DETAILED ACTION
This correspondence is responsive to the Application filed on October 24, 2023. Claims 1-20 are pending in the case, with claims 1, 8 and 15 in independent form.
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 .
Summary of Detailed Action
Claims 3 and 17 are objected to regarding informalities.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-3, 5-6 and 8-10, 12, 14-17 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hinton et al.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hinton et al., and further in view of Crow et al.
Claims 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hinton, and further in view of Susanne Taylor, Indeed, How to Write an Effective Resume Summary (With Examples).
Claim Objections
Claims 3 and 17 are objected to because of the following informalities:
Claim 3, line 3: change “an distance” to “a distance”.
Claim 17, line 3: change “an distance” to “a distance”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) subject matter at a general, high-level of a method comprising extracting a keyword from a content phrase from a resume; identifying an existing job title linked to the content phrase; identifying an additional job title from a knowledge graph based on the keyword; and in response to determining an association between the additional job title and the existing job title, linking the content phrase with the additional job title, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III). This judicial exception is not integrated into a practical application and the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 1-20 recite one of the four statutory categories of patent able subject matter and belong to the statutory class(es) of a process (method claims 1-7), a machine (system/apparatus claims 8-14), and an article of manufacture (non-transitory computer readable media claims 15-20).
Claim 1, recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, claim 1 further recites comprising extracting a keyword from a content phrase from a resume; identifying an existing job title linked to the content phrase; identifying an additional job title from a knowledge graph based on the keyword; and in response to determining an association between the additional job title and the existing job title, linking the content phrase with the additional job title, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application. Thus, the claim is directed to the abstract idea.
Claim 2, dependent on claim 1, recites only additional abstract ideas for wherein identifying the additional job title from the knowledge graph based on the keyword comprises searching for the additional job title based on a frequency of the additional job title to the keyword in the knowledge graph, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
Claim 3, dependent on claim 1, recites additional abstract ideas for wherein identifying the additional job title from the knowledge graph based on the keyword comprises searching for the additional job title based on an distance between the additional job title and the keyword in the knowledge graph, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
an embedding of (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 4, dependent on claim 1, recites only additional abstract ideas for wherein determining the association between the additional job title and the existing job title, comprises determining an industry associated with the existing job title and the industry associated with the additional job title are the same, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
Claim 5, dependent on claim 1, recites only additional abstract ideas for wherein determining the association between the additional job title and the existing job title, comprises: determining a similarity between the existing job title and the additional job title based on the knowledge graph is above a threshold, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
Claim 6, dependent on claim 1, recites additional abstract ideas for further comprising generating the knowledge graph, comprising: extracting one or more keywords from resume data; linking the extracted one or more keywords with a resume job title in the resume data; each of the extracted one or more keywords as a node in the knowledge graph; and the resume job title in the knowledge graph, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
embedding (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 7, dependent on claim 1, recites only additional abstract ideas for wherein the content phrase from the resume comprises a resume summary, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
Claim 8, recites a system, thus a machine, one of the four statutory categories of patentable subject matter. However, claim 8 further recites to extract a keyword from a content phrase from a resume; identify an existing job title linked to the content phrase; identify an additional job title from a knowledge graph based on the keyword; and in response to a determination of an association between the additional job title and the existing job title, link the content phrase with the additional job title, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
A processing system, comprising: a memory comprising computer-executable instructions; and a processor configured to execute the computer-executable instructions and cause the processing system to (An additional element merely that recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Thus, the claim is directed to the abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Claim 15, recites non-transitory computer-readable medium, thus an article of manufacture, one of the four statutory categories of patentable subject matter. However, claim 15 further recites extracting a keyword from a content phrase from a resume; identifying an existing job title linked to the content phrase; identifying an additional job title from a knowledge graph based on the keyword; and in response to determining an association between the additional job title and the existing job title, linking the content phrase with the additional job title, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
A non-transitory computer-readable medium storing program code for causing a processing system to perform the steps of (An additional element merely that recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Thus, the claim is directed to the abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Dependent claims 9-14 and 16-20 are comparably rejected as set forth above with respect to dependent claims 2-7.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-6, 8-10, 12, 14-17 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hinton et al. (Pub. No. US 2022/0343284 A1, published October 27, 2022) hereinafter Hinton. The Examiner notes that Hinton is cited on Applicant’s Information Disclosure Statement filed January 28, 2025.
Regarding claim 1, Hinton teaches:
A method (i.e., A data management server computer (“server”) and related methods are disclosed to enhance electronic documents or search processes related to skill data, including skill names, using machine learning techniques. Hinton, Figs 1-6, para 26), comprising:
extracting a keyword from a content phrase from a resume (i.e., In some embodiments, the server 102 is programmed to retrieve skill-related data, such as job postings and resumes (extracting a keyword (skill name keyword) from a content phrase from a resume), from the one or more third-party data sources 122. The server 102 can also be programmed to receive such data directly from users or the one or more client devices 112. The server 102 is programmed to then process the skill-related data, such as extracting skill names (extracting a keyword from a content phrase from a resume) and metadata related to the skill names. Hinton, Figs 1-6, para 40, 27. A skill name is the name of an ability to do something, which could be related to a tool or a subject matter, for example. Examples of skill names include “product engineering” or “object-oriented programming”. The server can also extract skill names and other skill information from data sources related to the search, training, evaluation, or utilization of skills, such as job search websites or human resources systems, using machine learning techniques. Hinton, Figs 1-6, para 27, 40.);
identifying an existing job title linked to the content phrase (i.e., [0065] In one embodiment, the server is programmed to receive skill data related to skill utilization, evaluation, training, or marketability; and determine from the skill data, for each skill name of the plurality of skill names, an applicable job category, a list of applicable job titles (identifying an existing job title linked to the content phrase), …. Hinton, Figs 1-6, para 65, 63.);
identifying an additional job title from a knowledge graph based on the keyword (i.e., The server can also return additional information associated with the skill names (identifying an additional job title (additional list of job titles, para 48) from a knowledge graph (knowledge graph for skill names, para 57) based on the keyword (skill name keyword)), in terms of utilization, marketability, training, or evaluation, for example. Hinton, Figs 1-6, para 62, 48. For instance, the additional information for a skill name may include an applicable job category, a list of applicable job titles (identifying an additional job title (additional list of job titles, para 48) from a knowledge graph (knowledge graph for skill names, para 57) based on the keyword (skill name keyword)), ... Hinton, Figs 1-6, para 62, 48, 57. Therefore, while “software” and “code” appear very different syntactically, the server can automatically group together their associated skill names as appropriate. The resulting superset of dependency graphs can then be considered as a knowledge graph for skill names((identifying an additional job title (additional list of job titles, para 48) from a knowledge graph (knowledge graph for skill names, para 57) based on the keyword (skill name keyword))). Hinton, Figs 1-6, para 57, 62, 48.); and
in response to determining an association between the additional job title and the existing job title, linking the content phrase with the additional job title (i.e., As a data generator, a request can include a skill name, and the server can (or cause the coupled system to) similarly identify a subgraph for the skill name from the knowledge graph. The request can also include a job category or a job title, and the server can identify a subgraph including all nodes corresponding to skill names associated with the job category or job title (in response to determining an association between the additional job title and the existing job title (identify subgraph), linking the content phrase with the additional job title (including all nodes in subgraph)). Hinton, para 63, 48.The system can similarly generate additional information related to skills and stores the additional information in association with the skill names (in response to determining an association between the additional job title and the existing job title, linking the content phrase with the additional job title). Hinton, Figs 1-6, para 48, 63, 32, 38.).
Regarding claim 2, which depends from claim 1 and recites:
wherein identifying the additional job title from the knowledge graph based on the keyword comprises searching for the additional job title based on a frequency of the additional job title to the keyword in the knowledge graph (i.e., Hinton teaches the method of claim 1, including identifying the additional job title from the knowledge graph based on the keyword. Hinton, Figs 1-6, para 57, 62, 48. Hinton teaches that, The skill names associated with the nodes or edges that are accessed more frequently or directly could be given higher priority in a ranking, for example (searching for the additional job title (skill name job title information, para 48) based on a frequency of the additional job title to the keyword (skill name keyword) in the knowledge graph). Hinton, para 60, 62, 57, 48. In some embodiments, when the request now includes a full query, the server can then return all the skill names in the subgraph, for example. The server can also return additional information associated with the skill names, in terms of utilization, marketability, training, or evaluation, for example. These skill names can be ranked based on the distance between the corresponding nodes to the node or the set of nodes representing the skill name in the full query. The skill names can also be ranked based on how frequently the corresponding nodes in the knowledge graph are accessed (searching for the additional job title (skill name job title information, para 48) based on a frequency of the additional job title (skill name job title information, para 48) to the keyword (skill name keyword) in the knowledge graph) or how they are accessed (directly, via one or more directed edges, etc.) in response to the request. Hinton, para 62, 60, 57, 48.).
Regarding claim 3, which depends from claim 1 and recites:
wherein identifying the additional job title from the knowledge graph based on the keyword comprises searching for the additional job title based on an distance between an embedding of the additional job title and an embedding of the keyword in the knowledge graph (i.e., Hinton teaches the method of claim 1 from which claim 3 depends, including identifying the additional job title from the knowledge graph. Hinton, Figs 1-6, para 57, 62, 48. Hinton teaches that, In some embodiments, the server assigns a meaning of a word to a skill name by comparing the generated embeddings. The server can measure a distance between the embedding for a skill name and one or more embeddings for each synset for a meaning of a word in the skill name, such as the synset for the word in the skill name designated as the syntactic root (searching for the additional job title (skill name job title information, para 48) based on an distance between an embedding of the additional job title (skill name job title information, para 48) and an embedding of the keyword (skill name) in the knowledge graph). The server can measure the distance between the embedding for the skill name and the embedding for each word or phrases in a synset and compute an aggregate distance of the distances as the distance between the skill name and the synset or the associated meaning of a word…. Alternatively, the server can compute the distance between the embedding for the skill name and the embedding for the summary description of a synset or the associated meaning of a word. The distance can be computed using any similarity or distance measure known to someone skilled in the art, such as the cosine similarity or the Euclidean distance. The server then assigns a meaning of a word that leads to the smallest distance to the skill name based on the generated embeddings. In certain embodiments, the server can assign a meaning of a word that leads to the smallest distance only when the distance is below a threshold. When the smallest distance is not below the threshold, a catch-all meaning of the word can be assigned to the skill name. Hinton, para 55, 59-62, 57, 48. In some embodiments, when the request now includes a full query, the server can then return all the skill names in the subgraph, for example. The server can also return additional information associated with the skill names, in terms of utilization, marketability, training, or evaluation, for example. These skill names can be ranked based on the distance between the corresponding nodes to the node or the set of nodes representing the skill name in the full query (searching for the additional job title (skill name job title information, para 48) based on an distance between an embedding of the additional job title (skill name job title information, para 48) and an embedding of the keyword (skill name) in the knowledge graph). The skill names can also be ranked based on how frequently the corresponding nodes in the knowledge graph are accessed or how they are accessed (directly, via one or more directed edges, etc.) in response to the request. Hinton, para 62, 59-62, 57, 48.).
Regarding claim 5, which depends from claim 1 and recites:
wherein determining the association between the additional job title and the existing job title, comprises: determining a similarity between the existing job title and the additional job title based on the knowledge graph is above a threshold (i.e., Hinton teaches the method of claim 1 from which claim 5 depends, including determining the association between the additional job title and the existing job title. Hinton, Figs 1-6, para 63, 48 Hinton teaches that, In some embodiments, in step 710, the server is programmed or configured to merge two dependency graphs for two skill names when two nodes for two meanings assigned to the two skill names are no more than a first threshold distance apart (determining a similarity between the existing job title and the additional job title (merge when determined similarity between an existing skill name job title and additional skill name job title based on the knowledge graph is above a threshold) based on the knowledge graph is above a threshold) in a hypernym tree, to obtain a knowledge graph. Hinton, Figs 1-6, para 74, 63, 48, 55-59.).
Regarding claim 6, which depends from claim 1 and recites:
further comprising generating the knowledge graph (i.e., In some embodiments, the server 102 broadly represents one or more computers, virtual computing instances, and/or instances of a server-based application that is programmed or configured with data structures and/or database records that are arranged to host or execute functions including but not limited to collecting skill data, generating knowledge graphs (generating the knowledge graph), and improving the generation or search of digital documents related to skills. Hinton, Figs 1-6, para 36, 26-27.), comprising:
extracting one or more keywords from resume data;
(i.e., In some embodiments, the server 102 is programmed to retrieve skill-related data, such as job postings and resumes (extracting one or more keywords (one or more skill name keywords) from resume data), from the one or more third-party data sources 122. The server 102 can also be programmed to receive such data directly from users or the one or more client devices 112. The server 102 is programmed to then process the skill-related data, such as extracting skill names (extracting one or more keywords (one or more skill name keywords) from resume data) and metadata related to the skill names. Hinton, Figs 1-6, para 40, 27. A skill name is the name of an ability to do something, which could be related to a tool or a subject matter, for example. Examples of skill names include “product engineering” or “object-oriented programming”. The server can also extract skill names and other skill information from data sources related to the search, training, evaluation, or utilization of skills, such as job search websites or human resources systems, using machine learning techniques. Hinton, Figs 1-6, para 27, 40.);
linking the extracted one or more keywords with a resume job title in the resume data (i.e., In some embodiments, the server 102 is programmed to retrieve skill-related data, such as job postings and resumes (extracted one or more keywords (skill names keywords)), from the one or more third-party data sources 122. The server 102 can also be programmed to receive such data directly from users or the one or more client devices 112. The server 102 is programmed to then process the skill-related data, such as extracting skill names (extracted one or more keywords (skill names keywords)) and metadata related to the skill names. Hinton, Figs 1-6, para 40, 27. A request can be a partial or full query including a partial or full skill name, and the response to the request can comprise extended queries or a search result for the query. A request can also be a job title or job category, and the response to the request comprise updated job descriptions or resumes for the job title or job category, for example (linking the extracted one or more keywords with a resume job title in the resume data). Hinton, Figs 1-6, para 32, 40, 27, 54-55.);
embedding each of the extracted one or more keywords as a node in the knowledge graph; and embedding the resume job title in the knowledge graph (i.e., In some embodiments, the server selects, for the word being the syntactic root of a skill name in the database, a particular meaning of the word using the hypernym trees involving the word. The server can create an embedding for each skill name in the database and each word or phrases in a synset of the hypernym trees involving a word in the skill names in the database with respect to a corpus (embedding each of the extracted one or more keywords (skill name keywords) as a node in the knowledge graph). An example of the corpus comprises the set of job descriptions or resumes from which the database of skill names is derived that also include the words in the synsets. In one implementation, two embeddings are closers with respect to a distance measure in a vector space typically when the two words or phrases represented by the two embeddings are closer in semantic meanings, where the semantic meanings could characterize words or phrases that appear together in a corpus. The skill names can co-occur with certain job titles, skill name descriptions, related competencies, and so on in the corpus (embedding the resume job title in the knowledge graph (embedding the resume corpus job title co-occurring with extracted keyword skill names as a node in knowledge graph). The hypernym trees which include inherent groupings of words that are related. Therefore, the skill names by themselves or extended with co-occurring phrases can be matched with specific word meanings or the corresponding synset of words. The embeddings can then be generated using a method known to someone skilled in the art, such as GloVe or Word2Vec, which typically utilize neural networks. In an example, “production” is part of the skill names “music production”, “video game production”, and so on. The hypernym trees involving “production” may include nodes representing multiple meanings of “production”, including the meaning summarily described by “a presentation for the stage or screen or radio or television”, which has a synset comprising “staging” and “presentation”. Therefore, embeddings could be created for each of the skill names, such as “music production” and “video game production”, and the summary description of a word meaning, such as “a presentation for the stage or screen or radio or television”, or each of the words or phrases in the synsets, such as “staging” or “presentation”. Hinton, Figs 1-6, para 54, 55, 32, 40, 27, 63, 71-73, 81.).
Claims 8-10, 12, and 14 recite systems that parallel the methods of claims 1-3 and 5-6. Therefore the analysis discussed above with respect to claims 1-3 and 5-6 also applies to claims 8-10, 12 and 14, respectively. Accordingly, claims 8-10, 12 and 14 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3 and 5-6, respectively. More specifically regarding A processing system, comprising: a memory comprising computer-executable instructions; and a processor configured to execute the computer-executable instructions and cause the processing system to (i.e., Hinton, Fig 8, para 83-94.)
Claims 15-17 and 19-20 recite non-transitory computer-readable media that parallel the methods of claims 1-3 and 5-6. Therefore the analysis discussed above with respect to claims 1-3 and 5-6 also applies to claims 15-17 and 19-20, respectively. Accordingly, claims 15-17 and 19-20 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3 and 5-6, respectively. More specifically regarding A non-transitory computer-readable medium storing program code for causing a processing system to perform the steps of (i.e., Hinton, Fig 8, para 88, 83-94.)
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.
Claim(s) 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hinton as applied to claims 1, 8 and 15 above, and further in view of Crow et al. (Pub. No. 2005/0080656 A1, published April 14, 20205) hereinafter Crow. The Examiner notes that Crow is cited on Applicant’s Information Disclosure Statement filed January 28, 2025.
Regarding claim 4, which depends from claim 1 and recites:
wherein determining the association between the additional job title and the existing job title, comprises determining an industry associated with the existing job title and the industry associated with the additional job title are the same.
Hinton teaches the method of claim 1 from which claim 4 depends, including determining the association between the additional job title and the existing job title. Hinton, Figs 1-6, para 63, 48, 32, 38. Hinton does not specifically disclose determining an industry associated with the existing job title and the industry associated with the additional job title are the same.
However, Crow teaches in the field related to automated job candidate selection via computer software. Crow, para 2. Crow, which is analogous to the claimed invention because Crow is directed to extracting concepts related to job skills and job title from resumes, teaches and illustrates in Figure 14 arranging and determining that an industry associated with the existing job title and the industry associated with the additional job title are the same (job titles1431- 14235 are associated with the same telecom engineering industry). For example, an exemplary excerpt 1400 of a roles taxonomy of an exemplary ontology is shown in FIG. 14. In the example, the roles are hierarchically arranged. In the example, the roles are hierarchically arranged. At the top of the excerpt 1400 is the "Technology" role 1410. Underneath is the role "Telecom Engineering" 1425 and possibly other roles (not shown). Underneath "Telecom Engineering" 1425 are five sibling roles, "Broadband Engineer" 1431, "Verification Test Engineer" 1432, "Voice Engineer" 1433, Telecom Test Engineer" 1434, and "Optical Engineer" 1435 (determining that an industry associated with the existing job title and the industry associated with the additional job title are the same (job titles1431- 14235 are associated with the same telecom engineering industry)). The taxonomy has been constructed by experts familiar with the technology areas depicted so that the roles represent hierarchical categories accepted as valid by those working in the field. Crow, Fig 14, para 126, 125-128
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement the method of determining the association between the additional job title and the existing job title of Hinton using the feature for determining that an industry associated with the existing job title and the industry associated with the additional job title are the same of Crow, with a reasonable expectation of success, in order to assist in the process of finding and hiring employees and reduce the time and effort involved in finding suitable employees. Crow, para 3-11. This would have provided the user with the advantages of improving the accuracy of identifying the different job titles that are closely related and are in the same industry.
Claim 11 recites a system that parallels the method of claim 4. Therefore the analysis discussed above with respect to claim 4 also applies to claim 11. Accordingly, claim 11 is rejected based on substantially the same rationale as set forth above with respect to claim 4.
Claim 18 recites a non-transitory computer-readable medium that parallels the method of claim 4. Therefore the analysis discussed above with respect to claim 4 also applies to claim 18. Accordingly, claim 18 is rejected based on substantially the same rationale as set forth above with respect to claim 4.
Claim(s) 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hinton as applied to claims 1, 8 and 15 above, and further in view of Susanne Taylor, Indeed, "How to Write an Effective Resume Summary (With Examples)", updated July 10, 2023, archived September 8, 2023, hereinafter Taylor.
Regarding claim 7, which depends from claim 1 and recites:
wherein the content phrase from the resume comprises a resume summary.
Hinton teaches the method of claim 1 from which claim 7 depends, including the content phrase from the resume. Hinton, Figs 1-6, para 27, 40. Hinton does not explicitly disclose a resume summary.
However, Taylor teaches in the field related to resumes. Taylor, page 1. Taylor, which is analogous to the claimed invention because Taylor is directed to examples of resumes, teaches that, A resume summary, also known as a professional summary or summary statement, is a short description at the top of your resume that describes your experience, qualities and skills. Taylor, page 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement the method including extracting a keyword from a content phrase from the resume of Hinton using the resume summary of Taylor, with a reasonable expectation of success, in order to help employers quickly learn whether someone has the skills and background required. Taylor. This would have provided the user with the advantages of improving the identification of important and valuable information and skills in resumes.
Claim 13 recites a system that parallels the method of claim 7. Therefore the analysis discussed above with respect to claim 7 also applies to claim 13. Accordingly, claim 13 is rejected based on substantially the same rationale as set forth above with respect to claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US-12373794-B2, US-20230004941-A1, US-20220129856-A1, US-20200118082-A1, US-20240176821-A1, US-20240296425-A1, US-20250225484-A1, US-12242989-B2.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BARBARA LEVEL whose telephone number is (303)297-4748. The examiner can normally be reached Monday through Friday 8:00 AM - 5:00 PM MT.
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/BARBARA M LEVEL/ Examiner, Art Unit 2142
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