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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Sep. 9, 2025 has been entered.
Status of the Claims
Claims 1, 3-14 and 16-28 are all the claims pending in the application.
Claims 1 and 27 are amended.
Claims 1, 3-14 and 16-28 are rejected.
The following is a Non-Final Office Action in response to amendments and remarks filed Sep. 9, 2025.
Response to Arguments
Regarding the 103 rejections, Applicant asserts the rejections should be withdrawn because the previously cited references do not teach the new claim limitations. Examiner respectfully does not find this assertion persuasive because Examiner finds Wu teaches the new claim limitations. Please see below for the complete rejections of the claims as amended.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 4, 9-13, 15-18, 20-22, and 25-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bykov et al, US Pub. No. 2021/0065126, herein referred to as “Bykov”, in view of Heidasch, US Pub. No. 2014/0108313, herein referred to as “Heidasch”, further in view of Wu, US Pub. No. 2018/0150739, herein referred to as “Wu”.
Regarding claim 1, Bykov teaches:
storing or accessing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language (memory, instructions, and computer-readable media, ¶¶[0022]-[0023], [0115] and Fig. 2);
where the data relates to job descriptions and job applicants' skills and experience (database of resumes and job postings, ¶[0085] and Fig. 8, ref. chars. 800, 805);
(b) automatically processing the structured representation of data to determine which jobs best match a job applicant's skills and experience (matches candidates to job postings based on a skills analysis, e.g., ¶¶[0031], [0072]; see also Fig. 6 summarizing process),
in which the structured, machine-readable representation of data that conforms to a machine-readable language comprises semantic nodes and passages (skill network with skills as nodes, ¶[0019] and Fig. 1; see also ¶¶[0090], [0098] and Fig. 9 discussing creating semantic network for job skills);
and a passage is either (i) a semantic node or (ii) a combination of semantic nodes (skill network with skills as nodes, ¶[0019] and Fig. 1);
and where machine-readable meaning comes from the choice of semantic nodes and the way they are combined and ordered as passages (skill network includes logical relationships between parent skills and child skills, ¶[0019]);
in which the structured, machine-readable representation of data stored in the non- transitory computer-readable medium, and the computation units are all represented in substantially the same structured, machine-readable representation of data that conforms to the machine-readable language (instructions are stored as computer readable instructions, e.g., ¶[0023] and system includes skill network with skills as nodes, ¶[0019] and Fig. 1).
However, Bykov does not teach but Heidasch does teach:
and in which a semantic node represents an entity and is itself represented by an identifier (generates a unique identifier for each node in semantic network, ¶[0024]; see also ¶[0052] noting unique identifier is a UUID).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov with the UUIDs of Heidasch because Bykov suggests doing so, see MPEP 2143.I.G. That is, Bykov teaches creating a semantic network of skills with skills as nodes, ¶¶[0019], [0090], [0098]. One of ordinary skill would have recognized the nodes in a semantic network need to been tracked or identified, i.e., with the UUIDs of Heidasch.
However, Bykov and Heidasch does not teach but Wu does teach:
including fetching and executing one or more computation units, wherein each computation unit is a semantic node, wherein the computation units represent computational capabilities (retrieves reference answer from question-answer index, ¶[0155]; see also ¶[0074] noting index is stored on database).
and (ii) uses a syntax that is a single shared syntax that applies to passages that represent factual statements, query statements and reasoning statements in which each reasoning statement explains how a conclusion has been reached (collects questions-answer pairs in index, ¶[0132], and answers include explanations for the answers, e.g., ¶¶[0092], [0134], [0137] and Figs. 3, 10, and 17A).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov and Heidasch with the automated interviewing of Wu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Bykov teaches matching candidates with jobs and vice versa, one of ordinary skill would have recognized users of Bykov (i.e., hiring entities) would likely be interested in interviewing some of these candidates and accordingly would have modified Bykov to include an interviewing process, e.g., as taught by Wu.
Regarding claim 3, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
including the step of matching candidates to open jobs (matches candidates to job postings based on a skills analysis, e.g., ¶¶[0031], [0072]).
using a data store containing: a plurality of candidate resumes where at least some parts of at least some of the candidate resumes are in a structured machine-readable form that encodes meaning; a plurality of job specifications for open roles where at least some of parts of at least some of the job specifications are stored in the structured machine-readable form that encodes meaning (database of resumes and job postings, ¶[0085] and Fig. 8, ref. chars. 800, 805; see also ¶¶[0104]-[0106] discussing storing the data in machine readable medium);
and where the method further includes the step of matching the plurality of candidate resumes with the plurality of job specifications to identify matches between candidates and open roles (matches candidates to job postings by scoring the matches, e.g., ¶¶[0031], [0072]).
Regarding claim 4, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
in which the structured machine-readable form is a language that represents meaning by creating combinations of identifiers and where at least some of the identifiers represent human skills and experience (skill network with skills as nodes includes logical relationships between parent skills and child skills, ¶[0019] and Fig. 1; see also ¶¶[0090], [0098] and Fig. 9 discussing creating semantic network for job skills).
Regarding claim 9, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
including the step of matching requirements in job specifications to the skills and experience of a candidate where there are no keywords in common between the relevant parts of the natural language versions of the candidate resume and job specification.
Nevertheless, it would have been obvious before the effective filing date of the claimed invention, in light of the teachings of Bykov for the matching to involve no common keywords between the resume and the job specification because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Bykov teaches generating a controlled vocabulary for standardizing the skills in job postings and applications, ¶¶[0048], [0086], [0091] when matching. One of ordinary skill would have recognized the standardizing process would results in matches with no common keywords (i.e., when the resumes and job postings include different words which are then standardized into the same, canonical form).
Regarding claim 10, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
including the step of making a sequence of logical reasoning steps in order to match the skills or experience of a candidate with a requirement in a job specification (matches candidates to job postings based on a skills analysis, e.g., ¶¶[0031], [0072]; see also Fig. 6 summarizing process).
Regarding claim 11, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
in which the structured representation of data further includes a representation of a spoken, written or graphical user interface (GUI) instruction provided by a human to a human/machine interface (receives job descriptions from user via a terminal, ¶[0037] and Fig. 2, ref. char. 260).
Regarding claim 12, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
in which representations of job descriptions and job applicants' skills and experience are automatically translated into the machine readable language (database of resumes and job postings, ¶[0085] and Fig. 8, ref. chars. 800, 805; see also ¶¶[0104]-[0106] discussing storing the data in machine readable medium).
Regarding claim 13, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
in which a machine learning system is used to generate the semantic nodes or passages that are the representations of job descriptions and job applicants' skills and experience (creates semantic network for job skills, ¶¶[0090], [0098] and Fig. 9, with skills as nodes, ¶[0019] and Fig. 1).
Regarding claim 16, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Heidasch further teaches:
in which the machine-readable language uses nesting of nodes and passages, as a substantially unambiguous syntax (meta-model semantic network links domains, terms, concepts and concept types; ¶¶[0032], [0051] and Fig. 2).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov with the UUIDs of Heidasch because Bykov suggests doing so, see MPEP 2143.I.G. That is, Bykov teaches creating a semantic network of skills with skills as nodes, ¶¶[0019], [0090], [0098]. One of ordinary skill would have recognized the nodes in a semantic network need to be organized in layers, i.e., as taught by Heidasch.
Regarding claim 17, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Heidasch further teaches:
in which the machine-readable language comprises a plurality of identifiers or IDs which are selected from an address space that is sufficiently large to enable users to select a new identifier with negligible risk of selecting a previously allocated identifier (generates a unique identifier for each node in semantic network, ¶[0024]; see also ¶[0052] noting unique identifier is a UUID).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov with the UUIDs of Heidasch because Bykov suggests doing so, see MPEP 2143.I.G. That is, Bykov teaches creating a semantic network of skills with skills as nodes, ¶¶[0019], [0090], [0098]. One of ordinary skill would have recognized the nodes in a semantic network need to been tracked or identified, i.e., with the UUIDs of Heidasch.
Regarding claim 18, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and does not explicitly teach:
in which the machine-readable language is scalable since there are no restrictions on which users can create a structured, machine-readable representation of data or related identifier.
Nevertheless, it would have been obvious before the effective filing date of the claimed invention, in light of the teachings of the combination of Bykov and Heidasch for the system to be scalable without restrictions on who can create representations of data or related identifier because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Heidasch teaches using UUIDs for identifying nodes, e.g., ¶¶[0019], [0090], [0098]. One of ordinary skill would have recognized using UUIDs is scalable since the large number of identifiers allows for scaling a semeiotic network. Further, neither Bykov nor Heidasch contemplates restrictions on creating data.
Regarding claim 20, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Wu further teaches:
includes the step of (a) the machine-readable language representing a question in a memory as a structured, machine-readable representation of data (collects questions in question-answer pairs, ¶[0146])
and the method further includes the step of (b) automatically generating a response to the question, using one or more of the following steps (retrieves reference answer from question-answer index, ¶[0155]):
(i) matching the question with structured, machine-readable representations of data previously stored in a memory store; (ii) fetching and executing one or more computation units, where computation units represent computational capabilities relevant to answering the question; (iii) fetching and execution of one or more reasoning passages, which are structured, machine-readable representations of data that represent the semantics of potentially applicable reasoning steps relevant to answering the question (retrieves reference answer from question-answer index, ¶[0155]; see also ¶[0074 noting index is stored on database);
and in which the representation of the question, the structured, machine-readable representations of data previously stored in the memory store, the computation units and the reasoning passages are all represented in substantially the same machine-readable language (system is stored as machine-readable media e.g., ¶¶[0179]-[0180]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov and Heidasch with the automated interviewing of Wu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Bykov teaches matching candidates with jobs and vice versa, one of ordinary skill would have recognized users of Bykov (i.e., hiring entities) would likely be interested in interviewing some of these candidates and accordingly would have modified Bykov to include an interviewing process, e.g., as taught by Wu.
Regarding claim 21, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
the step of learning new information and representing the new information in a structured, machine-readable representation of data that conforms to the machine-readable language (trains machine learning model to create semantic network of skills, ¶¶[0094], [0098] and Fig. 9).
Regarding claim 22, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
the step of (i) receiving a word or sequence of words in a natural language (extract standardized skills from the job postings, the job applications, the text description database, ¶[0092]);
and (ii) automatically translating that word or sequence of words into the machine-readable language by identifying or generating structured machine-readable representations that semantically represent meaning of the word or sequence of words in the machine-readable language (creates a semantic job skill network based on job postings, the job applications, the text description database, ¶[0098]; see also ¶[0019] and Fig. 1 discussing job skill network).
Regarding claim 25, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
the step of automatically and autonomously processing detected audio or text into the structured representation of data whenever audio or text is detected or received (extracts skills from text description database, ¶[0092] to create semantic network, ¶[0098] and Fig. 9).
Regarding claim 26, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and Bykov further teaches:
automatically selecting, deciding on or executing actions, and in which the structured representation of data includes one or more tenets, statements or other rules defining the objectives or motives, also represented using the structured representation of data (generates rules, ¶¶[0087]-[0088]);
and the method further includes the steps of (i) analysing a potential action to determine whether executing the action would optimize or otherwise affect achievement or realization of those tenets, statements or other rules (validates job skills, ¶[0088]);
(ii) automatically selecting, deciding on or executing actions only if they optimize or otherwise positively affect the achievement or realization of those tenets, statements or other rules (generates job skill vocabulary based on rules, ¶[0089]).
Regarding claim 27, claim 27 recites similar limitations as claim 1 ana accordingly is rejected for similar reasons as claim 1.
Regarding claim 28, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 7 and Bykov further teaches:
wherein the address space is Universal Unique Identifier (UUID) or Unicode (generates a unique identifier for each node in semantic network, ¶[0024]; see also ¶[0052] noting unique identifier is a UUID).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov with the UUIDs of Heidasch because Bykov suggests doing so, see MPEP 2143.I.G. That is, Bykov teaches creating a semantic network of skills with skills as nodes, ¶¶[0019], [0090], [0098]. One of ordinary skill would have recognized the nodes in a semantic network need to been tracked or identified, i.e., with the UUIDs of Heidasch.
Claim(s) 5-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bykov, Heidasch and Wu in view of Garimella et al, US Pub. No. 2018/0060823, herein referred to as “Garimella”.
Regarding claim 5, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and does not teach but Garimella does teach:
in which at least one data store stores a representation of candidates' desired roles at least partially represented in the structured machine-readable language (uses candidate preferences like employment types, work authorization, etc. ¶[0149])
and where the method includes the step of matching open roles against the representation of candidates' desired roles in order to improve the matches between candidates and open roles (recruiters search based on candidates’ preferences, ¶¶[0149]-[0150]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov, Heidasch and Wu with the candidate’s preferences of Garimella because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized that when presenting job postings to candidates, e.g., as in Fig. 6 of Bykov, candidates would likely have preferences in the types of jobs they are looking for and accordingly would have modified Bykov to incorporate a candidate’s preferences, i.e., as taught by Garimella.
Regarding claim 6, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and does not teach but Garimella does teach:
in which a push notification is sent to a mobile device when a match is found (send messages via push notifications, ¶[0174]; see also ¶¶[0071]-[0072] discussing mobile devices).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov, Heidasch and Wu with the push notifications of Garimella because simple substitution of one know part for another is obvious, see MPEP 2143.I.B. That is, Bykov teaches sending matching jobs and candidates to one another, ¶¶[073], [0083], via a network to a desktop, e.g., Fig. 2. One of ordinary skill would have simply substituted the reports in Bykov with the push notifications in Garimella in situations where notifications for mobile devices are more appropriate (e.g., when the user is accessing the system via smartphone).
Regarding claim 7, the combination of Bykov, Heidasch, Wu and Garimella teaches all the limitations of claim 5 and Garimella further teaches:
including the step of automatically explaining how the candidate matches the role by generating an explanation of which bits of the job specification match the skills and experience of the candidate (provides compatibility report based on job seeker’s match with the job, ¶[0127] and Figs. 9A-9C).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov, Heidasch and Wu with the compatibility report of Garimella because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized that when presenting job postings to candidates, e.g., as in Fig. 6 of Bykov, candidates would likely be interested in details about why they have been matched with a job and accordingly would have modified Bykov to incorporate additional details about the match, e.g., the compatibility reports as taught by Garimella.
Regarding claim 8, the combination of Bykov, Heidasch, Wu and Garimella teaches all the limitations of claim 7 and Garimella further teaches:
in which the explanation is in a natural language (compatibility report includes natural language explanations, Figs. 9A-9C).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov, Heidasch and Wu with the compatibility report of Garimella because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized that when presenting job postings to candidates, e.g., as in Fig. 6 of Bykov, candidates would likely be interested in details about why they have been matched with a job and accordingly would have modified Bykov to incorporate additional details about the match, e.g., the compatibility reports as taught by Garimella.
Claim(s) 14 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Bykov, Heidasch and Wu in view of Gupta et al, US Pub. No. 2020/0065769, herein referred to as “Gupta”.
Regarding claim 14, the combination of Bykov, Heidasch, Wu teaches all the limitations of claim 1 and does not teach but Gupta does teach:
in which the machine-readable language uses a single syntactical item to disambiguate the meaning of structured representations of data (Boolean queries uses parentheses to create syntax, ¶¶[0061]-[0062]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov, Heidasch, Wu with using parentheses to create syntax as taught by Gupta because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Bykov teaches identifying various sets of skills, e.g., ¶[0045], one of ordinary skill would have recognized a common method of organizing these concepts is via Boolean strings, e.g., as taught by Gupta and accordingly would have modified Bykov to use a common method of creating syntax.
Regarding claim 19, the combination of Bykov, Heidasch, Wu teaches all the limitations of claim 1 and Heidasch further teaches:
and (iii) uses a syntax that is a substantially unambiguous syntax comprising nesting of structured representations of data (meta-model semantic network links domains, terms, concepts and concept types; ¶¶[0032], [0051] and Fig. 2);
and (iv) uses an identifier selected from an address space that is sufficiently large to enable users to select a new identifier with negligible risk of selecting a previously allocated identifier (generates a unique identifier for each node in semantic network, ¶[0024]; see also ¶[0052] noting unique identifier is a UUID).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov with the UUIDs of Heidasch because Bykov suggests doing so, see MPEP 2143.I.G. That is, Bykov teaches creating a semantic network of skills with skills as nodes, ¶¶[0019], [0090], [0098]. One of ordinary skill would have recognized the nodes in a semantic network need to been tracked or identified, i.e., with the UUIDs of Heidasch.
However, the combination of Bykov and Heidasch does not explicitly teach:
and (v) is scalable since there are no restrictions on which users can create a structured representations of data or related identifier.
Nevertheless, it would have been obvious before the effective filing date of the claimed invention, in light of the teachings of the combination of Bykov and Heidasch for the system to be scalable without restrictions on who can create representations of data or related identifier because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Heidasch teaches using UUIDs for identifying nodes, e.g., ¶¶[0019], [0090], [0098]. One of ordinary skill would have recognized using UUIDs is scalable since the large number of identifiers allows for scaling a semeiotic network. Further, neither Bykov nor Heidasch contemplates restrictions on creating data.
However, the combination of Bykov, Heidasch and Wu does not teach but Gupta does teach:
in which the machine-readable language (i) uses a single syntactical item to disambiguate meaning of structured representations of data (Boolean queries uses parentheses to create syntax, ¶¶[0061]-[0062]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov, Heidasch and Wu with using parentheses to create syntax as taught by Gupta because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Bykov teaches identifying various sets of skills, e.g., ¶[0045], one of ordinary skill would have recognized a common method of organizing these concepts is via Boolean strings, e.g., as taught by Gupta and accordingly would have modified Bykov to use a common method of creating syntax.
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bykov, Heidasch and Wu in view of Petri et al, US Pub. No. 2010/0228724, herein referred to as “Petri”.
Regarding claim 23, the combination of Bykov, Heidasch and Wu teaches all the limitations of claim 1 and does not teach but Petri does teach:
the step of providing a service operable to receive a description of an entity and return one or more identifiers for structured, machine-readable representations of data corresponding to the entity, so that a user is able to use a shared identifier for the entity (user enters search and search returns domains of data object as well as unique identifier of the data object, ¶¶[0041]-[0042]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov, Heidasch and Wu with the data retrieval of Petri because known work in one field of endeavor or another may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Bykov teaches storing skills as nodes, e.g., ¶[0019]. One of ordinary skill would have recognized users may wish to view the details of the node skills and accordingly would have modified Bykov to include a search function, e.g., as taught by Petri.
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bykov, Heidasch and Wu in view of Devos et al, WO 2021/089129, herein referred to as “Devos”.
Regarding claim 24, Bykov, Heidasch and Wu teaches all the limitations of claim 1 and does not teach but Devos does teach:
translating between a first natural language and a second natural language, by: (a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language (adds translation pairs of documents to corpus, pg. 9, ll. 6-21);
(b) receiving a word or sequence of words in the first natural language to be translated into the second natural language (receives resume of job application, pg. 6, ll.16-22);
(c) automatically translating that word or sequence of words expressed in the first natural language into the second natural language by (i) identifying a structured, machine-readable representation of data that represents the semantics of the word or sequence of words in the first natural language and (ii) retrieving a word or sequence of words in the second natural language that corresponds in meaning to the identified structured, machine-readable representation of data (concept network includes interconnected concepts between natural languages, pg. 6, ll. 6-22).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the job skill taxonomy of Bykov, Heidasch and Wu with the data retrieval of Devos because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Bykov teaches matching candidates with jobs and vice versa. One of ordinary skill would have recognized some qualified candidates’ resumes may be in different languages than the job postings and accordingly would have modified matching in Bykov to include the translation-pairs of Devos so resumes and job postings in different languages can be matched.
Conclusion
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626