the edge 404. Therefore, the job description associated with the job opening (represented by the node 402 a) is similar to the job description associated with the job opening ad represented by the node 402 b in some aspects).
(G) As per Claim 24:
Sharma expressly discloses:
further comprising using the embedding mechanism to perform the contextualization of the plurality of concept nodes within the concept space; (Sharma ¶62 At step 304, the graph is transformed to generate a graph matrix. In an embodiment, the processor 202 transforms the graph. The graph matrix may be representative of a mapping of the one or more nodes in a predetermined dimensional space. In a scenario, the predetermined dimensional space may be a Laplacian embedding space of the graph. In an embodiment, the Laplacian embedding space is a K-dimensional space. In a further embodiment, each node in the graph is mapped to the predetermined dimensional space based on respective K neighboring nodes).
(H) As per Claim 25:
Although Sharma teaches methods and systems for human resource management in an organization, it doesn’t expressly disclose parsing a library using an embedding mechanism, however Lo teaches:
wherein the embedding mechanism parses a content library to generate the plurality of concept nodes; (Lo ¶94 at operation 806, a first tokenizer is used to modify the user session sequence data structure to encode each of the one or more actions to a token unique to a corresponding action type. At operation 808, a second tokenizer is used to modify the user session sequence data structure to encode each of the one or more items to a token unique to a corresponding item type. At operation 810, the user session sequence data structure is passed to a sequence encoder, which embeds each token in the user session sequence data structure to a vector embedding).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Sharma’s application server and have a first tokenizer used to modify the user session sequence data structure to encode each of the one or more actions to a token unique to a corresponding action type of Lo as both are analogous art which teach solutions to determining a first distance between a first set of nodes and a second node based on the graph matrix as taught in Sharma and have the user session sequence data structure passed to a sequence encoder, which embeds each token in the user session sequence data structure to a vector embedding as taught in Lo.
(I) As per Claim 26:
Sharma expressly discloses:
further comprising using the embedding mechanism to perform the contextualization of the plurality of job nodes within the job space; (Sharma ¶62-63 at step 304, the graph is transformed to generate a graph matrix. In an embodiment, the processor 202 transforms the graph. The graph matrix may be representative of a mapping of the one or more nodes in a predetermined dimensional space. Since the graph matrix is so generated that each node in the graph is represented in the graph matrix based on the K neighboring nodes. The graph matrix may be utilized to identify relationships that are not directly represented in the graph. For example, if there exists a job description 1 (represented as a node in the graph) that is directly connected to job description 2 (represented as a node in the graph) through an edge in the graph. The job description 2 is further connected to job description 3. If such a graph is transformed to generate the graph matrix, the job description 1 will be shown as related to the job description 3 in the graph matrix).
(J) As per Claim 27:
Sharma expressly discloses:
wherein the embedding mechanism parses job information to generate the plurality of job nodes; (Sharma ¶37 the application server 102 may extract the candidate profile information, the employee profile information, and the one or more job descriptions corresponding to the one or more job openings from the database server 106 to generate the graph. In an embodiment, the one or more candidates, the one or more employees, and the one or more job descriptions corresponding to the one or more job openings are represented as one or more nodes in the graph).
(K) As per Claim 28:
Although Sharma teaches methods and systems for human resource management in an organization, it doesn’t expressly disclose Large Language Model, however Lo teaches:
wherein the embedding mechanism comprises a large language model (LLM); (Lo ¶77 the concatenation component concatenates the encoded sequence data from the sequence encoder 404 with the embedded member features from embedding layer 406. This is similar to treating the user features as an extra element of the input sequence. This allows the transformer 400 to consider the context of the user and adds compatibility with Bidirectional Encoder Representations from Transformers (BERT) models which can be used with unsupervised training. However, it is also less flexible in setting the dimension of the transformed user features as it has the same dimension as other sequence elements. Bert applies bidirectional training of a model known as a transformer to language modelling).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Sharma’s application server and have the concatenation component concatenate the encoded sequence data from the sequence encoder with the embedded member features from embedding layer of Lo as both are analogous art which teach solutions to determining a first distance between a first set of nodes and a second node based on the graph matrix as taught in Sharma and allow the transformer to consider the context of the user and adds compatibility with Bidirectional Encoder Representations from Transformers models which can be used with unsupervised training as taught in Lo.
(L) As per Claim 29:
Sharma expressly discloses:
wherein the concept node represents a plurality of contents that includes the content; (Sharma ¶37 the application server 102 may extract the candidate profile information, the employee profile information, and the one or more job descriptions corresponding to the one or more job openings from the database server 106 to generate the graph. In an embodiment, the one or more candidates, the one or more employees, and the one or more job descriptions corresponding to the one or more job openings are represented as one or more nodes in the graph).
(M) As per Claim 30:
Sharma expressly discloses:
wherein the reporting the content corresponding to the concept node to the user of the computer system occurs on a user interface (UI) for the computing system; (Sharma ¶70 In a scenario where the complete list of the one or more candidates are sent to the hiring manager-computing device 104, at step 314, an input may be received deterministic of a selection of the set of candidates from the ranked list of the one or more candidates. In an embodiment, the hiring manager may select the set of candidates through a graphical user interface, presented by the processor 202, on the hiring manager-computing device 104).
(N) As per Claim 31:
Sharma expressly discloses:
wherein the contextualization of the plurality of job nodes in the job space corresponds to job characteristics of a plurality of jobs represented by the plurality of job nodes; (Sharma ¶23, 37 the application server 102 may extract the candidate profile information, the employee profile information, and the one or more job descriptions corresponding to the one or more job openings from the database server 106 to generate the graph. In an embodiment, the one or more candidates, the one or more employees, and the one or more job descriptions corresponding to the one or more job openings are represented as one or more nodes in the graph).
(O) As per Claim 32:
Sharma expressly discloses:
wherein the contextualization of the plurality of concept nodes in the concept space corresponds to a plurality of content represented by the plurality of concept nodes; (Sharma ¶40 the database server 106 is configured to store information pertaining to the profiles of one or more candidates, profiles of the one or more employees, and the job descriptions associated with the one or more job openings. The database server 106 may further store information related to social and professional connections associated with the one or more candidates, historical decisions, and job affiliations. In an embodiment, the database server 106 may receive a query from the application server 102 to extract/update the information).
(P) As per Claim 33:
Sharma expressly discloses:
wherein the learner is an employee of an organization comprising a plurality of jobs represented in the plurality of job nodes; (Sharma ¶31 the various types of relationships in the graph may include, but are not limited to, relationship between the one or more job openings, relationship between the one or more candidates, relationship between the one or more employees, relationship between the one or more candidates and the one or more job openings, relationship between the one or more employees and the one or more job openings, and relationship between the one or more candidates and the one or more employee).
(Q) As per Claim 34:
Sharma expressly discloses:
one or more processors; and a memory storing instructions that, when executed by the one or more processors, configure the learner evaluation system to: (Sharma ¶45 he processor 202 includes suitable logic, circuitry, and/or interfaces that are operable to execute one or more instructions stored in the memory 204 to perform predetermined operations. The processor 202 may be implemented using one or more processor technologies known in the art).
receive learner data of a learner; receive a plurality of jobs of an organization; (Sharma ¶53 for creating the graph, the processor 202 may transmit a query to the database server 106 to extract the data pertaining to profiles of the one or more candidates (who have applied for the one or more job openings), profiles of the employees of the organization, and job descriptions associated with the one or more job openings).
generate a job space corresponding to the plurality of jobs of the organization using the plurality of jobs of the organization; (Sharma ¶40 The database server 106 is configured to store information pertaining to the profiles of one or more candidates, profiles of the one or more employees, and the job descriptions associated with the one or more job openings).
identify a path of the learner in a concept space for a content library of the organization by applying the learner data to the concept space; (Sharma ¶37 the application server 102 may extract the candidate profile information, the employee profile information, and the one or more job descriptions corresponding to the one or more job openings from the database server 106 to generate the graph. The application server 102 may determine a first distance between a first set of nodes and a second node based on the graph matrix. The first set of nodes may correspond to the one or more candidates and the second node may correspond to a job opening under consideration).
evaluate an affinity of the learner for a first job of the plurality of jobs based on the path of the learner in the job space; (Sharma ¶32 a “first distance” refers to a measure of degree of similarity between profiles of one or more candidates (who have applied for one or more job openings in the organization) and job description of the one or more job openings in the organization).
report the affinity of the learner for the first job on a display of the computing apparatus; (Sharma ¶39 the hiring manager-computing device 104 refers to a computing device that may be utilized by a hiring manager to interact with the application server 102 for selecting a set of candidates for a job opening in the organization. In an embodiment, the hiring manager may require the set of candidates for the job opening associated with the team in the organization. In an embodiment, the hiring manager-computing device 104 may receive a user interface from the application server 102. The user interface may enable the hiring manager to input the one or more parameters based on which the set of candidates need to be selected).
Although Sharma teaches methods and systems for human resource management in an organization, it doesn’t expressly disclose determining a path of the job seeker in the job space, however Lo teaches:
determine a path of the learner in the job space based on the path of the learner in the concept space; (Lo ¶60-62, 92 each embedding layer 304A, 304B, 304C may be separately trained to create vector embeddings for the corresponding tokens, by learning such embeddings. Learning embeddings is a process whereby each token or combination of tokens is assigned a different set of coordinates in an n-dimensional space. Each of these sets of coordinates is considered a different embedding, and a set of coordinates is known as a vector (unlike “vectors” in the mathematical sense which are lines with directions). The embeddings may also be used for candidate generations, based on the distance property of the embeddings. Specifically, assuming the distance of embeddings is related to an application objective, then if a user A is a good candidate for user A, then those users that are close to user B in the embedding space may also be good candidates of user A).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Sharma’s application server and have each embedding layer be separately trained to create vector embeddings for the corresponding tokens of Lo as both are analogous art which teach solutions to determining a first distance between a first set of nodes and a second node based on the graph matrix as taught in Sharma and have the embeddings be used for candidate generations, based on the distance property of the embeddings as taught in Lo.
(R) As per Claim 35:
Although Sharma teaches methods and systems for human resource management in an organization, it doesn’t expressly disclose affinity between job seeker and first job based on job seeker current location, however Lo teaches:
wherein the affinity of the learner for the first job is evaluated based on a current location of the learner on the path of the learner in the job space; (Lo ¶53-55 the similarity score of these action/item embeddings and the user embeddings generated from the user building block portion of the user embedding block 208 can act as an affinity score that can be used as an input feature to the machine learning inference component 204. Additionally, due to the distance relationship inherent in the embeddings, the user embeddings can further be used to generate candidates. Additionally, the concept of an “analogy property” can be incorporated into the embeddings. For example, if it is observed that a job seeker in the IT field connects with a recruiter in the IT field, the relationship and distance between the embedding of the job seeker and the embedding of the recruiter in the n-dimensional space can be used to identify candidates for a recruiter in a different field, such as in the medical field).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Sharma’s application server and have a job seeker in the IT field connect with a recruiter in the IT field of Lo as both are analogous art which teach solutions to determining a first distance between a first set of nodes and a second node based on the graph matrix as taught in Sharma and the similarity score of these action/item embeddings and the user embeddings generated from the user building block portion of the user embedding block act as an affinity score that can be used as an input feature to the machine learning inference component as taught in Lo.
(S) As per Claim 36:
Although Sharma teaches methods and systems for human resource management in an organization, it doesn’t expressly disclose determining a path of the job seeker in the concept space, however Lo teaches:
wherein the learner data comprises a data structure describing a path taken by the learner in the concept space; (Lo ¶92 the embeddings may also be used for candidate generations, based on the distance property of the embeddings. Specifically, assuming the distance of embeddings is related to an application objective, then if a user A is a good candidate for user A, then those users that are close to user B in the embedding space may also be good candidates of user A).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Sharma’s application server and have each embedding layer be separately trained to create vector embeddings for the corresponding tokens of Lo as both are analogous art which teach solutions to determining a first distance between a first set of nodes and a second node based on the graph matrix as taught in Sharma and assuming the distance of embeddings is related to an application objective, then if a user A is a good candidate for user A, then those users that are close to user B in the embedding space may also be good candidates of user A as taught in Lo.
Claims 37-38 are rejected under 35 U.S.C. 103 as being obvious by the combination of US 20160132831 to Sharma et. al. (hereinafter referred to as “Sharma”) in further view of US 20250005346 to Lo et. al. (hereinafter referred to as “Lo”) and in even further view of US 20060265270 to Hyder et. al. (hereinafter referred to as “Hyder”).
(A) As per Claim 37:
Although Sharma in view of Lo teaches methods and systems for human resource management in an organization, it doesn’t expressly disclose evaluating the affinity of the job seeker based on a prior location, however Hyder teaches:
wherein the affinity of the learner for the first job is evaluated based on a prior location of the learner on the path of the learner in the job space; (Hyder ¶36, 46 such affinities preferably relate a job seeker to other job seekers based on, for example, a particular location, a job, skill set, job categories, spatial relationships, etc. Similarly, jobs can also be related to other jobs. In general, the affinity module 112 is used to identify commonalities and trends between otherwise disparate data. This information can then be utilized to identify alternative jobs to the job seeker or alternative job seeker candidates to an employer/recruiter user of the system 100 that otherwise might be missed. More particularly, the profile builder modules 200 and 202 feed the information obtained from the job seeker or the employer/recruiter, such as the job seeker's city, state, login ID, etc, and employer/recruiter provided job description information such as the job city, state, zip code, company name, job title, etc into an Extraction, Translation and Load (ETL) module 204 as shown in FIG. 2A. This ETL module 204 optionally can require input and translation of the input data from the resume extraction module 118 and from the location mapping module 114 in order to extract and load the information on the job and the job seeker properly into the database 104 as a job seeker profile 206 and a job profile 208 as is shown in FIG. 2B).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Sharma in view of Lo’s application server and have affinities preferably relate a job seeker to other job seekers based on a particular location, a job, skill set, job categories, spatial relationships of Hyder as both are analogous art which teach solutions to determining a first distance between a first set of nodes and a second node based on the graph matrix as taught in Sharma in view of Lo and have the profile builder modules feed the information obtained from the job seeker or the employer/recruiter, such as the job seeker's city, state, login ID, and employer/recruiter provided job description information such as the job city, state, zip code, company name, job title into an Extraction, Translation and Load (ETL) module as taught in Hyder.
(B) As per Claim 38:
Although Sharma in view of Lo teaches methods and systems for human resource management in an organization, it doesn’t expressly disclose evaluating the affinity of the job seeker based on a trajectory information, however Hyder teaches:
wherein the affinity of the learner for the first job is evaluated based on trajectory information for the path of the learner in the job space; (Hyder ¶109-110 the apply history and click-through matching score 322 is generally calculated using the affinity engine 112 and the user activity monitoring module 116. The affinity engine generates an affinity file using data from a “jobs applied for” (expression of interest) file as described in more detail below with reference to Table 5. This file tracks all jobs for which the job seeker has applied for or otherwise expressed an interest in).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Sharma in view of Lo’s application server and have the apply history and click-through matching score be generally calculated using the affinity engine and the user activity monitoring module of Hyder as both are analogous art which teach solutions to determining a first distance between a first set of nodes and a second node based on the graph matrix as taught in Sharma in view of Lo and have the affinity engine generate an affinity file using data from a “jobs applied for” as taught in Hyder.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant’s disclosure.
KR20250164080A
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEUS R STIVALETTI whose telephone number is 571-272-5758. The examiner can normally be reached on M-F 8:30-5:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1822.s
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/MATHEUS RIBEIRO STIVALETTI/Primary Examiner, Art Unit 3623 1/6/2026