DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-6 are all the claims pending in the application.
Claims 1 and 3 are amended.
Claims 1-6 are rejected.
This is a Final Office Action in response to amendments and remarks filed on May 25, 2025.
Response to Arguments
Applicant's arguments filed May 25, 2025 have been fully considered.
Regarding Claim Objections, Objections have been withdrawn in light of applicant’s amendments.
Regarding 112(a) rejection, the rejection is withdrawn in light of applicant’s arguments. The Applicant admits that one of ordinary skill in the art would be able to implement the features recited, specifically the general-purpose AI-based image and speech analysis algorithms, because the features are known and commercially available.
Regarding 101 rejections, the rejections are maintained for the following reasons:
Under Step 2A Prong One, applicant argues that Claims 1-6 qualify as patent-eligible because the data collected on preliminary applicants is collected through web crawling and Application Programming Interfaces (APIs). Examiner respectfully does not find this argument persuasive because the claims still recite the previously identified abstract idea of hiring and recruiting because the recited activities are a core function of a recruiter or HR specialist, thus is a certain method of organizing human activity.
Under Step 2A Prong Two, applicant argues that the amended additional elements, including the following:
web crawling and recruitment partner Application Programming Interfaces (APIs), to collect and process applicant data
Automated filtering to match resumes and cover letters to specific job postings
Generating video interview information, including customized question types and model answers
These additional elements are recited at a high level of generality amount to nothing more than instructions to implement the abstract idea without any improvement to technology, technical field, or to the functioning of the computer itself, do not meaningfully limit the abstract idea and thus do not integrate the invention into a practical application.
Under Step 2B, the applicant argues that the claimed invention embodies an inventive concept. Examiner respectfully does not find the claim limitations do not include additional elements, whether considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception because they are recited at a high level of generality amount to nothing more than instructions to apply the abstract idea without any improvement to technology, technical field, or to the functioning of the computer itself.
Accordingly, the 101 rejections are maintained, including the rejection for the dependent claims (Claims 2-6); please see below for the complete rejections of the claims as amended.
Regarding 103 rejections, Applicant's arguments filed May 25, 2025 have been fully considered but the Examiner respectfully does not find the arguments to be persuasive.
Applicant asserts that amended portion of the claim, including pre-posting activities and pre-screening activities, were not taught, whether individually or in combination, by the Wong, Jin and Sun references. Applicant’s arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant asserts that Wong, Jin and Sun do not teach or suggest web crawling or APIs provided by recruitment partners. Examiner respectfully does not find the argument persuasive. Rather, the prior art, specifically Wong, teaches the newly amended claim limitations. Please see below for the complete rejections of the claims as amended.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, in this case the exception is an abstract idea. A detailed three-pronged analysis to establish subject matter eligibility (MPEP 2106 (III)) is listed below.
Claim 1 – Independent Claim
Step 1 – Statutory Categories – Claims 1 - 6 are classified as a method, which is a process.
Step 2A – Judicial Exceptions
At Step 2A Prong One, the claim is evaluated to determine whether it recites a judicial exception. In this case, the claim is directed to abstract ideas since the invention recites a certain method of organizing human activity (with additional elements in brackets). See a listing of the functions recited below:
Customizing candidate evaluation criteria
Classifying and setting the candidate evaluation criteria
Creating a job posting
Collecting data on preliminary candidates
Selecting applicants
Generating video interview information
Conducting an interview for the applicant
Analyzing an interview video of the applicant by using image and speech analysis
Measuring an interview score of the applicant
Providing an analysis report
The abstract ideas are functions that are performed by HR specialists during a hiring process. HR specialists regularly perform the abstract ideas listed above, including customizing candidate evaluation criteria, creating job postings, selecting applicants, conduct the applicant interview, measure interview scores. These abstract ideas can be classified as “certain methods of organizing human activity”. The claim recites a judicial exception and will proceed to Prong Two.
Step 2A Prong Two –
At Step 2A Prong Two, these limitations need to have additional elements that integrate the judicial exception into a practical application. Below are a list of additional elements recited in the claim limitations:
Web crawling
Application Programming Interfaces (APIs)
Conducting an artificial intelligence interview
Artificial intelligence image and speech analysis algorithms
The additional elements listed above do not integrate the abstract idea into a practical application for the following reasons:
The additional elements that reference the use of artificial intelligence (AI) to augment the as mere instructions to apply the abstract ideas and encompass a generic computer function. The claim limitations do not amount to more than a recitation of the words “apply it” as outlined in MPEP 2106.05(f).
The additional elements that recite web crawling and APIs are recited at a high level of generality amount to nothing more than instructions to implement the abstract idea without any improvement to technology, technical field, or to the functioning of the computer itself, do not meaningfully limit the abstract idea and thus do not integrate the invention into a practical application.
Step 2B –
This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. At Step 2A, Prong Two, the additional elements were found to represent no more than mere instructions to apply the judicial exception on a computer using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. Under Step 2B of the patent eligibility analysis, the combination of additional elements is evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept). The independent claim does not amount to significantly more than the judicial exception given that mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 1 is not patent eligible.
Claims 2-6 – Dependent Claims
At Step 2A Prong 1, the dependent claims, Claims 2-6 are directed to an abstract idea since the invention recites a certain methods of organizing human activity (see MPEP 2106.04(a)) through the use of artificial intelligence (a computer related art) as a tool to augment existing human resources business processes with generic computing elements. These claims lack significantly the extra-solution activity that would warrant eligibility.
Claim 2 recites the same abstract idea as the independent claim by reciting the limitations and merely adding “through artificial intelligence”. The additional elements of the claim fail to recite details of how a solution to a problem is accomplished; whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and the particularity or generality of the application of the judicial exception. See MPEP 2106.05(f). Claim 2 is rejected due to being abstract and does not reflect a practical application.
Claim 3 recites the same abstract idea as the independent claim by reciting the limitations The additional elements of the claim fail to recite details of how a solution to a problem is accomplished; whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and the particularity or generality of the application of the judicial exception. See MPEP 2106.05(f). Claim 3 is rejected due to being abstract and does not reflect a practical application.
Claim 4 recites the same abstract idea as the independent claim by reciting the limitations and merely adding “through artificial intelligence”. The additional elements of the claim fail to recite details of how a solution to a problem is accomplished; whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and the particularity or generality of the application of the judicial exception. See MPEP 2106.05(f). Claim 4 is rejected due to being abstract and does not reflect a practical application.
Claim 5 recites the same abstract idea as the independent claim by reciting the limitations and merely adding “through artificial intelligence”. The additional elements of the claim fail to recite details of how a solution to a problem is accomplished; whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and the particularity or generality of the application of the judicial exception. See MPEP 2106.05(f). Claim 5 is rejected due to being abstract and does not reflect a practical application.
Claim 6 recites the same abstract idea as the independent claim by reciting the limitations and merely adding “through artificial intelligence”. The additional elements of the claim fail to recite details of how a solution to a problem is accomplished; whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and the particularity or generality of the application of the judicial exception. See MPEP 2106.05(f). Claim 6 is rejected due to being abstract and does not reflect a practical application.
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.
Claims 1-4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al, US Pub No. US20230088444A1, herein referred to as “Wong” in view of Jin et al, Korean Publication No. KR20210001419A, herein referred to as “Jin”.
Regarding Independent Claim 1, Wong teaches the following limitations:
setting a filtering format for proactive direct sourcing by customizing candidate evaluation criteria including a skillset, experience, and knowledge for each company and job
(“FIG. 10 illustrates exemplary diagrams for defining selection criteria hierarchy in accordance with embodiments of the current invention. The AHP creates a hierarchical structure of basic layers: a goal layer 1001, a criteria layer 1002, a sub-criteria layer 1003, and qualified candidates to be processed by the AHP 1004. Criteria can be further decomposed into sub-criteria.”)(¶0050 and Fig. 10)
classifying and setting the candidate evaluation criteria into essential elements and optional elements based on job description templates for each job group and job and providing scoring customization by assigning respective scores to individual skills
(“FIG. 10 illustrates exemplary diagrams for defining selection criteria hierarchy in accordance with embodiments of the current invention. The AHP creates a hierarchical structure of basic layers: a goal layer 1001, a criteria layer 1002, a sub-criteria layer 1003, and qualified candidates to be processed by the AHP 1004. Criteria can be further decomposed into sub-criteria.”)(¶0050 and Fig. 10)
creating a job posting, which includes the essential elements and the optional elements classified based on the job description templates for the each job group and job
(“In one embodiment, client management 221 gets job opening requirements from 211 and identifies TS based on the job opening requirements. The information of the identified TS and feedback information of talent behavior profile are inputs for AI-enabled personalized contents creator 231.”)(¶0038 and Fig. 2)
collecting data on preliminary applicants, in which big data on the preliminary applicants is collected through web crawling and Application Programming Interfaces (APIs) provided by recruitment partners
(“The knowledge base is categorized on the domain base. In another embodiment, the domain-based knowledge base is used to generate a subset of BOK knowledge bases, including the BOK candidate knowledge base, the BOK skill knowledge base, and the BOK question knowledge base. In one embodiment, machine learning unit 161 is distributed on the network and interacts with other modules of computer system 111 through the network interface. BOK skill knowledge base 151, BOK candidate knowledge base 152 and BOK question knowledge base 153 are based on the domain-specific knowledge base. Each of the BOK knowledge base, including the BOK skill knowledge base 151, BOK candidate knowledge base 152, and BOK question knowledge base 153, includes multiple baby BOKs, one for each domain. When a knowledge base is generated for a domain by scraping a domain-specific Big Data, each baby BOK of the domain is created. The adaptive algorithms are carried out based on each BOK knowledge base.”)(¶0036 and Fig. 1)
Also refer to (Formal source of inputs for the BOK 1501 are retrieved for a domain/discipline. Informal source of inputs for the BOK 1502 are to obtain pieces of the body of knowledge from the domain experts, be collected by survey of domain practitioners. Moreover, other informal body of knowledge can be crawled from forums and social media that are related to the domain.”)(¶0068 and Fig. 15)
selecting applicants with job suitability as candidates based on the filtering format for proactive direct sourcing for the each job group and job, wherein the job suitability is determined based on matching of resumes and self-introduction letters of the preliminary applicants with the job posting for the each job group and job
(“FIG. 12 illustrates exemplary diagrams for determining the candidate-criteria pairwise comparison matrix in accordance with embodiments of the current invention. The candidate-criteria matrix 1220 is a n×m pairwise comparison matrix of (candidate, criteria). First, a candidate pairwise comparison matrix 1211 is generated for a selection criterion S1. The candidate pairwise comparison matrix 1211 contains ranking by comparing one candidate to the other candidate for each criterion similar to the pairwise comparison of selection criteria above.”)(¶0056 and Fig. 12)
generating video interview information including question types, model answers, and answering time for the each company and job
(“Controller 111 receives job description from user interface 112 and generates interview questions. In one embodiment, an analysis is performed on the received job description to generate one or more interview question selection rules. In one embodiment, a set of interview questions is prepared by getting a subset of questions from BOK question knowledge base 153. The subset of the questions of question bank 116 is selected from BOK question knowledge base 153 based on analysis using BOK skill knowledge base 151 and one or more rules, such as industry match and skill level match. Upon generating the set of interview questions, controller 111 arranges user interface 112 to conduct the interview with the candidate of talents 130.”)(¶0035 and Fig. 1)
Conducting an artificial intelligence interview for the applicant
(“In one novel aspect, an adaptive interview is conducted using AI-enabled computer-based recruitment system. Procedure-wise, the adaptive recruitment system includes multiple procedures. Procedure 310 collects candidate information and generates candidate profile including authentication information; procedure 320 performs authentication, procedure 330 conducts adaptive interview; and procedure 340 generates feedback to the candidate.”)(¶0039 and Fig. 3);
Analyzing the interview of the applicant by using artificial intelligence (AI) image and speech analysis algorithms
(“In one embodiment, controller 111 uses RNN(NLP/NLU) model 115 to analyze the text transcribed from the speech audio of the candidate of talents 130 and generates an assessment result, which is an assessment of the correctness of the answer to the question. In other embodiment, emotional classifiers are also generated by analyzing the candidate answer. In one embodiment, the speech emotion classifier is generated using the CNN LSTM model. The generated emotion classifier is mapped to the sentiment classifier. The combination of the assessment result and the emotional classifier are summarized to the evaluation results.”)(¶0035 and Fig. 1);
Measuring an artificial intelligence interview score of the applicant
(“At step 452, an adaptive interview is conducted. The audio answers from the candidate are converted to text to be evaluated by RNN(NLP/NLU) unit 441. The assessment result of the adaptive interview is generated at step 453. The evaluation/assessment result of the adaptive interview procedure 450 is one input for the data mining unit 401. In one embodiment, other factors are used to generate the recommendation and feedback report 402.”)(¶0042 and Fig. 4);
Providing an artificial intelligence analysis report by analyzing images, voices, and attitudes in the interview of the applicant (underlined portion is disclosed by Jin)
(“Controller 111 analysis the answer audio and generates evaluation results. In one embodiment, controller 111 uses RNN(NLP/NLU) model 115 to analyze the text transcribed from the speech audio of the candidate of talents 130 and generates an assessment result, which is an assessment of the correctness of the answer to the question. In other embodiment, emotional classifiers are also generated by analyzing the candidate answer. In one embodiment, the speech emotion classifier is generated using the CNN LSTM model. The generated emotion classifier is mapped to the sentiment classifier. The combination of the assessment result and the emotional classifier are summarized to the evaluation results.”)(¶0035 and Fig. 1);
However, Wong does not teach “analyzing images, voices, and attitudes in the interview of the applicant”, but Jin does disclose analyzing images, voices, and attitudes in the interview of the applicant
(“In one embodiment, the step of analyzing a linguistic evaluation element and a non-verbal evaluation element based on the feature points includes: speed, volume, keyword, semantic range, and/or voice recognition technology as the linguistic evaluation elements by applying a speech recognition technology to the voice data. Analyzing one or more of concept, relationship, emotion, and emotion information, and applying one or more of face recognition, eye tracking, and motion detection technology to the image data to provide the interviewee's emotion information and attitude information as the non-verbal evaluation factor. It may include the step of analyzing.”)(¶0018).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the invention to combine the teaching of utilizing “RNN(NLP/NLU) model to analyze the text transcribed from the speech audio of the candidate of talents and generates an assessment result” with the “linguistic evaluation” of Jin to analyze the images, voices and attitudes of the interview applicant in order to create an applicant analysis report. Through the combination of the teachings of Wong’s and Jin, the automated screening process would improve the efficiency of the hiring staff and mitigate potential human bias that occurred during a face-to-face or online video interview process.
wherein the question types, model answers, and answering time are preset for the each company and job, and the artificial intelligence interview score is measured based thereon
(“User interface 112 sends the answer audio in response to a question to controller 111. Controller 111 analysis the answer audio and generates evaluation results. In one embodiment, controller 111 uses RNN(NLP/NLU) model 115 to analyze the text transcribed from the speech audio of the candidate of talents 130 and generates an assessment result, which is an assessment of the correctness of the answer to the question.”)(¶0035 and Fig. 1)
Regarding Claim 2, the combination of Wong and Jin teaches all the limitations of Claim 1 and Wong further teaches
Determining whether a document has passed or sorting the document according to its rank by tracking a text of a resume of the applicant and then performing filtering and weighing according to specifications desired by a company through artificial intelligence
(“a recruitment ranking process using analytic hierarchy process (AHP) in accordance with embodiments of the current invention. A recruitment system offers two-step processes to rank the candidates to be recommended to HR Client. A process 801 offers an online adaptive assessment that comprises both written test and audio interview through a chatbot. Results of the written test and audio interview are summarized and categorized. The assessment results along with job requirements (both functional and non-functional), candidate profiles and other industry specific knowledge are used to train a machine learning model 810.”) (¶ 48 and Fig 8.)
Also refers to (“At step 1404, the computer system ranks the group of primary candidates by performing an analytic hierarchy process (AHP) on a set of hierarchical criteria and the candidate-criteria pairwise comparison matrix.”)(¶0062 and Fig. 14)
Regarding Dependent Claim 3, the combination of Wong and Jin teaches all the limitations of Claim 1 and Wong further teaches
Providing a job-specific interview question template that can be referenced or utilized in the interview questions
(“In one embodiment, the evaluation results of one or more prior answers by the candidate are used to select the next question from question bank 116. The adaptive interview question selection enables an adaptive interview procedure, which is more accurately analyzed using AI technology, such as RNN(NLP/NLU) 115, and provides a more efficient way to evaluate the strength, weakness, and fit-ability of the candidate. The AI-enabled adaptive recruitment computer system 110 enhances the performance by using up-to-date knowledge, including BOK skill knowledge base 151, BOK candidate knowledge base 152, and question knowledge base 153. A machine learning unit 161 interacts and updates BOK skill knowledge base 151, BOK candidate knowledge base 152 and BOK question knowledge base 153. In one embodiment, machine learning unit also interacts with controller 111 and updates BOK skill knowledge base 151, BOK candidate knowledge base 152, and BOK question knowledge base 153 bases on instructions from controller 111”)(¶0036 and Fig. 1)
Also refer to (“Fig 5. illustrates exemplary diagrams for an adaptive interview by selecting questions from a question bank based on the candidate's prior answers in accordance with embodiments of the current invention. In one embodiment, a question bank 503 is generated using an RNN model based on BOK skill knowledge base 501 and BOK question knowledge base 502. Question bank 503 includes a list of questions. Each question is assigned/labeled with one or more attributes including an industry taxonomy, a skill name/index, and a skill level. At step 511, a question is selected from question bank 503 based on the evaluation of the candidate's prior answers. At step 512, the candidate's answer to the selected question is obtained and evaluated. In one embodiment, both the answer to the question and the emotional factor of the answer are evaluated. The evaluation of this question and evaluations of the prior questions are combined to generate a skill evaluation report.”)(¶0043 and Fig. 5).
Regarding Dependent Claim 4, the combination of Wong and Jin teaches all the limitations of Claim 1 and Wong further teaches
Constructing additional interactive questions by analyzing real-time interview content of the applicant through voice recognition
(Examiner Note: voice recognition is associated to speech audio, “In one embodiment, controller 111 uses RNN(NLP/NLU) model 115 to analyze the text transcribed from the speech audio of the candidate of talents 130 and generates an assessment result, which is an assessment of the correctness of the answer to the question. In other embodiment, emotional classifiers are also generated by analyzing the candidate answer. In one embodiment, the speech emotion classifier is generated using the CNN LSTM model. The generated emotion classifier is mapped to the sentiment classifier. The combination of the assessment result and the emotional classifier are summarized to the evaluation results.)
Also refer to (Examiner Note: real-time interview content is associated to an adaptive interview procedure, [0036] - In one embodiment, the evaluation results of one or more prior answers by the candidate are used to select the next question from question bank 116. The adaptive interview question selection enables an adaptive interview procedure, which is more accurately analyzed using AI technology, such as RNN(NLP/NLU) 115, and provides a more efficient way to evaluate the strength, weakness, and fit-ability of the candidate. The AI-enabled adaptive recruitment computer system 110 enhances the performance by using up-to-date knowledge, including BOK skill knowledge base 151, BOK candidate knowledge base 152, and question knowledge base 153. A machine learning unit 161 interacts and updates BOK skill knowledge base 151, BOK candidate knowledge base 152 and BOK question knowledge base 153. In one embodiment, machine learning unit also interacts with controller 111 and updates BOK skill knowledge base 151, BOK candidate knowledge base 152, and BOK question knowledge base 153 bases on instructions from controller 111.”)(¶0035-36 and Fig. 1)
Regarding Dependent Claim 6, the combination of Wong and Jin teaches all the limitations of Claim 1 and Wong further teaches
Performing language analysis, including words and sentences, and positive and negative semantic analysis in the interview video of the applicant
(“Candidate information 131 includes general candidate information and specific information for one or more job openings. In one embodiment, candidate information 131 includes the resume, recommendations, evaluation results, audio interview emotional analysis results, summarization of candidates' adaptively social media activities and events, and system ranking. In one embodiment, candidate information also includes authentication information, such as voice authentication sample extracted from the initial voice interview” )(¶0033 and Fig. 1)
Also refer to (“In one novel aspect, an adaptive interview is conducted using AI-enabled computer-based recruitment system. Procedure-wise, the adaptive recruitment system includes multiple procedures. Procedure 310 collects candidate information and generates candidate profile including authentication information; procedure 320 performs authentication, procedure 330 conducts adaptive interview; and procedure 340 generates feedback to the candidate.”)(¶0039 and Fig. 3)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wong et al, US Pub No. US20230088444A1, herein referred to as “Wong” in view of ”Jin” et al, Korean Publication No. KR20210001419A, herein referred to as “Jin” and Sun et al, Korean Publication No. KR 10-2084372 B1, herein referred to as “Sun”
Regarding Dependent Claim 5, the combination of Wong and Jin and Sun teaches all the limitations of Claim 1 and Wong further teaches
Processing and analyzing audio data in real-time by performing speaking speed detection, used word analysis and dialect recognition for the applicant. (underlined portion is disclosed by Sun)
(“The process and analyzing audio data in real-time by performing speaking speed detection, used work analysis, for the applicant as claimed wherein “In one embodiment, controller 111 uses RNN(NLP/NLU) model 115 to analyze the text transcribed from the speech audio of the candidate of talents 130 and generates an assessment result, which is an assessment of the correctness of the answer to the question. In other embodiment, emotional classifiers are also generated by analyzing the candidate answer. In one embodiment, the speech emotion classifier is generated using the CNN LSTM model. The generated emotion classifier is mapped to the sentiment classifier. The combination of the assessment result and the emotional classifier are summarized to the evaluation results.) (¶0036 and Fig. 1)
However, Wong does not teach “and dialect recognition”, but Sun does teach and dialect recognition for the applicant
(“The dialect-related database includes various dialect texts 500, standard word texts 510 corresponding thereto, and sample voice signals 520 corresponding to the dialect text 500. For reference, the database of FIG. 6 exemplifies a database for any dialect text without specific rules.
Referring back to FIG. 2, when the specific dialect text is included in the STT result, the subtitle providing service server 100 performs the specific dialect on the sample audio signal corresponding to the specific dialect text and the audio signal of the target video. The target voice signal parts corresponding to the text are compared (S150) to determine whether the two voice signals are similar (S160).:) (reference attached translated PDF (¶0044-45)).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the invention to combine the teaching of Wong and Jin and Sun. Because Sun teaches the use of Speech-To-Text (STT) translation methods using a dialect database to analyze dialects, one of ordinary skill in the art would combine the real-time (adaptive) speech analysis with the dialect recognition that Sun teaches. This would have been apparent in situations where candidates apply from many different regions or countries and traditional Speech-To-Text translations methods would not identify the nuance among different dialects.
Conclusion
Applicant' s amendment necessitate new ground(s) of rejection presented in this Office Action. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAHUL SHARMA whose telephone number is (571) 272-3058. The examiner can normally be reached Monday thru Friday, 8-5 CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached on (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RAHUL SHARMA/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626