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 Claims
The following is a non-final office action.
Claims [1-20] are currently pending and have been examined.
Claims 1, 8, and 15, are newly amended see REMARKS February 26, 2025.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 10, 2025 has been entered.
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 a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: Claims 1-7 recite a system, claims 8-14 recite a method (i.e. a process such as an act or series of steps), and claim 15-20 recites a non-transitory computer readable storage medium therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1, 8, and 15 recite: A method comprising: based on data including job opportunity and employee pairings, predict one or more optimal job opportunities for a user profile; receiving a description of employment data of a user; determining a unique code associated with a job profile based on the description of the employment data of the user; querying with the unique code to retrieve a list of skills and that are mapped to the unique code; transforming the list of skills associated with the job profile of the user and skills associated with other users than the user having skills attributes similar to skills in the description of the employment data of the user into a skills vector; determining, via the skills vector provided as an input thereto, one or more optimal job opportunities for the user, predicting the one or more optimal job opportunities for the user based on data including job opportunities for a community of users other than the user; and outputting information about the determined one or more optimal job opportunities; receive an indication of the user applying to at least one of the determined one or more optimal job opportunities for the user and update, in response to the received indication of the user applying to at least one of the determined one or more optimal job opportunities, a weight applied to at least one mapping of an optimal job opportunity and user profile.
The claims recite a certain method of organizing human activity. The claims are directed to a certain method of organizing human activity as the disclosure is directed to managing personal behavior or relationships or interactions between people. The claims recite a series of steps for determining a unique code associated with a job profile based on the description, retrieving a list of skills mapped to the unique code, and determining one or more optimal job opportunities for the user based on skills vector. The method of merely determining a plurality of job opportunities for a job user based on performing a series of actions to normalize a job description and finding associated skills is a certain method of organizing human activity.
Alternatively the claims recite a mental process. The claims recite a method of determining a unique code associated with a job profile, determining a list of skills mapped to the unique code, and determining one or more optimal job opportunities for a user. The claims therefore, recite a mental process as a person is capable of analyzing job description information to determine a job code associated with the job based on the description and determining a series of skills that are associated with the job code and use the determined information to identify and match job seekers with a job opening in their mind or by using simple tools such as pen and paper. Additionally, the courts have identified concepts such as observations, judgements, and opinions as being capable of being performed in the human mind. As such merely performing a series of steps to determine a job opportunity for a job seeker by determining a unique code or industry and associated skills of user employment history and job opportunity information and matching the information is a mental process. An individual such as a hiring agent would be able to process and normalize information pertaining to the employment history of a user as well as current job opportunity listings to identify similarities and overlapping skills. Therefore, the claims recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: A computing system comprising: a storage device and a processor, train, based on data including job opportunity and employee pairings, a machine learning model, an application programming interface (API) call, a database, execution of the trained machine learning model, via a user interface of a software application, and update the trained machine learning model.
Claim 8: Train, based on data including job opportunity and employee pairings, a machine learning model, an application programming interface (API) call, execution of the trained machine learning model, via a user interface of a software application, and update the trained machine learning model.
Claim 15: A non-transitory computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method, Train, based on data including job opportunity and employee pairings, a machine learning model, an application programming interface (API) call, execution of the trained machine learning model, via a user interface of a software application, and update the trained machine learning model.
The additional element of a system comprising generic computer elements are found to recite mere instructions to apply a generic computer and technology to execute the method in the recited claim limitations, as merely using a computer platform to transmit, display, and manipulate information is not an improvement to a technology or technical field. As the additional elements merely recite generic computer elements such as a processor and a user interface of a software application to receive and present information and execute the method as well as a generic machine learning model to analyze information to perform the abstract idea. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Therefore, the additional elements do not direct the claims to significantly more as they do not recite any meaningful limitations or an improvement to a technology or technical field.
The dependent claims 2-7, 9-14, and 16-20 further narrow the abstract idea of analyzing employment data to determine a unique code associated with a job and determine a plurality of skills associated with the job to determine optimal job opportunities for a user as recited in the independent claims 1, 8, and 15 and are therefore directed towards the same abstract idea.
Claims 2-7, 9-14, and 16-20 do not recite any further additional elements that were not disclosed in the above analysis.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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(s) 1, 5-8, 12-15, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grover (US 2018/0173803) in view of Bhardwaj (US 2021/0035046).
Claims 1, 8, and 15: Grover discloses (Claim 1) a computing system comprising: (Claim 8) A method comprising: (Claim 15) A non-transitory computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method comprising: a storage device configured to store a description of employment data of a user (Paragraph [0024]; [0034-0035]; [0044-0045]; Fig. 3, a method includes operations for performing semantic analysis of job application by a machine-learning program to identify similarity coefficients among the plurality of industries, and for receiving a job search query from a first member, with the job search query including a query industry from the plurality of industries. The method further includes an operation for causing presentation on a display of one or more top job results. When a user initially registers to become a member of the service the user is prompted to provide some personal information, such as name, interest, education background, employment history, professional industry, skills, and so on. This information is stored in the member profile database);
and a processor configured to: train, based on data including job opportunity and employee pairings, a machine learning model to predict one or more optimal job opportunities for a user profile (Paragraph [0025-0026]; [0058]; [0220]; Fig. 15, the instructions cause the one or more computer processors to perform operations including accessing the plurality of job applications, performing semantic analysis of the job applications by a machine learning program to identify similarity coefficients among the plurality of industries, receiving a job search query from a first member, with the job search query including a query industry form the plurality of industries, expanding the job search query with industries that are similar to the query industry, executing the expanded job search query to generate a plurality of job results, and causing presentation on a display of one or more to job results. The expanded job search query is executed by a machine learning algorithm trained with identified features that include the job industry and the member industry. Machine learning algorithms provide a score to qualify each job as a match for the user. Mache learning is also utilized to provide a score for finding similarities regarding titles, skills, or industries. The machine learning algorithms utilize training data to find correlations among identified features, and how the features value affect the outcome);
determine a unique code associated with a job profile based on the description of the employment data of the user (Paragraph [0048-0049]; [0144-0146]; Fig. 4, data structures for storing job and member information according to some embodiments. The member profile includes member information such as industry, employer, skills, and so forth. Within the member profile, the industry information is linked to industry data. The industry data is a table for storing the industries identified in the social network. In one example embodiment, the industry data includes an industry identifier (e.g. a numerical value or a text string) and an industry name, which is a text string associated with the industry (e.g. legal services). Job data and member profiles are linked to the industry data via the industry identifier);
query, via an application programming interface (API) call, a database with the unique code to retrieve a list of skills from the database that are mapped to the unique code at the database (Paragraph [0038]; [0051-0052]; [0167-0171]; [0224]; Fig. 8, a method for identifying similarities among member skills. In some example embodiments, the skills similarities are identified in order to improve job searching. Skills may be extracted from job posts. For example, by analyzing the job title, descriptions, or requirements, one or more skills may be identified for the job. Initially, a compressed skill vector is created for each skill. Afterwards, a concatenated skill table is created, where each row includes a sequence with all the skills for a corresponding member. Semantic analysis is then performed on the skill table. The skill data is a table for storing the different skills identified in the social network. In some example embodiments, the skill data includes a skill identifier (e.g. a numerical value) and a name for the skill. The skill identifier may be linked to the member profiles and job data);
transform the list of skills from the database associated with the job profile of the user and skills associated with other users than the user having skills attributes similar to skills in the description of the employment data of the user into a skills vector (Paragraph [0146]; [0167-0171]; [0205-0206]; Figs. 8 and 10, a method for identifying similarities among member skills. In some example embodiments, the skills similarities are identified in order to improve job searching. Skills may be extracted from job posts. For example, by analyzing the job title, descriptions, or requirements, one or more skills may be identified for the job. Initially, a compressed skill vector is created for each skill. Afterwards, a concatenated skill table is created, where each row includes a sequence with all the skills for a corresponding member. Semantic analysis is then performed on the skill table. A method for expanding a job search query with similar member skills. A social networking server for implementing example embodiments. The social networking server includes a skill similarity engine and a plurality of databases which include the social graph database and the member profile database. The search server includes a machine learning algorithm for performing the searches which utilizes a plurality of features for selection and scoring the jobs);
determine, via execution of the trained machine learning model having the skills vector provided as an input thereto, one or more optimal job opportunities for the user, the machine learning model predicting the one or more optimal job opportunities for the user based on data including job opportunities for a community of users other than the user (Paragraph [0056]; [0167-0171]; [0194]; [0197]; [0222]; Figs. 8 and 10, machine-learning algorithms are utilized to find similarities in order to improve the job search. A method for identifying similarities among member skills. In some example embodiments, the skills similarities are identified in order to improve job searching. Skills may be extracted from job posts. For example, by analyzing the job title, descriptions, or requirements, one or more skills may be identified for the job. Initially, a compressed skill vector is created for each skill. Afterwards, a concatenated skill table is created, where each row includes a sequence with all the skills for a corresponding member. Semantic analysis is then performed on the skill table. A method for expanding a job search query with similar member skills. After the similarities have been identified for the different industries, the similarity coefficient may be used to improve and expand job searching. The job search engine ranks the candidate jobs for presentation to the user. The method further includes identifying job recommendations for a second member without receiving a job search query, with the identifying job recommendations including accessing profile data of the second member. Executing by the social networking server a job search based on the profile data of the second member);
and output information about the determined one or more optimal job opportunities via a user interface of a software application (Paragraph [0056]; [0167-0171]; [0194]; [0197]; Figs. 8 and 15, machine-learning algorithms are utilized to find similarities in order to improve the job search. A method for identifying similarities among member skills. In some example embodiments, the skills similarities are identified in order to improve job searching. Skills may be extracted from job posts. For example, by analyzing the job title, descriptions, or requirements, one or more skills may be identified for the job. Initially, a compressed skill vector is created for each skill. Afterwards, a concatenated skill table is created, where each row includes a sequence with all the skills for a corresponding member. Semantic analysis is then performed on the skill table. A method for expanding a job search query with similar member skills. After the similarities have been identified for the different industries, the similarity coefficient may be used to improve and expand job searching. The job search engine ranks the candidate jobs for presentation to the user),
Grover discloses a system for identifying job opportunities based on user characteristics and recommending a list of potential opportunities. However, Grover does not specifically disclose the following claim limitations: receive an indication of the user applying to at least one of the determined one or more optimal job opportunities for the user; and update, in response to the received indication of the user applying to at least one of the determined one or more optimal job opportunities, the trained machine learning model to improve a weight applied to at least one mapping of an optimal job opportunity and user profile.
In the same field of endeavor of determining job recommendations Bhardwaj teaches receive an indication of the user applying to at least one of the determined one or more optimal job opportunities for the user (Paragraph [0014-0015]; [0030-0031]; Fig. 2, systems to provide real-time recommendations to a user operating a user device to interact with job listing data. The user device is operated to monitor the user’s interactions with a first set of job listing data to generate at least a first interaction event. Weight metrics of attributes of a specific job listing associated with at least first interaction event are calculated and stored. The weight metrics are transmitted to a recommendation engine for use by the recommendation engine in retrieving an updated set of job listing data containing job listings responsive to the weight metrics. The updated set of job listings data is then presented to the user. In some embodiments, the attributes may include event parameters associated with the interaction events, including a timestamp, a job category, an industry type, etc. These parameters are weighted and transmitted to the recommendation engine for use by the engine in retrieving job listing data that matches the user’s current intentions. In an example, a job seeker operating a user device can interact with job listings in two ways. First the user can navigate to a job detail page to view the details of a specific job listing. Next the user can apply to a specific job listing. Some embodiments calculate the weight of each category based on the recency of each of these actions);
and update, in response to the received indication of the user applying to at least one of the determined one or more optimal job opportunities, the trained machine learning model to improve a weight applied to at least one mapping of an optimal job opportunity and user profile (Paragraph [0014-0015]; [0030-0031]; Fig. 2, systems to provide real-time recommendations to a user operating a user device to interact with job listing data. The user device is operated to monitor the user’s interactions with a first set of job listing data to generate at least a first interaction event. Weight metrics of attributes of a specific job listing associated with at least first interaction event are calculated and stored. The weight metrics are transmitted to a recommendation engine for use by the recommendation engine in retrieving an updated set of job listing data containing job listings responsive to the weight metrics. The updated set of job listings data is then presented to the user. In some embodiments, the attributes may include event parameters associated with the interaction events, including a timestamp, a job category, an industry type, etc. These parameters are weighted and transmitted to the recommendation engine for use by the engine in retrieving job listing data that matches the user’s current intentions. In an example, a job seeker operating a user device can interact with job listings in two ways. First the user can navigate to a job detail page to view the details of a specific job listing. Next the user can apply to a specific job listing. Some embodiments calculate the weight of each category based on the recency of each of these actions).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining similarities amongst jobs by vectorizing job information and determining an industry identifier and recommending jobs to a user as disclosed by Grover (Grover [0022]) with the system of receive an indication of the user applying to at least one of the determined one or more optimal job opportunities for the user; and update, in response to the received indication of the user applying to at least one of the determined one or more optimal job opportunities, the trained machine learning model to improve a weight applied to at least one mapping of an optimal job opportunity and user profile as taught by Bhardwaj (Bhardwaj [0030]). With the motivation of helping job seekers find employment opportunities (Bhardwaj [0001]).
Claims 5, 12, and 19: Modified Grover discloses the computing system as per claim 1, the method as per claim 8, and the non-transitory computer-readable medium as per claim 15. Grover further discloses wherein the processor is configured to transform textual descriptions of a plurality of skills into a plurality of numerical values, respectively, and store the plurality of numerical values within the vector (Paragraph [0146]; [0167-0171]; Figs. 8 and 10, a method for identifying similarities among member skills. In some example embodiments, the skills similarities are identified in order to improve job searching. Skills may be extracted from job posts. For example, by analyzing the job title, descriptions, or requirements, one or more skills may be identified for the job. Initially, a compressed skill vector is created for each skill. Afterwards, a concatenated skill table is created, where each row includes a sequence with all the skills for a corresponding member. Semantic analysis is then performed on the skill table. A method for expanding a job search query with similar member skills. In one example embodiment, Word2vec is utilized. The result is skill similarities where each skill is associated with corresponding similar skills).
Claims 6 and 13: Modified Grover discloses the computing system as per claim 1 and the method as per claim 8. Grover further discloses wherein the processor is configured to transform a plurality of descriptions of a plurality of job profiles of the user into a plurality of vectors, respectively, aggregate values within the plurality of vectors to generate an aggregated vector, and execute the machine learning model on the aggregated vector to determine the optimal job opportunity (Paragraph [0056]; [0144-0148]; [0194]; [0197]; Figs. 8 and 10, machine-learning algorithms are utilized to find similarities in order to improve the job search. In one example embodiment, the industry data includes an industry identifier having a numerical value. The job data and member profile are linked to the industry data. The job-application table is configured where each row is associated with a job application and the row includes the job industry and the member industry. Semantic analysis is performed to capture the similarity among the different industries using the job-application table. In one example Word2vec is used. The result of the semantic analysis includes industry similarities, where for each industry ID a plurality of industries are identified with the respective similarity coefficient. The results obtained showed that similar industries had high similarity coefficients. A method for expanding a job search query with similar member skills. After the similarities have been identified for the different industries, the similarity coefficient may be used to improve and expand job searching. The job search engine ranks the candidate jobs for presentation to the user).
Claims 7, 14, and 20: Modified Grover discloses the computing system as per claim 1, the method as per claim 8, and the non-transitory computer-readable medium as per claim 15. Grover further discloses wherein the processor is configured to assign a greater weight to some but not all of the skills within the list of skills, and execute the machine learning model based on the assigned greater weight (Paragraph [0056]; [0167-0172]; [0194]; [0197]; Figs. 8 and 10, machine-learning algorithms are utilized to find similarities in order to improve the job search. A method for identifying similarities among member skills. In some example embodiments, the skills similarities are identified in order to improve job searching. Skills may be extracted from job posts. For example, by analyzing the job title, descriptions, or requirements, one or more skills may be identified for the job. Initially, a compressed skill vector is created for each skill. Afterwards, a concatenated skill table is created, where each row includes a sequence with all the skills for a corresponding member. Semantic analysis is then performed on the skill table. In some example embodiments, the skills are weighted by multiplying each compressed skill vector by a corresponding weight. A method for expanding a job search query with similar member skills. After the similarities have been identified for the different industries, the similarity coefficient may be used to improve and expand job searching. The job search engine ranks the candidate jobs for presentation to the user).
Claim(s) 2-3, 9-10, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grover (US 2018/0173803) in view of Bhardwaj (US 2021/0035046) further in view of Concordia (US 2008/0086366).
Claims 2, 9, and 16: Modified Grover discloses the computing system as per claim 1, the method as per claim 8, and the non-transitory computer-readable medium as per claim 15. However, Grover does not disclose wherein the processor is configured to receive a questionnaire with the description of the employment data and identify an Occupational Information Network (O*NET) job code corresponding to the description based on a string comparison between the description and a job title of the O*NET job code.
In the same field of endeavor of expanding a job search by normalizing job information Concordia teaches wherein the processor is configured to receive a questionnaire with the description of the employment data and identify an Occupational Information Network (O*NET) job code corresponding to the description based on a string comparison between the description and a job title of the O*NET job code (Paragraph [0004]; [0018]; [0021]; [0032]; the disclosure details the implementation for an interactive employment search platform enabling employment searching and skill specification. In one embodiment, the platform acts as a conduit for connecting job seekers with job listings supplied by employers. The search module allows a job seeker to enter personal information, such as a job title. This job title is compared to classifications of all jobs to be searched upon. The selected title is received by the platform and converted into an occupational classification code such as an occupation information network (O*net). Such a conversion may be accomplished by querying a jobs database based on the job title. In an embodiment, the narrowing of jobs is performed and all OC codes associated with the job titles may be quired and returned. The returned OC codes are then used to extract job listings that have the same or similar OC codes from a job listings database).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining similarities amongst jobs by vectorizing job information and determining an industry identifier as disclosed by Grover (Grover [0022]) with the system of wherein the processor is configured to receive a questionnaire with the description of the employment data and identify an Occupational Information Network (O*NET) job code corresponding to the description based on a string comparison between the description and a job title of the O*NET job code as taught by Concordia (Concordia [0021]). With the motivation of helping to expand a job search and identify job for a job seeker (Concordia [0003]).
Claims 3, 10, and 17: Modified Grover discloses the computing system as per claim 2, the method as per claim 9, and the non-transitory computer-readable medium as per claim 16. However, Grover does not disclose wherein the processor is configured to query an application programming interface (API) of the database via the API call with the O*NET job code therein to retrieve the list of skills.
In the same field of endeavor of expanding a job search by normalizing job information Concordia teaches wherein the processor is configured to query an application programming interface (API) of the database via an API call with the O*NET job code therein to retrieve the list of skills (Paragraph [0004]; [0018]; [0021]; [0032]; the disclosure details the implementation for an interactive employment search platform enabling employment searching and skill specification. In one embodiment, the platform acts as a conduit for connecting job seekers with job listings supplied by employers. The search module allows a job seeker to enter personal information, such as a job title. This job title is compared to classifications of all jobs to be searched upon. The selected title is received by the platform and converted into an occupational classification code such as an occupation information network (O*net). Such a conversion may be accomplished by querying a jobs database based on the job title. In an embodiment, the narrowing of jobs is performed and all OC codes associated with the job titles may be quired and returned. The returned OC codes are then used to extract job listings that have the same or similar OC codes from a job listings database. The jobs database is then queries using the resulting job titles or titles associated with the OC codes. In an embodiment, the job title is converted into OC codes. Skills associated with each OC code, such as those associated with O*net codes, may exist in data structures along with their associated OC codes and may be queried from the job database and displayed to the user. This may be a table of skills and weights keyed by code).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining similarities amongst jobs by vectorizing job information and determining an industry identifier as disclosed by Grover (Grover [0022]) with the system of wherein the processor is configured to query an application programming interface (API) of the database via an API call with the O*NET job code therein to retrieve the list of skills as taught by Concordia (Concordia [0021]). With the motivation of helping to expand a job search and identify job for a job seeker (Concordia [0003]).
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grover (US 2018/0173803) in view of Bhardwaj (US 2021/0035046) further in view of Lahti (US 2016/0283905).
Claims 4, 11, and 18: Modified Grover discloses the computing system as per claim 1, the method as per claim 8, and the non-transitory computer-readable medium as per claim 15. However, Grover does not disclose wherein the processor is configured to input payment data of the user pulled from a payroll system or financial institution into the machine learning model and predict the optimal job opportunity based on a combination of the skills vector and the payment data.
In the same field of endeavor of matching a job candidate with a potential position Lahti teaches wherein the processor is configured to input payment data of the user pulled from a payroll system or financial institution into the machine learning model and predict the optimal job opportunity based on a combination of the skills vector and the payment data (Paragraph [0212]; [0215-0216] Job match can be used whereby users select a job from a pre-defined database that translates job requirements into levels required on dimensions of job performance. Job profiling by the system is based on the O*net job classification and information about context variables. The information entered by the user is compared against a database which contains job profiles for a large number of jobs. The net results of the job analysis stage is a job profile output which can be used. The final job profile information form the job profile can be used to retrieve compensation data from jobs in available databases which can be further harnessed to provide projections for the user. The system may import/retrieve compensation data. Based on the job profile, compensation data is requested from available databases and is used to estimate potential compensation values for the job profile).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining similarities amongst jobs by vectorizing job information and determining an industry identifier as disclosed by Grover (Grover [0022]) with the system of wherein the processor is configured to input payment data of the user pulled from a payroll system or financial institution into the machine learning model and predict the optimal job opportunity based on a combination of the skills vector and the payment data as taught by Lahti (Lahti [0212]). With the motivation of helping to expand a job search and identify job for a job seeker (Lahti [0002]).
Therefore, claims 1-20 are rejected under 35 U.S.C. 103.
Response to arguments
Applicant’s arguments, see REMARKS, filed November 10, 2025, with respect to the rejections of claims 1-20 under U.S.C. 101 have been fully considered but are not persuasive.
Representative argues that the newly amended claims do not recite an abstract idea as they recite a system that is configured to “train, based on data including job opportunity and employee pairings, a machine learning model to predict one or more optimal job opportunities for a user profile; query via an application programming interface (API) call, a database with the unique code; determine via execution of the trained machine learning model having the skills vector provided as an input thereto, one or more optimal job opportunities for the user; receive an indication of the user applying to at least one of the determine one or more optimal job opportunities for the user; and update in response to the received indication of the user applying to at least one of the determined one or more optimal job opportunities, the trained machine learning model to improve a weight applied to at least one mapping of an optimal job opportunity and user profile.” However, the examiner respectfully disagrees as the claims are found to recite a method for identifying one or more optimal job opportunities for a user based on user data. The method comprises based on data including job opportunity and employee pairings, predicting one or more optimal job opportunities for a user profile, receiving a description of employment data of a user; determining a unique code associated with a job profile based on the description of the employment data of the user; querying with the unique code to retrieve a list of skills that are mapped to the unique code; transforming the list of skills from the database associated with the job profile into a skills vector; determining one or more optimal job opportunities; outputting information about the determine one or more optimal job opportunities; receiving an indication of the user applying to at least one of the determined one or more optimal job opportunities; and in response to the received indication of the user applying to at least one of the determined one or more optimal job opportunities, improve a weight applied to at least one mapping of an optimal job opportunity and user profile. The examiner finds that the method of analyzing user data to determine a unique code representation of a job profile associated with user employment data, identifying a list of skills mapped to the unique code, transforming the list of skills into a skills vector to determine one or more optimal job opportunities for a user, and updating or adjusting weights based on received information such as if a user applied to a job are a mental process. A person such as a recruiter is capable of mentally receiving user information and analyzing the information to perform actions such as determining code associated with a job to identify information such as its industry to be used to expand a job search by identifying further skills associated with the code or identifier and determining job opportunities for a user based on the expanded identified skills. As the courts have identified concepts such as observations, evaluations, judgement, and opinion as reciting a mental process. The claims merely recite a process to collect information, analyze it, and display a certain result. Furthermore, the courts have determined that claims performing a mental process in a computer environment or using a computer as a tool to perform a mental process still recite a mental process. The claims are found to recite a certain method of organizing human activity for managing personal behavior or interactions between people as they recite a series of steps for processing user information to identify potential job opportunities and determine if a user performs an action such as applying to a job opportunity to further update information such as weights used to identify further job opportunities. Therefore, the claims recite an abstract idea.
The applicant further argues that the claim limitations of “receive an indication of the user applying to at least one of the determine one or more optimal job opportunities for the user and update, based on the received indication of the user applying to at least open of the determined one or more optimal job opportunities, the trained machine learning model to improve a weight applied to at least one mapping of an optimal job opportunity and user profile” are a practical application. However, the examiner finds that the additional elements of training a machine learning model to predict one or more optimal job opportunities for a user profile; querying via an application programming interface call; determining via execution of the trained machine learning model one or more optimal job opportunities; outputting information via a user interface of a software application; and updating the trained machine learning model to improve a weight are directed to merely “apply it.” As the claims merely apply generic computer and machine learning elements to perform the abstract idea of receiving and analyzing information. Merely training a machine learning model to perform the abstract idea of predicting an optimal job opportunity for a user profile based on skill information is not an improvement to the technology of a machine learning model. The claims recite a generic process for utilizing a machine learning model to perform a basic function of receiving and analyzing information to generate an output such as job and user profile skill information to determine an “optimal job opportunity.” Therefore, the claims do not recite an improvement to a technology or technical problem but merely use a generic machine learning model to identify optimal job opportunities based on user information and generic computer elements to perform searching and displaying of information. The additional elements do not direct the claims to a practical application.
Therefore, the examiner maintains the current 101 rejection.
Claims 2-7, 9-14, and 16-20 are dependent on claims 1, 8, and 15 and therefore are rejected under the same rejection.
Applicant’s arguments, see REMARKS, filed February 26 2025, with respect to the rejections of 1, 5-8, 12-15, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grover (US 2018/0173803) in view of Bhardwaj (US 2021/0035046) are not persuasive as claims were amended which required further search and consideration.
Claims 1, 8, and 15: The applicant argues that the current combination of prior art does not disclose the claim limitation of “transform the list of skills form the database associated with the job profile of the user and skills associated with other users other than the user having skills attributes similar to skills in the description of the employment data of the user into a skills vector, the machine learning model predicting the one or more optimal job opportunities for the suer based on data including job opportunities for a community of users other than the user.” However, the examiner respectfully disagrees as the specification states Grover discloses a system that performs query expansion by identifying skills associated with the identified industry (Grover [0167]; Fig. 8). Grover further discloses a system where skill data which includes a skill identifier is linked to job data which can be extracted during a query (Grover [0051]). Extracted skills are then analyzed to identify similar skills. The skills are then turned into a vector to be used by the machine learning model to perform an expanded job search (Grover [0170]; [0199]). Grover additionally discloses a system of using a social networking server to be used in combination with the search server when identifying job opportunities (Grover [0205]). The system identifies job recommendations for individuals based on the profile data of other individuals in the social network (Grover [0222]). To accomplish this the system uses a machine learning algorithm that performs a search based on skill information of similar users in the social network and job profiles (Grover [0206]). Therefore, the examiner finds that Grover teaches the newly amended claim limitations.
Therefore, claims 1, 8, and 15 are rejected under U.S.C. 103
Claims 2-7, 9-14, and 16-20 are argued as being allowable as being dependent on claims 1, 8, and 15. Therefore, they are also rejected under the same rejection as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Duan (US 2021/0224750) Quality based scoring.
Zhang (US 2019/0095869) Methods and systems for surfacing employment opportunity listings to a user.
Abbasi Moghaddam (US 2021/0089603) Stacking model for recommendations.
Nigam (US 2021/0374681) System and method for providing job recommendations based on users’ latent skills.
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/COREY RUSS/Examiner, Art Unit 3629