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 Claim
This action is in reply in response to application filed on 21 of April 2023.
Claims 1-20 are currently pending and are rejected as described below.
Claim Rejections - 35 USC § 101
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machines, article of manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception.
The claims are then analyzed to determine whether the claims are directed to a judicial exception. MPEP §2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
With respect to 2A Prong 1, claim 1 recites “at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: to perform natural language processing of a first set of documents to generate a first data structure, the first set of documents characterizing information associated with a user of an organization; to perform natural language processing of a second set of documents to generate a second data structure, the second set of documents characterizing information associated with the organization; to process the first data structure and the second data structure utilizing one or more machine learning models to generate a third data structure, the third data structure characterizing resources of an information technology infrastructure to be allocated for use by the user of the organization; and to generate one or more control signals for allocating one or more resources of the information technology infrastructure for use by the user of the organization based at least in part on the third data structure”. Claims 15 and 18 disclose similar limitations as Claim 1, and therefore recite an abstract idea.
More specifically, claims 1, 15, and 18 are directed to “Certain Methods of Organizing Human Activity” in particular “managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)”, and “Mental Processes” in particular “concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” as discussed in MPEP §2106.04(a)(2), and in the 2019-01-08 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claims recite an abstract idea.
Dependent claims 2-14, 16, 17, 19, and 20 further recite abstract idea(s) contained within the independent claims, and do not contribute to significant more or enable practical application. Thus, the dependent claims are rejected under 101 based on the same rationale as the independent claims.
Under Prong Two of Step 2A of the Alice/Mayo test, the examiner acknowledges that Claims 1, 15, and 18 recite additional elements yet the additional elements do not integrate the abstract idea into a practical application. In order for the judicial exception to be “integrated into a practical application”, an additional element or a combination of additional elements in the claim “will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” PEG, 84 Fed. Reg. 54 (Jan. 7, 2019). The courts have identified examples in which a judicial exception has not been integrated into a practical application when “an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.” PEG, 84 Fed. Reg. 55 (Jan. 7, 2019); MPEP § 2106.05(h). The claims are directed to an abstract idea.
In particular, claims 1, 15, and 18 recite additional elements boldened and underlined above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
With respect to step 2B, claims 1, 15, and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claim recites the additional elements described above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process, as evidenced by at least in Pages 22-23 “As is apparent from the above, one or more of the processing modules or other components
of system 100 may each run on a computer, server, storage device or other processing platform element. A given such element may be viewed as an example of what is more generally referred to herein as a "processing device." The cloud infrastructure 1200 shown in FIG. 12 may represent
at least a portion of one processing platform. Another example of such a processing platform is
processing platform 1300 shown in FIG. 13. The processing platform 1300 in this embodiment comprises a portion of system 100 and includes a plurality of processing devices, denoted 1302-1, 1302-2, 1302-3, . . . 1302-K, which communicate with one another over a network 1304. The network 1304 may comprise any type of network, including by way of example a global computer network such as the Internet, a WAN, a LAN, a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. The processing device 1302-1 in the processing platform 1300 comprises a processor 1310 coupled to a memory 1312. The processor 1310 may comprise a microprocessor, a microcontroller, an application- specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a central processing unit (CPU), a graphical processing unit (GPU), a tensor processing unit (TPU), a video processing unit (VPU) or other type of processing circuitry, as well as portions or combinations of such circuitry elements. The memory 1312 may comprise random access memory (RAM), read-only memory (ROM), flash memory or other types of memory, in any combination. The memory 1312 and other memories disclosed herein should be viewed as illustrative examples of what are more generally referred to as "processor-readable storage media" storing executable program code of one or more software programs. Articles of manufacture comprising such processor-readable storage media are considered illustrative embodiments. A given such article of manufacture may comprise, for example, a storage array, a storage disk or an integrated circuit containing RAM, ROM, flash memory or other electronic memory, or any of a wide variety of other types of computer program products. The term "article of manufacture" as used herein should be understood to exclude transitory, propagating signals. Numerous other types of computer program products comprising processor- readable storage media can be used. Also included in the processing device 1302-1 is network interface circuitry 1314, which is used to interface the processing device with the network 1304 and other system components, and may comprise conventional transceivers”.
Claims 2-14, 16, 17, 19, and 20 do not disclose additional elements, further narrowing the abstract ideas of the independent claims and thus not practically integrated under prong 2A as part of a practical application or under 2B not significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Claim Rejections - 35 USC § 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 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.
Claims 1-2, 4, 10-11, and 13 are rejected under 35 U.S.C. 102 as being anticipated by US 20240330834 to Tiwari et. al. (hereinafter referred to as “Tiwari”).
(A) As per Claims 1, 15, and 18:
Tiwari expressly discloses:
at least one processing device comprising a processor coupled to a memory; (Tiwari ¶77 as shown in FIG. 11 , components of the system 1114 may be hosted in computing system 1108, which may have a memory 1112 and a processor).
the at least one processing device being configured: to perform natural language processing of a first set of documents to generate a first data structure, the first set of documents characterizing information associated with a user of an organization; to perform natural language processing of a second set of documents to generate a second data structure, the second set of documents characterizing information associated with the organization; (Tiwari ¶28 the match ML model 214 can receive the preprocessed data and determine the percentage by which the resume and JD represent a match or have correspondence. In some embodiments, the match ML model 214 can implement a term frequency-inverse document frequency (TF-IDF) vectorizer or other text classification and natural language processing (NLP) models that identify words that appear in the same context (e.g., continuous Bag-of-Words model (CBOW), Word2Vec, genism, Skip-Gram, etc.). For example, in one embodiment, the method may include determining that the first candidate description with the first skillset matches a first percentage of job descriptions from the plurality of job descriptions by converting the candidate description and the plurality of job descriptions to vectors and comparing similarity between the vectorized first candidate description and each vectorized job description of the plurality of job descriptions. In this way, the match ML model 214 can intelligently approximate the degree to which the particular candidate is suited to or an appropriate fit for the jobs that are listed in the JD 220).
to process the first data structure and the second data structure utilizing one or more machine learning models to generate a third data structure, the third data structure characterizing resources of an information technology infrastructure to be allocated for use by the user of the organization; (Tiwari ¶62, 68 the system is able to generate a personalized and effective upskilling program that is tailored to each candidate's unique needs and goals. The training plan is an AI-generated personalized learning path for the user that is tailor selected from thousands of combinations and possibilities).
to generate one or more control signals for allocating one or more resources of the information technology infrastructure for use by the user of the organization based at least in part on the third data structure; (Tiwari ¶72 FIG. 10 is a flow chart illustrating an embodiment of a method 1000 of controlling a system to generate and interactively implement a customized learning path).
Tiwari teaches a method of generating recommendations for individualized skill development at least in the Abstract and a non-transitory computer-readable medium storing software at least in ¶6.
(B) As per Claim 2:
Tiwari expressly discloses:
wherein the first set of documents comprises one or more documents characterizing a set of accrued skills associated with the user of the organization; (Tiwari ¶29 in some embodiments, the skill repository ML model 224 can receive the preprocessed data of both input types and identify and extract the candidate's current skillset (comprising one or more skills in which-based on the candidate description—it appears he or she is currently proficient in)).
(C) As per Claim 3:
Tiwari expressly discloses:
wherein the one or more documents characterizing the set of accrued skills associated with the user of the organization comprise at least one of a user self- assessment document, a periodic assessment document generated by a manager of the user in the organization, and a talent profile of the user; (Tiwari ¶27 as shown in FIG. 2 , during a first phase, the architecture 200 can first receive various inputs including a candidate description (e.g., a candidate profile or resume 210) and a plurality of job descriptions (jobs dataset, or simply “JD”) 220 for roles or potential upcoming and currently available opportunities in the organization).
(D) As per Claim 5:
Tiwari expressly discloses:
wherein the second set of documents comprises one or more documents characterizing a set of role-based skills associated with one or more roles that the user has within the organization; (Tiwari ¶27 . As shown in FIG. 2 , during a first phase, the architecture 200 can first receive various inputs including a candidate description (e.g., a candidate profile or resume 210) and a plurality of job descriptions (jobs dataset, or simply “JD”) 220 for roles or potential upcoming and currently available opportunities in the organization).
(E) As per Claim 6:
Tiwari expressly discloses:
one or more career-related skills; one or more productivity-related skills; and one or more project-related skills associated with one or more projects that the user is performing for the organization; (Tiwari ¶29 the skill repository ML model 224 can then determine whether any of the skills in the candidate's skillset match at least some of the skills that are needed for the upcoming jobs/roles).
(F) As per Claims 9, 17, and 20:
Tiwari expressly discloses:
wherein the first data structure specifies a set of accrued skills associated with the user of the organization, the second data structure specifies a set of required skills associated with one or more roles that the user has within the organization, and the third data structure comprises an individual development plan generated for the user, the individual development plan specifying one or more skills to be accrued by the user, the one or more skills to be accrued by the user being determined based at least in part on identifying one or more skill gaps between the set of accrued skills associated with the user of the organization and the set of required skills associated with the one or more roles that the user has within the organization; (Tiwari ¶26, 28, 62, 68 the match ML model 214 can receive the preprocessed data and determine the percentage by which the resume and JD represent a match or have correspondence. In some embodiments, the match ML model 214 can implement a term frequency-inverse document frequency (TF-IDF) vectorizer or other text classification and natural language processing (NLP) models that identify words that appear in the same context (e.g., continuous Bag-of-Words model (CBOW), Word2Vec, genism, Skip-Gram, etc.). The system is able to generate a personalized and effective upskilling program that is tailored to each candidate's unique needs and goals. The training plan is an AI-generated personalized learning path for the user that is tailor selected from thousands of combinations and possibilities. As shown in FIG. 1 , a talent management framework 100 can offer an array of features, including but not limited to a comprehensive and individualized skill gap analysis 102 for each employee, real-time suggestions and recommendations 104 that are targeted to the most up-to-date ongoing and anticipated needs of the organization, talent-build insights 106 tailored to guiding different levels of managers and employees and recommend learning paths for each person, intelligent staffing solutions 108 that intelligently identify or select the most appropriate or best-fit personnel for training and development toward specific goals or opportunities).
(G) As per Claim 10:
Tiwari expressly discloses:
wherein the one or more skills to be accrued by the user specified in the individual development plan comprise a first set of one or more required skills and a second set of one or more optional skills; (Tiwari ¶69 the training path can be modified in response to updated data about the candidate's proficiency in a particular domain. For example, while a candidate may have been considered highly proficient in a domain one year ago, their proficiency may be lower now as the underlying knowledge has advanced and changed over the year. Thus, what was a skill strength one year ago has become less useful. Thus, the training path can accommodate these fluctuations in domain knowledge that occur as advances in technology and methodologies take place in the real-world. The system is thereby able to continuously assess the candidate's skills and identify knowledge gaps that are based on the most current market trends to ensure the predictions and recommended personalized training paths are aligned with the actual state of the field and their relative experience).
(H) As per Claim 11:
Tiwari expressly discloses:
wherein the first set of one or more required skills is determined based at least in part on the one or more roles that the user has within the organization, and wherein the second set of one or more optional skills is determined based at least in part on one or more assessment documents associated with the user; (Tiwari ¶28, 69 the magnitude of the percentage generated by the match ML model 214 represents the extent of the candidate's knowledge gap as relevant to the organization (i.e. based on roles)—the higher the percentage, the smaller the knowledge gap, and the less likely that candidate is to require an upskilling program. The training path can be modified in response to updated data about the candidate's proficiency in a particular domain. For example, while a candidate may have been considered highly proficient in a domain one year ago, their proficiency may be lower now as the underlying knowledge has advanced and changed over the year. Thus, what was a skill strength one year ago has become less useful (e.g. assessment). Thus, the training path can accommodate these fluctuation in domain knowledge that occur as advances in technology and methodologies take place in the real-world).
(I) As per Claim 12:
Tiwari expressly discloses:
wherein allocating the one or more resources of the information technology infrastructure for use by the user of the organization comprises conditioning selection of one or more courses for developing at least a given one of the one or more skills to be accrued by the user based at least in part on user preferences related to sources of the one or more courses; (Tiwari ¶66 the app 500 can include provisions for collecting data that can be used to determine the candidate's current proficiency and/or awareness in a particular knowledge area. For example, depending on the experience needed for the recommended role, and the advice of the associated SME or domain expert, the candidate may be requested to work through a series of case studies, questionnaires, and live simulations targeting the required skills. Referring to FIG. 6 , an example of an automatically generated assessment interface 610 for app 500 is depicted. For purposes of this example, the first candidate 550 (“Marina Sayed”) with a current role of tester and one year experience in her role, was previously evaluated by the system as described with respect to FIGS. 2 and 3 above. Her predicted successful future role was identified as being directed to telco technologies, and she was recommended to attend to a series of online course modules through the system that would train her in this skillset).
(J) As per Claim 13:
Tiwari expressly discloses:
wherein allocating the one or more resources of the information technology infrastructure for use by the user of the organization comprises provisioning one or more workspaces for the user and assigning at least one of processing, memory, storage and network resources to the provisioned one or more workspaces based at least in part on the one or more skills to be accrued by the user as specified in the individual development plan; (Tiwari ¶66 the app 500 can include provisions for collecting data that can be used to determine the candidate's current proficiency and/or awareness in a particular knowledge area. For example, depending on the experience needed for the recommended role, and the advice of the associated SME or domain expert, the candidate may be requested to work through a series of case studies, questionnaires, and live simulations targeting the required skills. The system has tracked her progress through this training protocol and, after a first period of time from the prediction, automatically requests her participation in the knowledge test. In this case, the assessment interface 610 includes a series of questions that assess her knowledge based on the content that she has engaged with during the past week via the system-provided course module).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness
Claim 4 is rejected under 35 U.S.C. 103 as being obvious by the combination of US 20240330834 to Tiwari et. al. (hereinafter referred to as “Tiwari”) in view of CN 114153951 to Feng et. al. (hereinafter referred to as “Feng).
(A) As per Claims 1, 19, and 20:
Mishra expressly discloses:
wherein the natural language processing of the first set of documents utilizes… of the one or more documents characterizing the set of accrued skills associated with the user of the organization; (Tiwari ¶26 as shown in FIG. 1 , a talent management framework 100 can offer an array of features, including but not limited to a comprehensive and individualized skill gap analysis 102 for each employee, real-time suggestions and recommendations 104 that are targeted to the most up-to-date ongoing and anticipated needs of the organization, talent-build insights 106 tailored to guiding different levels of managers and employees and recommend learning paths for each person, intelligent staffing solutions 108 that intelligently identify or select the most appropriate or best-fit personnel for training and development toward specific goals or opportunities).
Although Tiwari teaches an intelligent machine learning-based systems and methods of generating recommendations for individualized skill development, it doesn’t expressly disclose using a target-dependent sentiment classification of content, however Feng teaches:
…target-dependent sentiment classification of content; (Feng Page 9 the TD-LSTM model proposed by the "LSTMs for Target-Dependent Sentiment Classification" is based on the position of the Target in the original text, and the original text is split into left, right two parts, the left and right parts respectively through different BiLSTM, then combined, outputting the corresponding emotion label. The data set used is the SemEval-2014Task 4, only the sentence level data set).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Tiwari’s talent management framework which offers an array of features, including a comprehensive and individualized skill gap analysis for each employee and apply the TD-LSTM model proposed by the LSTMs for Target-Dependent Sentiment Classification of Feng as both are analogous art which teaches intelligently identify or select the most appropriate or best-fit personnel for training and development toward specific goals or opportunities as taught in Tiwari and outputting the corresponding emotion label as taught in Feng.
Claim 7 is rejected under 35 U.S.C. 103 as being obvious by the combination of US 20240330834 to Tiwari et. al. (hereinafter referred to as “Tiwari”) in view of US 20230222150 to Sun et. al. (hereinafter referred to as “Sun”).
(A) As per Claims 7, 16, and 19:
Although Tiwari teaches an intelligent machine learning-based systems and methods of generating recommendations for individualized skill development, it doesn’t expressly disclose using a RNN ML, however Sun teaches:
wherein the one or more machine learning models utilized to process the first data structure and the second data structure comprise a recurrent neural network machine learning model; (Sun ¶6 a recurrent neural networks (RNN) is another example of a type of ANN that is sometimes used as a classifier. These types of RNNs are particularly well-suited for multi-variate time series data analysis and forecasting, handwriting recognition, natural language processing, and task synthesis).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Tiwari’s talent management framework which offers an array of features, including a comprehensive and individualized skill gap analysis for each employee and deploying recurrent neural networks of Sun as both are analogous art which teaches intelligently identify or select the most appropriate or best-fit personnel for training and development toward specific goals or opportunities as taught in Tiwari and having RNNs well-suited for multi-variate time series data analysis and forecasting, handwriting recognition, natural language processing, and task synthesis as taught in Sun.
Claims 8, 16, and 19 are rejected under 35 U.S.C. 103 as being obvious by the combination of US 20240330834 to Tiwari et. al. (hereinafter referred to as “Tiwari”) in view of US 20230222150 to Sun et. al. (hereinafter referred to as “Sun”) and in further in view of CN 114153951 to Feng et. al. (hereinafter referred to as “Feng).
(A) As per Claims 8, 16, and 19:
Although Tiwari in view of Sun teaches an intelligent machine learning-based systems and methods of generating recommendations for individualized skill development, it doesn’t expressly disclose using a RNN ML, however Feng teaches:
wherein the recurrent neural network machine learning model comprises one or more target dependent long short-term memory networks; (Feng Page 9 the TD-LSTM model proposed by the "LSTMs for Target-Dependent Sentiment Classification" is based on the position of the Target in the original text, and the original text is split into left, right two parts, the left and right parts respectively through different BiLSTM, then combined, outputting the corresponding emotion label. The data set used is the SemEval-2014Task 4, only the sentence level data set).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Tiwari in view of Sun’s talent management framework which offers an array of features, including a comprehensive and individualized skill gap analysis for each employee and apply the TD-LSTM model proposed by the LSTMs for Target-Dependent Sentiment Classification of Feng as both are analogous art which teaches intelligently identify or select the most appropriate or best-fit personnel for training and development toward specific goals or opportunities as taught in Tiwari in view of Sun and outputting the corresponding emotion label as taught in Feng.
Claim 14 is rejected under 35 U.S.C. 103 as being obvious by the combination of US 20240330834 to Tiwari et. al. (hereinafter referred to as “Tiwari”) in view of US 20240020618 to Singh et. al. (hereinafter referred to as “Singh”).
(A) As per Claim 14:
Although Tiwari teaches an intelligent machine learning-based systems and methods of generating recommendations for individualized skill development, it doesn’t expressly disclose loading software into the user’s device to pursue the accrual of new skills, however Singh teaches:
wherein allocating the one or more resources of the information technology infrastructure for use by the user of the organization comprises provisioning one or more workspaces for the user and loading software in the provisioned one or more workspaces based at least in part on the one or more skills to be accrued by the user as specified in the individual development plan; (Singh ¶53 the skill ontology for the enterprise may be available for performance of various functions within the enterprise, such as career planning 516, analytics/recommendations 518, workflow usage 520, and optimization/capacity planning 522. For career planning 516, employees may use an agent workspace or mobile application to manage a profile by setting goals, providing information about skills, achievements (e.g., awards, certifications, etc.), experiences, communicating interest in holding particular positions in the future, providing feedback to the enterprise, etc. For analytics 518, the enterprise may utilize the skill ontology to identify skills and/or skills gaps of individual employees, recommend training to close skills gaps, and forecast skill development of particular employees. These may be communicated to the employee via an agent workspace or mobile application).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Tiwari in view of Sun’s talent management framework which offers an array of features, including a comprehensive and individualized skill gap analysis for each employee and have employees use an agent workspace or mobile application to manage a profile of Singh as both are analogous art which teaches intelligently identify or select the most appropriate or best-fit personnel for training and development toward specific goals or opportunities as taught in Tiwari and have the enterprise utilize the skill ontology to identify skills and/or skills gaps of individual employees, recommend training to close skills gaps, and forecast skill development of particular employees as taught in Singh.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEUS R STIVALETTI whose telephone number is (571)272-5758. The examiner can normally be reached on M-F 8:30-5:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571)272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1822.
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/MATHEUS RIBEIRO STIVALETTI/Primary Examiner, Art Unit 3623 5/7/2026
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