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 final office action.
Claims 1, 4-13, and 15-20 are currently pending and have been examined on their merits.
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, 4-13, and 15-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1, 4-10 recite a system and claims 11-13, and 15-20 recites a method (i.e. a series of steps) and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claim 1 recites receiving candidate data and qualification data from a user, the user being a potential job candidate wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, Agreeableness, Neuroticism, Openness, Extraversion/Extroversion) assessment, or a cultural test; receiving position data associated with at least one open position at a company; identifying at least one criteria for the position based at least in part on the position data; determining that the user meets or exceeds the criteria for the position; generating a qualification score for the user based at least in part on the position data, the candidate data, and the qualification data; determining that the qualification score is greater than a qualification score threshold; notifying the user that the user would be a good fit for the position; receiving a request from the user, to apply for the position; providing the initial qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; concurrent with providing data to the representative, determining at least one recommendation for the user to improve the qualification score based at least one of additional position data, third party data, candidate data, and qualification data and providing the recommendation; receiving additional data from the user from the position data, third party data, candidate data, and/or qualification data; re-calculating the qualification score based at least in part on the additional data from the user; re-providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; providing feedback to the user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time; recalculating the qualification score after each time the user provides additional data; re-providing, the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing for each recalculation of the qualification score; providing feedback to the user after each recalculation of the qualification score, and providing a final qualification score at the end of the period of time to the location accessible to the representative of the company.
Claim 11 recites a method comprising: receiving, a referral of a first user from a second to fill a position listing hosted; receiving candidate data and qualification data from the first user wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, Agreeableness, Neuroticism, Openness, Extraversion/Extroversion) assessment, or a cultural test; generating a qualification score for the first user with respect to the position listing based at least in part on position data associated with the position listing, the candidate data, and the qualification data; that the determining at least one recommendation for the first user to improve the qualification score and providing the at least one recommendation to the first user; and receiving additional data from the first user in response to the recommendation; re-calculating the qualification score based at least in part on the additional data; re-providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; providing feedback to the first user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time; recalculating the qualification score after each time the user provides additional data; determining, at the end of the time period, qualification score is greater than a qualification score threshold; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the company.
Claim 16 recites a method comprising: receiving position data associated with at least one open position at a company; identifying at least one criteria for the position based at least in part on the position data; receiving candidate data and qualification data from a plurality of users of the system, each of the plurality of users indicting an interest in applying for the position listing; wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, Agreeableness, Neuroticism, Openness, Extraversion/Extroversion) assessment, or a cultural test; determining at least one recommendation for each of the subset of users to improve their qualification score based at least on the position data, third party data, candidate data, and qualification data and providing the recommendation to the subset of users; and receiving additional data from one or more of the subset of users in response to the recommendation; re-calculating the qualification score based at least in part on the additional data; determining that a subset of the plurality of users meets or exceeds the criteria for the position; generating a qualification score for each of the subset of plurality of users based at least in part on the position data, the candidate data, and the qualification data; determining that the qualification score is greater than a qualification score threshold for a set of users of the subset of the plurality of users; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company for each user of the set of users.
The claims recite a mental process as a person could mentally receive information pertaining to a job candidate and an open job position and evaluate the candidate based on the level to which they qualify for the position. As well as hire an individual to a position based on their qualifications. Additionally, it would also have been possible for individuals to refer other users to a recruiter and receive some compensation for referring a job seeker who is qualified for a position. Therefore, the claims are found to recite a mental process as the claims limitations for determining a qualification score for a candidate based on candidate information and position information are found to be concepts the courts have defined as being performed in the human mind such as observations, evaluations, judgements, and opinions.
The claims recite a certain method of organizing human activity, the recited process would also be considered a method of organizing human activity as it relates to tracking or organizing information (i.e. tracking remark data from individuals, processing it according to a series of rules, and displaying the results). The claims are directed towards managing personal behavior or relationships or interactions between people. As the claims recite a method for collecting information such as candidate information and job requirements and performing steps for assessing a job candidate to determine a qualification score for a job candidate for a position.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: A system comprising: one or more processors; non-transitory computer-readable media storing computer-executable instructions, which when executed by the one or more processors cause the one or more processors to perform operations comprising: a device associated with the user.
Claim 11: A recruiting system.
Claim 16: The system.
However, the limitations merely 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) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h). Merely utilizing a computer system to perform basic actions such as receiving and processing information and presenting a results is not an improvement to a technology. Furthermore, a method for transmitting, receiving, and processing information does not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). 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). See reasoning for Step 2A prong 2. Therefore, the additional elements do not amount to significantly more as they do not impose any meaningful limitations on the abstract idea.
Claims 2, 5-10, 12, and 17-20 are directed to further narrowing the abstract idea of evaluating a user for a job opening based on their characteristics and allowing a user to refer other individuals for the position.
Claims 4 and 15 are directed to further narrowing the abstract idea of matching a job candidate to a job position and recommending improvements to the job candidates.
Claim 13 recite the additional element of a device. However, these elements are directed to merely “apply it” or applying a known technology to perform the abstract idea.
Therefore, claims 1-2, 4-13, and 15-20 are rejected under 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 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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious before the effective filing date of the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived 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 non-obviousness.
Claims 1, 4-12, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ranade (US 2021/0158297) in view of Carter (US 2015/0006422) further in view of Krishnamoorthy (US 2015/0302359).
Claim 1: Ranade discloses a system comprising: one or more processors; non-transitory computer-readable media storing computer-executable instructions, which when executed by the one or more processors cause the one or more processors to perform operations comprising: receiving, via a device associated with a user, candidate data and qualification data from the user of the system, the user being a potential job candidate (Paragraph [0042]; [0047]; [0052]; Fig. 5, a system client application can collect different types of information with the user’s permission for example information from online social networks, different from online professional networks, and offline contacts. In some embodiments information may include particular skills the contact possesses or jobs the individual is seeking. The relevant user data is sent to the system for storage. The relevant user data can include data from sources such as an online social network, or behavior information collected passively by the client app. (The examiner notes that the broadest reasonable interpretation of personality data such as self-personality evaluation data or third-party personality data would include information about a person relevant for a job position that could be gathered from online social networks, recommendations from a user, and/or behavior information collected by a client app));
receiving, via the processor, position data associated with at least one open position at a company (Paragraph [0023]; [0034-0035]; Fig. 4, companies send job descriptions with details of the job like position, location, salary, benefits, and company background to the system. A summary of the process described includes the company looking to fill a job notifies the system about a new Job opening. It provides some details about location, salary, desired qualifications, and reward);
identifying, via the processor, at least one criteria for the position based at least in part on the position data (Paragraph [0023]; [0034-0035]; Fig. 4, companies send job descriptions with details of the job like position, location, salary, benefits, and company background to the system. A summary of the process described includes the company looking to fill a job notifies the system about a new Job opening. It provides some details about location, salary, desired qualifications, and reward);
determining, via the processor, that the user meets or exceeds the criteria for the position (Paragraph [0047]; Fig. 6, illustrates one way to compute a metric (qualification score) called a match score that serves as an indicator of how well a particular job opening relates to a particular user in the application network. For example, job information, e.g. requirements, can be combined with user data, e.g. work history and success rate, to arrive at a referrer match score for a job);
generating, via the processor, a qualification score for the user based at least in part on the position data, the candidate data, and the qualification data (Paragraph [0047]; Fig. 6, illustrates one way to compute a metric (qualification score) called a match score that serves as an indicator of how well a particular job opening relates to a particular user in the application network. For example, job information, e.g. requirements, can be combined with user data, e.g. work history and success rate, to arrive at a referrer match score for a job);
notifying, via the processor, the user that the user would be a good fit for the position via a device associated with the user (Paragraph [0024]; [0034-0037]; Figs. 4 and 6, the system chooses populations of users based on factors and notifies them of the new job posting);
receiving, via the processor, a request from the user via the device associated with the user, to apply for the position (Paragraph [0025] A user is able to either apply for the job directly or pass it on to someone else via ay means).
Ranade discloses a system of identifying candidates for a position. However, Ranade does not disclose wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test; determining, via the processor, that the qualification score is greater than a qualification score threshold; and providing, via the processor, the initial qualification score, candidate data, and qualification data to a location accessible by a representative with the position listing.
In the same field of endeavor of identifying candidates for a job position Carter teaches wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test (Paragraph [0074]; [0092]; [0100]; [0126]; [0132-0135]; Fig. 5, in many of these embodiments only automatic matching conducted by the system will require a minimum compatibility score using a threshold. Candidate profiles may be created using a profile building process including inputting information and completing questionnaires such as personality and value. Personality factors identified in a user personality questionnaire may include aggressiveness, agreeableness, athleticism, collaboration, conscientiousness, emotional stability, empathy, extraversion, openness, self-esteem, and others (The examiner notes that the broadest reasonable interpretation of a system that is capable of providing at least one personality assessment would include the recited system that allows a user to either submit personality information or take personality questionnaires that assess a variety of personality information). The system may present candidates with a psychographic questionnaire, workplace culture and satisfaction questionnaire, and others. Based on answers given in these and other questionnaires the system may create a candidate personality and values profile. Compatibility scorer may use correlational models in order to calculate predictive scores. These models may be created using self-provided information of users including personality and values information and then using those factors to predict individual job satisfaction and performance across employers. IN cases where a personality profile for the candidate is available the system may submit the jobs list to a personality fit scorer);
determining, via the processor, that the qualification score is greater than a qualification score threshold (Paragraph [0074]; [0092]; [0100]; [0126]; [0132-0135]; Fig. 5, in many of these embodiments only automatic matching conducted by the system will require a minimum compatibility score using a threshold. Candidate profiles may be created using a profile building process including inputting information and completing questionnaires such as personality and value. Personality factors identified in a user personality questionnaire may include aggressiveness, agreeableness, athleticism, collaboration, conscientiousness, emotional stability, empathy, extraversion, openness, self-esteem, and others. The system may present candidates with a psychographic questionnaire, workplace culture and satisfaction questionnaire, and others. Based on answers given in these and other questionnaires the system may create a candidate personality and values profile. Compatibility scorer may use correlational models in order to calculate predictive scores. These models may be created using self-provided information of users including personality and values information and then using those factors to predict individual job satisfaction and performance across employers. IN cases where a personality profile for the candidate is available the system may submit the jobs list to a personality fit scorer);
and providing, via the processor, the initial qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing (Paragraph [0074]; [0092]; [0100]; [0126]; [0132-0135]; Fig. 5, in many of these embodiments only automatic matching conducted by the system will require a minimum compatibility score using a threshold. Candidate profiles may be created using a profile building process including inputting information and completing questionnaires such as personality and value. Personality factors identified in a user personality questionnaire may include aggressiveness, agreeableness, athleticism, collaboration, conscientiousness, emotional stability, empathy, extraversion, openness, self-esteem, and others. The system may present candidates with a psychographic questionnaire, workplace culture and satisfaction questionnaire, and others. Based on answers given in these and other questionnaires the system may create a candidate personality and values profile. Compatibility scorer may use correlational models in order to calculate predictive scores. These models may be created using self-provided information of users including personality and values information and then using those factors to predict individual job satisfaction and performance across employers. In cases where a personality profile for the candidate is available the system may submit the jobs list to a personality fit scorer).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test; determining that the qualification score is greater than a qualification score threshold; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company as taught by Carter (Carter [0135]). With the motivation of helping to matching job candidates to open positions and hiring the best fit candidates (Carter [0003]).
The combination of Ranade and Carter disclose a system of identifying and scoring job candidates for a position based on referrals however the combination does not specifically disclose the following claim limitations:
concurrent with providing data to the representative, determining, at least one recommendation for the user to improve the qualification score based at least one of additional position data, third party data, candidate data, and/or qualification data and providing the recommendation to the device, via the process, to the user via the device associated with the user; receiving, via the processor, additional data from the user from the position data, third party data, candidate data, and/or qualification data; re-calculating, via the processor, the qualification score based at least in part on the additional data from the user; re-providing, via the processor, the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; providing, via the processor, feedback to the user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time; recalculating, the qualification score after each time the user provides additional data; re-providing, via the processor, the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing for each recalculation of the qualification score; providing, via the processor, feedback to the user each recalculation of the qualification score; and providing, via the processor, a final qualification score at the end of the period of time to the location accessible to the representative of the company.
In the same field of endeavor of scoring a job candidate for a position based on their likelihood of success Krishnamoorthy teaches concurrent with providing data to the representative, determining, at least one recommendation for the user to improve the qualification score based at least one of additional position data, third party data, candidate data, and/or qualification data and providing the recommendation to the device, via the process, to the user via the device associated with the user (Paragraph [0059-0062]; [0069]; Fig. 3D, in an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
receiving, via the processor, additional data from the user from the position data, third party data, candidate data, and/or qualification data (Paragraph [0059-0062]; [0069]; Fig. 3D, in an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. (The examiner notes that the broadest reasonable interpretation of receiving additional data from the user would include allowing a user to incorporate changes to their information based on received feedback such as including additional internal parameters and/or modify certain parameters of data objects as recited above));
re-calculating, via the processor, the qualification score based at least in part on the additional data from the user; re-providing, via the processor, the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing (Paragraph [0052-0054]; [0059-0062]; [0069]; Fig. 3D, the one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
providing, via the processor, feedback to the user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time (Paragraph [0035]; [0052-0054]; [0059-0062]; [0069]; Fig. 3D, In one embodiment, the internal parameters may be updated or refined in a pre-defined period of time. The one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
recalculating, the qualification score after each time the user provides additional data (Paragraph [0035]; [0052-0054]; [0059-0062]; [0069]; Fig. 3D, In one embodiment, the internal parameters may be updated or refined in a pre-defined period of time. The one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
re-providing, via the processor, the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing for each recalculation of the qualification score (Paragraph [0035]; [0052-0054]; [0059-0062]; [0069]; Fig. 3D, In one embodiment, the internal parameters may be updated or refined in a pre-defined period of time. The one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
providing, via the processor, feedback to the user each recalculation of the qualification score (Paragraph [0035]; [0052-0054]; [0059-0062]; [0069]; Fig. 3D, In one embodiment, the internal parameters may be updated or refined in a pre-defined period of time. The one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
and providing a final qualification score at the end of the period of time to the location accessible to the representative of the company (Paragraph [0035]; [0052-0054]; [0059-0062]; [0069]; Figs. 3D and 4, In one embodiment, the internal parameters may be updated or refined in a pre-defined period of time. The one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of determining at least one recommendation for the user to improve the qualification score based at least one of the position data, third party data, candidate data, and qualification data and providing the recommendation to the device; receiving additional data from the user of the system; re-calculating the qualification score based at least in part on the additional data from the user; re-providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; providing feedback to the user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time; recalculating the qualification score after each time the user provides additional data; and providing a final qualification score at the end of the period of time to the location accessible to the representative of the company as taught by Krishnamoorthy (Krishnamoorthy [0035]). With the motivation of helping to increase a user’s ability to receive a job offer for a position and improve the scoring of a job seeker compared to a position (Krishnamoorthy [0003]).
Claim 4: Modified Ranade discloses the system as per claim 1. However, Ranade does not disclose wherein prior to receiving the request to apply for the position from the user receiving additional data from the device and, in response to receiving the additional data, re- calculating, via the processor, the qualification score prior to providing the initial qualification score to the location accessible to the representative of the company.
In the same field of endeavor of scoring a job candidate for a position based on their likelihood of success Krishnamoorthy teaches wherein prior to receiving the request to apply for the position from the user receiving additional data from the device and, in response to receiving the additional data, re- calculating, via the processor, the qualification score prior to providing the initial qualification score to the location accessible to the representative of the company (Paragraph [0035]; [0052-0054]; [0059-0062]; [0069]; Fig. 3D, In one embodiment, the internal parameters may be updated or refined in a pre-defined period of time. The one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of determining at least one recommendation for the user to improve the qualification score based at least one of the position data, third party data, candidate data, and qualification data and providing the recommendation to the device; receiving additional data from the user of the system; re-calculating the qualification score based at least in part on the additional data from the user; re-providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; providing feedback to the user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time; recalculating the qualification score after each time the user provides additional data; and providing a final qualification score at the end of the period of time to the location accessible to the representative of the company as taught by Krishnamoorthy (Krishnamoorthy [0035]). With the motivation of helping to increase a user’s ability to receive a job offer for a position and improve the scoring of a job seeker compared to a position (Krishnamoorthy [0003]).
Claim 5: Modified Ranade discloses the system as per claim 1. Ranade further discloses wherein prior to notifying the user that the first user would be a good fit for the position, receiving a referral of the first user from a second user (Paragraph [0023-0026]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user (second user) is able to either apply for the job directly or pass it on to someone else (first user)).
Claim 6: Modified Ranade discloses the system as per claim 5. Ranade further discloses further comprising selecting, via the processor, the second user to receive an incentive for referring the first user for the position in response to receiving an input indicting that the first user was selected to fill the position (Paragraph [0023-0027]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user (second user) is able to either apply for the job directly or pass it on to someone else (first user). Some users in one of the chains of referral applies for the job. If they are hired everyone in that chain can get rewards from the pool of incentive bounty provided by the hiring company).
Claim 7: Modified Ranade discloses the system as per claim 5. Ranade further discloses further comprising: receiving, via the processor, a referral of the second user from a third user prior to receiving the referral of the first user from the second user; selecting, via the processor, the third user to receive an incentive for referring the first user for the position in response to receiving an input indicting that the first user was selected to fill the position (Paragraph [0023-0027]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user is able to either apply for the job directly or pass it on to someone else. Some users in one of the chains of referral applies for the job. If they are hired everyone in that chain can get rewards from the pool of incentive bounty provided by the hiring company. (The examiner notes that as per figure 2 the system teaches a referral chain of individuals who may subsequently continue to refer other individuals for a position and/or apply for the position themselves and therefore the broadest reasonable interpretation of any user disclosed in the system that refers other users or is referred by another user could be interpreted as a first, second, third, etc. user as recited in the claimed limitation)).
Claim 8: Modified Ranade discloses the system as per claim 5. Ranade further discloses further comprising: receiving, via the processor, a referral of the second user from a third user prior to receiving the referral of the first user from the second user; selecting, via the processor, the second user to receive an incentive for referring the first user for the position in response to receiving an input indicting that the first user was selected to fill the position (Paragraph [0023-0027]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user is able to either apply for the job directly or pass it on to someone else. Some users in one of the chains of referral applies for the job. If they are hired everyone in that chain can get rewards from the pool of incentive bounty provided by the hiring company. (The examiner notes that as per figure 2 the system teaches a referral chain of individuals who may subsequently continue to refer other individuals for a position and/or apply for the position themselves and therefore the broadest reasonable interpretation of any user disclosed in the system that refers other users or is referred by another user could be interpreted as a first, second, third, etc. user as recited in the claimed limitation)).
Claim 9: Modified Ranade discloses the system as per claim 5. Ranade further discloses further comprising: receiving, via the processor, a referral of the second user from a third user prior to receiving the referral of the first user from the second user; selecting, via the processor, the second user to receive a first incentive for referring the first user for the position in response to receiving an input indicting that the first user was selected to fill the position; and selecting, via the processor, the third user to receive a second incentive for referring the first user for the position in response to receiving an input indicting that the first user was selected to fill the position (Paragraph [0023-0027]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user is able to either apply for the job directly or pass it on to someone else. Some users in one of the chains of referral applies for the job. If they are hired everyone in that chain can get rewards from the pool of incentive bounty provided by the hiring company. (The examiner notes that as per figure 2 the system teaches a referral chain of individuals who may subsequently continue to refer other individuals for a position and/or apply for the position themselves and therefore the broadest reasonable interpretation of any user disclosed in the system that refers other users or is referred by another user could be interpreted as a first, second, third, etc. user as recited in the claimed limitation)).
Claim 10: Modified Ranade discloses the system as per claim 5. Ranade further discloses further comprising: receiving, via the processor, a referral of the second user from a third user prior to receiving the referral of the first user from the second user; receiving, via the processor, a referral of the third user from a fourth user prior to receiving the referral of the second user from the third user; selecting, via the processor, the second user to receive a first incentive for referring the first user for the position in response to receiving an input indicting that the first user was selected to fill the position; and selecting, via the processor, the fourth user to receive a second incentive for referring the first user for the position in response to receiving an input indicting that the first user was selected to fill the position (Paragraph [0023-0027]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user is able to either apply for the job directly or pass it on to someone else. Some users in one of the chains of referral applies for the job. If they are hired everyone in that chain can get rewards from the pool of incentive bounty provided by the hiring company. This distribution is calculated based on factors such as the number of links in the chain, the total amount of the bounty, some measure of user quality, user loyalty, and past referral quality (The examiner notes that as per figure 2 the system teaches a referral chain of individuals who may subsequently continue to refer other individuals for a position and/or apply for the position themselves and therefore the broadest reasonable interpretation of any user disclosed in the system that refers other users or is referred by another user could be interpreted as a first, second, third, etc. user as recited in the claimed limitation)).
Claim 11: Ranade discloses a method comprising: receiving, at a recruiting system, a referral of a first user from a second to fill a position listing hosted by the recruiting system (Paragraph [0023-0027]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user is able to either apply for the job directly or pass it on to someone else. Some users in one of the chains of referral applies for the job. If they are hired everyone in that chain can get rewards from the pool of incentive bounty provided by the hiring company);
receiving candidate data and qualification data from the first user (Paragraph [0047]; Fig. 6, illustrates one way to compute a metric (qualification score) called a match score that serves as an indicator of how well a particular job opening relates to a particular user in the application network. For example, job information, e.g. requirements, can be combined with user data, e.g. work history and success rate, to arrive at a referrer match score for a job);
generating a qualification score for the first user with respect to the position listing based at least in part on position data associated with the position listing, the candidate data, and the qualification data (Paragraph [0047]; Fig. 6, illustrates one way to compute a metric (qualification score) called a match score that serves as an indicator of how well a particular job opening relates to a particular user in the application network. For example, job information, e.g. requirements, can be combined with user data, e.g. work history and success rate, to arrive at a referrer match score for a job).
However, Ranade does not disclose the following claim limitations: at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test; determining that the qualification score is greater than a qualification score threshold; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company.
In the same field of endeavor of identifying candidates for a job position Carter teaches wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test (Paragraph [0074]; [0092]; [0100]; [0126]; [0132-0135]; Fig. 5, in many of these embodiments only automatic matching conducted by the system will require a minimum compatibility score using a threshold. Candidate profiles may be created using a profile building process including inputting information and completing questionnaires such as personality and value. Personality factors identified in a user personality questionnaire may include aggressiveness, agreeableness, athleticism, collaboration, conscientiousness, emotional stability, empathy, extraversion, openness, self-esteem, and others (The examiner notes that the broadest reasonable interpretation of a system that is capable of providing at least one personality assessment would include the recited system that allows a user to either submit personality information or take personality questionnaires that assess a variety of personality information). The system may present candidates with a psychographic questionnaire, workplace culture and satisfaction questionnaire, and others. Based on answers given in these and other questionnaires the system may create a candidate personality and values profile. Compatibility scorer may use correlational models in order to calculate predictive scores. These models may be created using self-provided information of users including personality and values information and then using those factors to predict individual job satisfaction and performance across employers. In cases where a personality profile for the candidate is available the system may submit the jobs list to a personality fit scorer);
determining that the qualification score is greater than a qualification score threshold; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company (Paragraph [0074]; [0092]; [0100]; [0126]; [0132-0135]; Fig. 5, in many of these embodiments only automatic matching conducted by the system will require a minimum compatibility score using a threshold. Candidate profiles may be created using a profile building process including inputting information and completing questionnaires such as personality and value. Personality factors identified in a user personality questionnaire may include aggressiveness, agreeableness, athleticism, collaboration, conscientiousness, emotional stability, empathy, extraversion, openness, self-esteem, and others. The system may present candidates with a psychographic questionnaire, workplace culture and satisfaction questionnaire, and others. Based on answers given in these and other questionnaires the system may create a candidate personality and values profile. Compatibility scorer may use correlational models in order to calculate predictive scores. These models may be created using self-provided information of users including personality and values information and then using those factors to predict individual job satisfaction and performance across employers. In cases where a personality profile for the candidate is available the system may submit the jobs list to a personality fit scorer).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test; determining that the qualification score is greater than a qualification score threshold; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company as taught by Carter (Carter [0135]). With the motivation of helping to matching job candidates to open positions and hiring the best fit candidates (Carter [0003]).
However, the combination of Ranade and Carter does not disclose determining at least one recommendation for the first user to improve the qualification score and providing the at least one recommendation to the first user; and receiving additional data from the first user in response to the recommendation; re-calculating the qualification score based at least in part on the additional data; re-providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; providing feedback to the first user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time; recalculating the qualification score after each time the user provides additional data; determining, at the end of the time period, determining that the qualification score is greater than a qualification score threshold; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company.
In the same field of endeavor of scoring a job candidate for a position based on their likelihood of success Krishnamoorthy teaches determining at least one recommendation for the first user to improve the qualification score and providing the at least one recommendation to the first user (Paragraph [0059-0062]; [0069]; Fig. 3D, in an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
and receiving additional data from the first user in response to the recommendation (Paragraph [0059-0062]; [0069]; Fig. 3D, in an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. (The examiner notes that the broadest reasonable interpretation of receiving additional data from the user would include allowing a user to incorporate changes to their information based on received feedback such as including additional internal parameters and/or modify certain parameters of data objects as recited above));
re-calculating the qualification score based at least in part on the additional data; re-providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; (Paragraph [0052-0054]; [0059-0062]; [0069]; Fig. 3D, the one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
providing feedback to the first user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time (Paragraph [0035]; [0052-0054]; [0059-0062]; [0069]; Fig. 3D, In one embodiment, the internal parameters may be updated or refined in a pre-defined period of time. The one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
recalculating the qualification score after each time the user provides additional data; determining, at the end of the time period (Paragraph [0035]; [0052-0054]; [0059-0062]; [0069]; Figs. 3D and 4, In one embodiment, the internal parameters may be updated or refined in a pre-defined period of time. The one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of determining at least one recommendation for the user to improve the qualification score based at least one of the position data, third party data, candidate data, and qualification data and providing the recommendation to the device; receiving additional data from the user of the system; re-calculating the qualification score based at least in part on the additional data from the user; re-providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; providing feedback to the user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time; recalculating the qualification score after each time the user provides additional data; and providing a final qualification score at the end of the period of time to the location accessible to the representative of the company as taught by Krishnamoorthy (Krishnamoorthy [0035]). With the motivation of helping to increase a user’s ability to receive a job offer for a position and improve the scoring of a job seeker compared to a position (Krishnamoorthy [0003]).
Claim 12: Modified Ranade discloses the method as per claim 11. Ranade further discloses further comprising: receiving an indication that the first user was selected to fill an opening associated with the position listing; and providing an incentive to the second user (Paragraph [0023-0027]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user is able to either apply for the job directly or pass it on to someone else. Some users in one of the chains of referral applies for the job. If they are hired everyone in that chain can get rewards from the pool of incentive bounty provided by the hiring company. This distribution is calculated based on factors such as the number of links in the chain, the total amount of the bounty, some measure of user quality, user loyalty, and past referral quality (The examiner notes that as per figure 2 the system teaches a referral chain of individuals who may subsequently continue to refer other individuals for a position and/or apply for the position themselves and therefore the broadest reasonable interpretation of any user disclosed in the system that refers other users or is referred by another user could be interpreted as a first, second, third, etc. user as recited in the claimed limitation)).
Claim 16: Ranade discloses a method comprising: receiving position data associated with at least one open position at a company (Paragraph [0047]; Fig. 6, illustrates one way to compute a metric (qualification score) called a match score that serves as an indicator of how well a particular job opening relates to a particular user in the application network. For example, job information, e.g. requirements, can be combined with user data, e.g. work history and success rate, to arrive at a referrer match score for a job);
identifying at least one criteria for the position based at least in part on the position data; receiving candidate data and qualification data from a plurality of users, each of the plurality of users indicting an interest in applying for the position listing (Paragraph [0042]; Fig. 5, a system client application can collect different types of information with the user’s permission for example different from online professional networks. In some embodiments information may include particular skills the contact possesses or jobs the individual is seeking);
determining that at least a subset of the plurality of users meets or exceeds the criteria for the position (Paragraph [0023-0027]; [0032]; [034-0036] Fig. 2, company sends job description details to the system. The system chooses or identifies a seed populations of users based on factors and notifies them of the new job posting. A user is able to either apply for the job directly or pass it on to someone else. Some users in one of the chains of referral applies for the job. If they are hired everyone in that chain can get rewards from the pool of incentive bounty provided by the hiring company);
generating a qualification score for each of the subset of plurality of users based at least in part on the position data, the candidate data, and the qualification data (Paragraph [0047]; Fig. 6, illustrates one way to compute a metric (qualification score) called a match score that serves as an indicator of how well a particular job opening relates to a particular user in the application network. For example, job information, e.g. requirements, can be combined with user data, e.g. work history and success rate, to arrive at a referrer match score for a job).
However, Ranade does not disclose the following claim limitations: at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test; determining that the qualification score is greater than a qualification score threshold for a set of users of the subset of the plurality of users; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company for each user of the set of users.
In the same field of endeavor of identifying candidates for a job position Carter teaches wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test (Paragraph [0074]; [0092]; [0100]; [0126]; [0132-0135]; Fig. 5, in many of these embodiments only automatic matching conducted by the system will require a minimum compatibility score using a threshold. Candidate profiles may be created using a profile building process including inputting information and completing questionnaires such as personality and value. Personality factors identified in a user personality questionnaire may include aggressiveness, agreeableness, athleticism, collaboration, conscientiousness, emotional stability, empathy, extraversion, openness, self-esteem, and others (The examiner notes that the broadest reasonable interpretation of a system that is capable of providing at least one personality assessment would include the recited system that allows a user to either submit personality information or take personality questionnaires that assess a variety of personality information). The system may present candidates with a psychographic questionnaire, workplace culture and satisfaction questionnaire, and others. Based on answers given in these and other questionnaires the system may create a candidate personality and values profile. Compatibility scorer may use correlational models in order to calculate predictive scores. These models may be created using self-provided information of users including personality and values information and then using those factors to predict individual job satisfaction and performance across employers. In cases where a personality profile for the candidate is available the system may submit the jobs list to a personality fit scorer);
determining that the qualification score is greater than a qualification score threshold for a set of users of the subset of the plurality of users; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company for each user of the set of users (Paragraph [0074]; [0092]; [0100]; [0126]; [0132-0135]; Fig. 5, in many of these embodiments only automatic matching conducted by the system will require a minimum compatibility score using a threshold. Candidate profiles may be created using a profile building process including inputting information and completing questionnaires such as personality and value. Personality factors identified in a user personality questionnaire may include aggressiveness, agreeableness, athleticism, collaboration, conscientiousness, emotional stability, empathy, extraversion, openness, self-esteem, and others. The system may present candidates with a psychographic questionnaire, workplace culture and satisfaction questionnaire, and others. Based on answers given in these and other questionnaires the system may create a candidate personality and values profile. Compatibility scorer may use correlational models in order to calculate predictive scores. These models may be created using self-provided information of users including personality and values information and then using those factors to predict individual job satisfaction and performance across employers. In cases where a personality profile for the candidate is available the system may submit the jobs list to a personality fit scorer).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, agreeableness, neuroticism, openness, extraversion/extroversion assessment or a cultural test; determining that the qualification score is greater than a qualification score threshold; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company as taught by Carter (Carter [0135]). With the motivation of helping to matching job candidates to open positions and hiring the best fit candidates (Carter [0003]).
However, the combination of Ranade and Carter does not disclose determining at least one recommendation for each of the subset of users to improve their qualification score based at least on the position data, third party data, candidate data, and qualification data and providing the recommendation to the subset of users; and receiving additional data from one or more of the subset of users in response to the recommendation; re-calculating the qualification score based at least in part on the additional data; determining that the qualification score is greater than a qualification score threshold for a set of users of the subset of the plurality of users; and providing the qualification score, candidate data, and qualification data to a location accessible by a representative of the company for each user of the set of users.
In the same field of endeavor of scoring a job candidate for a position based on their likelihood of success Krishnamoorthy teaches determining at least one recommendation for each of the subset of users to improve their qualification score based at least on the position data, third party data, candidate data, and qualification data and providing the recommendation to the subset of users; (Paragraph [0059-0062]; [0069]; Fig. 3D, in an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%);
and receiving additional data from one or more of the subset of users in response to the recommendation (Paragraph [0059-0062]; [0069]; Fig. 3D, in an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. (The examiner notes that the broadest reasonable interpretation of receiving additional data from the user would include allowing a user to incorporate changes to their information based on received feedback such as including additional internal parameters and/or modify certain parameters of data objects as recited above));
re-calculating the qualification score based at least in part on the additional data; (Paragraph [0052-0054]; [0059-0062]; [0069]; Fig. 3D, the one or more scores are calculated dynamically and may change under certain conditions. These conditions include change in internal parameters within the user’s scope, which may arise due to the feedback given by the feedback generator in order to improve the one or more scores computed. In an embodiment, feedback generator may also suggest the best match to the user’s requirement, by judging the minimum modifications that need to be made in order to achieve the best match judged by a high absolute score. For example, the user may be informed that on acquiring a particular skill set, it is estimated that he will be hired to his desired job position within a period of two months. Further, the feedback provides an option to the user to incorporate the one or more changes, which may include suggestions to include certain internal parameters, remove certain internal parameters, and/or modify certain internal parameters of the data object submitted by the user in order to improve the one or more computed scores. In one embodiment, the feedback generator may itself compute all the probable scores by automatically changing the various parameters. Feedback generator may utilize the one or more computed scores and the one or more rules stored in rule base database to provide an option of changing one or more internal parameters and thus may allow the user to improve his relative score. For example, the feedback may include one or more Suggestions such as, but not limited to, that the user may do additional professional courses in computer networking from the reputed institute ABC based on the fact that the competing user has done the same. Moreover, the feedback may also include Suggestions Such as, but not limited to, that if the user changes his residence to Los Angeles, his chances of acquiring his desired job will increase by 5%).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of determining at least one recommendation for the user to improve the qualification score based at least one of the position data, third party data, candidate data, and qualification data and providing the recommendation to the device; receiving additional data from the user of the system; re-calculating the qualification score based at least in part on the additional data from the user; re-providing the qualification score, candidate data, and qualification data to a location accessible by a representative associated with the position listing; providing feedback to the user after re-calculating the qualification score, allowing the user to continue providing additional data for a predetermined period of time; recalculating the qualification score after each time the user provides additional data; and providing a final qualification score at the end of the period of time to the location accessible to the representative of the company as taught by Krishnamoorthy (Krishnamoorthy [0035]). With the motivation of helping to increase a user’s ability to receive a job offer for a position and improve the scoring of a job seeker compared to a position (Krishnamoorthy [0003]).
Claim 17: Modified Ranade discloses the method as per claim 16. However, Ranade does not disclose wherein the candidate data includes career history, awards, demographic information, and educational history and the qualification data includes background check and certifications.
In the same field of endeavor of identifying candidates for a job position Carter teaches wherein the candidate data includes career history, awards, demographic information, and educational history and the qualification data includes background check and certifications (Paragraph [0100]; [0126]; [0132-0135]; Fig. 5, candidate profiles may be created using profile building process including uploading photos, inputting information and completing questionnaires such as general demographics, values and culture, personality and value, company value, and/or company satisfaction. General demographics questionnaires may include questions relevant to candidate’s demographics. Candidates may use a resume builder. A resume builder may contain sections including experience, skills, education, certifications, project, and others).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of wherein the candidate data includes career history, awards, demographic information, and educational history and the qualification data includes background check and certifications as taught by Carter (Carter [0100]). With the motivation of helping to matching job candidates to open positions and hiring the best fit candidates (Carter [0003]).
Claim 18: Modified Ranade discloses the method as per claim 16. However, Ranade does not disclose wherein the qualification data includes background check and certifications.
In the same field of endeavor of identifying candidates for a job position Carter teaches wherein the qualification data includes background check and certifications (Paragraph [0100]; [0126]; [0132-0135]; Fig. 5, candidate profiles may be created using profile building process including uploading photos, inputting information and completing questionnaires such as general demographics, values and culture, personality and value, company value, and/or company satisfaction. General demographics questionnaires may include questions relevant to candidate’s demographics. Candidates may use a resume builder. A resume builder may contain sections including experience, skills, education, certifications, project, and others).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of wherein the qualification data includes background check and certifications as taught by Carter (Carter [0100]). With the motivation of helping to matching job candidates to open positions and hiring the best fit candidates (Carter [0003]).
Claim 19: Modified Ranade discloses the method as per claim 16. Ranade further discloses wherein receiving candidate data and qualification data includes: receiving and parsing a resume uploaded to a recruiting system by a user (Paragraph [0026]; [0042]; Fig. 5, the application may consist of sending a resume/profile/candidate information. A system client application can collect different types of information with the user’s permission for example different from online professional networks. In some embodiments information may include particular skills the contact possesses or jobs the individual is seeking).
Claims 13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ranade (US 2021/0158297) in view of Carter (US 2015/0006422) further in view of Krishnamoorthy (US 2015/0302359) and further in view of Jersin (US 2019/0197487)
Claim 13: Modified Ranade discloses the method as per claim 11. However, Ranade does not disclose further comprising: receiving an indication that the first user was not selected to fill an opening associated with the position listing; prior to determining at least one recommendation for the first user to improve the qualification score.
In the same field of endeavor of improving a candidate stream for a position Jersin teaches further comprising: receiving an indication that the first user was not selected to fill an opening associated with the position listing; prior to determining at least one recommendation for the first user to improve the qualification score (Paragraph [0039]; [0052-0055]; Fig. 1, some embodiments present a user (e.g. a hiring manager) with a series of potential candidates in order to solicit feedback. When the user assigns negative rating to the candidates in the stream (e.g. by rejecting them) the user may be asked for further input on the stream in order to improve the stream. In an example, the user may be asked which attributes of the last rated candidate was displeasing (e.g. title, industry, and the like)).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of further comprising: receiving an indication that the first user was not selected to fill an opening associated with the position listing; prior to determining at least one recommendation for the first user to improve the qualification score as taught by Jersin (Jersin [0039]). With the motivation of helping to improve the recruiting of best matched candidates for a position (Jersin [0004]).
Claim 15: Modified Ranade discloses the method as per claim 11. However, Ranade does not disclose further comprising: identifying a plurality of similar position listings to the position listing; determining at least one recommendation for the representative associated with the position listing to improve the position listing based at least on the position data and aggerated data associated with the plurality of similar position listing and result data associated with the plurality of similar position listing; and providing the recommendation to the representative associated with the position listing.
In the same field of endeavor of helping a job seeker to fill an open job position Jersin teaches further comprising: identifying a plurality of similar position listings to the position listing; determining at least one recommendation for the representative associated with the position listing to improve the position listing based at least on the position data and aggerated data associated with the plurality of similar position listing and result data associated with the plurality of similar position listing; and providing the recommendation to the representative associated with the position listing (Paragraph [0029]; [0033-0035]; [0100-0106]; Fig. 3, to suggest a position for a hiring search (e.g. a job candidate search) certain embodiments can accomplish this in two ways. The first is to use a factorization machine to conduct collaborative filtering. In this method, companies are deemed similar if there is a flow of human capital between related companies. Once similar companies have been identified an embodiment can consider common titles held by employees at those companies and draw suggestions (e.g. suggested job titles) from this set of titles. A goal is to suggest the most common titles. By using titles that similar companies have hired people for, embodiments can product a list of suggested titles. In another embodiment a machine learning model is trained to make smart suggestions to the searcher as to how to modify the generated query).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of further comprising: receiving an indication that the first user was not selected to fill an opening associated with the position listing; prior to determining at least one recommendation for the first user to improve the qualification score as taught by Jersin (Jersin [0039]). With the motivation of helping to improve the recruiting of best matched candidates for a position (Jersin [0004]).
Claim 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ranade (US 2021/0158297) in view of Carter (US 2015/0006422) further in view of Krishnamoorthy (US 2015/0302359) further even further in view of Shin (US 2015/0199647).
Claim 20: Modified Ranade discloses the method as per claim 16. However, Ranade does not disclose wherein the candidate data includes at least one video.
In the same field of endeavor of helping a job seeker find and apply for a job opening Shin teaches wherein the candidate data includes at least one video (Paragraph [0014]; Fig. 1, an exemplary embodiment may provide job seekers or job candidates’ information in a visual or video format. Unlike the conventional text-based resume, the exemplary embodiments may generate visual or video representations (video introductions) of the job seekers of job candidates’ information. The embodiment may enable the job seekers or job candidates to submit their video introductions).
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 receiving a series of referrals to help identify and assess job candidates for a job opening as disclosed by Ranade (Ranade [0032]) with the system of wherein the candidate data includes at least one video as taught by Shin (Shin [0014]). With the motivation of helping to improve job applicant’s applications and acquire an open job position (Shin [0003]). As well as being a simple substitution as Ranade discloses received candidate data which can additionally include video information.
Therefore, claims 1, 4-13, and 15-20 are rejected under U.S.C. 103.
Response to Arguments
Applicant’s arguments, see REMARKS, filed December 17 2025, with respect to the rejections of claims 1, 4-13, and 15-20 under U.S.C. 101 have been fully considered but are not persuasive.
The applicant argues that the claims are do not recite an abstract idea as they recite using certain personality assessment, cultural information, and other certifications and checks to provide a subjective approach when evaluating job candidates. However, the examiner respectfully disagrees as the claims recite a system for receiving candidate data, receiving position data, identifying criteria for the position and determining a user meets or exceeds the criteria, determining a qualification score, notifying the user, receiving a request to apply for the position from the user, determining recommendations for the user to improve their qualification score, and recalculating the qualification score. The examiner finds these steps of merely receiving and analyzing information and providing feedback as reciting an abstract idea. The claims recite a mental process as receiving candidate data and qualification data such as the results of a personality assessment, receiving position data associated with an open position, identifying at least one criterion for the position, determining that a user meets or exceeds the criteria, generating a qualification score and determining if the qualification score is above a threshold, receiving a request to apply for a position, determining a recommendation for a user to improve their qualification score, and re-evaluating a candidate to generate a final qualification score are all processes that can be practically performed in the human mind or with simple tool such as pen and paper. A person such as a recruiter is capable of mentally receiving candidate and job information including the results of a candidate assessment, determining a qualification score, and recommending resources to help a candidate improve their qualification for a job. Additionally, the claims recite concepts the courts have identified as being mental processes including observation, evaluation, judgment, and opinions. The claims alternatively recite a certain method of organizing human activity as they recite a method for conducting a series of steps for evaluating a candidate for a job. Merely receiving candidate and job information to determine a qualification score for a candidate and suggesting further recommendations for the candidate to improve their score are steps of managing personal behavior or interactions between people. The claims recite a standard practice for recruiting job candidates. Therefore, the claims recite an abstract idea.
The applicant further argues that the claims recite a practical application as they require continued interactions between a user and a system to update information. However, the examiner respectfully disagrees and does not find this as an improvement in a technical field or a technical problem but as an “improvement” to an abstract idea which is insufficient in directing the claims to a practical application. Merely using a computer to perform the steps of the abstract idea such as receiving candidate information, analyzing their information compared to job criteria, and providing an output such as a score or feedback is not an improvement to a technology or technical field. The examiner finds the additional elements are directed to “apply it” or applying a technology to perform the abstract idea. The claims merely recite using generic computer elements to perform the abstract idea to receive candidate information and conduct a series of steps to generate a qualification score. Therefore, the additional elements do not direct the claims to a practical application.
Therefore, the examiner maintains the current 101 rejection.
Applicant argues that claims 4-10, 12-13, 15, and 17-20 are allowable as being dependent on claims 1, 11, and 16 and therefore are rejected under the same rejection.
Applicant’s arguments, see REMARKS, filed December 17, 2025, with respect to the rejections of claims 1, 4-12, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ranade (US 2021/0158297) in view of Carter (US 2015/0006422) further in view of Krishnamoorthy (US 2015/0302359) are not persuasive.
Claims 1, 11, and 16: Applicant argues that the combination of prior art does not teach the claim limitations of “receiving, via a device associated with a user, candidate data and qualification data from the user of the system, the user being a potential job candidate, wherein the qualification data includes at least one personality assessment chosen from a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, Agreeableness, Neuroticism, Openness, Extraversion/Extroversion) assessment or a cultural test.” As Ranade does not disclose receiving “personality” based information. However, the examiner does not claim Ranade to teach the claim limitation as argued by the applicant. Ranade discloses a system of determining a match score between a job opening and a user. To generate the match score Ranade acquires a plurality of user information such as work history, professional network data, success rate, as well as personal information such as social network information (Ranade [0042]). As such Ranade is capable of receiving a plurality of different types of candidate information for assessing a candidate for a position including information from a plurality of sources such as social networking information. Therefore, Ranade’s ability to receive a plurality of different types of candidate information from different sources can be used in combination with Carter which teaches a system of matching a job candidate to a position and determining a fit score for a user based on assessments such as a plurality of personality assessments (Carter [0100]). The examiner finds that the system taught by Carter of presenting a questionnaire to a user to evaluate their fit for a company (Carter [0126]) would include the broadest reasonable interpretation of a system that is capable of conducting any number or type of personality tests or questionnaires to evaluate a score or metric to be used in evaluating a candidate for a position. The applicant argues that Carter does not disclose the claim limitation of receiving candidate qualification data including at least one personality assessment chosen from “a Dominance, Influence, Compliance, and Steadiness (DISC) assessment, Big Five or OCEAN (Conscientiousness, Agreeableness, Neuroticism, Openness, Extraversion/Extroversion) assessment or a cultural test” because the personality tests in Carter are optional. However, the examiner respectfully disagrees as Carter recites a system which has the ability to provide personality questionnaires that are used to determine a candidate’s compatibility with a job or company teaches the function of the claim limitation. Therefore, the combination of Ranade and Carter teach a system of evaluating the personality and personal information of a job seeker when determine a score or an evaluation for a job opening.
Therefore, the examiner finds the combination of Ranade, Carter, and Krishnamoorthy capable of teaching the newly amended claim limitations.
Therefore, the examiner maintains the current 103 rejection of claims 1, 11, and 16.
Claims 5-10, 12-13, 15, and 17-20 were argued as being allowable only as being dependent on claims 1, 11, and 16. 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:
Schneiderman (US 2013/0198098) System and method for creating a dynamic customized employment profile and subsequent user thereof.
Bonmassar (US 2013/0290207) Method, apparatus and computer program product to generate psychological, emotional, and personality information for electronic job recruiting.
Smith (US 2022/0237531) Method of matching employers with job seekers including emotion recognition.
Goren (US 2018/0046987) System and methods of predicting fit for a job position.
Zes (US 2015/0286991) Determining job applicant fit score.
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/COREY RUSS/Examiner, Art Unit 3629